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PubMed2025

Impact of stromal maturity and proportion on prognosis and immune landscape in colorectal cancer.

BACKGROUND: Tumour microenvironment and cancer cells have constant interaction affecting cancer progression. Tumour-stroma ratio (TSR) in the tumour centre and desmoplastic reaction (DR) classification at the invasive margin are prognostic factors based on stroma evaluation on H&E slides. However, their combined value and immunological associations remain poorly defined. This study examines the prognostic and immunological value of TSR, DR, and their combination in two large colorectal cancer cohorts. METHODS: Two colorectal cancer cohorts (N = 1,876) were analyzed. We introduced a three-tiered Stromal Maturity and Proportion Score (SMAPS) based on the presence of high (>50%) TSR and myxoid stroma (immature DR classification). Alcian blue staining was used to further quantify myxoid stroma. Multiplex immunohistochemistry combined with digital image analyses, was utilized to study immune cell densities associated with SMAPS, TSR, DR, and Alcian blue intensity. RESULTS: In the study cohort (N = 1,100), SMAPS was a stronger predictor of cancer-specific mortality [HR for high (vs. low) SMAPS 2.01 (95% CI 1.47-2.75), p < 0.0001] compared to TSR [HR for stroma-high (vs. stroma-low) 1.49 (95% CI 1.15-1.93), p = 0.003] and DR classification [HR for immature (vs. mature) 1.84 (95% CI 1.39-2.45), p < 0.0001]. High SMAPS, stroma-high TSR, and immature DR correlated with lower densities of CD3+ T cells, B cells, M1-like macrophages, CD66B+ granulocytes, and mast cells. Alcian blue staining was associated with immature DR and corresponding immune cells. The validation cohort (N = 776) confirmed the association of SMAPS with survival and T cell densities. CONCLUSIONS: TSR and DR are independent prognostic factors for cancer-specific survival. SMAPS is a promising prognostic tool that integrates stromal maturity at the invasive margin and stromal proportion in the tumour centre. SMAPS has stronger prognostic value compared to TSR and DR classifications alone. A high stromal proportion and myxoid content are associated with an immunosuppressive microenvironment characterized by lower densities of antitumourigenic immune cells.

Humans↗

PubMed2026

A systematic approach to standardizing the visual appearance of endometriotic lesions for artificial intelligence recognition.

INTRODUCTION: Numerous studies have shown that the diagnostic performance and reproducibility of visual recognition of endometriosis during laparoscopy are poor. The use of artificial intelligence (AI) seems relevant for exhaustive lesion recognition. Standardization of the visual classification of lesions, in the form of an ontology, is an essential prerequisite to enable medical experts to annotate surgical data consistently and subsequently allow engineers to train and build an artificial intelligence tool for endometriosis recognition. MATERIAL AND METHODS: A systematic search was conducted in the MEDLINE (via PubMed), EMBASE, and the Cochrane Library databases up to May 2022, aiming to identify studies describing the laparoscopic visual appearance of superficial endometriosis, endometriomas, and deep infiltrating endometriosis. The accumulated data in the literature concerning the visual appearance of the different forms of endometriosis were used to create an ontology that could be used for artificial intelligence applications. RESULTS: Out of 932 articles screened, 35 studies were selected based on the inclusion criteria of human subjects with histologically confirmed endometriosis lesions visualized via laparoscopy. The selected studies were reviewed to develop a visual ontology of endometriosis lesions observed via laparoscopy. The lesions were categorized into 4 classes and further subdivided into 11 subclasses: superficial (black, red, white, or subtle), adhesions (dense or filmy), deep (obliteration, retraction, or deformation), and ovarian (endometrioma or chocolate fluid). The positive predictive value (PPV) varied across lesion types: black lesions (PPV 47%-97%), red lesions (PPV 33%-100%), white lesions (PPV 20%-81%), and ovarian endometriosis (PPV 42%-98%). Nonspecific lesions such as adhesions (PPV 16%-50%) and subtle superficial lesions (PPV 0%-67%) presented lower PPVs. Deep endometriosis lesions, often buried within organs, required indirect signs (obliteration, retraction, deformation) for identification. CONCLUSIONS: The visual ontology proposed in this systematic search could facilitate the detection and classification of endometriosis lesions using artificial intelligence. This study highlights the challenges of reaching a consensus on lesion recognition and classification in AI projects due to the diverse visual presentations of endometriosis.

