Zhongqing Xu

Background: Habitual snoring is a common symptom in primary care but is often overlooked as a minor nuisance. Although sleep-disordered breathing has been associated with adverse health outcomes, the association between self-reported habitual snoring and health-related quality of life (HRQoL) remains insufficiently characterized among adults attending community health centers in China. This study examined the association of habitual snoring with physical and mental HRQoL in this setting. Methods: We conducted a cross-sectional study among 2,312 adults recruited from community health centers in Shanghai, China. Habitual snoring was defined as self-reported snoring on at least 3 nights per week. HRQoL was assessed using the 12-item Short Form Health Survey (SF-12), from which the Physical Component Summary (PCS) and Mental Component Summary (MCS) scores were derived. PCS and MCS scores < 50 were classified as below norm and were not interpreted as diagnostic cutoffs. Multivariable logistic regression models were used to examine the associations of habitual snoring with below-norm HRQoL after adjustment for sociodemographic characteristics, health behaviors, BMI, and chronic disease. As a sensitivity analysis, PCS and MCS were additionally analyzed as continuous outcomes using multivariable linear regression models. Results: Of the 2,312 participants (mean age 44.4 years; 63% female), 36.9% reported habitual snoring (≥3 nights/week). Habitual snorers had significantly lower scores on both the Physical Component Summary (PCS: 42.9 vs. 46.0, P < 0.001) and the Mental Component Summary (MCS: 49.9 vs. 50.8, P = 0.023) compared to non-snorers. After multivariable adjustment, habitual snoring was associated with higher odds of below-norm physical HRQoL (ORm = 1.590, 95% CI: 1.291–1.957) and mental HRQoL (ORm = 1.250, 95% CI: 1.047–1.493). In sensitivity analyses, habitual snoring remained significantly associated with lower PCS (β = −2.38, 95% CI: −3.00 to −1.75) and lower MCS (β = −1.21, 95% CI: −2.04 to −0.38). Conclusion: Habitual snoring was common among adults attending community health centers in Shanghai and was associated with lower physical and mental HRQoL, particularly physical HRQoL. These findings support greater attention to simple symptom-based screening for snoring in community care, where it may help identify individuals who may benefit from further sleep-related evaluation and health management.

Thyroid nodule, as a common clinical endocrine disease, has become increasingly prevalent worldwide. Ultrasound, as the premier method of thyroid imaging, plays an important role in accurately diagnosing and managing thyroid nodules. However, there is a high degree of inter- and intra-observer variability in image interpretation due to the different knowledge and experience of sonographers who have huge ultrasound examination tasks everyday. Artificial intelligence based on computer-aided diagnosis technology maybe improve the accuracy and time efficiency of thyroid nodules diagnosis. This study introduced an artificial intelligence software called SW-TH01/II to evaluate ultrasound image characteristics of thyroid nodules including echogenicity, shape, border, margin, and calcification. We included 225 ultrasound images from two hospitals in Shanghai, respectively. The sonographers and software performed characteristics analysis on the same group of images. We analyzed the consistency of the two results and used the sonographers’ results as the gold standard to evaluate the accuracy of SW-TH01/II. A total of 449 images were included in the statistical analysis. For the seven indicators, the proportions of agreement between SW-TH01/II and sonographers’ analysis results were all greater than 0.8. For the echogenicity (with very hypoechoic), aspect ratio and margin, the kappa coefficient between the two methods were above 0.75 (P < 0.001). The kappa coefficients of echogenicity (echotexture and echogenicity level), border and calcification between the two methods were above 0.6 (P < 0.001). The median time it takes for software and sonographers to interpret an image were 3 (2, 3) seconds and 26.5 (21.17, 34.33) seconds, respectively, and the difference were statistically significant (z = -18.36, P < 0.001). SW-TH01/II has a high degree of accuracy and great time efficiency benefits in judging the characteristics of thyroid nodule. It can provide more objective results and improve the efficiency of ultrasound examination. SW-TH01/II can be used to assist the sonographers in characterizing the thyroid nodule ultrasound images.

