This cohort study aimed to investigate the association between steroid injections for shoulder diseases and the increased incidence of cuff tendon tears. The Kaohsiung Veterans General Hospital clinical database was used in this study. Patients were enrolled using the corresponding diagnostic codes for shoulder diseases. Patients who received steroid injections were included in the case group, and those without steroid injections were included in the control group. The outcome measure was the occurrence of cuff tendon tears during the study period. Adjusted hazard ratios for outcomes were calculated using Cox regression analysis adjusted for sex, age, and comorbidities. Of the 1025 patients with shoulder disease, 205 were in the case group and 820 were in the control group. The incidence of cuff tendon tears was 9.8% in patients who received steroid injections (p < 0.001). The adjusted hazard ratios for steroid injections, smoking, and chronic liver disease were 7.44 (p < 0.001), 2.40 (p = 0.046), 3.25 (p = 0.007), respectively. Steroid injections on the shoulder were associated with a raised risk of cuff tendon tears by 7.44 times compared to non-injection. The incidence of cuff tendon tears increased by 3.25 times with concurrent chronic liver disease and by 2.4 times with smoking.

Sialylation of glycoproteins is modified by distinct sialyltransferases such as ST3Gal, ST6Gal, ST6GalNAc, or ST8SIA with α2,3-, α2,6-, or α2,8-linkages. Alteration of these sialyltransferases causing aberrant sialylation is associated with the progression of colon cancer. However, among the ST8- sialyltransferases, the role of ST8SIA6 in colon cancer remains poorly understood. In this study, we explored the involvement of ST8SIA6 in colon cancer using multiple gene databases. The relationship between ST8SIA6 expression and tumor stages/grades was investigated by UALCAN analysis, and Kaplan–Meier Plotter analysis was used to analyze the expression of ST8SIA6 on the survival outcome of colon cancer patients. Moreover, the biological functions of ST8SIA6 in colon cancer were explored using LinkedOmics and cancer cell metabolism gene DB. Finally, TIMER and TISMO analyses were used to delineate ST8SIA6 levels in tumor immunity and immunotherapy responses, respectively. ST8SIA6 downregulation was associated with an advanced stage and poorly differentiated grade; however, ST8SIA6 expression did not affect the survival outcomes in patients with colon cancer. Gene ontology analysis suggested that ST8SIA6 participates in cell surface adhesion, angiogenesis, and membrane vesicle trafficking. In addition, ST8SIA6 levels affected immunocyte infiltration and immunotherapy responses in colon cancer. Collectively, these results suggest that ST8SIA6 may serve as a novel therapeutic target towards personalized medicine for colon cancer.

The identification of risk factors for future prediabetes in young men remains largely unexamined. This study enrolled 6247 young ethnic Chinese men with normal fasting plasma glucose at the baseline (FPG_base), and used machine learning (Mach-L) methods to predict prediabetes after 5.8 years. The study seeks to achieve the following: 1. Evaluate whether Mach-L outperformed traditional multiple linear regression (MLR). 2. Identify the most important risk factors. The baseline data included demographic, biochemistry, and lifestyle information. Two models were built, where Model 1 included all variables and Model 2 excluded FPG_base, since it had the most profound effect on prediction. Random forest, stochastic gradient boosting, eXtreme gradient boosting, and elastic net were used, and the model performance was compared using different error metrics. All the Mach-L errors were smaller than those for MLR, thus Mach-L provided the most accurate results. In descending order of importance, the key factors for Model 1 were FPG_base, body fat (BF), creatinine (Cr), thyroid stimulating hormone (TSH), WBC, and age, while those for Model 2 were BF, white blood cell, age, TSH, TG, and LDL-C. We concluded that FPG_base was the most important factor to predict future prediabetes. However, after removing FPG_base, WBC, TSH, BF, HDL-C, and age were the key factors after 5.8 years.

