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Background Nutrition impact symptoms (NIS) are common among cancer patients and influence prognosis. This study aimed to investigate the prognostic significance of NIS in gastric cancer patients using data from the Investigation on Nutrition Status and Clinical Outcome of Common Cancers (INSCOC) database. Methods We conducted a retrospective cohort study using data from 2,673 adult patients with confirmed gastric cancer enrolled in the INSCOC database between January 2013 to February 2020. NIS, including appetite loss, vomiting, dysphagia, and early satiety, were assessed using the Patient-Generated Subjective Global Assessment (PG-SGA). Overall survival (OS) was the primary outcome, while quality of life (QoL) was the secondary outcome. Statistical analyses included Kaplan–Meier survival analysis, Cox proportional hazards regression, and propensity score matching (PSM) to reduce confounding. Results Patients with NIS had significantly worse OS compared to those without (median OS: 74.1 vs. 81.3 months, p  < 0.001). In multivariate analysis, NIS was an independent predictor of mortality (HR: 1.28, 95% CI: 1.11–1.48, p  = 0.001). Vomiting and dysphagia were particularly associated with increased mortality (HR: 1.22, p  = 0.038 and HR: 1.80, p  < 0.001, respectively). Interaction analysis revealed that the prognostic impact of NIS was influenced by chemotherapy (P for interaction = 0.002). NIS was also strongly associated with severe malnutrition. Sensitivity analysis confirmed the robustness of these findings, even after excluding short-term mortalities within 180 days. Conclusions NIS are significant independent predictors of poor prognosis in gastric cancer patients, contributing to malnutrition and reduced survival. These findings highlight the importance of early symptom recognition and nutritional intervention to potentially improve outcomes for gastric cancer patients. Trial registration https://www.chictr.org.cn/showproj.html?proj=31813 , identifier ChiCTR1800020329.

Background Sepsis-induced metabolic dysregulation, particularly abnormal lactate metabolism, is closely associated with disease severity and mortality. This study aimed to systematically identify lactate-related genes involved in sepsis and evaluate their diagnostic and prognostic value using integrated transcriptomic analyses. Methods Single-cell RNA sequencing data were analyzed using AUCell, singscore, and ssGSEA algorithms combined with correlation analysis to identify lactate-related genes. A comprehensive machine learning framework incorporating 131 algorithms was applied for feature selection and construction of a diagnostic model. Survival analyses were performed to assess the prognostic significance of candidate genes. The expression of key genes was further validated by qPCR and western blotting in clinical samples and a sepsis animal model. Results Sepsis patients were stratified into distinct molecular subgroups exhibiting significant differences in prognosis, clinical characteristics, pathway enrichment, immune infiltration, and immune checkpoint gene expression. Single-cell analysis revealed that MARCO⁺ macrophages exhibited relatively high lactate-related activity compared with most immune populations, while plasma cells showed comparable levels. Sixteen genes were identified at the intersection of macrophage differentially expressed genes and lactate-related genes. Machine learning analysis further identified six core diagnostic genes. Survival analysis demonstrated that high LDHA expression was significantly associated with poor prognosis in sepsis patients. Consistently, elevated LDHA expression was observed in septic animals compared with controls. Conclusion By integrating single-cell and bulk RNA-seq data, we developed and validated a novel diagnostic model for sepsis, termed the Lacty Model, and identified LDHA as a key biomarker associated with poor prognosis. These findings highlight the potential clinical relevance of lactate metabolism–related genes in sepsis and provide a foundation for future mechanistic and therapeutic studies.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is highly contagious and causes lymphocytopenia, but the underlying mechanisms are poorly understood. We demonstrate here that heterotypic cell-in-cell structures with lymphocytes inside multinucleate syncytia are prevalent in the lung tissues of coronavirus disease 2019 (COVID-19) patients. These unique cellular structures are a direct result of SARS-CoV-2 infection, as the expression of the SARS-CoV-2 spike glycoprotein is sufficient to induce a rapid (~45.1 nm/s) membrane fusion to produce syncytium, which could readily internalize multiple lines of lymphocytes to form typical cell-in-cell structures, remarkably leading to the death of internalized cells. This membrane fusion is dictated by a bi-arginine motif within the polybasic S1/S2 cleavage site, which is frequently present in the surface glycoprotein of most highly contagious viruses. Moreover, candidate anti-viral drugs could efficiently inhibit spike glycoprotein processing, membrane fusion, and cell-in-cell formation. Together, we delineate a molecular and cellular rationale for SARS-CoV-2 pathogenesis and identify novel targets for COVID-19 therapy.

