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The effects of cold temperature on the development, microbiome, and transcriptome of the sea anemone Nematostella vectensis.

Thermal conditions impact essentially all aspects of the physiology for ectotherms. While the effects of high temperatures have been widely studied, cold temperature effects on aquatic invertebrates and their microbial communities have been poorly characterized. To determine the diverse effects of exposure to cold temperatures, we assessed acute and long-term impacts of ecologically relevant low temperatures on the development, microbiome, and gene expression of the sea anemone Nematostella vectensis. Two hours post fertilization, embryos were exposed to temperatures from 4°C to 35°C and development rate to the juvenile stage was quantified. We found temperature impacts the development rate of embryos, where lower temperatures extended development time and resulted in mortality below 10°C. For both microbiome and host transcriptomic responses, anemones were held at 20°C, 10°C, and 0°C and compared at 24 hours and 7 days. Extended exposures to colder temperatures caused restructuring of the host-associated microbiome, with the loss of common taxonomic groups from the class Bacteroidia and Bacilli. Lastly, cold stress induced significant changes in gene expression, which were more pronounced at the 10°C than 0°C but showed little change over time in each temperature. Interestingly, expression of genes associated with innate immunity were among the most differentially expressed genes including heat shock proteins and innate immune genes providing a potential host-imposed mechanism to explain the shift in the microbiome. Overall, cold temperatures have broad effects on many facets of this sea anemone and its microbial community and indicate the importance of cold temperature events when characterizing how ectotherms acclimate to thermal variation.

Animals

Non-coding RNAs and Mitochondrial Dysfunction in Alzheimer's Disease: A Systematic Review.

Alzheimer's disease (AD) is responsible for 70% of dementia cases worldwide, with tau hyperphosphorylation and amyloid-β plaque accumulation representing its core pathological hallmarks. Genetic predisposition, oxidative stress, and neuroinflammation contribute to disease onset and progression. Non-coding ribonucleic acids (ncRNAs) are a class of RNAs which control gene expression and whose dysregulation in AD patients has been linked to amyloid production, neuroinflammation, and mitochondrial dysfunction, which ranges from impaired energy metabolism to disrupted mitochondrial biogenesis and dynamics. Our descriptive systematic review surveyed the involvement of ncRNAs in mitochondrial dysfunction in AD across experimental and clinical literature. We identified multiple microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) that directly regulate mitophagy, mitochondrial biogenesis, mitochondrial autophagic, and apoptotic pathways, mitochondrial dynamics, and protein import mechanisms in AD models. Among the most important candidates demonstrating clinical dysregulation, miR-140 and lncRNA NEAT1 regulate mitophagy, while miR-9, miR-34a, miR-146a, miR-155, and miR-485 are implicated in mitochondrial biogenesis and miR-204 in mitochondrial autophagy. LncRNA BDNF-AS, miR-148a-3p, miR-21-5p, and miR-103a-3p emerged as regulators of the mitochondrial apoptosis pathway with confirmed clinical dysregulation. Multiple ncRNAs control mitochondrial dynamics, of which miR-195, miR-124, and miR-455-3p have also been studied in AD patients. Additionally, several ncRNAs were found to indirectly regulate mitochondrial fission, autophagy, and apoptosis, although the underlying mechanisms require further characterization. Thus, while ncRNA-centered AD research is in its early stages, current mechanistic and translational evidence supports mitochondrially relevant ncRNAs as promising candidates for biomarker and therapeutic development.

Alzheimer Disease

Can host genetics transform the sustainable control of tropical theileriosis? Insights from the Tick-Theileria interface.

Tropical theileriosis, caused by the tick-transmitted apicomplexan parasite Theileria annulata, remains a major constraint on cattle production across North Africa, the Mediterranean basin, the Middle East and South Asia. Current control depends on acaricides, the theilericidal drug buparvaquone and live attenuated schizont vaccines, but acaricide resistance, buparvaquone-resistance mutations and the logistical demands of vaccination are eroding the sustainability of these tools. Host genetics offers a complementary and durable alternative. Indigenous Bos indicus breeds are consistently more resistant to ticks and tolerate T. annulata infection better than exotic Bos taurus cattle, and this advantage has a measurable heritable component. Unlike previous reviews, which treat tick resistance, T. annulata immunobiology and livestock genomic selection as separate subjects, we integrate all three and assess host genetics specifically against the failure modes of current control. We review the tick, parasite and host interface, the evidence for natural resistance, and the genetic and immunological mechanisms involved, including signal-regulatory protein, bovine major histocompatibility complex class II and inflammatory pathway genes. We then assess whether genomic selection, multi-omics, machine learning and gene editing can translate these mechanisms into resistant cattle, and we weigh the biological, economic and infrastructural barriers to implementation. The evidence indicates that host genetics will not replace existing control but could reduce reliance on acaricides and chemotherapy. That contribution remains prospective rather than demonstrated: no resistance marker for T. annulata has yet been validated, prediction accuracies are moderate and transfer poorly between breeds, and no endemic production system has implemented selection for resistance.

