Hormone and Metabolic Research最新文献

After the COVID-19 pandemic, many patients have reported chronic fatigue and severe post-exertional malaise, with symptoms similar to those of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The accumulation of agonistic receptor autoantibodies targeting beta-adrenergic (β1 and β2) and muscarinic (M3 and M4) neurotransmitter receptors may play a crucial role in the pathomechanism of both ME/CFS and post-COVID conditions. Therapeutic apheresis has been suggested as an effective treatment option for alleviating and mitigating symptoms in this desperate group of patients. In this single-center pilot study, we analyzed autoantibodies in a cohort of 20 post-COVID patients before and after therapeutic apheresis. Apheresis resulted in a decline of β1 or β2 adrenergic receptor antibodies in all patients. Additionally, the majority of patients experienced a concurrent reduction in symptoms such as fatigue, physical activity restrictions, myalgia, post-exertional malaise, and concentration disorders. This study clearly demonstrates an association between autoantibodies and the clinical improvement of post-COVID patients. Even if future sham-controlled trials do not show a positive outcome, extracorporeal apheresis may still be valuable for this patient group by temporarily improving microperfusion and symptoms. Success in restoring patients to work and normal life, as observed in many individuals after therapeutic apheresis, should be recognized. Therefore, we believe that extracorporeal therapeutic apheresis, as part of a multimodal treatment, should be considered an early intervention for postinfectious syndromes in selected patients.

Glucagon-like peptide-1 (GLP-1) receptor agonists are effective hypoglycemic agents for type 2 diabetes mellitus (T2DM). It was reported that T2DM was implicated in pancreatic β-cell senescence. Whether loxenatide regulates cellular senescence of pancreatic β-cells is to be investigated. Our results revealed that high glucose (HG)-induced cellular senescence and elevated expression of SASP factors inhibited cell proliferation and stimulated DNA damage, which were reversed by loxenatide treatment. In addition, HG induction resulted in promoted insulin secretion and insulin synthesis of pancreatic β-cells and loxenatide treatment further strengthened these influences. In addition, loxenatide could inactivate the PERK/eIF2α signaling pathway via decreasing the levels of p-PERK and p-eIF2α in HG-induced pancreatic β-cells. Furthermore, CCT020312, an activator of the PERK/eIF2α signaling pathway, abolished loxenatide-mediated inhibiting cellular senescence, elevating cell proliferation and improving DNA damage and enhancing insulin secretion of HG-induced pancreatic β-cells. In conclusion, our results indicated that loxenatide impeded cellular senescence, promoted cell proliferation, improved DNA damage, enhanced insulin secretion and insulin synthesis of HG-induced pancreatic β-cells through modulating the PERK/eIF2α signaling pathway.

Insulin and thyroid hormones play important roles in our body. Insulin helps regulate the glucose level while the thyroid hormones affect various cells and tissues, metabolizing protein, lipids, and glucose. Hyperthyroidism and thyrotoxicosis are potential hazards for type 2 diabetes mellitus. There is a high prevalence of hypothyroidism being more common compared to hyperthyroidism coexisting with diabetes mellitus. Thyroid hormones affect glucose metabolism through its action on peripheral tissues (gastrointestinal tract, liver, skeletal muscles, adipose tissue, and pancreas). High-level thyroid hormone causes hyperglycemia, upregulation of glucose transport, and reduction in glycogen storage. The reverse is observed during low levels of thyroid hormone along with insulin clearance. The net result of thyroid disorder is insulin resistance. Type 2 diabetes mellitus can downsize the regulation of thyroid stimulating hormones and impair the conversion of thyroxine to triiodothyronine in peripheral tissues. Furthermore, poorly managed type 2 diabetes mellitus may result in insulin resistance and hyperinsulinemia, contributing to the proliferation of thyroid tissue and an increase in nodule formation and goiter size. Although metformin proves advantageous for both type 2 diabetes mellitus and thyroid disorder patients, other antidiabetics like sulfonylureas, pioglitazone, and thiazolidinediones may have adverse effects on thyroid disorders. Moreover, antithyroid drugs such as methimazole can weaken glycemic control in individuals with diabetes. Thus, an interplay between both endocrinopathies is observed and individualized care and management of the disorder needs to be facilitated.

There has been an increase in the diagnosis of thyroid nodules in recent years. In addition to the well-known criteria, features that are likely to increase the risk of malignancy are in the research process. In this study, we aimed to evaluate the malignant potential of nodules that expand the thyroid capsule ultrasonographically. A total of 109 patients with thyroid capsule-expanding nodules and 288 patients with non-expanding nodules were included in the study. Demographical data, ultrasonography features, and cytology results were noted, and histopathological findings were determined in operated patients. While malignant cytology was detected in 5.5% of capsule-expanding nodules in FNAB results, this rate was 0.7% in the non-capsule expanding group (p<0.001). According to the histopathological results, the incidence of malignancy was 15.6% in the capsule-expanding nodule group, while it was 3.1% in the other group (p=0.001). As a result of logistic regression analysis, it was determined that the risk of malignancy increased by 4.44-fold (95% CI 1.4-13.8, p=0.01) in patients with capsule-expanding nodules. Other features that increased the risk of malignancy were hypoechogenicity, microcalcification, and irregular margin presence. In this study, we found that the risk of malignancy increased in nodules expanding the thyroid capsule. Based on the increasing study data on this subject, monitoring thyroid capsule expansion in nodules may take its place among the criteria for malignancy in future evaluations.

