Panax notoginseng saponins against ischemic stroke: From molecule to clinic.

Piloting a Near-Peer Lay Counselor Based Problem Solving Therapy Intervention for Youth With and Without HIV in Botswana: An Adaptation of the Friendship Bench.

Treatment-resistant depression (TRD) remains a complex challenge, often requiring interventions beyond standard medications. This review explores factors that may predict positive response to electroconvulsive therapy (ECT), repetitive transcranial magnetic stimulation (rTMS) and ketamine-based treatments to help guide clinical decision-making. A systematic review was conducted following PRISMA 2020 guidelines. English-language, peer-reviewed studies were identified through PubMed, Embase and Google Scholar using search terms such as 'treatment-resistant,' 'outcome,' 'prediction,' 'ECT,' 'rTMS,' and 'ketamine.' Studies were included if they examined clinical, biological or imaging predictors of response in adults with TRD. Case reports, reviews, editorials and non-English articles were excluded. A total of 42 studies were selected from 408 screened. Among these, 23 focused on ketamine/esketamine, 14 on rTMS, and 11 on ECT. Predictive factors were grouped into clinical (e.g., symptom profile, illness duration), biological (e.g., IL-6, CRP, BDNF) and imaging (e.g., cingulate cortex activity, connectivity). Inflammation markers and fronto-limbic network findings appeared across treatments, though findings were inconsistent. While some predictors show promise, clinical use remains limited by methodological differences and small sample sizes. Larger studies are required to identify clinically useful predictors. Additionally, for optimal treatment decision-making, comparative studies are necessary.

Machine learning (ML) applied to diffusion tensor imaging (DTI) has emerged as a promising tool for detecting microstructural brain alterations in movement disorders. However, existing studies vary widely in design, sample size, imaging pipelines, and analytic rigor, resulting in high methodological heterogeneity that limits quantitative comparability. This exploratory meta-analysis and narrative synthesis aimed to characterize performance trends, methodological diversity, and sources of variability among ML models trained on DTI data for classifying movement disorders, rather than to infer a single pooled diagnostic effect. This was designated exploratory because extreme heterogeneity prevented confirmatory pooled effect inference, so the analysis focused on describing performance distributions and methodological patterns rather than estimating a unified diagnostic effect. A systematic search of PubMed, Web of Science, and Scopus identified human studies applying ML algorithms to DTI for diagnostic or classification purposes. Accuracy, sensitivity, specificity, and the area under the curve (AUC) were extracted, with multiple imputation used for incomplete metrics with missingness rates below 40%. Random-effects modeling was employed to provide descriptive summaries, and subgroup analyses were conducted to explore trends across disorders, model architectures, and imaging modalities. Study qualities were assessed with JBI tools. Forty-six studies (2016-2024) were included, spanning Parkinson's disease, Tourette syndrome, and essential tremor. Reported performance was generally high (median AUC ≈ 0.91), but between-study heterogeneity was extreme (I2 = 94.7%), indicating that studies were estimating distinct effects. Disorder-specific subgroup AUCs varied markedly: Essential Tremor (0.95), Parkinson's (0.90), Tourette's (0.88), and Other (0.79). Deep learning and radiomics-based models have reported higher accuracies, but they were often trained on small, single-center cohorts (37-139 participants), which limits their external validity. Pooled statistics were presented descriptively to illustrate performance ranges despite high heterogeneity, and were not interpreted as confirmatory effect sizes. ML models using DTI demonstrate high internal performance across studies, although generalizability remains limited across multiple movement disorders; however, current evidence remains exploratory due to small sample sizes, methodological fragmentation, and a lack of standardized imaging pipelines. Rather than confirmatory inference, these findings provide a descriptive map of emerging trends in ML-DTI diagnostics. Future progress will depend on data harmonization initiatives, multicenter collaborations, and federated learning frameworks that can support reproducible, generalizable, and clinically interpretable models.