Animals

Ecological Restoration of the Soil-Like Function in the Bauxite Residue: Natural Microbiomes Mediated Molecular Transformation of Dissolved Organic Matter.

Soilization of bauxite residues offers a scalable route for long-term carbon management and ecological restoration. However, the microbial processes that transform exogenous organic inputs into stable soil-like carbon pools remain poorly resolved. Here, we combined cross-ecosystem meta-analysis, machine-learning prediction, native synthetic community (SynCom) construction, 13C-labeled straw microcosms, field validation, Fourier transform ion cyclotron resonance mass spectrometry, and genome-resolved metagenomics to unravel microbiome-mediated carbon transformation at the dissolved organic matter (DOM) molecular scale. Our meta-analysis revealed that alkaline industrial wastes retained soil-like DOM signatures but were enriched in microbial humic- and protein-like components, indicating active yet incomplete carbon processing. Guided by these patterns, native SynCom inoculation increased 13C incorporation into total organic carbon (TOC) and dissolved organic carbon (DOC), enlarged biodegradable and adsorbable DOC fractions, and shifted DOM from recalcitrant aromatic pools toward oxygenated carbohydrate-, tannin-, and phenolic-like molecular classes. Genome-resolved analyses linked this transformation to complementary polymer degradation and nutrient-cycling functions across fungal and bacterial guilds, including enriched carbohydrate-active enzymes in straw-carbon-utilizing metagenome-assembled genomes. Null model and thermodynamic analyses further showed that microbial communities were constrained by homogeneous selection, whereas DOM molecules were diversified through variable selection and redox-dependent transformation. Field-scale validation confirmed that SynCom promoted TOC and DOC accumulation and humic-like, high-density DOM fractions under alkaline conditions. Together, these findings establish a mechanistic framework in which functional microbiomes couple plant carbon depolymerization, DOM molecular diversification, and mineral-interactive carbon stabilization, providing a microbiome-guided strategy for carbon sequestration and soilization in the bauxite residue.

Soil

Integrative machine learning and transcriptomic analysis reveals molecular mechanisms underlying low survival rate in larval Chinese Bahaba (Bahaba taipingensis).

Chinese Bahaba (Bahaba taipingensis) is a Class I protected marine fish endemic to China. Low larvae survival during artificial breeding severely hinder population recovery. To investigate the molecular mechanism of high mortality in larval fish, this study performed RNA-seq on liver from naturally deceased (ND) and mass-dead (MD) individuals, combined with least absolute shrinkage and selection operator (LASSO) regression and random forest (RF) algorithms to screen for core signature genes. A total of 873 differentially expressed genes (DEGs) were identified, including 112 upregulated and 761 downregulated genes. GO and KEGG enrichment analyses revealed significant enrichment in amino acid metabolism disorders, one‑carbon folate pool impairment, PPAR signaling abnormalities, ECM-receptor interaction, focal adhesion pathway, indicating widespread metabolic suppression accompanied by extracellular matrix remodeling and signaling disturbances in the livers of MD fish. MAD pre-filtering combined with dual machine learning algorithms yielded 18 robust core signature genes, among which SLC38A4, MMP1, FADD, FKBP5, and APOB were consistently identified as high-frequency core genes by both algorithms. SLC38A4 exhibited the highest importance score in the RF model and was significantly downregulated, making it the primary molecule distinguishing ND from MD phenotypes. ROC curve analysis showed that both models achieved an AUC of 1.000 (95% CI lower bound: 0.610), confirming the precise discriminatory ability of the core genes. GSEA further demonstrated significant enrichment of this core gene set in ND samples. This study provides the first systematic elucidation of the molecular mechanisms underlying liver dysfunction in low survival rate B. taipingensis, characterized by amino acid transport impairment, metabolic reprogramming, and structural remodeling, offering theoretical foundations for health assessment, early mortality risk warning, and artificial breeding conservation of this species.