Individuals of Asian descent are at higher risk for developing hyperuricemia and gout as compared to Western populations. Urate-lowering therapy (ULT) is an effective treatment for hyperuricemia and gout. It was reported that febuxostat, one of the ULTs, raises the risk of atrial fibrillation (AF) in elderly populations. Nevertheless, this association has not been properly investigated in Asian populations. We aimed to investigate the development of AF after ULT with different drugs in an Asian population. We conducted a retrospective cohort study using the clinical database at Kaohsiung Veterans General Hospital. Patients newly diagnosed with gout between 1 January 2013 and 31 December 2020 and with a documented baseline serum uric acid (sUA) level but no prior diagnosis of AF were identified. Patients were divided into three groups—allopurinol, benzbromarone, and febuxostat users. During the follow-up period, the risks of incident AF following the initiation of ULT with different drugs were assessed. Development of incident AF was noted in 43 (6%) of the 713 eligible patients during the follow-up period (mean, 49.4 ± 26.6 months). Febuxostat-treated patients had a higher prevalence of certain comorbidities (diabetes mellitus, heart failure, and chronic kidney disease) and higher CHA2DS2-VASc scores. Compared with allopurinol, neither febuxostat nor benzbromarone was associated with increased adjusted hazard ratios (HR) for incident AF (HR: 1.20, 95% confidence interval [CI]: 0.43–3.34; HR: 0.68, 95% CI: 0.22–2.08). There was no difference in the risk of incident AF among Asian patients with gout who received febuxostat, allopurinol, or benzbromarone. Further studies are needed to evaluate long-term cardiovascular outcomes in patients receiving different ULT drugs.

Clear cell renal cell carcinoma (ccRCC) is the most common RCC subtype with a high mortality. It has been reported that delta-like 1 homologue (DLK1) participates in the tumor microenvironmental remodeling of ccRCC, but the relationship between delta-like 2 homologue (DLK2, a DLK1 homologue) and ccRCC is still unclear. Thus, this study aims to investigate the role of DLK2 in the biological function and disease prognosis of ccRCC using bioinformatics analysis. The TNMplot database showed that DLK2 was upregulated in ccRCC tissues. From the UALCAN analysis, the overexpression of DLK2 was associated with advanced stage and high grade in ccRCC. Moreover, the Kaplan-Meier plotter (KM Plotter) database showed that DLK2 upregulation was associated with poor survival outcome in ccRCC. By the LinkedOmics analysis, DLK2 signaling may participated in the modulation of ccRCC extracellular matrix (ECM), cell metabolism, ribosome biogenesis, TGF-β signaling and Notch pathway. Besides, Tumor Immune Estimation Resource (TIMER) analysis showed that the macrophage and CD8⁺ T cell infiltrations were associated with good prognosis in ccRCC patients. Finally, DLK2 overexpression was associated with the reduced macrophage recruitments and the M1–M2 polarization of macrophage in ccRCC tissues. Together, DLK2 may acts as a novel biomarker, even therapeutic target in ccRCC. However, this study lacks experimental validation, and further studies are required to support this viewpoint.

During the Coronavirus disease 2019 (COVID-19) pandemic, smart technologies have been increasingly adopted in nursing education to reduce infection risk. However, the effectiveness and acceptance of smart glasses in clinical training remain unclear. This study aimed to identify factors influencing ratings of smart glasses and to examine rating correlations between paired nursing students and teachers following one-on-one training. We conducted a paired observational pilot study involving 30 nursing student–teacher pairs from Kaohsiung Armed Forces General Hospital, Taiwan, between November 2021 and December 2022. After completing clinical training using smart glasses, participants evaluated familiarity, satisfaction, time saving, and objectivity using a structured 5-point Likert-scale questionnaire. Group differences were analyzed using chi-square tests, independent t -tests, and 1-way ANOVA. Pearson correlation and linear regression analyses were performed to identify associated factors and inter-pair correlations. The results showed that teachers’ familiarity scores were significantly lower than students ( P = .004). The higher time saving scores was significantly showed in teachers compared with students ( P = .04). The satisfaction scores of students aged ≥24 years old were significantly lower than those of students aged <24 years old ( P = .02). Teachers with ≥15 years of work experience had significantly lower familiarity scores than teachers with < 15 years of work experience ( P = .03). Teachers with no experience using other smart technologies had significantly lower familiarity scores than teachers with experience using other smart technologies ( P = .03). Among teachers, age ( P = .01) and work experience ( P = .04) were negatively correlated with familiarity scores. Finally, the familiarity ( P = .03) and satisfaction scores ( P = .02) of the paired students were positively correlated with the familiarity and satisfaction scores of the paired teachers. Smart glasses were less familiar to senior teachers and those without prior smart technology experience. Age and professional experience significantly influenced acceptance levels. Positive interdependence between paired students and teachers suggests that mutual adaptation may enhance smart technology implementation in clinical nursing education.