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PubMed2026

Meta-analysis of growth and inactivation kinetics of Legionella.

Quantitative risk assessments intended to inform evidence-based water management plans and public health targets for Legionella in engineered water systems are constrained by fragmented and heterogeneous growth and inactivation kinetics. We conducted a meta-analysis of 25 growth and 39 thermal- and chemical-inactivation studies, fitting microbial persistence models to harmonize parameters. Nonlinear models outperformed first-order formulations, indicating that lag phases and resistant or protected subpopulations are central to Legionella persistence. Random forest analysis identified environmental and methodological drivers of variability based on 226 growth rates and reduction times for thermal (209) and chemical (135) inactivation. Growth was primarily governed by temperature, nutrient availability, and compatible Legionella-host pairings; thermal inactivation by quantification method, temperature, and turbidity; and chemical inactivation by inoculum size, disinfectant type, concentration, and host-associations. Accordingly, temperature-dependent growth parameters and exposure metrics for heat, free-chlorine, and monochloramine, expressed as TT (Temperature×time) and CT (Concentration×time), were derived as condition-specific inputs for predictive models. Growth optima around 37-40 °C, together with lag-time estimates, indicate that hot-water temperature setbacks and energy-saving practices may favor Legionella proliferation under repeated or prolonged lukewarm exposure. Culture- and viability-based TT differences highlight the need to consider viable‑but-non-culturable persistence in monitoring programs. CT comparisons suggest monochloramine may be advantageous because of its lower apparent sensitivity to host-associated protection. Although limited by restricted experimental conditions, the findings show that predictive models should account for microbial ecology, water matrix effects, and quantification endpoints. Future kinetic studies should prioritize realistic multi-host systems, strain pre-adaptation, complementary viability measurements, and standardized protocols and reporting to ensure reproducibility and enable robust system-level predictive modeling.

Legionella↗

PubMed2026

From commensal to pathobiont: The emergence of virulence-enhanced Escherichia coli in China's food-animal systems - insights with future implications.

A fundamental shift in Escherichia coli epidemiology is being driven by convergence of virulence determinants and antimicrobial resistance within linked human-animal-environment systems. In China, the rapid growth of food-animal production, extensive antimicrobial use, and complex food networks are accelerating the emergence and dissemination of virulence-enhanced E. coli pathobionts. This review synthesizes recent epidemiological, genomics, and outbreak data to characterize China's evolving landscape of food-animal-associated E. coli. We highlight a significant shift from classical pathotypes to hybrid lineages that simultaneously carry virulence factors and last-resort antibiotic resistance determinants, including mcr-1, tet(X4), and blaNDM. These traits disseminate rapidly via plasmid-mediated horizontal gene transfer, facilitating rapid adaptation and enabling cross-sectoral One Health transmission. National surveillance, foodborne outbreak investigations, and whole-genome sequencing data show that food-animal reservoirs are active evolutionary niches that drive pathogen diversity and fitness, rather than serving merely as contamination sources. Whole-genome sequencing also pinpoints high-risk clones (e.g., ST394) and plasmid-mediated co-selection of virulence and AMR. The emergence of hybrid pathotypes (e.g., STEC/ETEC) and AMR-virulence co-selection challenges traditional classification and limits the effectiveness of conventional surveillance approaches. The 2017 colistin ban reduced mcr-1, yet ongoing resistance and emerging tet(X4) demand integrated surveillance. Collectively, these findings call for reconceptualizing E. coli as a dynamic genomic entity embedded within a unified ecological network. Addressing this threat requires an integrated One Health strategy including genomic surveillance, agricultural antimicrobial stewardship, and coordinated food-environment-clinical monitoring to prevent high-risk clone emergence and global spread.

Animals↗

PubMed2026

Development and Validation of a Predictive Model for Identification of Cognitive Impairment Risk in Older Adults with Subjective Cognitive Decline：A Longitudinal Study.

