Immune signatures predict response to house dust mite subcutaneous immunotherapy in patients with allergic rhinitis.

BACKGROUND: Identifying predictive biomarkers for allergen immunotherapy response is crucial for enhancing clinical efficacy. This study aims to identify such biomarkers in patients with allergic rhinitis (AR) undergoing subcutaneous immunotherapy (SCIT) for house dust mite allergy. METHODS: The Tongji (discovery) cohort comprised 72 AR patients who completed 1-year SCIT follow-up. Circulating T and B cell subsets were characterized using multiplexed flow cytometry before SCIT. Serum immunoglobulin levels and combined symptom and medication score (CSMS) were assessed before and after 12-month SCIT. Responders, exhibiting ≥30% CSMS improvement, were identified. The random forest algorithm and logistic regression analysis were used to select biomarkers and establish predictive models for SCIT efficacy in the Tongji cohort, which was validated in another Wisco cohort with 43 AR patients. RESULTS: Positive SCIT response correlated with higher baseline CSMS, allergen-specific IgE (sIgE)/total IgE (tIgE) ratio, and frequencies of Type 2 helper T cells, Type 2 follicular helper T (TFH2) cells, and CD23+ nonswitched memory B (BNSM) and switched memory B (BSM) cells, as well as lower follicular regulatory T (TFR) cell frequency and TFR/TFH2 cell ratio. The random forest algorithm identified sIgE/tIgE ratio, TFR/TFH2 cell ratio, and BNSM frequency as the key biomarkers discriminating responders from nonresponders in the Tongji cohort. Logistic regression analysis confirmed the predictive value of a combination model, including sIgE/tIgE ratio, TFR/TFH2 cell ratio, and CD23+ BSM frequency (AUC = 0.899 in Tongji; validated AUC = 0.893 in Wisco). CONCLUSIONS: A T- and B-cell signature combination efficiently identified SCIT responders before treatment, enabling personalized approaches for AR patients.

Sinomenine prevents the development of cardiomyopathy in diabetic rats by inhibiting inflammatory responses and blocking activation of NF-κB.

Diabetic cardiomyopathy is a severe complication of diabetes mellitus (DM). The goal of current work was to study the effects of sinomenine on streptozotocin-induced cardiomyopathy in rats. DM in rats was induced by intraperitoneal injection of streptozotocin. Cardiac function was evaluated by measuring left ventricle end-diastolic diameter, left ventricle end-systolic diameter and ejection fraction. Cardiac inflammation was evaluated by the degree of infiltration of T lymphocytes and the levels of pro-inflammatory cytokines. Sinomenine attenuated diabetic symptoms without affecting plasma glucose. Cardiac dysfunction in the sinomenine-treated diabetic rats was significantly improved, as reflected by decreased levels of left ventricle end-diastolic diameter, left ventricle end systolic diameter and an increased level of ejection fraction. Sinomenine observably reduced cardiomyocyte hypertrophy in DM rats. Moreover, sinomenine reduced infiltration of CD3+ and CD68+ positive cells and decreased the levels of tumor necrosis factor-α, interlukin-1 and interlukin-6. Finally, sinomenine-treated rats showed a reduced expression of NF-κB and an increased expression of IκB in the myocardium compared with the myocardium of untreated diabetic rats. Our results indicate sinomenine significantly improves cardiac function in diabetic rats, which may be attributed to the deactivation of NF-κB and the blockade of inflammatory cytokine-mediated immune reactions.

Prevalence and variance of the sternalis muscle: a study in the Chinese population using multi-detector CT.

OBJECTIVE: To analyze the prevalence, anatomical features, as well as variance of the sternalis muscle in the Chinese population using multi-detector computed tomography (MDCT). METHODS: We retrospectively reviewed 6,000 adult axial MDCT images to determine the overall and gender prevalence of the sternalis muscles. We also analyzed the side prevalence and anatomical features, including shape, size, location and course. RESULTS: The sternalis muscle was present in 347 (5.8 %) of 6,000 adults. This muscle was more common in males (6.0 %, 187/3091) than in females (5.5 %, 160/2909). Among the 347 adults, 118 (34.0 %) had bilateral sternalis muscles; 148 (42.7 %) had right sternalis muscles; and 81 (23.3 %) had left sternalis muscles. The sternalis muscle was either flat or nodular and located superficial to the major pectoral muscles on CT axial transverse images. According to the muscle morphology and course, we classified sternalis muscles as three different types and nine subtypes. The muscles appeared with a single head and single belly in 58.5 %, double or multiple heads in 18.1 %, and double or multiple bellies in 23.4 %. The mean length, width and thickness were 111.1 ± 33.0, 17.7 ± 9.9 and 4.1 ± 1.7 mm measured on MDCT. CONCLUSION: The sternalis muscle was highly prevalent in normal Chinese adults. MDCT is an effective method to demonstrate this muscle in vivo.

Assessment of Physical Fitness and Health Status of Athletes Based on Intelligent Medical Treatment under Machine Learning.

Machine learning is an interdisciplinary study of how to make computer programs perform similar to human learning, and its techniques are widely used in the medical industry. The purpose of this paper is to study how to use machine learning-based intelligent medicine to analyze and study the assessment of athletes' physique and health status and describe the machine learning algorithm. This paper puts forward the problem of intelligent medical diagnosis, which is based on machine learning, and then elaborates on the concept of machine learning and related algorithms, and designs and analyzes a case of an athlete's physique monitoring and health status assessment system. The experimental results show that the athlete's physical fitness monitoring and health status evaluation system can meet the needs of users. The text classification effect based on the LSTM method is slightly inferior to the SVM effect, in which the recall rate of diabetes is not more than 40%, and the recall rate of cerebral infarction is improved by 26.5% after using fuzzy matching.