Neuroscience & Biobehavioral Reviews

Background At present, there are no approved pharmacological treatments for people at clinical high risk for psychosis (CHR-P). We sought to assess the acceptability of cannabidiol (CBD): a promising candidate treatment for this population. Methods CHR-P individuals completed a survey which assessed their views on the acceptability of CBD, its expected effectiveness and side effects, and on formulation preferences. Results The sample comprised 55 CHR-P individuals (24.3 years and 69% female). Most (91%) were familiar with CBD, and had previously used cannabis (64%), and around half (42%) had tried over-the-counter CBD. 75% were willing to take CBD as an intervention for mental health problems. Most participants anticipated fewer side effects with CBD than with existing medications, and preferred tablet or capsule formulations over liquids. Discussion CBD is perceived as a highly acceptable treatment among CHR-P individuals.

Deficits in inhibitory control are common across a wide range of psychiatric disorders and are closely linked to symptom severity, including emotional dysregulation, anxiety, substance misuse, and self-harm, making them an appealing target for intervention. Cognitive training offers a low-cost, scalable, and non-invasive strategy to strengthen inhibitory control; however, most existing paradigms target only a single facet of inhibition and rarely account for environmental influences, such as affective context. To address these gaps, we developed a computerized inhibitory control training paradigm to simultaneously engage three components of inhibition: preemptive, proactive, and reactive, while embedding trials within positive and negative affective contexts to assess the impact of emotional stimuli. Across two online experiments, participants completed the GAMBIT task in one session (Experiment 1, N = 300) or repeated over three sessions (Experiment 2, N = 65). The task included No-Go trials to train preemptive inhibition, stop-signal trials for reactive inhibition, and stop-signal anticipation trials to train proactive inhibition. Affective images of differing valence were presented as background stimuli to evaluate their impact on inhibitory performance. In Experiment 1, participants showed higher accuracy on No-Go versus reference Go trials ({beta}=1.45, SE=0.09, p<.001), confirming successful manipulation of preemptive inhibition. Reaction times were slower during anticipation trials across two different conditions ({beta}=0.16, SE=0.04, p<.001; {beta} = 0.07, SE = 0.04, p = 0.047), consistent with proactive slowing when anticipating a potential stop signal. Additionally, positive affective images ({beta} = 0.10, SE= 0.009, p < 0.001) further slowed RTs, indicating emotional interference with proactive control. In Experiment 2, the pattern of higher No-Go accuracy was replicated ({beta} = 0.91, SE = 0.11, p < .001) and accuracy generally improved over sessions ({beta} = 0.38, SE = 0.06, p < .001). In anticipation trials, RTs become shorter across sessions (session 2: {beta} = -0.25, SE = 0.06, p < .001; session 3: {beta} = -0.45, SE = 0.06, p < .001), reflecting practice-related gains, and SSRTs decreased over time (F(2,56) = 6.26, p = .004), consistent with enhanced reactive inhibition. Proactive inhibition was modulated by affective images, with both negative ({beta} = 0.04, SE = 0.02, p = .039) and positive ({beta} = 0.16, SE = 0.02, p < .001) affective images associated with slower RTs. Participants also reported reductions in self-assessed temper control by the last session (W = 25.5, p = .007, q = .037, d = -0.51) and usability ratings were high (all means [&ge;] 3.87/5). Together, these findings show that this paradigm recruits multiple forms of inhibitory control and yields training-related improvements in both performance and affective outcomes. This provides preliminary validation of a scalable, fully online inhibitory control training tool targeting multiple dissociable inhibitory processes within affective contexts. The approach holds promise as an accessible transdiagnostic intervention to support symptom improvement across psychiatric disorders, with future work needed to evaluate clinical efficacy in patient populations.

