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PubMed2026

Vaccine preferences and their role for vaccine confidence and uptake: a meta-ethnography.

Vaccine confidence and uptake are influenced by individuals' preferences regarding vaccine composition, quality, or administration pathways. However, literature synthesizing available qualitative insights into individuals' vaccine preferences remains limited. We therefore conducted a meta-ethnographic systematic review of the qualitative literature on vaccine preferences to identify opportunities for enhancing vaccine confidence and uptake. We implemented a comprehensive search strategy and screened 5,528 studies across seven research databases published between 2001 and 2023. We identified and synthesized 97 qualitative articles to delineate factors influencing consumers' vaccine preferences. Our findings revealed four primary domains shaping individuals' vaccine preferences: Product, Place, Price, and Promotion. First, individuals' preferences for vaccines often hinge on perceived quality and safety of the product itself, which can, for example, be associated with vaccine brand or origin, especially in the case of novel vaccines. Second, people prioritize convenience in terms of vaccination sites and delivery methods (wanting vaccinations offered at their doorstep or in local peripheral clinics); evidence regarding preferred groups to administer the vaccines was mixed. Third, the price of vaccines and the secondary costs associated with vaccination played a role in uptake considerations. Finally, both the sources of information (such as healthcare workers, community volunteers, and religious authorities) and the methods of promoting vaccine information (including face-to-face consultations during clinic visits and the distribution of leaflets or banners), emerged as crucial factors shaping decision-making processes. Overall findings highlight the importance of addressing multifaceted preferences to enhance vaccine confidence and uptake. By understanding individuals' vaccine preferences, strategic recommendations can be developed to optimize vaccination programs and ensure acceptability and utilization.

Humans↗

PubMed2026

Genome-wide insights into the evolutionary and demographic history of the red alga Mazzaella laminarioides: Evidence for speciation with ancient migration along the southeast Pacific coast.

The mechanisms driving lineage divergence in red algae remain unexplored, despite the group's remarkable diversity and ancient evolutionary history. The red alga Mazzaella laminarioides, a Chilean intertidal species complex composed of three parapatric cryptic lineages (North, Center, South), offers a valuable system to evaluate these processes, as its life history combines severe dispersal limitation with a haploid-diploid cycle that may influence the emergence of reproductive barriers. We reconstructed its evolutionary history using whole-genome sequencing and nuclear genome assembly of representative individuals from each lineage. Phylogenomic analyses based on 1,507 single-copy orthologs recovered three deeply divergent lineages with limited nuclear discordance consistent with incomplete lineage sorting. For both splits, demographic modelling was most consistent with an Ancient Migration scenario, although support over strict isolation was moderate, suggesting that divergence may have begun with low asymmetric ancestral gene flow followed by subsequent loss of connectivity, demographic bottlenecks, and later population expansion. Coding sequence analyses revealed lineage-specific dN/dS heterogeneity; only one South-lineage locus passed FDR correction (metaxin-1, mitochondrial protein import), with two further South-lineage candidates in chlorophyll and heme biosynthesis falling below the FDR threshold. Together, these signals suggest that divergent selective pressures on energy acquisition may have contributed to divergence at the southern end of the distribution. These results add to the small but growing body of whole-genome data for red algae and, alongside recent macroalgal studies, suggest that ancestral connectivity could be a recurrent feature of lineage divergence even in marine organisms with extremely restricted dispersal.

Rhodophyta↗

PubMed2026

Genomic determinants underlying biogenic amine detoxification phenotypes in food-associated lactic acid bacteria: Mechanism, evolutionary origin, and relevance to fermented food safety.

