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Pigment dispersion syndrome and pigmentary glaucoma are important causes of ocular hypertension and glaucomatous optic neuropathy, yet their genetic determinants remain incompletely defined, particularly across diverse ancestries. This study aimed to use a large multi-ancestry cohort from the All of Us Research Program to investigate the genetic basis of pigment dispersion syndrome and pigmentary glaucoma. Case-control study. 572 cases and 37,808 controls with array genotyping and 537 cases and 35,493 controls with whole genome sequencing. Using electronic health record phenotyping in the All of Us Research Program, we performed multi-ancestry genome-wide association analyses using both array-based data and whole genome sequencing-based data comparing patients with pigment dispersion syndrome or pigmentary glaucoma to those without either condition. We also performed Firth penalized regression and Fisher analyses, and we performed principal component analyses to assess effect sizes across genetic ancestries. We applied statistical fine-mapping, examined for cross-trait overlap, and assessed expression quantitative trait locus associations for lead variants. P-values and odds ratios of lead loci from genome-wide association analyses; size of credible sets determined from fine-mapping; allele frequency of lead variants in cases, controls, and the general population; expression quantitative trait locus effect size and p-values linking lead variants to gene expression. We identified four loci reaching genome-wide significance across analyses, including signals near EPHA7 (which mediates cell-cell signaling), within TYR (involved in melanin synthesis and replicated from prior studies), within LINC01138, and near OTX2. Statistical fine-mapping refined three of these loci to single-variant 95% credible sets and narrowed the TYR locus to small credible sets, prioritizing possible causal variants. Effect estimates were broadly consistent across genetic ancestry clusters. Lead variants showed regulatory evidence in expression quantitative trait locus, including reduced EPHA7 expression. These findings implicate both melanogenesis and cell-cell adhesion and signaling pathways in pigment dispersion syndrome and pigmentary glaucoma.
There is growing interest in identifying brain function underlying adolescent cognition, personality, and psychopathology. One promising approach is Precision Functional Mapping (PFM) of MRI functional connectivity, a data-intensive method for characterizing individualized brain networks. Foundational studies suggest that PFM can detect stable, task-responsive, and clinically relevant networks. Studies demonstrate that both functional connectivity reliability and network stability improve with increasing data quantity, although benchmark estimates vary across populations, preprocessing pipelines, and MRI acquisition approaches. Accordingly, it is important to understand how PFM performs in adolescent populations and with multi-echo fMRI acquisition. In a case study of eight youth (ages 10-17), we applied PFM to 80 minutes of combined resting-state and task-based fMRI. The resulting networks were highly modular, consistent with adult templates, and without evidence of structural registration artifacts. Functional connectivity reliability compared favorably to prior single-echo studies, with multivariate similarity and ICC estimates showing early stabilization around 10-15 min despite continued improvement with additional data. Trait-like stability increased gradually with acquisition time, and a Bayesian algorithm (MS-HBM) demonstrated higher stability than Infomap. Across algorithms, stability was greatest in the somatosensory, auditory, visual, and parietal networks. Furthermore, when evaluating task-based responses to threat and attention paradigms, only the auditory network consistently benefited from individualized mapping over group template networks. These findings suggest that, with constrained scanning time, PFM is especially effective for characterizing sensory and perceptual networks in adolescents. Bridging the methodological divide between deeply sampled individual cases and large-scale developmental studies will require further innovation and validation.
