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PubMed2026

Innovations in microbial physical mutagenesis for food fermentation: An overview from traditional to emerging technologies.

Microbial strains serve as an important factor affecting fermentation efficiency and product quality. To obtain superior strains, mutation breeding is a classic strategy. Compared to chemical mutagenesis, physical mutagenesis directly induces genomic changes, providing notable advantages such as the elimination of chemical residues and environmental sustainability, hence rendering it a favored method for enhancing food-grade microorganisms. Conventional physical mutagenesis mostly depends on UV, rays, high pressure, or space radiation. As physical technologies advance, emerging methods such as ion implantation, plasma, microwave, ultrasound, and pulsed light are widely utilized for genetic modification. Mutagenesis technologies are progressively transitioning from single-effect to multi-effect synergy. Recent evaluations indicate that emerging technologies can enhance microbial mutation efficiency at the application level relative to established technologies. Nonetheless, the systematic clarification and comparative analysis at the mechanistic level remain inadequate, hindering intuitive comprehension of the qualities and distinctions across techniques. Furthermore, physical mutagenesis encounters several significant obstacles, such as cellular damage, limited rates of advantageous mutations, and laborious screening processes. This review carefully elucidates the mechanisms and properties of physical mutagenesis technology and delineates the distinctions among approaches through comparative analysis. Simultaneously, solutions for optimizing mutagenesis are presented to tackle the principal challenges mentioned above. This review aims to offer a theoretical foundation and practical guidance for the enhanced application of physical mutagenesis technologies in microbial breeding.

Mutagenesis↗

PubMed2026

Identification Matters: How Data Sharing Affects Pupil Honesty and Engagement in Universal School Well-Being Assessments.

PURPOSE: Universal well-being assessments in schools may support early identification of pupils needing mental health support. However, little is known about how privacy and confidentiality concerns influence pupils' acceptability of assessments and willingness to engage authentically. This study examined how hypothetical identification, where responses are linked to pupils and shared with key stakeholders, affects pupils' anticipated honesty and engagement, and whether known help-seeking barriers predict negative responses. METHODS: Cross-sectional data were collected from 12,377 primary (ages 8-10) and secondary pupils (ages 11-17) across 55 schools in England. Pupils reported whether their responses would change if identifiable and shared with school staff, parents/guardians, or external professionals. Responses indicating reduced honesty or likelihood of disengagement were coded as negative. Predictors were examined using mixed-effects logistic regression models, including demographics, school connectedness, and mental well-being. RESULTS: Identification and data sharing influenced pupils' anticipated engagement, particularly in secondary schools. Identification by school staff elicited the highest proportion of negative responses in both phases, whereas external professionals elicited the fewest. Most primary pupils reported they would respond authentically, while a larger proportion of secondary pupils indicated they would respond less honestly or disengage when responses were identifiable and shared. Across primary and secondary samples, low well-being, low school connectedness, and being female were associated with greater likelihood of negative response. DISCUSSION: Pupils' anticipated engagement with well-being assessments is shaped by who accesses their data, with marked developmental differences. Strengthening trust, privacy, and connectedness, and supporting pupils' autonomy, may improve the acceptability and response accuracy.

Humans↗

PubMed2026

Retinal microstructural alterations as early phenotypes of depression in radiogenomics analysis.

