Data Analytics and Psychiatry

Exposure to repetitive head impacts (RHI) is linked to the development of chronic traumatic encephalopathy (CTE), which can only be diagnosed at post-mortem. The presence of a cavum septum pellucidum (CSP) is a common finding in post-mortem studies of confirmed CTE and in neuroimaging studies of individuals exposed to RHI. This study examines CSP in living former American football players, investigating its association with RHI exposure, traumatic encephalopathy syndrome (TES) diagnosis, and provisional levels of certainty for CTE pathology.

Affective recognition and sensory processing are impaired in people with autism. However, no mouse model of autism comanifesting these symptoms is available, thereby limiting the exploration of the relationship between affective recognition and sensory processing in autism and the molecular mechanisms involved.

Childhood cerebral adrenoleukodystrophy (C-ALD) is a severe inflammatory demyelinating disease that must be treated at an early stage to prevent permanent brain injury and neurocognitive decline. In standard clinical practice, C-ALD lesions are detected and characterized by a neuroradiologist reviewing anatomical MRI scans. We aimed to assess whether diffusion tensor imaging (DTI) is sensitive to the presence and severity of C-ALD lesions and to investigate associations with neurocognitive outcomes after hematopoietic cell therapy (HCT).

Updates in Alzheimer disease (AD) diagnostic guidelines by the National Institute on Aging-Alzheimer's Association (NIA-AA) and the International Working Group (IWG) over the past 11 years may affect clinical diagnoses. We assessed how these guidelines affect clinical AD diagnosis in a cohort of cognitively unimpaired (CU) and cognitively impaired (CI) individuals.

Tian L. Effects of cognitive behavioral therapy on anxiety and depressive symptoms in advanced cancer patients: A meta-analysis. Gen Hosp Psychiatry. 2024 Jan 20;87:20-32. doi:10.1016/j.genhosppsych.2024.01.006. Epub ahead of print. PMID: 38280276.

Repetitive transcranial magnetic stimulation (rTMS) is frequently used as an adjunctive treatment with antidepressants for depression. We aimed to evaluate the clinical efficacy and safety of antidepressant classes when administered concurrently with rTMS for the management of major depressive disorder (MDD).

Ataxia telangiectasia is a multisystem disorder with progressive neurodegeneration. Corticosteroids can improve neurological functioning in patients with the disorder but adrenal suppression and symptom recurrence on treatment discontinuation has limited their use, prompting the development of novel steroid delivery systems. The aim of the ATTeST study was to evaluate the efficacy and safety of intra-erythrocyte delivery of dexamethasone sodium phosphate compared with placebo in children with ataxia telangiectasia.

Low loss aversion (LA) and high delay discounting (DD) are behavioral-economic decision-making biases that independently predict cigarette smoking and other risky substance use. Here we examine (1) whether low-LA and high-DD co-occur, (2) does co-occurrence increase the odds of current smoking and other substance use compared to only low-LA, high-DD, or neither; and (3) potential gender differences in these associations.

Given the chronic nature of schizophrenia, it is important to examine age-specific prevalence and incidence to understand the scope of the burden of schizophrenia across the lifespan. Estimates of lifetime prevalence of schizophrenia have varied widely and have often relied upon community-based data estimates from over two decades ago, while more recent studies have shown considerable promise by leveraging pooled datasets. However, the validity of measures of schizophrenia, particularly new onset schizophrenia, has not been well studied in these large health databases. The current study examines prevalence and validity of incidence measures of new diagnoses of schizophrenia in 2019 using two U.S. administrative health databases: MarketScan, a national database of individuals receiving employer-sponsored commercial insurance (N = 16,365,997), and NYS Medicaid, a large state public insurance program (N = 4,414,153). Our results indicate that the prevalence of schizophrenia is over 10-fold higher, and the incidence two-fold higher, in the NYS Medicaid population compared to the MarketScan database. In addition, prevalence increased over the lifespan in the Medicaid population, but decreased in the employment based MarketScan database beginning in early adulthood. Incident measures of new diagnoses of schizophrenia had excellent validity, with positive predictive values and specificity exceeding 95%, but required a longer lookback period for Medicaid compared to MarketScan. Further work is needed to leverage these findings to develop robust clinical outcome predictors for new onset of schizophrenia within large administrative health data systems.

Sleep remains a cornerstone for sociopsychological well-being, but it is in decline, especially among today's youth. Simultaneously, engagement with social media is escalating. Research has identified a link between social networking sites use and sleep problems; however, the nature and direction of the relations remain obscure. Therefore, it is imperative to pursue longitudinal research to elucidate this correlation and guide suitable intervention practices. The present study aimed to examine the reciprocal relationship between social networking sites use and sleep problems.

Shame has been linked to the experience of psychosis, with implications for clinical outcomes, however, a meta-analysis of the relationship has not yet been conducted. This systematic review and meta-analysis aimed to examine the strength of the association between shame and psychosis, and any variations between clinical and non-clinical populations and shame type (internal vs external shame).

The browsing history of a medical institution's website can potentially reveal or identify information about the health condition of the website visitor through browser cookies and fingerprints. In Japan, although the Personal Information Protection Law was revised in April 2022, the use of access analysis services to collect browsing history on medical institution websites has not been investigated. Therefore, this study investigates the actual usage of access analysis services on Japanese medical institution websites and compares it with the current situation in France, which follows the General Data Protection Regulation. The results revealed that the larger the size of the hospital, the higher the percentage of adoption of access analytics services in Japan. However, the implementation of a system for obtaining consent for the use of access analysis in Japan was found to be poor compared to that of French medical institutions. While access analysis tools are used in the websites of several medical institutions in Japan, the implementation of the process of obtaining consent to acquire browsing history is poor.

Mobile health (mHealth) offers significant benefits for patients with stroke, facilitating remote monitoring and personalized health care solutions beyond traditional settings. However, there is a dearth of comprehensive data, particularly qualitative insights, on the barriers to mHealth access. Understanding these barriers is crucial for devising strategies to enhance mHealth use among patients with stroke.

Early identification of patients with substance use disorders (SUDs) with a higher risk of emergency department (ED) presentations after being discharged can be useful. We performed a chart review of patients from the Intensive Recovery Discharge Team (IRDT) program, which provides two weeks of outpatient support for patients with SUDs discharged from a mental health hospital.

Motor impairments and sensory processing abnormalities are prevalent in autism spectrum disorder (ASD), closely related to the core functions of the primary motor cortex (M1) and the primary somatosensory cortex (S1). Currently, there is limited knowledge about potential therapeutic targets in the subregions of M1 and S1 in ASD patients. This study aims to map clinically significant functional subregions of M1 and S1.

Data sharing spaces for medical data are necessary to facilitate research. To make medical data available for research, a mechanism is preferable that not only provides data a researcher has legal access to, but also contributes to the investigation of their specific research hypothesis. We propose a three-party two-stage search algorithm initiated by a researcher on centrally stored but technically and organizationally separated data. The search seeks to minimize the risk of reidentification of patients and to enable data minimization. In the first stage, we only access data IDs of patients meeting the cohort criteria. In the second stage, the actual data is downloaded if the set of matching patients satisfies the minimum cohort size. Our approach is privacy preserving, as only the researcher is able to connect medical and demographic data, while no single malicious party can get data access. We thereby hope to pave the way for a privacy-aware health data sharing space as currently proposed by the EU.

Data Science emerged as a new cross-disciplinary discipline at the intersection of statistics, computer science, and expertise in a specific domain, such as health and biology. The data science field, alongside other data-related professions, is continuously evolving. We conducted a study examining tasks assigned to first-year internship students pursuing a Master's degree in Health Data Science, exploring the missions, technologies employed and skills required, and internship alignment with students' training through semi-structured interviews with 32 participants. Three quarters of the students were placed in teams within the public sector. Among these entities, there were 11 hospitals and 12 universities. Although the majority of students did their internship as part of a methodological team, they often had a healthcare professional on their team. Nearly half of the missions involved descriptive analysis, followed by 9 missions focused on etiology or prediction and 8 missions on implementing a data warehouse. The majority of students had to perform data management and produce graphs, while only half conducted statistical analysis. The findings highlighted that data management remains a major challenge, and it should be taken into consideration when designing training programs. In future, it remains to determine whether this trend will continue with second-year students or if, with experience, they are more often assigned statistical analyses.

