Objective Catatonia in young people is increasingly recognized, but pediatric cohorts remain limited. We characterized clinical presentation and treatment course and examined factors associated with electroconvulsive therapy (ECT) use and exploratory inflammatory correlates of psychomotor dimensions. Methods We retrospectively reviewed 57 consecutive patients aged <18 years treated at a tertiary referral center; one patient with a medical etiology was excluded from inferential analyses, leaving 56. DSM-5-TR signs were operationalized as hypokinetic, hyperkinetic, and parakinetic scores. Associations with ECT use were estimated using Firth penalized logistic regression. Admission neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and systemic inflammation response index (SIRI) were examined with false-discovery-rate correction. Results Catatonia occurred across schizophrenia spectrum (n = 28), bipolar (11), depressive (11), and autism spectrum (6) disorders. ECT was used in 21 patients (37.5%). Older age, male sex, higher lorazepam-equivalent dose, and greater catatonia symptom burden were associated with ECT use. Each additional parakinetic sign was associated with higher odds of ECT use (OR 2.94, 95% CI 1.52-7.29), including after diagnostic-group adjustment; however, the parakinetic score overlapped substantially with total symptom burden, and their independent contributions could not be resolved. NLR and SIRI were associated only with the hyperkinetic score, which consisted of documented agitation. Thirty-five patients (62.5%) had a discharge CGI-catatonia score of 1. Conclusion ECT use was associated with greater catatonia symptom burden and higher parakinetic scores, but overlap between these measures and retrospective treatment selection preclude dimension-specific or causal inference. The laboratory findings were limited to agitation and require prospective confirmation.
BACKGROUND:Disorders of consciousness (DoC; encompassing coma, unresponsive wakefulness syndrome, and minimally conscious state) are sequelae of critical illness that prolong hospitalization and interfere with engagement in care. Due to heterogeneous and incompletely understood pathophysiology, no pharmacologic standard of care has been established. We aimed to systematically examine the efficacy, safety, and dosing of neurostimulants among adult ICU patients with DoC. METHODS:We conducted a systematic search of PubMed, Embase, CINAHL, and Web of Science from inception to December 2025. Studies that examined the use of neurostimulants for DoC in critically ill adults were included. We followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and used the Effective Public Health Practice Project framework to appraise bias. RESULTS:35 studies met inclusion criteria: 11 randomized controlled trials, 19 observational, and 5 case-based studies. Amantadine was the most studied agent (N = 2743), with several studies reporting improvements in standardized measures of consciousness and earlier transition to rehabilitation. Modafinil (N = 1776) was primarily evaluated for hypersomnolence and demonstrated mixed effects on Glasgow Coma Scale (GCS) scores. Methylphenidate (N = 1044) demonstrated signals of benefit in both randomized studies and augmentation strategies with amantadine. Apomorphine was evaluated in one placebo-controlled ICU trial and carbidopa-levodopa in one case series. Despite inclusion in our search strategy, no studies examined caffeine, amphetamines, or bromocriptine. CONCLUSIONS:Neurostimulants were associated with short-term improvements in measures of arousal and recovery, although evidence remains heterogeneous and largely observational. Amantadine has the largest ICU evidence base; methylphenidate has been studied as monotherapy and augmentation; modafinil has primarily been evaluated for hypersomnolence. Further placebo-controlled trials are needed to establish efficacy across different causes of DoC; head-to-head studies are needed to clarify comparative effectiveness among available neurostimulants.