Biogenic amines (BAs) are toxic metabolites that accumulate in fermented foods and pose significant food safety concerns. Although several lactic acid bacteria (LAB) have previously been reported to exhibit strain-specific BA-degrading phenotypes, the genetic determinants underlying these activities have remained largely uncharacterized. Here, we analyzed 8251 LAB genomes to validate BA-degrading phenotypes. We predicted five BA-associated genes, including two direct biogenic amine-degrading genes (BADGs), mco and patA, and three polyamine-modifying genes (PMGs), speG, paiA, and bltD. Among BADGs, mco was broadly distributed across LAB and strongly enriched across food-associated niches. patA, organized within a conserved potD-glnB-potABC-patA cassette, is a putative, functionally distinct BADG in LAB, revealing a nitrogen-responsive polyamine uptake-catabolism module. Phylogenomics, phylogenetic reconciliation, and synteny analysis established that all five genes entered the LAB through episodic horizontal gene transfer followed by lineage-specific fixation. GC compositional bias and mobile genetic element association further corroborated the horizontal origin of the two BADGs. Structural analysis confirmed the conservation of catalytic core residues of BADGs across LAB, indicating strong purifying selection. Phenotype-to-genotype correlation with experimentally reported LAB suggested mco as a reliable genomic predictor of degrading phenotype. Integration of degradation and biosynthetic profiles predicted multiple LAB species capable of both synthesizing and degrading BA, along with 1823 genomes with degradation potential but lacking detectable BA biosynthesis genes. This study provides the first large-scale genome framework linking BA-degrading phenotypes with their genetic determinants in LAB and offers a rational basis for selecting BA-detoxifying strains for fermented food applications.

Biogenic Amines↗

PubMed2026

Diversity and population connectivity of members of the family Eunicidae inhabiting deep-water corals in the North Atlantic.

Eunicid polychaetes are often found in association with Cold Water Corals (CWCs), even establishing symbiotic relationships, such as those described between Desmophyllum pertusum and Eunice norvegica. While genetic connectivity of CWCs across the North Atlantic has been widely studied, little is known about their associated fauna in this regard. Here, we present a study combining a focused analysis of the genetic and genomic connectivity of E. norvegica with a regional assessment of the distribution and evolutionary relationships of three CWC-associated eunicid species from the Cantabrian Sea and the North of the United Kingdom (190-1,230 m depth). An integrative approach using genetic (16S, COI and 18S), morphological and ecological data allowed the identification of the eunicids studied, with new records of Eunice cf. nicidioformis and Leodice cf. antarctica in the Cantabrian Sea, as well as previously undocumented associations with CWC species. In addition, RADseq data contributed to the delimitation of the closely related species E. norvegica and Eunice philocorallia. Moreover, the genetic connectivity of E. norvegica was studied trough a RADseq (1,067 neutral SNPs) approach. Our results indicate a single panmictic population across approximately 2,000 km, suggesting that oceanographic currents facilitate passive dispersal of E. norvegica lecithotrophic larvae, aided by coral host stepping-stones. The connectivity patterns observed for E. norvegica mirror those of D. pertusum, on which the worm is ecologically dependent. Our study highlights the importance of using integrated genetic, morphological and ecological data to characterise and delineate understudied CWC-associated species and improve our understanding of their dispersal capabilities and genetic connectivity to inform future conservation recommendations.

Animals↗

PubMed2026

Genomic epidemiology of clinically critical antibiotic resistance in Salmonella enterica causing bloodstream infections across six Chinese provinces, 1994-2023.

Clinically critical antibiotic-resistant Salmonella enterica (S. enterica) causing bloodstream infections remains a public health challenge. Here, we aim to reveal the emergence and trends of clinically important antibiotic resistance in S. enterica causing bloodstream infections using 833 isolates from six Chinese provincial-level administrative areas during 1994-2023. We identified 48 serovars and 64 sequence types (STs). Overall, 8.52% of 833 isolates were resistant or had decreased susceptibility to ciprofloxacin, 4.32% and 6.84% reported resistance or decreased susceptibility to third- and fourth-generation cephalosporins (3GCs and 4GCs), 1.80% reported resistance to fosfomycin, and 2.16% reported resistance to azithromycin. Across these six regions, azithromycin and fosfomycin resistance is increasing, as is decreased susceptibility or resistance to ciprofloxacin, 3GCs, and 4GCs, especially among younger children and elderly people. Clinically prioritized antibiotic resistance also varies by region, serovar, and age group. S. Paratyphi A genotype 2.3.3 strains are mainly divided into 2 lineages distributed in Guangxi and Shanghai. Within the scope of this passive surveillance dataset, S. Typhi genotype 4.3.1.2.1 was identified as the earliest documented case among the collected isolates. Our retrospective and longitudinal genomic epidemiology study provides critical data for the formulation of treatment guidelines and policies for bloodstream infections and for the monitoring and control of antimicrobial resistance.