BACKGROUND: With the increasing prevalence of depression, there is an urgent clinical need for early screening in depression. The retina offers a promising window for early screening in depression due to its rapid, non-invasive, objective, eye-brain correlated characteristics, but previous research has yielded conflicting alterations in retinal microstructure in depression. METHODS: We screened retinal optical coherence tomography and brain magnetic resonance imaging data in the UK Biobank to enroll 23,225 participants for retinal study of depression occurrence, and 1475 participants for the eye-brain association study. We also used genetic data (ID: ebi-a-GCST90014267 and ukb-d-20,448) from the Integrative Epidemiology Unit Open Genome-Wide Association Study for Mendelian randomization analysis. We used Cox regression to assess the association between retinal microstructure and depression risk, Mendelian randomization to infer causality, and mediation analysis to explore retina-brain pathway association. RESULTS: The Cox regression analysis showed that retinal ganglion cell-inner plexiform layer (GCIPL) thickness remained a significant predictor of depression. The Mendelian randomization analysis indicated a positive statistical association between GCIPL thickness and depression. Moreover, there was a significant positive correlation (all p < 0.001) between the volume of specific depression-related brain regions and the GCIPL thickness. Adjusting for age, sex, and head size, the mediation analysis provided preliminary evidence for a potential anatomical pathway linking retinal GCIPL thickness to depression-related brain regions through primary visual cortex and secondary visual cortex volumes. CONCLUSION: Thickened retinal GCIPL is a potential early phenotype of depression and has a potential association pathway with depression-related brain regions using a radiogenomics approach.

Humans↗

PubMed2026

Prevalence of Theileria luwenshuni in goats (Capra hircus) on Tarama Island, Okinawa, Japan.

Caprine theileriosis is an economically important tick-borne disease caused by various Theileria species, particularly Theileria lestoquardi, Theileria luwenshuni, and Theileria uilenbergi, in goats (Capra hircus). Goat farming plays an economically and culturally important role on Tarama Island, Okinawa, Japan. Because goats on the island are mainly managed under an extensive grazing system, tick infestation is common. However, Theileria infections have not previously been investigated in goats on Tarama Island. To address this, archived DNA samples prepared from blood collected from 44 goats on Tarama Island were screened using a universal PCR assay targeting 18S rRNA sequences of Theileria and Babesia species. Two DNA samples were positive, and sequencing analysis of the amplicons identified T. luwenshuni. To further investigate the epidemiology of T. luwenshuni on Tarama Island, blood samples were subsequently collected from 96 goats across 19 farms. From each blood sample, a thin blood smear was prepared and genomic DNA was extracted. Microscopic examination of Diff-Quik-stained smears detected intraerythrocytic Theileria-like organisms in 35 (36.5%) goats. In addition, screening of DNA samples using a newly developed T. luwenshuni-specific PCR assay detected 77 (80.2%) positive goats, and the subsequent sequencing analysis confirmed the PCR results. Given that T. luwenshuni can cause severe disease in small ruminants, our findings highlight the importance of managing T. luwenshuni infection in goats on Tarama Island.

Animals↗

PubMed2026

Genome-wide identification and expression profiling of CSP and OBP genes in Stictocephala bisonia reveals candidate genes potentially associated with insecticide response.

Stictocephala bisonia is an important invasive agricultural pest. Due to the frequent application of insecticides in its habitat, this species is under intense selection pressure. Chemosensory proteins (CSPs) and odorant-binding proteins (OBPs) are known to play key roles in insecticide resistance, but their specific functions in S. bisonia remain unclear. In this study, we identified a total of 22 SbisCSPs and 16 SbisOBPs based on the S. bisonia genome. To screen for candidate genes potentially linked to insecticide resistance, we adopted a multi-criteria screening strategy that integrated phylogenetic analysis, molecular docking with three insecticides, and tissue-specific expression profiling. Phylogenetic analysis identified several SbisCSPs and SbisOBPs clustering with genes known to be involved in insecticide resistance, serving as an initial evolutionary filter. Molecular docking results indicated that λ-Cyhalothrin exhibited the strong predicted binding affinity with most of SbisCSPs and SbisOBPs. Subsequent qPCR validation of seven prioritized candidates revealed distinct expression patterns: SbisCSP22 was highly expressed in adults and demonstrated strong binding affinity to all three insecticides tested, suggesting a potential role in mediating multi-insecticide response. Conversely, SbisCSP17 was significantly upregulated in larvae, clustered with genes known to mediate imidacloprid resistance, and exhibited strong binding affinity to imidacloprid. Given its larval-specific expression and the soil-dwelling behavior of larvae, we hypothesize that SbisCSP17 is a key candidate gene for larvae coping with soil-treated insecticides.