Functional connectivity MRI studies have identified widespread dysconnectivity in schizophrenia and bipolar disorder. However, most approaches rely on group-defined atlases that assume fixed network boundaries, potentially obscuring effects due to inter-individual variability in network organization. Here, we examined topographic abnormalities using individualized functional mapping. Resting-state fMRI data (1 h acquisition) were obtained from 56 healthy controls (HC), 45 bipolar disorder (BP), and 31 schizophrenia (SZ) participants (ages 18-33). Individualized functional networks were derived using template matching. Topographic Abnormality Index (TAI) quantified network-specific spatial deviations relative to normative boundaries, while Vertexwise Functional Deviation Index (VFDI) reflected global deviation across all cortical vertices. Group differences were assessed using ANOVA and ANCOVA (covarying age, sex, and motion), with false discovery rate correction. Clinical correlations were examined within BP and SZ. Significant group effects were observed across multiple networks, including temporo-insular (TIN), cingulo-opercular (CON), sensorimotor, dorsal attention (DAN), language (LAN), and default mode (DMN) networks (q < 0.05). BP showed prominent abnormalities in sensorimotor and perceptual networks, whereas SZ exhibited greater involvement of higher-order associative networks. Global metrics demonstrated robust group discrimination: TAI average (F = 9.01, p = 2 × 10⁻4) and VFDI (F = 11.06, p = 3.7 × 10⁻⁵), with both BP and SZ elevated relative to HC. Mania severity correlated with sensorimotor (body) network TAI (q = 0.002). Global metrics were not related to symptom severity. Schizophrenia and bipolar disorder are characterized by widespread but distinct disruptions in functional network topography. Global measures, particularly VFDI, provide sensitive indices of cross-network abnormality and may offer utility for biomarker development beyond traditional network-specific approaches.
This cohort study assesses long-term oncologic and quality-of-life outcomes after transperineal template-guided mapping biopsy in patients with prostate cancer.
Floods are among the most devastating disasters, posing significant risks to communities and infrastructure, particularly in tropical regions where hazard mapping is often constrained by limited data availability. This study applies an integrated geospatial and machine learning (ML) approach to improve flood hazard assessment in a mountainous tropical region of Aceh Jaya, Indonesia, with the aim of evaluating model performance and identifying dominant causative factors linked to spatially targeted mitigation strategies. A set of ten flood causative factors, together with historical flood inventory data, was analyzed using four ML algorithms: Random Forest (RF), Support Vector Machine (SVM), Boosted Regression Tree (BRT), and Generalized Linear Model (GLM). Flood hazard maps were classified into five levels, ranging from very low to very high susceptibility. High to very high hazard zones cover approximately 14-22% of the study area and are primarily concentrated in low-elevation downstream areas. Model evaluation using Area Under the Curve (AUC), True Skill Statistics (TSS), correlation, and deviance indicates that RF achieves the highest predictive performance (AUC = 0.983; TSS = 0.92). The results consistently identify elevation as the dominant controlling factor, underscoring the influence of terrain-driven hydrodynamic processes. The coherence between model outputs, underlying physical mechanisms, and observed spatial patterns strengthens the basis for delineating flood hazard zones and informing mitigation priorities. This integrated approach enhances the applicability of the study by supporting evidence-based decision-making and enabling more targeted flood risk management in data-limited tropical regions, with a transferable framework that can be readily applied to similar mountainous settings.
Chronic and complex wounds involve prolonged care processes that may affect patient experience and perceived quality of care. This study aimed to map the patient journey of people living with chronic or complex wounds using a journey map and to identify critical points and levels of satisfaction with the healthcare services used. We conducted a mixed-methods qualitative-quantitative study. Semi-structured interviews were held with patients aged ≥18 years with at least one complex wound, under treatment and follow-up in specialised wound care units within Osakidetza between May and October 2025, using purposive sampling to maximise heterogeneity. Participants' narratives were used to develop a journey map of the care process, and satisfaction at each level of care was assessed with two questions based on the Net Promoter Score (NPS). Theoretical saturation was reached after 36 interviews. Seven stages along the care journey were identified (from wound onset to changes in routines and loss of autonomy), with critical points related to initial uncertainty, perceived minimisation of the problem at first contact, discontinuity of professionals and delays in referral. NPS indices were lower in primary care centres (13 and 16) and higher in specialised units (83 and 92), indicating greater satisfaction with care in the latter. Mapping the patient journey of people with chronic or complex wounds allows potentially modifiable stages and critical points to be identified, as well as differences in satisfaction between levels of care. These findings may help to guide interventions aimed at improving cross-level coordination, clarifying referral criteria and strengthening the problem-solving capacity of primary care, thereby supporting a more satisfactory care experience.