Humans↗

PubMed2026

Association of ERBB4 and SHBG gene polymorphisms with polycystic ovarian syndrome in South Indian women: a case-control genetic analysis.

INTRODUCTION: Polycystic ovary syndrome (PCOS) is a multifactorial endocrinological disorder with a substantial genetic component. However, the role of genes involved in follicular development and androgen regulation remains incompletely understood, particularly in South Indian populations. This study aimed to evaluate how variations in the ERBB4 and SHBG genes affect PCOS risk. METHODOLOGY: A hospital-based case-control study was conducted among 400 South Indian women, comprising 200 women with PCOS and 200 age-matched healthy controls. Genomic DNA was extracted to study SNPs at ERBB4 (rs2178575 and rs1351592) and SHBG (rs1799941 and rs727428) using ARMS-PCR genotyping. The study compared genotype and allele frequencies between cases and controls while assessing their associations with allelic, homozygous, heterozygous, dominant, recessive, and over-dominant genetic models. Genotyping accuracy was confirmed by re-genotyping and Sanger sequencing of a subset of samples. RESULTS: The ERBB4 rs2178575 polymorphism demonstrated a significant association with PCOS, as the AA genotype and A allele combination increased risk across all three genetic models, including homozygous, recessive, and allelic models. The ERBB4 rs1351592 variant was associated with 3-fold higher risk of PCOS in heterozygous and GC carriers. The SHBG rs1799941 polymorphism showed a significant link to PCOS through its effects on heterozygous and allelic states, whereas rs727428 displayed no significant connection due to its monomorphic distribution. CONCLUSION: These findings suggest that polymorphisms in ERBB4 and SHBG may contribute to PCOS susceptibility in South Indian women in a locus- and model-specific manner, revealing the intricate genetic structure that defines this medical condition.

Humans↗

PubMed2026

First identification and molecular subtyping of Blastocystis spp. in donkeys in Aksaray province, Türkiye.

Blastocystis is a common intestinal protist worldwide that can infect humans and animals. Although its molecular epidemiology in Türkiye is mostly focused primarily on humans and livestock, equids have received limited attention despite their traditional roles and frequent contact with humans and other animals in rural environments. This study aimed to determine the molecular prevalence and subtype (ST) distribution of Blastocystis spp. in donkeys in Aksaray Province, providing the first molecular data on donkeys in Türkiye. A total of 182 fresh fecal samples were collected from donkeys in nine villages within Aksaray province. Genomic DNA was extracted, and the small subunit ribosomal RNA (SSU rRNA) gene fragment of Blastocystis spp. was amplified via PCR analysis. Positive isolates were sequenced bidirectionally for identification and subsequent phylogenetic analysis of Blastocystis in donkeys. The overall molecular prevalence of Blastocystis spp. in donkeys was 4.4% (8/182). The infection rate was higher in young donkeys (under 3 years old; 8.33%) than in adults (3 years or older; 2.46%). However, this difference was not statistically significant. Sequence analysis of the positive PCR products revealed the presence of one known livestock-specific subtype, ST10. Phylogenetic analysis showed that the ST10 isolates characterized in this study clustered with isolates identified from different hosts. This study provides the first molecular data on Blastocystis presence in donkeys in Türkiye. The exclusive detection of ST10 suggests potential cross-species transmission, likely facilitated by the traditional practice of co-housing donkeys with other animals in confined barns. These findings indicate that donkeys may contribute to Blastocystis transmission, underscoring the importance of a "One Health" approach in future epidemiological surveillance.

Animals↗

PubMed2026

Investigating telomere length and hTERT-MNS16A VNTR polymorphism in Bipolar disorder: Insights into clinical features.