Background Lung cancer remains a major global health threat, with its biological complexity and patient heterogeneity posing significant challenges. Novel machine learning approaches now offer effective tools to interpret complex biological information hierarchies, showing promise to transform lung cancer treatment approaches. Methods We analyzed comprehensive biological datasets from TCGA and other databases, integrating DNA, RNA, miRNA, protein, and metabolite information. Multiple machine learning methods were employed to build diagnostic tools, treatment response predictors, and survival estimation models. Results Our machine learning approaches effectively distinguished cancer patients from healthy controls. Analysis identified unique molecular characteristics between lung cancer subtypes and discovered biomarkers that help predict treatment efficacy and patient prognosis. Adding clinical data to biological information significantly improved model accuracy and enhanced patient stratification. Conclusion This study marks significant progress toward precision cancer therapy by demonstrating how machine learning can help decode the complex biology of lung cancer.

Background: Doxorubicin (DOX) is a widely used chemotherapeutic agent known for its efficacy against various cancers, but its clinical application is often limited by its cardiotoxic effects. The exact mechanisms of DOX-induced cardiotoxicity remain unclear, requiring further investigation. Early diagnosis is essential to enhance the quality of life and prognosis for patients with malignancies. This study aims to identify biomarkers and therapeutic targets for DOX cardiotoxicity.Methods: Heart tissue samples from 20 DOX-treated cardiotoxic mice and 19 normal controls were analyzed using liquid chromatography-mass spectrometry (LC-MS). Multivariate statistical analysis identified differential metabolites. Key metabolites were assessed using a random forest algorithm, and ROC curves evaluated diagnostic value. H9C2 rat cardiomyoblast cells were cultured to investigate the protective effects of these metabolites.Results: Among 291 metabolites, significant differences emerged between cardiotoxic and normal mice. Five metabolites-4-hydroxy-valeric acid, 2-methylbutanoic acid, traumatic acid, PI (18:2 (9Z, 12Z)/0:0), and MIPC (t18:0/24:0 (2OH))-showed diagnostic potential. ROC analysis indicated excellent value for 4-hydroxy-valeric acid and PI (18:2 (9Z, 12Z)/0:0) and high discriminatory power for 2-methylbutanoic acid (AUC = 0. 99). Pathway analysis highlighted glycosylphosphatidylinositol-anchor biosynthesis, unsaturated fatty acids biosynthesis, pantothenate and CoA pathways, among others, associated with DOX-induced cardiotoxicity. In addition, we found that the differential metabolite Cer (d18:0/12:0) can improve DOX-induced myocardial cell damage and inhibit apoptosis-related protein expression at the cellular level.Conclusion: Heart tissue metabolomics with LC-MS identified critical metabolites and pathways associated with DOX cardiotoxicity, suggesting biomarkers for early diagnosis and potential therapeutic targets to mitigate DOX-related cardiotoxicity and improve clinical outcomes.

Diabetes, especially type 2 diabetes (T2D), poses an unprecedented challenge to global public health. Hydration status also plays a fundamental role in human health, especially in people with T2D, which is often overlooked. This study aimed to explore the longitudinal associations between hydration status and the risk of T2D among the Chinese population. This study used data from the large community-based Kailuan cohort, which included adults who attended physical examinations from 2006 to 2007 and were followed until 2020. A total of 71,526 participants who eventually met the standards were divided into five hydration-status groups based on their levels of urine specific gravity (USG). Multivariable and time-dependent Cox proportional hazards models were employed to evaluate the associations of baseline and time-dependent hydration status with T2D incidence. Restricted cubic splines (RCS) analysis was used to examine the dose–response relationship between hydration status and the risk of T2D. Over a median 12.22-year follow-up time, 11,804 of the participants developed T2D. Compared with the optimal hydration-status group, participants with dehydration and severe dehydration had a significantly increased risk of diabetes, with adjusted hazard ratios (95% CI) of 1.30 (1.04–1.63) and 1.38 (1.10–1.74). Time-dependent analyses further confirmed the adverse effects of impending dehydration, dehydration, and severe dehydration on T2D incidence by 16%, 26%, and 33% compared with the reference group. Inadequate hydration is significantly associated with increased risks of T2D among Chinese adults. Our findings provided new epidemiological evidence and highlighted the potential role of adequate hydration status in the early prevention of T2D development.