STOP-HCV-HCC program to screen and treat hepatitis C, vaccinate for hepatitis B, and prevent hepatocellular carcinoma is implementing a cloud-based privacy-preserving platform to overcome electronic health record barriers to reporting, without data transfer, at four federally qualified health centers in South Texas, USA.

Sharing biomedical data for research can help to improve disease understanding and support the development of preventive, diagnostic, and therapeutic methods. However, it is vital to balance the amount of data shared and the sharing mechanism chosen with the privacy protection provided. This requires a detailed understanding of potential adversaries who might attempt to re-identify data and the consequences of their actions. The aim of this paper is to present a comprehensive list of potential types of adversaries, motivations, and harms to targeted individuals. A group of 13 researchers performed a three-step process in a one-day workshop, involving the identification of adversaries, the categorization by motivation, and the deduction of potential harms. The group collected 28 suggestions and categorized them into six types, each associated with several of six distinct harms. The findings align with previous efforts in structuring threat actors and outcomes and we believe that they provide a robust foundation for evaluating re-identification risks and developing protection measures in health data sharing scenarios.

The identification of vulnerable records (targets) is an important step for many privacy attacks on protected health data. We implemented and evaluated three outlier metrics for detecting potential targets. Next, we assessed differences and similarities between the top-k targets suggested by the different methods and studied how susceptible those targets are to membership inference attacks on synthetic data. Our results suggest that there is no one-size-fits-all approach and that target selection methods should be chosen based on the type of attack that is to be performed.

Generative machine learning models such as Generative Adversarial Networks (GANs) have been shown to be especially successful in generating realistic synthetic data in image and tabular domains. However, it has been shown that such generative models, as well as the generated synthetic data, can reveal information contained in their privacy-sensitive training data, and therefore must be carefully evaluated before being used. The gold standard method through which such privacy leakage can be estimated is simulating membership inference attacks (MIAs), in which an attacker attempts to learn whether a given sample was part of the training data of a generative model. The state-of-the art MIAs against generative models, however, rely on strong assumptions (knowledge of the exact training dataset size), or require a lot of computational power (to retrain many "surrogate" generative models), which make them hard to use in practice. In this work, we propose a technique for evaluating privacy risks in GANs which exploits the outputs of the discriminator part of the standard GAN architecture. We evaluate our attacks in terms of performance in two synthetic image generation applications in radiology and ophthalmology, showing that our technique provides a more complete picture of the threats by performing worst-case privacy risk estimation and by identifying attacks with higher precision than the prior work.

Prior researchers have identified distinct differences in functional connectivity neuroimaging characteristics among MDD patients. However, the auxiliary diagnosis and subtype differentiation roles of VMHC values in MDD patients have yet to be fully understood. We aim to explore the separating ability of VMHC values in patients with anxious MDD or with non-anxious MDD and HCs.

In this Review, we explore the transformative potential of decentralized clinical trials (DCTs) in addressing the limitations of traditional randomized controlled trials (RCTs). We highlight the merits of DCTs fostering greater inclusivity, efficiency, and adaptability. We emphasize the challenges of RCTs, including limited participant diversity and logistical barriers, geographical constraints, and mistrust in research institutions, showing how DCTs are preferred in addressing these challenges by utilizing remote digital technologies and community providers to enable broader, more inclusive participation. Furthermore, we underscore the potential of DCTs for democratizing clinical research. We also stress the importance of addressing unresolved challenges, including data security and privacy, remote patient monitoring, and regulatory variations. Research is needed to devise standardized protocols to streamline DCT processes, explore its long-term impacts on patient outcomes, and overcome challenges through equitable stakeholder engagement.

To assess the effect of Long-acting injectable (LAI) antipsychotics in acutely ill patients, we systematically searched major databases for randomized controlled trials (RCTs) comparing LAIs with other LAIs, oral antipsychotics, or placebo in acutely symptomatic adults with schizophrenia-spectrum disorders. Data were analyzed with a random-effects network meta-analysis. Co-primary outcomes were efficacy (mean change in psychopathology rating scales) and acceptability (all-cause discontinuations) at study endpoint. Of 25 RCTs, 19 studies tested second-generation LAIs (SGA-LAIs) and six first-generation LAIs (FGA-LAIs). Due to a disconnected network, FGA-LAIs were analyzed separately, with poor data quality. The SGA-LAIs network included 8,418 individuals (males=63%, mean age=39.3 years). All SGA-LAIs outperformed placebo in reducing acute symptoms at study endpoint (median follow-up=13 weeks). They were more acceptable than placebo with the only exception of olanzapine, for which no differences with placebo emerged. Additionally, we distinguished between different LAI formulations of the same antipsychotic to explore potential pharmacokinetic differences. Most formulations outperformed placebo in the very short-term (2 weeks or less), regardless of the need for initial oral supplementation. SGA-LAIs are evidence-based treatments in acutely ill individuals with schizophrenia-spectrum disorders. Findings support the use of SGA-LAIs to manage psychopathology and improve adherence right from the acute phases of illness.

There is high comorbidity of opioid use disorder (OUD) and chronic pain (CP), which is often addressed by prescribing buprenorphine (BUP). While BUP is effective in preventing overdose, it does not address the psychological aspects of OUD and CP comorbidity and treatment retention rates are as low as 50%. The Virtual Opioid use disorder Integrated Chronic Pain Treatment (VOICE) study (NCT05039554) is a novel effectiveness-implementation trial to test a 12-week virtual group Acceptance and Commitment Therapy (ACT) protocol and a care management smartphone application (app; Valera Health) on pain and opioid use in patients with OUD and CP receiving BUP. Using a 2 × 2 factorial design, participants (expected N = 280) are randomized into: ACT, Valera app, ACT + Valera, or Treatment as Usual arm. This study is taking place in the Bronx, NY, a racially/ethnically diverse community that faces numerous socioeconomic stressors and is one of the nation's epicenters of the opioid epidemic. We created a culturally responsive ACT group protocol, and Valera psychoeducational material. Outcome measures include NIH HEAL Common Data Elements and ACT and Valera-specific measures. We are conducting a novel 2 × 2 trial investigating augmenting BUP treatment with ACT and Valera, with the goal that improved mental health and access to care will result in decreased and opioid use and pain interference.

One-fifth of US adults experience chronic pain, which is associated with increased tobacco and cannabis use. Although bidirectional relationships between tobacco and pain have been demonstrated, pathways between pain, cannabis use, and co-use of cannabis and tobacco are understudied. We aimed to estimate the effects of (1) substance use (exclusive and co-use of cannabis and tobacco) on later pain intensity, and (2) pain intensity on later substance use. Data were from 31,983 adults in biennial surveys (2015-2021) of the US nationally representative longitudinal Population Assessment of Tobacco and Health Study (n = 71,055 pairs of consecutive surveys; T1 and T2). Past-week pain intensity was dichotomized (≤4/10 no/low pain; >4/10 moderate/severe pain). Mutually exclusive substance use categories (past 30 days) were no cannabis/tobacco use; exclusive cannabis/tobacco use; and co-use. Logistic regression assessed whether T1 substance use affected moderate/severe pain at T2. Multinomial models assessed whether pain status at T1 affected substance use at T2. Compared with no cannabis/tobacco use at T1, co-use (OR: 2.29 [95% CI: 2.09-2.51]), exclusive tobacco use (2.00 [1.86-2.14]), and exclusive cannabis use (1.35 [1.13-1.61]) were all associated with moderate/severe pain at T2. Moderate/severe pain at T1 increased odds of co-use (2.43 [2.22-2.66]), exclusive tobacco (2.12 [1.98-2.28]), and exclusive cannabis use (1.46 [1.29-1.65]) compared with no cannabis/tobacco use at T2, and increased odds of co-use at T2 compared with exclusive cannabis/tobacco use. Findings demonstrated bidirectional relationships between pain and the exclusive use and co-use of cannabis and tobacco and indicate potential synergy in the co-use of cannabis and tobacco with respect to pain.