Cocaine use disorder (CUD) is a significant public health problem with few treatment options. Repetitive Transcranial Magnetic Stimulation (rTMS) targeting the left dorsolateral prefrontal cortex has shown promise as a therapeutic tool for neural alterations in CUD. However, its effects on white matter (WM) microstructure and their role in treatment efficacy remain uncertain. This study aimed to assess the global impact of rTMS on WM microstructure in CUD patients. In this study, we made a longitudinal correlational tractography analysis that was conducted using Quantitative Anisotropy (QA) on diffusion MRI data from CUD patients who received either active rTMS (n = 22) or sham rTMS (n = 18) treatment. Imaging data were collected before (T0) and after two weeks of treatment (T1). Correlations were derived using nonparametric Spearman partial correlation, accounting for gender, age, and age at substance initiation through multiple regression. Tracks were selected using a p-FDR threshold of 0.05. A significant QA increase was found in 9718 tracts across the whole brain in the active rTMS group compared to the sham group, with no observed reduction in QA. The affected WM tracts included cerebellar, commissural, associative, and projective fibers, mainly in the left hemisphere. The study suggests that rTMS induces widespread changes in WM microstructure, potentially improving communication between brain regions and cognitive control in CUD patients. However, the small sample size limits the findings' generalizability, highlighting the need for larger, longitudinal studies.

With the increasing adoption of discovery -omics in the life sciences, a large number of analysis tools for differential expression analysis (DEA) have been introduced over the years. While such tools tend to be developed with one particular -omics modality in mind, they can often be applied across technologies to solve common issues. This is particularly the case when -omics data share statistical and distributional properties. Herein, we showcase how tools originally developed for transcriptomics analysis are especially well-suited to solving problems in discovery proteomics and metabolomics. Using data from our own experimental work as examples of real-world implementation, we demonstrate how these methods can be used to tackle common DEA issues, such as variable sample quality, hidden batch effects, normalization, and small sample size. We believe this can be useful to novices and seasoned practitioners alike by expanding their toolkits. As multiomic and integrative analyses become commonplace, it is especially useful to capitalize on the similarities of otherwise different -omics.

Objective: to describe the initial experience and evaluate the learning curve of Thulium Fiber Laser Enucleation of the Prostate (ThuFLEP) in a training program. Methods: an ambispective study included consecutive patients with benign prostatic enlargement who underwent ThuFLEP between March 2024 and January 2025. Collected variables included demographics, operative times, enucleation and morcellation efficiency, perioperative complications, and functional outcomes at 3 months. Learning progression was assessed using linear regression of enucleation efficiency and operative time across consecutive cases, and by stratifying patients into quartiles according to case order. Results: thirty‑eight patients were included. Mean prostate volume was 74.5 ± 19.6 g, and mean operative time was 83.6 ± 40.3 minutes. Mean enucleation efficiency was 0.75 ± 0.30 g/min. Total operative time decreased by approximately 3 minutes per case (p < 0.01). Most complications were low grade (Clavien-Dindo grade I–II). Significant improvement at 3 months was observed in PSA, IPSS, QoL, and PVR (all p < 0.0001). Operative proficiency reached around case 10-20. Limitations of this study: small sample size, ambispective design, and lack of a comparative group. Originality: this is the first Mexican study describing early experience and the learning curve of ThuFLEP in a training program. Conclusions: ThuFLEP shows a reproducible learning curve in a supervised training environment. Its safety profile, hemostatic properties, and predictable tissue interaction support its use as a technique for surgical education.

