Community-based digital weight management programs have shown potential to address the public health concern of obesity, but often face implementation barriers. This study aimed to identify and evaluate current implementation barriers, facilitators, and further develop strategies. A prospective qualitative study was conducted via workshops of stakeholders from community-based weight loss management services across Singapore, alongside a separate series of interviews for adopters. A hybrid deductive-inductive approach to thematic analysis was then adopted, informed by the Consolidated Framework for Implementation Research (CFIR), the Theoretical Domains Framework (TDF), and the Expert Recommendations for Implementing Change framework (ERIC). Interview transcripts were qualitatively coded based on relevant CFIR and TDF domains using a coding template. Subsequently, identified barriers and domains were mapped to ERIC strategies. A total of 48 and 145 barriers were identified based on adopter and implementer transcripts, spanning across 26 and 35 subdomains of the TDF and CFIR, respectively. Several barriers were consistently identified by both adopters and implementers, with the main consistency being a mismatch of expectations. These include differing expectations of public awareness on obesity as a disease, expectations on the flexibility of implementations, and expectations on support for personal motivation. Furthermore, 43 adopter and 57 implementer facilitators were identified, spanning across 25 and 28 distinct subdomains of the TDF and CFIR framework, with the provision of incentives, societal and doctor endorsements as common facilitators. A total of 53 distinct ERIC implementation strategies subthemes were also identified. The study identified underlying themes linking barriers and facilitators across implementers and adopters of community-based weight management services. These were mapped to ERIC strategies and contextualized using the Action, Actor, Context, Target, Time (AACTT) framework to specify actors and actions. Many of these strategies could be operationalized by policymakers and nurses, highlighting their central role in facilitating program adoption and delivery.
Excited-state symmetry breaking (ES-SB) is a photophysical process wherein centrosymmetric quadrupolar molecules acquire a pronounced dipolar character after photoexcitation. This ultrafast excitation localization and the emergence of dipolar excited states stem from the interplay of electronic coupling, vibronic interactions, and environmental polarization, whose respective contributions are often difficult to disentangle in spectroscopic analysis. In this work, we investigate a linearly aligned centrosymmetric quadrupolar dye with a D-π-A-π-D motif to elucidate how solvent effects and electronic coupling between branches cooperatively govern the onset and degree of ES-SB. By combining femtosecond broadband transient absorption spectroscopy using Laporte-forbidden transitions as symmetry markers with an essential-state model incorporating vibronic coupling and solvation effects, we directly tracked ES-SB dynamics in real time and mapped the associated relaxation potential energy surfaces. Our results reveal that, in polar solvents, ES-SB proceeds via a cascaded relaxation pathway: an initial symmetry-preserving step along symmetric vibrational coordinates, followed by symmetry breaking along antisymmetric vibrational coordinates. Furthermore, we establish a quantitative criterion for ES-SB onset (Stokes-shift energy >3V, where V is electronic interbranch coupling). This work clearly elucidates the dynamics of ES-SB and establish a predictable and tunable framework for rationally controlling ES-SB behavior in quadrupolar chromophores.
To develop a structured, pharmacy-specific framework that defines the essential digital health and telehealth knowledge and skills required for student pharmacists and practicing pharmacists across educational and practice settings. An expert panel convened by the American Society of Health-System Pharmacists conducted a targeted literature review and environmental scan, followed by an iterative, consensus-driven development process over 18 months. The resulting framework organizes competencies into 7 domains: Technology and Digital Literacy; Patient Communication, Education, and Engagement; Documentation and Information Management; Clinical Assessment and Decision-Making; Operational Management and Workflow Integration; Regulatory Compliance and Ethical Practice; and Quality Improvement and Evaluation. Each domain includes actionable skills aligned with real-world telehealth workflows and mapped to entrustable professional activities to support competency-based education. The framework also incorporates practical curricular applications, including learning objectives, assessment strategies, and experiential activities, to facilitate integration into didactic curricula, experiential training, and continuing professional development. This approach addresses gaps in current pharmacy education, where digital health training is often fragmented, inconsistent, or insufficiently tailored to the pharmacist's role in interdisciplinary care. This framework provides a comprehensive and adaptable foundation for integrating digital health and telehealth competencies into pharmacy education and practice. By standardizing expectations and aligning training with evolving healthcare technologies, it supports the preparation of pharmacists to deliver safe, effective, and patient-centered care in digitally enabled environments while promoting ongoing professional development and workforce readiness.
