Background: The recent outbreak of coronavirus disease 2019 (COVID-19) has been rapidly spreading on a global scale and poses a great threat to human health. Acute respiratory distress syndrome, characterized by a rapid onset of generalized inflammation, is the leading cause of mortality in patients with COVID-19. We thus aimed to explore the effect of risk factors on the severity of the disease, focusing on immune-inflammatory parameters, which represent the immune status of patients. Methods: A comprehensive systematic search for relevant studies published up to April 2020 was performed by using the PubMed, Web of Science, EMBASE, and China National Knowledge Internet (CNKI) databases. After extracting all available data of immune-inflammatory indicators, we statistically analyzed the risk factors of severe and non-severe COVID-19 patients with a meta-analysis. Results: A total of 4,911 patients from 29 studies were included in the final meta-analysis. The results demonstrated that severe patients tend to present with increased white blood cell (WBC) and neutrophil counts, neutrophil-lymphocyte ratio (NLR), procalcitonin (PCT), C-reaction protein (CRP), erythrocyte sedimentation rate (ESR), and Interleukin-6 (IL-6) and a decreased number of total lymphocyte and lymphocyte subtypes, such as CD4+ T lymphocyte and CD8+ T lymphocyte, compared to the non-severe patients. In addition, the WBC count>10 × 10⁹/L, lymphocyte count0.5 ng/mL, and CRP>10 mg/L were risk factors for disease progression in patients with COVID-19 (WBC count>10 × 10⁹/L: OR = 2.92, 95% CI: 1.96–4.35; lymphocyte count0.5 ng/mL: OR = 6.33, 95% CI: 3.97–10.10; CRP>10 mg/L: OR = 3.51, 95% CI: 2.38–5.16). Furthermore, we found that NLR, as a novel marker of systemic inflammatory response, can also help predict clinical severity in patients with COVID-19 (OR = 2.50, 95% CI: 2.04–3.06). Conclusions: Immune-inflammatory parameters, such as WBC, lymphocyte, PCT, CRP, and NLR, could imply the progression of COVID-19. NLR has taken both the levels of neutrophil and lymphocyte into account, indicating a more complete, accurate, and reliable inspection efficiency; surveillance of NLR may help clinicians identify high-risk COVID-19 patients at an early stage.

Human epidermal growth factor receptor (HER) family plays an important role in gastric cancer (GC), especially HER2. Too much attention has been paid to HER2; however, the functions of HER3 and HER4 overexpression in GC are always ignored. The clinicopathological and prognostic roles of HER3 and HER4 in GC are controversial. In this study, a systematic review and meta-analysis was conducted to evaluate the use of HER3 or HER4 as a predictor of clinicopathology and survival time in GC patients. Eligible studies were searched on PubMed, Ovid, Web of Science, and Cochrane databases through multiple search strategies. Data collection and statistical analysis were carried out by the Revman 5.3 software. The Newcastle-Ottawa scale was used to assess the quality of included studies. A total of 448 studies about HER3 overexpression and GC, and 398 studies about HER4 overexpression and GC were searched. Of these, 5 eligible studies about HER3 including 1016 GC patients and 3 eligible studies about HER4 including 793 GC patients met the inclusion criteria. The results showed that HER3 and HER4 overexpression were significantly associated with depth of tumor invasion (OR = 0.44, 95%CI 0.29–0.67, P = 0.0002 and OR = 0.50, 95%CI 0.38–0.86, P = 0.007) and lymph node metastasis (OR = 0.40, 95%CI 0.20–0.77, P = 0.007 and OR = 0.57, 95%CI 0.38–0.86, P = 0.007), and HER3 overexpression reveals a tendency of later tumor node metastases (TNM) stage (OR = 0.50, 95%CI 0.22–1.15, P = 0.10) and predicts a worse survival time (RR = 0.71, 95%CI 0.61–0.84, P<0.00001), while HER4 overexpression had no correlation with TNM stage (OR = 0.60, 95%CI 0.20–1.78) and survival time (RR = 1.09, 95%CI 0.91–1.30). This meta-analysis indicated that HER3 plays an essential role in the clinicopathology and prognosis of GC. However, HER4 may not be an ideal prognostic factor for GC.

