Studying pulmonary fibrosis due to microbial infection via automated microscopic image analysis.

Introduction: Pulmonary fibrosis is a consequential complication of microbial infections, which has notably been observed in SARS-CoV-2 infections in recent times. Macrophage polarization, specifically the M2-type, is a significant mechanism that induces pulmonary fibrosis, and its role in the development of Post- COVID-19 Pulmonary Fibrosis is worth investigating. While pathological examination is the gold standard for studying pulmonary fibrosis, manual review is subject to limitations. In light of this, we have constructed a novel method that utilizes artificial intelligence techniques to analyze fibro-pathological images. This method involves image registration, cropping, fibrosis degree classification, cell counting and calibration, and it has been utilized to analyze microscopic images of COVID-19 lung tissue. Methods: Our approach combines the Transformer network with ResNet for fibrosis degree classification, leading to a significant improvement over the use of ResNet or Transformer individually. Furthermore, we employ semi-supervised learning which utilize both labeled and unlabeled data to enhance the ability of the classification network in analyzing complex samples. To facilitate cell counting, we applied the Trimap method to localize target cells. To further improve the accuracy of the counting results, we utilized an effective area calibration method that better reflects the positive density of target cells. Results: The image analysis method developed in this paper allows for standardization, precision, and staging of pulmonary fibrosis. Analysis of microscopic images of COVID-19 lung tissue revealed a significant number of macrophage aggregates, among which the number of M2-type macrophages was proportional to the degree of fibrosis. Discussion: The image analysis method provids a more standardized approach and more accurate data for correlation studies on the degree of pulmonary fibrosis. This advancement can assist in the treatment and prevention of pulmonary fibrosis. And M2-type macrophage polarization is a critical mechanism that affects pulmonary fibrosis, and its specific molecular mechanism warrants further exploration.

Immune-related adverse events induced by programmed death protein-1 inhibitors from the perspective of lymphoma immunotherapy.

Lymphoma, which is highly malignant, stems from lymph nodes and lymphoid tissue. Lymphoma cells express programmed death-ligand 1/2 (PD-L1/PD-L2), which binds with programmed cell death 1 protein (PD-1) to establish inhibitory signaling that impedes the normal function of T cells and allows tumor cells to escape immune system surveillance. Recently, immune checkpoint inhibitor immunotherapies such as PD-1 inhibitors (nivolumab and pembrolizumab) have been introduced into the lymphoma treatment algorithm and have shown remarkable clinical efficacy and greatly improve prognosis in lymphoma patients. Accordingly, the number of lymphoma patients who are seeking treatment with PD-1 inhibitors is growing annually, which results in an increasing number of patients developing immune-related adverse events (irAEs). The occurrence of irAEs inevitably affects the benefits provided by immunotherapy, particularly when PD-1 inhibitors are applied. However, the mechanisms and characteristics of irAEs induced by PD-1 inhibitors in lymphoma need further investigation. This review article summarizes the latest research advances in irAEs during treatment of lymphoma with PD-1 inhibitors. A comprehensive understanding of irAEs incurred in immunotherapy can help to achieve better efficacy with PD-1 inhibitors in lymphoma.