Esophageal cancer (EC) is a highly fatal malignancy for which radiotherapy plays a critical role in treatment. However, anatomical changes during radiotherapy can lead to increased doses to surrounding organs at risk (OARs). Adaptive radiotherapy (ART) is a strategy that incorporates patient-specific anatomic changes into treatment plan modification to minimize overdose to surrounding healthy tissues. Identifying appropriate triggers with in-room verification imaging is critical to maximize the benefits of ART and prevent additional imaging dose to a certain extent for patients without compromising clinical resources or operations. To develop an automatic ART triggering procedure by predicting the patient's anatomical changes and their resulted dose-volume metrics differences on OARs using deep learning (DL) based on cone-beam computed tomography (CBCT) to efficiently and effectively balance the benefit and frequency of ART. A DL network was first trained to automatically segment OARs on the CBCT of 136 EC patients underwent volumetric modulated arc therapy (VMAT). The dose distributions on CBCT with the automatically contoured OARs were predicted with Unet. A set of trigger criteria based on OAR dosimetry deformed form original treatment plan was established to assess replanning necessity. The feasibility and accuracy of automatic segmentation and dose prediction on CBCT were verified with rescan CT (rCT) and CBCT at the same time point. The average dices of the automatic segmentation model for the lung, heart, and spinal cord were 0.92, 0.91, and 0.80, respectively. The predicted dose distributions on CBCT were close to mapped dose distributions. The ART trigger decision agreement between rCT and CBCT was 81.8%. CBCT with automatic segmentation OARs achieved an area under curve, accuracy, sensitivity, and specificity of 0.86, 0.82, 1.0, and 0.71 in the triggering of ART for EC patients, respectively. An automatic ART triggering procedure was established based on CBCT directly for EC patients underwent VMAT. It is a feasible and promising ART methods to improve the management of EC patients without additional patients appoints and resources.
The fire ant Solenopsis invicta is an aggressive invasive species whose venom frequently triggers hypersensitivity reactions, including severe anaphylaxis. In endemic regions, its stings represent a significant cause of Hymenoptera-related allergy. Four venom allergens have been identified - phospholipase A1 (Sol i 1), antigen 2 (Sol i 2), antigen 3 (Sol i 3), and antigen 4 (Sol i 4) - with Sol i 3 recognized as the predominant sensitizer. However, the molecular determinants that drive Sol i 3 allergenicity and its potential cross-reactivity with other Hymenoptera venoms remain insufficiently understood. This study identified the linear immunoglobulin E (IgE) epitopes of Sol i 3 and examined their recognition by sera from yellow jacket venom (YJV) - and Polistes wasp-allergic patients. Two linear epitopes were mapped: Sol i 3_e1 (ELRQRVASGKEMRG) and Sol i 3_e2 (WAKTTKIGCGRIMF). Although Sol i 3 exhibits limited sequence and structural similarity to other antigen 5 proteins, it contains a conserved immunoreactive core (WAKTTK), analogous to the WAKTKE motif described for the allergen Poly p 5 from Polybia paulista. This conserved region may represent a shared epitope contributing to cross-reactivity among Hymenoptera venoms. Consistently, sera from P. dominula-sensitized patients and YJV-sensitized patients recognized Sol i 3_e2. These findings define key B-cell epitopes of Sol i 3 and reveal a conserved motif that may underlie cross-reactivity, offering implications for improved diagnosis and immunotherapy.
