Laryngeal squamous cell carcinoma (LSCC) remains clinically challenging because of immune escape and resistance to chemotherapy, radiotherapy, and immune checkpoint blockade. Although immunometabolism encompasses diverse nutrient, redox, and stromal pathways, LSCC-specific evidence is currently strongest for glycolysis/lactate metabolism, mitochondrial remodeling, oxidative stress adaptation, ferroptosis-related regulation, extracellular-vesicle-mediated macrophage remodeling, and checkpoint-associated T-cell dysfunction. This focused review therefore examines resistance-oriented immunometabolic circuits rather than providing an exhaustive catalogue of all metabolic pathways. We discuss how glycolytic activation and lactate accumulation may generate nutrient-competitive and acidic niches; how mitochondrial stress, ROS adaptation, and ferroptosis-related processes influence tumor survival; and how tumor-derived vesicles, TAMs, TILs, Tregs, pDCs, and emerging neutrophil/CAF-related signals shape immune escape. Underexplored axes, including lipid and amino-acid metabolism, glutamine dependence, arginine metabolism, tryptophan-IDO signaling, adenosine metabolism, hypoxia/HIF signaling, NK cells, MDSCs, endothelial cells, and broader stromal-immune interactions, are highlighted as evidence gaps requiring LSCC-specific validation. This review proposes a focused framework for biomarker development and rational combination therapy.

To investigate the effects of metformin on the proliferation, migration, and invasion of prostate cancer cells, and to determine whether its antitumor effects are mediated by regulation of human antigen R (HuR) and the AKT/mammalian target of rapamycin (mTOR) signaling pathway.

Human prostate cancer cell lines PC3 and 22RV1 were used as the research models and were treated with metformin. HuR knockdown and overexpression experiments were performed to further verify the underlying mechanism. Cell proliferation was assessed using the cell counting kit-8 (CCK-8) assay. Cell migration and invasion were evaluated by Transwell assays. The expression levels of HuR and AKT/mTOR pathway-related proteins were measured by quantitative real-time polymerase chain reaction (qRT-PCR) and Western blotting.

Metformin treatment reduced the proliferation, migration, and invasion abilities of PC3 and 22RV1 cells, and inhibited the expression of proteins associated with the AKT/mTOR signaling pathway (P < 0.05). HuR knockdown also decreased the proliferation, migration, and invasion abilities of prostate cancer cells and inhibited AKT/mTOR pathway activity. In contrast, HuR overexpression partially attenuated the inhibitory effects of metformin on the malignant biological behaviors of prostate cancer cells and its inhibitory effects on the AKT/mTOR signaling pathway. In addition, metformin downregulated HuR mRNA and protein expression levels in PC3 and 22RV1 cells in a concentration-dependent manner (P < 0.01).

Metformin may inhibit the activation of the AKT/mTOR signaling pathway by downregulating HuR expression, thereby reducing the proliferation, migration, and invasion of prostate cancer cells.

TATA-binding protein-associated factor 3 (TAF3), a member of the TAF family, plays a crucial role in safeguarding finely balanced transcriptional programs. Previous research identified TAF3 as a critical prognostic marker in hepatocellular carcinoma (HCC). This study aimed to elucidate TAF3's functional role and mechanistic underpinnings in liver cancer progression. Through the establishment of stable TAF3-knockdown and overexpression cell lines in human HCC cells (HepG2 and MHCC97H), comprehensive functional analyses revealed that TAF3 knockdown significantly inhibited cancer cell proliferation, migration, and invasion, while its overexpression promoted these malignant phenotypes. Clinically, elevated TAF3 expression correlated with aggressive clinicopathological features and poor prognosis in HCC patients. Mechanistic investigations demonstrated that TAF3 transcriptionally activates sterol regulatory element-binding protein 2 (SREBP2), a master regulator of cholesterol synthesis, leading to increased intracellular cholesterol accumulation. This cholesterol elevation functionally contributed to oncogenic processes, as exogenous cholesterol supplementation reversed the impaired malignancy in TAF3-deficient cells. In vivo validation using a subcutaneous xenograft mouse model confirmed that TAF3 knockdown suppressed tumor growth, an effect effectively counteracted by high-cholesterol dietary intervention. Collectively, these findings establish that TAF3 promotes liver cancer progression through transcriptional activation of SREBP2 and subsequent enhancement of cholesterol biosynthesis. The study identifies a novel TAF3/SREBP2/cholesterol axis as a promising therapeutic target in TAF3-overexpressing hepatocellular carcinomas, providing mechanistic insights into the interplay between transcriptional regulation and metabolic reprogramming in cancer progression.