Animals↗

PubMed2026

Transcriptomic responses of Porphyrophora sophorae larvae during licorice root colonization reveal coordinated remodeling of translation, mitochondrial energy metabolism and defense-related genes.

BACKGROUND: Porphyrophora sophorae is a subterranean piercing-sucking scale insect that damages licorice (Glycyrrhiza uralensis) roots, but the molecular responses associated with larval root colonization remain insufficiently defined. METHODS: We compared non-parasitic larvae (NP) and root-colonizing larvae (RC) using six RNA-seq libraries, de novo transcriptome assembly, DESeq2-based differential expression analysis, GO/KEGG enrichment, annotation-based candidate gene screening, and RT-qPCR validation of selected genes. RESULTS: Sequencing yielded 260.91 million clean reads, and de novo assembly produced 60,794 non-redundant transcripts. DESeq2 identified 703 FDR-significant DEGs, including 49 upregulated and 654 downregulated genes in RC larvae. Upregulated genes were mainly associated with translation- and ribosome-related processes, whereas downregulated genes were enriched in mitochondrial, oxidation-reduction, energy metabolism, and oxidative phosphorylation-related functions. Annotation-based screening identified 75 FDR-significant candidate genes associated with chemosensation, defense-related responses, and energy metabolism, with mitochondrial energy metabolism-related genes forming the largest module. RT-qPCR validation based on the raw Ct data showed concordant expression directions for ten selected transcript targets. CONCLUSIONS: Root colonization in P. sophorae larvae was associated with coordinated transcriptional remodeling involving selective activation of translation-related processes, adjustment of mitochondrial energy metabolism, and changes in defense-related gene expression. These results provide candidate molecular targets for future functional studies of host contact, feeding establishment, and physiological adjustment in this subterranean scale insect.

Animals↗

PubMed2026

Long-term mortality in pediatric sepsis: a systematic review and meta-analysis.

BACKGROUND: Pediatric sepsis represents a significant factor in the mortality rates among children, with survivors remaining highly fragile during the period following discharge. While in-hospital and short-term mortality have been widely studied, the long-term mortality of pediatric sepsis is not adequately synthesized or appreciated. This study aims to estimate the long-term mortality associated with pediatric sepsis, providing a basis for optimizing post-discharge surveillance and care protocols. METHODS: This systematic review and meta-analysis followed PRISMA guidelines and was registered in PROSPERO (CRD420251137504). Exhaustive searches were conducted in PubMed, Embase, the Cochrane Library, and Web of Science for studies published from the inception of each database to June 30, 2025. Studies reporting long-term mortality in pediatric sepsis patients diagnosed using international consensus criteria were included. After literature screening, long-term mortality was pooled using a random effects meta-analysis in R statistical software. RESULTS: A total of 72,065 records were identified through database searching. After removing duplicates and screening, six studies comprising 11,318 pediatric sepsis patients were included. The pooled long-term mortality in pediatric sepsis was 11% (95% CI: 7-16%), though significant heterogeneity was observed (I2 = 98.2%, p < 0.001). Sensitivity analyses yielded similar results, and evidence of publication bias was limited. CONCLUSION: Long-term mortality after pediatric sepsis was 11%, highlighting the persistent risk of mortality after hospital discharge. Further high-quality longitudinal studies are required to identify modifiable risk factors and guide evidence-based follow-up and personalized care.

Humans↗

PubMed2026

A conserved distal-tail helical extension defines a tailspike attachment architecture in Gram-negative siphophages.