Active engagement is crucial in psychoeducational interventions for care partners of persons living with dementia, yet measurement is limited. This manuscript explores participant engagement in an arts-based intervention designed to increase engagement in addressing dementia-related behavioral symptoms. The intervention uses multisensory activities, including caregiver-informed vignettes, to foster engagement and process caregiving experiences. Care partners of persons living with dementia (n = 9) participated in six focus groups. Focus group data were analyzed using process coding to define and map patterns of active engagement across 27 intervention activities. The findings informed development of a conceptual model of active engagement. Four levels of engagement were identified-Participating, Clarifying, Contributing, and Applying-and explored across participants, time points, and activities. The conceptual model illustrates that (1) antecedents contribute to (2) levels of engagement, leading to (3) the intervention's hypothesized mechanisms of action, proximal outcomes (capacity to adapt, appraisal of caregiving demands), and distal outcomes (perceived stress, caregiver well-being). This manuscript provides a clear framework for operationalizing and measuring active engagement and demonstrates how engagement patterns can be conceptually linked to the intervention's proposed mechanisms of action and outcomes. These findings provide transparency in reporting engagement patterns within an intervention, offering valuable insight into the components of active engagement and how these may be measured. The ability to track active engagement during intervention development and testing has the potential to improve our understanding of intervention dose and fidelity and how active engagement interacts with these to improve outcomes for participants.
C5 inhibition is a proven therapeutic target with several monoclonal antibody (mAb) and small molecule drugs in the clinic. To explore alternative modes of inhibition, novel C5-blocking mAbs were generated and compared to therapeutic mAbs eculizumab and crovalimab. Blocking mAbs were selected using complement inhibition in haemolysis assays. C5 binding and competition between mAbs were tested by ELISA and surface plasmon resonance. Impact on C5 cleavage by CVF and native convertases was tested by western blotting of C5 fragments and ELISA for C5a release. Four novel C5-blocking mAbs were identified and shown to inhibit haemolysis with comparable efficacy to crovalimab and eculizumab. Competition assays identified four distinct function-blocking epitopes; mAb 7D4 and eculizumab competed for an α-chain epitope (epitope 1), while 10B6 and crovalimab competed for a β-chain epitope (epitope 2). mAbs 2B11 and 4G2 were not competitive with epitope 1/2 binders or each other, marking two distinct blocking α-chain epitopes (epitopes 3 and 4). Epitope 1 and epitope 2 binders blocked C5 convertase-mediated C5 cleavage and C5a release, while epitope 3 and epitope 4 binders permitted C5 cleavage, demonstrating distinct modes of inhibition. We identified four distinct C5 blocking epitopes; epitope 1 targeted by eculizumab and 7D4, epitope 2 targeted by crovalimab and 10B6 and epitopes 3 and 4 targeted by 2B11 and 4G2 respectively. Although all efficiently blocked haemolysis, epitope 3 and 4 binders permitted C5a release. We reveal (at least) four blocking epitopes on C5 and (at least) two distinct mechanisms of inhibition.
