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.

CARD9 deficiency promotes pancreatic cancer growth by blocking dendritic cell maturation via SLC6A8-mediated creatine transport.

Pancreatic cancer (PC) is featured with low survival rate and poor outcomes. Herein, we found that the expression of caspase-recruitment domain-containing protein 9 (CARD9), predominantly expressed in innate immune cells, was positively related to the prognosis of PC patients. CARD9-deficient PC mice exhibited rapider cancer progression and poorer survival rate. CARD9 knockout decreased dendritic cell (DC) maturation and impaired DC ability to activate T cells in vivo and in vitro. Adoptive DC transfer confirmed that the role of CARD9 deficiency in PC relied on DCs. Creatine was identified as the most significant differential metabolite between WT DCs and CARD9-/- DCs wherein it played an essential role in maintaining DC maturation and function. CARD9 deficiency led to decreased creatine levels in DCs by inhibiting the transcription of the creatine-specific transporter, solute carrier family 6 member 8 (SLC6A8). Furtherly, CARD9 deletion blocked p65 activation by abolishing the formation of CARD9-BCL10-MALT1 complex, which prevented the binding between p65 and SLC6A8 promoter. These events decreased the creatine transport into DCs, and led to DC immaturity and impairment in antitumor immunity, consequently promoting PC progression.

A bibliometric analysis of the cause of sudden unexplained death in forensic medicine: Research trends, hot spots and prospects.

To better understand the cause of sudden unexplained death, our group evaluated the scientific results of related studies in a global context. A systematic search of the Web of Science, PubMed and MEDLINE databases identified 2001 studies related to this field published from 1997 to 2020. The studies were analyzed using bibliometric methods, and statistical maps were drawn to explore research trends and research frontiers. Sudden cardiac death and sudden unexpected epilepsy death were the two major causes of sudden unexplained deaths. With the rapid development of high-throughput sequencing technology and bioinformatics in the past 10 years, molecular autopsy has become an effective research method as well as a research hotspot for exploring the cause of sudden unexplained deaths. However, molecular autopsy is underutilized in the investigation of sudden unexpected death in epilepsy. Developing standardized guidelines for diagnostic strategies for the deceased and their families, expanding the screening of mutation spectrum of related diseases, studying the association between variants and diseases in complex genetic diseases, and improving variants interpretation guidelines and disease sequencing databases are future research directions.

Rare Variants and Polymorphisms of FBN1 Gene May Increase the Risk of Non-Syndromic Aortic Dissection.

Aortic dissection (AD) is a cardiovascular disease characterized by high mortality and poor prognosis. Although FBN1 is associated with syndromic AD, its association with non-syndromic AD remains unclear. In this study, DNA samples from 90 Chinese individuals with non-syndromic AD (60 Stanford A, 30 Stanford B types) were analyzed to determine the relationship between diverse genotypes of the FBN1 gene and non-syndromic AD. Eleven pathogenic/likely pathogenic variants (1 novel) were identified in 12.2% of patients with non-syndromic AD. Patients with positive variants suffered from AD at a younger age than those in the negative variant group. Among the six positive missense mutations associated with cysteine residue hosts, four (66.7%) were Stanford A AD, whereas two (33.3%) were Stanford B AD. Three (100%) positive splicing/truncation variant hosts were Stanford A AD. The splicing/truncation variants and missense variants involving cysteine residues in the FBN1 gene increased the risk of Stanford A AD. Ten common SNPs that increased susceptibility to AD were identified. In particular, five SNPs were detected significantly in Stanford A AD, whereas another four SNPs were significantly detected in Stanford B AD. These significant variants can function as biomarkers for the identification of patients at risk for AD. Our findings have the potential to broaden the database of positive mutations and common SNPs of FBN1 in non-syndromic AD among the Chinese population.