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PubMed2026

Metabolic engineering of Candida yeasts for biotechnological applications.

Candida yeasts represent a versatile yet underexploited platform for industrial biotechnology. These yeasts utilize a remarkably broad range of carbon sources, particularly for hydrophobic carbon sources, coupled with robust growth and diverse biosynthetic capacities, making them promising hosts for sustainable production of chemicals, fuels, and proteins. Despite these advantages, industrial deployment of Candida species has been hindered by concerns regarding opportunistic pathogenicity and the historical lack of efficient genetic manipulation tools, leading to a substantial gap between metabolic potential and practical utilization. Recent advances in functional genomics, genome editing, and systems metabolic engineering are rapidly overcoming these barriers, enabling more precise and efficient strain development. In this review, we systematically summarize recent progress in the metabolic engineering of Candida species as microbial cell factories, with particular emphasis on expanding genetic toolkits, utilizting renewable and non-conventional carbon sources, and biosynthesizing high-value compounds. In addition, we propose a biosafety-oriented classification framework to support their safe industrial deployment. Finally, we discuss current challenges and emerging opportunities, emphasizing that the synergy of synthetic biology and artificial intelligence-driven design holds the key to unlocking the biotechnological potential of Candida yeasts.

Candida↗

PubMed2026

The impact of body mass index classification on operative characteristics and perioperative outcomes in lumbar microdiscectomy.

INTRODUCTION: Body mass index (BMI) stratification helps classify obesity severity. In patients undergoing microdiscectomy for symptomatic lumbar disc herniation, the effect of obesity on perioperative risk remains incompletely understood. This retrospective single-institution study evaluated whether BMI class influences perioperative risk in a large surgical cohort. METHODS: Adults older than 18 years who underwent primary, elective single-level lumbar microdiscectomy between June 2018 and March 2025 with at least 3 months of follow-up were included. Patients were grouped by BMI: without obesity (WO, BMI < 30), class I (CI, 30-34.9), class II (CII, 35-39.9), and class III (CIII, ≥40). Outcomes were analyzed separately for open microdiscectomy (OM), tubular microdiscectomy (TM), and endoscopic discectomy (ED). Continuous variables were compared using Kruskal-Wallis testing with Dunn post hoc analysis; categorical variables were compared with chi-square tests. Significance was set at p < 0.05. RESULTS: A total of 757 patients were included (OM 422, TM 190, ED 145). Higher obesity classes underwent ED more frequently (p = 0.038). In the OM cohort (WO 258, CI 97, CII 50, CIII 17), CI had a higher proportion of males and CII a lower proportion (p = 0.007). Operative time, length of stay, and estimated blood loss were greatest in CII and CIII patients (all p < 0.001). CII patients also had more emergency department visits within 1 year than other classes (p = 0.026). No differences were found in age, smoking status, disc herniation type, dural tears, intraoperative or postoperative complications, or revision presence/time. In the TM cohort (WO 117, CI 47, CII 21, CIII 5), WO patients were oldest and CIII youngest (p < 0.001), with no other significant differences. In the ED cohort (WO 79, CI 31, CII 20, CIII 15), WO patients were oldest and CIII youngest (p = 0.004). CIII patients had higher estimated blood loss (p = 0.028) and shorter time to revision (p < 0.001), while other variables were similar. CONCLUSIONS: ED was used more often in higher obesity classes. In OM, CII and CIII obesity were associated with longer operative time, longer hospital stay, and greater blood loss, likely due to increased exposure requirements. TM and ED showed few obesity-related differences in complications, suggesting minimally invasive approaches may mitigate obesity-related perioperative risk. However, the retrospective design and small number of CIII patients warrant further study.

Humans↗

PubMed2026

Functional neuroimaging subtypes of obsessive-compulsive disorder: A systematic review and meta-analysis.