OBJECTIVE: To compare leukocyte telomere length (LTL; T/S ratio) and hTERT-MNS16A VNTR polymorphism between patients with bipolar disorder (BD) and healthy controls, and to examine their associations with clinical features in BD. METHODS: A total of 179 participants (100 BD patients, 79 healthy controls) were enrolled. Relative LTL was assessed by qPCR-based T/S ratio; hTERT-MNS16A VNTR genotyping by PCR and gel electrophoresis. Clinical variables including episode frequency, illness duration, age at onset, symptom severity scales, first episode polarity, and family history of mood disorder were evaluated. RESULTS: No significant differences were observed between BD patients and healthy controls in T/S ratio or hTERT-MNS16A VNTR genotype distributions (all p > 0.05). Within the BD group, S allele carriers (L/S or S/S) had significantly more depressive episodes than L/L homozygotes (1.45 ± 2.58 vs. 0.61 ± 1.52; p = .040). Significant inverse correlations were identified between T/S ratio and depressive episode count (ρ = -0.220, p = .028) and total mood episodes (ρ = -0.207, p = .039). Multivariable negative binomial regression revealed four independent predictors of depressive episode frequency: lower T/S ratio (p = 0.005), S allele carriage (L/S or S/S genotypes) (p = 0.001), first depressive episode polarity (p < 0.001), and family history of mood disorder (p = 0.035). CONCLUSION: Although LTL and hTERT-MNS16A VNTR genotype did not differ between BD patients and healthy controls, shorter telomere length and S allele carriage were independently associated with higher depressive episode frequency within the BD group, implicating telomere biology and hTERT genetic variation in the biological substrate of depressive illness burden.

Humans↗

PubMed2026

Toward real-time quantification of driving risks: a systematic review and research agenda of risk field theory.

In complex traffic systems, driving risk often evolves in a continuous and progressive manner prior to crash occurrence. How to effectively represent and analyze such latent risk states remains a central challenge in traffic safety research. In recent years, risk field-based approaches have introduced spatial and spatiotemporal continuous modeling paradigms, providing new perspectives for characterizing the distribution of traffic risk and its dynamic evolution. Motivated by the rapid growth of this research area and the lack of a systematic synthesis, this paper presents a comprehensive review of studies applying risk field theory to driving safety and traffic risk analysis. Following the PRISMA guidelines, relevant literature was collected through multi-database searches and analyzed using a combination of bibliometric analysis and qualitative review. The review systematically summarizes the theoretical foundations, modeling elements, data sources, analytical methods, and application domains of risk field-related research. Particular attention is given to studies that conceptualize traffic risk as a continuous field, complemented by a broader review of traffic risk factor literature to identify key elements and analytical dimensions involved in risk field modeling. On this basis, the paper synthesizes research progress in major application areas, including traffic safety state representation, driving behavior analysis, traffic conflict assessment, and autonomous driving and human-machine cooperative systems. Differences and commonalities among existing studies are compared in terms of modeling strategies, data support, and application scenarios. Through this systematic review, the paper clarifies the main research themes and methodological trends of risk field-based studies, providing a structured framework for understanding the evolution and application of this approach and offering methodological insights for risk perception modeling and safety-oriented decision support in intelligent transportation systems (ITS).

Humans↗

PubMed2025

An integrated multiscale air quality modelling framework for industrial park pollution: Linking local emissions to regional transport.

Capturing the spatiotemporal distribution of pollutants in industrial parks remains challenging for regional air quality models because of their coarse resolution (3 km), resulting in uncertainties in local emission quantification. To address this, we developed the Integrated Multiscale Air Quality Modelling System for Industry (IAQMS-Industry), coupling the regional Nested Air Quality Prediction Modelling System (NAQPMS) with a city-scale chemical transport model. This framework integrates point-source locations and Gaussian plume dispersion to simulate particulate matter with a diameter smaller than 2.5 micrometres (PM2.5) at 100 m resolution. Applied to the Beijing Yi Zhuang and Tangshan industrial parks and evaluated against observations. The coupled model achieved a normalized mean bias (NMB) ranging from 3.1 % to 6.2 %, improving upon NAQPMS (-16.9 % to -7.7 %). Spatial analysis revealed that coarse regional grids underestimated the PM2.5 concentrations at industrial sites by smoothing gradients, whereas IAQMS-Industry successfully resolved spatial patterns. Industrial point emissions accounted for 22.9 %-26.4 % of PM2.5 in the coupled model, which was significantly greater than the regional model estimates of 1.6 %-13.7 %. These findings indicate that regional models overestimate pollutant dispersion processes in industrial parks while underestimating local industrial impacts. By explicitly resolving point-source dynamics and linking them to regional transport, IAQMS-Industry provides a robust tool for designing targeted emission controls in industrial cities and balancing local air quality improvements with minimized regional pollution outflow. This study underscores the necessity of multiscale modelling for accurate source apportionment and informed environmental governance in industrial zones.