Bipolar disorder is a mental disorder characterized by recurrent episodes of mania/hypomania and depression, with a strong genetic contribution and substantial functional burden. Recent genomic studies implicate multiple risk variants converging on intracellular calcium (Ca2+) signaling and synaptic function, while neurons derived from induced pluripotent stem cells of patients with bipolar disorder demonstrate altered neuronal excitability and lithium-responsive phenotypes. Building on early neuroimaging and postmortem observations, accumulating evidence supports the mitochondrial dysfunction hypothesis, which proposes that impaired mitochondrial Ca2+ buffering disrupts neuronal Ca2+ homeostasis and contributes to mood instability. Diverse findings align with this framework: altered brain energy metabolism, increased mitochondrial DNA (mtDNA) deletions, elevated lactate, reduced mitochondrial gene expression and complex I proteins, enrichment of deleterious de novo and mosaic variants in Ca2+ signaling- and mitochondrial/endoplasmic reticulum-related genes, and a higher prevalence of the MELAS (mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes)-associated m.3243A>G mutation in individuals with bipolar disorder. Animal models further strengthen causal inference. Neuron-specific Ant1 knockout mice exhibit reduced mitochondrial Ca2+ uptake and serotonergic hyperexcitability, while mice with neuron-specific mutant Polg accumulate mtDNA deletions and show recurrent depression-like episodes responsive to lithium and switch-like manic behaviors following treatment with a tricyclic antidepressant, indicating construct, face, and predictive validity. To identify the critical brain substrate, mtDNA deletions were mapped and found to accumulate most prominently in the paraventricular thalamic nucleus (PVT), a serotonergic-recipient hub projecting to limbic circuits involved in emotional salience. Human postmortem single-nucleus analyses reveal marked reductions of PVT neurons and prominent gene expression changes in the PVT, including enrichment of GWAS (genome-wide association study) signals among downregulated genes, as well as neuropathological alterations such as granulovacuolar degeneration in the PVT in late-onset cases. These convergent data suggest that genetically driven Ca2+ dysregulation and mitochondrial vulnerability promote circuit-level dysfunction-particularly within the serotonin-PVT-limbic pathway-leading to dysregulated emotion-cognition balance and mood swings.
Morocco is experiencing rapid demographic aging alongside a rising cancer burden, creating structural challenges for the care of older adults with cancer. This review synthesizes current evidence on geriatric oncology in Morocco and proposes a conceptual framework to guide system-level adaptation. We conducted a structured narrative review of peer-reviewed publications, population-based registry data, national demographic reports, and policy documents published between 2000 and 2025 (last search: January 2026). Evidence was synthesized qualitatively and organized into six predefined analytical domains: (1) demographic transition, (2) cancer epidemiology, (3) health system organization, (4) access to care, (5) workforce capacity, and (6) geriatric assessment and clinical practice. These domains are applied consistently as the organizing framework across the Results sections and are explicitly mapped onto the WHO Health System Building Blocks and the Four-Phase Oncogeriatric Transition framework in the Discussion. In 2024, adults aged ≥60 years accounted for 13.8% of Morocco's population, while individuals aged ≥65 years represented approximately 8%, with projections indicating a marked increase by 2050. Population-based registries report age-standardized cancer incidence rates around 120-137 per 100,000. Available cohorts indicate high vulnerability prevalence (e.g., >80% abnormal G8 in some series), substantial metastatic presentation at diagnosis, limited geriatric workforce capacity, and a strong urban concentration of oncology services. Structured geriatric assessment is not yet consistently implemented in routine oncology care. These findings suggest that Morocco is entering an oncogeriatric transition characterized by a growing mismatch between demographic acceleration and geriatric-integrated oncology capacity. We propose a Four-Phase Oncogeriatric Transition framework to conceptualize this evolution and inform policy, workforce planning, and phased implementation strategies. Early integration of geriatric assessment, registry adaptation, and multidisciplinary coordination will be essential to ensure equitable, age-adapted cancer care in an aging society.
A research competent workforce is essential for embedding and sustaining evidence-based practice with health and care services. Despite strategic emphasis, many practitioners lack the confidence, skills, and opportunities to engage in research. This study explored the experiences of health and care practitioners from a range of clinical and nonclinical roles undertaking a project-based research internship. It aimed to identify key barriers and enablers to research engagement. A qualitative study was conducted with participants engaged in a 12-month research internship program in the north west of England. Using normalization process theory as a framework, an online semistructured group interview was held with six interns postprogram. Data were analyzed thematically and mapped to normalization process theory domains: coherence, cognitive participation, collective action, and reflexive monitoring. Four themes were identified: discovering purpose from initial uncertainty; negotiating commitment and researcher identity; applying research practice within real-world contexts; and establishing a sense of research legitimacy. Subthemes provide nuance around clarifying expectations, balancing responsibilities, support structures, adapting to organizational constraints, and the internship as a foundation for future practice. The internship program successfully contributed to the development of confidence and researcher identity among novice practitioners. Engagement was maintained through supervision, peer interaction, and pastoral input from coordinators, while workload pressures and differing expectations across stakeholders created challenges. Future delivery should continue to prioritize supportive supervision, peer connection, and pastoral input, while also attending to alignment of expectations, reliable provision of protected time, and training timing that aligns with project milestones.
