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PubMed2026

Measurement of low-density lipoprotein cholesterol and other circulating lipids in Brazil: a systematic literature review.

Accurate laboratory assessment of circulating lipids underpins cardiovascular risk stratification, yet clinical interpretation depends not only on the assays but on the formula chosen to estimate low-density lipoprotein cholesterol (LDL-C). This review integrates the 2019-2025 evidence on laboratory methods for triglycerides (TG), total cholesterol (TC), and high-density lipoprotein cholesterol (HDLC), and on the formulas estimating LDL-C, VLDL-C, and non-HDL cholesterol, to determine how these should be measured, reported, and harmonized in Brazil, where lipid thresholds are adapted from international consensus. A PRISMA 2020 systematic search (PROSPERO CRD420251241064) of PubMed/MEDLINE, Scopus, SciELO, LILACS, Web of Science, and Embase retrieved 57,915 records; after removing 38,210 duplicates, 19,705 titles/abstracts were screened, 312 full texts assessed, and 25 sources included. Enzymatic colorimetric assays remain standard for TG, TC, and HDLC. For LDL-C, Martin/Hopkins classifies more accurately than Friedewald (89.6% vs 83.2% correct categorization in 5,051,467 patients), particularly at high TG and low LDL-C, while Sampson/NIH and modified Sampson/NIH extend reliable estimation into hypertriglyceridemia and very low LDL-C; direct measurement is reserved for TG beyond the validated range. Although the review centers on the Friedewald, Martin/Hopkins, and Sampson/NIH families that dominate guideline practice, other published equations exist and are addressed in context. In Brazil, atherogenic-lipid thresholds are risk-based decision limits rather than reference intervals; national surveys describe lipid distributions but were not designed to establish them. Analytical standardization through traceability programs, multicenter validation of formulas, and-where the distribution-based construct applies (HDLC, pediatrics)-nationally derived reference intervals are priorities for equitable cardiovascular risk assessment in Brazil.

Humans↗

PubMed2026

Resistance versus concurrent training with three assigned protein targets in middle-aged and older women: a randomized 2 × 3 factorial trial.

BACKGROUND: Evidence is limited regarding whether assigned protein targets modify responses to resistance training (RT) alone or to the same RT program plus cycling (concurrent training [CT]) in middle-aged and older women. This randomized 2 × 3 factorial trial examined bioelectrical impedance analysis (BIA)-derived skeletal muscle mass (SMM; primary outcome), other body composition outcomes, muscular and functional performance, and cycle-derived estimated VO₂max. METHODS: In this randomized 2 × 3 factorial trial, 108 women aged 40-77 years were assigned to 12 weeks of supervised RT or CT (identical RT followed by cycling) and protein targets of 0.8, 1.6, or 2.2 g·kg-1·d-1. Baseline-adjusted ANCOVA tested training × protein interactions and marginal training and protein effects. Complete-case analyses included 83 participants. RESULTS: For SMM, no training-condition × protein-target interaction (p = 0.856), marginal protein-target effect (p = 0.726), or marginal training-condition effect (p = 0.273) was detected. CT had a lower baseline-adjusted week-12 BFP than RT (adjusted difference, -2.04 percentage points; 95% CI, -2.94 to -1.14; p < 0.001). RT had a higher baseline-adjusted week-12 leg-press estimated 1-RM than CT (CT - RT: -6.68 kg; 95% CI, -8.32 to -5.04; p < 0.001), whereas CT had a higher baseline-adjusted week-12 cycle-derived estimated VO₂max (adjusted difference, 4.53 mL·kg-1·min-1; 95% CI, 3.80 to 5.25; p < 0.001). No detectable marginal protein-target effects or training-condition × protein-target interactions were observed for the key secondary outcomes. CONCLUSIONS: No detectable differences in SMM or key secondary outcomes were attributable to assigned protein target. Compared with RT, CT favored estimated aerobic fitness and BFP, whereas RT favored leg-press strength. Because CT included additional cycling and greater exercise exposure, these differences cannot be attributed solely to training modality. Null protein findings do not establish equivalence among doses.