Background Sepsis remains a life-threatening condition in intensive care units (ICU) with high morbidity and mortality rates. Some biomarkers commonly used in clinic do not have the characteristics of rapid and specific growth and rapid decline after effective treatment. Machine learning has shown great potential in early diagnosis, subtype analysis, accurate treatment and prognosis evaluation of sepsis. Methods Gene expression matrices from GSE13904 and GSE26440 were combined into a training model after quality control and standardization. Then, the intersection genes were obtained by crossing the screened differentially expressed genes (DEGs) and the module genes with the strongest correlation obtained by WGCNA analysis. 113 combined machine learning algorithms to build a diagnosis model. Then the CIBERSORT algorithm is used to analyze the relationship between the change of core gene expression and immune response in sepsis. Construct nomogram, DCA and CIC to further verify the reliability of the diagnosis model. The potential molecular compounds interacting with key genes were searched from the Traditional Chinese Medicine Active Compound Library (TCMACL). Results We screened 405 DEGs, including 334 up-regulated and 71 down-regulated genes. The 308 potential genes were obtained by intersection of MEturquoise module genes in WGCNA analysis and DEGs for subsequent machine learning analysis. GO and KEGG enrichment analysis showed that sepsis was mainly related to immune response and bacterial infection. Then 113 combined machine learning algorithms are applied to construct a diagnosis model to screen 22 hub genes. Four four key genes (CD177, GNLY, ANKRD22, and IFIT1) are obtained through further analysis of PPI network constructed by 22 hub genes. Subsequently, the diagnostic model is proved to have good predictive value by nomogram, DCA and CIC. Finally, molecular compounds (Dieckol, Grosvenorine and Tellimagrandin II) were screened out as potential drugs. Conclusion 113 combinated machine learning algorithms screened out four key genes that can distinguish sepsis patients. At the same time, potential therapeutic molecular compounds interacting with key genes genes were screened out by molecular docking.

Background No previous prospective research has explored the association of the TyG (fasting triglyceride-glucose) index and TG/HDL-C ratio as insulin resistance markers with the risk of colorectal cancer (CRC) incidence in the Northern Chinese population. Methods In this prospective cohort study, we included 93,659 cancer-free participants with the measurements of TyG index and TG/HDL-C ratio. Participants were divided by the quartiles of the TyG index or TG/HDL-C ratio. The associations of TyG index, TG/HDL-C ratio, and their components with CRC risk were assessed using Cox proportional hazards regression models. Results During a median follow-up of 13.02 years, 593 incident CRC cases were identified. Compared with the lowest quartile of the TyG index (Q1), the risk of CRC was higher in persons in the third (Q3) and highest quartiles (Q4) of the TyG index, with corresponding multivariable-adjusted HRs (95% CI) of 1.36 (1.06, 1.76) and 1.50 (1.19, 1.91), respectively. The elevated risks of CRC incidence were observed in people in the second, third, and highest quartiles of the TG/HDL-C ratio groups, with corresponding multivariable-adjusted HRs (95% CI) of 1.33 (1.05, 1.70), 1.36 (1.07, 1.73) and 1.37 (1.07, 1.75), respectively. Conclusions Elevated TyG index and TG/HDL-C ratio were associated with a higher risk of developing CRC among adults in Northern China.