OBJECTIVE:To evaluate a delivery-based bedside screening workflow for identifying risk of postpartum depressive symptoms within one year postpartum and compare its performance with a parsimonious electronic health record (EHR)-derived approach. METHODS:This retrospective cohort study included individuals screened during delivery hospitalization at two hospitals within a single integrated health system between November 2023 and November 2024. A structured bedside screening tool incorporating psychiatric, psychosocial, and obstetric risk factors was administered at admission. The primary outcome was elevated postpartum depressive symptoms, defined as Patient Health Questionnaire-9 (PHQ-9) score ≥ 10 within one year postpartum. Associations between screening variables and postpartum PHQ-9 scores were evaluated using nonparametric analyses. A parsimonious multivariable logistic regression model incorporating routinely available EHR-derived variables was also evaluated. RESULTS:Among 575 individuals with postpartum PHQ-9 data, 67 (11.7%) had PHQ-9 scores ≥10 postpartum. In the analytic cohort (n = 512), individuals classified as high risk at delivery had higher postpartum PHQ-9 scores than those not classified as high risk (median 3 vs 1, p < 0.001). Low social support, intimate partner violence, prior postpartum depression, and PHQ-2 ≥ 3 demonstrated strong univariate associations with postpartum symptom severity. A parsimonious EHR-based model including prenatal PHQ-9 score and insurance status demonstrated higher discrimination for PHQ-9 ≥ 10 than the bedside high-risk designation (AUC 0.74 vs 0.61). CONCLUSIONS:Delivery-based bedside screening identified individuals at elevated risk for postpartum depressive symptoms, while EHR-derived variables provided scalable risk stratification. Integrating EHR-based indicators alongside psychosocial screening may support more proactive and individualized perinatal mental health care.
BACKGROUND:This review synthesises literature on the implementation of digital mental health (MH) screening in neurology healthcare services and summarises information about its feasibility, acceptability and associated clinical outcomes. METHODS:Following prospective registration (CRD420251010397) we searched Embase, Medline, PsycINFO, and Central for peer-reviewed articles describing a digitally delivered MH screening procedure using a validated screener implemented in a real-world neurological care service for people of any age with a neurological disorder. Two authors reviewed articles and extracted data. Study quality was assessed for articles reporting outcome data. Of 10,633 abstracts and 228 full-text articles, 31 met eligibility criteria. RESULTS:Articles spanned 10 neurological disorders (most commonly epilepsy) and primarily screened depression and anxiety. Screening was delivered via tablets, kiosks/clinic computers, or patients' devices and often integrated with electronic medical/health record systems. Completion rates varied but acceptability was generally high among clinicians and patients. Positive MH screening results were commonly associated with clinical follow-up such as symptom discussion, medication initiation or adjustment, and referrals. Some services used predefined score thresholds to trigger MH care pathways. Limited pre-post evidence suggested digital screening may increase detection of depression and anxiety, but risk of bias was common, and no articles assessed impacts on MH outcomes over time. CONCLUSION:A subset of articles suggest that digital MH screening appears feasible and acceptable in real-world neurology settings and may improve detection, facilitate doctor-patient communication, and prompt treatment-related actions. These findings may inform the integration of MH screening in neurological care, particularly during pre-implementation planning.
PURPOSE:This study aims to examine the relationship between adverse childhood experiences (ACEs), physical activity (PA), and cognitive frailty (CF). METHODS:Data were obtained from the Survey of Health, Aging, and Retirement in Europe (SHARE). ACEs, PA, and CF were collected through questionnaires and physical measurements. Statistical analyses were conducted using Cox regression models and causal mediation analysis. RESULTS:A total of 23,385 participants were included. After adjusting for all covariates, each additional ACE was associated with a 9% increased risk of CF (HR = 1.09). Conversely, each additional year of sufficient PA was associated with a 25% reduced risk of CF (HR = 0.75). Compared with the I-PA _ I-PA group, the S-PA _ I-PA group (HR = 0.61), the I-PA _ S-PA group (HR = 0.87), and the S-PA _ S-PA group (HR = 0.46) reduced the risk of CF occurrence by 39%, 13%, and 54% respectively. Maintaining sufficient PA for over 6 years significantly mitigated the negative effects of ACEs on CF, particularly in individuals with poor childhood socioeconomic status (HR = 0.59) and poor neighbor quality (HR = 0.45). Mediation analysis indicated that changes in PA and the maintenance of sufficient PA mediated the association between ACEs and CF, with mediation proportions of 11.26% and 23.00%, respectively. CONCLUSION:A higher number of ACEs is associated with an increased risk of CF. However, increasing and maintaining sufficient PA for at least four years reduces this risk. Furthermore, PA can counteract the adverse effects of ACEs on CF.