Humans↗

PubMed2026

Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer.

BACKGROUND: Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. OBJECTIVE: To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n = 907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. RESULTS: The 65 studies comprised breast cancer (n = 35), colorectal cancer (n = 21), and pancreatic cancer (n = 9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). CONCLUSION: AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.

Humans↗

PubMed2026

Genetic evidence for a causal relationship between melatonin metabolism and depression.

To investigate the causal relevance of melatonin metabolism, which provides the biological basis for circulating melatonin levels, to specific depression symptom subtypes, we performed a targeted systematic review of melatonin metabolism pathways in the human brain and liver. Using two-sample Mendelian randomization (MR), we assessed the causal effects of metabolism pathways and/or individual genes on major depressive disorder (MDD) and nine symptom subtypes derived from Patient Health Questionnaire-9 (PHQ-9). Instrumental variables (IVs) were expression quantitative trait loci (eQTL) for eight individual genes, one synthesis route, and three degradation routes. Results were assessed using Bayesian colocalization and phenome-wide association analyses. At the pathway-level, the genetically proxied synthesis-route signal was associated with PHQ-9 Assessment 5 (PHQ9A5, OR: 0.89, 95% CI: 0.85-0.93), but sensitivity analyses suggested this association was primarily driven by TPH1 and may reflect serotonin-related biology. In contrast, higher brain melatonin degradation raised the risk of both PHQ9A1 (OR: 1.03, 95% CI: 1.02-1.04) and PHQ9A7 (OR: 1.03, 95% CI: 1.02-1.03). Within degradation, up-regulation of the kynurenine sub-pathway increased the odds of PHQ9A3 (OR: 1.05, 95% CI: 1.02-1.07), PHQ9A4 (OR = 1.04, 95% CI: 1.02-1.06) and PHQ9A7 (OR: 1.05, 95% CI: 1.02-1.07). Gene-level analyses were largely concordant, except for SULT1A1, whose higher expression was genetically protective for PHQ9A3 but risk-increased for PHQ9A1 and PHQ9A4. Overall, these results demonstrate that melatonin metabolism exerts symptom-specific and pathway-specific causal effects on depression. A stratified view of melatonin's role may help optimize the application of exogenous melatonin supplementation.

Melatonin↗

PubMed2026

Sleep stage-dependent distribution of interictal epileptiform discharges in epilepsy: A systematic review.

BACKGROUND: Sleep and epilepsy interact through complex bidirectional mechanisms. Although NREM sleep facilitates interictal epileptiform discharges (IED), the diagnostic contribution of individual sleep stages remains uncertain. In particular, it is unclear whether deeper sleep stages such as N3 provide an advantage over N2 for spike detection or localization in clinical (electroencephalography) EEG practice. METHODS: This systematic review followed PRISMA 2020 guidelines. PubMed and Web of Science were searched for studies reporting quantitative IED measures across sleep stages in patients with epilepsy. Eligible studies included scalp EEG, video-EEG, polysomnography, or intracranial recordings. Mean IED rates per minute were derived when possible. Comparisons between NREM and REM sleep and between N2 and N3 stages were performed using study level non-parametric tests. Risk of bias was assessed with the ROBINS-I tool. RESULTS: Ten observational studies including 266 patients (mean age 30.1 years) were analyzed. IED rates were significantly higher during NREM than REM sleep (Wilcoxon signed-rank test, W = 0, p = 0.0019, r = 0.87). No significant difference was observed between N2 and N3 sleep, although median spike rates were slightly higher during N3 than N2 (0.99 vs 0.86 IED/min). REM showed the lowest activity. CONCLUSIONS: NREM sleep consistently exhibited higher IED rates than REM sleep, reinforcing the neurophysiological association between sleep stage and epileptiform activity without establishing diagnostic superiority.

Humans↗

PubMed2026

Synergistic transcriptional modules in Trichoderma asperellum enhance glutathione detoxification to counteract fungal pathogen toxins.