Background Malnutrition is associated with poor overall survival (OS) in breast cancer patients; however, the most predictive nutritional indicators for the prognosis of patients with breast cancer are not well-established. This study aimed to compare the predictive effects of common nutritional indicators on OS and to refine existing nutritional indicators, thereby identifying a more effective nutritional evaluation indicator for predicting the prognosis in breast cancer patients. Methods This prospective study analyzed data from 776 breast cancer patients enrolled in the “Investigation on Nutritional Status and its Clinical Outcome of Common Cancers” (INSCOC) project, which was conducted in 40 hospitals in China. We used the time-dependent receiver operating characteristic curve (ROC), Kaplan–Meier survival curve, and Cox regression analysis to evaluate the predictive effects of several nutritional assessments. These assessments included the patient-generated subjective nutrition assessment (PGSGA), the global leadership initiative on malnutrition (GLIM), the controlling nutritional status (CONUT), the nutritional risk index (NRI), and the prognostic nutritional index (PNI). Utilizing machine learning, these nutritional indicators were screened through single-factor analysis, and relatively important variables were selected to modify the PNI. The modified PNI, termed the cholesterol-modified prognostic nutritional index (CPNI), was evaluated for its predictive effect on the prognosis of patients. Results Among the nutritional assessments (including PGSGA, GLIM, CONUT, NRI, and PNI), PNI showed the highest predictive ability for patient prognosis (time-dependent ROC = 0.58). CPNI, which evolved from PNI, emerged as the superior nutritional index for OS in breast cancer patients, with the time-dependent ROC of 0.65. It also acted as an independent risk factor for mortality ( p  < 0.05). Moreover, the risk of malnutrition and mortality was observed to increase gradually among both premenopausal and postmenopausal age women, as well as among women categorized as non-overweight, overweight, and obese. Conclusions The CPNI proves to be an effective nutritional assessment tool for predicting the prognosis of patients with breast cancer.

Background Serum albumin (ALB) has traditionally been regarded as a marker of nutritional status. However, recent studies suggest its changes are closely linked to inflammation, metabolic dysregulation, and disease severity, limiting its role as a sole indicator of nutritional status. Yet, clinical practice continues to rely on ALB to monitor nutritional interventions, with a paucity of high-quality evidence on its dynamic associations with clinical outcomes. This study aimed to investigate the comprehensive associations of ALB dynamics with inflammation, nutritional status, and clinical outcomes in hospitalized patients, providing evidence to optimize clinical management. Methods This secondary analysis utilized data from a prospective observational cohort study conducted in 34 tertiary hospitals across China between June and September 2014. A total of 2959 patients hospitalized for 7–30 days with complete data were included. Standardized protocols were used to collect demographics, nutritional indices (Nutritional Risk Screening 2002, Subjective Global Assessment), hematology, biochemistry results, and clinical outcomes (complications, length of stay, costs). Subgroup analyses were performed based on inflammatory status changes, nutritional therapy administration, department type, baseline nutritional status, and advanced age. Receiver operating characteristic curves identified cutoff values for infection-related complications. Correlation analyses and multivariable linear regression models determined independent predictors of ALB changes. Results Among 2959 patients, 1894 (64.0%) experienced a decrease in ALB during hospitalization, which significantly impacted primary outcomes, including prolonged length of stay, increased hospitalization costs, and higher complication rates. Significant ALB decline was also strongly associated with worsened nutritional status, weight loss at discharge, exacerbated gastrointestinal symptoms, functional impairments, and edema ( P  < 0.001 for all). Compared to binary categorization (increase vs. decrease), the magnitude of ALB change demonstrated a stronger correlation with infection-related complications across all subgroups. Subgroup-specific cutoff values stratified patients into high- and low-risk groups, with significant differences in infection-related complication rates ( P  < 0.05), aiding early identification and intervention. Independent predictors of ALB decline included advanced age, surgical status, lower baseline handgrip strength and its change during hospitalization, higher baseline ALB and globulin levels, baseline Prognostic Nutritional Index, baseline inflammatory status and its exacerbation, elevated platelet-to-lymphocyte ratio, and intensive care unit admission. Conclusions Dynamic changes in ALB serve as a critical indicator of inflammation–nutrition interplay, with its reduction effectively predicting infection-related complications, clinical outcomes, and nutritional deterioration. This is particularly valuable in older adults, inflammatory-variable, surgical, and non-malnourished patients. The conventional view of ALB as a pure nutritional marker requires revision. Joint monitoring with inflammatory biomarkers and multidisciplinary interventions targeting high-risk populations are recommended.

