The Singapore National Precision Medicine (NPM) program is a three-phase whole-of-nation effort designed to develop scalable, evidence-based solutions for precision health tailored to Asia's diverse populations. Here we present NPM phase II (2020-2025), highlighting how large-scale precision medicine initiatives can drive new research insights, enable healthcare innovations and create economic value. Key achievements include the PRECISE-SG100K population dataset, which reflects Singapore's unique multi-ancestry Asian population; the successful translation of research findings into mainstream national healthcare using familial hypercholesterolemia as a use case; and the establishment of strategic public-private partnerships with diverse industry sectors. We also outline phase III (2025-2031), which aims to establish whole-genome sequencing as a foundational element of a patient's lifelong healthcare record for 10% of the population. NPM provides a model for how smaller countries, despite limited populations and finite resources, can contribute meaningfully to global scientific movements while preserving strategic independence and addressing national health challenges.

The growing integration of digital technologies into healthcare requires understanding the impact of different consultation modalities on patient stress, memory and perceived credibility. This study compared different consultation modalities (human physician, in person or via video call; AI-style physician, as a chatbot or avatar) in standardized simulated medical consultations involving the delivery of bad news. For the AI-styled physician consultations, a Wizard of Oz design was used, in which participants were told they interacted with an AI physician while a human controlled the interaction. 163 healthy participants experienced either a human or an AI physician. Stress was measured through ratings and salivary cortisol, alongside memory retrieval and situation credibility. Human-based consultations led to higher stress levels than AI-styled formats. Greater perceived credibility was associated with stronger stress responses. Memory retrieval was lowest in the AI-styled chatbot condition. These findings show that different consultation modalities are associated with varying levels of stress and memory in medical settings. AI-styled physician interactions may reduce stress in routine medical communication, but their use should be carefully considered for critical medical communication.

To map research on nursing workforce preparedness and resilience in health emergencies from 2000 to 2025 and propose an integrated framework.

Bibliometric review with science mapping and thematic evolution.

Scopus was searched for journal articles and reviews on nursing workforce preparedness and resilience in health emergencies published from 2000 to 2025. Records were screened using year, language, document type, duplicate title, and metadata-based scope criteria. Co-citation and co-word analyses were conducted to map intellectual foundations and conceptual structures. Time-sliced analysis examined thematic evolution across 2000-2009, 2010-2019, and 2020-2025, while emerging hotspots were identified using keyword growth and recency indicators.

The final retained corpus included 7570 documents, composed of 6950 articles and 620 reviews. Publication output showed a marked post-2020 surge. Co-citation analysis highlighted intellectual foundations related to disaster nursing competence, psychological resilience and mental health, leadership, staffing, safety, and workforce systems. Co-word analysis indicated major domains of clinical competence, emergency and acute-care delivery, pandemic-related psychological burden and resilience, workforce organization and emergency service capacity, and nursing education for disaster preparedness. Emerging hotspots included burnout, moral distress, resilience, telemedicine, mental health, and qualitative inquiry.

Nursing workforce preparedness research has evolved into a multidimensional, system-oriented agenda that integrates competence, resilience, and structural readiness.

Nurse managers should treat emergency preparedness as a strategic workforce-management responsibility, not only as a clinical or disaster-planning task. The findings support competency-based preparedness training, workforce policies for surge staffing and role deployment, organizational capacity building, mental health and resilience supports, leadership preparation, safety infrastructure, and data-informed preparedness planning to sustain nursing performance during future health emergencies.

Major depressive disorder (MDD) is a serious mental health disorder that is understood to affect an individual's speech signals, including observed variations in acoustic, prosodic, and laryngeal characteristics of a person's speech. There is a need for noninvasive, objective, and automated diagnostic methods for the diagnosis of depression. In this study, after applying the Empirical Mode Decomposition (EMD) to speech signals, it is proposed to classify the extracted features using a hybrid deep learning model (CNN-BiLSTM) for the early diagnosis of MDD. Nonstationary speech signals were decomposed into 7 intrinsic mode functions (IMF) using the EMD method. A total of 325 acoustic features (MFCC, LPCC, formant frequencies, jitter, shimmer, spectral entropy, glottal parameters, etc) were extracted from both the raw speech signal and the 7 IMF segments and classified using the CNN-BiLSTM model. Performance of the proposed method was compared with the performances of five different machine learning models for the MODMA dataset: Fine Tree, Linear SVM, Ensemble Boosted Trees, Kernel Logistic Regression, and Narrow Neural Network. The results show that EMD-based adaptive features significantly improve classification performance compared with features extracted without EMD. The study demonstrated better performance than MFCC or spectrogram-based studies in the literature, with 94.7% accuracy and an average F1-score value of 0.95. The results of 20% hold-out validation and 5-fold cross-validation demonstrate that the proposed method offers high accuracy, stability, interpretability, and generalizability. Additionally, a MATLAB-based clinical decision support interface supporting the CNN-BiLSTM model for MDD diagnosis from speech signals has been developed.