Trichoderma fungi are potent biocontrol agents. However, their defence mechanisms against pathogen-derived toxins remain poorly understood. We identified two synergistic transcription factor modules in T. asperellum that orchestrate the detoxification of cytotoxic secondary metabolites from the poplar blight pathogen Alternaria alternata. Overexpression of the central regulator TasMYB46 reduced disease lesion area by approximately 22% and was associated with decreased pathogen-induced reactive oxygen species (ROS) accumulation. Mechanistically, TasMYB46 directly activates the glutathione S-transferases TasGST61.1 and TasGST56.1 through distinct promoter binding sites (G-box/as-1/MBS), forming dedicated detoxification modules. Crucially, we identified urolithin C as the most abundant phytotoxin in A. alternata metabolites, which is efficiently detoxified through the TasMYB46-TasGST61.1 module. The transcription enhancer TasbHLH53.8 amplifies this system by binding to TasMYB46, boosting TasGST expression and enhancing glutathione-dependent detoxification capacity. This coordinated response elevates glutathione pools and antioxidant enzyme activities (GST/GPx), conferring increased oxidative stress resistance. This study reveals a novel defence mechanism in Trichoderma in which MYB-bHLH-GST modules enable biocontrol agents to neutralise pathogen-derived toxins. Given that Alternaria toxins threaten crops globally (tomatoes, potatoes, citrus), the discovered regulatory synergy represents a strategic advance in developing next-generation biocontrol solutions against toxin-producing plant pathogens.

Alternaria↗

PubMed2026

Bioactive peptides for meat quality and preservation: Integrating peptidomics and computational screening.

Bioactive peptides generated from meat proteins, fermented meat products, and slaughter by-products have attracted increasing attention as functional molecules for improving meat quality and preservation. In meat systems, peptides can be produced through endogenous postmortem proteolysis, microbial fermentation, gastrointestinal digestion, or controlled enzymatic hydrolysis of underutilized animal by-products. These peptides are closely associated with key meat science endpoints, including postmortem tenderization, oxidative stability, color retention, flavor development, microbial inhibition, and the valorization of processing by-products. However, although high-resolution peptidomics has greatly expanded the identification of meat-derived peptide sequences, their translation into practical meat applications remains limited by matrix interactions, processing stability, sensory constraints, safety concerns, and insufficient validation in real meat systems. This review synthesizes recent advances in meat-related peptidomics and computational screening, including sequence-based prediction, machine learning, molecular docking, molecular dynamics, stability assessment, and safety-oriented filtering. Particular attention is given to how these approaches can prioritize peptides with antioxidant, antimicrobial, flavor-modulating, and preservation-related functions under meat-specific technological constraints. By integrating peptide generation pathways, mass spectrometry-based identification, in silico prioritization, and meat quality endpoints, this review proposes a stage-gated framework for translating meat-derived bioactive peptides from discovery to application. Future research should strengthen matrix-specific validation, standardized peptidomic reporting, and safety assessment to support the use of bioactive peptides in meat quality improvement, clean-label preservation, and circular utilization of meat industry by-products.

Animals↗

PubMed2026

Advances in cereal protein applications for infant and Young child nutrition.

BACKGROUND: The increasing use of plant-derived proteins in infant and young child nutrition necessitates tailored amino acid profiles, high digestibility and strict safety controls. Cereal proteins, such as rice, oat, maize, millet, barley, and wheat, are widely used in complementary foods but face intrinsic limitations, notably lysine and tryptophan deficits, antinutritional factors that reduce bioavailability, gluten immunogenicity in wheat/barley, and inorganic arsenic risks in rice. SCOPE AND APPROACH: This review synthesizes recent advances in processing and formulation strategies, including enzymatic hydrolysis, fermentation, germination, extrusion, cereal-legume complementation, and micronutrient fortification. Their impacts on digestibility, techno-functionality, iron and zinc bioavailability, and protein quality, including Protein Digestibility-Corrected Amino Acid Score (PDCAAS) and Digestible Indispensable Amino Acid Score (DIAAS) are critically reviewed using data from in vitro assays, product development, and clinical trials. KEY FINDINGS AND CONCLUSIONS: Processing and blending approaches can substantially improve protein digestibility, amino acid balance and micronutrient availability, and hydrolyzed rice protein holds clinical promise for cow's milk protein allergy (CMPA). However, most evidence is preclinical, reporting of protein quality is inconsistent, and industrial translation is constrained by sensory, shelf-life, contaminant and cost issues. We recommend standardized DIAAS-based reporting, large-scale feeding trials, sensory /stability optimization, and targeted exploration of underutilized grains (e.g., oat, millet) with active allergen monitoring. Prioritizing amino acid-focused formulation coupled with strategies to enhance micronutrient bioavailability will accelerate safe adoption of cereal proteins in early-life nutrition.