Targeted protein degradation has emerged as a promising therapeutic strategy, yet rational degrader design remains challenged by the dynamic nature of protein of interest (POI)-E3 ligase interactions. While X-ray crystallography and cryo-EM provide valuable structural snapshots, they are insufficient for capturing the conformational heterogeneity underpinning efficient ubiquitination and degradation. Here, we present a unified computational workflow to systematically generate and evaluate POI-E3 ligase conformational states for CRBN- and VHL-mediated proteolysis-targeting chimeras (PROTACs). The workflow integrates warhead connectivity analysis, conformational clustering, ubiquitination accessibility assessment and molecular dynamics simulations to identify productive POI-E3 ligase geometries. Analysis of experimental structures revealed that PROTAC linkers do not exceed 15 Å, providing a practical attachment-atom distance based filter for docking-derived models. Furthermore, POI-E3 ligase conformations differing by more than 7.5 Å Cα RMSD exhibited distinct ubiquitination profiles, offering quantitative guidance for defining structurally and functionally divergent states. Experimental ternary complexes consistently positioned one or more solvent-exposed POI lysine residues within 50 Å of the E2 catalytic Cys111, establishing a mechanistically grounded criterion for ubiquitination competence. Validation against 34 experimentally determined PROTAC ternary complexes achieved a 97% recovery rate and identified multiple ubiquitination competent conformations beyond experimental snapshots. The workflow was subsequently applied to model productive WEE1-CRBN and PKMYT1-CRBN conformations, for which no experimental structures are available. PROTAC induced-fit docking demonstrated that active PROTACs selectively engage productive POI-E3 ligase geometries with linker-compatible attachment atom distances. Overall, this study provides a quantitative, structure-based framework for guiding the rational design of ubiquitination-based degraders. The code and example data supporting this workflow are openly available at https://github.com/Husam-PSE/PROTACMap .Scientific contributionThis study offers a generalizable computational framework for identifying productive POI-E3 ligase conformations. It demonstrates that effective degradation depends on the interplay between conformational diversity, feasible warhead connectivity and preserved ubiquitination competence, rather than solely on ternary complex stability or binding affinity. Our computational approach was applied on WEE1 and PKMYT1 PROTACs for which no experimental ternary structure is available.
A novel diagnostic device combining Scheimpflug and anterior segment optical coherence tomography promises increased accuracy in corneal diagnostics. The aim of this study was to analyze the epithelial thickness measurement repeatability in healthy and keratoconus eyes and define severity-dependent thresholds. This prospective, cross-sectional, observational, monocentric study included a total of 217 eyes of 217 subjects divided into a keratoconus group (166 eyes of 166 subjects) and a healthy control group (51 eyes of 51 subjects). Keratoconus diagnosis and stage was established using multimodal imaging. Three measurements were performed with the novel combined Scheimpflug and optical coherence tomography device (Pentacam Cornea optical coherence tomography) on the same day. Repeatability coefficients (RCs) with two-sided 95% confidence intervals (CIs) were calculated for epithelial parameters. The repeatability was compared between groups and according to keratoconus severity and crosslinking status. The median repeatability of the epithelial thickness was slightly better in the healthy group (RC = 3.3) compared with the keratoconus group (RC = 3.9; P < 0.001) when the tear film was excluded. The overall median repeatability with (RC = 3.7) and without tear film (RC = 3.8) did not differ significantly (P > 0.05). Repeatability worsened significantly in severe keratoconus stages (P < 0.01), whereas crosslinking status had only a marginal effect. The hybrid tomographic device demonstrated high intrasubject repeatability for epithelial thickness assessment in healthy and keratoconus eyes, supporting its technical feasibility for clinical use. Further studies are needed to analyze the benefit of incorporating epithelium into established keratoconus scores using the hybrid system.
Health care systems face escalating cyberattacks, including the UK Synnovis ransomware attack, which halted pathology services for 14 weeks; the Ascension Health breach affecting 5.6 million patients; and the Change Healthcare breach costing US $2.5 billion. Conventional cybersecurity measures in health care remain reactive and inadequate against evolving threats. Machine learning (ML) offers adaptive, predictive, real-time cyber defense; yet, there is limited clarity on how ML tools are applied across cybersecurity domains, their real-world effectiveness, and where gaps remain. This study aims to map ML applications in health care cybersecurity against the National Institute of Standards and Technology Cybersecurity Framework version 2.0, summarize ML performance, and identify research gaps and implementation considerations. A systematic search of Ovid MEDLINE, Embase, and Scopus was conducted on July 30, 2025, for studies between 2019 and 2025. Eligible studies applied ML-based approaches to organizational-level cybersecurity in health care settings, with outcomes related to data privacy or cybersecurity strengthening. Studies on smart devices, blockchain, or those lacking empirical data were excluded. Title and abstract and full-text screening were conducted