Obsessive-compulsive disorder (OCD) exhibits substantial clinical heterogeneity that may reflect underlying neurobiological diversity. Neuroimaging-based subtyping may advance precision psychiatry by identifying biologically distinct subgroups with differential treatment responses. This study systematically synthesized evidence from functional neuroimaging subtyping studies in OCD to identify reproducible neurobiological subtypes, characterize their clinical profiles, and establish a consensus-based classification framework. We reviewed 40 original studies employing machine learning, clustering, normative modeling, or classification approaches, encompassing approximately 8,150 patients. Consensus clustering identified three reproducible neurobiological subtypes. The Limbic-Hyperactive subtype, comprising approximately 40% of patients, exhibited amygdala and insula hyperconnectivity, elevated anxiety levels, predominant contamination and washing symptoms, and favorable response to cognitive-behavioral therapy. The Fronto-Striatal-Hypoconnected subtype, comprising approximately 35% of patients, demonstrated reduced orbitofrontal-striatal connectivity, cognitive inflexibility, predominant checking and ordering symptoms, and a favorable response to selective serotonin reuptake inhibitors. The Global-Disrupted subtype, comprising approximately 25% of patients, exhibited widespread connectivity disruption, greater symptom severity, and poor treatment response. Support vector machine classification achieved 81.5% accuracy for subtype assignment, though classification of OCD versus healthy controls showed limited generalizability in multisite settings (AUC 0.567-0.673). These findings support a neuroimaging-based framework for personalized treatment selection but require prospective validation.

Humans↗

PubMed2026

The incidence and descriptive factors of calcaneal malunion after surgical fixation of intra-articular calcaneal fractures using the sinus tarsi approach: A retrospective cohort study with binary logistic regression analysis.

BACKGROUND: This study evaluated the incidence of calcaneal malunion after minimally invasive sinus tarsi approach (MIS-STA) in displaced intra-articular calcaneal fractures (I-ACFs) and identified related descriptive factors of calcaneal malunion. METHODS: A retrospective review of 99 displaced I-ACFs treated with MIS-STA was conducted. Demographic data, pre-operative radiographs, and operative details were analyzed. Outcomes included numerical rating scale (NRS) pain scores at rest and during activities of daily living (ADL), Foot and Ankle Ability Measure (FAAM) for ADL and radiographic parameters. Logistic regression was used to identify descriptive factors associated with malunion. RESULTS: Malunion occurred in 33/99 cases (33.3%). The significant descriptive factors were the initial Böhler angle < 0.5 °, time to surgery > 12.5 days, and Sanders type ≥ III. Malunion patients had significantly worse NRS and FAAM scores (p ≤ 0.001). CONCLUSION: Calcaneal malunion after MIS-STA occurred in one-third of cases, with three descriptive factors identified and poorer outcomes observed. LEVEL OF EVIDENCE: III, Comparative retrospective study with binary logistic regression analysis.

Humans↗

PubMed2026

Meta-PseU: A meta-classifier for robust prediction of RNA pseudouridine modification sites from long sequences.

BACKGROUND AND OBJECTIVES: Pseudouridine (Ψ) represents one of the most abundant and conserved RNA modifications. Ψ provides an additional hydrogen-bond donor that enhances RNA structural stability and modulates translation. It participates in diverse biological processes, including RNA-protein interactions, splicing, translational control, and stress responses. Aberrant pseudouridylation is implicated in cancer, neurodegenerative disorders, and autoimmune diseases. Despite its biological importance, experimental identification of Ψ sites remains time-consuming and costly, limiting the feasibility of transcriptome-wide profiling. Computational approaches have therefore become essential complements to experimental techniques. However, state-of-the-art machine-learning and deep-learning predictors often suffer from limited generalizability due to small training datasets. To overcome these issues, we aim at constructing new long-sequence datasets and developing a novel Ψ site predictor. METHODS: New long-sequence datasets were constructed as benchmarks for RNA Ψ-site prediction. The Ψ modification sites in RMBase 3.0 were mapped to the reference genomes across three species of human, mouse, and yeast, and the RNA sequences with a length of 201 were generated by extending the upstream and downstream from the mapped, central sites. To eliminate sequence redundancy, the sequences were clustered using CD-HIT with a 70% sequence identity threshold. We developed Meta-PseU, a logistic regression-based meta-classifier that considered 118 machine learning and deep learning classifiers. The datasets and programs are freely accessible at https://github.com/kuratahiroyuki/MetaPseU. RESULTS: By optimizing model configuration, we proposed the Meta-PseU model stacking 32 machine learning and deep learning classifiers out of 118 classifiers. Meta-PseU substantially improved model generalizability, overcoming a key limitation of existing approaches. It greatly outperformed state-of-the-art predictors and achieved increasing accuracy with increasing sequence length. CONCLUSIONS: Long-sequence datasets were newly constructed as benchmarks for RNA Ψ-site prediction. Meta-PseU offers a new framework for robust Ψ-site identification by using long sequences.

