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PubMed2026

HPV circulating tumor DNA as a potential prognostic and predictive biomarker in head and neck squamous cell carcinoma: a systematic review.

PURPOSE: Human papillomavirus circulating tumor DNA (HPVctDNA) has emerged as a promising prognostic biomarker in HPV-related head and neck squamous cell carcinoma (HNSCC). This systematic review aimed to synthesize current evidence on the diagnostic accuracy and prognostic value of HPVctDNA in HNSCC management. MATERIAL/METHODS: We systematically reviewed a PubMed-indexed database of studies published between January 2012 and September 2025. Eligible studies were assessed for design, primary tumor site and stage, treatment modality, HPVctDNA detection method, diagnostic accuracy (sensitivity and specificity), and reported clinical endpoints. Descriptive syntheses were performed; sensitivity and specificity were standardized to proportions and summarized as median values per group. RESULTS: A total of 60 studies, including 8,234 patients were analyzed, of which 41 (68.3%) focused exclusively on oropharyngeal squamous cell carcinoma (OPSCC) and 17 (28.3%) included mixed HPV-related HNSCC subsites and HPV-positive cancers of unknown primary. The median follow-up across the included studies was 23 months. Among the included studies, 19 were retrospective (31.7%) and 33 were prospective (55.0%), with a small proportion of cross-sectional and randomized clinical trials. Overall, 40 (66.7%) evaluated the role of HPVctDNA in a curative setting. Plasma was the most common sample type, analyzed in 55 studies (91.7%), while 5 studies also included saliva. Detection methods varied: 40 employed droplet digital PCR (ddPCR), 16 used quantitative PCR (qPCR) and 4 applied NGS-based assays. Most of these studies (38, 63.3%) evaluated the prognostic utility of HPVctDNA, while only 4 (6.7%) assessed HPVctDNA in a screening or diagnostic setting. Regarding diagnostic accuracy, the median sensitivity across evaluable studies was 91.1%, while the median specificity was 99.4%. In OPSCC-only cohorts, the median sensitivity and specificity were 89.4% and 99.4%, respectively. Dynamic changes in HPVctDNA levels during or after treatment were consistently associated with outcomes: clearance or sustained negativity correlated with higher response rates, improved progression-free survival and overall survival, while persistent positivity or increasing levels predicted disease progression and recurrence. CONCLUSIONS: HPVctDNA demonstrates high diagnostic and prognostic accuracy in HPV-related HNSCC, especially OPSCC, supporting its use for prognosis, treatment monitoring and early detection of recurrence. However, prospective interventional studies are still required to demonstrate that HPVctDNA-guided treatment decisions improve clinical outcomes before routine implementation.

Humans↗

PubMed2026

Prognostic effect of serum glial fibrillary acidic protein and neurofilament light chain for predicting progression independent of relapse activity in multiple sclerosis: A systematic review.

BACKGROUND: Progression independent of relapse activity (PIRA) is increasingly appreciated as one of the important factors contributing to disability accumulation in MS. sGFAP and sNfL could represent markers reflecting two separate biological processes related to relapse-independent progression in MS. OBJECTIVE: To perform a systematic review of the literature on blood GFAP and/or NfL measured in relation to PIRA or other similar relapse-independent progression endpoints in people with MS. METHODS: PubMed, Scopus, and Web of Science databases were searched from inception to 1 June 2026. The eligible studies were original human studies measuring blood GFAP and/or NfL concentrations in serum, plasma, or any other type of blood-derived material and assessing PIRA, PIRMA, CDP/CDW without relapses, relapse-free EDSS progression, non-inflammatory progression, or comparable relapse-independent disability worsening outcomes. Methodological quality was assessed according to the Newcastle-Ottawa scale and the QUIPS instrument for bias detection in the body of evidence on prognostic factors. Due to heterogeneity of outcomes, biomarker measurements and effect estimates, results were synthesized qualitatively rather than quantitatively. RESULTS: After removing duplicates, 1206 records were screened, followed by full-text review of 120 reports. A total of 18 reports were included. Overall, sGFAP was associated more frequently with PIRA or PIRA-like disability progression, particularly in cohorts with suppressed or limited overt inflammatory activity. Evidence for sNfL was more variable and context-dependent: several studies reported associations with PIRA-like or relapse-independent disability worsening when acute inflammatory activity was absent, suppressed, or analytically separated, whereas other studies reported negative or inconclusive findings. Negative or inconclusive results were reported by several articles, particularly when broad outcomes were evaluated or the study population was small. CONCLUSION: Blood GFAP and NfL give complementary but non-interchangeable information concerning PIRA in MS patients. The existing evidence base does not allow us to perform meta-analysis because of heterogeneity in terms of outcomes, standardization of biomarkers, and treatment context. Further prospective investigations with uniform criteria will be necessary for their use as biomarkers of PIRA in clinical settings.