The rapid growth of digital health initiatives has heightened reliance on frontline health workers (FLHWs) to deliver, document, and manage services through digital tools, particularly in low- and middleincome countries (LMICs). In India, the widespread rollout of platforms under the Ayushman Bharat Digital Mission (ABDM) is not yet matched by a standardized digital health competency framework (DHCF) for FLHWs, hindering systematic skill development, assessment, and integration. This study designed, developed, and evaluated a theory-driven, evidence-based, and scalable DHCF for India's health workforce. Framed as a feasibility and proof-ofconcept study, it was piloted among FLHWs in Uttar Pradesh using a three-stage approach comprising design, implementation, and evaluation. The framework development drew on a systematic literature review and the Government of India's Framework for Roles, Activities, and Competencies (FRAC). A cadre-agnostic competency dictionary was created, spanning functional, behavioral, domainspecific, and intervention-specific skills across graded proficiency levels. Competencies were mapped to FLHW roles, and aligned training materials and assessments were developed. The framework was piloted through in-person, instructor-led sessions for Auxiliary Nurse Midwives (ANMs) in two districts (n = 70), alongside baseline assessments for Accredited Social Health Activists (ASHAs; n = 32). The resulting DHCF comprises a three-component package: (i) a cadre-agnostic competency dictionary with progressive proficiency levels; (ii) systematic role-tocompetency mapping using the FRAC methodology; and (iii) integrated training content and assessment scaffolding designed for institutional embedding. The framework defined 10 core competencies, enabling role-specific mapping across cadres. Feasibility testing demonstrated significant gains in ANMs' knowledge and digital skills: Wilcoxon signed-rank tests showed significant improvements in two of four competency levels (C1L1 and C2L1; both P < .001), with the largest effect for data collection basics (r = 0.84). ASHA baseline assessments revealed substantial foundational literacy gaps (mean total score 11.97/30 [39.9%]; data collection was the weakest competency at 32.5%, with no ASHA scoring above 60% on C2L1). Stakeholders affirmed the framework's relevance, feasibility, and adaptability, while identifying the need for hybrid training models and stronger institutional embedding.The DHCF offers a structured, scalable approach to standardizing digital health training for FLHWs and strengthening workforce preparedness in resource-limited settings during India's digital health transition. This feasibility study establishes the framework's relevance and applicability; future work is needed to evaluate effectiveness at scale, long-term competency retention, and linkage to service delivery outcomes. Parallel attention to digital tool design and usability will be essential to complement competency-building efforts.
Fragile X Syndrome (FXS) is a neurodevelopmental disorder caused by mutations in the FMR1 gene, resulting in the loss of FMRP, an RNA-binding protein regulating translation of hundreds of mRNAs. The Fmr1 knock-out mouse models this deficiency and is used to study molecular perturbations in the FXS brain. Omics analysis shows that FMRP loss disrupts coordination between transcriptomic and translatomic layers. But limited sample availability and dataset heterogeneity hinder detection of subtle, coordinated multi-omic dysregulations. To address this, we trained a Multi-Channel Variational Autoencoder (MCVAE) on wild-type samples to learn a shared latent representation of transcriptomic and translatomic modalities via cross-modal reconstruction. Testing MCVAE on Fmr1-knock-out samples revealed deviations from wild-type as anomalies, uncovering known and novel perturbations. Compared to alternative methods, MCVAE shows stronger enrichment for FMRP mRNA targets and improved genotype discriminative power in post-hoc tests. Translatomic anomalies exhibited coordinated relationships with transcriptomic anomalies, as supported by publicly available databases exploration. Moreover, these anomalies mapped to validated FMRP regulators and neurodevelopmental pathways, establishing MCVAE as a framework to uncover coordinated molecular perturbations underlying the FXS pathophysiology and guide biomarker and therapeutic target identification.