The study aimed to develop a risk prognostic model using platelet-related genes (PRGs) to predict sepsis patient outcomes. Sepsis patient data from the Gene Expression Omnibus (GEO) database and PRGs from the Molecular Signatures Database (MSigDB) were analyzed. Differential analysis identified 1139 differentially expressed genes (DEGs) between sepsis and control groups. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed enrichment in functions related to immune cell regulation and pathways associated with immune response and infectious diseases. A risk prognostic model was established using LASSO and Cox regression analyses, incorporating 10 PRGs selected based on their association with sepsis prognosis. The model demonstrated good stratification and prognostic effects, confirmed by survival and receiver operating characteristic (ROC) curve analyses. It served as an independent prognostic factor in sepsis patients. Further analysis using the CIBERSORT algorithm showed higher infiltration of activated natural killer (NK) cells and lower infiltration of CD8 T cells and CD4 T cells naïve in the high-risk group compared to the low-risk group. Additionally, expression levels of human leukocyte antigen (HLA) genes were significantly lower in the high-risk group. In conclusion, the 10-gene risk model based on PRGs accurately predicted sepsis patient prognosis and immune infiltration levels. This study provides valuable insights into the role of platelets in sepsis prognosis and diagnosis, offering potential implications for personalized treatment strategies.

Radioiodine (RAI) therapy after surgery, is an important component for the treatment of patients with papillary thyroid cancer (PTC), the most common thyroid cancer. In this study we sought to evaluate the cancer-specific survival (CSS) impact of RAI in specific thyroid cancer subgroups. The Surveillance, Epidemiology, and End Results (SEER) database were used to identify patients with PTC who underwent surgery between 2000 and 2019. Patients not treated with RAI were compared to those treated with RAI using propensity score matching (PSM) on the basis of identical inclusion criteria. A total of 106 333 patients were identified from the SEER database. RAI therapy was associated with improved CSS in the matched cohort (HR: 0.83; 95% CI: 0.72-0.96, p=0.01) but not in the unmatched data set (HR: 1.46; 95% CI: 1.30-1.64, p<0.001) among all PTC patients regardless of disease stage. Detailed analyses, however, showed that only patients with high-risk disease (pT3N1, pT4N1) experienced the greatest benefit in CSS. In the lower disease stages, no significant differences were recognized in the group of PTC patients with or without RAI therapy. One exception: in the group of PTC patients in stage pT1bN0, a significant difference was seen towards RAI. This is, however, most likely due to the large number of patients investigated. In summary, RAI therapy should not be used in low-risk PTC patients and might be used to some extent in intermediate-risk PTC patients. The histological suptype of the tumor needs to be considered in this context.

Hepatocellular carcinoma (HCC) is a primary liver cancer with a high mortality rate. The search for a new biomarker could help the prognosis of HCC patients. We identified the glycolytic gene set associated with HCC and the glycolytic lncRNA based on TCGA and MsigDB databases. According to these lncRNAs, K-means clustering, and regression analysis were performed on the patients. Two groups of HCC patients with different lncRNA expression levels were obtained based on K-means clustering results. The results of difference analysis and enrichment analysis showed that DEmRNA in the two HCC populations with significant survival differences was mainly enriched in transmembrane transporter complex, RNA polymerase II specificity, cAMP signaling pathway, and calcium signaling pathway. In addition, a prognostic model of HCC with 4 DElncRNAs was constructed based on regression analysis. ROC curve analysis showed that the model had good predictive performance. Drug predictionresults showed that the efficacy of JQ1, niraparib, and teniposide was higher in the low-risk group than in the high-risk group. In conclusion, this study preliminarily identified glycolytic-related prognostic features of lncRNAs in HCC and constructed a risk assessment model. The results of this study are expected to guide the prognosis assessment of clinical HCC patients.

This study attempted to build a prostate cancer (PC) prognostic risk model with mitochondrial feature genes. PC-related MTGs were screened for Cox regression analyses, followed by establishing a prognostic model. Model validity was analyzed via survival analysis and receiver operating characteristic (ROC) curves, and model accuracy was validated in the GEO dataset. Combining risk score with clinical factors, the independence of the risk score was verified by using Cox analysis, followed by generating a nomogram. The Gleason score, microsatellite instability (MSI), immune microenvironment, and tumor mutation burden were analyzed in two risk groups. Finally, the prognostic feature genes were verified through a q-PCR test. Ten PC-associated MTGs were screened, and a prognostic model was built. Survival analysis and ROC curves illustrated that the model was a good predictor for the risk of PC. Cox regression analysis revealed that risk score acted as an independent prognostic factor. The Gleason score and MSI in the high-risk group were substantially higher than in the low-risk group. Levels of ESTIMATE Score, Immune Score, Stromal Score, immune cells, immune function, immune checkpoint, and immunopheno score of partial immune checkpoints in the high-risk group were significantly lower than in the low-risk group. Genes with the highest mutation frequencies in the two groups were SPOP, TTN, and TP53. The q-PCR results of the feature genes were consistent with the gene expression results in the database. The 10-gene model based on MTGs could accurately predict the prognosis of PC patients and their responses to immunotherapy.