Optimizing Levothyroxine Replacement: A Precision Dosage Model for Post-Thyroidectomy Patients.

Background: Thyroidectomy is commonly performed for benign or malignant thyroid tumors, often resulting in hypothyroidism. Levothyroxine (LT4) supplementation is crucial to maintain hormone levels within the normal range and suppress TSH for cancer control. However, determining the optimal dosage remains challenging, leading to uncertain outcomes and potential side effects. Methods: We analyzed clinical examination data from 510 total thyroidectomy patients, including demographic information, blood tests, and thyroid function. Using R, we applied data preprocessing techniques and identified 274 samples with 98 variables. Principal Component Analysis, correlation analysis, and regression analysis were conducted to identify factors associated with optimal LT4 dosage. Results: The analysis revealed that only eight variables significantly influenced the final satisfactory dosage of LT4 in tablets: Benign0/Malignant1 (benign or malignant), BQB (electrophoretic albumin ratio), TP (total protein), FDP (fibrin degradation products), TRAB_1 (thyroid-stimulating hormone receptor antibody), PT (prothrombin time), MONO# (monocyte count), and HCV0C (hepatitis C antibody). The resulting predictive model was: . Conclusion: Parameters such as benign/malignant status, TRAB_1, and BQB ratio during medication can serve as observational indicators for postoperative LT4 dosage. The calculated linear model can predict the LT4 dosage for patients after thyroidectomy, leading to improved treatment effectiveness and conserving medical resources.

High-dimensional analysis reveals an immune atlas and novel neutrophil clusters in the lungs of model animals with Actinobacillus pleuropneumoniae-induced pneumonia.

Due to the increase in bacterial resistance, improving the anti-infectious immunity of the host is rapidly becoming a new strategy for the prevention and treatment of bacterial pneumonia. However, the specific lung immune responses and key immune cell subsets involved in bacterial infection are obscure. Actinobacillus pleuropneumoniae (APP) can cause porcine pleuropneumonia, a highly contagious respiratory disease that has caused severe economic losses in the swine industry. Here, using high-dimensional mass cytometry, the major immune cell repertoire in the lungs of mice with APP infection was profiled. Various phenotypically distinct neutrophil subsets and Ly-6C+ inflammatory monocytes/macrophages accumulated post-infection. Moreover, a linear differentiation trajectory from inactivated to activated to apoptotic neutrophils corresponded with the stages of uninfected, onset, and recovery of APP infection. CD14+ neutrophils, which mainly increased in number during the recovery stage of infection, were revealed to have a stronger ability to produce cytokines, especially IL-10 and IL-21, than their CD14- counterparts. Importantly, MHC-II+ neutrophils with antigen-presenting cell features were identified, and their numbers increased in the lung after APP infection. Similar results were further confirmed in the lungs of piglets infected with APP and Klebsiella pneumoniae infection by using a single-cell RNA-seq technique. Additionally, a correlation analysis between cluster composition and the infection process yielded a dynamic and temporally associated immune landscape where key immune clusters, including previously unrecognized ones, marked various stages of infection. Thus, these results reveal the characteristics of key neutrophil clusters and provide a detailed understanding of the immune response to bacterial pneumonia.