Postprostatectomy incontinence (PPI) is a common complication after robot-assisted radical prostatectomy and significantly impairs patients' quality of life. Although behavioral interventions such as pelvic floor muscle training and bladder diaries are evidence-based, their effectiveness is often limited by poor adherence and lack of personalization. This study aimed to develop and evaluate a reinforcement learning (RL)-driven clinical behavioral intervention-supporting system (CBISs) for adaptive, personalized rehabilitation in patients with PPI. The study comprised 2 sequential stages. First, the CBISs was developed through (1) construction of a medical record database from a prospective cohort of PPI patients using standardized 3-day bladder diaries, (2) design of functional modules and user interfaces based on clinical rehabilitation needs, and (3) development of an RL model using XGBoost (extreme gradient boosting) and Bayesian optimization to generate individualized training plans. Second, a separate cohort of 16 patients participated in a single-arm, pre-post pilot study to evaluate feasibility and preliminary outcome trends over a 3-month intervention period, with assessments based on bladder diary parameters and system usage metrics. The CBISs successfully implemented an adaptive, closed-loop behavioral rehabilitation framework that dynamically tailored training recommendations according to individual voiding patterns, fluid intake behaviors, and adherence signals. Feasibility outcomes were favorable, with high system engagement observed throughout the intervention (mean usage frequency 5.2, SD 1.1 times per day). In exploratory pre-post analyses (n=16), consistent directional improvements were observed across multiple outcomes. Mean daytime urinary frequency decreased from 5.74 (SD 1.21) episodes per day to 4.69 (SD 1.08) episodes per day, while median nighttime urinary frequency declined from 1.8 (IQR 1.6-2.2) episodes per night to 1.0 (IQR 1.0-1.6) episodes per night. Median incontinence episodes were reduced from 7.0 (IQR 6.0-11.0) episodes per day to 4.0 (IQR 2.0-6.0) episodes per day. Objective urine leakage measured by the 1-hour pad test decreased from a median of 8.5 (IQR 4.0-19.0) g to 3.5 (IQR 2.0-9.0) g. Patient-reported symptom burden, assessed using the International Consultation on Incontinence Questionnaire-Short Form (ICIQ-UI SF), showed a median reduction from 14.0 (IQR 12.0-20.0) points to 9.0 (IQR 6.0-16.0) points. Although several within-participant changes were statistically detectable, effect magnitudes varied across individuals. Given the single-arm design, small sample size, and lack of a control group, findings are presented as exploratory and hypothesis-generating rather than confirmatory of clinical efficacy. The CBISs represents the first RL-powered digital therapeutic system for PPI, enabling adaptive, evidence-based behavioral optimization. By addressing limitations of static rehabilitation protocols and declining adherence, it offers a scalable approach for personalized PPI management. Future multicenter trials are needed to confirm its clinical effectiveness.

The B cell repertoire contains the recombined sequences that encode the entire antibody repertoire of an individual. The repertoire is made from three antigenic binding chains, namely the immunoglobulin heavy chain (IGH) and two immunoglobulin light chains, κ (IGK) and λ (IGL). Compared to the T cell repertoire, the B cell repertoire is understudied in inflammatory bowel diseases (IBD) even though different antibodies such as ASCA (Anti-Saccharomyces cerevisiae) and ANCA (Anti-Neutrophil Cytoplasmic Antibodies) have been shown to be elevated in individuals with IBD. To address this limitation, we profiled the B cell repertoire of peripheral blood from 27 treatment-naive individuals with CD and 21 age-matched symptomatic controls using bulk B cell receptor sequencing. The repertoire of individuals with CD showed a reduction in diversity and an increase in clonality. Furthermore, we observed a significant reduction in the expansion of IgM and IgD and an expansion of IgA2, and IgG2 clonotypes in individuals with CD relative to controls, suggesting an antigen-driven expansion. This was also supported by higher levels of somatic hypermutations, particularly in the complementary determining region 2 (CDR2) of immunoglobulin heavy chain, in individuals with CD relative to the control group. Thus, despite the small sample size, we identified multiple alterations in the B cell repertoire of individuals with CD, highlighting the potential of the B cell repertoire in identifying antigenic exposures implicated in the disease, demanding now larger international studies, ideally including also treatment-naive and pre-clinical cases.