Humans↗

PubMed2026

Nitrogen sources and concentrations shape algal odor compounds: Key drivers of β-cyclocitral and β-ionone in water bodies of the lower Yangtze River.

Taste and odor (T&O) compounds derived from cyanobacterial blooms pose escalating threats to freshwater security worldwide, yet the drivers of specific T&O metabolites remain poorly constrained. Here, we investigated the dual effects of nitrogen (N) sources and concentrations on the production of β-cyclocitral and β-ionone, two algal-derived T&O compounds, through integrated field surveys (54 sites across lakes and rivers) in the eutrophic lower Yangtze River, China, and laboratory cultivation of typical cyanobacteria (Microcystis aeruginosa and Pseudanabaena cinerea). Our field data revealed that the concentrations of β-cyclocitral and β-ionone in lakes and rivers were not significantly different, but increased with the trophic level index. Redundancy analysis and Mantel analysis showed that Microcystis and Pseudanabaena were potentially dominant contributors to β-cyclocitral and β-ionone in the water column. Structural equation modeling and variation partitioning analysis showed that enhanced nitrate (NO3--N) significantly promoted the production of these compounds. Laboratory experiments demonstrated that inorganic N (NaNO₃) maximized total T&O yields by promoting algal biomass, whereas organic N (urea and glutamic acid) elevated the T&O production per unit biomass by 1.5- to 9.5-fold. Notably, Pseudanabaena exhibited a 2.3-fold higher β-ionone yield than Microcystis, with greater sensitivity to N concentrations. Our study highlights the critical role of nitrogen pollution, both source and concentration, in the production of T&O compounds by phytoplankton and provides reference data for managing T&O issues in rivers and shallow lakes.

Norisoprenoids↗

PubMed2026

The gut microbiota-obesity axis in the pathogenesis and prognosis of breast cancer.

BACKGROUND: Breast cancer (BC) remains a major global health concern, accounting for 11.7% of all cancer cases and ranking as the second leading cause of female cancer-related deaths worldwide. Increasing evidence highlights the interplay between gut microbiota (GM) dysbiosis and obesity-associated metabolic dysfunction in BC progression. This review aims to elucidate the role of GM in obese patients with BC. METHODS: A systematic literature search was conducted in PubMed and Web of Science databases for publications from July 2015 to January 2025. Search terms combined BC, GM, obesity, dysbiosis, immunity, and microbiome. Article selection prioritized studies investigating microbial alterations in BC patients, mechanistic links between obesity and cancer progression, and GM-targeted interventions. Both original studies and authoritative reviews were included, supplemented by manual reference screening. DISCUSSION: Obesity may trigger systemic inflammation, altered adipokine secretion, and disrupted steroid hormone metabolism via gut-derived β-glucuronidase activity, thereby exacerbating BC occurrence and recurrence. GM dysbiosis-driven metabolites such as branched-chain amino acids (BCAAs) and short-chain fatty acids (SCFAs) can activate oncogenic signaling pathways and immunosuppressive myeloid-derived suppressor cells (MDSCs), fostering tumor immune evasion. Conversely, dietary interventions, probiotics, and fecal microbiota transplantation (FMT) can alleviate dysbiosis, strengthen gut barriers, and restore anti-tumor immunity, improving chemotherapy response and reducing recurrence. However, challenges persist in deciphering BC subtype-related microbial signatures and optimizing microbiota-targeted therapies. CONCLUSION: Future longitudinal studies are needed to clarify causal relationships, validate microbial biomarkers, and translate preclinical findings into clinical applications. Addressing the gut-breast axis may offer transformative potential for precision oncology in obesity-driven BC.

