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Psychological problems (e.g., depression and anxiety) have been widely studied as an important public health issue in the background of the pandemic, whereas the positive aspects (e.g., well-being) have been paid little attention. The present study aimed to investigate the effects of the COVID-19 pandemic and generations on well-being by adopting 5 groups (Post-00s, Post-90s, Post-80s, Post-70s, and Post-60s) × 2 time points (before and during the COVID-19 pandemic) mixed factorial design. A total of 1579 Chinese adults completed the self-report survey, and a valid sample of 1529 adults from five generational cohorts was included in the data analysis. Results of the mixed factorial ANOVAs and simple effects analyses showed significant interaction effects on some dimensions of well-being. Specifically, the Post-80s exhibited a significant increase in both Engagement and Accomplishment of well-being during the pandemic, and the Post-60s generation demonstrated a significant improvement in Engagement of well-being. However, the other generations did not show significant changes. It could be concluded that the effects of the pandemic on well-being are complicated across generations.

Gastric cancer (GC) represents a significant burden of cancer-related mortality worldwide, underscoring an urgent need for the development of early detection strategies and precise postoperative interventions. However, the identification of non-invasive biomarkers for early diagnosis and patient risk stratification remains underexplored. Here, we conduct a targeted metabolomics analysis of 702 plasma samples from multi-center participants to elucidate the GC metabolic reprogramming. Our machine learning analysis reveals a 10-metabolite GC diagnostic model, which is validated in an external test set with a sensitivity of 0.905, outperforming conventional methods leveraging cancer protein markers (sensitivity < 0.40). Additionally, our machine learning-derived prognostic model demonstrates superior performance to traditional models utilizing clinical parameters and effectively stratifies patients into different risk groups to guide precision interventions. Collectively, our findings reveal the metabolic landscape of GC and identify two distinct biomarker panels that enable early detection and prognosis prediction respectively, thus facilitating precision medicine in GC. Gastric cancer detection by endoscopy is intrusive and time-consuming, and early detection is key to improving survival. Here, the authors propose a metabolite-based model to enable early detection.

Background Positron emission tomography (PET) and PET/computed tomography (PET/CT) imaging with 3,4-dihydroxy-6-[ 18 F] fluoro-L-phenylalanine ( 18 F-FDOPA) has been used in the evaluation of gliomas. We performed a meta-analysis to obtain the diagnostic and grading accuracy of 18 F-FDOPA PET and PET/CT in patients with gliomas. Methods PubMed, Embase, Cochrane Library and Web of Science were searched through 13 May 2019. We included studies reporting the diagnostic performance of 18 F-FDOPA PET or PET/CT in glioma patients. Pooled sensitivity, specificity, and area under the summary receiver operating characteristic (SROC) curve were calculated from eligible studies on a per-lesion basis. Results Eventually, 19 studies were included. Across 13 studies (370 patients) for glioma diagnosis, the pooled sensitivity and specificity of 18 F-FDOPA PET and PET/CT were 0.90 (95%CI: 0.86–0.93) and 0.75 (95%CI: 0.65–0.83). Across 7 studies (219 patients) for glioma grading, 18 F-FDOPA PET and PET/CT showed a pooled sensitivity of 0.88 (95%CI: 0.81–0.93) and a pooled specificity of 0.73 (95%CI: 0.64–0.81). Conclusions 18 F-FDOPA PET and PET/CT demonstrated good performance for diagnosing gliomas and differentiating high-grade gliomas (HGGs) from low-grade gliomas (LGGs). Further studies implementing standardized PET protocols and investigating the grading parameters are needed.

Hypertrophic cardiomyopathy (HCM) is a common inherited cardiovascular disease with heterogeneous clinical presentations, governed by multiple molecular mechanisms. Metabolic perturbations underlie most cardiovascular diseases; however, the metabolic alterations and their function in HCM are unknown. Here, we describe the metabolome and lipidome of heart and plasma samples from individuals with and without HCM. Correlation analyses showed strong association between metabolic alterations and cardiac function and prognosis of patients with HCM. Using machine learning we identified metabolite panels as potential HCM diagnostic markers or predictors of survival. Clustering based on metabolome and lipidome of heart enabled stratification of patients with HCM into three subgroups with distinct cardiac function and survival. Integration of metabolomics and proteomics data identified metabolic pathways significantly altered in patients with HCM, with the pentose phosphate pathway and oxidative stress being particularly upregulated. Thus, targeting the pentose phosphate pathway and oxidative stress may serve as potential therapeutic strategies for HCM.

Positron emission tomography (PET) and PET/computed tomography (PET/CT) imaging with 3,4-dihydroxy-6-[ F] fluoro-L-phenylalanine ( F-FDOPA) has been used in the evaluation of gliomas. We performed a meta-analysis to obtain the diagnostic and grading accuracy of F-FDOPA PET and PET/CT in patients with gliomas. PubMed, Embase, Cochrane Library and Web of Science were searched through 13 May 2019. We included studies reporting the diagnostic performance of F-FDOPA PET or PET/CT in glioma patients. Pooled sensitivity, specificity, and area under the summary receiver operating characteristic (SROC) curve were calculated from eligible studies on a per-lesion basis. Eventually, 19 studies were included. Across 13 studies (370 patients) for glioma diagnosis, the pooled sensitivity and specificity of F-FDOPA PET and PET/CT were 0.90 (95%CI: 0.86-0.93) and 0.75 (95%CI: 0.65-0.83). Across 7 studies (219 patients) for glioma grading, F-FDOPA PET and PET/CT showed a pooled sensitivity of 0.88 (95%CI: 0.81-0.93) and a pooled specificity of 0.73 (95%CI: 0.64-0.81). F-FDOPA PET and PET/CT demonstrated good performance for diagnosing gliomas and differentiating high-grade gliomas (HGGs) from low-grade gliomas (LGGs). Further studies implementing standardized PET protocols and investigating the grading parameters are needed.