Animals

Symptom Burden After Dialysis Initiation and Its Association With Hospitalization.

RATIONALE & OBJECTIVE: Symptom burden is distressing for patients living with kidney failure, but there is limited information about the combination of symptoms and individual symptoms that most strongly predict health care use in this group. We classified and summarized patients' symptom burden levels and changes over time and estimated associations with hospitalizations among patients receiving incident hemodialysis. STUDY DESIGN: Longitudinal, observational. SETTING & PARTICIPANTS: Individuals initiating dialysis in the United States. EXPOSURE: Kidney Disease Quality of Life-36 (KDQOL-36) measure. OUTCOME: First hospitalization after dialysis initiation. ANALYTICAL APPROACH: Latent transition analysis was used to identify symptom burden classes using the KDQOL-36. Cox regression models were used to assess whether individual KDQOL-36 symptoms and symptom burden groups were associated with hospitalization risk after dialysis initiation, independent of demographics and comorbid conditions. RESULTS: 1,818 participants were Black (29%), were aged >65 years (59%), were women (42%), had diabetes (49%), and had hypertension (74%). Latent transition analysis identified the following 3 symptom burden groups: (1) low (low severity of all symptoms and kidney disease impacts), (2) moderate (high physical health impact and overall burden of kidney disease), and (3) high (high levels of all symptoms and kidney disease impact). After adjusting for patient characteristics, all KDQOL-36 scales except the Effects of Kidney Disease scale were associated with a higher hazard of hospitalization. Using the symptom burden groups, a high symptom burden was associated with a 20% increase in the hazard of hospitalization. A 1-category worsening in pain interference and in fatigue was associated with a 12% and an 8% increased hazard of hospitalization, respectively. LIMITATIONS: Findings may not generalize outside the United States. CONCLUSIONS: Pain interference and fatigue, as well as an overall symptom burden, are useful prognostic indicators in patients receiving in-center hemodialysis. Symptom burden should remain a treatment target in hemodialysis.

Hemodialysis

Herd-level heterogeneity of antimicrobial resistance in commensal Escherichia coli: A nationwide high-throughput survey of Australian pig herds.

Antimicrobial resistance in commensal Escherichia coli provides a useful indicator for overall antimicrobial resistance burden. We applied this approach to assess antimicrobial resistance within and between commercial pig herds across Australia. A high-throughput robotic workflow was used to isolate 2730 E. coli colonies from rectal contents collected in 2022 from healthy slaughter pigs (n = 300) representing 30 herds (∼70% of national production). Up to 94 isolates per herd underwent antimicrobial susceptibility testing using the Robotic Antimicrobial Susceptibility Platform. Isolate- and herd-level antimicrobial resistance indices were calculated, weighting antimicrobials by their human health importance. Resistance to first-line agents was widespread: ampicillin 77% and tetracycline 79%. By contrast, resistance to critically important antimicrobials was rare (ciprofloxacin 0.11%; extended-spectrum cephalosporins 0.04%), and no clinical resistance to carbapenems or colistin was detected. Overall, 56.9% of isolates were multi-class resistant. Herd-level antimicrobial resistance within indices ranged from 1.51 to 5.76, revealing substantial between-herd heterogeneity. Three herds carried critically important antimicrobials-resistant isolates that would likely have been missed using conventional, lower-density sampling approaches. Whole-genome sequencing identified fluoroquinolone-resistant isolates belonging to ST10 and ST69 (both qnrS1), and ST744 (Quinolone Resistance Determining Region mutations plus blaCTX-M-27). By testing approximately tenfold more isolates than conventional surveys, we uncovered considerable antimicrobial resistance with heterogeneity within and between animals and herds, including farm-specific variability. This expanded sampling also enabled detection of critically important antimicrobial resistance at very low prevalence. In conclusion, high-throughput, high-density testing offers a practical early-warning system and herd-level benchmark to inform surveillance and targeted interventions.

Animals

Plasma proteomics reveal SERPINA1 and CD59 as candidate biomarkers for COVID-19 severity stratification and prognosis prediction.

