Canine memory T-cell subsets in health and disease

Canine Models of Inflammatory Skin Diseases and Their Application in Pharmacological Research

The purpose of this article is to provide an overview of existing pharmacological models of canine dermatitis. Canine models of dermatitis have contributed significantly to our current understanding of the pathology of dermatitis and to the development of corresponding pharmacological interventions. Specifically, canine atopic dermatitis (AD) is reviewed here, as it is one of the most common inflammatory skin diseases in dogs. Canine AD also shares clinicopathological features with human AD, making the dog a natural and optimal model for human disease. Thus, pharmacological models of canine AD may be uniquely applicable to human pharmacological research. In this article, particular attention is dedicated to relevant in vivo, in vitro, and ex vivo models of canine AD, skin barrier defect models, pruritus models, and skin immunology models. Additionally, models of superficial pyoderma and food allergy are also discussed. With understanding of findings from canine models, researchers can select the most salient features for future pharmacological drug development. © 2023 Wiley Periodicals LLC.

Plasma Extracellular Vesicle Long RNA in Diagnosis and Prediction in Small Cell Lung Cancer

(1) Introduction: The aim of this study was to identify the plasma extracellular vesicle (EV)-specific transcriptional profile in small-cell lung cancer (SCLC) and to explore the application value of plasma EV long RNA (exLR) in SCLC treatment prediction and diagnosis. (2) Methods: Plasma samples were collected from 57 SCLC treatment-naive patients, 104 non-small-cell lung cancer (NSCLC) patients and 59 healthy participants. The SCLC patients were divided into chemo-sensitive and chemo-refractory groups based on the therapeutic effects. The exLR profiles of the plasma samples were analyzed by high-throughput sequencing. Bioinformatics approaches were used to investigate the differentially expressed exLRs and their biofunctions. Finally, a t-signature was constructed using logistic regression for SCLC treatment prediction and diagnosis. (3) Results: We obtained 220 plasma exLRs profiles in all the participants. Totals of 5787 and 1207 differentially expressed exLRs were identified between SCLC/healthy controls, between the chemo-sensitive/chemo-refractory groups, respectively. Furthermore, we constructed a t-signature that comprised ten exLRs, including EPCAM, CCNE2, CDC6, KRT8, LAMB1, CALB2, STMN1, UCHL1, HOXB7 and CDCA7, for SCLC treatment prediction and diagnosis. The exLR t-score effectively distinguished the chemo-sensitive from the chemo-refractory group (p = 9.268 × 10^-9) with an area under the receiver operating characteristic curve (AUC) of 0.9091 (95% CI: 0.837 to 0.9811) and distinguished SCLC from healthy controls (AUC: 0.9643; 95% CI: 0.9256-1) and NSCLC (AUC: 0.721; 95% CI: 0.6384-0.8036). (4) Conclusions: This study firstly characterized the plasma exLR profiles of SCLC patients and verified the feasibility and value of identifying biomarkers based on exLR profiles in SCLC diagnosis and treatment prediction.