independently by 2 (KR and SZ) reviewers following the Arksey and O'Malley framework and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, with discrepancies resolved by consensus. Data were synthesized narratively and mapped against the 6 National Institute of Standards and Technology Cybersecurity Framework version 2.0 functions (Identify, Protect, Detect, Respond, Recover, and Govern). From 10,348 articles identified, 45 studies across 18 countries were included, applying 80 ML models. Most studies addressed "Protect" (n=22, 48.9%), encompassing federated learning, homomorphic encryption, and deidentification pipelines. "Detect" (n=13, 28.9%) covered intrusion detection and anomaly-based threat detection. "Identify" (n=8, 17.8%) addressed risk assessment and vulnerability prediction. Only 2 studies addressed "Respond," and none addressed "Recover" or "Govern." Classical ML, deep learning, and natural language processing predominated, with intrusion detection being the most common application (n=29). Despite strong controlled performance, only one study demonstrated real-world deployment; most rely on synthetic or outdated benchmark datasets and inconsistent reporting metrics. To our knowledge, this is the first scoping review to map ML-driven cybersecurity solutions against all six National Institute of Standards and Technology Cybersecurity Framework version 2.0 functions, offering a structured, policy-relevant evidence base. ML applications remain concentrated in preincident functions, with gaps in response, recovery, and governance. This highlights systematic blind spots and priorities for researchers and implementers. The evidence supports a phased implementation approach: beginning with detection systems, where evidence is most established and integration is most feasible, progressing to privacy-preserving architectures, for which the literature currently offers no guidance. Implementation requires sustained investment in infrastructure, representative datasets, explainable AI, and real-world validation. Limitations include language bias, methodological heterogeneity, and exclusion of medical devices and proprietary solutions.
Schistosomiasis is an acute and chronic illness caused by parasites of the genus Schistosoma. It is present in 79 countries worldwide and primarily affects socioeconomically vulnerable populations due to exposure to infested water during daily activities. Praziquantel is the primary drug used in international schistosomiasis treatment. However, adverse effects and parasite resistance mechanisms have been reported, ranging from experimentally induced resistance to naturally occurring resistance in real-world populations. New therapeutic targets have been identified to address these issues. Consequently, drug repurposing is often considered a faster alternative that may offer lower development risks and potentially fewer adverse outcomes than newly synthesized drugs. Building on this potential, this study aimed to identify drug repurposing candidates as alternatives for schistosomiasis treatment through a systematic review. Two databases (ScienceDirect and PubMed) were searched. The selection of studies and writing of this systematic review followed the PRISMA guidelines. Of the 313 articles identified, 28 were selected after applying the exclusion criteria. The best results observed were celecoxib (over 90% for egg burden and parasite load), mefenamic acid (92% for parasite load and 82% for egg burden), and chlorambucil (75% parasite load and 85% egg burden). This review highlights the data supporting this strategy as a viable alternative for developing new therapies for schistosomiasis. As these compounds have been used clinically for a long time, substantial preclinical, biosafety, and pharmacovigilance data are already available. Their adverse effects and toxicities are well characterized, which may contribute to a reduction in overall costs and development timelines, although robust clinical validation remains a prerequisite for their use.
Two chikungunya (CHIKV) vaccines have now been licensed, but the human clinical evidence base remains fragmented across vaccine platforms, populations, follow-up periods, and safety settings, complicating product-specific interpretation of durability and benefit-risk. We conducted a PRISMA-ScR-guided scoping review of human CHIKV vaccine evidence indexed in PubMed, Embase, and Web of Science from January 2000 to June 2026. After screening 890 records, we included 77 sources of evidence and mapped them at both the record level and the candidate/product level. The included evidence clustered around a limited number of vaccine programs, including TSI-GSD-218, VRC-CHKVLP059-00-VP/PXVX0317/Vimkunya, MV-CHIK/V184, VLA1553/IXCHIQ, ChAdOx1 Chik, and mRNA-1388/VAL-181388. Late-stage and post-authorization evidence was concentrated mainly in VLA1553/IXCHIQ and PXVX0317/Vimkunya, whereas viral-vector and mRNA candidates remained largely restricted to early-phase adult studies. Evidence has expanded to adolescents and adults aged ≥65 years for selected products but remains limited or product-specific for children, pregnant individuals, immunocompromised populations, and medically complex older adults. Short-term trial safety data were characterized primarily by mild or moderate local and systemic reactogenicity, while post-authorization safety evidence remains recent and concentrated in licensed products. This scoping review provides a structured evidence map for CHIKV vaccine development and highlights priorities for standardized immunogenicity assessment, longer-term durability data, broader population representation, endemic-region effectiveness studies, and continued post-marketing surveillance.