BACKGROUND:The mechanisms underlying the high comorbidity between internalizing disorders (IDs) and functional disorders (FDs) remain unclear. This study aimed to identify data-driven subgroups of major depressive disorder (MDD), generalized anxiety disorder (GAD), myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), fibromyalgia (FM), and irritable bowel syndrome (IBS) symptoms in the general population, capturing shared symptom patterns while accounting for variation in symptom severity. METHOD:We analyzed cross-sectional data from 72,919 adults in the Dutch Lifelines Cohort Study, randomly divided into training and validation subsets. Twenty-seven ID and FD criteria symptoms were examined using mixed-measurement item response theory models in the training set, with the optimal model validated in the validation set. Class characteristics were assessed through associations with risk factors, ID/FD diagnosis and comorbidity patterns. RESULTS:Six classes best described the data: Healthy (57.5%), Pain (13.8%), Tension/Pain (9.1%), Anxiety (9.0%), Cognition/Fatigue (6.4%), and Depression (4.3%). All classes showed a combination of ID and FD symptoms, with the Cognition/Fatigue and Depression classes being most mixed, and the Pain and Anxiety classes most domain-specific. Transdiagnostic symptoms were highly endorsed across all classes. ID-FD comorbidity was highest in the Cognition/Fatigue and Depression classes, with the Depression class showing the greatest functional impairment. CONCLUSIONS:The identified classes comprised mixed symptoms from multiple disorders, rather than disorder-specific profiles. ID-FD comorbidity may partly result from non-specific defining symptoms lowering the threshold for fulfilling criteria for multiple disorders. Symptom-level evaluation can help identify more homogenous and clinically relevant subgroups. Longitudinal research is needed to clarify underlying mechanisms.
Background International guidelines recommend risk-adapted depression screening in primary care. However, empirical evidence on the diagnostic accuracy of depression screening questionnaires in patients at risk for depression remains limited. Objective To evaluate the diagnostic accuracy of the Patient Health Questionnaire-9 (PHQ-9) for detecting major depressive disorder (MDD) in primary care, stratified by the presence of single depression-related risk factors and the amount of risk factors. Methods This secondary analysis used data from 985 primary care patients participating in the GET.FEEDBACK.GP trial who completed the PHQ-9, a depression-related risk factor assessment, and underwent evaluation for MDD using the Mini-International Neuropsychiatric Interview (MINI). Accounting for partial verification bias, this study applied an inverse probability weighting normalized for a sample of 985. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the area under the curve (AUC) were calculated for different PHQ-9 cut-off scores across single risk factors and the amount of risk factors. The analysis was pre-registered (https://osf.io/wzctq). Results Of 985 participants, 89 (9.1%) had a MDD diagnosis. The best-performing PHQ-9 cut-offs, stratified by the amount of risk factors, varied, ranging from 7 to 13, with a higher number of risk factors being associated with a higher best-performing PHQ-9 cut-off score. Sensitivity ranged from 0.78 to 0.99; specificity from 0.80 to 0.91. PPV ranged from 0.40 to 0.88 and NPV from 0.80 to 0.99. AUCs ranged from 0.92 to 0.97, indicating excellent diagnostic accuracy. Similar results were found when stratifying by single risk factors. Conclusions The PHQ-9 demonstrated high diagnostic accuracy for detecting MDD in patients at risk for depression. Although optimal cut-offs vary slightly according to the number and type of risk factors present, the findings support the validity of risk-adapted depression screening using the PHQ-9 in primary care.