Pseudouridine↗

PubMed2026

Integrated salivary proteomic and metabolomic analyses reveal molecular characterization and novel biomarker panels of chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a respiratory disorder characterized by chronic inflammation, oxidative stress, and metabolic dysregulation. The lack of convenient and easily-accessible non-invasive diagnostic approaches remains a major clinical challenge. This study applied an integrated saliva-based proteomic and untargeted metabolomic strategy to identify potential biomarkers for COPD classification. Comprehensive multi-omics analyses identified 225 differentially abundant proteins and 60 differentially abundant metabolites between patients with COPD and healthy controls, including 24 biologically relevant endogenous metabolites. Functional enrichment analyses revealed pronounced dysregulation of mitochondrial energy metabolism, redox homeostasis, lipid remodeling, and inflammatory-related pathways in COPD. By integrating salivary proteomic and metabolomic biomarkers, a stepwise feature selection combined with LASSO logistic regression was used to construct diagnostic models, yielding an optimized biomarker panel consisting of 11 proteins and 2 endogenous metabolites. This integrated model achieved excellent diagnostic performance, with an area under the ROC curve of 0.96. Collectively, these findings demonstrate that integrated salivary proteomic and metabolomic profiling provides a robust, non-invasive approach for COPD classification and offers a promising foundation for the development of biosensor-based diagnostic platforms and early disease detection. SIGNIFICANCE: Chronic obstructive pulmonary disease (COPD) remains a major global health burden. Current diagnostic approaches rely largely on spirometry and clinical assessment, which are limited in sensitivity for early-stage disease and unsuitable for large-scale screening. This study employs an integrated saliva-based proteomic and metabolomic strategy to identify non-invasive biomarkers for COPD classification. Our findings reveal coordinated dysregulation of mitochondrial energy metabolism, redox homeostasis, and lipid remodeling in COPD, highlighting the interconnected roles of metabolic reprogramming, oxidative stress, and inflammation in disease pathophysiology. Notably, a robust diagnostic panel comprising 11 proteins and 2 endogenous metabolites was established, achieving excellent classification performance (AUC of 0.96). To our knowledge, the integrated application of salivary proteomics and metabolomics for COPD diagnosis remains largely unexplored, underscoring the significance and translational potential of our findings.

Humans↗

PubMed2026

Transverse testicular ectopia with fused vas deferens: A systematic review.

BACKGROUND: Transverse testicular ectopia (TTE) with fused vas deferens is an extremely rare anomaly, often diagnosed intraoperatively. Current TTE classifications do not address internal ductal variations, limiting surgical guidance. OBJECTIVE: To systematically review cases of TTE with fused vas deferens, summarize presentation, operative strategies, outcomes and identify patterns that highlight the need for classification refinement. METHODS: A PRISMA 2020-compliant systematic review (PROSPERO; CRD420251247785) was performed across PubMed, ScienceDirect and citation of included articles through December 2025. Case reports and series confirming fused vas deferens were included. Data extracted comprised demographics, presentation, imaging, surgical approach, and outcomes. Quality assessment used JBI checklists. RESULTS: 12 studies (16 patients) were included. Most presented with unilateral inguinal hernia (62%) and contralateral undescended testis (68%); 81% were diagnosed intraoperatively. Anatomical patterns included common/proximal fused vas (87%), Y-shaped fusion (6%), and long-loop vas (6%). Trans-septal orchidopexy was the preferred approach, with preservation of vas integrity. Postoperative outcomes were favorable; long-term follow-up was limited. CONCLUSION: TTE with fused vas deferens represents a distinct variant requiring careful intraoperative recognition. We propose a Type IV TTE category for internal ductal fusion to guide surgical planning and classification refinement. Further accumulation of case-based evidence may help clarify its anatomical patterns and operative implications.