The results and details of the clinical studies and research must be securely stored to ensure reliability, accountability, and prevent malicious misuse. To accomplish this, a secure method for storing metadata and study results is crucial. Also, a mechanism to ensure accountability for both data owners and researchers is needed. In this way, data owners and the scientific community can rely on and verify results and methods presented by researchers, while researchers can check the validity of the analyzed data and have proof of authorship for their work. A modular framework is presented in this paper, which utilizes blockchain and cryptography to store study results and metadata, along with proof of accountability. The framework has been tested within a privacy-preserving distributed analytics infrastructure.

Within mental health services, persons in recovery from their own experiences of mental health challenges (peers) are increasingly being trained to provide peer support. This study describes individual and organizational outcomes related to engaging peers in a multisite demonstration project in California that sought to integrate them as cocreators throughout planning and implementation of digital mental health interventions. We collected data from key informants across 11 sites. Quarterly online surveys invited key informants to report perceived outcomes of the peer component. Biannual interviews elicited details regarding survey-reported outcomes. Quantitative data provided indications of outcome prevalence and consistency, and quotes from the interviews illustrated the complex realities underlying survey responses. One hundred three quarterly surveys and 39 biannual interviews were completed between Summer 2020 and Fall 2022. Key informants reported diverse outcomes, including integration of peer input into local decision making, mental health benefits to peers and community members, reduced workplace mental health stigma, and new cross-site collaborations. Five sites reported outcomes with greater consistency compared to the other six sites. Reports of increased peer visibility in the workplace coincided with reports of reduced stigma and increased value of peer input by mental health professionals. This study offers encouragement for the potential positive impact of engaging peers as cocreators of mental health interventions. Data suggest integrating peers does not increase mental health stigma and may instead result in various positive outcomes. The degree to which these outcomes manifest in a specific setting, however, may vary. Future research should seek to identify contextual factors that support actualization of positive outcomes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Survivors of childhood B-acute lymphoblastic leukemia (B-ALL) are at risk for difficulties with attention and executive functioning (EF) as a late effect of treatment. The present study aimed to identify treatment and demographic factors associated with risk for difficulties with EF in youth treated for high-risk B-ALL.

We report a mixed-methods process evaluation embedded within a randomised controlled trial. We aimed to test and refine a theory of change model hypothesising key causal assumptions to understand how the New Interventions for Independence in Dementia Study (NIDUS)-Family (a manualised, multimodal psychosocial intervention), was effective relative to usual care, on the primary outcome of Goal Attainment Scaling (GAS) over 1 year.

Although previous reviews have examined the relationship between heritage cultural maintenance and mental health outcomes among migrants, none have focussed specifically on migrant adolescents (i.e. those aged 10-24 years).

Associations have been found between COVID-19 and subsequent mental illness in both hospital- and population-based studies. However, evidence regarding which mental illnesses are associated with COVID-19 by vaccination status in these populations is limited.

Neuroimaging databases for neuro-psychiatric disorders enable researchers to implement data-driven research approaches by providing access to rich data that can be used to study disease, build and validate machine learning models, and even redefine disease spectra. The importance of sharing large, multi-center, multi-disorder databases has gradually been recognized in order to truly translate brain imaging knowledge into real-world clinical practice. Here, we review MRI databases that share data globally to serve multiple psychiatric or neurological disorders. We found 42 datasets consisting of 23,293 samples from patients with psychiatry and neurological disorders and healthy controls; 1245 samples from mood disorders (major depressive disorder and bipolar disorder), 2015 samples from developmental disorders (autism spectrum disorder, attention-deficit hyperactivity disorder), 675 samples from schizophrenia, 1194 samples from Parkinson's disease, 5865 samples from dementia (including Alzheimer's disease), We recognize that large, multi-center databases should include governance processes that allow data to be shared across national boundaries. Addressing technical and regulatory issues of existing databases can lead to better design and implementation and improve data access for the research community. The current trend toward the development of shareable MRI databases will contribute to a better understanding of the pathophysiology, diagnosis and assessment, and development of early interventions for neuropsychiatric disorders.

The paper discusses biases in medical imaging analysis, particularly focusing on the challenges posed by the development of machine learning algorithms and generative models. It introduces a taxonomy of bias problems and addresses them through a data infrastructure initiative: the PADME (Platform for Analytics and Distributed Machine-Learning for Enterprises), which is a part of the National Research Data Infrastructure for Personal Health Data (NFDI4Health) project. The PADME facilitates the structuring and sharing of health data while ensuring privacy and adherence to FAIR principles. The paper presents experimental results that show that generative methods can be effective in data augmentation. Complying with PADME infrastructure, this work proposes a solution framework to deal with bias in the different data stations and preserve privacy when transferring images. It highlights the importance of standardized data infrastructure in mitigating biases and promoting FAIR, reusable, and privacy-preserving research environments in healthcare.

Postpartum depression (PPD) significantly impacts mothers and children's health. China aims to incorporate PPD screening in postpartum home visits, but research on implementation barriers and facilitators is scarce. We designed and implemented a new PPD screening program in Changsha, China, requiring maternal health workers to integrate PPD screening into their postpartum home visits.

Studies of psychedelic-assisted therapy with LSD, psilocybin, MDMA, and related substances show clinical promise but inadequately assess side effects. Measuring side effects is challenging because they are not always easily differentiated from treatment effects or disease symptoms and show high heterogeneity, variable duration and impact, and sensitivity to context. A systematic questionnaire describing important characteristics of side effects of psychedelics and MDMA would greatly improve on previous methods. We aimed to create a standardized tool for recording clinically relevant side effects of psychedelics and MDMA, including their severity, duration, impact, and treatment-relatedness.

The rising rate of unsheltered homelessness is a troubling and important public health issue. This narrative review sought to answer the question: What are the reasons that homeless individuals report for being unsheltered? To identify studies, systematic search methods were applied to PubMed, Google Scholar, and PsycINFO databases using the following eligibility criteria: English-written, peer-reviewed studies published from 2000 to 2023 that reported qualitative or quantitative data related to reasons why homeless individuals in a Western country were unsheltered. After duplicates were removed, 14,690 studies were screened and filtered to 10 final studies that fit all eligibility criteria and were included in the review. Eight of the 10 studies reported qualitative data from interviews and focus groups; the two quantitative studies reported data from interviewer-administered surveys. Across studies, eight thematic categories were identified as barriers to staying in shelters or other sheltered locations: lack of safety, triggers for substance use, strict shelter rules, triggers for substance use, perceived inadequate care, unsanitary conditions, required treatment to participate in housing program, no pet or family accommodations, and lack of accessibility for individuals with disabilities. Together, these findings indicate the delicate balance needed in homeless shelters to have both an inclusive/permissive and structured/safe environment for homeless individuals. Attention to the design and operation of homeless shelters, as well as staff training and accommodation for clients with special needs may improve some of these issues.

To evaluate the comparative effectiveness and acceptability of oral monotherapy using psychedelics and escitalopram in patients with depressive symptoms, considering the potential for overestimated effectiveness due to unsuccessful blinding.

While patients affected by mild cognitive impairment (MCI) exhibit characteristic voxel-mirrored homotopic connectivity (VMHC) alterations, the ability of such VMHC abnormalities to predict the diagnosis of MCI in these patients remains uncertain. As such, this study was performed to evaluate the potential role of VMHC abnormalities in the diagnosis of MCI.

Social isolation (SI) and food insecurity (FI) are important social determinants of health that can negatively impact well-being in old age. While research on the association between FI and SI is limited in LMICs, the mediators of this association are largely unknown. This cross-sectional study examined whether FI is associated with SI among older adults in Ghana and whether psychological factors (i.e., depression, anxiety, and sleep problems) mediated the association.