BACKGROUND: Subjective cognitive decline (SCD) is a transitional state between objective cognitive impairment and cognitively intact mental status, providing a critical window for implementing preventive interventions to delay objective cognitive decline. AIMS: We aimed to develop a predictive model for SCD progression in older adults with mild cognitive impairment (MCI). This model will facilitate the identification of risk factors and establishment of targeted interventions for community-based SCD management. METHODS: Data from the China Health and Retirement Longitudinal Study (CHARLS) was utilized in this study, extracting 18 indicators. Potential predictors selected through univariate Cox regression and LASSO regression analyses were sequentially incorporated into a multivariable Cox regression model. A nomogram was constructed to establish a predictive model. Model validation encompassed Area Under Curve (AUC) metrics for discriminative capacity, complemented by quantitative assessments using calibration curve analysis for precision verification and decision curve analysis (DCA) for clinical utility evaluation. RESULTS: A total of 1099 older adults with SCD were included in the final analysis, of whom 114 (10.3%) developed MCI. Multivariable Cox regression identified residence, marital status, educational level, social participation, gait speed, and baseline cognitive function. The model demonstrated time-dependent AUC values of 0.885, 0.830, 0.839, and 0.836 in the training set when evaluating discriminative capacity at 2-, 4-, 7-, and 9-year, respectively. The predictive model showed excellent predictive ability according to AUC, calibration curve, and DCA. CONCLUSIONS: A predictive model was created to estimate the risk of developing MCI in older individuals with SCD, offering clinician-actionable intervention benchmarks for preventive care.

Humans↗

PubMed2026

An individualized nomogram for predicting progression-free survival in systemic anaplastic large cell lymphoma: a multicenter, retrospective, and internally validated study.

OBJECTIVES: To develop an individualized nomogram for predicting disease progression risk in systemic anaplastic large cell lymphoma (sALCL). METHODS: Independent predictors of progression-free survival (PFS) were identified using Cox regression in a multicenter retrospective cohort of 109 sALCL patients (2010-2022). These were incorporated into a three-factor nomogram, evaluated via bootstrapped internal validation (1000 resamples), ROC analysis, C-index, decision curve analysis (DCA), and clinical impact curve (CIC). RESULTS: A total of 29 PFS events occurred during a median follow-up of 31 months. Multivariable modelling selected serum β2-microglobulin elevation, extranodal disease, and front-line chemotherapy choice (CHOP versus CHOPE or BV+CHP) as autonomous progression drivers. Upon internal bootstrap validation, the nomogram yielded strong prognostic accuracy, achieving AUCs of 0.81, 0.85 and 0.87 for 1-, 3- and 5-year progression-free survival, alongside a corrected C-index of 0.779 (95% CI: 0.699 - 0.861). Calibration plots showed close agreement between predicted and observed outcomes, while DCA confirmed superior net clinical benefit versus conventional IPI or Ann Arbor stratification across multiple decision thresholds. CONCLUSION: This first sALCL-specific nomogram integrates clinical and treatment variables to provide personalized PFS risk estimation. While internally validated, this exploratory, observation-based tool requires external validation and recalibration in prospective cohorts before clinical implementation.

Humans↗

PubMed2026

Predictive evolutionary genomics: principles, validation, and practice.

Climate change and habitat loss are driving rapid evolutionary responses in populations world-wide, which creates an urgent need for evolutionary forecasting in conservation and agriculture. Such forecasting can be categorized into three time scales: trait-based models that use multivariate quantitative genetic equations to project correlated phenotypic responses up to c. 20 generations, allele-based analyses that model allele frequency dynamics up to 100 generations, and composite adaptation scores that aggregate many small effects to yield predictions across longer horizons. However, these approaches have remained largely disconnected. Here, we present a Bayesian framework that integrates these three complementary approaches for evolutionary prediction. Our framework combines genomic, phenotypic, and environmental data to yield probabilistic predictions with explicit uncertainty. We show how predictive evolutionary forecasts can be validated with experimental evolution, field experimentation, historical specimens, and reciprocal transplants. These validated forecasts can help advance conservation and agricultural programmes by helping predict which populations are at risk of future extinction, optimizing breeding programmes for future climates, and planning ecosystem management under environmental change. By supporting a shift towards more predictive approaches in evolutionary biology, this framework may help improve our ability to manage biodiversity and food security in a changing world.

Genomics↗

PubMed2026

Development and validation of a comprehensive prognostic model for 28-day ICU mortality in non-traumatic subarachnoid hemorrhage: an analysis based on the MIMIC-IV database.