Moving from an association signal to a clinically credible biomarker requires several links that are often conflated: verified variant identity, aligned allelic effects, reproducible gene-level association, relevant cellular expression, and a plausible functional consequence. We developed a source-aware multiomics framework to assess MPHOSPH6 in lung adenocarcinoma (LUAD) while keeping those evidence classes separate. Six prespecified rsIDs were recovered from the harmonized TRICL LUAD dataset, of which five reached p < 5 × 10 - 8. Only rs112333466 and rs76474922 were available with alignable alleles in FinnGen R10, and both showed concordant directions. Fixed-effect estimates were OR = 1.592 for rs112333466-T (95% CI, 1.401-1.809; p = 9.91 × 10 - 13) and OR = 0.819 for rs76474922-C (95% CI, 0.773-0.867; p = 1.03 × 10 - 11). In a prespecified two-variant GTEx v8 lung model, genetically predicted MPHOSPH6 expression was positively associated with LUAD in TRICL (Z = 3.341, p = 8.35 × 10 - 4) and FinnGen (Z = 2.697, p = 0.0070). This gene-level result did not establish colocalization or connect MPHOSPH6 to the six susceptibility rsIDs. Patient-level analysis of 89,241 immune cells from six paired tumor and normal-adjacent lung samples found no significant difference in MPHOSPH6 pseudobulk abundance (exact paired Wilcoxon p = 0.3125). None of 688 lung-lineage pharmacogenomic tests remained significant after false-discovery-rate correction. Ten recorded MPHOSPH6 missense alleles, including five ClinVar variants of uncertain significance, were curated; structural analysis identified I58 at an experimental RNA-exosome interface and defined a focused perturbation series. MPHOSPH6 is therefore supported as a human-variation-informed candidate for functional evaluation, not as a validated LUAD biomarker, pathogenic gene, drug-response predictor, or therapeutic target.

To investigate changes in the levels of serum amyloid-A (SAA) and coagulation indicators in children with severe pneumonia complicated by mycoplasma infection and their relationship with the prognosis.

A total of 300 children with severe pneumonia were retrospectively enrolled. All the participants were admitted to the Department of Pediatrics of our hospital and received treatment there between July 2022 and December 2024. The patients were divided into a non-mycoplasma infection group (n = 180) and a mycoplasma infection group (n = 120) according to the presence or absence of mycoplasma infection. Then, children with severe pneumonia complicated by mycoplasma infection were further divided into a favorable prognosis subgroup (n = 85) and a poor prognosis subgroup (n = 35). Demographic and clinical data were collected for all participants. Logistic regression analysis was performed to determine whether age, SAA, fibrinogen (Fib), lung ultrasound (LUS) score, and other variables were influencing factors associated with prognosis, and their diagnostic performance was evaluated using receiver operating characteristic (ROC) curve.

The SAA Fib levels and LUS scores of the participants in the mycoplasma infection group were all higher than those in the non-mycoplasma group (all P < 0.05). Spearman correlation analysis showed that serum SAA levels, Fib levels, and LUS scores were positively correlated with both the occurrence of mycoplasma infection and poor prognosis after mycoplasma infection in children with severe pneumonia (all P < 0.05). The ROC curve analysis showed that the combined use of SAA, Fib, and LUS score predicted mycoplasma infection with an area under the curve (AUC) of 0.928 (95% CI, 0.899-0.957). Through multivariate logistic regression analysis, age (odds ratio [OR], 0.053; 95% CI, 0.003-0.997), SAA (OR, 1.045; 95% CI, 1.003-1.085), Fib (OR, 1.757; 95% CI, 1.378-8.158), and LUS score (OR, 3.538; 95% CI, 1.480-8.457) were identified as influencing factors associated with prognosis in children with severe pneumonia complicated by mycoplasma infection (all P < 0.05). ROC curve analysis demonstrated that the AUC of age, SAA, Fib, and LUS score for predicting prognosis were 0.837 (95% CI, 0.764-0.856), 0.792 (95% CI, 0.701-0.884), 0.755 (95% CI, 0.655-0.909), and 0.917 (95% CI, 0.828-1.000), respectively. The combined model incorporating the 4 parameters achieved an AUC of 0.997 (95% CI, 0.992-1.000).