Existential distress is commonly experienced by patients diagnosed with a life-threatening illness. This condition has been shown to adversely impact quality of life and is correlated with increased suicidal ideation and requests for hastened death. While palliative care teams are experienced in treating depression and anxiety, existential distress is a distinct clinical condition for which traditional medications and psychotherapy approaches demonstrate limited efficacy or duration of effect. Psychedelic drugs, including psilocybin and lysergic acid diethylamide (LSD), in conjunction with psychotherapy have been shown to produce rapid and sustained reductions in existential and psychiatric distress and may be a promising treatment for patients facing existential distress in palliative care settings. In this narrative review article, we describe the history of psychedelic medicine including early studies and the modern wave of research over the past 20 years, which includes high quality clinical trial data. This review outlines specific considerations for therapeutic application of psilocybin including pharmacokinetics, patient selection, dosing, protocol designs, and safeguards to reduce potential adverse effects to help guide future psychedelic practitioners. With growing public interest and evolving state level policy reforms allowing access to psychedelic treatments, it is critical for palliative care providers to gain familiarity with the current state of science and the potential of psilocybin assisted psychotherapy in the treatment of existential distress.

Physical pain and negative emotions represent two distinct drinking motives that contribute to harmful alcohol use. Proactive avoidance, in contrast, can reduce consumption in response to these motives but appears to be impaired in those with problem drinking. Despite such evidence, proactive avoidance and its underlying neural deficits have not been assessed experimentally. How these deficits inter-relate with drinking motives to influence alcohol use also remains unclear. The current study leveraged neuroimaging data in forty-one problem and forty-one social drinkers who performed a probabilistic learning go/nogo task featuring proactive avoidance of painful outcomes. We identified the brain responses to proactive avoidance and contrasted the neural correlates of drinking to avoid negative emotions vs. physical pain. Behavioral results confirmed proactive avoidance deficits in problem drinking individuals' learning rate and performance accuracy, both which were associated with greater alcohol use. Imaging findings in the problem drinking group showed that negative emotions as a drinking motive predicted attenuated right anterior insula activation during proactive avoidance. In contrast, physical pain motive predicted reduced right putamen response. These regions' activations as well as functional connectivity with the somatomotor cortex also demonstrated a negative relationship with drinking severity and positive relationship with proactive avoidance performance. Path modeling further delineated the pathways through which physical pain and negative emotions influenced the neural and behavioral measures of proactive avoidance. Taken together, the current findings provide experimental evidence for proactive avoidance deficits in alcohol misuse and establish the link between their neural underpinnings and drinking behavior.

Tele-mental health (TMH) services, including both mental and behavioral healthcare (MBH) services, emerged as a cornerstone in delivering pediatric mental healthcare during the coronavirus disease 2019 (COVID-19) pandemic, yet their utilization and effects on healthcare resource utilization (HCRU) and medical expenditures remain unclear. To bridge the gap, this study aims to investigate the association between TMH utilization and sociodemographic factors and assess its associated HCRU and medical expenditures within a pediatric population in Mississippi. Studying 1,972 insured pediatric patients who accessed outpatient MBH services at the study institution between January 2020 and June 2023, age, race, insurance type, rural residency, and household income were identified as key determinants of TMH utilization. Adjusting for sociodemographics, TMH utilization was associated with 122% more MBH-associated outpatient visits and 36% higher related medical expenditures, but 27% less overall medical expenditures. This study reveals sociodemographic disparities in pediatric TMH utilization, highlights its role in augmenting outpatient mental healthcare access, and shows its potential for cost savings. Future efforts should aim at fostering more digitally inclusive, equitable, and affordable pediatric mental healthcare services.

Suicidal thought and behavior (STB) is highly stigmatized and taboo. Prone to censorship, yet pervasive online, STB risk detection may be improved through development of uniquely insightful digital markers. Focusing on Sanctioned Suicide, an online pro-choice suicide forum, this work derived 17 egocentric network features to capture dynamics of social interaction and engagement within this uniquely uncensored community. Using network data generated from over 3.2 million unique interactions of N = 192 individuals, n = 48 of which were determined to be highest risk users (HRUs), a machine learning classification model was trained, validated, and tested to predict HRU status. Model prediction dynamics were analyzed using introspection techniques to uncover patterns in feature influence and highlight social phenomena. The model achieved a test AUC = 0.73 ([0.61, 0.85], 95% CI), suggesting that network-based socio-behavioral patterns of online interaction can signal for heightened suicide risk. Transitivity, density, and in-degree centrality were among the most important features driving this performance. Moreover, predicted HRUs tended to be targets of social exchanges with lesser frequency and possessed egocentric networks with "small world" network properties. Through the implementation of an underutilized method on an unlikely data source, findings support future incorporation of network-based social interaction features in descriptive, predictive, and preventative STB research.

Severe fatigue is a common symptom for people with visual impairment, with a detrimental effect on emotional functioning, cognition, work capacity and activities of daily living. A previous study found that depression was one of the most important determinants of fatigue, but less is known about disease-specific factors in this patient population. This study aimed to explore the association between visual impairment severity and fatigue in adults with low vision, both directly and indirectly, with vision-specific factors and depression as potential mediators.

With the rapid urbanization and aging population, depression has become a severe public health issue globally, affecting millions of individuals and significantly impacting their quality of life and healthcare costs. Urban resilience refers to a city's ability to absorb, recover, and prepare for future shocks, ensuring sustainable development despite challenges. This study aims to explore the impact of urban resilience on depressive symptoms among middle-aged and elderly adults. Using data from the China Health and Retirement Longitudinal Study (CHARLS), we analyzed five dimensions of urban resilience: economic, social, ecological, institutional, and infrastructural resilience. The results of cox proportional hazards model indicate that high levels of urban resilience significantly reduce the risk of depressive symptoms (HR = 0.875, 95% CI: 0.832-0.920, P < 0.001). Specifically, economic resilience (HR = 0.883, 95% CI: 0.846-0.921, P < 0.001), social resilience (HR = 0.916, 95% CI: 0.876-0.958, P < 0.001), ecological resilience (HR = 0.670, 95% CI: 0.516-0.869, P = 0.003), institutional resilience (HR = 0.922, 95% CI: 0.886-0.960, P < 0.001), and infrastructural resilience (HR = 0.875, 95% CI: 0.826-0.926, P < 0.001) all show significant negative correlations with depressive symptoms. Furthermore, the mitigation of depressive symptoms risk resulting from improved urban resilience disproportionately benefits vulnerable groups and those with healthy living habits. These findings provide scientific evidence for urban planning and policy formulation, contributing to the promotion of mental health and healthy aging among middle-aged and elderly populations.

Selective Serotonin Reuptake Inhibitors (SSRIs) represent a diverse class of medications widely prescribed for depression and anxiety. Despite their common use, there is an absence of large-scale, real-world evidence capturing the heterogeneity in their effects on individuals. This study addresses this gap by utilizing naturalistic search data to explore the varied impact of six different SSRIs on user behavior.

The rapid increase in lipidomic studies has led to a collaborative effort within the community to establish standards and criteria for producing, documenting, and disseminating data. Creating a dynamic easy-to-use checklist that condenses key information about lipidomic experiments into common terminology will enhance the field's consistency, comparability, and repeatability. Here, we describe the structure and rationale of the established Lipidomics Minimal Reporting Checklist to increase transparency in lipidomics research.

The patient-centered communication principles in Western countries are widely esteemed. In Eastern countries, a family-centered approach to medical decision-making is preferred. However, the predicaments faced by attending physicians and their coping strategies in the process of truth-telling about cancer are unknown. Therefore, this study aimed to understand attending physicians' predicaments and coping strategies in implementing truth-telling for cancer in Taiwan.

There are significant rural/urban disparities that exist in cancer and chronic disease morbidity and mortality, many of which are attributed to increased tobacco use prevalence in rural populations compared to urban. Understanding differences in rural and urban tobacco use patterns is key to developing targeted interventions.

This study aimed to explore the reciprocal relationships between implementation leadership and practitioner implementation citizenship behavior during the implementation of evidence-based practices (EBPs). Data were collected at two timepoints with a time lag of six months during a national implementation of evidence-based treatment for post-traumatic stress disorder in Norwegian mental health clinics. Data from 72 leaders and 346 practitioners were analyzed with a two-wave cross-lagged panel model, accounting for the nested structure and adjusting for demographic variables. Significant positive autoregressive effects for both implementation leadership and implementation citizenship behavior indicated some stability in ratings across time. Significant cross-lagged effects in both directions indicated that practitioners who experienced greater implementation leadership from their leaders demonstrated greater implementation citizenship behavior six months later, and vice versa. Findings hence supported both the social exchange hypothesis and the followership hypothesis, suggesting reciprocal associations between the constructs. The findings underscore the mutually influential relationship between leaders' behavior and practitioners' engagement in citizenship behavior during EBP implementation. The study emphasizes the importance of interventions focusing on leadership behaviors that encourage practitioner engagement and mutually beneficial behavior patterns, highlighting the reciprocal and vital roles that both leaders and practitioners play in successful EBP implementation.