To combat/control the COVID-19 pandemic, a complete lockdown was implemented in China for almost 6 months during 2020. To determine the impact of a long-term lockdown on the academic performance of first-year nursing students via mandatory online learning, and to determine the benefits of online teaching. The recruitment and academic performance of 1^st-year nursing students were assessed between 2019 [prior to COVID-19, n = 195, (146 women)] and 2020 [during COVID-19, n = 180 (142 women)]. The independent sample t test or Mann-Whitney test was applied for a comparison between these two groups. There was no significant difference in student recruitment between 2019 and 2020. The overall performance of the first-year students improved in the Biochemistry, Immunopathology, Traditional Chinese Medicine Nursing and Combined Nursing courses via mandatory online teaching in 2020 compared with traditional teaching in 2019. Suspension of in-class learning but continuing education virtually online has occurred without negatively impacting academic performance, thus academic goals are more than achievable in a complete lockdown situation. This study offers firm evidence to forge a path for developments in teaching methods to better incorporate virtual learning and technology in order to adapt to fast-changing environments. However, the psychological/psychiatric and physical impact of the COVID-19 lockdown and the lack of face-to-face interaction on these students remains to be explored.

Background: Oxidative stress and cell apoptosis play pivotal roles in the pathogenesis of doxorubicin (DOX)-induced myocardial injury. Heat shock protein-derived peptide (HSP-17) is a peptide which is low-expressed in DOX treated mouse heart tissue. It has high bioactivity and interspecies sequence consistency, and is predicted to have myocardial protective effect. Methods: Firstly, we added 1 µM DOX to H9c2 cell culture medium for 24 hours to construct the myocardial cytotoxicity model. Then we detected the effect of HSP-17 on DOX induced H9c2 cardiomyocyte injury by measuring cell viability and lactate dehydrogenase (LDH) level. In addition, reactive oxygen species (ROS) and tetraethylbenzimidazolylcarbocyanine iodide kits are used to evaluate the effect of the HSP-17 peptide on DOX-induced oxidative stress injury to cardiomyocytes, and the detection of apoptosis related proteins and flow cytometry were applied to detect the level of apoptosis. Furthermore, the protein expression levels [phosphorylated Akt (p-Akt) and phosphorylated PI3K (p-PI3K)] of the PI3K/Akt pathway were also detected by western blotting. Results: We found that the HSP-17 peptide can increase cell viability, protect mitochondrial potential, reduce LDH levels, and reduce ROS and cardiomyocyte apoptosis. In addition, we also observed that HSP-17 upregulated the expression level of p-Akt, and LY294002, a typical inhibitor of PI3K/Akt, was found to eliminate the protective roles of HSP-17. Conclusions: In conclusion, this study demonstrated that the HSP-17 peptide protected H9c2 cells against oxidative stress and apoptosis via PI3K/Akt pathway activation, which provides a new idea for the treatment of DOX-induced myocardial injury.

Background: Gastric cancer (GC) is a highly heterogeneous tumor with different responses to immunotherapy. Identifying immune subtypes and landscape of GC could improve immunotherapeutic strategies. Methods: Based on the abundance of tumor-infiltrating immune cells in GC patients from The Cancer Genome Atlas, we used unsupervised consensus clustering algorithm to identify robust clusters of patients, and assessed their reproducibility in an independent cohort from Gene Expression Omnibus. We further confirmed the feasibility of our immune subtypes in five independent pan-cancer cohorts. Finally, functional enrichment analyses were provided, and a deep learning model studying the pathological images was constructed to identify the immune subtypes. Results: We identified and validated three reproducible immune subtypes presented with diverse components of tumor-infiltrating immune cells, molecular features, and clinical characteristics. An immune-inflamed subtype 3, with better prognosis and the highest immune score, had the highest abundance of CD8+ T cells, CD4+ T–activated cells, follicular helper T cells, M1 macrophages, and NK cells among three subtypes. By contrast, an immune-excluded subtype 1, with the worst prognosis and the highest stromal score, demonstrated the highest infiltration of CD4+ T resting cells, regulatory T cells, B cells, and dendritic cells, while an immune-desert subtype 2, with an intermediate prognosis and the lowest immune score, demonstrated the highest infiltration of M2 macrophages and mast cells, and the lowest infiltration of M1 macrophages. Besides, higher proportion of EVB and MSI of TCGA molecular subtyping, over expression of CTLA4, PD1, PDL1, and TP53, and low expression of JAK1 were observed in immune subtype 3, which consisted with the results from Gene Set Enrichment Analysis. These subtypes may suggest different immunotherapy strategies. Finally, deep learning can predict the immune subtypes well. Conclusion: This study offers a conceptual frame to better understand the tumor immune microenvironment of GC. Future work is required to estimate its reference value for the design of immune-related studies and immunotherapy selection.