Backgrounds Malnutrition and systemic inflammatory responses are associated with poor overall survival (OS) in lung cancer patients, but it remains unclear which biomarkers are better for predicting their prognosis. This study tried to determine the best one among the existing common nutrition/inflammation‐based indicators of OS for patients with lung cancer. Materials and methods There were 16 nutrition or systemic inflammation‐based indicators included in this study. The cut‐off points for the indicators were calculated using maximally selected rank statistics. The OS was evaluated using the Kaplan–Meier estimator, and univariate and multivariate Cox proportional hazard models were used to determine the relationship between the indicators and OS. A time‐dependent receiver operating characteristic curves (time‐ROC) and C‐index were calculated to assess the predictive ability of the different indicators. Results There were 1772 patients with lung cancer included in this study. In univariate analysis, all 16 indicators were significantly associated with OS of the patients (all P < 0.001). Except for platelet‐to‐lymphocyte ratio, all other indicators were independent predictors of OS in multivariate analysis (all P < 0.05). Low advanced lung cancer inflammation index (ALI) was associated with higher mortality risk of lung cancer [hazard ratio, 1.30; 95% confidence interval (CI), 1.13–1.49]. The results of the time‐AUC and C‐index analyses indicated that the ALI (C‐index: 0.611) had the best predictive ability on the OS in patients with lung cancer. In different sub‐groups, the ALI was the best indicator for predicting the OS of lung cancer patients regardless of sex (C‐index, 0.609 for men and 0.613 for women) or smoking status (C‐index, 0.629 for non‐smoker and 0.601 for smoker) and in patients aged <65 years (C‐index, 0.613). However, the modified Glasgow prognostic score was superior to the other indicators in non‐small cell lung cancer patients (C‐index, 0.639) or patients aged ≥65 years (C‐index, 0.610), and the glucose‐to‐lymphocyte ratio performed better prognostic ability in patients with small cell lung cancer (C‐index, 0.601). Conclusions The prognostic ability of the ALI is superior to the other inflammation/nutrition‐based indicators for all patients with lung cancer.

Background Systemic inflammation, the most representative tumour–host interaction, plays a crucial role in disease progression and prognosis in patients with non‐small cell lung cancer (NSCLC). Few studies have compared the performance of existing haematological systemic inflammation biomarkers in predicting the prognosis of NSCLC patients. The purpose of this study was to compare the prognostic value of existing systemic inflammation biomarkers and determine the optimal systemic inflammation biomarker in patients with NSCLC through a multicentre prospective study. Methods The predictive accuracy of systemic inflammation biomarkers for prognostic assessment in NSCLC was assessed using C‐statistics. Inter‐group differences in survival were assessed using the log‐rank test and visualized using the Kaplan–Meier method. A restricted cubic spline (RCS) curve was used to explore the association between the biomarkers and survival. Independent prognostic biomarkers for overall survival were determined using multivariable Cox proportional hazards regression analysis. Logistic regression analysis was used to determine independent predictors of 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia. Results The inflammatory burden index (IBI) had the highest C‐statistic for predicting the prognosis of patients with NSCLC, reaching 0.640 (0.617, 0.663). Patients with a high IBI had significantly worse outcomes than those with a low IBI (35.46% vs. 57.22%; log‐rank P < 0.001). The IBI was also able to differentiate the prognosis of patients with NSCLC with the same pathological stage. The RCS curve showed an inverted L‐shaped dose–response relationship between the IBI and survival of patients with NSCLC. Multivariable Cox proportional hazards regression analysis showed that a high IBI was an independent risk factor for death of patients with NSCLC (hazard ratio = 1.229, 95% confidence interval [CI]: 1.131–1.335, P < 0.001). A high IBI was an independent predictor of 90‐day outcomes (odds ratio [OR] = 1.789, 95% CI: 1.489–2.151, P < 0.001), prolonged hospital stays (OR = 1.560, 95% CI: 1.256–1.938, P < 0.001), high hospitalization expenses (OR = 1.476, 95% CI: 1.195–1.822, P < 0.001) and cachexia (OR = 1.741, 95%CI = 1.374–2.207, P < 0.001) in patients with NSCLC. Conclusions The IBI was independently associated with overall survival, 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia in NSCLC patients. As an optimal systemic inflammation biomarker, the IBI has broad clinical application prospects in predicting the prognosis of patients with NSCLC.