Humans↗

PubMed2026

Transcranial Photobiomodulation Variables Assessment Battery: Development and Validation.

Transcranial photobiomodulation (tPBM) response variability is partly driven by biophysical characteristics such as skin tone and hair properties that attenuate photon penetration, and by lifestyle factors including sleep quality, alcohol use, and nicotine consumption that disrupt the mitochondrial and vascular pathways on which tPBM acts. To date, no validated self-report tool exists to capture these moderators systematically. To address this gap, the tPBM Variables Assessment Battery was developed and psychometrically evaluated. It integrates adapted versions of established measures (Brief Pittsburgh Sleep Quality Index, E-cigarette Dependence Scale, Hair Scale Assessment PRO, Monk Skin Tone Scale, and Heaviness of Smoking Index), validated wellbeing evaluators (Ryff's Psychological Wellbeing), and custom measures (Hairstyle Classification, Hair Color Classification). Face and content validity met recommended expert thresholds, internal consistency was acceptable across adapted subscales, and criterion validity analyses confirmed meaningful associations between the lifestyle components and PROMIS-10 global health outcomes. The battery is low-burden, digitally deployable, and psychometrically defensible, offering a practical tool for characterizing the variables most likely to moderate tPBM response in home-use studies.

Humans↗

PubMed2026

Mitochondrial DNA diversity in Ecuadorian populations: Recurrence of variant 16136 within haplogroup B2.

The identification of lineage-defining variants, frequently found in the coding region of mitochondrial DNA (mtDNA), is essential for refining haplogroup classification. Most mtDNA studies in South American populations have focused on the control region (CR), which has provided important insights into population structure and maternal lineage origins, although information needed for more robust phylogenetic resolution has been neglected. This study investigates the maternal genetic structure of Ecuadorian populations by combining CR and whole mitogenome analyses. Sequences from the mtDNA CR were obtained from 461 individuals (253 Mestizos and 208 Native Americans), while complete mitogenomes were sequenced for 127 individuals to improve phylogenetic resolution by identifying lineage-defining variants present in coding region. Most mtDNA haplogroups in the two population groups analyzed were of Native American origin (A2, B2, B4, C1, D1, D4), with significant differences in the distribution of specific lineages between them. Among Mestizos, African haplogroups (all within the L branches) and Eurasian haplogroups (H, K, R, U) were detected at low frequencies, whereas no African lineages were observed among Native Americans. The results obtained highlighted a heterogeneity within Ecuadorian populations that must be considered when developing mtDNA haplotype databases for forensic purposes. Whole mitogenome sequences enabled the identification of variants that refined haplogroup classifications, provided a more accurate reconstruction of the maternal genetic diversity, and improve the discrimination between Native American and Asian maternal lineages within haplogroup B4b.

Humans↗

PubMed2026

Early infantile developmental and epileptic encephalopathy: clinical spectrum, diagnosis, outcomes, and evolving treatment strategies.