Histologic assessment of endomyocardial biopsy (EMB) remains the standard for diagnosing acute cardiac allograft rejection, yet molecular profiling may provide complementary quantitative insights. Microarray data (GSE2596; GPL1053) were obtained from the Gene Expression Omnibus. Acute rejection (R, n = 16) and non-rejection (N, n = 27) samples were analyzed after excluding infection-related groups. Probes were mapped to gene symbols and summarized per gene. Differential expression was assessed using Welch's t-test with Benjamini-Hochberg false discovery rate control. Reactome over-representation analysis was performed for significant genes. Immune cell scores were estimated using MCP-counter markers. An L1-penalized logistic regression model was evaluated by 5-fold cross-validation. Experimental validation was performed by quantitative PCR in a cervical heterotopic cardiac xenotransplantation model (BALB/c to C57), including normal control and sham groups. Among 3,968 genes, 1,032 were differentially expressed (FDR<0.05), including 135 with |log2FC|>1. Upregulated genes included HLA-DMA, DEF6, TRB@, and CD74. Enrichment analysis highlighted interferon signaling, T-cell receptor signaling, chemokine pathways, and antigen presentation. Immune scoring indicated increased monocytic, B-lineage, and T/cytotoxic lymphocyte signals in rejection. The diagnostic model achieved strong discrimination (5-fold AUC=0.993). qPCR confirmed coordinated upregulation of interferon-related (Ifng, Stat1), chemokine (Cxcl10), and cytotoxic (Prf1) genes in transplanted grafts. Acute rejection EMB transcriptomes demonstrate coordinated interferon-driven immune activation. A compact gene signature shows strong internal diagnostic performance, supported by experimental validation, warranting external confirmation.
Poincaré beams can be mapped on the hybrid-order Poincaré sphere using longitude and latitude coordinates. Mapping on a geometrical sphere is crucial in the study of the topological aspects and applications of singular beams. Currently, there is a dearth of methods that enable the mapping of Poincaré beams on these spheres. This paper presents a simple and robust detection method to determine the coordinates of Poincaré beams on a hybrid-order Poincaré sphere (HyOPS). The method requires only a single intensity measurement obtained through a fixed linear polarizer. The transmitted intensity pattern contains unique null points whose positions encode both longitude and latitude coordinates of the beam. This approach eliminates the need for polarizer rotation or multiple projections, thereby reducing experimental errors and improving reproducibility. The method is applicable to beams belonging to all three types of HyOPS, where the beams are formed by linear/circular state superpositions. Experimental results demonstrate accurate mapping of beam coordinates, consistent with theoretical predictions.
Implementation strength (IS), a cardinal concept in the field of implementation science, is used to assess how the implementation of health services and specific health programme influences health outcomes. It offers a unique opportunity to determine how much implementation effort is required to achieve a meaningful level of change in outcomes. Despite its growing use, a lack of conceptual clarity has led to inconsistent measurement and sub-optimal application. We explored how IS is defined, measured, and associated with outcomes, and proposed a set of recommendations for its future use. Using an integrated approach combining systematic review and principles of concept analysis, a comprehensive search of PubMed, Scopus, ERIC, and Google Scholar was conducted up to July, 2025. Peer-reviewed theoretical and empirical studies conducted within health systems-regardless of study design, location, or population were eligible for inclusion. JBI tools were used for quality assessment. Data were synthesised using a content analysis approach. IS is often vaguely defined, with mismatches between its definitions and how it is measured. The concept has primarily been applied to health programmes implemented in real-life settings; however, no study has applied it to routine healthcare services. Studies conceptually-grounded in 2011-13 IS guidance differed notably from their counterparts: they used fewer but more relevant indicators, advanced statistical methods for composite indices, and examined dose-response relationship. Yet, most studies overlooked broader programme components outlined in the WHO monitoring & evaluation framework. Measurement practices varied widely and often conflating IS with implementation fidelity. Few studies analysed the relationship between IS and outcomes, but those that did reported a strong positive association. While half of the studies were of high quality, most lacked a reliable and valid composite IS index. Our review comprehensively mapped existing confusion surrounding the conceptualisation of IS, identified shortcomings in its application, and offers a scientifically grounded resolution aimed at standardising its conceptualization and application, thereby enabling consistent comparisons across settings. Strong evidence of a positive relationship between IS and health system outcomes reinforces the value and potential of this approach in strengthening health systems. Future research should focus on developing standardised and validated IS indices across health themes, along with effective and feasible approaches for integrating them into routine health systems for use by health administrators. Not applicable.