The relationship between HIV and frailty, a predictor of poor outcomes in the face of stressors, remains unknown in older people in sub-Saharan Africa.

To systematically evaluate the measurement properties of 12 patient-reported outcome measures (PROMs) used to measure depression symptom severity in adolescents with depression. Depression symptom severity was chosen as the outcome of focus given its importance as an outcome to measure in adolescents with depression across clinical trials and/or care.

With the rapid development of emerging information technologies such as artificial intelligence, cloud computing, and the Internet of Things, the world has entered the era of big data. In the face of growing medical big data, research on the privacy protection of personal information has attracted more and more attention, but few studies have analyzed and forecasted the research hotspots and future development trends on the privacy protection. Presently, to systematically and comprehensively summarize the relevant privacy protection literature in the context of big healthcare data, a bibliometric analysis was conducted to clarify the spatial and temporal distribution and research hotspots of privacy protection using the information visualization software CiteSpace. The literature papers related to privacy protection in the Web of Science were collected from 2012 to 2023. Through analysis of the time, author and countries distribution of relevant publications, we found that after 2013, research on the privacy protection has received increasing attention and the core institution of privacy protection research is the university, but the countries show weak cooperation. Additionally, keywords like privacy, big data, internet, challenge, care, and information have high centralities and frequency, indicating the research hotspots and research trends in the field of the privacy protection. All the findings will provide a comprehensive privacy protection research knowledge structure for scholars in the field of privacy protection research under the background of health big data, which can help them quickly grasp the research hotspots and choose future research projects.

More than half of suicide deaths in the United States result from self-inflicted firearm injuries. Extreme risk protection order (ERPO) laws in 21 states and the District of Columbia temporarily limit access to firearms for individuals found in a civil court process to pose an imminent risk of harm to themselves or others. Research with large multistate study populations has been lacking to determine effectiveness of these laws. This study assembled records pertaining to 4,583 ERPO respondents in California, Connecticut, Maryland, and Washington. Matched records identified suicide decedents and self-injury method. Researchers applied case fatality rates for each suicide method to estimate nonfatal suicide attempts corresponding to observed deaths. Comparison of counterfactual to observed data patterns yielded estimates of the number of lives saved and number of ERPOs needed to avert one suicide. Estimates varied depending on the assumed probability that a gun owner who attempts suicide will use a gun. Two evidence-based approaches yielded estimates of 17 and 23 ERPOs needed to prevent one suicide. For the subset of 2,850 ERPO respondents with documented suicide concern, comparable estimates were 13 and 18, respectively. This study's findings add to growing evidence that ERPOs can be an effective and important suicide prevention tool.

The allergic airway disease, such as allergic rhinitis, chronic rhinosinusitis, asthma, is a general term of a range of inflammatory disorders affecting the upper and lower airways and lung parenchyma. This study aimed to investigate the short-term effects of air pollutants and meteorological factors on AAD-related daily outpatient visits.

Positive and negative affect influence an individual's ability to utilize available physical, psychological, and social resources to maximize responses to life events. Little research has examined the factors that influence the development of positive affect or reduction of negative affective responses among deployed military personnel. The present study aimed to investigate the relationship between deployment-related stressors and symptoms of behavioral health concerns with affectivity among deployed U.S. service members.

This study proposes an approach for analyzing mental health through publicly available social media data, employing Large Language Models (LLMs) and visualization techniques to transform textual data into Chernoff Faces. The analysis began with a dataset comprising 15,744 posts sourced from major social media platforms, which was refined down to 2,621 posts through meticulous data cleaning, feature extraction, and visualization processes. Our methodology includes stages of Data Preparation, Feature Extraction, Chernoff Face Visualization, and Clinical Validation. Dimensionality reduction techniques such as PCA, t-SNE, and UMAP were employed to transform complex mental health data into comprehensible visual representations. Validation involved a survey among 60 volunteer psychiatrists, underscoring the visualizations' potential for enhancing clinical assessments. This work sets the stage for future evaluations, specifically focusing on a combined features method to further refine the visual representation of mental health conditions and to augment the diagnostic tools available to mental health professionals.

This study examined whether maternal depression is related to Early Childhood Developmental (ECD) delay among children by quantifying the mediating contribution of responsive caregiving. We used data from 1235 children (Children's mean age = 50.4 months; 582 girls, 653 boys, 93.9% were Han), selected through convenience sampling, in 2021. 4.7% of children had ECD delay, 34.3% of mothers had depression. Children with depressed mothers were less likely to receive responsive caregiving (OR 4.35, 95% CI 2.60-7.27), and those who did not receive responsive caregiving were more likely to experience ECD delay (OR 3.89, 95% CI 1.89-8.02). Responsive caregiving partly mediated the relationship between maternal depression and ECD. Early intervention for children with depressed mothers is worthy of further investigation.

Mental disorders are a significant contributor to disease burden. However, there is a large treatment gap for common mental disorders worldwide. This systematic review summarizes the factors associated with mental health service use.

Measurement of cognitive functions is typically based on the implicit assumption that the mental architecture underlying cognitive task performance is constant throughout the task. In contrast, skill learning theory implies that cognitively demanding task performance is an adaptive process that progresses from initial heavy engagement of effortful and task-general metacognitive and executive control processes towards more automatic and task-specific performance. However, this hypothesis is rarely applied to the short time spans of traditional cognitive tasks such as working memory (WM) tasks. We utilized longitudinal structural equation models on two well-powered data sets to test the hypothesis that the initial stages of WM task performances load heavily on a task-general g-factor and then start to diverge towards factors specific to task structure. In line with the hypothesis, data from the first experiment (N = 296) was successfully fitted in a model with task-initial unity of the WM paradigm-specific latent factors, after which their intercorrelations started to diverge. The second experiment (N = 201) replicated this pattern except for one paradigm-specific latent factor. These preliminary results suggest that the processes underlying working memory task performance tend to progress rapidly from more task-general towards task-specific, in line with the cognitive skill learning framework. Such task-internal dynamics has important implications for the measurement of complex cognitive functions.

To investigate the influence of aerobic exercise on depression among the young people.

The Dynamic Appraisal of Situational Aggression: Youth Version (DASA:YV) is a brief instrument, most often used by nurses and was specifically designed to assess risk of imminent violence in youth settings. To date, it has been recommended that DASA:YV scores are interpreted in a linear manner, with high scores indicating a greater level of risk and therefore need more assertive and immediate intervention. This study re-analyses an existing data set using contemporary robust data analytic procedures to examine the predictive validity of the DASA:YV, and to determine appropriate risk bands. Mixed effect logistic regression models were used to determine whether the DASA:YV predicted aggression when the observations are correlated. Two approaches were employed to identify and test novel DASA:YV risk bands, where (1) three risk bands as previously generated for the adult DASA were used as a starting point to consider recategorising the DASA:YV into three risk bands, and (2) using a decision tree analysis method known as Chi-square automated interaction detection to produce risk bands. There was no statistically significant difference between a four and three category of risk band. AUC values were 0.85 for the four- and three-category options. A three-category approach is recommended for the DASA:YV. The new risk bands may assist nursing staff by providing more accurate categorisation of risk state. Identification of escalation in risk state may prompt early intervention, which may also prevent reliance on the use of restrictive practices when young people are at risk of acting aggressively.

With the urgent need for clinicians capable of responding to the opioid crisis, an interprofessional education (IPE) pilot curriculum was launched to assess trainee self-efficacy in managing chronic pain and mental health conditions, and attitudes toward interprofessional practice among resident physicians, family nurse practitioners (FNP), and physician assistant (PA) students.