Humans↗

PubMed2026

From prediction to mechanism: Explainable AI uncovers plasma and CSF proteomic signatures of Alzheimer's disease.

Alzheimer's disease (AD) plasma and cerebrospinal fluid (CSF) proteomics can distinguish AD from cognitively normal controls, but the generalizability of machine learning performance and the recurrence of biological signals across datasets require cautious interpretation. We developed an explainable artificial intelligence framework spanning two fluids and four ADNI proteomic datasets, covering 2082 modality specific samples, all analysed internally within ADNI. Phase 1 analysed plasma using a 119 analyte NULISA and targeted UPENN panel (n = 727; 216 CE, 511 controls). Phase 2 extended the analysis to CSF using SOMAscan7k, TMT-MS and targeted SET2, with Elecsys Aβ42, Aβ40, total tau and p-tau181 as anchor biomarkers. Only SOMAscan was subject-independent relative to Phase 1 plasma; TMT-MS and SET2 overlapped with Phase 1 for 96.0% and 97.7% of subjects and therefore are not independent replication cohorts. Under subject-level splits with fold internal preprocessing, we compared Elastic Net, Explainable Boosting Machines and gradient boosted trees with SHAP-based explanations. Among the candidate pipelines, we selected the pipeline with the highest held-out test ROC AUC for each platform; the selected values were 0.927 in plasma and 0.954-0.973 across the three CSF datasets. Because the same held out test performance was used for pipeline selection and headline reporting, these are optimistically selected single-holdout estimates, not unbiased estimates of generalizable or clinical performance. Explanations identified five recurring biological axes within ADNI: cholinergic (ACHE), tau/14-3-3 (YWHAG, YWHAZ, YWHAB, YWHAE), neuro-axonal (NEFL, NEFH), microglial/complement (CHIT1, SMOC1, CHI3L1, C7, CFH) and synaptic (NPTXR, NPTX2, DLG4, SYT5, VSNL1, ELAVL2). CSF analyses showed synaptic vesicle-cycle enrichment (q = 2 × 10-6), and CSF YWHAG correlated strongly with total tau (ρ = 0.87). Cross-fluid directional concordance was modest overall (54-57%) but increased to 73-80% among mapped analyte/protein rows reaching q < 0.05 in CSF. These findings provide hypothesis-generating, internally supported evidence within ADNI. Independent external cohorts with locked pipelines are required to evaluate generalizable performance and biological reproducibility; the overlapping TMT-MS and SET2 analyses should not be interpreted as independent replication.

Alzheimer Disease↗

PubMed2026

Analyzing salinity tolerance in grass carp (Ctenopharyngodon idella): Insights from genome-wide association study and genomic selection.