BACKGROUND: Due to the complex pathophysiology of non-traumatic subarachnoid hemorrhage (SAH), accurate risk prediction remains a challenge. Our aim is to develop and validate a comprehensive prognostic model that integrates demographic characteristics, vital signs, laboratory parameters, and more, to provide clinical decision-making support in real-world practice. METHODS: We conducted a retrospective cohort study of 785 Non-traumatic subarachnoid hemorrhage patients. The cohort was randomly divided into a training set (n = 549) and a validation set (n = 236). Feature selection was performed using LASSO regression, followed by backward stepwise Cox regression for optimization. A nomogram was constructed based on independent predictive factors, and model performance was assessed using discrimination, calibration, and decision curve analysis. To prevent immortal-time bias, all predictors were anchored to a fixed early (first-24-hour) measurement window, treatment variables were modelled as binary indicators rather than cumulative exposures, and a five-model sensitivity analysis with baseline-severity adjustment was performed. RESULTS: The development of our model followed a systematic approach: first, 15 potential predictive factors were selected via LASSO regression, which were then refined to 12 independent predictors using backward stepwise Cox regression. The final predictive factors included: Ventilation, AHT, Nimodipine 60 mg, Age, SAPS.II, Input amount, Calcium total, Platelet count, White blood cells, Anion gap, pH, and Chloride. The integrated model demonstrated excellent predictive ability for 7-day, 14-day, and 21-day mortality in both the training set (AUC: 0.972, 0.934, 0.898) and the validation set (AUC: 0.968, 0.948, 0.911). Calibration curves and decision curve analysis confirmed the model's reliability and clinical utility across different time points. We constructed a nomogram for individualized risk prediction. Univariate Kaplan-Meier survival analysis demonstrated significant stratification of survival outcomes by each predictor, while restricted cubic spline analysis revealed non-linear relationships between continuous variables and mortality risk. Random survival forest analysis identified the top three predictive factors (Nimodipine 60 mg, Ventilation, AHT) and compared them with our full 12-variable model, confirming superior performance of the integrated model at all time points. At the 28-day primary endpoint, the model achieved a time-dependent AUC of 0.898 (training) and 0.904 (validation); after restricting predictors to the early baseline window, the leakage-controlled model retained good discrimination (validation C-index 0.803). CONCLUSIONS: Our ICU 28-day mortality prognosis model demonstrated robust performance in predicting ICU 28-day mortality in non-traumatic subarachnoid hemorrhage. The model, through the nomogram, provides individualized risk assessment, aiding clinical decision-making and patient stratification.

Humans↗

PubMed2026

Validation of the lung immune prognostic index in extensive-stage small cell lung cancer: Post hoc analysis of the caspian and IMpower133 phase 3 trials.

BACKGROUND: The Lung Immune Prognostic Index (LIPI) is an inflammation-based biomarker associated with outcomes to immunotherapy across several tumor types. Its prognostic value in extensive-stage small-cell lung cancer (ES-SCLC), however, remains insufficiently validated. We aimed to validate the prognostic impact of LIPI in ES-SCLC using data from two phase III trials. METHODS: Patients enrolled in the CASPIAN (NCT03043872) and IMpower133 (NCT02763579) trials were included. LIPI groups were defined as good (dNLR<3 and LDH<ULN), intermediate (dNLR≥3 or LDH≥ULN) and poor (dNLR≥3 and LDH≥ULN). Overall survival (OS) and progression-free survival (PFS) were assessed across LIPI categories and treatment arms. RESULTS: LIPI was available for 1140 patients (Good: 34%, Intermediate: 49%, Poor: 17%), including 708 treated with chemotherapy-immunotherapy and 432 with chemotherapy alone. Poor LIPI was associated with unfavorable characteristics, including lower albumin levels and higher rate of liver metastases. Median OS was 14.6 months (95%CI: 12.4-15.9) for LIPI Good, 10.9 (10.1-11.5) for Intermediate, and 8.4 (7.1-9.3) for Poor (p < 0.0001). In multivariate models adjusted on gender, age, ECOG, treatment arm and metastatic sites, LIPI remained an independent prognostic factor for OS (HR Poor vs. Good: 1.76, 95%CI: 1.45-2.15, p < 0.001) and PFS (HR: 1.59, 95%CI: 1.33-1.90, p < 0.001). Although patients with poor LIPI derived limited benefit from immunotherapy, no significant treatment-LIPI interaction was observed. CONCLUSION: This large post hoc analysis confirms LIPI as a robust and clinically applicable prognostic biomarker in ES-SCLC. Patients with poor LIPI have substantially worse outcomes and limited benefit from immunotherapy, highlighting the need for novel therapeutic strategies in this subgroup.

