Allogeneic haematopoietic stem cell transplantation is a critical treatment for acute myeloid leukaemia (AML) and acute lymphoblastic leukaemia (ALL), yet the risk of treatment failure still persists. Chimerism analysis serves as a potential tool for predicting disease recurrence and survival rates, but its specific role has not yet been clearly defined. This study aimed to explore the role of decreased T-cell and bone marrow (BM) chimerism in predicting post-transplant relapse and survival in patients with acute myeloid leukaemia (AML) and acute lymphoblastic leukaemia (ALL). The study subjects were 305 AML and ALL patients who underwent allogeneic haematopoietic stem cell transplantation at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, from January 1, 2018, to September 1, 2023. We monitored the chimerism rate at monthly intervals after transplantation until either patient relapse or the end of follow-up. T-cell and BM chimerism were tested at the same time points. Relapse probabilities were estimated by the Kaplan-Meier method and compared with the log-rank test. Gehan-Breslow-Wilcoxon test was used to assess the impact of T-cell and BM chimerism levels on survival probabilities. In AML patients, our analysis revealed no significant correlation between the presence of initial mixed chimerism (at Day +30 post-transplantation) and the relapse rate. Among patients with ALL, the number with initial mixed chimerism was insufficient for statistical analysis. In AML patients whose bone marrow chimerism rate decreased first (n = 13) had a higher relapse rate and a lower survival rate than those whose T-cell chimerism rate decreased first (n = 11) (p < 0.01). In patients with ALL, there was no significant difference in the relapse or survival rates between patients whose bone marrow chimerism rate decreased first (n = 12) and those whose T-cell chimerism rate decreased first (n = 15) (p > 0.05). While a decrease in bone marrow chimerism effectively predicts AML relapse, T-cell chimerism demonstrates lower predictive efficacy. Further research is necessary to identify reliable predictors for relapse in ALL patients. The integration of chimerism analysis with other prognostic indicators, along with early monitoring and preemptive intervention, may enhance patient survival and quality of life.

Pulmonary alveolar proteinosis (PAP) is a rare lung disease characterised by progressive accumulation of surfactant-derived lipoproteinaceous material within alveoli, impairing gas exchange and causing respiratory insufficiency. Despite therapeutic advances such as whole lung lavage, substantial heterogeneity persists in outcome selection and reporting across studies, limiting comparability and evidence synthesis.

To systematically map and categorise outcomes and outcome measures reported in studies of PAP treatments and establish a foundation for developing a standardised core outcome set (COS).

A scoping review was conducted across four databases and two clinical trial registries (May 2024, updated May 2025). Eligible studies included those reporting treatment outcomes in patients of any age with PAP. Outcomes and measures were extracted and categorised using the Core Outcome Measures in Effectiveness Trials taxonomy.

From 8475 screened records, 62 studies met the inclusion criteria, encompassing 31 distinct outcomes and 92 corresponding outcome measures. Physiological parameters dominated reporting, including arterial oxygenation (n=55, 89%; such as arterial oxygen tension and alveolar-arterial oxygen gradient) and lung function indices (n=53, 85%; such as diffusing capacity of the lung for carbon monoxide and forced vital capacity) were most frequently assessed. In contrast, patient-centred outcomes such as quality of life were reported in only 10 (16%) studies, while adverse events were relatively well-reported (n=41, 66%).

The reporting of outcomes and outcome measures in PAP studies is highly variable. There is an urgent need for a COS tailored to PAP that focuses on physiological outcomes, adverse events and patient-reported outcomes.

Globally, unaccompanied minors face more significant risks for poorer mental health and other psychosocial issues while also grappling with mental health, language, and acculturation challenges. The United States admitted 1550 unaccompanied Afghan minors (UAMs) as humanitarian parolees among some 76,000 Afghans evacuated following the US withdrawal and Taliban takeover in 2021. Most UAMs had lived all their lives in Afghanistan's protracted conflict. Now resettled in the U.S., it is critical to understand their experiences to inform services, prevent common mental health problems, and strengthen pathways for their development. This study examined the recently arrived UAMs' needs and challenges in resettlement. The study gathered data from free listing interviews and focus groups with UAMs and their caregivers (n = 29). Guided by a transactional ecological model, thematic content analysis was used to identify and categorize problem clusters and common themes outlined by UAMs and caregivers. UAMs were primarily concerned about being separated from their families and expressed sadness, longing, and grief. Other problems include language barriers and mental health symptoms that affect functioning across their ecological levels of development. Caregivers emphasized challenges related to adjusting to the American cultural context. Findings support the pressing need to design and provide culturally relevant services to reduce mental health problems, strengthen family relationships, and engender positive future outcomes. The results have implications for the provision of equitable mental health services for resettled Afghan youth.