Robotic cholecystectomy is increasingly adopted as an alternative to laparoscopic cholecystectomy and proposed as an entry‑level procedure in robotic training curricula, yet real‑world data on surgeon‑specific learning curves and their impact on perioperative safety remain limited. This study aimed to map the learning curve for robotic cholecystectomy across a multi-surgeon, multicenter cohort and assess associated safety outcomes. This retrospective cohort study analyzed the first 50 consecutive robotic cholecystectomies performed independently by each of five surgeons (n = 250) between January 2023 and June 2025. For each surgeon, the learning-curve endpoint was identified as the breakpoint of a piecewise linear regression of skin-to-skin operative time against case sequence, and the Mann-Whitney U test assessed whether operative time differed significantly between the early and late phases. A linear mixed-effects model, with surgeon as random intercept, identified independent predictors of operative time accounting for within-surgeon clustering. The learning-curve endpoint ranged from 11 to 34 cases across surgeons. The breakpoint corresponded to a significant reduction in operative time for three of five surgeons (p = 0.017, p = 0.015, p < 0.001), but not for the remaining two (p = 0.853, p = 0.233). Cohort-level median operative time decreased significantly from early to late phase (66 vs 50 min; p < 0.001), while length of stay did not differ (p = 0.354). Case sequence number (- 0.72 min/case) and Nassar difficulty grade (+ 9.8 min/grade) were independent predictors of operative time (p < 0.001). No bile duct injuries occurred and severe complications occurred in 1/250 patients (0.4%). Most surgeons showed a statistically confirmed reduction in operative time within their first 11-24 robotic cholecystectomies, while for others no significant improvement was confirmed. Complication rates remained low, although this cohort was not adequately powered to formally demonstrate safety equivalence across the learning process. These findings support robotic cholecystectomy as a feasible early procedure within structured robotic training pathways.
Individuals with psychiatric disorders frequently experience comorbid cardiometabolic conditions, complicating treatment and worsening health outcomes. Both psychiatric and cardiometabolic disorders have been individually associated with alterations in brain structure. Yet, it remains unclear whether these associations stem from a shared genetic basis that underlies their frequent co-occurrence. We analyzed genome-wide association summary statistics from large international consortia of individuals of European ancestry, including psychiatric disorder GWAS with case-control sample sizes ranging from ~18,000 to ~158,000 cases, cardiometabolic disease GWAS with up to ~242,000 cases, and cortical morphology GWAS from UK Biobank comprising ~39,000 individuals. We applied complementary multivariate, causal, and mediation genetic analyses to disentangle genetic factors underlying brain alterations and comorbidity. Here we show that patterns of genetic overlap differ across disorders. Schizophrenia exhibits substantial polygenic overlap with cortical thickness and type 2 diabetes, despite low genetic correlation. In contrast, attention-deficit/hyperactivity disorder (ADHD) is more strongly correlated with cardiometabolic disease but shows limited overlap with cortical morphology. Notably, cortical surface area partly mediates the genetic association between ADHD and type 2 diabetes. Pathway analyses highlight metabolic stress processes in ADHD as well as neurodevelopmental and immune processes in schizophrenia. These findings indicate that psychiatric-cardiometabolic comorbidity arises through both shared and disorder-specific genetic pathways. This work clarifies the genetic architecture of multimorbidity and highlights opportunities for trait-targeted prevention strategies in psychiatry. Many people with psychiatric disorders also experience physical health problems, such as heart disease or diabetes. It remains unclear whether these links are due to shared genetic factors. In this study, we used large genetic datasets from hundreds of thousands of participants to investigate how psychiatric disorders, brain structure, and cardiometabolic diseases are genetically connected. We applied statistical methods to identify shared genetic influences and to explore whether one trait may partly influence another. We found that schizophrenia and ADHD show distinct genetic patterns: schizophrenia shares more genetic factors with brain structure and diabetes, while ADHD is more strongly linked to metabolic pathways. These findings highlight that different mental disorders may involve different biological routes, which could inform prevention and treatment strategies in the future.