The aim of this study was to assess the potential of FTIR spectroscopy for monitoring biochemical changes in serum samples of individuals with carotid atherosclerosis following surgical intervention. Principal Component Analysis (PCA) of FTIR spectra from serum samples reveals distinct biochemical patterns at different time points: pre-surgery, 24 h post-surgery, and 48 h post-surgery. Two spectral ranges, 800–1800 cm−1 and 2800–3000 cm−1, were analyzed. PCA demonstrated that pre-surgery samples can be clearly differentiated from those taken 24 and 48 h post-surgery. However, no significant distinction was found between the 24-hour and 48-hour post-surgery samples. For the 800–1800 cm−1 range, the first principal component (PC1) explained 77.49% of the variance, highlighting the molecular vibrations of lipids, proteins, and carbohydrates. In the 2800–3000 cm−1 range, PC1 accounted for 94.89% of the variance, primarily reflecting lipid-related vibrations. These findings indicate a clear separation between pre-surgery and post-surgery samples, with the most significant variance explained by PC1. Additionally, the Boruta algorithm identified a key spectral range between 1506 cm−1 and 1673 cm−1, critical for distinguishing the samples. Classification models, including k-Nearest Neighbors, Gradient Boosting, Support Vector Machine, and Neural Network, demonstrated excellent performance in differentiating pre-surgery and post-surgery samples. However, the models struggled to distinguish between the 24-hour and 48-hour post-surgery time points. This suggests that FTIR spectroscopy may be useful for monitoring post-surgery recovery in carotid artery atherosclerosis, although subtle changes in the biochemical profile are challenging to detect between 24 and 48 h post-surgery.
Objective Delirium remains under-recognized. We evaluated feasibility and exploratory outcomes of a DELirium Team Approach-based intervention combining a 1-h educational program with an electronic medical record-integrated application. Methods We conducted a single-center before-after study in an intermediate care unit with 8-week pre-intervention and intervention periods. Adults without delirium on admission were enrolled. Primary outcome was chart-based identification of new-onset delirium within 48 h. Process evaluation included application logs, post-implementation nurse questionnaires, and structured interviews. Results Among 104 patients, new-onset delirium occurred in 28.9% (13/45) during the pre-intervention period and 15.3% (9/59) during the intervention period (risk difference, −13.6%; 95% CI, −29.7% to 2.5%; relative risk, 0.53; 95% CI, 0.25 to 1.13; p = 0.15). Patient-level application utilization was 100%. Of 23 nurses who used the application, 19 completed care-plan preparation within 10 min. Approximately 80% reported assessing pain, dehydration, and constipation in at least 50% of applicable opportunities, whereas corresponding actions were reported by 41.7%–58.3% and discussions of risk medications by 25.0%. Conclusions This pilot study supports the feasibility of integrating DELTA-based structured assessments into routine acute care, although translation into corresponding bedside and multidisciplinary actions remained inconsistent. Large-scale controlled studies using objective process measures are warranted.
Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) provides information on the molecular composition and structure of samples. The use of ATR-FTIR was evaluated for biochemical analysis and taxonomic differentiation of entomopathogenic nematodes (EPNs). Spectra were obtained from a small sample (pellet) of a nematode population recovered from commercial EPN packages, which was placed directly on the ATR plate. Differences in signal intensity at multiple peaks associated with biomolecules critical to the survival of EPN (trehalose, glycogen, and triglyceride) were measured and visualized using Non-Metric Multidimensional Scaling (nMDS) and Principal Component Analysis (PCA). Statistically significant differences in peak signal intensity were observed between EPN species for each biochemical parameter, providing a basis for assessing the likelihood of their performance success in the field conditions. The present study also evaluated FTIR analysis of EPN for taxonomic differentiation. Results demonstrate that FTIR can be used to identify and differentiate Steinernema and Heterorhabditis genera/species, offering a potentially faster, less expensive alternative to molecular identification techniques. Ultimately, this study demonstrates the efficacy of ATR-FTIR as a reliable method for assessing the biochemical suitability of EPN products for field applications and differentiating between EPNs.