Accurate quantitative survival prediction in advanced non-small cell lung cancer (NSCLC) remains an unmet clinical need. While liquid biopsy is widely used, single circulating tumor DNA (ctDNA) shows limited predictive power. We developed an interpretable deep-learning model to quantitatively predict outcomes.

We integrated data from 1373 advanced NSCLC patients profiled by two ultra-deep ctDNA sequencing assays (MSK-ACCESS and ctDx Lung). Features associated with overall survival (OS) were incorporated into a deep-learning network (DeepSurv), which estimates time-to-event survival probabilities. Model performance was evaluated by time-dependent area under the curve (AUC). SHapley Additive exPlanations (SHAP) were employed to interpret model output.

A total of 1373 patients were analyzed, with 1012 using MSK-ACCESS (discovery) and 361 using ctDx Lung (validation). Among over 40 clinicopathological features, ctDNA status, cell-free DNA (cfDNA) concentration, age, blood-based TP53, EGFR, PIK3CA, ARID1A, STK11 and MET mutations significantly predicted OS. In ctDNA-positive patients, TP53/PIK3CA/ARID1A/STK11/MET-mutated patients had significantly inferior OS compared with wildtype patients (P < 0.001). Using above variables, DeepSurv was trained and tested in the MSK-ACCESS cohort (12-month AUC = 0.75), outperforming single cfDNA (AUC = 0.66) or ctDNA (AUC = 0.59), and externally validated in the ctDx Lung cohort. Compared with high-risk patients, DeepSurv-identified low-risk patients had significantly longer OS in both discovery (12-month OS 87.8% vs 53.8%, HR 0.32, P < 0.001) and validation cohorts (73.2% vs 48.4%, HR 0.42, P < 0.001). SHAP revealed TP53 and cfDNA concentration > 4.8 ng/mL had the most important contributions.

The interpretable DeepSurv model, integrating multimodal features, enables quantitative survival prediction and risk stratification in advanced NSCLC, facilitating personalized decision-making.

Traditional medicines can contribute to achieving universal health coverage, particularly in low- and middle-income countries where access to conventional treatments is limited. International collaboration is crucial to bridge the lag in modernised research and promote access to traditional medicines. This study focused on China's global collaborative research and development (R&D) efforts on traditional medicine, in the hope of improving global recognition for traditional medicine.

We conducted a cross-sectional study to analyse collaborative R&D outputs on Chinese patent medicines from 1996 to 2022. The study cohort included the collaborative outputs of scientific research, patent applications, and clinical trials between China and other countries. We analysed the outputs using data from the Web of Science, Worldwide Patent Statistical Database, and the International Clinical Trials Registry Platform. The Zero Inflated Negative Binomial regression model was employed to investigate the association between outputs and the characteristics of participating countries.

The majority of collaborative outputs (n = 964, 92.4%) originated from collaborations with high-income countries, with only 7.6% involving low- and middle- income countries (LMICs). The percentage of R&D collaborations with LMICs showed an increasing trend from 0% in 1996 to 11.7% in 2022. Most collaborations focused on non-communicable diseases (n = 912, 87.4%). Low-income countries accounted for a larger share of collaborative R&D on communicable diseases (14.3%) compared with high-income countries (1.4%). The total number of outputs was positively associated with the degree of cooperative institutionalisation and the collaborator's traditional medicine development score.

Gaps still remain in the involvement of low- and middle-income members compared with high income countries. Findings highlight the importance of encouraging greater engagement of low- and middle- income countries in global R & D collaboration on traditional medicine, particularly through South-South partnerships. Such collaborations should prioritise research agendas that address local health priorities, especially those related to communicable diseases.