Both mental health and mental illness unfold in complex and unpredictable ways. Novel artificial intelligence approaches from the area of dynamical systems reconstruction can characterize such dynamics and help understand the underlying brain mechanisms, which can also be used as potential biomarkers. However, applying deep learning to model dynamical systems at the individual level must overcome numerous computational challenges to be reproducible and clinically useful. In this study, we performed an extensive analysis of these challenges using generative modeling of brain dynamics from fMRI data as an example and demonstrated their impact on classifying patients with schizophrenia and major depression. This study highlights the tendency of deep learning models to identify functionally unique solutions during parameter optimization, which severely impacts the reproducibility of downstream predictions. We hope this study guides the future development of individual-level generative models and similar machine learning approaches aimed at identifying reproducible biomarkers of mental illness.

Rotating shiftwork schedules are known to disrupt sleep in a manner that can negatively impact safety. Consumer sleep technologies (CSTs) may be a useful tool for sleep tracking, but the standard feedback provided by CSTs may not be salient to shift-working populations. SleepTank is an app that uses the total sleep time data scored by a CST to compute a percentage that equates hours of sleep to the fuel in a car and warns the user to sleep when the "tank" is low. Royal Australian Navy aircraft maintenance workers operating on a novel rotational shift schedule were given Fitbit Versa 2s to assess sleep timing, duration, and efficiency across a 10-week period. Half of the participants had access to just the Fitbit app while the other half had access to Fitbit and the SleepTank app. The goal of this study was to evaluate differences in sleep behavior between shifts using an off-the-shelf CST and to investigate the potential of the SleepTank app to increase sleep duration during the 10-week rotational shift work schedule.

Restoring motor function after stroke necessitates involvement of numerous cognitive systems. However, the impact of damage to motor and cognitive network organization on recovery is not well understood. To discover correlates of successful recovery, we explored imaging characteristics in chronic stroke subjects by combining noninvasive brain stimulation and fMRI. Twenty stroke survivors (6 months or more after stroke) were randomly assigned to a single session of transcranial direct current stimulation (tDCS) or sham during image acquisition. Twenty healthy subjects were included as controls. tDCS was limited to 10 min at 2 mA to serve as a mode of network modulation rather than therapeutic delivery. Fugl-Meyer Assessments (FMA) revealed significant motor improvement in the chronic stroke group receiving active stimulation (p = 0.0005). Motor changes in this group were correlated in a data-driven fashion with imaging features, including functional connectivity (FC), surface-based morphometry, electric field modeling and network topology, focusing on relevant regions of interest. We observed stimulation-related changes in FC in supplementary motor (p = 0.0029), inferior frontal gyrus (p = 0.0058), and temporo-occipital (p = 0.0095) areas, though these were not directly related to motor improvement. The feature most strongly associated with FMA improvement in the chronic stroke cohort was graph topology of the dorsal attention network (DAN), one of the regions surveyed and one with direct connections to each of the areas with FC changes. Chronic stroke subjects with a greater degree of motor improvement had lower signal transmission cost through the DAN (p = 0.029). While the study was limited by a small stroke cohort with moderate severity and variable lesion location, these results nevertheless suggest a top-down role for higher order areas such as attention in helping to orchestrate the stroke recovery process.

Difficulty controlling anger is a common postdeployment problem in military personnel. Chronic and unregulated anger can lead to inappropriate aggression and is associated with behavioral health, legal, employment, and relationship problems for military service members. Military-related betrayal (e.g., military sexual assault, insider attacks) is experienced by over a quarter of combat service members and is associated with chronic anger and aggression. The high level of physical risk involved in military deployments make interconnectedness and trust in the military organization of utmost importance for survival during missions. While this has many protective functions, it also creates a vulnerability to experiencing military-related betrayal. Betrayal is related to chronic anger and aggression. Individuals with betrayal-related injuries express overgeneralized anger, irritability, blaming others, expectations of injustice, inability to forgive others, and ruminations of revenge. Current approaches to treating anger and aggression in military populations are inadequate. Standard anger treatment is not trauma-informed and does not consider the unique cultural context of anger and aggression in military populations, therefore is not well suited for anger stemming from military-related betrayal. While trauma-informed interventions targeting anger for military personnel exist, anger outcomes are mixed, and aggression and interpersonal functioning outcomes are poor. Also, these anger interventions are designed for patients with posttraumatic stress disorder. However, not all military-related betrayal meets the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition-5 definition of trauma, though it may still lead to chronic anger and aggression. As a result, these patients lack access to treatment that appropriately targets the function of their anger and aggression.

Neurofilament light chain (NFL) is elevated in neurodegenerative diseases, including Parkinson's disease (PD). This study aimed to investigate serum NFL in newly diagnosed PD and its association with cognitive and motor decline over 10 years. Serum NFL levels were measured in PD patients and controls from the ParkWest study at diagnosis (baseline) and after 3 and 5 years. Mixed-effects regression analyzed changes in NFL and the association with annual changes in MMSE and UPDRS-III scores over 10 years. PD patients had elevated serum NFL at all visits and a faster annual increase over 5 years compared to controls (0.09 pg/mL per year; p = 0.029). Higher baseline NFL predicted faster cognitive decline β -0.77 transformed MMSE; p = 0.010), and a 40% NFL increase predicted future motor decline (β 0.28 UPDRS-III; p = 0.004). Elevated serum NFL in early PD is linked to faster cognitive and motor impairment, suggesting its prognostic value in PD biomarker panels.

The rapid rise in the availability and scale of scRNA-seq data needs scalable methods for integrative analysis. Though many methods for data integration have been developed, few focus on understanding the heterogeneous effects of biological conditions across different cell populations in integrative analysis. Our proposed scalable approach, scParser, models the heterogeneous effects from biological conditions, which unveils the key mechanisms by which gene expression contributes to phenotypes. Notably, the extended scParser pinpoints biological processes in cell subpopulations that contribute to disease pathogenesis. scParser achieves favorable performance in cell clustering compared to state-of-the-art methods and has a broad and diverse applicability.

Research suggests an association between COVID-19 infection and certain financial hardships in the shorter term and among single-state and privately insured samples. Whether COVID-19 is associated with financial hardship in the longer-term or among socially vulnerable populations is unknown. Therefore, we examined whether COVID-19 was associated with a range of financial hardships 18 months after initial infection among a national cohort of Veterans enrolled in the Veterans Health Administration (VHA)-the largest national integrated health system in the US. We additionally explored the association between Veteran characteristics and financial hardship during the pandemic, irrespective of COVID-19.

Problem adaptation therapy (PATH) is a relatively new psychotherapy that recognises the importance of simultaneously targeting cognitive impairment and functional disability in the treatment of late-life depression. This is the first systematic review to examine the effectiveness of PATH.

Depression is one of the most common mental disorders and substantially decreases socioemotional well-being and health-related quality of life. Analyzing temporal patterns in depressive symptoms can reveal emerging risks that require attention and have implications for mental health promotion. The present study disentangled age, period, and cohort (APC) effects on trends in depressive symptoms and their gender disparities among China's nationally representative samples of middle-aged and older adults.

Internal circadian phase assessment is increasingly acknowledged as a critical clinical tool for the diagnosis, monitoring, and treatment of circadian rhythm sleep-wake disorders and for investigating circadian timing in other medical disorders. The widespread use of in-laboratory circadian phase assessments in routine practice has been limited, most likely because circadian phase assessment is not required by formal diagnostic nosologies, and is not generally covered by insurance. At-home assessment of salivary dim light melatonin onset (DLMO, a validated circadian phase marker) is an increasingly accepted approach to assess circadian phase. This approach may help meet the increased demand for assessments and has the advantages of lower cost and greater patient convenience. We reviewed the literature describing at-home salivary DLMO assessment methods and identified factors deemed to be important to successful implementation. Here, we provide specific protocol recommendations for conducting at-home salivary DLMO assessments to facilitate a standardized approach for clinical and research purposes. Key factors include control of lighting, sampling rate, and timing, and measures of patient compliance. We include findings from implementation of an optimization algorithm to determine the most efficient number and timing of samples in patients with Delayed Sleep-Wake Phase Disorder. We also provide recommendations for assay methods and interpretation. Providing definitive criteria for each factor, along with detailed instructions for protocol implementation, will enable more widespread adoption of at-home circadian phase assessments as a standardized clinical diagnostic, monitoring, and treatment tool.

