Tumors can induce the generation and accumulation of immunosuppressive cells such as myeloid-derived suppressor cells in the tumor microenvironment, contributing to tumor immunological escapes. Many studies have demonstrated that multiple factors could induce myeloid precursor cells into myeloid-derived suppressor cells, not dendritic cells. In our study, we found that tumor supernatants could induce the generation of myeloid-derived suppressor cells by disturbing the development of dendritic cells. Twist and miR-34a may regulate the effect of tumor cells inducing myeloid-derived suppressor cells via TGF-β and/or IL-10.

Introduction: Cough is a common symptom of respiratory diseases, and prolonged monitoring of cough can help assist doctors in making judgments about patients’ conditions, among which cough frequency is an indicator that characterizes the state of the patient’s lungs. Therefore, the aim of this paper is to design an automatic cough counting system to monitor the number of coughs per minute for a long period of time.Methods: In this paper, a complete cough counting process is proposed, including denoising, segment extraction, eigenvalue calculation, recognition, and counting process; and a wearable automatic cough counting device containing acquisition and reception software. The design and construction of the algorithm is based on realistically captured cough-containing audio from 50 patients, combined with short-time features, and Meier cepstrum coefficients as features characterizing the cough.Results: The accuracy, sensitivity, specificity, and F1 score of the method were 93.24%, 97.58%, 86.97%, and 94.47%, respectively, with a Kappa value of 0.9209, an average counting error of 0.46 counts for a 60-s speech segment, and an average runtime of 2.80 ± 2.27 s.Discussion: This method improves the double threshold method in terms of the threshold and eigenvalues of the cough segments’ sensitivity and has better performance in terms of accuracy, real-time performance, and computing speed, which can be applied to real-time cough counting and monitoring in small portable devices with limited computing power. The developed wearable portable automatic cough counting device and the accompanying host computer software application can realize the long-term monitoring of patients’ coughing condition.

Background: Ovarian cancer (OC) is a significant health concern due to the complex nature of its causes, difficulties in early detection, and low 5-year survival rate. The function of mechanosensitive ion channel (MIC)-related prognostic gene signatures in OC is still not clearly defined. Our aim was to clarify the function of the MIC in OC. Methods: We created OC subtypes and a prognostic model based on MICs to forecast patient outcomes using RNA sequencing and clinical data from The Cancer Genome Atlas (TCGA) database. Results: In this study, the top 20 genes were identified based on their relevance scores and included PIEZO1, SCN5A, KCNQ1, CFTR, PIEZO2, KCNMA1, ASIC2, CACNA1C, ASIC3, SCN1A, TRPV4, TRPV1, KCNN4, SCNN1B, SCNN1A, CACNA1B, SCNN1G, TRPM7, KCNK2, and TRPA1. Patients were distinctly categorized into a high-risk group (cluster 1) and a low-risk group (cluster 2) based on genes related to MICs. Functional analysis revealed that the upregulated differentially expressed genes (DEGs) in cluster 1 were significantly enriched in pathways such as focal adhesion, axon guidance, proteoglycans in cancer, extracellular matrix (ECM)-receptor interaction, Wnt signaling pathway, Hippo signaling pathway, and thyroid hormone signaling pathway. Conversely, the downregulated DEGs in cluster 1 were predominantly enriched in pathways including oxidative phosphorylation, chemical carcinogenesis-reactive oxygen species, and nonalcoholic fatty liver disease. Gene Ontology (GO) analysis of the upregulated DEGs in cluster 1 indicated significant enrichment in biological pathways related to ECM organization, cell-substrate adhesion, and cell junction assembly. Conversely, the downregulated DEGs in cluster 1 were significantly enriched in pathways associated with oxidative phosphorylation, adenosine triphosphate metabolic processes, and cellular respiration. The estimation of immune scores revealed differences between the high- and low-risk groups. Using least absolute shrinkage and selection operator and Cox regression analyses, we identified a set of 20 genes linked to MICs in OC, from which three key genes—PIEZO1, CACNA1C, and TRPV4—were further selected. Single-cell RNA sequencing demonstrated that CACNA1C was expressed in fibroblasts and myofibroblasts, PIEZO1 was expressed across all five cell subtypes, and TRPV4 was expressed in fibroblasts and monocytes or macrophages. Conclusions: This study initially identified unique molecular subtypes and key genes for patients with OC from the novel angle of MICs.