Humans↗

PubMed2026

Machine learning vs. traditional methods for predicting postoperative cardiac complications after non-cardiac surgery: a systematic review and Bayesian network meta-analysis.

INTRODUCTION: Accurate prediction of peri-operative cardiac complications is critical to optimise pre-operative decision-making. Traditional risk prediction scores, such as the Revised Cardiac Risk Index, show only modest discrimination. Machine learning can model complex, non-linear relationships but their predictive performance compared with traditional scores remains unclear. METHODS: We performed a systematic review and Bayesian network meta-analysis. The primary outcome was postoperative adverse cardiac events following non-cardiac surgery. Prediction models were assessed relative to the Revised Cardiac Risk Index. As many studies evaluated multiple versions of each model type, the highest performing ('best version') and lowest performing ('worst version') results were analysed. Models were ranked using the surface under the cumulative ranking curve (SUCRA). RESULTS: Thirteen studies evaluating 54 models and 927,113 patients were included. Machine learning approaches generally outperformed traditional risk scores. Automated machine learning ranked highest (SUCRA 96.6) showed the greatest improvement in the best version analysis (mean difference (MD) 0.28 (95%CrI 0.16-0.40)) and remained superior in the sensitivity analysis (MD 0.30 (95%CrI 0.14-0.45)). Gradient boosting models showed superior performance over the Revised Cardiac Risk Index across analysis (best version: MD 0.20 (95%CrI 0.14-0.26), worst version: MD 0.18 (95%CrI 0.12-0.25), SUCRA 82.4). The Gupta Perioperative Risk for Myocardial Infarction or Cardiac Arrest score outperformed the Revised Cardiac Risk Index in the best version analysis (MD 0.16 (95%CrI 0.01-0.32)). Between-study heterogeneity was low. None of the included studies externally validated their machine learning models and only six were judged to be at low risk of bias. DISCUSSION: Most machine learning models showed better discrimination than traditional risk scores, with automated machine learning and gradient boosting models ranking highest. However, study quality, calibration reporting and absence of external validation limit immediate clinical adoption. Prospective, multicentre evaluation is required before integration of these models into peri-operative practice.

Humans↗

PubMed2026

Predicting ACL injury risk in athletes: A systematic review of machine learning-based models.

BACKGROUND: Early ACL injury risk identification in athletes is essential. This systematic review examines machine learning (ML) models for predicting ACL injuries, evaluating their methodological quality, performance, and reliability. METHOD: A comprehensive electronic search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore databases, supplemented by Google Scholar for grey literature, covering articles published between January 1, 2015, and August 30, 2025. Eligible studies were appraised using the Prediction Model Study Risk of Bias Assessment Tool (PROBAST) for methodological quality and risk of bias, and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines for quality of evidence. RESULTS: Ten studies were included. PROBAST showed eight studies had moderate risk of bias and two low risk. TRIPOD found only two studies met quality criteria. ML models included logistic regression (n = 5), support vector machines (n = 4), k-nearest neighbor (n = 3), decision trees (n = 3), random forests (n = 5), neural networks (n = 2), linear discriminant analysis (n = 1), and pre-trained CNNs (n = 1). AUC ranged from 0.63 to 0.98. Accuracy (reported in six studies) ranged from 26% to 95%; however, these values should be interpreted with caution due to the absence of confidence intervals, lack of class imbalance handling, and limited external validation across studies. Tree-based ensemble methods such as random forest achieved competitive accuracy (74-86%), while SVM, a non-ensemble classifier, reported accuracy ranging from 71% to 95%; however, the highest values were obtained in studies with notably small sample sizes (n = 12 to n = 39), raising concerns about overfitting and generalizability. CONCLUSION: Current ML algorithms show promise for identifying athletes at high ACL injury risk and detecting relevant risk factors. Although study quality was generally satisfactory, future research should prioritize external validation and model interpretability to support clinical translation.