Air Pollution↗

PubMed2026

Is ophthalmology a pain-free career? A systematic review and meta-analysis of musculoskeletal pain among ophthalmologists.

OBJECTIVE: To estimate the pooled prevalence, anatomical distribution, and occupational impact of work-related musculoskeletal (MSK) pain among ophthalmologists, and to identify modifiable ergonomic risk factors. DESIGN: A systematic review and meta-analysis of published data. The protocol was preregistered (PROSPERO CRD42023422368) and reported in accordance with PRISMA. PARTICIPANTS: The participants were 6691 ophthalmologists from 21 studies. METHODS: Electronic databases (MEDLINE, Embase, Cochrane, and PubMed) were searched for peer-reviewed quantitative studies reporting MSK pain prevalence in ophthalmologists. Pooled estimates with 95% CI were calculated using random-effects models; heterogeneity was quantified using I². MAIN OUTCOME MEASURES: Prevalence of overall and site-specific MSK pain; effects on workload and productivity; prevalence of treatment or mitigation strategies. RESULTS: The pooled prevalence of any MSK pain was 70% (95% CI: 65%-75%; I² = 92%); neck (41%; 95% CI: 35%-47%; I² = 95%), lower back (36%; 95% CI: 32%-39%; I² = 87%), and shoulder pain (28%; 95% CI: 22%-35%; I² = 91%) were most common. Pain led to workload modification in up to 44% and contributed to reduced clinical volume, sick leave, or contemplation of early retirement. Forty-six percent (95% CI: 22%-71%) pursued no treatment; 39% (95% CI: 30%-49%) used medical therapy; and 29% (95% CI: 20%-41%) sought physiotherapy. Heterogeneity was high across studies, reflecting differing case definitions and self-reported measures. CONCLUSIONS: MSK pain affects most ophthalmologists, particularly in the cervical and lumbar regions and frequently alters practice patterns. These findings underscore the need for early ergonomic training, structured micro-breaks, and workspace redesign. Prospective intervention trials are required to establish causality and quantify benefit.

Humans↗

PubMed2026

Regional and statewide hysterectomy-corrected endometrial cancer incidence and five-year relative survival in Texas.

BACKGROUND: Rising endometrial cancer (EC) incidence nationwide, particularly among Hispanic women, and high prevalence of risk factors such as obesity and comorbidities in Texas, motivated us to estimate EC incidence rates (IRs) and survival by age (<50 years/ early-onset, ≥50 years/late-onset), race-ethnicity (Non-Hispanic-White [NHW], -Black [NHB], Hispanic), histology (endometrioid, non-endometrioid), and area-based socioeconomic (SES) factors across Texas Health Service Regions (HSRs). STUDY DESIGN: Between 2000 and 2019, a total of 42,571 women (20-79 years) with EC were reported from Texas within the Surveillance, Epidemiology, and End Results Program. IRs and 5-year relative survival were calculated using SEER*Stat. IRs were corrected for hysterectomy using Behavioral Risk Factor Surveillance System data. RESULTS: Statewide EC IRs rose from 38.5 (2000-2009) to 44.5 (2010-2019), with the highest increase in the Upper-South (42.6 to 53.8). Across HSRs, Upper-South consistently had higher IRs among women < 50 (13.9) and ≥ 50 years (112.7). Among those < 50 years, Hispanics had the highest IRs (12.4), predominantly endometrioid tumors, whereas in women ≥ 50 years, NHB had the highest IRs (119.2) with a large proportion of non-endometrioid tumors. IRs were higher in areas with lower poverty, and higher education, income, and urbanization. Associations with unemployment were mixed. Worse survival outcomes were observed among NHBs, non-endometrioid, advanced-stage, and lower SES. Central Texas had more favorable survival outcomes compared to other HSR. CONCLUSION: EC IRs and survival rates in Texas largely mirror national trends, with regional differences likely reflecting sociodemographic and histologic distributions.

Humans↗

PubMed2026

Quantitative susceptibility mapping in neurodegenerative diseases: An umbrella review of iron-related biomarkers and mechanisms.