Humans↗

PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗

PubMed2026

Beyond multidimensionality: a systematic review of recurrent frailty archetypes in community-dwelling older adults.

BACKGROUND: Frailty is a clinically heterogeneous geriatric syndrome commonly summarised using physical or multidomain severity scores. Whether person-centred analyses identify recurring within-frailty configurations has not been systematically examined in community-dwelling older adults. METHODS: We searched PubMed, Embase, MEDLINE, and CINAHL (January 2000-November 2025) for cross-sectional studies using latent class, latent profile, or analogous clustering methods to derive frailty subgroups. Quality was assessed using the AHRQ checklist and a purpose-built appraisal of person-centred model reporting. Study-derived classes were mapped in duplicate to a structured archetype framework developed through comparison of class-defining features across studies. RESULTS: Fourteen reports representing 12 independent datasets from eight countries were included. Six configurations were identified: minimally impaired reference, mobility-physical, nutritional-metabolic, cognitive-predominant, combined cognitive-physical, and psychosocial/mood-predominant. Convergence was measurement-dependent. The reference and mobility-physical configurations recurred across physical-only and multidomain indicator sets, while the combined cognitive-physical configuration appeared across several multidomain frameworks but required cognition to be measured. The remaining configurations emerged only when their defining domains were included. Evidence of prognostic value beyond aggregate frailty severity came from one deficit-index study. Collapsing shared-provenance reports and excluding the boundary-eligible study did not alter recurrence; excluding the Croatian dataset left five configurations recurrent, with the cognitive-predominant configuration supported by one independent dataset. CONCLUSIONS: Person-centred analyses identify recurring within-frailty configurations, but their apparent stability is partly measurement-dependent. A five-configuration core persisted after exclusion of the Croatian dataset, whereas the cognitive-predominant configuration remained weakly replicated. Harmonised indicators and rigorous external validation are needed before clinical application.

Humans↗

PubMed2026

Coordinated use of three homocysteine methyltransferases supports l-methionine biosynthesis and environmental adaptation among plant-associated bacteria.

Plant pathogens colonize multiple plant-associated habitats throughout their life cycle, encountering distinct nutrient conditions and microbial communities. l-methionine is required for bacterial growth and environmental adaptation. However, how plant pathogens coordinate l-methionine biosynthetic pathways to adapt to different plant-associated environments remains poorly understood. Here, using the plant pathogen Xanthomonas campestris pv. campestris strain XC1 as a model, we show that three homocysteine methyltransferase pathways allow XC1 to catalyze the final step of l-methionine biosynthesis using different methyl donors and cofactors under different environmental conditions. Bioinformatic and transcriptional analyses identified three homocysteine methyltransferase-associated operons in XC1, mesMXD, mmuPM, and metHRHaHb, corresponding to the MesD-, MmuM-, and MetHaHb-dependent pathways, respectively. MesD uses an endogenously synthesized methyl donor and functions as the dominant homocysteine methyltransferase under l-methionine-limiting conditions, supporting bacterial growth, intracellular l-methionine accumulation, and full virulence. Furthermore, MmuM enables XC1 to use plant-derived S-methylmethionine for l-methionine biosynthesis, whereas MetHaHb enables XC1 to use vitamin B12 supplied by a neighboring bacterium for l-methionine biosynthesis in co-culture. Expression analyses showed that mesMXD was the only homocysteine methyltransferase-associated operon that responded to l-methionine availability, and its expression also decreased when S-methylmethionine- or vitamin B12-dependent pathways supported l-methionine biosynthesis. Comparative genomic analysis further showed that the three-homocysteine methyltransferase configuration is conserved in Xanthomonas and is also present in other plant-associated bacteria. Together, these findings show that a plant pathogen can coordinate endogenous, plant-derived, and microbially supported homocysteine methyltransferase pathways to maintain l-methionine biosynthesis, providing a metabolic strategy for adaptation to plant-associated environments.

Methionine↗

PubMed2026

Beyond antigen matching: compatibility intelligence theory for transfusion as an emergent biological system.