Humans↗

PubMed2026

Self-Report Health Screening Tools in Female Athletes: A Systematic Review of Domain Coverage, Validation, and Use Across Participation Levels.

BACKGROUND: Female athlete health encompasses multiple interconnected domains; however, the self-report screening tools used to assess these domains have not been comprehensively synthesised. OBJECTIVE: To systematically identify self-report health screening tools used to assess female athlete health, map domain coverage, determine validation reporting, and describe application across participation levels. METHODS: This systematic review was pre-registered with PROSPERO ( CRD420251056910 ) and conducted in accordance with PRISMA guidelines. Four databases (PubMed, MEDLINE, SPORTDiscus and Web of Science) were searched from inception to January 2026 using female health and screening-related terms. Methodological quality was appraised using Joanna Briggs Institute and National Institutes of Health tools, and findings were synthesised descriptively. Eligible, peer-reviewed studies reported the use, development or validation of self-report health screening tools assessing one or more domains relevant to female health applied in female athlete populations, spanning recreational through elite participation levels. All sports and activities were included. The search was restricted to English language with no date limits. RESULTS: In total, 360 studies (1990-2026) representing 134,506 female participants spanning recreational to elite sport and 273 screening tools were included. Mental health (n = 77, 34.1%), disordered eating (n = 33, 14.6%) and body image (n = 30, 13.3%) predominated. Domains related to female health, including menstrual health, pelvic floor health, pregnancy/postpartum and breast health were comparatively underrepresented. Most studies reported tools were used for risk identification (n = 323, 80.3%). Validation reporting was inconsistent, with half (n = 180, 50%) reporting use of at least one validated tool. Tool use was concentrated in professional and elite sport, with limited inclusion of recreational, masters and disability athlete cohorts. Health literacy constructs were explicitly assessed in 12.5% of studies (n = 45). CONCLUSIONS: Health screening in female athlete populations remains fragmented and uneven in domain coverage, with inconsistent validation reporting. Development of integrated, multi-domain and contextually inclusive screening frameworks is warranted.

Journal Article↗

PubMed2026

Development and validation of a liquid chromatography-tandem mass spectrometry method for the quantification of twenty-five steroids in equine serum.

Steroids are potential biomarkers for monitoring equine pregnancy. However, immunoassays currently used for their quantification suffer from cross-reactivity and limited specificity, thus requiring more accurate methods. This study reports the development and validation of a robust liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for simultaneous quantification of 25 steroids covering the main biosynthetic pathways of progestogens, corticosteroids, androgens, and estrogens. Steroids were extracted by protein precipitation followed by evaporation, derivatization, and reconstitution before LC-MS/MS analysis. A surrogate matrix was used for calibration and validation to avoid endogenous interference. Validation was performed according to and partly adapted from Clinical and Laboratory Standards Institute guidelines (CLSI), including linearity, trueness, precision, limits of detection and quantification, measurement uncertainty, recovery, matrix effects, carryover, selectivity, and stability. Calibration curves were fitted using the best-performing weighted linear or quadratic regression model, yielding excellent linearity (R2 > 0.990), trueness between -9.0% and 2.3%, and intra- and inter-day precision <6.3%. Lower limits of quantification ranged from 2.07 to 2250 pg/mL depending on physiological analytes concentration. Extraction recovery averaged 24.3-114.9%, matrix effects were acceptable, and accuracy ranged from 94.4% to 98.9%. No carryover or interferences were detected. Measurement uncertainty remained <15%. This study presents the first LC-MS/MS method partially validated per CLSI criteria for the quantification of 24 steroids in equine serum. The method offers a sensitive and specific alternative to immunoassays and provides a robust tool for equine steroid profiling with potential applications in pregnancy monitoring, placentitis diagnosis, and fetal sex determination.