Negative emotions and associated avoidance behaviors are core symptoms of anxiety. Current treatments aim to resolve dysfunctional coupling between them. However, precise interactions between emotions and avoidance in patients' everyday lives and changes from pre- to post-treatment remain unclear. We analyzed data from a randomized controlled trial where patients with anxiety disorders underwent 16 sessions of cognitive behavioral therapy (CBT). Fifty-six patients (68 % female, age: M = 33.31, SD = 12.45) completed ecological momentary assessments five times a day on 14 consecutive days before and after treatment, rating negative emotions and avoidance behaviors experienced within the past 30 min. We computed multilevel vector autoregressive models to investigate contemporaneous and time-lagged associations between anxiety, depression, anger, and avoidance behaviors within patients, separately at pre- and post-treatment. We examined pre-post changes in network density and avoidance centrality, and related these metrics to changes in symptom severity. Network density significantly decreased from pre- to post-treatment, indicating that after therapy, mutual interactions between negative emotions and avoidance were attenuated. Specifically, contemporaneous associations between anxiety and avoidance observed before CBT were no longer significant at post-treatment. Effects of negative emotions on avoidance assessed at a later time point (avoidance instrength) decreased, but not significantly. Reduction in avoidance instrength positively correlated with reduction in depressive symptom severity, meaning that as patients improved, they were less likely to avoid situations after experiencing negative emotions. Our results elucidate mechanisms of successful CBT observed in patients' daily lives and may help improve and personalize CBT to increase its effectiveness.

This study aimed to characterize unmet reproductive health needs of female young adult survivors of childhood cancer (YASCC), as understood by oncology providers ( = 10) caring for this patient population. Providers completed brief online questionnaires and a one-time semi-structured interview. Descriptive statistics characterized quantitative data, and rapid qualitative analysis identified themes in interview data. In survey data, providers rated fertility as a top concern among YASCC. Qualitative data identified specific under-addressed reproductive sexual health information needs for patients and also for their providers. The results provide preliminary knowledge to inform the development of behavioral interventions to address patients' unmet needs.

Self-efficacy is the strongest predictor of completing home exercise programs (HEPs). How physical therapists address low levels of self-efficacy is unknown. Our objectives were to determine (1) knowledge and confidence in addressing patients' self-efficacy; (2) strategies used to address low self-efficacy; and (3) barriers.

Attending to heartbeats for interoceptive awareness initiates distinct electrophysiological responses synchronized with the R-peaks of an electrocardiogram (ECG), such as the heartbeat-evoked potential (HEP). Beyond HEP, this study proposes heartbeat-related spectral perturbation (HRSP), a time-frequency map of the R-peak locked electroencephalogram (EEG), and explores its characteristics in identifying interoceptive attention states using a classification approach. HRSPs of EEG brain components specified by independent component analysis (ICA) were used for the offline and online classification of interoceptive states. A convolutional neural network (CNN) designed specifically for HRSP was applied to publicly available data from a binary-state experiment (attending to self-heartbeats and white noise) and data from our four-state classification experiment (attending to self-heartbeats, white noise, time passage, and toe) with diverse input feature conditions of HRSP. From the dynamic state perspective, we evaluated the primary frequency bands of HRSP and the minimal number of averaging epochs required to reflect changing interoceptive attention states without compromising accuracy. We also assessed the utility of group ICA and models for classifying HRSP in new participants. The CNN for trial-by-trial HRSP with actual R-peaks demonstrated significantly higher classification accuracy than HRSP with sham, i.e., randomly positioned, R-peaks. Gradient-weighted class activation mapping highlighted the prominent role of theta and alpha bands between 200-600 ms post-R-peak-features absent in classifications using sham HRSPs. Online classification benefits from employing a group ICA and classification model, ensuring reliable accuracy without individual EEG precollection. These results suggest HRSP's potential to reflect interoceptive attention states, proposing transformative implications for clinical applications.

Internet gaming disorder (IGD) and tobacco use disorder (TUD) are two major addiction disorders that result in substantial financial loss. Identifying the similarities and differences between these two disorders is important to understand substance addiction and behavioral addiction. The current study was designed to compare these two disorders utilizing dynamic analysis.

Abnormal brain aging is suggested in epilepsy. Given the brain network dysfunction in epilepsy, the white matter tracts, which primarily interconnect brain regions, could be of special importance. We focused on white matter brain aging in diverse forms of epilepsy and comorbid psychosis. We obtained brain diffusion tensor imaging (DTI) data at 3 T-MRI in 257 patients with epilepsy and 429 healthy subjects. The tract-based fractional anisotropy values of the healthy subjects were used to build a brain-age prediction model, and we calculated the brain-predicted age difference (brain-PAD: predicted age-chronological age) of all subjects. As a result, almost all epilepsy categories showed significantly increased brain-PAD (p < 0.001), including temporal lobe epilepsy (TLE) with no MRI-lesion (+ 4.2 yr), TLE with hippocampal sclerosis (+ 9.1 yr), extratemporal focal epilepsy (+ 5.1 yr), epileptic encephalopathy or progressive myoclonus epilepsy (+ 18.4 yr), except for idiopathic generalized epilepsy (IGE). Patients with psychogenic non-epileptic seizures also presented increased brain-PAD. In TLE, interictal psychosis significantly raised brain-PAD by 8.7 years. In conclusion, we observed increased brain aging in most types of epilepsy, which was generally consistent with brain morphological aging results in previous studies. Psychosis may accelerate brain aging in TLE. These findings may suggest abnormal aging mechanisms in epilepsy and comorbid psychotic symptoms.

Schizophrenia is a serious and debilitating psychiatric disorder that is linked to marked social and occupational impairment. Despite the vital relevance of medication, non-adherence with recommended pharmacological treatments has been identified as a worldwide problem and is perhaps the most difficult component of treating schizophrenia. There are limited studies conducted on magnitude and potential factors of medication non-adherence among peoples with schizophrenia in Ethiopia.

Mechanisms underlying neurodegeneration in multiple sclerosis (MS) remain poorly understood but mostly implicate molecular pathways that are not unique to MS. Recently detected tau seeding activity in MS brain tissues corroborates previous neuropathological reports of hyperphosphorylated tau (p-tau) accumulation in secondary and primary progressive MS (PPMS). We aimed to investigate whether aberrant tau phosphorylation can be detected in the cerebrospinal fluid (CSF) of MS patients by using novel ultrasensitive immunoassays for different p-tau biomarkers.

Our group previously showed that genetic or pharmacological inhibition of the cystine/glutamate antiporter, system x , mitigates excitotoxicity after anoxia by increasing latency to anoxic depolarization, thus attenuating the ischaemic core. Hypoxia, however, which prevails in the ischaemic penumbra, is a condition where neurotransmission is altered, but excitotoxicity is not triggered. The present study employed mild hypoxia to further probe ischaemia-induced changes in neuronal responsiveness from wild-type and xCT KO (xCT) mice. Synaptic transmission was monitored in hippocampal slices from both genotypes before, during and after a hypoxic episode. Although wild-type and xCT slices showed equal suppression of synaptic transmission during hypoxia, mutant slices exhibited a persistent potentiation upon re-oxygenation, an effect we termed 'post-hypoxic long-term potentiation (LTP)'. Blocking synaptic suppression during hypoxia by antagonizing adenosine A receptors did not preclude post-hypoxic LTP. Further examination of the induction and expression mechanisms of this plasticity revealed that post-hypoxic LTP was driven by NMDA receptor activation, as well as increased calcium influx, with no change in paired-pulse facilitation. Hence, the observed phenomenon engaged similar mechanisms as classical LTP. This was a remarkable finding as theta-burst stimulation-induced LTP was equivalent between genotypes. Importantly, post-hypoxic LTP was generated in wild-type slices pretreated with system x inhibitor, S-4-carboxyphenylglycine, thereby confirming the antiporter's role in this phenomenon. Collectively, these data indicate that system x interference enables neuroplasticity in response to mild hypoxia, and, together with its regulation of cellular damage in the ischaemic core, suggest a role for the antiporter in post-ischaemic recovery of the penumbra.

Mental health in adolescence is critical in its own right and a predictor of later symptoms of anxiety and depression. To address these mental health challenges, it is crucial to understand the variables linked to anxiety and depression in adolescence.