Early infantile developmental and epileptic encephalopathy (EIDEE) is among the most severe epilepsy syndromes, with onset before three months of age and an estimated incidence of approximately 10 per 100,000 live births. The 2022 International League Against Epilepsy classification unified the historically distinct Ohtahara syndrome and early myoclonic encephalopathy under a single diagnostic framework defined by frequent drug-resistant tonic and/or myoclonic seizures, an abnormal neurological examination, and an abnormal interictal electroencephalogram-most characteristically a burst-suppression pattern. This narrative review synthesizes the clinical, electrophysiological, neuroimaging, genetic, and therapeutic literature within the EIDEE framework. The clinical phenotype is characterized by central hypotonia, postnatal microcephaly, cortical visual impairment, and age-dependent syndromic evolution toward infantile epileptic spasms syndrome or Lennox-Gastaut syndrome in the majority of patients. Electroencephalography remains essential for syndromic classification, while systematic metabolic screening and early trio whole-exome or whole-genome sequencing are central to the etiologic workup, achieving diagnostic yields of 60-65%. The most commonly identified genetic causes include STXBP1, KCNQ2, and SCN2A variants. Outcomes are poor overall and strongly etiology-dependent: vitamin-responsive disorders carry a substantially more favorable prognosis, whereas mortality reaches 25% in genetic cohorts. Genotype-guided pharmacotherapy is now applicable to a clinically meaningful subset of patients, with sodium channel blockers, potassium channel openers, and emerging antisense oligonucleotide therapies representing important therapeutic advances. Gene therapy trials are underway but have encountered early safety signals, underscoring the vulnerability of this population. Critical unmet needs include earlier molecular diagnosis, precision therapies targeting developmental outcomes beyond seizure control, and prospective international registries to characterize the long-term natural history of EIDEE.

Humans↗

PubMed2026

Female genital mutilation knowledge, attitudes and training needs among health professionals in non-practicing countries: A literature review.

BACKGROUND: With increasing globalization and migration, the number of women affected by female genital mutilation who reside in countries where the practice is not traditionally performed is constantly increasing. Healthcare providers in these settings are required to address the complex health needs of this vulnerable population. We aimed to synthesize recent literature on their knowledge, preparedness, and educational background. METHODS: We conducted a systematic review across PubMed, Scopus and Embase, identifying papers published from January 2015 onwards, examining providers' knowledge, education and attitudes toward female genital mutilation in non-practicing countries. Both quantitative and qualitative observational studies were eligible. Given heterogeneity in study populations, outcome definitions, and assessment tools, findings were synthesized narratively. The review protocol was registered with the International Prospective Register of Systematic Reviews (CRD420251044761). FINDINGS: 1046 records were screened by title and abstract, and 140 full-text articles were assessed for eligibility. 31 studies met the inclusion criteria (23 quantitative, 8 qualitative). Many providers reported clinical experience with women affected by female genital mutilation, yet substantial variability was observed in knowledge, training, and attitudes. Gaps were particularly evident regarding legislation, World Health Organization classification, clinical guidelines, referral pathways, workplace protocols. Midwives and younger professionals tended to demonstrate higher knowledge levels. Training exposure ranged from 5% to 91%, and many participants perceived it as insufficient. Qualitative findings echoed these patterns, highlighting challenges in female genital mutilation classification, legal awareness, documentation systems, the impact of providers' cultural beliefs on care delivery. CONCLUSION: Considerable efforts are needed to equip healthcare providers to deliver high-quality, culturally competent care to women affected by female genital mutilation. Research should develop validated tools to assess preparedness, adopt mixed-methods strategies to capture patient and provider perspectives, and guide standardized, up-to-date training programs, strengthening knowledge in managing female genital mutilation.

Humans↗

PubMed2026

Assessing the accuracy and efficiency of an electronic platform for managing childhood illnesses in rural China: A cluster randomized controlled trial.