Grass carp (Ctenopharyngodon idella) is one of the most widely cultured freshwater fish species globally. However, the expansion of its farming scale faces severe limitation owing to freshwater scarcity; therefore, the development of strains with greater salinity tolerance is key for expanding production using brackish water resources. To investigate the genetic basis of salinity tolerance in grass carp, a genome-wide association study (GWAS) was conducted using 200 individuals representing extreme phenotypes, namely salinity-tolerant and salinity-sensitive groups. In total, 17 single nucleotide polymorphisms (SNPs) related to salinity tolerance were detected, which were distributed across 11 chromosomes. Through gene annotation, 38 candidate genes were obtained from these loci. Enrichment analysis revealed these candidate genes are primarily implicated in key biological processes, including osmotic regulation, energy metabolism, and stress responses. Analyses of different SNP densities revealed that the 5 K SNP density panel can balance prediction accuracy and computational efficiency. The BayesA model achieved the highest prediction accuracy under the GWAS_Evenly selection strategy, with substantial reductions in mean absolute error and mean square error. This study reveals the genetic mechanisms of salinity tolerance in grass carp, which might be optimized through genomic selection, and provides insights for selectively breeding new varieties with greater salinity tolerance.

Animals↗

PubMed2026

ORBIT: Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space for cancer driver gene identification.

Accurate identification of cancer driver genes is crucial for precision oncology but remains challenging due to the complexity of integrating heterogeneous data and modeling dynamic biological systems. To address these limitations, we propose ORBIT (Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space). Our framework synergistically fuses multi-omics profiles with functional network data using a context-adaptive graph reweighting mechanism to capture cancer-specific dynamics. The model employs a bi-prototype contrastive learning strategy within hyperbolic space, which aligns gene representations around distinct driver and non-driver semantic anchors while preserving the intrinsic hierarchy of biological networks. Comprehensive evaluations demonstrate that ORBIT achieves highly competitive stability in pan-cancer analysis while consistently outperforming state-of-the-art methods in cancer-specific predictions. Furthermore, functional enrichment analysis confirms that the model effectively segregates core cancer pathways, and drug sensitivity profiling validates the clinical relevance of the identified drivers. By integrating hyperbolic geometry with context-adaptive learning, ORBIT offers a robust and interpretable paradigm for precision medicine. The source codes and datasets are publicly accessible at https://github.com/spcho-dev/ORBIT.

Humans↗

PubMed2026

Stage-specific ROMO1 in rheumatoid arthritis: predictive immune insights into the MIF pathway and HLA-DR/IL2RA axis via integrated GWAS, transcriptomic, single-cell, and spatial profiling.

Emerging evidence links reactive oxygen species modulator 1 (ROMO1), a key mitochondrial ROS regulator, to rheumatoid arthritis (RA) pathogenesis. However, its exact mechanism remains elusive given the conflicting evidence about its specific function. We used a four-level integrative framework combining multi-omics data and literature‑supported mechanistic inference. At the genetic level, Mendelian randomization (MR) was performed to explore potential causal relationships between ROMO1, IL2RA, HLA-DR, MIF, and RA risk, followed by differential expression analysis and machine learning-based feature selection to identify key mROS genes. The temporal expression dynamics of ROMO1 were assessed in RA progression. At the cellular and tissue levels, we integrated single-cell RNA sequencing and spatial transcriptomics to map cell-type-specific expression and synovial localization of ROMO1-related immune cells and pathways. Finally, our multi-omics findings were contextualized with literature-supported mechanistic inference. (1) MR results were consistent with a potential protective effect of ROMO1 on RA (OR = 0.52) and its potential regulation of risk factors IL2RA (OR = 0.46) and HLA-DR (OR = 0.40). Conversely, IL2RA (OR = 1.42), HLA-DR (OR = 1.88), and MIF (OR = 1.17) were positively associated with RA risk. Additionally, ROMO1 was identified as a top candidate diagnostic predictor with stage-specific dynamics: downregulated in the early but upregulated in the late/remission stages. (2) Single-cell RNA sequencing showed ROMO1's cell-specific expression in CD14+ HLA-DR+ CD74+ monocytes and CD4+ IL2RA+ T cells. Cell communication analysis further suggested that these cells may participate in MIF pathway regulation. Spatial transcriptomics subsequently identified that ROMO1-related cells localized to synovial pathological regions, with MIF pathway changes correlated with RA progression. (3) Finally, literature-supported mechanistic inference suggests that ROMO1 may modulate mROS levels to promote anti-inflammatory M2 macrophage polarization, which could theoretically contribute to reduced systemic inflammation and the alleviation of multi-organ decline in RA. This integrated multi-omics investigation, supported by literature-based mechanistic inference, suggests ROMO1 as a stage-dependent biomarker candidate and potential immune regulator in RA.