Pathological iron accumulation is a common pathophysiological hallmark across multiple neurodegenerative diseases (NDDs), motivating the need for accurate, non-invasive quantification methods. Quantitative susceptibility mapping (QSM) is an advanced magnetic resonance imaging (MRI) technique that enables in vivo measurement of tissue magnetic susceptibility (χ), providing a sensitive proxy for iron content. This umbrella review systematically evaluates the diagnostic accuracy, clinical correlations, and distinct iron distribution patterns of QSM in major NDDs, such as Parkinson's disease (PD), Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), and atypical Parkinsonism. We included 15 (13/15 were rated Low or Critically Low on AMSTAR 2) systematic reviews and meta-analyses (through July 15, 2026); however, the findings should be interpreted cautiously because of heterogeneity and the low methodological quality. A Corrected Covered Area (CCA) analysis demonstrated only slight overlap of primary studies across the included reviews (CCA = 5.42%). Collectively, the evidence indicates that QSM provides comparable or higher diagnostic sensitivity and reliability than conventional R2* and SWI techniques, particularly for deep gray matter structures. The findings support significant iron overload in the substantia nigra, particularly in the pars compacta, as a robust biomarker for PD that correlates with motor severity and disease duration. Furthermore, regional iron profiling in the basal ganglia is critical for differential diagnosis; specifically, elevated χ in the putamen and globus pallidus effectively distinguishes multiple system atrophy and progressive supranuclear palsy from idiopathic PD. Distinctively, AD and ALS exhibit specific χ alterations in the thalamus, motor cortex, and hippocampus, reflecting divergent iron-related pathophysiological mechanisms, which correlate with cognitive impairment and upper motor neuron signs. Overall, QSM shows diagnostic promise and offers mechanistic insights into iron-related neurodegenerative processes.

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

MET expression by immunohistochemistry as a biomarker in pancreatic neuroendocrine tumours.

INTRODUCTION: MET (c-MET) is a receptor tyrosine kinase implicated in numerous cancers, including pancreatic neuroendocrine tumours (pNETs), by promoting cell proliferation, survival, invasion and angiogenesis. Recognizing its oncogenic potential, there is significant interest in MET-targeted therapies for malignancies like pNETs, which often develop treatment resistance. Immunohistochemistry (IHC) has become a practical method for detecting MET overexpression in cancers. This study evaluates MET expression in pNETs by IHC and assesses its correlation with prognostic variables and survival outcomes. METHODS AND RESULTS: Tissue microarrays containing well-differentiated neuroendocrine tumours from the gastrointestinal tract were analysed. The study included 125 pNET cores from 112 patients after application of inclusion criteria. MET expression was determined using the H-score system. Different variables were assessed for H-score distribution and cross-tables. Survival analyses were conducted based on progression-free survival and overall survival. Positive MET expression was found in 83.5% of cases. Higher MET H-scores were seen in patients with lymphovascular invasion (LVI), distant metastases and higher tumour grade (P < 0.05). When assessing different variables for higher MET H-scores, a significant association emerged at the 150-cut-off-point for LVI, perineural invasion, radiological evidence of progression and overall survival. For survival analysis, at a MET H-score threshold of 200, high MET expression was significantly associated with shorter progression-free survival (mean 8.7 versus 13.4 years, P < 0.05) and overall survival (mean 3.6 versus 7.7 years, P < 0.05). CONCLUSION: Elevated MET expression is linked to adverse histopathological features and worse clinical outcomes in pNET. Standardizing MET IHC evaluation is critical as anti-MET therapies develop, and identifying patients likely to benefit from these treatments remains essential.

MET protein↗

PubMed2026

Introgression shapes the genomic conflict landscape of Malus, providing evidence for a reticulate backbone in a woody crop lineage.