Objective: Posttraumatic stress disorder (PTSD) after a traumatic birth is a serious but overlooked maternal morbidity, affecting ~20% of women following medically complicated deliveries. PTSD can undermine maternal caregiving. Rapid screening tools suited to busy obstetric settings are lacking. We developed and evaluated a brief screener, derived from the 20-item PTSD Checklist for DSM-5 (PCL-5), to identify PTSD related to childbirth. Study Design: We enrolled 107 women with traumatic childbirth. Participants completed the PCL-5 and the gold-standard clinician diagnostic interview for PTSD (CAPS-5); depression was measured with the Edinburgh Postnatal Depression Scale (EPDS). Bootstrap resampling with LASSO regression identified PCL-5 items most associated with PTSD. Firth logistic regression models estimated diagnostic accuracy. Sensitivity, specificity, area under the ROC curve (AUC), and Youden's J statistic determined performance and optimal cut-off. Results: A six-item version of the PCL-5 (PCL-5 R6), statistically derived from the full scale, showed excellent discrimination for PTSD compared with clinician evaluation (AUC = 0.95; 95% CI, 0.89-1.00). A cut-off score of 7 yielded high sensitivity (0.96) and good specificity (0.83), with an overall diagnostic efficiency of 0.86, detecting most PTSD cases while minimizing false positives. The PCL-5 R6 correlated moderately with the EPDS (rho = 0.53), showing that a depression screen alone cannot reliably detect PTSD. Conclusions: A short, 6-item PCL-5 provides a valid, efficient tool for detecting childbirth PTSD. Its brevity and accuracy make it practical for integration into routine postpartum care, enabling timely mental health screening.

Background: Nursing interns are at high risk of psychological distress due to academic and clinical stressors. While poor sleep quality is linked to anxiety and depression, the buffering role of social support remains underexplored in this population. Aims: To explore the role of social support in regulating the relationship between sleep and mental health among nursing interns. Methods: A total of 396 nursing interns completed self-administered questionnaires including the Pittsburgh Sleep Quality Index (PSQI), Social Support Rate Scale (SSRS), Generalized Anxiety Disorder-7 (GAD-7), and Patient Health Questionnaire-9 (PHQ-9). Hierarchical regression and simple slope analyses were used to test moderation effects. Results: Poor sleep quality was significantly associated with higher anxiety ({beta}=0.449, P<0.001) and depression ({beta}=0.535, P<0.001). Social support significantly moderated these relationships. Under low social support, the effects of sleep quality on anxiety ({beta} = 0.602) and depression ({beta} = 0.779) were stronger than under high support (anxiety: {beta} = 0.396; depression: {beta} = 0.515). Conclusions: Social support buffers the adverse psychological effects of poor sleep among nursing interns. Interventions should integrate sleep hygiene education with strategies to enhance social support.

Resting-state electroencephalography (EEG) has been proposed as a scalable source of biomarkers for chronic pain, but its clinical potential remains uncertain. To systematically evaluate this potential, we benchmarked nine modeling strategies, spanning conventional machine learning with handcrafted features to state-of-the-art deep learning. Across 72 configurations of signal representations and model architectures, we trained models to predict self-reported pain intensity, using chronological age decoding as a positive control. Pain prediction performance was limited (R=0.15), with the best results achieved by conventional connectivity-based models. In contrast, age was robustly decoded from the same dataset (R=0.53), confirming technical efficacy. These findings indicate that resting-state EEG contains limited information about inter-individual differences in chronic pain intensity, making it unlikely to yield clinically actionable biomarkers in cross-sectional settings. Instead, its potential may lie in intra-individual modeling of pain dynamics, which could advance individualized mechanistic insights and more personalized treatment of chronic pain.

Actigraphy is a popular behavioral sleep assessment tool in research and clinical practice. Hierarchical hand-scoring approaches remain the standard for actigraphy rest interval estimation, but can be impractical for large cohort studies and suffer from reproducibility problems. We developed a semi-automated pipeline (actiSleep) to set rest intervals consistent with best-practice hand-scoring algorithms incorporating event marker, diary, light, and activity data. To evaluate actiSleep performance, we used data from an observational study of 51 adolescents (14-19yr), with and without family history of bipolar disorder. Participants completed 2 weeks of wrist actigraphy and daily sleep diary. We first hand-scored records using a standardized hierarchical algorithm incorporating event marker, diary, light, and activity data. We then compared the hand-scored rest intervals to those from actiSleep and two automated activity-based algorithms (Activity-Merged, Activity-Only). Activity-Only used activity-based sleep estimation and Activity-Merged joined closely adjacent rest intervals. For rest onset, rest offset, and rest duration, all algorithms had strong mean agreement with hand-scoring: actiSleep estimates were within 1-3 minutes, Activity-Merged within 2-4 minutes, and Activity-Only within 7-14 minutes. However, actiSleep had notably better (narrower) margins of agreement with hand-scoring, as evidenced by Bland-Altman plots, and greater positive predictive value and true positive rates for rest detection, especially in the 60 minutes surrounding the onset and offset of the rest interval. The actiSleep algorithm successfully estimates actigraphy rest intervals comparable to hand-scoring while avoiding pitfalls of activity-only algorithms. actiSleep has potential to replace hand-scoring for research in adolescents but requires further testing and validation in other samples.