Warfarin remains one of the most widely used anticoagulants; however, its narrow therapeutic index means that even small dosing deviations can result in thromboembolic or bleeding events, necessitating close monitoring and strict control of the international normalized ratio (INR). Although traditional warfarin dosing algorithms incorporating CYP2C9 and VKORC1 genotypes improve upon fixed-dose regimens, they explain less than 50% of dose variability and perform inconsistently across populations. These limitations underscore the need for more adaptive and precise dosing methodologies. Artificial intelligence (AI) and machine learning (ML) have been recognized as powerful approaches to advance warfarin dose individualization. This narrative review synthesizes literature on machine learning approaches to warfarin dosing, including support vector regression, neural networks, ensemble models, and reinforcement learning, with a focus on predictive performance and clinical relevance. Overall, the literature indicates that ML-based warfarin dosing models may improve prediction of the therapeutic warfarin dose and regulation of INR levels compared with traditional clinical and pharmacogenetic interventions. However, many published models are constrained by small sample sizes and limited external validation, reducing generalizability. Methodological heterogeneity and inconsistent reporting further underscore persistent gaps in the evidence base. AI and ML approaches have shown potential advantages over clinical and pharmacogenetic dosing methods for warfarin, with some studies reporting lower prediction errors and improved therapeutic INR control. However, further studies are needed to draw definitive conclusions about their comparative effectiveness.

Computer-aided diagnosis (CAD) systems based on deep learning have shown significant potential for Alzheimer’s disease (AD) stage classification from Magnetic Resonance Imaging (MRI). Nevertheless, challenges such as class imbalance, small sample sizes, and the presence of multiple slices per subject may lead to biased evaluation and statistically unreliable performance, particularly for minority classes. In this study, a Vision Transformer (ViT)-based framework is proposed for multi-class AD classification using a Kaggle dataset containing 6400 MRI slices across four cognitive stages. A subject-wise data-splitting strategy is employed to prevent information leakage between the training and testing sets, and the statistical unreliability of near-perfect scores in underrepresented classes is critically examined. An ablation study is conducted to assess the contribution of key architectural components, demonstrating the effectiveness of self-attention and patch embedding in capturing discriminative features. Furthermore, attention-based visualization maps are incorporated to highlight brain regions influencing the model’s decisions and to illustrate subtle anatomical differences between MildDemented and VeryMildDemented cases. The proposed approach achieves a test accuracy of 97.98%, outperforming existing methods on the same dataset while providing improved interpretability. It supports early and accurate AD stage identification.

Abstract Presence is a central concept in virtual reality (VR) research but is most often assessed using post-experience questionnaires that are subjective and susceptible to bias. Behavioral measures may offer a complementary, objective perspective on presence, particularly when examined in relation to specific situational demands. Rather than attempting to validate postural control as a global measure of presence, this Forum article explores whether postural responses show promise as a stimulus-evoked behavioral correlate of presence in an immersive virtual environment. Participants stood wearing a head-mounted display while viewing a VR-based construction site in which walls and ceiling elements moved toward the user, while postural responses were recorded using motion capture. Trunk sway magnitude and direction were quantified and compared to quiet stance, and subjective presence was assessed following each trial. Results showed greater postural sway during many virtual events relative to baseline, with moderate but non-significant associations with self-reported presence. Although limited by a small sample size, these exploratory findings highlight the potential and limitations of postural control as a context-dependent indicator of presence and are intended to stimulate further methodological discussion.

Abstract Hierarchical occupancy and abundance models (HOAMs) have become a leading approach for inferring wildlife population dynamics because they explicitly account for imperfect detection. HOAMs are suitable for sampling approaches that produce detection histories from repeated visits to the same sites, including direct observations (e.g. bird point counts), indirect observations (e.g. tracks, dung) and remote and passive sensors (e.g. camera traps, acoustic recorders). Wildlife often exhibits non‐linear temporal trends or threshold‐like responses to environmental conditions. However, traditional HOAMs address non‐linearity crudely using global polynomial functions, despite well‐documented limitations. Generalised additive models (GAMs) provide a more flexible approach to non‐linearity, allowing smooth data‐driven estimation through basis functions and penalised splines. Yet, GAMs have remained sparsely adopted in hierarchical occupancy modelling, in part due to the need for custom code in Bayesian modelling languages. We demonstrate the applicability of GAMs within the occupancy and abundance modelling framework (hereafter ‘OccuGAMs’) by comparing traditional HOAMs with polynomials to OccuGAMs. In simulations, OccuGAMs recovered non‐linear relationships more accurately and more often, scoring better on energy and variogram metrics and produced more stable responses at smaller sample sizes. Polynomials performed well in some scenarios but were less generalisable, making OccuGAMs the more robust overall choice, especially when there is no a priori guidance about the functional form. Limitations of OccuGAMs include interpretability of model parameters and sensitivity to the choice and number of basis functions, which can be assessed with diagnostic tools. To promote wider accessibility, we provide code for OccuGAM implementation in JAGS and Stan as well as in the R packages flocker and mvgam .