BACKGROUND: Despite major advances in serologic testing, extended phenotyping, and blood group genomics, clinically similar transfusion exposures may result in markedly different immune and clinical outcomes. Existing compatibility strategies do not fully explain this biological variability. OBJECTIVES: To examine transfusion compatibility as an emergent donor-recipient biological state and propose a systems-level conceptual framework that integrates established biological determinants into a testable model for future precision transfusion medicine. METHODS: This narrative review critically synthesizes current evidence from blood group genomics, recipient immunobiology, inflammation, disease-specific biology, transfusion medicine, and computational prediction. The proposed framework distinguishes Compatibility Intelligence Theory (CIT) as a biological interpretation from Precision Transfusion Intelligence (PTI) as its potential clinician-supervised translational application. RESULTS: The review argues that transfusion compatibility is shaped by interactions among donor genetics, recipient immune biology, inflammatory physiology, disease context, transfusion history, and longitudinal adaptation rather than by antigen matching alone. CIT provides an organizational framework for integrating these determinants, whereas PTI describes a possible clinician-supervised translation. To address current feasibility, the revised framework separates variables into routinely measurable, contextually available but incompletely standardized, and research-stage domains, and proposes a staged strategy for deriving rather than assuming their quantitative weights. Any clinical implementation would require comparative validation against current serologic, phenotypic, and genotype-based practice. CONCLUSIONS: Compatibility Intelligence Theory offers a testable systems-level framework for understanding transfusion compatibility without replacing established transfusion practices. The framework is not presented as a ready-to-use score: currently measurable variables can be organized for structured risk review, whereas inflammatory, immunogenetic, and multi-omic inputs require prospective standardization and validation. If future studies demonstrate incremental predictive and patient-centered benefit, CIT-informed PTI could support an adaptive, evidence-based extension of current precision transfusion practice.

Humans↗

PubMed2026

Evaluation of three Aspergillus antibody assays for screening of chronic pulmonary aspergillosis: prospective diagnostic accuracy study.

OBJECTIVES: Chronic pulmonary aspergillosis (CPA) is a frequent complication of pulmonary tuberculosis (PTB), particularly in high-burden settings where access to reliable serological diagnostics remains limited. We evaluated the diagnostic performance of two immunochromatographic technology (ICT) lateral flow assays (LFAs) and an ELISA for CPA screening among patients with active or previously treated PTB. METHODS: In this two-year prospective multicentre diagnostic evaluation, serum from adults with prior or active PTB was tested using the Era Biology Aspergillus IgG ICT LFA, LDBio Aspergillus IgG/IgM ICT LFA, and Bordier Aspergillus fumigatus IgG ELISA. CPA diagnosis was established using a consensus composite reference standard incorporating clinical, immunological, radiological, and microbiological criteria. The Bordier ELISA was used as part of the immunological component of the consensus CPA diagnosis, with a cutoff optical density of ≥1.0. Diagnostic accuracy, agreement statistics, receiver operating characteristic analysis, and latent class analysis (LCA) were performed. RESULTS: Among 340 participants, 24 (7.06%) had CPA. Proportion of participants with positive antibody tests among all tested individuals were 6.76% for LDBio ICT LFA, 20.0% for Era Biology ICT LFA, and 11.47% for Bordier ELISA. Against consensus CPA diagnosis, Bordier ELISA showed 87.50% sensitivity and 94.30% specificity, LDBio ICT LFA 58.33% sensitivity and 97.15% specificity, and Era Biology LFA 66.67% sensitivity and 83.54% specificity. LCA estimated CPA prevalence at 7.72%. LCA-derived sensitivities and specificities were 86.58% and 99.92% for LDBio ICT LFA, 83.39% and 85.31% for Era Biology LFA, and 79.10% and 94.19% for Bordier ELISA. CONCLUSIONS: The Bordier ELISA showed high sensitivity and specificity, while the LDBio ICT LFA demonstrated very high specificity with strong LCA-derived performance. These findings support the use of ELISA for laboratory diagnosis and ICT as a point-of-care screening tool for CPA in resource-limited settings. Era Biology Aspergillus IgG LFA demonstrated moderate sensitivity and acceptable diagnostic performance, indicating its potential utility as a supplementary screening assay for CPA in settings where rapid, point-of-care testing is required.