Based on the quenching of the fluorescence intensity of quantum dots, the interaction of CdSe/ZnS quantum dots with four flavonoid compounds, including quercetin, rutin, luteolin, and 5,7,3′,4′-tetrahydroxy-flavanone, and their mechanism were studied. A fluorescence method with a detection limit of 0.14 mg L⁻¹ for the determination of quercetin at concentrations between 0.576 and 184 mg L⁻¹ was proposed according to its quenching effect on the QDs. The quercetin samples were determined by this method with satisfactory results. The study indicated that CdSe/ZnS QDs could be a potential excellent fluorescence probe to detect flavonoids.

BACKGROUND Intermittent fasting (IF), particularly time-restricted feeding (TRF), is increasingly popular has gained popularity for weight loss, yet management, but its effects impact on gut health remain unclear. Remains inadequately understood. This study explores how investigated the effects of TRF effects on intestinal health and explored the underlying mechanisms. AIM To assess the effects of IF on intestinal health, elucidate the mechanisms involved. METHODS Mice were divided into two groups: Normal control (NC) and IF. The IF group underwent TRF, while the NC group had unrestricted access to food. Body weight, fecal characteristics, and intestinal morphology were analyzed. Colon tissue, serum, and fecal samples were collected for histological analysis, enzyme-linked immunosorbent assay, flow cytometry, 16S rRNA sequencing, and metabolomic profiling. RESULTS IF significantly affected body weight and intestinal morphology, compromised the intestinal barrier, increased pro-inflammatory cytokines, and heightened gut immune activation (P < 0.05). It also disrupted the gut microbiota, promoting pro-inflammatory bacteria, reducing anti-inflammatory metabolites, and elevating pro-inflammatory metabolites (P < 0.05). Indoleacrylic acid (IAA) supplementation notably alleviated intestinal inflammation and reversed immune dysfunction induced by IF (P < 0.05). CONCLUSION Prolonged IF exacerbates intestinal inflammation by impairing gut barrier integrity and disrupting microbial homeostasis. However, IAA supplementation effectively mitigates fasting-induced intestinal inflammation and immune imbalance, suggesting its potential as a therapeutic agent.

Background: The rapid advancement of radiomics and artificial intelligence (AI) technology has provided novel tools for the diagnosis of esophageal cancer. This study innovatively combines muscle imaging features with conventional esophageal imaging features to construct deep learning diagnostic models. Methods: This retrospective study included 1066 patients undergoing radical esophagectomy. Preoperative computed tomography (CT) images covering esophageal, stomach, and muscle (bilateral iliopsoas and erector spinae) regions were segmented automatically with manual adjustments. Diagnostic models were developed using deep learning (2D and 3D neural networks) and traditional machine learning (11 algorithms with PyRadiomics-derived features). Multimodal features underwent Principal Component Analysis (PCA) for dimension reduction and were fused for final analysis. Results: Comparative analysis of 1066 patients’ CT imaging revealed the muscle-based model outperformed the esophageal plus stomach model in predicting N2 staging (0.63 ± 0.11 vs. 0.52 ± 0.11, p = 0.03). Subsequently, multimodal fusion models were established for predicting pathological subtypes, T staging, and N staging. The logistic regression (LR) fusion model showed optimal performance in predicting pathological subtypes, achieving accuracy (ACC) of 0.919 in the training set and 0.884 in the validation set. For predicting T staging, the support vector machine (SVM) model demonstrated the highest accuracy, with training and validation accuracies of 0.909 and 0.907, respectively. The multilayer perceptron (MLP) fusion model achieved the best performance among all models tested for N staging prediction, although the accuracy remained moderate (ACC = 0.704 in the training set and 0.685 in the validation set), indicating potential for further optimization. Fusion models significantly outperformed single-modality models. Conclusions: Based on CT imaging data from 1066 patients, this study systematically constructed predictive models for pathological subtypes, T staging, and N staging of esophageal cancer. Comparative analysis of models using esophageal, esophageal plus stomach, and muscle modalities demonstrated that muscle imaging features contribute to diagnostic accuracy. Multimodal fusion models consistently showed superior performance.

The functional exhaustion of CD8+ T cells in the tumor microenvironment (TME) severely limits anti-tumor immunity in gastric cardia adenocarcinoma (GCA). H