Endometrial cancer (EC) is increasingly prevalent worldwide, highlighting the need for non-invasive blood-based diagnostic triage tools. ATR-FTIR spectroscopy enables rapid, label-free biochemical profiling of plasma or serum for experimental cancer detection. To date, no systematic review or meta-analysis has evaluated the experimental performance of infrared spectroscopy for discriminating EC from non-cancer in blood-based samples. This study synthesizes available evidence to characterize the strength, consistency, and heterogeneity of the underlying spectroscopic signal across preclinical and proof-of-concept studies. MEDLINE, Web of Science, EMBASE, Scopus, Google Scholar, and CENTRAL were searched without language restrictions. Eligible studies evaluated ATR-FTIR spectroscopy of plasma or serum using histopathology as the reference standard. Pooled sensitivity, specificity, likelihood ratios, and diagnostic odds ratios were estimated using a bivariate random-effects model, with assessment of heterogeneity, threshold effects, and publication bias. Five case–control studies comprising 1376 participants were included. For plasma-based analyses, pooled sensitivity was 0.61 (95% CI: 0.59–0.68) and specificity was 0.73 (95% CI: 0.69–0.76), with a diagnostic odds ratio of 4.23 (95% CI: 3.33–5.37). For serum-based analyses, pooled sensitivity and specificity were both 0.62 (95% CI: 0.59–0.65), with a diagnostic odds ratio of 2.65 (95% CI: 2.16–3.25). Substantial heterogeneity and significant threshold effects were observed. Current evidence supports reproducible spectroscopic differences between EC and non-cancer blood samples under experimental conditions. However, methodological heterogeneity and retrospective case–control study designs limit clinical interpretability. These findings provide a benchmark for future prospective validation rather than immediate clinical application.
Background Individuals with serious mental illness (SMI) experience disproportionately high rates of trauma exposure and trauma-related symptoms relative to the general population, yet many receive treatment in acute psychiatric settings that may inadvertently contribute to replication of traumatic experiences. Trauma-informed care (TIC), has emerged as a framework to mitigate and address trauma-related responses by implementing systems and individual level clinical interventions to improve patient care and reduce adverse outcomes. While research on TIC implementation in psychiatric settings is growing internationally, the unique features of the U.S. mental healthcare system warrant a focused examination of TIC practices including systems level models and trauma specific treatments. To our knowledge, this is the first systematic review examining trauma informed models and trauma specific interventions in U.S. inpatient psychiatric settings. Methods A systematic review of articles published between January 2014 through November 2024 following PRISMA 2020 guidelines using a PROSPERO-registered protocol (ID 600061) with PubMed and SCOPUS databases, and supplementation through Google Scholar. Included studies examined TIC models or trauma-specific treatments or interventions in U.S. inpatient psychiatric settings and reported clinical or system-level outcomes. Results Of 2269 identified records, six studies met inclusion criteria, and all were conducted in child or adolescent inpatient psychiatric units. Five studies examined TIC systems level implementation while only one study described a trauma-specific therapeutic intervention (Brief STAIR-A). Conclusions There is a substantial gap in the literature on TIC implementation, and trauma specific treatments in U.S. inpatient psychiatric settings. Findings center around pediatric populations with an absence of literature in adult acute care. The quality of evidence of most studies was limited and focused on implementation of milieu level TIC but not on clinical symptom assessment or outcomes. Development and evaluation of brief, scalable trauma-focused treatments or interventions may be a more flexible implementation strategy that can directly influence measurable inpatient outcomes.
INTRODUCTION:People with concurrent substance use and mental disorders (CD) experience a disproportionately higher risk of overdose compared to people with substance use disorder alone. However, predictors among individuals with severe concurrent disorders (SCD) in tertiary care settings remain poorly characterized. We aimed to identify factors associated with frequent non-fatal overdose using statistical learning methods. METHODS:Data were obtained from the Reducing Overdose and Relapse: Concurrent Attention to Neuropsychiatric Ailments and Drug Addiction (ROAR CANADA) longitudinal cohort of individuals with SCD treated at three tertiary centres in British Columbia (N = 326). Frequent overdose (FO) was defined as more than the median number of lifetime self-reported overdoses (>2). Candidate variables included sociodemographic characteristics, substance use patterns, impulsivity, and trauma history. Elastic Net regularized regression with bootstrap stability selection identified key predictors. Stability-selected variables were entered into a principal component logistic regression model to assess predictive performance. RESULTS:Recent heroin or fentanyl use was the most stable predictor of FO (100% selection), followed by Hepatitis C diagnosis (98%), being single (98%), recent crack cocaine use (95%), history of sexual abuse (88%), and history of physical abuse (87%). Being unhoused, prior hospitalization, and life-threatening illness were also associated with increased risk. Variables inversely associated with FO included recent cannabis use (94%), white ethnicity (87%), and perceived access to instrumental support (84%). Chi-square analyses showed significant associations between FO and recent heroin or fentanyl use (OR 6.99; 95% CI 4.29-11.38), Hepatitis C diagnosis (OR 4.02; 95% CI 2.12-7.64), recent crack cocaine use (OR 2.36; 95% CI 1.49-3.75), and history of physical trauma (OR 2.06; 95% CI 1.28-3.31). The model demonstrated moderate discrimination (area under the curve (AUC) = 0.78; sensitivity 66.2%; specificity 77.5%). CONCLUSION:To address the prolonged overdose crisis, findings suggest that integrated treatment of patients' polysubstance use, comorbidities, and social support is important for risk prevention. Future studies should involve validation in a larger, demographically diverse, sample.