Humans↗

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

Effects of semaglutide and empagliflozin on markers of endothelial function in persons with type 2 diabetes: A post hoc analysis of a randomized clinical trial.

AIMS: The endothelium maintains vascular health by regulating blood flow and protecting against inflammatory damage. In type 2 diabetes (T2DM), however, hyperglycemia, hypertension, and hyperlipidemia place a significant burden on the endothelium, potentially leading to atherosclerosis and cardiovascular disease (CVD). While semaglutide and empagliflozin have been shown to reduce CVD risk in T2DM, it remains unclear whether these benefits are mediated by improved endothelial function. This post-hoc analysis explores the effects of these agents on markers of endothelial function, i.e. the reactive hyperaemic index (RHI) and the endothelial-derived cell adhesion molecules (CAMs) E-Selectin, ICAM-1, P-Selectin, and VCAM-1. METHODS: This was a post-hoc analysis of a 32-week randomized trial evaluating the separate and combined effects of semaglutide and empagliflozin on cardio-renal organ damage. One hundred and twenty participants with type 2 diabetes, age ≥ 50 were randomized to four groups (semaglutide, empagliflozin, the combination or placebo). An increase in RHI and/or a decrease in CAMs were considered beneficial. RESULTS: RHI increased compared to baseline (0.11, 95%CI [0.008;0.21], p = 0.03) but not compared to placebo in the semaglutide group (0.11, 95%CI [-0.04;0.24], p = 0.16). There was no effect on RHI in the empagliflozin group. Compared to placebo, E-Selectin decreased significantly in the semaglutide and combination groups (-9, 95%CI [-14.1;-5.1] p < 0.01 and - 9, 95%CI [-14.3; -5.2] p < 0.01, respectively). VCAM-1 increased in the same groups, compared to placebo (12.3, 95%CI[2.8;20.8] p = 0.01 and 16.2, 95%CI [7.2;24.3], p < 0.01, respectively).P-Selectin and ICAM-1 was not significantly affected in any of the groups, compared to placebo (p ≥ 0.11 and p ≥ 0.09, respectively). CONCLUSION: Semaglutide improved endothelial function compared to baseline, but not significantly versus placebo, which likely is due to limited power. CAM responses were heterogeneous, suggesting distinct roles in endothelial dysfunction and atherosclerosis. ClinicalTrialsRegister.eu: EudraCT 2019-000781-38.

Aged↗

PubMed2026

From host response to genomic targets: electrochemical biosensing of tuberculosis biomarkers.

Tuberculosis (TB) remains one of the leading causes of death from a single infectious agent worldwide, with timely diagnosis continuing to be a major challenge, particularly in resource-limited settings. Conventional TB diagnostic methods are limited by low sensitivity, long turnaround times, and an inability to reliably differentiate latent from active disease. Biomarker-based diagnostic strategies have therefore gained increasing attention as they offer the potential to improve early detection, disease differentiation, and treatment monitoring. Herein, we examine electrochemical biosensing strategies for TB diagnostics using a biomarker-class-driven framework, covering host-response biomarkers (IFN-γ and TNF-α), pathogen-derived antigens (ESAT6, CFP10, CFP10-ESAT6, MPT64, Ag85, HspX and LpqH), cell-wall signatures and whole-cell markers (LAM and whole cell Mtb), and genomic markers (Mtb DNA and IS6110). Through structured comparison of recognition elements, biointerface designs, signal amplification strategies, electrochemical techniques, matrices, and validation levels, this review identifies the most promising technical approaches for different TB biomarker classes. It further highlights key translational bottlenecks, including limited clinical validation, buffer-based testing, complex multistep amplification, redox-probe dependence, matrix fouling, and insufficient evidence of manufacturability. This review therefore provides practical guidance for developing electrochemical TB biosensors that are analytically sensitive, clinically relevant, and suitable for decentralized diagnostic applications.

Biosensing Techniques↗