Wearable devices present transformative opportunities for personalized healthcare through continuous monitoring of digital biomarkers; however, individual variations in device wear time could mask or otherwise impact signal identification. Despite the widespread adoption of wearable devices in research, no comprehensive framework exists for understanding how wear time varies across populations or for addressing wear time-related biases in analysis. Using Fitbit data from 11,901 participants in the All of Us Research Program, we conducted the first large-scale systematic assessment of wearable device wear time across demographics, social determinants of health, lifestyle factors, mental health symptoms, and disease. Our findings revealed that wear time was higher among males and increased with age, income, and education, but decreased with depressive, anxiety, and anhedonia symptoms, with reductions more pronounced following clinical diagnoses compared to symptom-based classifications. Individuals with chronic conditions displayed differential levels of wear time compared to healthy controls. Critically, we demonstrate that the widely used [&ge;]10-hour daily compliance threshold, while appropriate for some research contexts, can disproportionately exclude days of data from disease populations: among individuals with major depressive disorder, 74.4% of data days were excluded compared to 20.9% for controls. We propose a flexible methodological framework including standard compliance thresholds, wear time covariate adjustment, metric normalization, propensity score matching, and adaptive thresholds that can be applied individually or in combination to optimize wearable data retention across diverse research contexts. These findings establish wear time as a critical methodological consideration for wearable device research and provide guidance for advancing equitable and rigorous digital health analytics.

Importance: Dementia is common in Parkinson's disease (PD), causing greater disability than other symptoms, but varies in timing. Although visual deficits are linked with PD dementia, how these interact with genetic factors to predict PD dementia has not been characterised. Objective: To investigate whether visual deficits and genetic factors predict PD dementia. Design: Large prospective longitudinal case-control study, mean follow-up 32.7 (SD=12.3) months. Setting: Cases were recruited between 2017-2020 at 35 UK PD clinics. Participants: People with PD without dementia at baseline were included. Main outcomes and measures: Visual function was measured using a web-based platform. The main outcome measure was global cognition, measured as the Montreal Cognitive Assessment (MoCA). Blood samples were collected for genetics. Results: 450 patients with PD were included. Mean age of PD patients was 71.7 (SD=7.8), 68% male. Mean baseline MoCA was 27.7 (SD=1.7). 263 patients with PD were classed as poor-vision based on baseline visual tests: mean age 74.4 (SD=6.8) compared to 69.7 (SD=7.5) with good-vision. Poor-vision PD patients had higher rates of progression to mild cognitive impairment (PD-MCI) (HR=2.34, CI=1.58-3.48, pFDR=0.00062, age- and sex-corrected). The combination of genetic factors together with vision influenced outcomes. In good-vision PD patients, high-risk GBA1 gene variants were linked with greater progression to PD-MCI (HR=4.61, CI=1.73-12.28, pFDR=0.0068). Polygenic Risk Score (PRS) for both PD and Alzheimer's disease (AD) also modified cognitive survival when combined with vision status. High PD-PRS was associated with greater progression to PD-MCI in good-vision patients (HR=2.66, CI=1.21-5.81, pFDR=0.0381); and high AD-PRS with greater progression to PD-MCI in poor-vision PD patients (HR=1.91, CI=1.10-3.32, pFDR=0.04999). Combining high PD- and AD-PRS, compared to low PD- and AD-PRS in good-vision PD showed even higher progression to PD-MCI (HR=6.14, CI=1.36-27.83, pFDR=0.046). Simulations showed that adding visual and genetic stratification reduced sample size from n=705 to n=160 for clinical trials. Conclusions and relevance: Poor vision in PD predicts progression to PD-MCI and dementia. This combines with the effects of genetic factors including GBA risk variants and PD- and AD-PRS. These findings can enable enrichment of clinical trials for patients at higher risk of PD dementia, for more efficient trial design for interventions to slow progression.