Dementia with Lewy bodies shares clinical and pathological features with both Parkinson's disease and Alzheimer's disease, but the local biological factors that render specific cortical regions vulnerable to atrophy remain poorly defined. In particular, it is unclear whether cortical thinning in dementia with Lewy bodies reflects generic neurodegenerative mechanisms, processes shared with Parkinson's disease and Alzheimer's disease, or dementia with Lewy bodies-specific molecular and network susceptibilities. A total of 89 patients with dementia with Lewy bodies and 89 matched controls underwent T1-weighted brain MRI. Scans were processed to generate surface-based cortical thickness maps. Regional cortical thickness estimates, after slice-by-slice manual correction, were mapped to gene expression data from healthy postmortem human brains to identify transcriptomic signatures associated with decreased thickness in dementia with Lewy bodies. We assessed whether genes whose expression was increased with regional thinning converged onto established Parkinson's disease- and Alzheimer's disease-related pathways and identified genes uniquely implicated in dementia with Lewy bodies. Spatial annotation mapping was then used to test whether patterns of cortical thinning overlapped with in vivo neurotransmitter system distributions and whether the observed thickness pattern was constrained by large-scale structural connectivity, consistent with a network-based propagation process. Cortical thinning predominated in regions that, in the healthy brain, show higher expression of genes involved in mitochondrial function and synaptic transmission. The transcriptomic profile associated with thinning significantly overlapped with genes belonging to Parkinson's disease and Alzheimer's disease pathways, supporting shared pathogenic mechanisms across Lewy body- and Alzheimer-type neurodegeneration. However, 90 genes associated with cortical thinning did not overlap with Parkinson's disease or Alzheimer's disease pathways and were enriched for GABAergic signalling. Spatial mapping analyses showed that regions with greatest thickness reductions colocalized with GABAA, serotoninergic 5-HT1A, 5-HT1B, 5-HT4, and dopaminergic D2 receptor distributions, and that the thickness pattern followed structural connectivity. MRI-derived cortical thickness changes in dementia with Lewy bodies reflect selective molecular and network vulnerabilities rather than a non-specific degenerative process. Mitochondrial and synaptic genes, together with a distinct GABAergic association and connectivity constraints, delineate mechanisms explaining why some cortical territories are more affected in dementia with Lewy bodies.
Intellectual disability (ID) affects approximately 45% of children with cerebral palsy (CP), yet early identification is frequently hindered by severe motor and communication impairments. This study aimed to develop and validate an interpretable machine learning (ML) framework for predicting ID risk in children with CP. In this retrospective, registry-based study, data from 807 children with CP were analysed. To ensure temporal validity, all predictors were restricted to clinical and neuroimaging assessments confirmed by 2 years of age. Eight ML algorithms were trained and compared on an independent test set, and SHapley Additive exPlanations (SHAP) were applied to interpret model output at both the global and the individual levels. The optimized models achieved robust discriminative performance, with the highest area under the receiver operating characteristic curve (AUC) reaching 0.813 on the independent test set. SHAP analysis revealed a highly skewed distribution of predictive features: The inability to achieve independent sitting by age 2 was the most critical risk factor, followed by early-onset epilepsy, spastic quadriplegia and severe Gross Motor Function Classification System (GMFCS) levels. Baseline perinatal factors demonstrated lower direct predictive utility, and local SHAP analyses successfully mapped individualized risk trajectories. This transparent ML approach functions as a reliable decision-support tool, translating complex algorithmic output into clinically intuitive insights. It may empower clinicians to move from 'wait-and-see' approaches towards timely, personalized neurodevelopmental interventions for high-risk children.