BACKGROUND: COVID-19 has been closely associated with coagulation abnormalities. However, existing biomarkers, including D-dimer and fibrin degradation products (FDP), exhibit limited accuracy in stratifying disease severity and predicting long-term clinical outcomes. OBJECTIVES: This study aimed to use proteomic analysis to identify plasma biomarkers associated with COVID-19 severity and prognosis, and validate their predictive utility for mortality and thromboembolic complications. METHODS: Plasma proteomic profiles were analyzed across three COVID-19 severity classes. Differential expression analysis and functional analysis were performed. Clustering analysis was used to identify proteins correlated with disease severity. Candidate biomarkers were validated in an independent cohort. Predictive performance of the biomarkers for mortality, sepsis and venous thromboembolism was evaluated using bootstrap-corrected ROC analyses and multivariable regression analyses. RESULTS: Proteomic analysis revealed progressive involvement of the coagulation and complement pathway with increasing disease severity. SERPINA1 and CD59 were identified as candidate biomarkers and exhibited significantly higher plasma levels in severe cases. Bootstrap-corrected ROC analyses demonstrated strong predictive performance: SERPINA1 achieved AUCs of 0.775 and 0.924 for 30-day and 12-month mortality, and CD59 achieved AUCs of 0.720 for sepsis; the combined model further improved prediction of 12-month mortality (AUC 0.946) and sepsis (AUC 0.904), outperforming D-dimer and FDP. Multivariable regression confirmed their independent prognostic value. CONCLUSION: This exploratory study identifies SERPINA1 and CD59 as candidate prognostic biomarkers in COVID-19, highlighting the role of coagulation and complement-related pathways in disease severity and warranting further prospective validation.

Humans

ALG-020572, an Antisense Oligonucleotide for the Treatment of Chronic Hepatitis B Virus Infection Discontinued for Drug-Induced Liver Injury.

Current treatment options for chronic HBV infection are suboptimal in that they fail to suppress HBsAg levels. ALG-020572 is an antisense oligonucleotide designed to reduce viral protein synthesis through degradation of HBV mRNA. ALG-020572-401 was a double-blind, randomized, placebo-controlled trial consisting of two parts. Part 1 (single-ascending doses) evaluated the pharmacokinetics, safety and tolerability of single doses of ALG-020572 or placebo in healthy participants. In Part 2 (multiple dosing), participants with non-cirrhotic HBeAg-negative, virologically suppressed chronic HBV infection were administered up to 7 doses of ALG-020572 to evaluate safety, pharmacokinetics and antiviral activity. In Part 1, 32 participants were randomized to ALG 020572 or placebo. Single doses of ALG-020572 up to 480 mg were well tolerated. The most common treatment-emergent adverse event reported was injection site reaction. ALG-020572 was rapidly absorbed and plasma exposures increased with dose. In Part 2, 8 participants with non-cirrhotic HBeAg-negative virologically suppressed chronic hepatitis B infection were enrolled and received up to 7 doses of ALG-020572. The study was prematurely discontinued after 4 participants experienced significant alanine aminotransferase elevations that were subsequently attributed to drug-induced liver injury. Single doses of ALG-020572 demonstrated a favourable pharmacokinetic and safety profile in healthy participants. Unexpectedly, ALG-020572 was poorly tolerated in participants with chronic HBV infection, resulting in the early termination of the study and further development of ALG-020572 due to idiosyncratic drug-induced liver injury, suggesting caution is required in the development of this class of drugs. Trial Registration: Registered at clinicaltrials.gov: NCT0500102.

Adult

Co-Use of Some Substances Associated With Higher Doses in Adolescents: Findings From the Adolescent Brain Cognitive Development Study.