OBJECTIVES: The Integrated Management of Childhood Illness (IMCI) faces challenges in capacity building and quality control. This trial aims to assess an electronic IMCI (eIMCI) platform in improving the effectiveness and efficiency in disease classification and management by community health workers (CHWs). DESIGN: Cluster randomized controlled trial. SETTING: Rural western China. PARTICIPANTS: 24 CHWs and 72 ill children aged 2 months to 5 years (3 children per CHW). CHWs were randomly assigned to intervention or control groups. INTERVENTIONS: The intervention CHWs received online training and performed disease management using the eIMCI platform featuring integrated training modules and decision-support tools. The control group received traditional face-to-face training and used paper-based IMCI protocols. MAIN OUTCOME MEASURES: Proportion of children correctly diagnosed or classified by CHWs, as determined by a pediatric specialist. Relative risk (RR) between groups was estimated using Poisson Generalized Linear Mixed Models incorporating a random intercept for CHW to account for clustering of children within individual CHWs and adjusting for key covariates at both the CHW and child levels. RESULTS: The intervention group (13 CHWs, 39 children) had a higher rate of correct classification (64.1%) compared to the control group (11 CHWs, 33 children) (39.4%, P = .056). Multivariable regression analysis confirmed this (RR = 2.1, 95% CI: 1.5-3.1; P < .001). No significant difference was found in correct treatment rates (38.5% vs. 27.3%, P = .316). Online training reduced time and costs by approximately 80%, though with a slight decrease in post-training evaluation scores. CONCLUSIONS: The eIMCI platform shows potential in enhancing IMCI implementation and significantly reducing the training burden in resource-limited settings. Trial registration: Chinese Clinical Trial Registry: ChiCTR2100042533, https://www.chictr.org.cn/showproj.html?proj=119995.

Humans↗

PubMed2026

Molecular Landscape and Advanced Diagnostic Technologies for BRAF Mutations in Cancer: From Quantitative PCR and ddPCR to CRISPR-Based Platforms.

BRAF mutations are key oncogenic alterations across multiple malignancies, including melanoma, thyroid carcinoma, colorectal cancer, non-small cell lung cancer, glioma, and hairy cell leukemia. The most prevalent variant, BRAF-V600E, induces constitutive activation of the MAPK signaling pathway, promoting tumor progression and influencing therapeutic responsiveness. Accurate detection of BRAF alterations is therefore essential for molecular classification, prognostic assessment, treatment selection, and resistance surveillance. This review summarizes the molecular heterogeneity of BRAF mutations and critically evaluates current diagnostic methodologies. Conventional approaches such as allele-specific PCR and Sanger sequencing are compared with advanced quantitative platforms, including high-resolution melting analysis, droplet digital PCR, and next-generation sequencing, with emphasis on analytical sensitivity, mutation coverage, and clinical applicability. Emerging technologies such as CRISPR-based assays, rolling circle amplification systems, and nanoparticle-based biosensors and point-of-care diagnostic platforms are also discussed for their potential to enhance ultra-sensitive detection, particularly in liquid biopsy settings. These emerging tools are highlighted for their potential to enable ultra-sensitive, rapid, and decentralized mutation detection, particularly in liquid biopsy settings. Key challenges, including intratumoral heterogeneity, low allele-frequency variants, FFPE-associated artifacts, and clonal evolution under therapeutic pressure, are examined within a translational framework. In addition, we examine critical barriers to clinical implementation, including standardization, cost, and global accessibility of molecular diagnostics, and outline potential solutions through scalable technologies and decentralized testing strategies. We propose that optimal BRAF testing requires a mutation subclass-informed and clinically integrated strategy combining comprehensive baseline profiling with longitudinal molecular monitoring. Future diagnostic paradigms will likely integrate multi-omics data and artificial intelligence (AI)-assisted interpretation to refine precision oncology implementation. Looking forward, we propose that optimal BRAF testing will require integration of multi-omics profiling with AI-assisted interpretation, enabling automated variant classification, real-time clinical decision support, and improved prediction of therapeutic response and resistance.

Humans↗

PubMed2026

A Meta-learning-driven strategy for adulteration detection in sweet potato starch and vermicelli using Raman spectroscopy.