Humans↗

PubMed2026

Reinforcement learning-based dynamic ensemble for missense variant effect prediction and tiered prioritization of VUS.

BACKGROUND: Accurate classification of missense variants remains a challenging task despite major advances in genomics. Numerous computational models have been developed to assist in variant classification, but often require repeated integration and benchmarking efforts. Ensemble methods have been proposed to overcome the limitations of single predictors, but mostly rely on fixed, predefined weights that constrain their ability to capture interactions among predictive signals. METHODS: We present GenixRL, a dynamic ensemble framework that reformulates model fusion as a reinforcement learning optimization problem. GenixRL uses a Q-learning agent to learn a policy that dynamically weights the probabilistic outputs of complementary predictors, including BayesDel (addAF and noAF), ClinPred, and MetaRNN. Replacing static weighting with policy learning allows GenixRL to adaptively identify optimal weightings and substantially improve classification accuracy. RESULTS: In benchmark evaluation against 25 state-of-the-art predictors, GenixRL achieved an AUROC of 0.9644 on an independent ClinVar dataset. On saturation genome editing assays for BRCA1 and BRCA2, GenixRL achieved the best performance and ranked highest on 14 of 17 clinically significant genes in a zero-shot evaluation. Applied to uncertain and conflicting ClinVar variants, GenixRL enabled tiered, evidence-based prioritization of hundreds of thousands of variants as likely pathogenic or pathogenic with high confidence, supported by orthogonal population evidence from gnomAD. CONCLUSION: GenixRL advances pathogenicity prediction for missense variants and provides an adaptive ensemble that sorts variants of uncertain significance into tiered candidates for expert curation and functional validation.

Mutation, Missense↗

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

The journey of fluxapyroxad, mandipropamid and mefentrifluconazole residues in two morphologically distinct chilli peppers: A comprehensive risk assessment from field to processing.

Understanding the residue fate of novel pesticides in crops is crucial for ensuring their safe application and safeguarding public health. This study examined the dissipation, processing factors (PFs), and risk assessment of fluxapyroxad, mandipropamid, and mefentrifluconazole in two morphologically distinct varieties of chilli peppers from field to processing. The half-lives of the three pesticides ranged from 5.42 to 10.05 days, following first-order kinetics. The initial residues were higher in Chaotian chilli peppers (CCP) than in long green chilli peppers (GCP). However, dissipation occurred more rapidly in CCP. Washing notably reduced the residues (PF: 0.60-0.89), whereas sun drying and oven drying concentrated them (PF: 1.92-3.74), with oven drying leading to greater concentrations. Both chronic and acute dietary risk assessments suggested acceptable risk levels for the general population. This study offers reliable guidance for the rational application of these three pesticides in chilli pepper cultivation.

Capsicum↗

PubMed2026

Urban Design Quality and Clinical Mental Health: A Systematic Review.

ObjectivesThis systematic review synthesizes empirical evidence on core urban design dimensions that affect clinical mental health outcomes and examines how environmental exposures mediate or moderate these relationships.BackgroundUrban design has increasingly been recognized as a determinant of psychological well-being, yet a standardized framework to evaluate its mental health impact remains underdeveloped.MethodsFollowing PRISMA 2020 guidelines, we systematically reviewed 19 quantitative empirical studies published through January 2025, examining relationships between outdoor urban design features and validated clinical mental health indicators across four major databases.ResultsFindings reveal that urban design influences clinical mental health outcomes (depression, anxiety, stress, cognitive decline) through two objective spatial scales: street-level features (imageability, enclosure, human scale, complexity) and neighborhood environments (land use mix, density, green infrastructure). Environmental exposures (traffic, noise, air pollution) operate as perceptual and experiential mechanisms that mediate or moderate the mental health effects of these spatial design features.ConclusionsWe propose an integrated three-domain conceptual framework distinguishing objective spatial design scales from subjective exposure mechanisms. This framework provides evidence-based guidance for urban planners and policymakers toward creating mentally healthier urban environments.