PURPOSE: Adolescence is a critical developmental period marked by heightened vulnerability to initiating substance use, with long-term implications for health and behavior. Nicotine, alcohol, and cannabis are the most commonly used substances among adolescents. Their use often co-occurs but few studies have described or quantified the patterns and doses of substance co-use in this vulnerable population. METHODS: We used data from six waves of the Adolescent Brain Cognitive Development study. Substance use was measured through a web-based timeline followback interview, and outcomes are operationalized into substance use quantity (in standard units), single use, and co-use (use of two substances on the same day) of alcohol, cannabis, and nicotine. Group differences of average dose in standard units between single use and co-use for each substance class were analyzed using linear mixed-effects modeling in years 4-6. RESULTS: The average dose of nicotine was significantly higher when co-used with alcohol (4.32; 95% confidence interval [CI]: 2.55-6.09; p < .001) or when co-used with cannabis (4.41; 95% CI: 2.76-6.05; p < .001) in year 6; nicotine trends were similar in years four and five. In year 6, the average dose of alcohol was higher when co-used with cannabis (1.87; 95% CI: 0.37-3.36; p = .004) or co-used with nicotine (1.64; 95% CI: 0.06-3.22; p = .03) compared to single use of alcohol. The average dose of cannabis was not significantly different when used singularly or co-used with either alcohol or nicotine in our study years. DISCUSSION: During adolescence, the co-use of substances (namely alcohol and nicotine) may lead to higher average doses compared to single-substance use.

Humans

An automated geometric modeling framework in GATE for the design and optimization of high-sensitivity converging-beam SPECT collimators.

Objective.The trade-off between detection sensitivity and spatial resolution is a fundamental challenge in designing organ-dedicated Single-photon emission computed tomography (SPECT) collimators. While converging-hole geometries offer a solution, their optimization is often hindered by the lack of flexible computational tools capable of modeling large-scale, non-parallel hole arrays. This study aims to develop an automated geometric modeling framework to facilitate the design and evaluation of complex converging- and diverging-hole collimators within standard Monte Carlo environments.Approach.We developed a specialized modeling framework by implementing custom C++ classes and a vector-based alignment algorithm within GATE. This platform enables automated, orientation-consistent construction of large-scale converging arrays not natively supported by standard implementations. A high-sensitivity pure cone-beam collimator (CBC) was designed using this framework. The evaluation used hot-rod, disc, and Jaszczak phantoms for physical characterization, while XCAT and dedicated brain models were employed for clinical tasks, including cardiac, brain perfusion, and DaTscan SPECT simulations.Main results.The CBC achieved a nearly fourfold sensitivity increase compared to a conventional low-energy high-resolution parallel-hole collimator at a 20 cm radius of rotation, while maintaining comparable spatial resolution. Despite a 52.3% field of view reduction, the CBC yielded a 2.2-fold noise reduction (CV: 11.7% vs 25.9%) and mitigated partial volume effects via geometric magnification. XCAT and brain phantom simulations confirmed enhanced anatomical definition and contrast recovery in cardiac, perfusion, and DaTscan tasks.Significance.This work provides an efficient computational tool for rapid design space exploration of advanced collimator geometries. The results demonstrate that the proposed CBC design offers a significant sensitivity advantage, making it highly suitable for high-performance, small-volume clinical applications such as brain and cardiac molecular imaging.

Tomography, Emission-Computed, Single-Photon

Molecular evaluation of residual disease following neoadjuvant chemotherapy in triple-negative breast cancer CALGB 40603 (Alliance).

BACKGROUNDDespite therapeutic advances in early-stage triple-negative breast cancer (TNBC), residual disease (RD) following neoadjuvant therapy remains a key predictor of a worse prognosis and obstacle to improving patient outcomes.METHODSTo better characterize RD and identify survival-associated features, we performed comprehensive transcriptomic profiling of 340 pretreatment stage II/III TNBCs and 70 matched posttreatment RD samples from the randomized CALGB 40603 (Alliance) phase II clinical trial. To explore preclinical treatment strategies for RD, patient-derived xenograft (PDX) mouse models mimicking RD were treated with antibody-drug conjugates (ADCs).RESULTSOur study shows prognostic genomic features measured pretreatment may differ from prognostic features measured posttreatment from RD specimens. Patients with a genomic PAM50 subtype of basal-like in RD specimens had a poor survival outcome, and their matching pretreatment tumors were characterized by elevated chromosomal amplifications of oncogenic drivers and significantly reduced B and T cell expression features. Paired analyses of basal-like RD and matched pretreatment tumors revealed further lymphocyte depletion in RD, along with lower expression of MHC class I and interferon signaling, indicating an immune-cold RD microenvironment. Treatment of a basal-like and conventional chemotherapy-resistant PDX model, resembling basal-like RD, with sacituzumab govitecan or trastuzumab deruxtecan produced a marked antitumor response.CONCLUSIONRD biology differs from pretreatment tumors, with basal-like subtype RD following neoadjuvant chemotherapy being immune cold and associated with poor survival. Preclinical modeling suggests this high-risk group may benefit from adjuvant ADC therapy.TRIAL REGISTRATIONClinicalTrials.gov NCT00861705.FUNDINGNIH NCI U10CA180821 (Alliance for Clinical Trials in Oncology), NCI U24CA176171 (Alliance for Clinical Trials in Oncology), NCI UG1CA233373 (Alliance for Clinical Trials in Oncology), NCI Breast SPORE program P50-CA058223; Susan G. Komen SAC-160074; Breast Cancer Research Foundation BCRF-23-127; NIH NCI R01-CA229409; UNC LCCC Triple Negative Breast Cancer Center.