Objective To test the effectiveness of a postpartum behavioural intervention delivered by automated text messaging in reducing weight. Design Two parallel group, multicentre, randomised controlled trial. Setting Recruitment from five areas across the United Kingdom (Belfast, Bradford, Stirling, London and Cardiff) through healthcare and community pathways, including social media. Participants A diverse sample of 892 women between 6 weeks and 24 months postpartum, aged 18 years or more and with a body mass index of 25 kg/m2 or more, enrolled between May 2022 and May 2023: 445 were randomised to the intervention and 447 to an active control (comparator). Interventions Twelve months of fully automated text messages with embedded behaviour change techniques and two-way messaging components to support weight loss and maintenance of weight loss in the postpartum period by targeting dietary, physical activity and weight management behaviours. The comparator group received 12 months of text messages on child health and development tailored to child age. Main outcome measures Primary outcome: weight in kilograms at 12 months (end of intervention). Secondary outcomes recorded at 6 and 12 months were changes in weight (at 6 months), body mass index, proportions of women with weight gain or loss of 5 kg or more, waist circumference, self-reported dietary intake, physical activity and infant feeding practices. Results 674 (75.6%) participants were included in the primary analysis. There was no statistically significant difference found in the adjusted mean weight change between the intervention and active control groups (-0.1 kg (95% confidence interval -1.0 to 0.8, P= 0.84). Sensitivity analyses did not change these results. There was a small statistically significant improvement in Fat and Fibre Barometer scores at 12 months in the intervention compared with control group (adjusted mean difference 0.09, 95% CI: 0.04 to 0.14; P <0.001) and a statistically significant increase in physical activity scores (International Physical Activity Questionnaire Short Form) at 12 months in the intervention group compared with the control group (adjusted mean difference 405.3 total MET minutes/week, 95% CI: 141.3 to 669.3; P= 0.003). Conclusions A 12 month automated, interactive behavioural weight management intervention delivered by text message did not support weight loss for postpartum women but did have a positive impact on diet and physical activity behaviours.

Background: Quadriceps weakness may reduce sagittal plane shock absorption during landing, shifting load toward the frontal plane and increasing knee abduction moment (KAM), a biomechanical risk factor for anterior cruciate ligament (ACL) injuries. Purpose: The purpose of this study was to evaluate the association between isokinetic quadriceps strength and peak KAM during drop vertical jump landing in adolescent athletes. Study Design: Secondary analysis of previously collected data. Methods: Healthy adolescent athletes completed quadriceps strength testing using an isokinetic dynamometer and a biomechanical assessment during a drop vertical jump task. Quadriceps strength was quantified as peak concentric torque and the peak external KAM was calculated during the landing phase on the dominant limb. Both strength and KAM were normalized to body mass. Linear regression was used to examine the association between normalized quadriceps strength and peak external KAM on the dominant limb. Results: The association between quadriceps strength and peak normalized KAM on the dominant limb was not statistically significant ({beta} = -0.053 (95% CI [-0.137 to 0.030]), F(1,119) = 1.62, R2 = 0.013, p = 0.206). Quadriceps strength explained only 1.3% of the variance in peak KAM, indicating a negligible association between these variables in this cohort. Discussion: Quadriceps strength was not associated with peak normalized KAM during landing, suggesting that frontal-plane knee loading during a drop vertical jump is not meaningfully explained by maximal concentric quadriceps strength alone. KAM appears to be driven more by multi-joint movement strategy and neuromuscular coordination than by the capacity of a single muscle group.