Age, SAA, Fib, and LUS scores can be used as diagnostic indicators for assessing the prognosis of children with severe pneumonia complicated by mycoplasma infections. The combined model demonstrates the highest predictive performance and may contribute to improved clinical prognostic assessment.

Vaccine uptake among Black and Latine populations remains persistently lower than among white populations, yet drop-offs between screening and enrollment in clinical trials is underexamined. The current sociopolitical moment, marked by "DEI" rollbacks, disruptions of federally funded health research, and sensitivity to health equity language, heightens the urgency of this work and threatens infrastructure supporting equitable recruitment. This qualitative study investigated non-enrollment among individuals positively screened for a vaccine trial at Indiana University School of Medicine and the Indiana Clinical and Translational Sciences Institute. Seven themes emerged: institutional reputation; communication and awareness; healthcare professionals' role; public and community engagement; recruitment practices; research participant motivation; and barriers to enrollment. From a critical lens, the findings of this study make clear that closing the gap between interest and enrollment in clinical trials requires more than improved recruitment messaging but structural transformation within every step.

Ankylosing spondylitis (AS) and pulmonary diseases (PD) comorbidities accelerates functional decline and increases disease burden, but risk stratification and identification of these comorbidities are lacking. This study developed machine learning (ML) models based on immune-related cells and immuno-inflammatory markers to identify risk factors for pulmonary comorbidities in AS.

Demographic characteristics, immuno-inflammatory indices (e.g., neutrophils, erythrocyte sedimentation rate [ESR], C-reactive protein [CRP], immunoglobulin G, neutrophil-to-lymphocyte Ratio [NLR], platelet-to-neutrophil Ratio [PNR], pan-immune-inflammation value [PIV]) were measured and calculated for each patient. Feature selection was performed via least absolute shrinkage and selection operator (LASSO). Four ML algorithms, including decision tree, random forest, XGBoost, and support vector Machine (SVM), were developed and evaluated to distinguish between AS and AS+PD. Subgroup analyses were conducted for pulmonary nodules (PN) and chronic obstructive pulmonary disease (COPD), and sensitivity analysis was performed using unimputed data.

363 AS patients were enrolled, comprising 193 AS alone and 170 AS+PD patients (including 123 with PN and 47 with COPD). LASSO regression identified age, neutrophils, IgG, ESR, CRP, NLR, PNR, and PIV as key predictors for AS+PD. In the overall population, the SVM model demonstrated superior generalization and resistance to overfitting, whereas the random forest and XGBoost showed overfitting. In subgroup analyses, XGBoost emerged as the optimal classifier for AS+PN and AS+COPD, with good calibration and net clinical benefit. Sensitivity analyses confirmed the robustness and consistency of these findings.

AS patients with pulmonary comorbidities exhibit distinct immune-related cells and immuno-inflammatory markers (especially neutrophils, IgG, NLR, and PIV). Of the four ML models, SVM showed best overall generalization, whereas XGBoost performed excellently in PN and COPD subgroups. These findings support the integration of immuno-inflammatory markers with ML algorithms to improve identification of current status of pulmonary complications for pulmonary comorbidities in AS.

Artificial intelligence (AI) medical devices have become an important technological foundation for enhancing healthcare system resilience and promoting the equitable allocation of medical innovation resources. However, limited attention has been paid to the distribution of upstream innovation resources and the spatial organization of this industry in the wake of the pandemic. Using AI medical device patent collaboration data from the Yangtze River Delta during 2018-2025, this study constructs local and external collaborative innovation networks and applies social network analysis and the Geodetector to examine their spatial evolution and associated factors across different phases of the COVID-19 pandemic. The results show that all innovation networks expanded continuously, particularly the external collaborative network. Nevertheless, cross-regional collaboration remained concentrated in a limited number of core cities, resulting in a persistent core-periphery structure. Regional GDP, retail market size, higher education resources, science expenditure, and financial support consistently exhibited strong explanatory power for collaboration intensity, while GDP growth and openness became increasingly associated with network differentiation following the COVID-19 shock. Moreover, the explanatory power of most variables was stronger for external collaboration than for local collaboration, suggesting that cross-regional knowledge exchange relies more heavily on comprehensive innovation capacity. This study advances the understanding of the spatial organization of AI medical innovation and provides empirical evidence for promoting a more balanced allocation of cross-regional medical R&D resources.