Navigating global crises like the Coronavirus Disease 2019 (COVID-19) demands strategic and impactful health interventions. Evaluating these interventions is crucial for fortifying health systems at both national and global scales. This article described and appraised projects carried out under the United States Government-funded, Reaching Impact Saturation and Epidemic Control (RISE) initiative during the pandemic in Ghana, offering insights and lessons learned through six health system building blocks. A mixed study design using qualitative and quantitative approaches: comprehensive document review, engagement with project managers and case narratives of data collections were adopted to appraise 10 novel interventions. Projects were selected using a census method, which included all projects that had been completed at the time of appraisal. Following selection, each project was mapped to the six building components using well-defined criteria; sustainable health financing, service provision, health management and leadership, products and logistics, information systems and data integration, and human resource. Overall, significant improvements in service delivery and health system strength were noted. COVID-19 immunization was successfully integrated into routine service delivery, resulting in 93.2% coverage attainment. Data quality audits and saturation analyses result in the institutionalization of standardized reporting and real-time data utilization. Under the Test-2-Treat (T2T) project, access to COVID-19 medications was provided to 79% confirmed cases. Additionally, RISE-supported oxygen interventions (LOX/PSA systems) improved equitable access to medical oxygen, reducing travel distances for peripheral facilities by 12.1% to 69.7% to procure medical oxygen. Capacity-building activities further strengthened service delivery and equipment maintenance, while generating critical lessons for the Ghana Health Service on integrating pandemic response interventions into routine health system functions.
Postpartum depression (PPD) is a common mental health condition affecting women after childbirth and has been linked to long-term health outcomes. However, it remains unclear whether PPD is associated with accelerated aging and increased burden of aging-related diseases later in life. We conducted a cross-sectional analysis among 38 551 parous women from the UK Biobank. Phenotypic biological aging was assessed using two aging clocks, PhenoAge and Klemera-Doubal biological age (KDMAge), derived from clinical biomarkers. Age acceleration was quantified as standardized BAGs after regressing biological age on chronological age. Aging-related disease burden was defined as the presence of at least one of 69 chronic diseases mapped to molecular hallmarks of aging. Multivariable logistic regression models were used to examine associations between PPD and phenotypic BAGs or aging-related disease phenotypes. Subgroup analyses were conducted across demographic, socioeconomic, and lifestyle factors. Women with a history of PPD showed modestly higher phenotypic aging measures than women without PPD. PPD was associated with higher PhenoAge BAGs and KDMAge BAGs, as well as a higher likelihood of having at least one aging-related disease. These associations were largely consistent across socioeconomic strata defined by education, employment, and Townsend Deprivation Index. Subgroup analyses demonstrated similar patterns across demographic and lifestyle factors, with limited evidence of effect modification. PPD was associated with modestly accelerated biological aging and a greater burden of aging-related diseases in later life. These findings suggest that postpartum mental health may have long-term implications for systemic aging processes and women's health trajectories.
To evaluate the prognostic value of cardiovascular magnetic resonance imaging (MRI)-derived left ventricular filling pressure (MRI-wedge) and pulmonary blood volume index (PBVi), and to assess their association with non-invasive markers of myocardial fibrosis. MRI-wedge pressure was computed from left-atrial volume and left-ventricular mass, and PBVi was measured from first-pass transit analysis. Patients were assigned to one of four MRI haemodynamic stages based on normal or elevated MRI-wedge and PBVi: stage 1 (normal profile), stage 2 (isolated volume overload), stage 3 (isolated pressure overload), and stage 4 (combined overload). Non-invasive myocardial tissue indices and clinical outcomes were compared across stages. The primary endpoint was a composite of cardiovascular death and cardiac hospitalisation. Among 262 participants (mean age 52 ± 17 years; 34% women), mean MRI-wedge was 13.4 ± 2.3 mmHg and mean PBVi was 333 ± 150 mL/m². Higher MRI-wedge values were associated with greater PBVi and prolonged pulmonary transit time (both p < 0.001) and increased in parallel with native T1 mapping and indexed extracellular volume (iECV, both p < 0.001). Over a median follow-up of 30 months, 29 patients (12%) met the primary endpoint. Event-free survival was reduced in patients with MRI-wedge ≥ 15 mmHg, PBVi ≥ 492 mL/m², and iECV ≥ 16 mL/m², and declined progressively across MRI haemodynamic stages, with stage 4 having the lowest survival (p < 0.001). In multivariable analysis, the MRI-based haemodynamic congestion staging system (p = 0.009) and iECV (HR 1.072; 95% CI 1.003-1.146; p = 0.04) each remained independent predictors of adverse events. MRI-wedge, PBVi and iECV capture complementary and progressive biological features of haemodynamic congestion-from early structural adaptation to overt circulatory overload-and identify patients at increased risk of adverse clinical events. Question How do MRI-derived markers of haemodynamic congestion relate to one another, and can their integration improve non-invasive prognostic stratification? Findings Higher MRI-wedge was associated with increased PBVi, prolonged pulmonary transit time, and extracellular matrix expansion. A four-stage haemodynamic congestion grading framework predicted clinical outcomes. Clinical relevance An integrated MRI-based system combining filling-pressure surrogates and pulmonary blood volume identifies progressively higher haemodynamic congestion states, while iECV provides complementary tissue-level prognostic information.