Hypertension and diabetes are major risk factors for heart disease, which remains among the leading causes of morbidity and mortality worldwide. Heart disease includes heart failure, myocardial infarction, stroke, and atherosclerosis. The identification and monitoring of these risk factors are crucial for early intervention and effective management. Machine learning techniques have the potential to improve the management and prevention of heart disease by enabling the automatic detection of risk factors, which in turn can help doctors personalize treatment and facilitate preventive interventions. In this study, heart disease risk factors were automatically extracted using a combination of multimodal data: both unstructured clinical narratives (e.g., the PrevComp corpus) and structured datasets (e.g., the UCI heart disease dataset) were used to predict the presence or absence of heart disease. The classification model is based on the Light Gradient Boosting Machine (LightGBM), a state-of-the-art implementation of the gradient boosting framework that employs tree-based learning algorithms. The developed classification model demonstrated a robust predictive accuracy of 83% for the presence/absence of heart disease, supporting its potential to accurately identify high-risk patients and improve clinical outcomes.
Precise anatomical mapping of the deep inferior epigastric perforator (DIEP) flap is essential for successful breast reconstruction. Although computed tomography angiography (CTA) is the gold standard for preoperative planning, manual identification of perforators is time-consuming and subject to inter-rater variability. We aimed to validate a self-developed image-processing tool designed to standardize coordinate extraction and automate two-dimensional spatial mapping of pre-selected abdominal wall perforators. A retrospective analysis was conducted on 52 patients who underwent DIEP flap reconstruction between 2017 and 2021. A total of 170 perforators were analyzed. The custom software processed axial CTA slices through histogram analysis and frequency-range filtering (1800-2200 HU) to enhance vascular visualization. Cartesian coordinates of clinician-selected perforators were calculated relative to the navel base (0, 0) and compared with manual radiological assessments to validate spatial fidelity. Analysis of 170 cutaneous perforators (n=52) showed predominant concentration in the lower abdominal quadrants. Centroids were (1.4, -20.2) mm for radiological assessment and (1.8, -21.2) mm for our software, relative to the umbilicus. Validation demonstrated high concordance, with a minimal systematic bias of (-0.4, 1.0) mm and a root mean square error of 6.04 mm, within clinically acceptable margins. Standardized mapping and visual report generation averaged 8 ± 6 min per patient, thereby providing a rapid turnaround for converting coordinate data into a standardized surgical guide. The validated tool provides a reliable, objective framework for standardizing and visualizing existing radiological data in DIEP flap planning. Although it does not alter clinical perforator selection or advance surgical technique, this technical utility provides an autonomous data-processing pathway to generate standardized spatial roadmaps, thereby optimizing the preoperative imaging workflow and safeguarding administrative timelines against potential interdepartmental reporting bottlenecks.