Bullying and school violence contribute directly to mental health difficulties among youth in the United States. Background/Objectives: This study describes the development and initial evaluation of a technology-enabled, multi-component school safety framework designed to support bullying prevention in middle and high schools. Methods: Students (n = 46), school personnel (n = 79), and parents/caregivers (n = 28) participated in three waves of usability and feasibility testing focused on a mobile application (Speak Out with Advocatr), companion classroom instructional materials, and guidelines for a school-wide safety campaign. Quantitative data were summarized using descriptive statistics and benchmark comparisons, and group differences across respondent roles were examined using analysis of variance with post hoc pairwise tests. Given small and unequal sample sizes, bootstrap resampling with 1000 resamples was used to obtain robust estimates of group means and confidence intervals. Qualitative responses were analyzed using content analysis. Results: Across waves, mean ratings generally met or exceeded predefined usability benchmarks, indicating favorable perceptions of the system. Findings indicated strong student acceptance and engagement with the framework. Adult participants expressed particular interest in restorative approaches to addressing student conflict, as well as concerns about preventing the recurrence of bullying behaviors. Conclusions: Findings provide initial support for the usability and feasibility of a multi-component, technology-enabled approach to school-based bullying prevention. Results also highlight the value of role-specific feedback for refining integrated mental health and safety interventions within school settings.

This study evaluated the feasibility of two minimally equipped, blended supervised and tele-supported exercise programs, a moderate intensity training (MIT) and high intensity training (HIT) program, in healthy sedentary adults, and explored signals of efficacy on central pain processing. Participants were randomized to a 10-week MIT or HIT program, each comprising one supervised group session and two tele-supported home-based sessions per week. MIT involved a Start-to-Run protocol at 60-70% of heart rate reserve, while HIT involved strength exercises at > 80% of predicted maximum heart rate. Feasibility outcomes included recruitment, retention, adherence, and acceptability. Quantitative sensory testing (QST) assessed central pain processing pre- and post-intervention. Eighteen participants were enrolled (8 MIT, 10 HIT). Retention was lower in the MIT group (5 completed) than the HIT group (11 completed), with high adherence in both. Participants appreciated the blended format but noted challenges with digital tools, and suggested refinements. No significant differences in QST outcomes were observed, likely due to small sample size and inter-individual variability. Individual responses suggested reduced pain inhibition following MIT and enhanced inhibition after HIT. Both programs were feasible, supporting a larger-scale study to optimize implementation and confirm effects. ClinicalTrails.gov (ID: NCT06207422).

This systematic review evaluates the accuracy, reliability, and clinical utility of jaw motion tracking (JMT) systems compared to traditional occlusal assessment methods, providing an overview of current evidence supporting their integration in dental practice. The review followed PRISMA guidelines (PROSPERO Registration: CRD42024567619). A comprehensive search was conducted in PubMed, Scopus, Web of Science, and the Cochrane Library (January 2005- December 2025) using predefined keywords related to JMT systems and digital occlusal analysis. Studies were included if they assessed JMT device performance in clinical, experimental, or in vitro settings, comparing them to conventional mechanical articulators or manual occlusal techniques. The QUADAS-2 tool evaluated study quality, focusing on bias, patient selection, and applicability concerns. Twenty studies met inclusion criteria, ranging from single-patient case studies to larger clinical trials. Optical tracking systems demonstrated higher precision and accuracy in recording mandibular movements compared to traditional methods. JMT systems showed potential in reducing chair time and improving prosthetic outcomes. The risk of bias was high, mainly due to small sample sizes and the lack of long-term evaluations. Digital JMT technologies improve occlusal assessment and treatment planning, particularly in prosthodontics and TMJ diagnostics. However, further research is needed to standardize protocols, validate these systems across diverse populations, and ensure clinical applicability. Despite the potential benefits, methodological inconsistencies highlight the need for more rigorously designed studies.