Rapid growth of bacteriophage genome collections has outpaced functional annotation of tail-tip proteins, limiting comparative analysis of host-recognition structures. Starting from a shared distal-tail gene organization in the Salmonella phages 9NA and Jersey, I developed a morphogenetic bioinformatic framework integrating gene synteny, sequence comparison, profile hidden Markov model (HMM) screening, structural evidence, structure-aware searching, and AlphaFold modeling. Comparison with the experimentally characterized lambda and Sf11 tail assemblies identified a predominantly alpha-helical C-terminal extension of the distal-tail (DT) protein associated with tailspike attachment, termed the distal-tail helical extension (DT-helix). Screening 541,986 proteins from 5167 complete NCBI RefSeq tailed-phage genomes, followed by evidence-based evaluation of sequence, genomic context, and structural architecture, identified 165 curated DT-helical-extension-associated phages. Their DT proteins segregated into six sequence groups. In the four principal multi-member groups, cognate tailspikes showed group-specific conservation in proximal N-terminal regions but substantially greater downstream diversity, consistent with sequence constraint at the DT-tailspike attachment boundary. A complementary ProstT5/Foldseek search supported the established groups but revealed no convincing additional highly divergent family. Together with the experimentally characterized Sf11 attachment interface, these findings define a recurrent morphogenetic architecture linking conserved distal-tail scaffolds to more variable receptor-binding proteins across siphophages infecting Gram-negative bacteria. Although universal exchangeability is not established, the identified scaffold-receptor-binding boundaries provide a framework for molecular characterization and rational phage engineering. Accession-level information for the 165 curated phages is available through PhageTailDB.

Viral Tail Proteins↗

PubMed2026

Privacy, security, and reliability risks of artificial intelligence in healthcare: a systematic review of empirical evidence.

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into healthcare information systems, supporting clinical decision-making, imaging analysis, and predictive modeling. While these applications offer operational and clinical benefits, they also introduce emerging risks to patient privacy, data security, and system reliability. OBJECTIVE: To systematically review empirical evidence on privacy breaches, security vulnerabilities, and misuse associated with AI applications in healthcare settings. METHODS: PubMed, Embase, Web of Science, Scopus, IEEE Xplore, and ACM Digital Library were searched for empirical studies published between January 2015 and November 2025 that evaluated AI use or misuse in clinical diagnosis, treatment, or decision-making. Two reviewers independently screened studies and extracted data using a standardized form. Findings were synthesized narratively due to heterogeneity in study designs, AI methods, and reported outcomes. RESULTS: Of 7,285 records identified through database searches and 205 through citation screening, 22 empirical studies met the inclusion criteria, spanning multiple clinical domains and data modalities, predominantly medical imaging applications. Five recurring threat categories were identified: patient re-identification, membership inference, unauthorized access and adversarial exploitation, input manipulation, and misuse or overinterpretation of AI outputs. Across studies, AI models were shown to encode latent biometric signals across diverse data types, limiting the effectiveness of traditional anonymization and synthetic data approaches. Adversarial attacks and input manipulation were also shown to compromise diagnostic performance and system integrity. CONCLUSION: This systematic review provides empirical evidence suggesting that contemporary AI systems in healthcare introduce privacy and security risks that may challenge traditional assumptions about data protection. These findings underscore the need for privacy- and security-by-design approaches and governance frameworks that address risks across the AI lifecycle.

Humans↗

PubMed2026

Enhancing Hemoglobin Bart's hydrops fetalis syndrome prevention: a single-tube multiplex real-time PCR assay for the comprehensive detection of four significant α0-thalassemia deletions (--SEA, --THAI, --CR, and --SA) found in Thailand.

BACKGROUND: Hemoglobin (Hb) Bart's hydrops fetalis is a major public health concern in Southeast Asia, particularly in Thailand. Current screening strategies target the two most common α0 -thalassemia deletions (--SEA and --THAI). METHOD: In this study, we developed a single-tube multiplex real-time PCR assay for the simultaneous detection of four clinically relevant α0-thalassemia deletions (--SEA, --THAI, --CR, and --SA). The assay was validated using 538 clinical samples with diverse thalassemia genotypes and compared against conventional gap-PCR as the reference method. Analytical performance, including sensitivity, specificity, and limit of detection (LOD), was evaluated. In addition, clinical utility was assessed in 22 prenatal diagnosis cases at risk of Hb Bart's hydrops fetalis. RESULTS: The study cohort demonstrated substantial genetic heterogeneity, comprising 43 distinct genotypes. The developed assay achieved 100% sensitivity and specificity for all targeted deletions, with complete concordance with gap-PCR results. No cross-reactivity was observed with α+-thalassemia. The assay demonstrated a high analytical sensitivity with a LOD of 9.76 × 10-3 ng per reaction. Whereas in prenatal diagnosis, all 22 fetal genotypes were accurately identified, including five cases of homozygous --SEA and one rare compound heterozygous --SEA/--CR fetus. CONCLUSIONS: This study presents a rapid, accurate, and cost-effective multiplex real-time PCR assay capable of detecting both common and rare α0-thalassemia deletions in a single reaction. The assay demonstrates strong potential for implementation in routine clinical laboratories and large-scale population screening, contributing to improved prevention and control of severe thalassemia syndromes in high-prevalence regions.