Studies on the prevalence of suicidal ideation (SI) and its associated factors among the elderly in China show considerable variability. This meta-analysis aims to clarify the epidemiological features of SI in this population.

Neuroprotection aims to safeguard neurons from damage caused by various factors like stress, potentially leading to the rescue, recovery, or regeneration of the nervous system and its functions [J Clin Neurosci. 2002;9(1):4-8]. Conversely, neuroplasticity refers to the brain's ability to adapt and change throughout life, involving structural and functional alterations in cells and synaptic transmission [Neural Plast. 2014;2014:541870]. Neuroprotection is a broad and multidisciplinary field encompassing various approaches and strategies aimed at preserving and promoting neuronal health. It is a critical area of research in neuroscience and neurology, with the potential to lead to new therapies for a wide range of neurological disorders and conditions. Neuroprotection can take various forms and may involve pharmacological agents, lifestyle modifications, or behavioral interventions. Accordingly, also the perspective and the meaning of neuroprotection differs due to different angles of interpretation. The primary interpretation is from the pharmacological point of view since the most consistent data come from this field. In addition, we will discuss also alternative, yet less considered, perspectives on neuroprotection, focusing on specific neuroprotective targets, interactions with surrounding microglia, different levels of neuroprotective effects, the reversive/adaptative dimension, and its use as anticipatory/prophylactic intervention.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with onset in infancy. Early intervention is critical to improve the prognosis for these children. E-health interventions have tremendous potential. This review aimed to determine the status and effectiveness of family interventions for parents of children aged 0-6 years with ASD in the context of e-health.

Systemic sclerosis (SSc) is a rare autoimmune disease with multiple organ involvement; however, the contribution of the nervous system (NS) remains relatively understudied. There are no specific data on the role of the autoimmune response and inflammation in the development of peripheral nerve system (PNS) damage in SSc and markers to assess this damage have yet to be identified.

The association of shift work (SW) and disrupted circadian rhythm with markers of large artery atherosclerosis and cerebral small vessel disease is uncertain. We aimed to study the separate association of current and former SW with these markers.

Common atypical antipsychotics include risperidone, paliperidone, olanzapine, lurasidone, quetiapine, clozapine, aripiprazole, ziprasidone, asenapine, brexpiprazole, and cariprazine. Previous studies on ocular adverse reactions of antipsychotics were mainly focused on typical antipsychotics. Systematic research on atypical antipsychotics remains limited.

Rising anxiety and depression in primary school students adversely affect their development and academics, burdening families and schools. This trend necessitates urgent, focused research within this young demographic. This alarming trend calls for a systematic bibliometric analysis to develop effective preventative and remedial strategies.

Digital technologies are essential for connecting to the Internet and communicating with others. This study used data from the 2022 Project Community Homelessness Assessment, Local Education and Networking Groups (CHALENG) survey, which surveyed a national sample of 1992 homeless-experienced veterans (HEV) about use of cell phones, smart phones, computers, laptops, and the Internet. Data were analyzed to compare currently and formerly homeless veterans on digital access and to identify characteristics associated with never using the Internet. Over 75% of HEV reported having a cellphone and over 65% reported having a smartphone. Internet use was common among HEV as 74% of currently homeless veterans and 77% of formerly homeless veterans reported using the Internet. Among HEV who used the Internet, over 70% used the Internet at least weekly. There was no significant difference between currently and formerly homeless veterans on their Internet use in the total sample and subsamples of only black and only Hispanic veterans. Overall, HEV who were older, black non-Hispanic, and living in the Southeast were more likely to never use the Internet. These findings highlight the potential for technology-based interventions among HEV, and suggest a digital divide based on age, race, and geography.

Adequate eHealth literacy and health promotion behavior (HPB) are important to achieve good health-related quality of life. There is limited information on the influence of eHealth literacy and HPB on body mass index (BMI) in our setting and among public service workers.

Depression is a chronic psychiatric condition that places significant burdens on individuals, families, and societies. The rapid evolution of non-invasive brain stimulation techniques has facilitated the extensive clinical use of Transcranial Magnetic Stimulation (TMS) for depression treatment. In light of the substantial recent increase in related research, this study aims to employ bibliometric methods to systematically review the global research status and trends of TMS in depression, providing a reference and guiding future studies in this field.

Screening, brief intervention, and referral to treatment (SBIRT) for adolescent alcohol and drug (AOD) use is recommended to occur with adolescents admitted to pediatric trauma centers. Most metrics on SBIRT service delivery only reference medical record documentation. In this analysis we examined changes in adolescents' perception of SBIRT services and concordance of adolescent-report and medical record data, among a sample of adolescents admitted before and after institutional SBIRT implementation.

Compared to civilians and non-medical personnel, military medical doctors are at increased risk for sleep disturbances and impaired psychological well-being. Despite their responsibility and workload, no research has examined sleep disturbances and psychological well-being among the medical doctors (MDs) of the Swiss Armed Forces (SAF). Thus, the aims of the proposed study are (1) to conduct a cross-sectional study (labeled 'Survey-Study 1') of sleep disturbances and psychological well-being among MDs of the SAF; (2) to identify MDs who report sleep disturbances (insomnia severity index >8), along with low psychological well-being such as symptoms of depression, anxiety and stress, but also emotion regulation, concentration, social life, strengths and difficulties, and mental toughness both in the private/professional and military context and (3) to offer those MDs with sleep disturbances an evidence-based and standardized online interventional group program of cognitive behavioral therapy for insomnia (eCBTi) over a time lapse of 6 weeks (labeled 'Intervention-Study 2').

Lifestyle intervention can produce clinically significant weight loss and reduced disease risk/severity for many individuals with overweight/obesity. Dietary lapses, instances of non-adherence to the recommended dietary goal(s) in lifestyle intervention, are associated with less weight loss and higher energy intake. There are distinct "types" of dietary lapse (e.g., eating an off-plan food, eating a larger portion), and behavioral, psychosocial, and contextual mechanisms may differ across dietary lapse types. Some lapse types also appear to impact weight more than others. Elucidating clear lapse types thus has potential for understanding and improving adherence to lifestyle intervention.

Chronic motor impairments are a leading cause of disability after stroke. Previous studies have associated motor outcomes with the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to model chronic motor outcomes after stroke and compares the accuracy of these associations to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients from the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA) Stroke Recovery Working Group. Using the explained variance metric to measure the strength of the association between chronic motor outcomes and imaging biomarkers, we compared theory-based biomarkers, like lesion load to known motor tracts, to three data-driven biomarkers: lesion load of lesion-behaviour maps, lesion load of structural networks associated with lesion-behaviour maps, and measures of regional structural disconnection. In general, data-driven biomarkers had stronger associations with chronic motor outcomes accuracy than theory-based biomarkers. Data-driven models of regional structural disconnection performed the best of all models tested ( = 0.210, < 0.001), performing significantly better than the theory-based biomarkers of lesion load of the corticospinal tract ( = 0.132, < 0.001) and of multiple descending motor tracts ( = 0.180, < 0.001). They also performed slightly, but significantly, better than other data-driven biomarkers including lesion load of lesion-behaviour maps ( = 0.200, < 0.001) and lesion load of structural networks associated with lesion-behaviour maps ( = 0.167, < 0.001). Ensemble models - combining basic demographic variables like age, sex, and time since stroke - improved the strength of associations for theory-based and data-driven biomarkers. Combining both theory-based and data-driven biomarkers with demographic variables improved predictions, and the best ensemble model achieved = 0.241, < 0.001. Overall, these results demonstrate that out-of-sample associations between chronic motor outcomes and data-driven imaging features, particularly when lesion data is represented in terms of structural disconnection, are stronger than associations between chronic motor outcomes and theory-based biomarkers. However, combining both theory-based and data-driven models provides the most robust associations.

The case of "a multimillionaire who was sent to a psychiatric hospital after an argument with his son" has sparked heated debate in the Chinese mainland. This incident is particularly significant as 2023 marks the 10 anniversary of the implementation of the Mental Health Law of the People's Republic of China. The focus of the ongoing debate, as brought to light by the aforementioned case, is centered on the right to refuse treatment for patients with mental disorders.