This article explores the mental health concerns of transgender and gender diverse (TGD) individuals within a mutual aid and harm reduction framework. Despite historical strides toward equity and care in the public health sector, TGD people face structural discrimination, polyvictimization, and worsening political hostility that heighten mental health disparities. The authors critique medical and nursing practices shaped by carceral logic and call for new approaches rooted in equity and care. Clinical recommendations emphasize affirming, trauma-informed, and community-based care, while highlighting harm reduction and mutual aid as essential, evidence-based strategies for collective healing, resilience, and a move toward communal care.

Overutilization of the emergency department (ED) is costly and often reflects unmet needs and suboptimal care, raising concerns when patients rely on the ED persistently over many years. Long-term ED use trajectories for patients with substance-related disorders (SRDs) have never been investigated. This study aimed to identify ED use trajectories over a 10-year period and examine associations with patients' sociodemographic and clinical characteristics, quality-of-care indicators, and subsequent adverse outcomes.

A cohort of 9642 patients with long-standing SRDs was examined using Québec (Canada) medical records (1996-2022). Group-Based Trajectory Modeling identified 10-year ED use trajectories. Multinomial logistic regression assessed associations between trajectories and covariates, while Cox models examined links to subsequent adverse outcomes (suicidal behavior, hospitalization, death).

Four ED use trajectories were identified: Trajectory 1 ("Low ED users," 50% of cohort), Trajectory 2 ("Increasing ED users," 20%), Trajectory 3 ("Sinusoidal ED users," 20%), and Trajectory 4 ("Very frequent ED users," 10%). Half of the cohort showed frequent ED use (≥3 visits/year), including 10% who were very frequent users (≥8 visits/year), underscoring substantial unmet needs. ED use frequency was strongly linked to social and health conditions-most favorable in Trajectory 1, who were mostly men, and markedly worse in Trajectories 4, 2, and 3. Adverse outcomes were also strongly associated with poorer social and health conditions and lower treatment motivation, even though Trajectories 4, 2, and 3 received more healthcare services.

Findings indicate that the healthcare system is not adequately structured to meet the needs of patients with severe multimorbidity involving SRDs, mental disorders, chronic physical illnesses, and social instability, contributing to care quality insufficiently meeting the complex needs of Trajectories 2-4. Tailored interventions may be warranted, including Integrated Treatment for Dual Disorders and Assertive Community Treatment for Trajectories 2 and 4, and Intensive Case Management, Recovery Management Checkups, or mobile health technologies for Trajectory 3. Across Trajectories 2-4, enhanced motivational interventions, social support, reduced stigma, optimized medication management, peer-helper programs, and stronger ED-outpatient collaboration remain essential. Improving services for patients with frequent ED use-particularly those in Trajectory 4 with recurrent very frequent use-should be prioritized to reduce costs, optimize service use, and support recovery.

Chronic psychological stress and poor cardiometabolic health are major public health concerns that can accelerate cellular ageing, yet their combined impact on shorter telomere length (TL) - a biomarker of cellular ageing - remains poorly understood in occupational settings. This study examined their associations and potential interactions among university staff in Australia.

TL was measured from blood leukocytes. Cardiometabolic indicators included adiposity, blood pressure (BP), lipid profile, fasting plasma glucose (FPG), insulin resistance, C-reactive protein, and cortisol. Psychological stress, burnout, well-being, and health-related quality of life (HRQoL) were assessed using validated questionnaires, and wearable devices captured sleep and nocturnal autonomic nervous system activity. Correlation, multiple linear regression, and moderator analyses were used to analyse the data.