Identify genetic variants associated with amblyopia in African American (AFR) and Admixed American (AMR) ancestry groups, expanding upon a previous studies conducted in European ancestry. Retrospective ancestry-stratified genome-wide association study (GWAS) and gene-level rare variant association study (RVAS). Participants in the All of Us Research Program from AFR and AMR ancestry groups with whole genome sequencing available. Cases and controls were distinguished based on presence of ICD-9/10/SNOMED diagnosis codes for amblyopia in electronic health records. This yielded ancestry-stratified subsets of 269 cases and 71,585 controls of AMR ancestry and 366 cases and 79,460 controls of AFR ancestry. Stratified logistic regression models adjusted for age, sex, and the top 10 principal components of genomic ancestry. GWAS was limited to common variants (mean allele frequency or MAF > 1%) and RVAS was limited to rare variants with coding sequence-altering effects (MAF < 1%, exonic only, excluded synonymous variants) aggregated at the gene level using the SKAT algorithm. Downstream analyses of the significant variants were performed using KEGG and GO pathway analysis and STRING database queries for protein-protein interactions and gene-gene interactions. Single-nucleotide polymorphisms (SNPs) were determined to have genome-wide significance if p < 5e-8 in the GWAS and genes were determined to have significant association with amblyopia in the RVAS if p < 8.0 x 10-4. In the AMR GWAS, 245 unique SNPs mapping to 97 distinct loci were identified, notably within neurodevelopmental and axonal guidance genes, including ROBO1, SEMA4B, PTPRD, NRXN1, and CAMK2D. The AFR GWAS identified 11 significant variants corresponding to 6 loci mapping primarily to long-noncoding RNAs and pseudogenes. The AMR RVAS identified 15 genes, including axonal transport genes (KIF1B, KIF7) and growth factor signaling genes (EGF, ERBIN, and AKAP17A). The AFR RVAS identified a single gene, DLG2, which encodes the postsynaptic protein PSD-93, which promotes the closure of the sensitive period of neuroplasticity for vision in early childhood. Genetic risk architectures for amblyopia differ across ancestries but fundamentally converge on neurodevelopmental signaling, cortical synapse assembly, and sensitive period plasticity rather than ocular structural dynamics.
Accurate age- and sex-specific T1 and T2 reference values are critical for differentiating myocardial pathologies, yet existing data are inconsistent due to methodological variability. This study proposes standardized T1 and T2 mapping protocols with clinically feasible breath-hold durations and reports site-, sequence-, age-, sex-, and segment-specific reference values. This prospective, single-center study enrolled 183 healthy Caucasian subjects (median age 34 y [IQR 22, 50], range 11-70 y, 98 females) who underwent both global and AHA segment-based native T1 and T2 mapping at 3 T. T1 was assessed using Modified Look-Locker Inversion-Recovery sequence, MOLLI 5s(3s)3s, while T2 was performed using a gradient spin-echo (GraSE) protocol with echo-planar-imaging readout. Assessment of T1 and T2 relaxation times was highly reproducible, with low intra- and interobserver, scan-rescan, and test-retest variability. Mean global T1 was 1238 ± 28 ms, and median global T2 was 45.4 ms [44.3, 46.8]. T1 was significantly higher in women than in men (1245 ± 28 ms vs. 1230 ± 7 ms, P < 0.001), while T2 did not differ significantly (45.3 ms [44.2, 46.6] vs. 45.6 ± 2.2 ms, P = 0.453). Segmental analysis showed higher T1 and T2 values in the septum than in the lateral wall (P < 0.005). Subtle associations of age with T1 and T2 were observed. This single-center study supports the use of sex- and segment-specific reference values for standardized T1-MOLLI and T2-GraSE mapping at 3 T to facilitate the characterization of myocardial pathologies, while age seems to be negligible.
Orthorexia nervosa (ON) is increasingly understood as a morally saturated health orientation in which dietary restraint is organised through ideals of purity, discipline, and self-worth. Building on social theory that treats health as a cultural project and a site of identity governance, this study theorises ON as moral identity work enacted and contested in digital environments. Using a large-scale qualitative design integrating thematic corpus mapping and Critical Discourse Analysis (CDA), 1,100 texts from Reddit, YouTube, and blogs (2020-2025) were analysed; an intensified CDA subset (n=80) traced how key meanings were rhetorically accomplished. Across platforms, moralised purity/contamination vocabularies and identity-relevant self-positioning were common but genre-sensitive. In the CDA subset, a recurring configuration framed restrictive eating as (a) morally evaluative, (b) identity-defining and non-negotiable, and (c) defended via boundary work and resistance to pathologisation; recovery narratives more often disrupted these binaries and decoupled selfhood from dietary purity. Extending existing accounts that document morality narratives around ON, the analysis specifies the discursive mechanisms and genre- and platform-sensitive configurations through which moralisation becomes identity-defensive and resistant to critique. These findings clarify the discursive mechanisms through which ON becomes intelligible and defensible within digital wellness culture, with implications for prevention and care focused on moralised self-concept and digitally reinforced rigidity.