Humans↗

PubMed2026

Prognostic Value of Frailty in Aortic Surgery: A Systematic Review and Meta-Analysis Comparing Frailty Assessment Tools.

BACKGROUND: Frailty is increasingly recognized as an important determinant of outcomes after aortic vascular surgery, but assessment methods vary substantially and the optimal tool for risk stratification remains uncertain. This systematic review and meta-analysis evaluated the prognostic value of preoperative frailty and compared the predictive performance of different frailty instruments in aortic surgery. METHODS: PubMed, Embase, and Cochrane Library were searched from inception to April 27, 2026. Eligible studies included patients undergoing open, endovascular, or hybrid aortic procedures involving abdominal, thoracic, thoracoabdominal, arch, and proximal aortic diseases, including aneurysms and dissections, assessed frailty preoperatively, and reported postoperative outcomes. RESULTS: Thirty studies comprising 419,459 patients were included. Frailty was associated with higher early mortality (odds ratio [OR] 2.20; 95% confidence interval [CI] 1.54-3.14) and late mortality (hazard ratio 2.18; 95% CI 1.64-2.90). Frail patients also had increased risks of major complications (OR 2.52; 95% CI 1.22-5.19), acute kidney injury (OR 1.64; 95% CI 1.34-2.02), and nonhome discharge (OR 5.50; 95% CI 3.05-9.92). Associations were consistent across surgical approaches and aortic segments. Judgment-based or phenotype-like tools yielded higher effect estimates than deficit-accumulation indices, although differences were not statistically significant; among index-based tools, Modified Frailty Index (mFI)-11 outperformed mFI-5. CONCLUSION: Preoperative frailty strongly predicts mortality, morbidity, and loss of functional independence after open, endovascular, and hybrid aortic surgery across different aortic segments and pathologies, including aneurysmal and dissecting aortic disease. Routine frailty assessment may improve risk stratification and perioperative decision-making.

Humans↗

PubMed2026

Exploring China's Clean Air Act and associated cardiovascular disease risk: a prospective, quasi-experimental, and causal inference modelling study.

BACKGROUND: Substantial improvements in air quality have been recorded following the implementation of China's Clean Air Act (CCAA) in 2013. However, the association between CCAA implementation and individual-level cardiovascular disease (CVD) risk remains unclear. We aimed to examine the long-term association between CCAA implementation and individual-level predicted CVD risk. METHODS: In this prospective, quasi-experimental study, we used data from the China Kadoorie Biobank, a prospective cohort study that recruited participants from five urban and five rural areas across China between 2004 and 2008, with three resurveys conducted after the baseline survey (in 2008, 2013-14, and 2020-21). We included 34 862 individuals (mean age 51·3 years) who participated in at least one resurvey and had no history of CVD at baseline. Participants were classified into intervention (n=25 497) and control (n=9365) groups based on the local government's targets for particulate matter reduction. We estimated the 10-year risk of incident CVD morbidity or mortality using a validated risk prediction model. We used a difference-in-difference model to assess the long-term association between CCAA implementation and predicted risk, with adjustments made for regional confounders and individual-level characteristics, including demographics, lifestyle factors, medical history, and indoor air pollution exposure. The relationship between changes in long-term exposure to PM2·5, PM10, and O3 and predicted risk after CCAA implementation was analysed using a linear model. The estimated risk differences associated with air pollutant changes were estimated based on the magnitude of changes and their corresponding effect sizes. FINDINGS: After the CCAA was implemented, PM2·5 and PM10 concentrations declined in both groups, but O3 concentrations increased. The intervention group showed a 3·95% (95% CI 3·18-4·72%) lower increase in predicted risk than the control group, with larger estimated differences under stricter enforcement. Between 2013 and 2021, each 10 μg/m3 change in PM2·5 concentration was positively associated with a 1·80 (1·34-2·27) percentage point change in predicted CVD risk, whereas each 10 μg/m3 change in PM10 concentration was associated with a 1·24 (0·84-1·63) percentage point change and each 10 μg/m3 change in O3 concentration with a 0·58 (0·33-0·83) percentage point change. Overall, the observed changes in air pollutants during the study period were associated with an average 6·6 percentage point reduction in predicted CVD risk. INTERPRETATION: The CCAA and improved air quality were associated with a slower increase in predicted CVD risk, supporting the necessity for stricter, multipollutant air quality policies to maximise public health benefits. FUNDING: National Natural Science Foundation of China, Kadoorie Charitable Foundation, Noncommunicable Chronic Diseases-National Science and Technology Major Project, National Key R&D Program of China, Chinese Ministry of Science and Technology, and UK Wellcome Trust.