Among 126 participants [65% female, mean age 48.84 (± 9.43) years], the average TL was 7.06 ± 1.00 kilobase. While visceral fat, systolic BP, and FPG were associated with TL, these associations were attenuated in age-adjusted models. Psychological variables were not directly associate with the TL; however, burnout (B = -0.26 [95% CI: -0.51, -0.01], p = .042), sleep-related impairment (B = -0.02 [95% CI: -0.04, 0.0004], p = .046), and HRQoL-mental component (B = 0.02 [95% CI: 0.001, 0.04], p = .034) significantly moderated the association between FPG and TL.

Age was the primary correlate of TL. Burnout, sleep-related impairment, and mental quality of life may moderate the association between FPG and TL. These exploratory findings suggest that addressing both psychological and cardiometabolic health may support TL in working adults.

ACTRN12625000134426.

Psoriasis is a common, chronic, immune-mediated skin disease affecting more than 40 million individuals worldwide. Psoriasis, along with psoriatic arthritis, are part of a broader psoriatic disease spectrum, and present with inflammatory skin and musculoskeletal manifestations driven by shared genetic, environmental, and immunological mechanisms. Skin psoriasis presents with diverse clinical phenotypes, including chronic plaque psoriasis, guttate, palmoplantar, erythrodermic, and pustular forms. Beyond cutaneous manifestations, psoriasis is associated with a substantial psychosocial burden and a wide range of comorbidities, including mental health disorders and cardio-metabolic diseases. Key psoriasis risk factors include genetic polymorphisms (most notably HLA-C*06:02), obesity, and environmental factors such as trauma, infections, and specific medications. Advances in understanding the key role of the IL-23-IL-17 inflammatory axis and related immunogenetic pathways have redefined psoriasis as a complex immune disorder, leading to the development of highly effective targeted biologic and small-molecule therapies that modulate cytokine signalling and intracellular pathways, offering improved disease control across cutaneous and musculoskeletal domains.

Hallucinogens have been prescribed for the treatment of mental illnesses; however, reports on the dependence potential of hallucinogens across species remain inconsistent. Non-human primate (NHP) self-administration model are suitable for evaluating species-specific reinforcement behavior induced by hallucinogens. In this study, we demonstrated that 4-bromo-2,5-dimethoxyphenethylamine (2C-B) was self-administered orally in freely moving marmoset (Callithrix jacchus). 2C-B-treated mice did not demonstrate reinforcement behavior in self-administration paradigm as well as brain stimulation behavior in intracranial self-stimulation (ICSS) test. Interestingly, 2C-B-induced dopamine neuronal activity was demonstrated only in the brain of marmosets as evidenced by fluorescent false neurotransmitter assessment and measurement of synaptosome dopamine levels. We sought to study possible molecular mechanisms underlying this and docking simulations revealed that 2C-B occupied a binding pocket overlapping the canonical dopamine-binding site in both species but adopted a distinct interaction pattern in marmoset DAT involving Leu321. Pull-down assays demonstrated stronger DAT-associated 2C-B binding in marmosets than in mice. Furthermore, 2C-B produced greater inhibition of DAT-mediated FFN102 uptake in cells expressing marmoset or human DAT than in cells expressing mouse DAT, whereas substitution of Leu321 abolished this effect. Collectively, these findings demonstrate that 2C-B induces reward-related behaviors in marmosets and identify species-specific DAT-2C-B interactions as a potential mechanism underlying differential dopaminergic and behavioral responses across species.

The comparative efficacy of acceptance and commitment therapy (ACT) relative to other bona fide psychotherapies has been obscured by methodological limitations and variability in how ACT and active comparators are operationalized. We therefore conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) comparing full-model ACT (therapist-delivered, multi-session interventions targeting the core ACT processes) to bona fide psychotherapies. We identified 34 RCTs meeting our inclusion criteria. In line with prior critiques of the ACT literature, we identified several common methodological shortcomings, such as lack of preregistration of primary outcomes and hypotheses, inadequate statistical power for detecting differential effects, limited use of intent-to-treat analyses, and interpretive overreach. Random-effects three-level meta-analyses indicated that full-model ACT was not superior to bona fide psychotherapies on mental and behavioral health outcomes (g = -0.01, p = .86). Secondary, hypothesis-generating post hoc analyses examined clinical significance, ACT-targeted processes, and an exploratory TOST comparison against a prespecified equivalence region (g = ±0.20). Current RCT evidence does not justify recommending full-model ACT over other established treatments. Further progress will require more rigorous comparative trials and idiographic designs that better align with the ACT model.