Humans↗

PubMed2026

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↗

PubMed2026

Machine learning-based prediction of unplanned readmission and construction of an online calculator for elderly patients with mild ischemic stroke.

OBJECTIVE: To screen for independent risk factors for unplanned readmission in elderly patients with mild ischemic stroke, and to construct and validate an online risk prediction calculator based on an interpretable machine learning model, thereby providing a promising practical tool for accurate clinical assessment of 30‑day all‑cause unplanned readmission risk in this population. METHODS: A prospective cohort study was conducted, including 1050 patients aged ≥ 60 years with mild ischemic stroke admitted between August 2023 and September 2024. Participants were randomly divided into a training set (840 cases) and a test set (210 cases) at a ratio of 8:2. Risk factors were screened by univariate analysis and multivariable Logistic regression. Four machine learning models, namely LightGBM, XGBoost, Random Forest, and K‑Nearest Neighbors (KNN), were developed and their performance was evaluated using AUC, accuracy, sensitivity, and specificity as metrics. The SHAP framework was used for interpretability analysis, and an online calculator was subsequently developed based on the optimal model. RESULTS: Univariate analysis showed significant differences (P < 0.05) in 13 factors including age, smoking, AIP, TyG index, HALP score, etc. Multivariable Logistic regression identified age (OR = 9.752), smoking (OR = 5.171), AIP (OR = 6.691), TyG index (OR = 4.393), HALP score (OR = 2.831), and ≥ 2 comorbidities (OR = 3.664) as independent risk factors. All four machine learning models demonstrated good predictive performance. Based on a comprehensive evaluation of multiple metrics and computational efficiency, the LightGBM model exhibited the best predictive performance (AUC = 0.884, accuracy = 0.829, sensitivity = 0.812, specificity = 0.875). SHAP analysis showed that age, AIP, TyG index, smoking, and HALP score were key predictors. An online calculator developed based on this model enables individualized risk predictions. CONCLUSION: Key risk factors associated with 30‑day unplanned readmission in elderly patients with mild ischemic stroke were identified. The LightGBM model demonstrated high predictive accuracy, and together with the interpretability analysis and online calculator, offers a practical tool to support clinical risk assessment. However, this tool requires future external validation.

Humans↗

PubMed2026

Integrated transcriptomic and metabolomic analysis of fluoride tolerance-related pathways and differentially expressed genes in silkworm strain XSKD.

XueSong KD (XSKD) silkworm strain exhibits prominent fluoride tolerance, yet the underlying molecular mechanisms of fluoride tolerance remains unclear. In the present study, fourth-instar pre-molting XSKD silkworms were used as experimental materials for integrated transcriptomic and untargeted metabolomic analyses. In total, 572 differentially expressed genes and 90 differential metabolites were screened. GO enrichment and KEGG enrichment based on the hypergeometric distribution model revealed that 13-Hydroxy-9Z,11E-octadecadienoic acid (13-(S)-HODE) acts as the core differential metabolite, which is significantly enriched in the linoleic acid metabolism pathway. Within this pathway, LOC101737302 and CYP338A1 display opposite expression trends and show correlations with pathway metabolites. Based on multi-omics data, this study preliminarily characterizes the lipid metabolic response under fluoride stress, providing omics dataset support for further in-depth exploration of the molecular mechanism of fluoride tolerance in silkworms.

Animals↗

PubMed2026

Metal-organic frameworks nanozyme-integrated portable microneedle patch for visual bacterial monitoring in meat.