Supporting learners in complex skill acquisition is a demanding process that requires substantial knowledge of the scientific principles and the individual, environmental, and task constraints defining each learner’s optimal movement execution. Future applied practitioners (sport scientists, coaches, physical education teachers, etc.) have to develop this knowledge and skills throughout their studies. However, research shows that due to limited lesson time in tertiary education, many students may not acquire good proficiency in some of these competencies (Lounsbery & Coker, 2008). Digital tools have therefore been identified as a promising way to support students’ learning outside the lesson time. Insights into their effectiveness in ecologically valid settings provide information about their efficiency and potential directions for their improvement. The aim of this study was therefore to investigate the utilization and effectiveness of a digital tool as support to enhance students’ swimming performance (time and technique) and competency development (content knowledge, self-assessment of technique). In a pre-post observational study, 26 sport science students were assessed on swimming performance (time and technique), self-assessment accuracy and content knowledge at the beginning and the end of the semester. Students had unrestricted access to the digital tool during the course, and its utilization was monitored through learning analytics. No control group was included due to course-structure constraints. Dependent t-tests and Wilcoxon signed-rank test showed statistically significant improvements in students’ swimming time (t(24) = 5.91, p < .001), technique (t(25)= -7.25, p < .001), self-assessment of technique (t(25) = -10.09, p < .001) and theoretical knowledge (V = 118, p = .009), all with moderate effect sizes (Cohen’s d = 0.57-0.58; r = .42). However, none of these improvements were correlated with the frequency of digital tool use. The frequency of digital tool use did, however, significantly correlate with both students’ swimming time on the first (τ = 0.33, p = .020) and the last lesson (τ = 0.39, p = .005), suggesting that students with lower swimming performance tended to use the digital tool more often. Students’ self-assessment of technique did significantly correlate with their swimming time, both at the first lesson (t(23) = -4.9, p < .001) and last lesson (t(23)= -2.83, p = .009), as well as with expert’s assessment of their technique on the first lesson (t(24) = 2.81, p = .010) and last lesson (t(23) = 3.14, p = .004). While we can confirm significant progress in students’ swimming performance, technique, theoretical knowledge and self-assessment of technique, our findings couldn’t establish a link between the utilization of the digital tool and students’ improvements. Apart from students making significant progress in their self-assessment of technique, we additionally identified good accuracy in self-assessment on their first lesson, highlighting good starting proficiency in this important competency. Overall implications of this research, while presenting realistic insight into ecologically valid settings, are somehow limited due to small sample size and lack of a control group.

Prurigo nodularis (PN) is a chronic, intensely pruritic dermatosis driven by neuroimmune dysregulation for which targeted therapies are emerging. The objective of our study is to evaluate the efficacy and safety of Janus kinase inhibitors (JAK-Is) for the treatment of PN. We conducted a systematic search in sources including PubMed/Medline, Scopus, Web of Science, and Embase; the search was completed on September 9, 2025. Inclusion criteria were the use of any JAK-I in a patient with confirmed PN, and exclusion criteria were reviews, duplicate reports, and studies with incomplete outcome data. Risk of bias was assessed using the NHLBI quality assessment tools for observational studies and the Murad etal. checklist for case reports and case series. Ten clinical studies (n = 180 patients) and 21 case reports/series (n = 31 patients) were included (total n = 211). Agents evaluated included upadacitinib, tofacitinib, abrocitinib, and baricitinib. Applying standardized response criteria, 139/180 patients (77.2%) in clinical studies achieved meaningful clinical improvement. Rapid itch relief was frequently observed (days to weeks). Quality of life improved substantially (mean reduction from 20.4 to 3.9). Reported adverse events were generally mild (infections, laboratory abnormalities, dyslipidemia, acneiform eruption); serious events were rare. Limitations include small sample sizes, predominance of uncontrolled and observational designs, heterogeneity of outcomes, and limited long-term safety data. Current evidence suggests JAK-Is are highly effective and generally well tolerated for PN, warranting larger randomized controlled trials with extended follow-up.