To address the widespread adulteration of sweet potato starch and its vermicelli with cheaper starches and overcome conventional supervised learning's dependency on large labeled datasets, this study developed a few-shot discrimination method integrating Raman spectroscopy with meta-learning. We constructed a meta-learning framework using cassava- and wheat-adulterated sweet potato starch as the source domain for training, with potato-adulterated sweet potato starch and cassava-adulterated sweet potato vermicelli as two target domains for testing. Raman spectra showed high consistency between sweet potato vermicelli and its raw starch, laying the foundation for cross-domain detection. Testing yielded comprehensive classification accuracies of 95.33% and 98.00% for the two target domains, significantly outperforming SVM, RF, and CNN (max. 85.24%). This approach effectively identifies subtle starch variety differences in complex adulteration, providing novel food quality inspection solutions and verifying the feasibility of raw material-to-finished product cross-domain detection.

Ipomoea batatas↗

PubMed2026

Machine learning-assisted Mn-N-C nanozyme colorimetric sensor array for trace-level detection of biogenic amines in meat.

Accurate detection of biogenic amines (BAs) in meat remains challenging due to their high structural similarity and co-occurrence. Herein, an Mn-N-C nanozyme was synthesized via a metal-organic framework confined pyrolysis strategy, possessing excellent oxidase (OXD)- and peroxidase (POD)-like activities. The dual enzyme-like activity showed Km values of 0.1584 mM (OXD) and 0.1498 mM (POD), respectively, in detection system. Leveraging these properties, a colorimetric sensor array was constructed, enabling the detection of four representative BAs within a concentration range of 2-10 ppm with 100% classification accuracy. In addition, a concentration independent recognition model based on an artificial neural network was developed to address signal nonlinearity interference in meat. The integrated system achieved accurate trace-level identification of BAs in perishable fish, pork, and chicken, demonstrating its applicability for early-stage BAs monitoring and quality deterioration warning during storage and transportation.

Biogenic Amines↗

PubMed2026

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

Humans↗

PubMed2026

AI-enabled viral genomics: from virus discovery to host prediction and emerging variant forecasting.

The rapid expansion of metagenomic sequencing has generated vast repositories of viral sequence data that far outpace our capacity to interpret them using conventional approaches. Highly divergent sequences, sparse functional annotation, and taxonomically uneven sampling present fundamental challenges for reference-dependent methods, which lose sensitivity precisely for novel and understudied viruses with high public health relevance. Artificial intelligence (AI) provides a new avenue to address these challenges by enabling predictive inference from viral genomes and proteins while reducing dependence on sequence similarity. In this Review, we discuss representative advances in AI for virus discovery, taxonomic classification and functional annotation, prediction of host range and zoonotic potential, and efforts toward forecasting emerging variants. These advances are transforming viral genomics from a largely descriptive discipline into one with increasing predictive capability. We also critically assess the major challenges that constrain current approaches, including the availability of high-quality and representative datasets, rigorous model evaluation, biological interpretability and responsible governance for increasingly capable AI models.

Artificial Intelligence↗

PubMed2026

External load metrics and monitoring in women's football match play: A systematic review.

This systematic review aimed to identify the most commonly used variables for monitoring external load during elite women's football matches and to compare reporting practices internationally and in Brazil. Searches were conducted in Web of Science, PubMed, and SciELO using the PICOS framework between February and March 2026, resulting in the inclusion of 35 studies. The main outcomes analysed were total distance covered (TD), distance covered across speed zones (HSR, VHSR, sprint), number of accelerations and decelerations, and maximum speed. TD and distance covered across speed zones were the most frequently reported indicators (94.3%), followed by HSR (82.8%) and sprint distance (68.5%). Considerable variability was observed in the classification of speed zones and thresholds used to define accelerations and decelerations, limiting comparisons between studies. External load values varied according to playing position and competition level, with international matches generally imposing greater demands than national competitions. Brazilian research remains limited and demonstrates notable methodological variability. This review proposes standardised speed and acceleration/deceleration thresholds based on the most recurrent ranges reported in the literature, supporting improved consistency in monitoring practices across elite women's football contexts.