Journal Article↗

PubMed2026

Liver transcriptome analysis revealed multiple immune processes and lipid metabolism pathways involved in the defense response of the turbot (Scophthalmus maximus) against Aeromonas salmonicida.

Aeromonas salmonicida is a significant pathogen causing notable economic losses in Scophthalmus maximus aquaculture. This study utilized Illumina sequencing technology to examine the transcriptional response characteristics of S. maximus liver at 24 h following A. salmonicida infection. A total of 2363 differentially expressed genes (DEGs) were identified when compared to the negative control group. The immunity-related Toll-like receptor signaling pathway, NOD-like receptor signaling pathway, as well as metabolism-related PPAR signaling pathway and insulin signaling pathway, were notably enriched. Significant differences exist in the expression of key genes within the PPAR pathway, particularly cd36, acsl4a, pparαa, and plin2, all of which mediate the interaction between lipid metabolism and the immune response. These results offer valuable insights into the immunometabolic regulatory mechanism of S. maximus response to A. salmonicida infection.

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

Making waves: toward systems-level interpretation of hormonal and endogenous biomarkers in wastewater-based epidemiology.

Wastewater-based epidemiology (WBE) has proven invaluable for population health monitoring, most notably during the COVID-19 pandemic. Yet current WBE largely relies on exogenous markers such as drugs, pathogens, and their metabolites, limiting surveillance to what communities are exposed to. We argue for expanding WBE towards endogenous biomarkers, particularly hormones, which provide insights into physiological stress, metabolic function, and endocrine activity. Hormone-based WBE offers new opportunities to capture population-level biological responses to societal and environmental stressors, disasters, and chronic disease burdens at the community scale. This perspective outlines a systems-level framework for integrating hormonal signals in wastewater with clinical data, behavioral indicators, environmental factors, and digital markers to support more robust and context-aware public health surveillance. We highlight key technical considerations, interpretive challenges, and opportunities for translational pilot studies. By moving beyond exposure tracking toward more integrated interpretation of biological responses, hormone-informed WBE may contribute to more resilient, inclusive, and actionable public health infrastructure.

Humans↗

PubMed2026

Diagnostic performance of machine learning models for malignant and non-malignant pleural effusion: Systematic review and meta-analysis.

BACKGROUND: Accurately distinguishing malignant pleural effusion (MPE) from non-malignant pleural effusion is clinically important, but the generalisability and methodological quality of machine-learning (ML) models remain uncertain. METHODS: We searched eight databases to 23 April 2026. Diagnostic performance was pooled using random-effects and Reitsma bivariate models, and study quality was assessed using PROBAST+AI. RESULTS: Forty-two studies were included; 17 contributed to the AUC meta-analysis and 14 to the bivariate analysis. The pooled AUC was 0.90 (95 % CI 0.85-0.94; 95 % prediction interval 0.62-0.98), with sensitivity of 0.80 (95 % CI 0.77-0.83) and specificity of 0.87 (95 % CI 0.79-0.92). Only nine studies reported external, temporal or independent validation. Externally validated studies had a lower pooled AUC than studies without external validation (0.83 vs 0.92), with lower specificity observed in the two externally validated studies contributing sensitivity and specificity data. All 42 development assessments had high overall quality concerns, and all 42 model evaluations were judged at high risk of bias. CONCLUSIONS: ML models showed good apparent accuracy for distinguishing MPE from non-MPE, but the evidence was limited by substantial heterogeneity, high risk of bias and scarce external validation. The pooled estimates reflect the average performance of different selected models rather than the expected accuracy of a single clinical test. ML models should be regarded as adjuncts to existing diagnostic pathways until they are confirmed by rigorous multicentre prospective external validation and clinical-impact studies.