Animals↗

PubMed2026

Experimental validation of an AI-driven digital healthcare platform for oral health behavior and plaque assessment among vietnamese children.

BACKGROUND: Oral health among children in developing countries, including Vietnam, remains a significant public health concern. Innovative approaches leveraging artificial intelligence AI-based digital health platforms may offer effective strategies for managing dental plaque and promoting better oral hygiene behaviors among school-aged children. This study aimed to evaluate the effectiveness of an AI-driven oral healthcare platform (Denti-i Vietnam) in improving oral hygiene and behavioral outcomes among Vietnamese primary school students. METHODS: A total of 204 primary school students aged 8-10 years in Hanoi, Vietnam, participated in this experimental study. Participants were randomly assigned to an intervention group (n = 107), which used the AI-driven oral healthcare platform, and a comparison group (n = 97), which received traditional oral health education via pamphlets. Oral health behaviors, dental plaque levels (Simplified Oral Hygiene Index; OHI-S), and caries indices (dft/DMFT) were assessed at baseline and after the intervention period. RESULTS: The intervention group demonstrated a significant reduction in the OHI-S score compared to baseline (2.49 ± 0.60 to 1.70 ± 0.76, p < 0.001), particularly in the debris component, indicating enhanced plaque control. Notable improvements were also observed in oral hygiene behaviors, including increased frequency of toothbrushing before and after breakfast (p < 0.01) and more frequent parental assistance during brushing (p = 0.03). Furthermore, parental awareness of dental caries significantly increased in the intervention group (p = 0.001). CONCLUSIONS: The AI-driven oral healthcare platform significantly improved both oral hygiene behaviors and plaque control among Vietnamese primary school children. These findings suggest that AI-driven digital health tools can serve as practical and scalable solutions for promoting oral health in developing countries.

Humans↗

PubMed2026

Physical reconfiguration of limb electrodes for Precordial Bipolar Lead acquisition: Morphological validation against digital subtraction.

BACKGROUND: The V2 - V1 Precordial Bipolar Lead (PBL) selectively evaluates the right-to-left retrosternal axis and has shown diagnostic value beyond the standard 12‑lead electrocardiogram. However, its use has been limited by the need for raw electrocardiographic data and post-processing software. This study evaluated whether a simple physical reconfiguration of limb electrodes could reproduce the digitally derived V2 - V1 morphology with sufficient accuracy for clinical application. METHODS: Thirty-seven subjects underwent two sequential 10-s 12‑lead recordings using a Cardiovit FT-1 electrocardiograph sampled at 1000 Hz. In the standard recording, the digital PBL was calculated as V2 - V1. In the second recording, the right-arm and left-arm electrodes were repositioned to the V1 and V2 sites so that Lead I directly recorded the retrosternal dipole. Signals were filtered, synchronized, and analyzed using median beats. Morphological agreement was assessed with Pearson correlation on Z-normalized signals, while absolute agreement was evaluated using Lin's concordance correlation coefficient (CCC), intraclass correlation coefficient (ICC (Lewis, 1931; Nehb, 1938 [1,2])), root mean square error (RMSE), and Bland-Altman analysis. RESULTS: Mean Pearson correlation between digital and physical PBL was 0.955 (SD 0.043), with segment-specific correlations of 0.953 (SD 0.054) for QRS and 0.967 (SD 0.052) for ST-T. Lin's CCC and ICC(2,1) were both 0.871 (SD 0.110), and RMSE was 0.091 (SD 0.049) mV. Bland-Altman analysis showed minimal bias (-0.008 mV). CONCLUSIONS: Physical acquisition of the V2 - V1 PBL achieved high agreement with the digitally derived signal, supporting a simplified analog method for broader clinical implementation.

Humans↗

PubMed2026

Relational care in community mental health: Evaluating staff experiences in Intensive Community Care Services (ICCS) vs treatment as usual.