Chronic non-communicable diseases (NCDs)-including cardiovascular disease, diabetes, chronic obstructive pulmonary disease (COPD), and chronic kidney disease-pose a major 21st-century global public health challenge. They drive high morbidity, mortality, and escalating healthcare costs. Traditional reactive, clinic-centered care models are ill-equipped to meet the ongoing, complex needs of chronic disease patients. This has prompted a shift toward proactive, personalized, and patient-centered approaches. This narrative review examines the transformative potential of emerging digital health technologies (DHTs) in chronic disease prevention and control. It emphasizes the synergistic integration of four key domains: Internet Plus ecosystems, wearable devices and sensors, artificial intelligence (AI) and machine learning, and interactive voice-based follow-up or conversational agents. Internet Plus serves as the foundational infrastructure. It enables seamless data integration, care coordination, telemedicine, and patient empowerment across stakeholders. Wearable devices facilitate continuous, real-time monitoring of physiological and behavioral data, yielding valuable insights for timely interventions in cardiovascular, metabolic, respiratory, and musculoskeletal disorders. AI and machine learning drive predictive diagnostics, risk stratification, and personalized digital therapeutics, demonstrating superior efficacy and cost-effectiveness in areas like pulmonary rehabilitation and orthopedic care. Voice-based technologies provide scalable, low-cost solutions for medication adherence, symptom monitoring, and health education. They particularly benefit older adults and rural populations. Despite these advances, significant challenges remain. These include data security and privacy risks, health inequities amplified by the digital divide and device biases, and AI limitations (e.g., reproducibility, opacity or "black-box" issues, and unclear legal accountability). In conclusion, the convergence of these technologies promises a more precise, proactive, and inclusive paradigm for chronic disease management. Future success hinges on robust privacy protections, inclusive design, diverse real-world validation, and refined regulatory frameworks to ensure equitable and sustainable implementation.

Metabolic dysfunction-associated fatty liver disease (MAFLD) has emerged as a major non-communicable comorbidity in people living with HIV (PLWH). The role of inflammatory metabolic parameters-including the lymphocyte-to-high-density lipoprotein cholesterol ratio (LHR), platelet to high-density lipoprotein cholesterol ratio (PHR), and aggregate index of systemic inflammation (AISI)-in predicting MAFLD in PLWH remains unclear. This study aimed to evaluate the prognostic value of inflammatory metabolic parameters for predicting MAFLD in PLWH.

We conducted a retrospective cohort study of 814 PLWH receiving stable antiretroviral therapy (ART) between 2018 and 2025 at a tertiary care center. Baseline demographic, clinical, and laboratory data were collected. LHR, PHR, and AISI were calculated and categorized into tertiles. The primary outcome was incident MAFLD, diagnosed according to Chinese guidelines. Kaplan-Meier survival analysis, Cox proportional hazards regression, restricted cubic spline (RCS) models, and time-dependent receiver operating characteristic (ROC) curves were applied to assess associations and predictive performance. Sensitivity analyses were performed across subgroups.

During a median follow-up of 2.8 years, 116 participants (14.3%) developed MAFLD, corresponding to an incidence rate of 50.4 per 1,000 person-years. Higher LHR and PHR tertiles were significantly associated with increased MAFLD risk, whereas AISI showed no predictive value. In fully adjusted Cox models, the high LHR tertile remained an independent risk factor (HR = 2.315, 95% CI: 1.208-4.436, p = 0.011), while only the middle PHR tertile retained significance. RCS analyses showed no significant non-linear associations for LHR, PHR, or AISI. Time-dependent ROC analyses demonstrated that LHR had the strongest short-term predictive ability (AUC = 0.713 at 1 year), followed by PHR (AUC = 0.644), while AISI consistently performed poorly (AUC < 0.600). Subgroup analyses confirmed the robustness of LHR and PHR associations across demographic, clinical, and metabolic subgroups.

In this large cohort of PLWH on ART, LHR and PHR were independent predictors of incident MAFLD, with LHR demonstrating the strongest and most consistent predictive value. AISI was not associated with MAFLD. LHR and PHR, as simple, low-cost indices derived from routine laboratory tests, may serve as practical tools for identifying high-risk individuals who could benefit from early monitoring and targeted intervention.

Musculoskeletal pain and noncommunicable diseases are major contributors to disability among adults aged ≥50 years, yet their coexistence and modifying factors are not well characterized in Saudi Arabia. This study aimed to estimate age-group-specific prevalence of musculoskeletal pain, identify independent predictors of site-specific and multisite pain and of major morbidities, and evaluate effect modification among adults ≥50 years.

A community-based cross-sectional survey was administered to participants aged ≥50 years. Data on sociodemographic characteristics, smoking status, body mass index (BMI), morbidities, and musculoskeletal pain sites were collected.