The presence and severity of acute kidney injury (AKI) upon ICU admission provides important short- and long-term prognostic information. Existing reports have been limited by inadequate baseline kidney function assessment, incomplete outcome capture, limited adjustment for illness severity, and small sample sizes. We conducted a population-level study of all adult (≥ 18years) Ontario, Canada residents with available outpatient baseline creatinine measurements admitted to the ICU from 2009-2021. AKI at the time of ICU admission was determined according to the Kidney Disease Improving Global Outcomes (KDIGO) criteria. Outcomes included death and kidney replacement therapy (KRT). Multivariable logistic regression modeling was used to estimate adjusted odds ratios (aOR). The study cohort included 484,956 adults (43% female) admitted to the ICU. Mean (SD) age and baseline eGFR were 68 (15) years and 77 (25) mL/min/1.73 m2, respectively. AKI was present in 105,671 (22%). Relative to no AKI, stage 1 AKI was associated with approximately twofold higher odds for 90-day mortality (aOR 1.89 [95% CI 1.85-1.93]) while stages 2 and 3 AKI were associated with approximately 2.5-fold higher odds (stage 2 aOR 2.64 [95% CI 2.54-2.73], stage 3 aOR 2.54 [95% CI 2.45-2.63]). Relative to no AKI, there was a progressively increased risk for KRT dependence at 90days: stage 1 (aOR 2.05 [95% CI 1.79-2.34]), stage 2 (aOR 4.28 [95% CI 3.40-5.40]), and stage 3 (aOR 8.61 [95% CI 7.71-9.62]). The presence and severity of AKI at the time of ICU admission are strongly associated with adverse health outcomes. Stage 2 and 3 AKI portend a similarly high risk of mortality.

Burnout is a debilitating syndrome associated with emotional exhaustion, depersonalization, and a low sense of personal accomplishment. For medical students, studies suggest that burnout related to stress may begin as early as their first year and can be associated with poor academic performance, general distress, substance abuse, and suicidal ideation. Previous literature suggests that mindfulness training, breathing exercises, and social support can be protective against the effects of burnout. Our study aimed to evaluate the efficacy of a new wellness program consisting mainly of brief, daily, mindfulness-based micro-habit training sessions. The study adopted a single-group before-and-after design for a duration of eight weeks. Twelve medical students consented to participate (7 female); mean age = 26.25 years (SD = 4.4). The micro-habit intervention is delivered by a mobile app and is supported by weekly peer-circle meetings. The micro-habits are taught by listening to self-contained 90-second mindfulness exercises, at least once daily, five days per week. Maslach burnout inventory, a well-established 22-item self-report survey; Depression Anxiety Stress Scale (DASS21), a 21-item version of the DASS42; and Weekly Stress Scale, for which participants reported their stress level in terms of one of the categories (i.e., green, amber, or red) each week. The six participants who completed the post-intervention Maslach inventory exhibited a significant decline in emotional exhaustion (P = .03, 1-tailed) with a large effect size (d = 0.99). The five participants who completed the DASS21 at post-intervention averaged a considerable but non-significant decline in anxiety (d = 0.57). Despite the small sample size, it is encouraging that even a brief micro-habit intervention, one suited to busy medical students, can produce a considerable decrease in burnout symptoms, warranting a study with a larger cohort. burnout, mindfulness, habits, micro-habits, stress, wellness, medical students.