Humans↗

PubMed2026

Genomic and Phenotypic Characterization of Two Novel Enterobacter Phages With EDTA-Enhanced Antibiofilm Activity.

Multidrug-resistant members of the Enterobacter cloacae complex (ECC) are increasingly linked to difficult-to-treat infections and biofilm-mediated antimicrobial tolerance. Here, two lytic phages, vB_EhoIP_HHH and vB_EluM_RZH, displaying podovirus-like and myovirus-like morphology, respectively, were isolated from the River Chelt. HHH has a 39,582 bp genome (51.2% GC, 63 ORFs), while RZH has a 174,197 bp genome (39.4% GC, 314 ORFs), with neither genome carrying antimicrobial resistance, virulence or lysogeny-associated genes. VIRIDIC and VICTOR analyses placed HHH within Kayfunavirus and RZH within Karamvirus, supporting their classification as distinct species. Both phages demonstrated rapid adsorption, short latent periods and stability across physiological pH and temperature ranges. A phage cocktail targeting MDR ECC strain was evaluated with EDTA against established biofilms. Crystal violet assays showed the greatest biomass reduction at MOI 10 with 0.5-0.75 mM EDTA. Bliss independence analysis revealed localized synergy within this window but significant overall antagonism at higher EDTA concentrations. CFU enumeration confirmed greater activity against 24 h than 48 h biofilms. The optimized combination also reduced recoverable bacteria in a fibroblast infection model while maintaining low LDH release. These findings identify two novel lytic Enterobacter phages and support a narrow EDTA concentration window for enhanced phage-mediated antibiofilm activity.

Biofilms↗

PubMed2026

Comparative genomic and proteomic analysis reveals orthogroup structured evolution of tick protease inhibitors.

Protease inhibitors (PIs) play central roles in regulating endogenous proteolysis and host-parasite interactions in ticks. However, the evolutionary architecture underlying their diversification across tick lineages remains insufficiently resolved. Here, we performed a genome-wide comparative analysis of predicted proteomes from 14 tick species to systematically characterize PI repertoires. In total, 4931 putative PIs were identified and grouped into 20 families using the MEROPS classification system. Further, PI families such as Antistasin, WAP-type, and Pacifastin, which have not previously been systematically reported in tick genomes, were classified. Orthogroup inference demonstrated that PI expansion is structured at the level of evolutionary lineages rather than uniformly across families. By stratifying orthogroups according to duplication burden and taxonomic conservation, we identified a broadly conserved single-copy core under strong purifying selection. Motif level analysis of serpin reactive center loops further revealed conservation of inhibitory specificity within single copy orthogroups and diversification of key functional residues in duplication-associated lineages. Integration of secretion prediction and tissue-resolved proteomics from Hyalomma anatolicum and Rhipicephalus microplus demonstrated that evolutionary stratification is reflected at the protein level. Together, these findings provide an orthogroup-resolved evolutionary framework linking duplication dynamics, molecular evolution, and tissue-level protein deployment. This integrative approach offers a systematic basis for prioritizing conserved and diversified PI lineages for future functional and anti-tick intervention studies.

Animals↗

PubMed2026

Multimodal intervention benefits: Responder analysis of J-MINT PRIME Kanagawa trial.

INTRODUCTION: The J-MINT PRIME Kanagawa trial was an 18-month multimodal intervention (incorporating exercise, nutrition, and metabolic management) for dementia prevention. Because the primary analysis showed no significant benefits, we performed an exploratory responder analysis to identify responsive subpopulations. METHODS: We analyzed the Full Analysis Set comprising 188 participants. Classification and regression tree (CART) analysis, applied to the intervention arm, identified baseline predictors of cognitive improvement. These rules were then applied to the entire cohort to evaluate treatment effects on the Mini-Mental State Examination (MMSE) using fully adjusted mixed-effects models for repeated measures (MMRM). RESULTS: CART identified a "Target Group" (N = 108) characterized by baseline profiles such as an MMSE score < 28 or specific metabolic ranges (e.g., LDL-C < 135 mg/dL). Within this target group, the intervention significantly preserved MMSE trajectories compared with the control group (group × time interaction, P = 0.022). In contrast, the Non-Target Group (N = 80), consisting of high-functioning individuals (MMSE ≥ 28), exhibited no significant group × time interaction. DISCUSSION: Multimodal interventions may effectively preserve global cognition in older adults with sub-threshold cognitive decline. Careful targeting of appropriate populations, while considering potential longitudinal measurement artifacts (e.g., practice effects), is essential. These findings provide a hypothesis-generating framework that warrants external validation in future prevention trials.