Among 298 participants (mean age 58.2 ± 6.3 years; 47.0% overweight; 32.9% obese), musculoskeletal pain was reported by 73.2%, most commonly at the lower back (30.9%), knee (26.2%), neck (23.8%), and shoulder (21.8%). Hypertension (29.5%) and diabetes (28.2%) were the most prevalent morbidities; multimorbidity was present in 24.5% of participants. Female sex was the most consistent predictor across pain sites, with higher odds for low back pain (OR 2.83, 95% CI 1.60-5.00; p < 0.001), shoulder pain (OR 2.99, 95% CI 1.64-5.44; p < 0.001), and neck pain (OR 2.58, 95% CI 1.44-4.63; p = 0.002). A significant smoking × BMI interaction was observed for hypertension (OR 0.22, 95% CI 0.06-0.86; p = 0.029), indicating that the positive association of smoking with hypertension was strongest at normal BMI and attenuated at higher BMI. For diabetes, the age-related increase in risk was greater among participants with hypertension (OR 1.02 per year, 95% CI 1.01-1.03; p < 0.001) and was further increased at higher BMI (OR 1.59, 95% CI 1.24-2.05; p < 0.001).

Musculoskeletal pain co-occurring with cardiometabolic conditions was common among older adults in Saudi Arabia. Female sex consistently predicted pain, and obesity was more strongly associated with hypertension. Effect-modification patterns identify high-risk strata; therefore, routine cardiometabolic screening should be incorporated into musculoskeletal management, with first-line non-pharmacologic care and targeted counselling for obese women and smokers.

The World Health Organization (WHO) recommends integrating hypertension and human immunodeficiency virus (HIV) care; however, evidence for implementing integrated care in primary healthcare (PHC) HIV clinics remains limited.

To assess the feasibility and acceptability of a pilot model for integrating hypertension care into HIV services and to describe the hypertension care cascade among people living with HIV (PLHIV) and hypertension.

Two PHC HIV clinics in Wakiso district, Uganda.

We conducted a parallel convergent mixed methods study. The pilot intervention included providing blood pressure (BP) cuffs, antihypertensive medications, a treatment algorithm and training healthcare provider (HCP) on hypertension care. Quantitative data were collected from February 2022 to December 2022. Using the consolidated framework for implementation research, we conducted interviews with HCPs (n = 12) and PLHIV with hypertension (n = 8) to explore implementation determinants. We performed descriptive analysis for hypertension care cascades. Qualitative data identified barriers and facilitators to integrating HIV and hypertension care.

Of 3802 PLHIV in care, 3502 (92%) were screened for hypertension. Among these, 290 (8.3%) had a chart diagnosis of hypertension, 282 (97.2%) were treated and 128 (50.2%) achieved BP control. Key facilitators included access to medications, BP monitors and improved provider knowledge on management of BP among PLHIV. Barriers included unsynchronised clinic visits and increased provider workload.

Integrating hypertension and HIV services in Ugandan HIV clinics is feasible and acceptable. Availability of resources (BP medications and monitors) and trained personnel facilitates integration of these services.

This pilot study provides evidence that integrating hypertension care into existing PHC HIV in Uganda and other similar settings is both feasible and acceptable but may necessitate additional human resources for health.

While international evidence suggests seasonal variations may influence outcomes of interventions for pediatric obesity, data for Aotearoa New Zealand are limited. We examined seasonal variations in changes in body mass index standard deviation score (BMI SDS) in young people with obesity enrolled in an intervention programme.

We studied 397 children and adolescents (median = 10.1 years; range 3.7-16.8 years) from Whānau Pakari, a multidisciplinary community-based healthy lifestyle programme (initially a randomised clinical trial that subsequently transitioned into the regional childhood obesity service). Participants were stratified by season at entry and 6-month BMI SDS changes (Δ) were evaluated. Lifestyle factors were also assessed. Data were analysed using traditional linear models and machine learning (random forest).

68% of participants had BMI SDS reductions at 6 months (mean = - 0.16 SDS; P < 0.0001). Linear models showed seasonal variations in programme effectiveness, with BMI SDS reductions among summer (- 0.17 SDS), autumn (- 0.19 SDS) and winter (- 0.18 SDS) but not among spring entrants. Random forest modelling identified higher baseline BMI SDS and younger age as the most influential predictors of greater 6-month reductions in BMI SDS. Season of entry was more important than any single lifestyle factor; spring entrants exhibited attenuated reductions relative to other seasons.

The season at programme entry was an important factor associated with intervention effectiveness. Spring entry was associated with attenuated BMI SDS reductions, likely due to the inclusion of the summer holidays within the 6-month intervention. These findings highlight the need for targeted support during such unstructured periods to improve participant outcomes.