Protocadherin-12 (PCDH12), a cell-adhesion protein belonging to the non-clustered protocadherin family, plays a crucial role in the establishment and regulation of neuronal connections and communication. Bi-allelic loss-of-function (LoF) variants in the PCDH12 gene have been associated with several neurodevelopmental disorders (NDDs) such as diencephalic-mesencephalic junction dysplasia (DMJD) syndrome, cerebral palsy, and cerebellar ataxia, often accompanied by ocular abnormalities. However, genotypes exhibit variable expressivity. Affected individuals sharing the same PCDH12 variant presenting differing phenotypic severities have posed major challenges towards identification of the underlying pathogenic mechanisms. Here, we report three affected individuals from two families, each harbouring non-truncating pathogenic missense variants in PCDH12. The patients are compound heterozygous, with each individual carrying one extracellular [c.1742T>G (p.Val581Gly) and c.1861_2del/insCA (p.Ile621His)] and one intracellular variant [c.3370C>T (p.Arg1124Cys) and c.3445G>A (p.Asp1149Asn] on each allele. The children present with a range of phenotypes similar to those associated with LoF variants. One child exhibited microcephaly and seizures, while the two siblings displayed developmental delays and severe behavioral disorders. All three children experienced some degree of visual impairment. The missense variants provided new insights into the neurodevelopmental consequences of compromised PCDH12 function by distinguishing the specific consequences associated with dysfunction in the extracellular versus intracellular domains of PCDH12. All identified missense variants are predicted to be deleterious and destabilizing. The expression of PCDH12 in HEK293T and HeLa cells demonstrated that PCDH12 is expressed effectively, regardless of the presence of missense variants. However, the extracellular variants p.Val581Gly and p.Ile621His compromised the stability of PCDH12's homophilic adhesion. Additionally, we found evidence of an interaction between PCDH12 and the extracellular domain of the epilepsy-associated PCDH19 protein. PCDH12 extracellular missense variants also negatively impact this interaction. Our study provides evidence that PCDH12 mediates both homophilic and heterophilic interactions. Our findings also highlight the importance of stable PCDH12-mediated adhesion, emphasizing the need to further study the functional consequences of PCDH12 missense variants on brain and visual system development.

Abstract Background: Conventional MRI cannot reliably distinguish radiation necrosis (RN) from recurrent metastasis after cranial radiotherapy, as both can show similar enhancement despite different biology. We tested whether these entities are mechanically non-equivalent in vivo and separable by MRE-derived viscoelastic metrics and perilesional interface-instability features. Methods: In a prospective, histopathology-anchored cohort, 11 post-radiotherapy enhancing lesions were classified as RN (n=3) or recurrent/progressive tumor (n=8). MRE was acquired at 3.0 T with single-frequency 60-Hz excitation to derive storage modulus (G'), loss modulus (G''), and complex shear modulus magnitude (|G*|). Co-primary endpoints were median tumor G' and |G*|, each tested one-sided (RN > tumor) with Holm correction across the two co-primary tests. Median tumor G'' was tested two-sided. A prespecified secondary 6-endpoint family (absolute and tumor/NAWM-normalized G', G'', and |G*|) was analyzed with Benjamini-Hochberg FDR control. Exploratory instability mapping in a 0- 6 mm peritumoral shell generated interface-topology metrics, including convexity. Results: Absolute tumor-core medians were higher in RN than tumor for |G*| (1.79 vs 1.32 kPa; Cliff's {delta} = 0.67; q = 0.10), G' (1.62 vs 1.09 kPa; {delta} = 0.50; q = 0.14), and G'' (0.81 vs 0.46 kPa; {delta} = 0.75; q = 0.10). NAWM normalization improved separation: tumor/NAWM |G*| (2.26 vs 1.41; {delta} = 0.92; q = 0.04) and tumor/NAWM G'' (2.67 vs 0.87; {delta} = 1.00; q = 0.04) were FDR-significant. Convexity also differentiated RN from tumor (0.49 vs 0.36; {delta} = 1.00; MWU p = 0.01). Conclusions: Tumor/NAWM G'', tumor/NAWM |G*|, convexity, and tumor G'' emerged as the strongest candidate features, indicating that RN is mechanically harder and more dissipative than recurrent metastasis. Signal strength was high (Cliff's {delta} up to 1.00) but should be interpreted cautiously given sample size. Exploratory analyses further suggest that instability mapping captures biologically relevant interface behavior. These findings support a mechanics-based RN-versus-recurrence framework and justify prespecified, preregistered external validation.