Humans↗

PubMed2026

Lipid-mediated activation of BLT2 promotes membrane repair to prevent cell death.

Various pathogenic microorganisms produce toxins that create pores in cell membranes, causing cell damage and disrupting the host epithelial barrier. Recently, we reported that mice lacking the G protein-coupled receptor leukotriene B4 receptor 2 (BLT2), which is expressed in vascular endothelial and alveolar epithelial cells, are highly susceptible to pneumolysin (PLY), a pneumococci-generated toxin. Although we clarified the protective roles of BLT2 in vascular endothelial cells, those in alveolar epithelial cells have not been elucidated. Here, we report that lipid mediator 12-hydroxyheptadecatrienoic acid (12-HHT), which is produced by membrane-damaged epithelial cells, prevents cell death by promoting membrane repair through BLT2. BLT2 promoted the release of PLY-bound plasma membranes as extracellular vesicles in a sphingomyelinase-dependent manner. Additionally, BLT2 activated Rac1 and subsequent actin polymerization, leading to resistance to cell death. Furthermore, inhibition of 12-HHT production by aspirin and treatment with a BLT2 antagonist abolished the protective effect of BLT2. These findings provide a new therapeutic strategy for bacterial infection.

Receptors, Leukotriene B4↗

PubMed2026

Ensemble DNA methylation clock demonstrates Immune-metabolic aging signatures associated with mortality.

Aging is a multifactorial process that is best described in terms of the progressive acquisition of multiple layers of phenotypic changes, such as epigenetic modifications, inflammation, and metabolic dysregulation. DNA methylation clocks have been extensively used to construct epigenetic clocks based on the DNAm profiles that can be used to estimate biological age and predict age-associated outcomes. Nevertheless, the vast majority of clocks constructed so far have been based on linear models, which are unlikely to fully account for the heterogeneity and non-linearity of survival-related DNAm signatures. In this work, we constructed a heterogeneous stacked ensemble survival model based on DNAm data obtained from the Framingham Heart Study. We first identified 190 CpG loci using elastic net Cox regression and subsequently constructed a survival prediction model based on the fusion of five complementary survival models by means of a neural network meta-learner. The prediction power of the survival model was evaluated in an external validation cohort, where we observed strong performance for predicting all-cause mortality that significantly exceeded PhenoAge and was statistically comparable to GrimAge. These performance estimates were derived in cohorts of European ancestry and externally validated in postmenopausal women aged 50-79 years, and should therefore be interpreted as applicable only to demographically similar populations.

Humans↗

PubMed2026

PdIr bimetallic nanozyme engineered metal-organic frameworks integrated dual-mode sensor toward Stx2 detection in food.

Shiga toxin II (Stx2) has attracted extensive attention due to its toxicity and pathogenicity, making the development of sensitive detection methods urgent. This study constructed a dual-mode sensing platform for the sensitive detection of Stx2 in food. Composite material UIO-66@PdIr with peroxidase-like activity and fluorescent properties was synthesized and combined with cDNA as the signal probe, while aptamer-modified magnetic beads served as the capture probe. Specific binding of Stx2 to the aptamer triggered the release of the signal probe, enabling colorimetric and fluorescence signal readout. The colorimetric mode showed a linear range of 0.05-100 ng/mL with an LOD of 0.039 ng/mL, and the fluorescence mode exhibited 0.01-1000 ng/mL with an LOD of 0.0097 ng/mL. Additionally, this method was successfully applied to the detection of Stx2 in food, and the recovery rates were 94.33% ∼ 102.20%. It indicated that the constructed sensor holds great practical potential for Stx2 detection.

Food Contamination↗

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

Clinical applications of digital twin technology in In Vitro Fertilisation.

