Gene expression and immune infiltration analysis comparing lesioned and preserved subchondral bone in osteoarthritis.

Background: Osteoarthritis (OA) is a degenerative disease requiring additional research. This study compared gene expression and immune infiltration between lesioned and preserved subchondral bone. The results were validated using multiple tissue datasets and experiments. Methods: Differentially expressed genes (DEGs) between the lesioned and preserved tibial plateaus of OA patients were identified in the GSE51588 dataset. Moreover, functional annotation and protein-protein interaction (PPI) network analyses were performed on the lesioned and preserved sides to explore potential therapeutic targets in OA subchondral bones. In addition, multiple tissues were used to screen coexpressed genes, and the expression levels of identified candidate DEGs in OA were measured by quantitative real-time polymerase chain reaction. Finally, an immune infiltration analysis was conducted. Results: A total of 1,010 DEGs were identified, 423 upregulated and 587 downregulated. The biological process (BP) terms enriched in the upregulated genes included "skeletal system development", "sister chromatid cohesion", and "ossification". Pathways were enriched in "Wnt signaling pathway" and "proteoglycans in cancer". The BP terms enriched in the downregulated genes included "inflammatory response", "xenobiotic metabolic process", and "positive regulation of inflammatory response". The enriched pathways included "neuroactive ligand-receptor interaction" and "AMP-activated protein kinase signaling". JUN, tumor necrosis factor α, and interleukin-1ß were the hub genes in the PPI network. Collagen XI A1 and leucine-rich repeat-containing 15 were screened from multiple datasets and experimentally validated. Immune infiltration analyses showed fewer infiltrating adipocytes and endothelial cells in the lesioned versus preserved samples. Conclusion: Our findings provide valuable information for future studies on the pathogenic mechanism of OA and potential therapeutic and diagnostic targets.

Process indicators outshine outcome measures: assessing hospital quality of care in breast cancer treatment in China.

Reporting the results of quality indicators can narrow the gap in the quality of care between hospitals. While most studies rely on outcome indicators, they may not accurately measure the quality of care. Process indicators are not only strongly associated with treatment outcomes, but are also more sensitive to whether patients are treated accurately, enabling timely intervention. Our study aims to investigate whether process indicators provide a more reasonable assessment of hospital quality of care compared to outcome indicators. Data were sourced from the Specific Disease Medical Service Quality Management and Control System in China. A total of 113,942 patients with breast cancer treated in 298 hospitals between January 2019 and April 2023 were included in this retrospective study. The rankability of 11 process indicators was calculated and used as a weight to create a new composite indicator. The composite indicators and outcome measures were compared using the O/E ratio categories. Finally, in order to determine the impact of different years on the results, a sensitivity analysis was conducted using bootstrap sampling. The rankability ( ρ ) values of the eleven process indicators showed significant differences, with the highest ρ value for preoperative cytological or histological examination before surgery (0.919). The ρ value for the outcome indicator was 0.011. The rankability-weighting method yielded a comprehensive score ( ρ  = 0.883). The comparison with categorical results of the outcome indicator has different performance classifications for 113 hospitals (37.92%) for composite scores and 140 (46.98%) for preoperative cytological or histological examinationbefore surgery. Process indicators are more suitable than outcome indicators for assessing the quality of breast cancer care in hospitals. Healthcare providers can use process indicators to identify specific areas for improvement, thereby driving continuous quality improvement efforts.

Global, regional, and national temporal trend in burden of major depressive disorder from 1990 to 2019: An analysis of the global burden of disease study.

Major depressive disorder (MDD) is one of the leading causes of disability worldwide. Comprehensive description of the global burden of MDD and its attributable risk factors is essential for policymaking but currently lacking. In this study, we aim to estimate the burden of MDD in terms of incidence, prevalence, and years lived with disability (YLDs), along with its attributable risk factors at global, regional, and rational level between 1990 and 2019, using data from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019. Data analysis was completed on July 1, 2023. In 2019, 274.80 million (95 % uncertainty interval [UI], 241.28 to 312.77) new cases of MDD were identified globally, with an increase of 59 % from 1990. A total of 37.20 million (25.65 to 51.22) YLDs were attributable to MDD, accounting for the largest proportion of mental disorder YLDs (29.7 %). Countries in the low sociodemographic index quantile exhibited the highest age-standardized incidence rate of MDD, with Uganda (7836.2, per 100,000 person-years, 6713.7 to 9181.1) and Palestine (7687.7, 6546.1 to 9023.9) reporting the highest rates among them. The United States had the highest increase in age-standardized rates, with an average annual percent change of 0.99. Females had 1.6 times higher age-standardised rates than males, ranging from 1.2 (Oceania) to 2.2 (tropical Latin America) times across 21 regions. Globally, the proportions of YLDs due to MDD attributable to bullying victimization, childhood sexual abuse, and intimate partner violence were 4.86 %, 5.46 %, and 8.43 % in 2019, respectively. The heavy burden of MDD serves as a stark reminder that a coordinated response from governments and health communities is urgently needed to scale up mental health services and implement effective interventions, particularly in low-income countries.

Automated measurement and grading of knee cartilage thickness: a deep learning-based approach.

Background: Knee cartilage is the most crucial structure in the knee, and the reduction of cartilage thickness is a significant factor in the occurrence and development of osteoarthritis. Measuring cartilage thickness allows for a more accurate assessment of cartilage wear, but this process is relatively time-consuming. Our objectives encompass using various DL methods to segment knee cartilage from MRIs taken with different equipment and parameters, building a DL-based model for measuring and grading knee cartilage, and establishing a standardized database of knee cartilage thickness. Methods: In this retrospective study, we selected a mixed knee MRI dataset consisting of 700 cases from four datasets with varying cartilage thickness. We employed four convolutional neural networks-UNet, UNet++, ResUNet, and TransUNet-to train and segment the mixed dataset, leveraging an extensive array of labeled data for effective supervised learning. Subsequently, we measured and graded the thickness of knee cartilage in 12 regions. Finally, a standard knee cartilage thickness dataset was established using 291 cases with ages ranging from 20 to 45 years and a Kellgren-Lawrence grading of 0. Results: The validation results of network segmentation showed that TransUNet performed the best in the mixed dataset, with an overall dice similarity coefficient of 0.813 and an Intersection over Union of 0.692. The model's mean absolute percentage error for automatic measurement and grading after segmentation was 0.831. The experiment also yielded standard knee cartilage thickness, with an average thickness of 1.98 mm for the femoral cartilage and 2.14 mm for the tibial cartilage. Conclusion: By selecting the best knee cartilage segmentation network, we built a model with a stronger generalization ability to automatically segment, measure, and grade cartilage thickness. This model can assist surgeons in more accurately and efficiently diagnosing changes in patients' cartilage thickness.