Humans

Feasibility and barriers to same-day physical therapy following lumbar fusion surgery.

OBJECTIVE: To evaluate the feasibility of same-day (postoperative day 0; POD0) physical therapy (PT) following lumbar fusion and to identify factors associated with failure to participate. METHODS: This retrospective study analyzed prospectively collected data from patients undergoing single-level posterior spinal fusion (PSF), with or without anterior (ALIF) or lateral (LLIF) interbody fusion, between January and December 2024 at a single institution. A standardized POD0 PT protocol was implemented for eligible patients. Patients were categorized into two groups: successful POD0 PT (ambulatory on POD0) and unable to participate. Demographic and surgical variables were compared between groups. Reasons for inability to participate were recorded and categorized. RESULTS: Among 129 patients in whom POD0 PT was attempted, 84 (65%) successfully participated, while 45 (35%) were unable. There were no significant differences in age, sex, BMI, ASA class, operative time, estimated blood loss, or surgical approach between groups. Patients who successfully completed POD0 PT had a significantly shorter hospital length of stay compared to those who did not (3.4&#xa0;&#xb1;&#xa0;1.6 vs 5.8&#xa0;&#xb1;&#xa0;2.9&#xa0;days, P&#xa0;<&#xa0;0.001), with no differences in complication rates, discharge disposition, emergency department visits, or reoperation rates. The most common barriers to POD0 PT were postoperative pain, medical issues (e.g., orthostatic hypotension, nausea, dizziness), and anesthesia-related somnolence. Less common factors included postoperative restrictions and logistical issues such as brace availability. CONCLUSIONS: POD0 PT following lumbar fusion is feasible in the majority of patients and is associated with a shorter hospital stay without increased complications. Failure to participate was not associated with the baseline patient or surgical characteristics evaluated in this study. Instead, the most common barriers were postoperative pain, transient medical issues, and anesthesia-related somnolence, suggesting that optimization of modifiable perioperative factors may improve the implementation of POD0 PT.

Humans

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

Data-centric, robust, and explainable multimodal deep learning for clinical decision support: A systematic review.

PURPOSE: Multimodal deep learning is increasingly proposed for clinical decision support (CDS) under a "data-centric" framing that prioritizes label quality, missing-modality robustness, distribution shift, calibration, and explainability. Prior reviews have examined multimodal medical AI, CDS, and data-centric methods separately, but none address their intersection. We mapped the modalities, fusion strategies, and data-centric and explainability techniques used in this recent literature, quantified how often each is implemented rather than merely mentioned, assessed deployment-relevant evidence (external validation, clinical-outcome measurement, equity), and formally appraised study-level risk of bias. METHODS: Following the PRISMA 2020 statement (PROSPERO CRD420261427815; registered retrospectively), we screened 150 records and included primary, clinical, multimodal studies that applied machine or deep learning to a decision-support task and reported at least one quantitative result. Two reviewers screened and extracted data with consensus adjudication. Each study was coded against pre-specified operational definitions, separating implemented or empirically evaluated techniques from those only mentioned. Study-level risk of bias was assessed with PROBAST + AI. Synthesis was narrative. RESULTS: Thirty-one studies met inclusion; 30 (97%) were published between 2024 and 2026, with a median of three modalities (range 2-6), most commonly structured EHR (71%) and imaging (39%). Data-centric techniques were frequently reported (74-84% across label-noise, distribution-shift, calibration, missing-modality and class-imbalance handling; equity 61%). However, external validation was reported in only 4/31 studies (13%), a clinical or provider outcome in 3/31 (10%), and no study reported routine deployment. Overall risk of bias was high in 27/31 studies (87%), driven by the analysis domain. CONCLUSION: Within this recent, self-selected slice of the field, technical robustness and explainability techniques are widely reported but rarely validated out-of-distribution or against clinical outcomes, and the underlying evidence is at high risk of bias. Progress requires external multi-site validation, clinical-outcome measurement, formal bias appraisal, and adherence to AI reporting standards (e.g., TRIPOD + AI) before deployment can be justified.