Foodborne microbial contamination is a major global health concern, with conventional methods often being time-consuming and complex. Herein, we developed a novel portable biosensor by integrating microneedle patch technology and a metal-organic framework (Fe/Cu-NBDC MOF) nanozyme, enabling rapid, on-site, visual detection of bacteria in meat. The sensing system works by encapsulating aptamer-functionalized MOF nanozymes within a hydrogel patch, where their catalytic sites are initially blocked by the aptamer. In the presence of Staphylococcus aureus (S. aureus) as the target, the specific aptamer's binding to bacteria exposes numerous catalytic sites, further activating the chromogenic reaction of the tetramethylbenzidine‑hydrogen peroxide (TMB-H₂O₂) system, enabling visual detection of S. aureus. The biosensor demonstrates a detection limit of 82 CFU/mL with excellent specificity to successfully apply to commercial mutton. By integrating sampling, enrichment, and visual detection into a single compact device, this platform offers a practical, efficient solution for rapid on-site screening of foodborne pathogens.

Biosensing Techniques↗

PubMed2026

Algae-to-host horizontal gene transfer in Paramecium bursaria is associated with host adaptation during endosymbiosis.

Paramecium bursaria maintains a stable endosymbiosis with green algae, yet the evolutionary consequences of this association remain unclear. Here, we screened the host genome for algal-derived horizontally transferred genes (HTGs) using a lineage-aware workflow designed to detect horizontal gene transfer (HGT) between two defined lineages. We identified 16 candidate HTGs, including four putative newly transferred genes and 12 homologous transferred genes, most of which were functionally associated with redox homeostasis and metabolism. Five HTGs showed symbiosis-dependent expression. RNAi knockdown of GH32s and SATs reduced host proliferation, total cell area, and motility, while GH32s knockdown also reduced endosymbiont load. Duplication patterns suggest that most transfers may have occurred after the P. bursaria lineage diverged from the sampled Paramecium species but before its lineage-specific whole-genome duplication (WGD). The HTGs also showed host-associated shifts in GC content and gene length, while representative HTGs retained conserved domains and functional motifs. Together, our results support algae-to-host HGT in P. bursaria and suggest that some transferred genes may contribute to metabolic integration during endosymbiosis.

Gene Transfer, Horizontal↗

PubMed2026

Once-weekly IcoSema versus once-daily insulin glargine U100 in type 2 diabetes management (COMBINE 4): an open-label, multicentre, treat-to-target, randomised, phase 3b trial.

BACKGROUND: Stepwise treatment intensification is recommended for managing type 2 diabetes, including insulin initiation when non-insulin glucose-lowering medications are insufficient. Guidelines recommend combining a GLP-1 receptor agonist with basal insulin to improve glycaemic efficacy while reducing weight gain and hypoglycaemia risk. COMBINE 4 evaluated the efficacy and safety of IcoSema, a once-weekly combination therapy of basal insulin icodec and semaglutide (a GLP-1-receptor agonist) versus insulin glargine U100 (glargine U100) in people with type 2 diabetes on oral glucose-lowering medications. METHODS: COMBINE 4 was a 40-week, randomised, open-label, treat-to-target, phase 3b trial conducted across 97 sites in nine countries. Adults (aged ≥18 years) with type 2 diabetes (HbA1c ≥8·0%) receiving oral glucose-lowering medications were randomly allocated in a 1:1 ratio without stratification to IcoSema or once-daily glargine U100. The titration target was 3·9-5·0 mmol/L (70-90 mg/dL). The primary endpoint was change in HbA1c and the secondary confirmatory endpoint was change in bodyweight, both from baseline to week 40, evaluated in all randomly allocated participants. Adverse events were recorded during weeks 0-45. This trial is registered with ClinicalTrials.gov (NCT06269107) and is complete. FINDINGS: Of 653 individuals screened between Feb 15 and Aug 6, 2024, 151 did not meet screening criteria and 17 withdrew before initiating treatment; 243 were randomised to IcoSema and 242 to glargine U100. Of the 485 randomly allocated participants, 286 (59%) were male and 199 (41%) were female, and median age was 58 years (range 26-82). For HbA1c, from baseline (9·57% for IcoSema and 9·50% for glargine U100), mean change to week 40 was greater with IcoSema versus glargine U100 (-3·32 vs -2·44 percentage points; estimated treatment difference [ETD] -0·88 percentage points [95% CI -1·12 to -0·63]), confirming superiority of IcoSema (p<0·001). From baseline to week 40, mean bodyweight decreased with IcoSema and increased with glargine U100 (-0·79 vs 3·81 kg; ETD -4·61 kg [95% CI -5·46 to -3·75]), confirming superiority of IcoSema (p<0·001). Rate of combined clinically significant (blood glucose <3·0 mmol/L [<54 mg/dL], confirmed with a blood glucose meter) or severe hypoglycaemia (severe cognitive impairment requiring external assistance for recovery) was statistically significantly lower with IcoSema versus glargine U100 (0·29 vs 0·59 episodes per person-year of exposure; estimated rate ratio 0·56 [95% CI 0·32 to 0·97]; p=0·04). Gastrointestinal disorders were the most frequently reported adverse events with IcoSema. INTERPRETATION: Once-weekly IcoSema demonstrated superior HbA1c reduction and bodyweight change, with lower rates of clinically significant or severe hypoglycaemia, versus glargine U100, suggesting that IcoSema might be an effective once-weekly treatment option for insulin-naive individuals with type 2 diabetes inadequately controlled on oral glucose-lowering medications. FUNDING: Novo Nordisk.