BACKGROUND: The quality of healthcare delivery relies heavily on building strong relationships between healthcare providers (HCPs) and clients. This study presents the results of a process evaluation for a Randomised Controlled Trial (RCT) examining the effectiveness of Intensive Community Care Service (ICCS) vs Treatment as Usual (TAU; inpatient or core community CAMHS). METHODS: Thirty-four semi-structured interviews were conducted with staff across various services, including 20 from ICCS and 14 other TAU services. A thematic decomposition analysis was conducted on the data, and specific themes relevant to staff experiences of young people's engagement with services and overall recovery. RESULTS: Three main themes were observed in the HCP data (1. Relational Ecologies: barriers and enablers to engagement, 2. flexibility of approach amidst systemic pressures and 3. the web of trust in the relationship-building process). HCPs highlighted the necessity of developing trust and rapport through non-clinical engagement strategies, such as informal visits and personalised interactions. HCPs emphasised that without trust, treatment effectiveness diminishes, necessitating a tailored approach rather than a one-size-fits-all model. The flexibility in duration of treatment and methods of engagement was noted as crucial in accommodating individual client needs and fostering an open, trusting environment necessary for long-term recovery. CONCLUSION: The findings highlight the vital importance of relational care models, especially ICCS, in addressing the complex needs of Children and Young People (CYP). Flexible, family-centred approaches improve trust, engagement, and long-term recovery outcomes. Recommendations include tackling systemic barriers and expanding relational care models within CAMHS to meet increasing mental health demands effectively. Further research should investigate scalable strategies for integrating these insights into wider mental health service frameworks.

Humans↗

PubMed2026

Measuring what we count, counting what we measure: Two extensions to the case for modernizing cannabis use disorder surveillance.

Explore the source record for details and available documents.

CUDIT‐R↗

PubMed2026

A machine learning-derived and functionally validated circadian rhythm signature predicts clinical outcomes and in silico drug sensitivity in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) displays considerable heterogeneity in clinical outcomes, highlighting the need for reliable prognostic biomarkers. While the aberrant expression of circadian rhythm-related genes has been implicated in cancer pathogenesis, its comprehensive role in CRC progression and predicted therapeutic vulnerabilities remains inadequately characterized. METHODS: Bulk and single-cell RNA-sequencing data were integrated from multiple CRC cohorts. A circadian rhythm signature (CRS) was developed through machine learning algorithms and validated for prognostic value. Comprehensive analyses of tumor microenvironment, genomic alterations, and drug sensitivity were performed. Furthermore, the biological function of the core gene, BHLHE40, was validated in CRC cell lines through CCK-8, EdU, and wound healing assays. RESULTS: Single-cell analysis demonstrated an elevated expression signature of circadian rhythm-related genes in dendritic cells. The optimized CRS, comprising 14 circadian rhythm-related genes, successfully categorized patients into high- and low-risk groups. Patients with a high CRS showed markedly poorer overall survival and computationally inferred immunosuppressive features, including reduced CD8+ T cell infiltration and increased M2 macrophage polarization. Genomic analysis revealed enhanced mutation burden in TP53 and alterations in RTK-RAS/WNT pathways. Notably, in vitro assays confirmed that BHLHE40 is significantly overexpressed in CRC cells. Knockdown of BHLHE40 markedly inhibited tumor cell proliferation and migration. Drug sensitivity profiling identified bexarotene and SMER-3 as potential therapeutic options for high-CRS patients. A nomogram integrating CRS with clinical parameters demonstrated superior predictive accuracy for 1-, 3-, and 5-year survival. CONCLUSIONS: The CRS represents a promising prognostic biomarker that reflects tumor immune status and genomic features, providing valuable insights for personalized treatment strategies in CRC.

Circadian rhythm↗

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↗

PubMed2026

How prevalent is fear of cancer recurrence beyond 5 years: a systematic review of validated assessments across tumour types.