Humans↗

PubMed2026

Boosting kynurenic acid in kombucha via substrate selection: metagenomic and biochemical insights.

Kombucha is gaining global popularity for its health benefits. This study explored the use of chestnut honey, a rich source of kynurenic acid (KYNA), to produce kombucha enriched with this metabolite. Five variants were prepared using different green/black tea blends and carbon sources: white sugar or acacia honey (controls) versus chestnut honey. Samples were analyzed for tryptophan metabolites, physicochemical properties, and microbial diversity. Komagataeibacter and Enterobacter were predominant bacterial genera in SCOBY. Candida and Aspergillus were predominated in the single sample analyzed for fungi. During fermentation, tryptophan decreased, while kynurenine increased. KYNA levels remained largely stable during fermentation and were mainly influenced by the fermentation substrate. No melatonin pathway derivatives were detected. On day 7, chestnut honey yielded kombucha with 381.680-739.915 μmol/L KYNA and elevated myricetin. Overall, chestnut honey-based kombucha represents a system in which substrate composition appears to be the main factor influencing KYNA levels in the final beverage.

Kynurenic Acid↗

PubMed2026

Integrated widely targeted metabolomics and GC-IMS reveal dynamic flavor, nutritional, functional, and metabolic profiles in macadamia kernels during processing.

Different processing stages influence the color, flavor, and antioxidant activities of macadamia kernels. However, the biochemical mechanisms that occur during processing are not well known. This study integrated widely targeted metabolomics (UPLC-MS/MS) with GC-IMS to systematically characterize non-volatile and volatile compounds in macadamia kernels across key three sample groups: fresh kernels (FMN), low-temperature-dried kernels (DMN), and roasted kernels (BMN). A total of 622 non-volatile metabolites and 52 volatile compounds were identified. Low-temperature drying promoted the accumulation of phenolic acids and flavonoids, enhancing antioxidant capacity. Roasting degraded heat-sensitive nutrients but generated flavor compounds via Maillard reaction and lipid oxidation, shifting aroma from green to nutty notes. Nutritional assessment confirmed that roasting significantly reduced antioxidant activities and bile acid binding capacity. Pearson correlation analysis verified the key metabolite-antioxidant relationships. These findings provide critical insights into metabolic dynamics during nut processing and establish a scientific basis for optimizing thermal processing strategies.

Metabolomics↗

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

Evolutionary architecture and lineage-specific diversification of Forkhead box transcription factors in Perna viridis.

The Forkhead box (Fox) transcription factors are evolutionarily conserved regulators of development, cell cycle, and apoptosis across metazoans. This study provides the first comprehensive genome-wide analysis of the Fox gene family in the Asian green mussel (Perna viridis). We identified 28 Fox genes distributed across 10 chromosomes. Comparative analysis reveals the absence of the FoxI, FoxQ1, FoxR and FoxS subfamily, consistent with other bivalves and indicative of lineage-specific gene loss during molluscan evolution. Notably, gene duplications in the FoxAB, FoxD, FoxH, FoxN1-4, FoxQ2 and FoxQD subfamilies may reflect functional diversification associated with environmental adaptation. Exon-intron structural variability, including intron loss in several paralogues, suggests structural diversification and potential regulatory variation. Phylogenetic reconstruction confirmed the monophyly of core Fox classes while highlighting divergent expansion patterns in lophotrochozoans. Selection analyses showed strong purifying selection across duplicated Fox paralogs, supporting functional conservation after lineage-specific expansion. Gene Ontology enrichment linked Fox genes to stress response, apoptosis, and transcriptional regulation. By integrating phylogenetic, structural, and transcriptomic analyses, this study provides a genomic framework for understanding Fox gene organisation, evolution, and tissue-associated expression patterns in Perna viridis and establishes a comparative resource for future functional studies in bivalves.

Animals↗