Humans↗

PubMed2026

Association between anaemia and osteoporosis: a systematic review and meta-analysis.

BACKGROUND: Osteoporosis significantly impacts global morbidity. Recent evidence suggests anaemia may contribute to osteoporosis risk. This systematic review and meta-analysis investigates this association. METHODS: PubMed, Scopus, EBSCO, and ScienceDirect were searched for papers. Studies with definition of anaemia and assessing osteoporosis outcomes were included. Meta-analysis utilized random-effects models (DerSimonian-Laird method), and study quality was assessed via Newcastle-Ottawa Scale (NOS). Analyses were performed using R Studio. RESULT: Eighteen studies (861,540 participants) were analyzed. Anaemia significantly increased osteoporosis risk in univariate analysis (OR 1.62; 95% CI 1.33-1.98; p < 0.001), despite high heterogeneity (I2 = 92.7%). The results remain significant in studies that reported multivariate analysis (OR 2.01; 95% CI 1.26-3.21; p = 0.004). Sensitivity analyses confirmed the robustness of our result. CONCLUSION: Anaemia significantly associated with osteoporosis, emphasizing the need for targeted screening in anaemic individuals. Further studies should consider incorporating anaemia into osteoporosis and fracture prediction tools.

Humans↗

PubMed2026

Association between seminal and serum iron parameters and male infertility: a systematic review and meta-analysis.

BACKGROUND: Iron is an essential trace element for normal spermatogenesis, yet excessive iron accumulation may impair male fertility. Preliminary studies imply a link between elevated iron levels and male infertility, but evidence remains limited without systematic quantitative synthesis. This metaanalysis assessed the association between iron concentrations and male infertility. METHODS: We systematically searched PubMed, CBM, CNKI and Cochrane Library. RevMan, Stata and R were used for data analysis. Randomeffects models pooled effect sizes, with forest and funnel plots generated to evaluate seminal and serum iron levels in male infertility. RESULTS: After screening studies published up to April 2025, a total of ten eligible articles involving 985 participants were finally included in this meta-analysis. Pooled results revealed that seminal and serum iron concentrations were notably higher in infertile males compared with fertile controls. Specifically, infertile men presented higher seminal iron levels (SMD = 0.44, 95% CI: 0.12-0.76, P < 0.05), as well as elevated serum iron levels (SMD = 3.77, 95% CI: 1.68-5.87, P < 0.05). The present results suggest that increased seminal and serum iron concentrations may be potentially correlated with male infertility risk.

Humans↗