Background and objectives STXBP1-related disorder (STXBP1-RD), caused by pathogenic variants in the STXBP1 gene, is a rare neurodevelopmental condition, characterized by early-onset seizures, developmental delay, intellectual disability (ID), and prominent motor dysfunction. Despite the high prevalence of motor symptoms, systematic gait characterization remains limited. We therefore aimed to quantitively assess gait in individuals with STXBP1-RD. Methods In this cross-sectional study, we included ambulatory patients aged 6 years or older with genetically confirmed STXBP1-RD. Instrumented 3D Gait Analysis (i3DGA) was performed to objectively quantify gait. Functional mobility was assessed with the Functional mobility scale (FMS) and Mobility Questionnaire 28 (MobQues28). Caregiver health-related quality of life was evaluated using the PedsQL-Family Impact Module (PedsQL-FIM). We explored associations between gait, functional mobility, STXBP1-variant type and clinical features (ID, age at seizure onset, seizure frequency, age at onset of independent walking). Correspondence between i3DGA and the Edinburgh Visual Gait Score (EVGS), an observational gait assessment, was investigated. Results Eighteen participants were included. Compared to typically developing peers, individuals with STXBP1-RD had significantly reduced walking speed, step and stride length. Gait patterns were highly variable, with the most frequent pattern being an externally rotated foot progression angle (FPA), present in 11/18 participants. At home, 93.75% of the participants (16/18) walked independently, yet community mobility was more variable: 11/16 (68.75%) walked independently, 2/16 (12.50%) with aid and 3/16 (18.75%) used a wheelchair, indicating increasing limitations with distance and environmental complexity. Earlier acquisition of independent walking strongly predicted later unassisted ambulation at community level (p<0.001). Median MobQues28 score was 57.14% and median PedsQL-FIM score was 60.42%, indicating a moderate level of mobility limitations and reduced health-related quality of life of caregivers. EVGS was highly positive correlated with i3DGA (p= 0.001). Discussion Quantitative gait analysis in individuals with STXBP1-RD demonstrates heterogenous kinematic deviations, with an externally rotated FPA emerging as the most common pattern. Age at independent walking was a clinically relevant predictor of later functional mobility. EVGS showed strong correspondence with i3DGA and may offer a more practical, semi-quantitative assessment for broader use. These findings inform clinical decision-making and guide the selection of scalable outcome measures for natural history studies and interventional trials.

Multiple sclerosis (MS) is a chronic neurodegenerative disease characterised by progressive neurological disability and heterogeneous symptom trajectories. Current clinical monitoring methods, including magnetic resonance imaging (MRI) and episodic neurological assessments, provide limited insight into subtle disease progression and functional changes. Digital health technologies integrating multimodal biosignals and behavioural assessments may enable continuous monitoring and personalised rehabilitation in patients with MS. This study aims to evaluate the clinical utility of the BodyMirror Clinical MS platform, a multimodal SaMD that combines wearable biosensors, neuroscience-based games, and machine learning to remotely monitor disease progression and deliver personalised neurorehabilitation for individuals with multiple sclerosis. This study is a prospective, randomised, double-blind, controlled, multisite clinical trial enrolling 400 participants (300 individuals with multiple sclerosis and 100 healthy controls). MS participants will be randomly assigned (1:1) to either an adaptive neurorehabilitation intervention group or a control group receiving non therapeutic digital activities matched for engagement and exposure. Participants will perform three 30-minute sessions per week over 24 months using the BodyMirror platform. The system integrates multiple biosignals, including electroencephalography (EEG), electromyography (EMG), inertial measurement unit (IMU) motion data, speech analysis, and behavioural performance metrics to generate digital biomarkers of neurological function. The primary endpoint is a change in Expanded Disability Status Scale (EDSS) score from baseline to 24 months. Secondary outcomes include changes in Multiple Sclerosis Functional Composite (MSFC), MRI brain volume, cognitive performance, patient-reported outcomes, adherence to digital rehabilitation, and health economic outcomes.

Background Persistent inequities in immunisation coverage, particularly among zero-dose and under-immunised children, continue to challenge Pakistan's Expanded Programme on Immunization. Weak feedback loop, inconsistent data quality, and limited real-time monitoring impede effective decision-making. This Implementation Research was conducted under the MAINSTREAM Initiative funded by Alliance for Health Policy and Systems Research (AHPSR) and supported by the Aga Khan Community Health Services Department and National Institutes of Health Pakistan to design, implement, and evaluate a digital monitoring and action planning tool to strengthen data-driven decision-making within routine immunisation systems. Methodology/Principal Findings A co-creation approach was employed to design a digital monitoring solution through inclusive consultations, key informant interviews, and focus group discussions with EPI Punjab at provincial and district levels. The solution included a customised mobile application for data collection and a Power BI visualisation dashboard to map low-coverage areas, identify drivers of dropouts and zero-dose children, and capture caregivers' information sources to inform targeted communication. The intervention was piloted in 60 households across six clusters of a Union Council of District Lahore. Advanced analytics identified reasons for non-vaccination and missed opportunities, generating tailored recommendations and practical plans for program managers. The analysis assessed acceptability, adoption, fidelity, and perceived scalability through field observations, system use, and stakeholder feedback. The co-developed digital tool enhanced visibility of coverage gaps through UC-level mapping, real-time dashboards, and structured action planning. Pilot testing in Lahore showed strong acceptability, ease of use, fidelity, and adaptability among managers, supervisors, and vaccinators. Scalability and sustainability potential were demonstrated, though barriers included leadership turnover, system fragmentation, workload pressures, and resource constraints. Conclusion The tool demonstrated feasibility to strengthen immunisation equity, accountability, and responsiveness. Co-creation with stakeholders enhanced ownership, operational relevance, and adoption, while complementing existing platforms. Sustainability will depend on effective integration, local ownership, capacity building, and accountability, while scalability requires interoperability, resource commitment, policy support, and alignment with existing workflows.