Unsupervised, self-collected nasal swabs (SCNS) are a convenient alternative to collection by health-care workers for influenza-like illness (ILI) surveillance. We compared viral respiratory pathogen detection in paired SCNS versus research coordinator-collected nasal swabs (CCNS).

Adult Military Health System beneficiaries were enrolled in a prospective influenza vaccine effectiveness trial. Following vaccination, participants were contacted weekly to ascertain ILI symptoms. In the event of an ILI, participants completed a symptom diary, a SCNS, and were offered a visit for CCNS. We evaluated respiratory pathogen detection by PCR, concordance between paired swabs (Cohen's Kappa statistic), and the impact of the timing (days post symptom onset [DPSO]) of collection on detection.

3,357 ILIs were reported during the study period and paired (SCNS and CCNS) swabs were obtained during 1,048 ILIs. Among the paired swabs, SCNS were generally collected earlier than CCNS (median DPSO: 3.0 versus CCNS: 7.0; p < 0.001) and a higher proportion reported moderate or severe symptoms at the time of SCNS collection (43.9% versus 33.3%; p < 0.001). Among 988 swab pairs collected 0-7 days apart, viral pathogen detection was higher in SCNS (39%) versus CCNS (31%) (p < 0.001; K = 0.59). Paired swabs collected within 7 DPSO (K: 0.63 versus 0.42) were associated with a higher concordance.

SCNS are a feasible and useful alternative to CCNS for respiratory virus surveillance and research as it allows an average earlier collection time. Detection of viral pathogens is increased by obtaining swabs within 7 days of symptom onset.

https://clinicaltrials.gov/study/NCT03734237?term=NCT03734237&rank=1, NCT03734237.

Hypercytokinemia is a major contributor of tissue damage and mortality in severe influenza. However, most studies rely on static, single-time-point measurements, providing limited insight into the evolving inflammatory response. The prognostic relevance of longitudinal cytokine trajectories remains poorly defined.

We conducted a multi-cohort analysis of 186 patients from three public datasets to identify cytokines with influenza-severity-dependent expression patterns. We then analyzed a longitudinal cohort of 33 patients with severe influenza who underwent serial cytokine measurements, using linear mixed-effects models to identify the top 10 severity-associated cytokines. Integrating both analyses, we defined a core cytokine panel and a composite core panel score. Prognostic value was assessed using time-varying Cox regression and landmark analyses, with comparisons against clinical predictors, including LODS scores and CRP, as well as sensitivity analyses adjusting for diabetes.

A core panel of six pro-inflammatory cytokines (IL-8, IL-6, G-CSF, MCP-1, TNF-α, and MIP-1α) was identified. Non-survivors showed distinct longitudinal trajectories of the core panel score, characterized by a progressive increase over the disease course. Paired sputum analyses suggested that these systemic cytokine signatures partially reflected pulmonary inflammation. Baseline core panel scores were not significantly associated with mortality (HR 3.29, 95% CI 0.95-11.38; P = 0.059), whereas time-varying monitoring showed better model fit (AIC 29.34 vs 34.85). Core panel scores remained associated with mortality in analyses accounting for LODS scores, baseline CRP, and diabetes. Landmark analysis further suggested that updated core panel scores may provide increasing prognostic information over time compared with baseline assessment, with delta AUC increasing from near zero at day 5 to 0.24 by day 17 after ICU admission.

Dynamic monitoring of a core cytokine panel may provide greater prognostic information than single-time-point assessment in severe influenza, although these findings require validation in larger independent cohorts. Longitudinal inflammatory profiling may help refine risk stratification and provide a framework for future precision studies.