OBJECTIVES:Several studies have found mild elevations of serum neurofilament light chain (sNfL) - a marker of neuroaxonal injury - in various psychiatric disorders (PD) compared to controls. Given the documented role of neuroinflammation in PD, this study investigated the relationship between inflammatory markers and sNfL in a transdiagnostic cohort of PD. METHODS:PD data was obtained from Signature Biobank (BbS) participants aged 40-81 that presented to a psychiatric emergency department with various PDs (n = 291, M = 181, F = 110), and a control group (n = 69, M = 36, F = 33). Serum NfL was measured using SIMOA technology, inflammatory markers interleukin-6 (IL-6) and tumour necrosis factor-alpha (TNF-α) were measured using ELISA, and C-reactive protein (CRP) was measured using Atellica CH Wide range method. Linear regression models were used to test associations between sNfL and inflammatory markers while controlling for age, sex, BMI, in addition to identified statistically significant medical/lifestyle covariates of sNfL and the inflammatory markers. RESULTS:In the PD transdiagnostic group, sNfL was positively associated with IL-6 (np2 = .065, p < .001) with a medium effect size and positively associated with TNF-α (np2 = .075, p = .002) with a medium effect size; no associations between CRP and sNfL were detected. At the subgroup levels, the associations remained statistically significant in the mood and psychotic disorders groups, and only for IL-6 in anxiety disorders group. CONCLUSIONS:This study provides evidence of an association between levels of inflammation (IL-6 and TNF-α) and neuroaxonal injury (sNfL) in PD.
BACKGROUND:The comorbidity of mental disorders and diabetes is on the rise, presenting a significant global public health challenge that gravely impacts the physical and psychological health of patients and presents obstacles to their effective management. The coronavirus disease 2019 (COVID-19) pandemic, in particular, aggravated these challenges owing to restrictions on in-person care. Artificial intelligence (AI) interventions have emerged as a promising solution to alleviate this burden. Thus, we conducted a scoping review to map the current evidence in the literature and provide a clear understanding of AI for mental disorders and comorbid diabetes. METHODS:This scoping review utilized the Arksey & O'Malley framework. Five electronic databases were systematically searched for studies published in the post-COVID era, focusing on AI-assisted approaches for individuals with mental disorders and comorbid diabetes. RESULTS:Twenty-four studies were reviewed. Supervised learning algorithms were most commonly employed, with studies reporting positive performance metrics, while generative AI was essentially absent. The findings demonstrated promising outcomes in four functional domains: Risk assessment, Prediction, Diagnosis, and Personalised care. AI applications are predominantly at the capability stage of translation maturity, focusing mainly on model development and validation, with limited adoption in existing clinical workflows. Other limitations include little demographic reporting, limited external validation, scarce intervention-focused research, and ethical concerns. CONCLUSION:AI shows promise in enhancing support for individuals with mental disorders and diabetes through targeted, data-driven strategies. Future studies should prioritize clinically integrated, patient-centred AI interventions and evaluate their effectiveness in improving functional outcomes, while addressing ethical considerations.
