Semaglutide mitigates testicular damage in diabetes by inhibiting ferroptosis.

Diabetes is linked to male infertility, but the mechanisms and therapeutic options remain unclear. This study investigates the effects of semaglutide on testicular function in a diabetes mouse model. Clinical data shows that diabetes affects blood glucose, lipid levels, and sperm quality. Single-cell and transcriptome analyses reveal changes in testicular tissue cell proportions and activation of ferroptosis pathways in diabetic patients/rats. In the diabetes mouse model, sperm quality decreases significantly. Treatment with semaglutide (Sem) and the ferroptosis inhibitor ferrostatin-1 (Fer-1) alleviates testicular damage, as evidenced by improved lipid peroxidation and ferroptosis markers. Moreover, the diabetes-induced decrease in the TM-3 cell line's vitality, increased lipid peroxidation, ROS, ferrous ions, and mitochondrial membrane potential damage are all improved by semaglutide and ferrostatin-1 intervention. Overall, these findings highlight semaglutide's potential as a therapeutic approach for mitigating diabetes-induced testicular damage through modulation of the ferroptosis pathway.

Upregulation of ROBO3 promotes proliferation, migration and adhesion of AML cells and affects the survival of AML patients.

Acute myeloid leukemia (AML) is a heterogeneous hematological malignancy, which is the most common and severe acute leukemia in adults. Its occurrence, development and prognosis are affected by many factors, and more research is still needed to further guide its treatment. Here, we found that roundabout3 (ROBO3) was associated with poor prognosis in AML through bioinformatics analysis. We then found that overexpression of ROBO3 promoted AML cell proliferation, adhesion and migration while knockdown of ROBO3 had opposite effects. We subsequently found that ROBO3 regulated CD34 expression in AML cells, and this regulatory effect may be achieved through the Hippo-YAP pathway. The inhibitors of this pathway, K-975 and verteporfin, showed an inhibitory effect on AML cells with high ROBO3 expression. ROBO3 was also found to be significantly increased in bone marrow samples from AML patients. Our research indicates that ROBO3 plays an important role in the development of AML, which suggests that ROBO3 can be a prognostic biomarker and potential therapeutic target for AML.

A Multi-Machine Learning Consensus Model Based on Clinical Features Reveals That Interleukin-10 Derived from Monocytes Leads to a Poor Prognosis in Patients with Coronavirus Disease-2019.

Background: Despite ongoing interventions, SARS-CoV-2 continues to cause significant global morbidity and mortality. Early diagnosis and intervention are crucial for effective clinical management. However, prognostic features based on transcriptional data have shown limited effectiveness, highlighting the need for more precise biomarkers to improve COVID-19 treatment outcomes. Methods: We retrospectively analyzed 149 clinical features from 189 COVID-19 patients, identifying prognostic features via univariate Cox regression. The cohort was split into training and validation sets, and 77 prognostic models were developed using seven machine learning algorithms. Among these, the least absolute shrinkage and selection operator (Lasso) method was employed to refine the selection of prognostic variables by ten-fold cross-validation strategy, which were then integrated with random survival forests (RSF) to build a robust COVID-19-related prognostic model (CRM). Model accuracy was evaluated across training, validation, and entire cohorts. The diagnostic relevance of interleukin-10 (IL-10) was confirmed in bulk transcriptional data and validated at the single-cell level, where we also examined changes in cellular communication between mononuclear cells with differing IL-10 expression and other immune cells. Results: Univariate Cox regression identified 43 prognostic features. Among the 77 machine learning models, the combination of Lasso and RSF produced the most robust CRM. This model consistently performed well across training, validation, and entire cohorts. IL-10 emerged as a key prognostic feature within the CRM, validated by single-cell transcriptional data. Transcriptome analysis confirmed the stable diagnostic value of IL-10, with mononuclear cells identified as the primary IL-10 source. Moreover, differential IL-10 expression in these cells was linked to altered cellular communication in the COVID-19 immune microenvironment. Conclusion: The CRM provides accurate prognostic predictions for COVID-19 patients. Additionally, the study underscores the importance of early IL-10 level testing upon hospital admission, which could inform therapeutic strategies.