Phylogenomic discordance is widespread across plants, but its evolutionary significance is often obscured when conflict is treated primarily as analytical noise rather than as evidence of underlying processes. In woody lineages in particular, incomplete lineage sorting, introgression, and genome duplication can interact over long timescales to produce complex genomic histories that are not adequately summarized by a strictly bifurcating tree. Here, we use Malus as a model woody genus to investigate how these processes structure conflict across a genus-scale, accession-based phylogenomic framework. Using broad taxon sampling, hundreds of nuclear loci, plastid genomes, and genome-wide SNP summaries, we reconstruct a robust nuclear backbone for sampled Malus lineages and evaluate where discordance is concentrated and which processes best explain it. Nuclear analyses resolve eight major clades, whereas conflict is non-random and localized to recurrent hotspots rather than evenly distributed across the tree. Cytonuclear discordance is similarly concentrated, especially around Clade H, represented by sampled accessions of M. tschonoskii, where localized plastid-nuclear disagreement is consistent with candidate plastid capture or organellar introgression. Multiple complementary analyses further indicate that the strongest conflict is not explained by ILS alone, but instead reflects lineage-structured introgression, while polyploid complexes represent additional localized sources of evolutionary complexity. Together, these results provide evidence for a reticulate genomic backbone in Malus and show how integrating nuclear, plastid, and genome-wide conflict analyses can help distinguish background discordance from process-specific signals in woody plant radiations. Several lineage-level reticulation hypotheses identified here should now be tested with broader population-level sampling and curated reference accessions.

Malus↗

PubMed2026

Genetic Ancestry and Carrier Variant Frequency Enrichment in a Colombian Andean Population: Insights From the Eje Cafetero.

Colombia is one of the most genetically diverse populations in Latin America, and its demographic process has promoted the persistence and local enrichment of deleterious alleles, increasing the frequency of autosomal recessive disorders, particularly in semi-isolated Andean populations such as the Eje Cafetero. However, exome-based reference data from this region remain scarce, limiting ancestry-aware variant interpretation and carrier screening strategies. We aimed to characterize the ancestry proportions of this population using exome data, and to estimate the carrier frequency and distribution of pathogenic and likely pathogenic (P/LP) variants in clinically relevant recessive genes. We conducted a cross-sectional study with whole-exome sequencing (WES) in 316 unrelated individuals from the Colombian Eje Cafetero. P/LP variants were evaluated in 454 genes associated with autosomal recessive disorders. The global ancestry proportions were estimated using a validated panel of 250 exome-compatible ancestry-informative markers. Carrier frequencies were compared against Non-Finnish Europeans (NFE) and Admixed Americans (AMX) from gnomAD v4. The cohort showed predominant European ancestry (mean 51%), followed by Native American (36%) and African (13%) components. We identified 151 carriers of 89 distinct pathogenic variants across autosomal recessive genes. The most frequent variants were SERPINA1 c.863A>T (5.5%), CFTR c.1210-11T>G (3.5%), and PYGM c.1094C>T (1.5%). Also, recurrent variants were significantly enriched compared with both NFE and AMX populations, supporting regional founder effects. This study represents one of the most comprehensive exome-based genetic characterizations of the Colombian Eje Cafetero, revealing ancestry-specific enrichment of clinically relevant autosomal recessive variants driven by founder effects.

Female↗

PubMed2026

Development and validation of an LC-MS/MS method for the quantification of the KRASG12C inhibitor divarasib.

Divarasib is a newly developed covalent KRASG12C inhibitor, currently under clinical investigation in a phase 3 trial in patients with non-small cell lung cancer (NSCLC). At the moment, very limited pharmacokinetic data are publicly known. However, obtaining more insight into the pharmacokinetic properties of divarasib is important, since this may provide a better understanding of its efficacy and safety risks. Pre-clinical studies have been performed in mouse models to evaluate the effect of drug transporters and drug-metabolizing enzymes on the plasma exposure and tissue distribution of divarasib. Therefore, a reliable quantification method is required. To our knowledge, no bioanalytical assay of divarasib has been published yet. Therefore, in this study we developed and validated an assay to quantify divarasib in human plasma and in eight different mouse-related matrices, and partially in mouse plasma, using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The method was initially evaluated over a concentration range of 1-10,000 nM. However, due to carry-over observed at 10,000 nM, the validated calibration range was established at 1-2000 nM, with matrix-dependent LLOQs of 1-10 nM. Erlotinib was used as an internal standard and acetonitrile was utilized to perform protein precipitation as sample pretreatment. Divarasib demonstrated stability in human plasma and in mouse plasma and tissue homogenates under various experimental conditions. A pilot in vivo study showed the applicability of our validated LC-MS/MS method. Ongoing clinical trials may collect plasma samples, and this developed method enables quantification of divarasib in both mouse and human plasma samples.

Animals↗