Deep Learning

Emotional and Cognitive Processes Underlying Persuasion, Moderating Factors, and Physiological Reactions: A Systematic Review.

Persuasion is a type of social influence aiming to produce changes in others' attitudes or behaviors. This study explores the relationship between emotions and persuasion, principal moderating factors, and physiological reactions during persuasive attempts. Following PRISMA guidelines, 28 empirical articles were analyzed, addressing emotions, affective/cognitive orientations, framing effects, and psychophysiological reactions. Mixed findings emerged regarding emotions, with fear appeals being effective in health education, while more recent studies favor the use of positive persuasive messages to increase behavior intention. Principal moderating factors included personal relevance, need for cognition (NFC) and need for affect (NFA), thought confidence, vulnerability, and efficacy beliefs. Psychophysiological studies revealed distinct physiological arousal during persuasion processing compared to a rest state. In addition, a greater misalignment between current behavior and the persuasive attempt led to perceived freedom threat and psychological reactance. These insights enhance persuasive effectiveness and deepen understanding of persuasion processes, guiding future research directions.

Humans

Clinical Outcomes and Genomic Epidemiology of Multidrug-Resistant Methicillin-Resistant Staphylococcus aureus Keratitis.

PURPOSE: To characterize the clinical features, management, antimicrobial resistance patterns, and genomic epidemiology of methicillin-resistant Staphylococcus aureus (MRSA) keratitis at two North American centers. DESIGN: Retrospective interventional case series combined with laboratory investigation PARTICIPANTS: Seventy eyes of 67 patients presenting laboratory-confirmed MRSA keratitis were included METHODS: We performed a multicenter retrospective case series of patients with culture-proven MRSA keratitis treated between 2005 and 2022. Demographic and clinical data were collected. Antimicrobial susceptibility testing was conducted, and multidrug resistance (MDR) was defined as resistance to &#x2265;3 antibiotic classes. A subset of isolates underwent whole-genome sequencing with core genome multilocus sequence typing. Vancomycin susceptibility, heteroresistance screening, and tolerance testing were performed on available isolates. MAIN OUTCOME MEASURES: Antimicrobial susceptibility and multidrug resistance rates, vancomycin phenotypic profiles, MRSA genotypic distribution, and final best-corrected visual acuity RESULTS: Median age was 63.5 years, and 61.4% were female. Ocular surface disease (67.7%) and prior ocular surgery (65.2%) were common. Only 25.4% had significant healthcare exposure in the preceding year. Most isolates (85.7%) were MDR. Fluoroquinolone susceptibility was low (moxifloxacin 19.7%). All isolates were susceptible to vancomycin (MIC&#x2089;&#x2080; 2 &#xb5;g/mL), and no vancomycin-intermediate, heteroresistant, or tolerant phenotypes were identified. Whole genome sequencing (n = 41) demonstrated predominance of clonal complexes 5 (68.3%) and 8 (29.2%). Visual outcomes were poor, with most patients (85.2%) having a final visual acuity worse than 20/60 among those with follow-up. CONCLUSIONS: MRSA keratitis is associated with high rates of multidrug resistance and poor visual outcomes despite guideline-based therapy. Infections were predominantly caused by CC5 MDR strains despite limited recent healthcare exposure. These findings highlight the persistence of highly resistant MRSA lineages in community-associated corneal infection and underscore the need for ongoing antimicrobial surveillance and optimized treatment strategies.

Humans

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&#xa0;=&#xa0;5), support vector machines (n&#xa0;=&#xa0;4), k-nearest neighbor (n&#xa0;=&#xa0;3), decision trees (n&#xa0;=&#xa0;3), random forests (n&#xa0;=&#xa0;5), neural networks (n&#xa0;=&#xa0;2), linear discriminant analysis (n&#xa0;=&#xa0;1), and pre-trained CNNs (n&#xa0;=&#xa0;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&#xa0;=&#xa0;12 to n&#xa0;=&#xa0;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