PURPOSE: Fear of cancer recurrence (FCR) is an established challenge for cancer survivors. Research however has largely focussed early in treatment, with varying assessments and often single tumour sites. This systematic review set out to determine prevalence of FCR in survivors beyond 5 years across all tumour types. METHOD: We designed a search strategy to identify publications assessing FCR in survivors beyond 5 years with sample size greater than 50, using validated measures. Applying PRISMA methodology and with defined inclusion and exclusion criteria two authors independently assessed the studies for eligibility. Data extraction recorded number of participants, tumour type, study design, FCR tool, time points for assessment and reported prevalence. Risk of bias was assessed to address quality. RESULTS: Ten papers were included, reporting FCR from 5 years to beyond 20 years. Validated tools employed were FCRI-SF and FOP-Q-SF. Sample sizes ranged from 64 to 5983 participants, with a heterogeneous mix of tumour types and age. Only two studies reported longitudinal measurements. Prevalence of FCR above defined threshold ranged from 13 to 33.9% for those studies with acceptable risk of bias reporting distinct cohorts beyond 5 years. CONCLUSION: The limited evidence suggests that clinically relevant FCR persists in some survivors at 5 years. Our review demonstrates the challenge of heterogeneous patient populations in FCR research emphasising the need for improved consensus on measurements and more prospective longitudinal research representing a comprehensive variety of tumour types. We address the clinical implications of persistent FCR and the need to implement effective interventions.

Humans↗

PubMed2026

Development and validation of a novel LC-MS/MS method for simultaneous quantification of fidaxomicin and metabolite (OP-1118) from feces for gut pharmacobiome studies.

Fidaxomicin is a first-line antibiotic for treating Clostridioides difficile infection. While it has low systemic absorption and reaches high colonic concentrations, it is hydrolyzed to a less active metabolite, OP-1118. Few studies have completely described critical experimental details of liquid chromatography-tandem mass spectrometry (LC-MS/MS) for quantifying fecal fidaxomicin and OP-1118. This study developed and validated a simple, fast, and sensitive LC-MS/MS method to quantify fidaxomicin and OP-1118 in human and mouse feces. This method simplified fecal sample preparation without the use of solid phase extraction and optimized LC-MS/MS parameters. A broad working range (0.3-1000 ng/ml) in both diluted human and murine fecal matrices was achieved with good intra- and inter-day accuracy (93-107%), precision (1-7%), and recovery (70-105%) as well as little IS-normalized matrix effects. This method was utilized to quantify fidaxomicin and OP-1118 in human and murine fecal samples. This novel method was simple, fast, sensitive, and accurate in analyzing fecal fidaxomicin and OP-1118 and could be deployed to facilitate gut pharmacobiome research.

Feces↗

PubMed2026

Integration of single-cell transcriptomics and genomic mutation analysis identifies an immunotherapy-resistant tumor subcluster and validates ARNTL2 as a malignant driver in lung adenocarcinoma.

BACKGROUND: Immunotherapy resistance in lung adenocarcinoma (LUAD) remains a critical clinical challenge, and the mechanisms underlying resistance-associated intratumoral heterogeneity are poorly characterized. METHODS: We performed single-cell RNA sequencing of LUAD patients receiving neoadjuvant immunotherapy (responders vs. non-responders), integrating inferCNV, GSVA, and differential expression analyses. Cluster-specific genes were validated across seven independent cohorts (TCGA-LUAD, GSE13213, GSE26939, GSE29016, GSE30219, GSE31210, GSE42127). A multi-algorithm machine learning framework was used to construct a prognostic model, and the immune microenvironment was characterized using TCIA scoring, seven infiltration algorithms, and ESTIMATE. ARNTL2 function was assessed by CCK-8 and Transwell assays in A549 and H1299 cells. RESULTS: Non-responders showed significant enrichment of epithelial cells, depletion of cytotoxic T/NK cells, and elevated copy number variation burden versus responders (p < 0.0001). A resistance-enriched malignant subcluster (Cluster 2) exhibited hyperproliferative and metabolic reprogramming signatures with upregulated KRT17, S100A2, and CST6, which showed tumor-specific overexpression, adverse prognostic value, and genomic amplification across cohorts. CoxBoost combined with survivalSVM achieved optimal predictive performance (C-index = 0.686), yielding robust risk stratification (HR: 2.54-10.51, all p < 0.05). Low-risk patients showed greater immune infiltration and higher TCIA immunophenoscores. ARNTL2 was an independent prognostic factor (HR: 2.07-4.64) strongly correlated with risk score (r = 0.69), and its knockdown suppressed proliferation and invasion in both LUAD cell lines (all p < 0.05). CONCLUSION: This study identifies a resistance-associated malignant subcluster in LUAD, constructs a validated CoxBoost + survivalSVM prognostic model with robust immune stratification, and establishes ARNTL2 as a core oncogenic driver and therapeutic target.

ARNTL2↗