Single-shot fringe projection profilometry based on deep learning has emerged as a promising approach for real-time three-dimensional (3D) measurement. However, existing methods face challenges when dealing with practical fringe patterns that contain various degradations. Specifically, the captured fringe images inevitably include high-frequency sensor noise and shadow regions where fringe modulation is weak or absent. Most existing networks learn features indiscriminately from both reliable and corrupted regions, leading to biased depth estimation. Moreover, conventional attention mechanisms treat all spatial directions equally, ignoring the inherent directional periodicity of fringe patterns. In this paper, a novel deep learning framework, to our knowledge, named FPDNet is proposed, incorporating two specifically designed modules to address these issues. First, a pixel-wise reliability attention module (PRAM) is introduced, which incorporates local mean and variance statistics as physics-guided noise-discriminative features. Periodic fringe patterns, governed by their sinusoidal nature, exhibit predictable and structured local statistics, while noise introduces irregular statistical fluctuations that deviate from these expected patterns. PRAM leverages this distinction to generate pixel-wise reliability maps, enabling adaptive fusion of original features in reliable regions and context-compensated features in degraded regions through cascaded dilated convolutions. This physics-guided design effectively suppresses the adverse impact of noise on depth mapping learning. Second, a factorized efficient spatial attention (FESA) module is designed, which decomposes spatial attention into horizontal and vertical components using elongated convolutional kernels (7×1 and 1×7), explicitly exploiting the directional characteristics of fringe patterns for enhanced periodic structure extraction while suppressing directionally inconsistent noise. Comprehensive experiments on a public dataset with three noise levels demonstrate that the proposed FPDNet achieves a mean absolute error (MAE) of 1.339 mm under low-noise conditions, representing a 54.7% reduction compared to the baseline. Furthermore, the proposed method exhibits superior noise robustness, with only a 12.3% increase in MAE from low to high noise levels, significantly outperforming existing methods while maintaining real-time processing at 52 FPS.
Postoperative pulmonary complications (PPCs) remain a major determinant of morbidity, delayed functional recovery, and long-term outcomes in older surgical patients. While positive end-expiratory pressure (PEEP) is a cornerstone of lung-protective ventilation, fixed or uniformly high PEEP strategies have yielded inconsistent benefits in the elderly and are frequently limited by hemodynamic intolerance. Accumulating evidence suggests that the optimal PEEP in older patients lies within a narrow therapeutic window defined by competing risks of atelectasis, overdistension, and circulatory compromise. This narrative review synthesizes current evidence linking individualized perioperative PEEP to PPCs and functional recovery in older adults, with a particular focus on the limitations of one-size-fits-all approaches. We propose a pragmatic three-dimensional titration framework that integrates respiratory mechanics (driving pressure and compliance), regional lung imaging (lung ultrasound and electrical impedance tomography), and hemodynamic tolerance to guide PEEP selection. By mapping physiological endpoints to clinical and functional outcomes, we highlight how individualized PEEP strategies may improve perioperative safety and recovery trajectories in the aging population. Finally, we discuss translational pathways, implementation challenges, and key research gaps to inform future trials and clinical adoption.