Understanding host susceptibility to Mycobacterium tuberculosis (Mtb) is critical for the development of new vaccines. Certain individuals "resist" becoming infected with Mtb despite intensive exposure; however, it is unknown whether there is a genetic basis for "resistance" to Mtb infection across populations. Here we conducted a genome-wide association study (GWAS) of resistance to Mtb infection by carefully characterizing exposure to TB patients among 4,058 close contacts in India, Brazil, and South Africa. 476 (12%) "resisters" remained free of Mtb infection despite substantial exposure to highly infectious TB patients. GWAS identified a novel chromosome 13 locus (rs1295104126) associated with resistance across the multi-ancestry meta-analysis. Comparing Mtb-infection to all uninfected contacts, irrespective of exposure, yielded a different locus on chromosome 6 (rs28752534), near the HLA-II region. These findings demonstrate a common genetic basis for resistance to Mtb infection across multi-ancestral cohorts with potential to elucidate novel mechanisms of protection from Mtb infection.

The use of semaglutide (SE), a glucagon-like peptide-1 receptor agonist (GLP-1RA) with glucose-lowering and weight-loss effects, has risen rapidly, particularly among women of reproductive age. While preclinical studies suggest benefits for ovarian function via the hypothalamic-pituitary-ovarian axis, its impact on the endometrial-embryo interface remains unclear. Here, we show that GLP-1R is dynamically expressed in fertile human endometrium, restricted to epithelial cells and markedly upregulated during the mid-secretory phase of the menstrual cycle. In a preclinical model of endometrial epithelial organoids, SE at physiological concentrations activates intracellular cAMP signaling, enhances epithelial metabolism, and upregulates receptivity markers without steroid hormone priming, whereas higher concentrations modestly reduce expression of a key receptivity marker PAEP/glycodelin and shift metabolism towards oxidative phosphorylation. By contrast, in stromal cells lacking detectable GLP-1R, SE disrupts decidualization, induces endoplasmic reticulum stress and suppresses cell-cycle at G2/M phase. Human embryo models, blastoids, expressed GLP-1R and underwent concordant SE-mediated transcriptional remodeling in epiblast and trophectoderm lineages, encompassing changes in metabolism and epigenetic regulation, but without shifts in lineage proportions. Notably, SE increased blastoid attachment to the endometrial epithelium in the absence of exogenous steroid hormones, suggesting enhanced epithelial-embryo interaction. Together, these findings reveal a compartment-specific mismatch, as SE augments epithelial and embryonic metabolic activity but compromises stromal support for implantation, with potential consequences for implantation due to stromal dysfunction.

Background Artificial intelligence (AI) is increasingly being integrated into healthcare systems, with growing applications in clinical decision support, workflow optimization, and population health management. While substantial investments have been made in digital infrastructure, the successful adoption of AI in primary care depends critically on the readiness, awareness, and educational preparedness of healthcare professionals. Global health authorities emphasize the need for ethically grounded and workforce-focused approaches to AI integration; however, evidence on clinicians readiness for AI, particularly in primary care settings and in the Middle East region, remains limited. Objectives This study aims to assess the level of awareness, perceptions, attitudes, and educational needs related to AI among healthcare professionals working within Qatars Primary Health Care Corporation (PHCC). In addition, it seeks to examine organizational factors influencing the integration of AI-focused education in primary care and to develop an AI readiness framework that can inform targeted training strategies and policy planning. Methods This study will adopt a mixed-methods design guided by the Organizational Readiness for Change (ORC) framework, adapted for AI integration in primary care. The quantitative component will consist of an anonymous, census-style online survey distributed to all healthcare professionals across PHCC health centers and headquarters, assessing AI awareness, attitudes, training needs, and perceived infrastructure readiness. Composite AI awareness and attitude scores will be calculated, and regression analyses will be used to explore factors associated with AI readiness. The qualitative component will include semi-structured interviews and focus group discussions using maximum variation sampling to capture diverse professional perspectives. Qualitative data will be analyzed thematically, following COREQ and SRQR reporting standards. Quantitative and qualitative findings will be integrated to generate an AI readiness profile and an actionable education roadmap aligned with national digital health priorities. Discussion This study will provide the first comprehensive assessment of AI readiness among primary care healthcare professionals in Qatar. By identifying knowledge gaps, training priorities, and organizational enablers and barriers, the findings are expected to inform the development of evidence-based AI education strategies within continuing professional development frameworks. The proposed AI readiness framework may also offer a transferable model for other health systems seeking to align workforce development with responsible AI implementation in primary care.