BACKGROUND: Digital twin technology, originating from aerospace and manufacturing industries, has emerged as a transformative tool in healthcare. In vitro fertilisation (IVF) faces persistent challenges including suboptimal embryo selection, unpredictable treatment outcomes, and limited personalisation of protocols. Despite advances in assisted reproductive technology, existing literature exhibits fragmentation: artificial intelligence applications in embryo selection, ovarian stimulation, and endometrial assessment have been developed independently without systematic integration into comprehensive treatment frameworks. Digital twin technology offers unprecedented opportunities to create virtual replicas of biological systems, enabling real-time monitoring, predictive modelling, and personalised treatment strategies. AIM: This narrative review aims to critically examine the current applications of digital twin technology in IVF, evaluate its potential benefits and limitations, synthesize existing evidence into an integrative conceptual model, and identify future directions for implementation in reproductive medicine. METHOD: A comprehensive narrative review was conducted using PubMed, Scopus, Web of Science, and IEEE Xplore databases. A narrative review approach was selected over systematic review to accommodate the heterogeneity of evidence types in this emerging field, including theoretical frameworks, simulation studies, and proof-of-concept implementations that would be excluded from systematic reviews. Search terms included "digital twin," "IVF," "in vitro fertilisation," "assisted reproductive technology," "embryo selection," and "predictive modelling." Studies published between 2015 and 2025 were included, focusing on original research articles, systematic reviews, and proof-of-concept studies describing digital twin applications in reproductive medicine. RESULTS: Digital twin technology in IVF demonstrates significant potential across multiple domains including embryo development simulation, ovarian response prediction, endometrial receptivity modelling, and personalised stimulation protocols. Current applications integrate artificial intelligence, machine learning algorithms, time-lapse imaging, and omics data to create comprehensive virtual models. Early evidence suggests improvements in embryo selection accuracy, ovarian response prediction, and treatment protocol optimization, though large-scale randomized controlled trials remain limited. Implementation challenges include data integration complexity, computational requirements, regulatory considerations, and validation requirements. CONCLUSION: Digital twin technology represents a paradigm shift in IVF practice, offering personalised, predictive, and precision medicine approaches. This review synthesizes existing evidence to propose an integrative conceptual model for digital twin implementation across the IVF treatment spectrum, identifies critical knowledge gaps, and establishes research priorities to advance clinical translation. Despite current limitations, continued advancement promises improved success rates and patient outcomes.

Humans↗

PubMed2026

Sitosterolemia: evolving strategies for earlier diagnosis.

PURPOSE OF REVIEW: Sitosterolemia is a rare autosomal recessive lipid disorder caused by biallelic pathogenic variants in ABCG5 or ABCG8 , resulting in excessive intestinal absorption and impaired biliary excretion of plant sterols. Although historically considered exceptionally rare, recent genetic studies suggest the disorder is substantially underdiagnosed, with marked phenotypic heterogeneity ranging from xanthomas and premature atherosclerosis to hematologic abnormalities, and frequently mimics familial hypercholesterolemia. This review summarizes recent advances in the clinical, biological, and genetic diagnosis of sitosterolemia, with a focus on strategies that may facilitate earlier detection. RECENT FINDINGS: Phytosterol quantification, particularly sitosterol, campesterol, and stigmasterol, remains indispensable for accurate diagnosis. Hematologic abnormalities, including hemolytic anemia, stomatocytosis, and macrothrombocytopenia, are increasingly recognized as valuable diagnostic clues complementing the biochemical approach. Expanded variant catalogs for ABCG5/ABCG8 and genome-wide association studies have revealed potentially polygenic contributions to phytosterol metabolism extending beyond these two genes. However, no specific guidelines have yet been established for cascade screening. SUMMARY: Earlier diagnosis requires integration of clinical, biochemical, hematologic, and genetic data. Plasma phytosterol measurement remains the diagnostic cornerstone. Improved disease awareness, broader access to sterol testing, and expanded genetic screening may reduce diagnostic delays and enable timely management, including ezetimibe and dietary phytosterol restriction.

Humans↗