Humans↗

PubMed2026

Factors influencing the enhancement of the new iron triangle in healthcare organisations.

PURPOSE: A new paradigm, "healthcare's new iron triangle," has been developed to emphasise the technological perspective of healthcare delivery, focusing on automation, value and empathy. The study aims to build a conceptual model and to identify factors for the enhancement of the new iron triangle in healthcare organisations. DESIGN/METHODOLOGY/APPROACH: The healthcare organisation is the primary focus point of the current study. To determine the factors, a survey of the literature and healthcare experts' opinions was conducted. The healthcare professionals validated the identified factors. Data for this study were gathered using a closed-ended questionnaire and scheduled interviews. The study employed "Total Interpretive Structural Modeling methodology and Matriced' Impacts Croise´s Multiplication Appliqué´ a UN Classement/Cross-Impact Matrix Multiplication Applied to a Classification (MICMAC) analysis" to address the "why" and "how" the factors interact and prioritise the identified factors. FINDINGS: The study found that organisational structure (F8), artificial intelligence (F1), innovation (F2) and human resources (F5) are the driving or key factors of the study. RESEARCH LIMITATIONS/IMPLICATIONS: The study primarily focused on identifying factors for the enhancement of a new iron triangle in healthcare organisations. The scope could eventually be expanded to explore more areas. PRACTICAL IMPLICATIONS: Academics and other stakeholders will have a better understanding of the key drivers for the enhancement of the new iron triangle in healthcare organisations. ORIGINALITY/VALUE: In this study, total interpretive structural modeling and cross-impact MICMAC analysis are proposed as an innovative approach to address the new iron triangle in healthcare organisations.

Humans↗

PubMed2026

Non-tobacco nicotine dependence and postoperative complications after total ankle arthroplasty: A propensity-matched cohort study.

BACKGROUND: The clinical impact of non-tobacco nicotine dependence (NTND) is poorly defined. This study evaluated the association between NTND and complications following total ankle arthroplasty (TAA). METHODS: A retrospective cohort study using the TriNetX Research Network was performed. Adults undergoing primary TAA (Current Procedural Terminology [CPT] 27702) between 2010 and 2025 were included. Patients were categorized into NTND (International Classification of Diseases, Tenth Revision [ICD-10]: F17, excluding tobacco-specific codes) and nonsmoker cohorts. Propensity score matching (1:1) yielded 939 NTND patients and 939 controls. Ninety-day medical and wound complications and ≥ 2-year mechanical outcomes were assessed. RESULTS: NTND patients had higher rates of 90-day readmission (11.7% vs 6.5%; OR 1.9), wound disruption (4.3% vs 2.6%; OR 1.7), surgical site infection (3.1% vs 1.6%; OR 2.0), and sepsis (2.4% vs 1.1%; OR 2.3). At a minimum 2-year follow-up, NTND was not associated with increased risk of mechanical complications. CONCLUSION: These findings challenge the assumption that smokeless nicotine products are benign in the perioperative setting and support incorporating NTND screening and cessation counseling into preoperative optimization protocols. Future prospective studies are warranted to further characterize the dose-dependent effects of non-tobacco nicotine exposure and to evaluate the impact of perioperative cessation strategies on outcomes following TAA. LEVEL OF EVIDENCE: IV.

Humans↗