Objectives: Compare Anterior Cruciate Ligament (ACL) Return to Sport after Injury (ACL-RSI) scores over time following ACL reconstruction (ACLR) between male and female patients aged 15 to 25 years with primary ACL injuries and ACL reinjuries. Design: Retrospective cohort design. Setting: Sports physical therapy clinics. Participants: 332 patients aged 15-25 years who underwent ACLR following either primary ACL injury or ACL reinjury, either contralateral or ipsilateral graft reinjury, and had at least one observation of the ACL-RSI. Main Outcome Measures: ACL-RSI score. Results: ACL-RSI scores significantly increased over time post- ACLR (p < .001), males reported significantly higher scores compared to females (p < .001), and patients with contralateral ACL reinjury demonstrated higher scores than those with ipsilateral ACL graft reinjury (p = .006), though there was no difference in scores between patients with primary ACL injury and ACL reinjury. A significant interaction effect of sex and injury status was also observed (p = .009), generally demonstrating that females had lower psychological readiness compared to males across injury statuses. Conclusions: ACL-RSI following ACLR varies based on biological sex and time post-ACLR, though ACL reinjury, independent of the reinjured leg, does not appear to effect scores compared to primary ACL injury.

Background: Psychological readiness to return to sport and subjective knee function are critical outcomes following ACL reconstruction (ACLR), yet they do not always progress in parallel. An athlete may demonstrate high subjective knee function but low psychological readiness, suggesting a mental barrier to return, or conversely, report high readiness despite persistent functional limitations, raising concerns of overconfidence and reinjury risk. Understanding how these domains change together during recovery is essential for identifying mismatches that may require targeted intervention. Purpose: The purpose of this study is to examine the relationship between changes in psychological readiness (ACL-RSI) and subjective knee function (IKDC) from early to late recovery following ACLR. Study Design: Secondary analysis of prospectively collected data. Methods: Athletes (N = 48, Age at ACLR = 17.7 {+/-} 1.8 y) aged 15-25 years who underwent ACLR with an ipsilateral autograft, had a pre-injury MARX score > 8, and completed the ACL-RSI and IKDC questionnaires at 3.5 {+/-} 1 and 7 {+/-} 1 months post-ACLR were included. Percent changes in ACL-RSI and IKDC scores between early and late recovery were calculated. Spearman's rank correlation was used to examine the association between changes in psychological readiness and subjective knee function. Significance was set to p < .05. Results: The mean percent change in ACL-RSI was 40.7 {+/-} 57.1% and the mean percent change in IKDC was 24.8 {+/-} 18.1% from 3.5 {+/-} 1 months to 7 {+/-} 1 months post-ACLR. The percent changes in ACL-RSI and IKDC scores from 3.5 {+/-} 1 months to 7 {+/-} 1 months post-ACLR were moderately correlated ({rho} = 0.350 (95% CI [0.089, 0.584]), p = 0.012). Discussion: The main finding of this study was that subjective knee function and psychological readiness to return to sport changed in parallel from 3.5 to 7 months following ACLR. Clinicians can use this information regarding the concordant progression of psychological readiness to return to sport and subjective knee function to personalize ACL rehabilitation for future patients. Overall, clinicians can understand that if psychological readiness improves, subjective knee function will likely improve over the 3.5- to 7-month post-ACLR time frame, and vice versa. Therefore, focusing on both of these components at multiple time points during the recovery process may be influential to ensure the greatest likelihood of returning to sport in athletes following ACLR.