A novel "prime and pull" strategy mediated by the combination of two dendritic cell-targeting designs induced protective lung tissue-resident memory T cells against H1N1 influenza virus challenge.

Vaccination is still the most promising strategy for combating influenza virus pandemics. However, the highly variable characteristics of influenza virus make it difficult to develop antibody-based universal vaccines, until now. Lung tissue-resident memory T cells (TRM), which actively survey tissues for signs of infection and react rapidly to eliminate infected cells without the need for a systemic immune reaction, have recently drawn increasing attention towards the development of a universal influenza vaccine. We previously designed a sequential immunization strategy based on orally administered Salmonella vectored vaccine candidates. To further improve our vaccine design, in this study, we used two different dendritic cell (DC)-targeting strategies, including a single chain variable fragment (scFv) targeting the surface marker DC-CD11c and DC targeting peptide 3 (DCpep3). Oral immunization with Salmonella harboring plasmid pYL230 (S230), which displayed scFv-CD11c on the bacterial surface, induced dramatic production of spleen effector memory T cells (TEM). On the other hand, intranasal boost immunization using purified DCpep3-decorated 3M2e-ferritin nanoparticles in mice orally immunized twice with S230 (S230inDC) significantly stimulated the differentiation of lung CD11b+ DCs, increased intracellular IL-17 production in lung CD4+ T cells and elevated chemokine production in lung sections, such as CXCL13 and CXCL15, as determined by RNAseq and qRT‒PCR assays, resulting in significantly increased percentages of lung TRMs, which could provide efficient protection against influenza virus challenge. The dual DC targeting strategy, together with the sequential immunization approach described in this study, provides us with a novel "prime and pull" strategy for addressing the production of protective TRM cells in vaccine design.

Progress in research on the molecular biological detection techniques of avian encephalomyelitis.

Avian encephalomyelitis (AE) is a highly infectious disease caused by the avian encephalomyelitis virus (AEV), which primarily affects the central nervous system of 1- to 4-week-old chicks and causes significant economic losses in the worldwide poultry industry. Despite heavy dependency on vaccine immunization, AEV has persisted on farms for extended periods, which increases its virulence and makes quick and accurate detection crucial to preventing and controlling the disease. Classical diagnostic methods have been unable to meet the current requirements for rapid diagnosis of AE cases. To address this issue, this paper reviews the etiological and molecular biological detection techniques of AE, and it seeks to provide a reference for future research and to establish differential diagnostic techniques for AE epidemiological investigation, identification of epidemic strains, and early diagnosis of clinical cases. Through improving our understanding of AE, we can better combat the disease and protect the global poultry industry.

Research Progress of Respiratory Disease and Idiopathic Pulmonary Fibrosis Based on Artificial Intelligence.

Machine Learning (ML) is an algorithm based on big data, which learns patterns from the previously observed data through classifying, predicting, and optimizing to accomplish specific tasks. In recent years, there has been rapid development in the field of ML in medicine, including lung imaging analysis, intensive medical monitoring, mechanical ventilation, and there is need for intubation etiology prediction evaluation, pulmonary function evaluation and prediction, obstructive sleep apnea, such as biological information monitoring and so on. ML can have good performance and is a great potential tool, especially in the imaging diagnosis of interstitial lung disease. Idiopathic pulmonary fibrosis (IPF) is a major problem in the treatment of respiratory diseases, due to the abnormal proliferation of fibroblasts, leading to lung tissue destruction. The diagnosis mainly depends on the early detection of imaging and early treatment, which can effectively prolong the life of patients. If the computer can be used to assist the examination results related to the effects of fibrosis, a timely diagnosis of such diseases will be of great value to both doctors and patients. We also previously proposed a machine learning algorithm model that can play a good clinical guiding role in early imaging prediction of idiopathic pulmonary fibrosis. At present, AI and machine learning have great potential and ability to transform many aspects of respiratory medicine and are the focus and hotspot of research. AI needs to become an invisible, seamless, and impartial auxiliary tool to help patients and doctors make better decisions in an efficient, effective, and acceptable way. The purpose of this paper is to review the current application of machine learning in various aspects of respiratory diseases, with the hope to provide some help and guidance for clinicians when applying algorithm models.

Prevalence and characteristics of multidrug-resistant Proteus mirabilis from broiler farms in Shandong Province, China.

Animal-derived Proteus mirabilis (P. mirabilis) is an important food-borne zoonotic bacillus and widely exists in the broiler-breeding industry. The present study was designed to explore the P. mirabilis prevalence and antimicrobial resistance characteristics in 6 conventional broiler-fattening farms in Shandong Province, China. The overall isolation rate of P. mirabilis was 7.07% (50/707). Antimicrobial resistance was very common in the P. mirabilis isolated from these farms and varied for different antibacterial drugs, with chloramphenicol, ciprofloxacin, and trimethoprim-sulfamethoxazole having the highest resistance rate (98%) and aztreonam the lowest (0%). Multidrug resistance was as high as 100%. The majority of the MDR isolates were resistant to between 9 and 12 of the antibiotics, with these accounting for 76% (38/50) of multidrug resistant strains. These P. mirabilis isolates carried 24 drug-resistance genes in 6 types, with stcM having the highest rate (96%) and cmlA, blaTEM, and qnrC the lowest (2%). Superdrug resistance gene blaNDM-1 was found in 10% (5/50) of isolates from poultry farms in Shandong. All the P. mirabilis isolates carried at least 6 virulence genes, with 100% detection rates of the ireA and hpmA genes. Our study revealed that the P. mirabilis strains isolated in the Shandong area all showed the MDR phenotype and the poultry-derived carbapenem-resistant MDR P. mirabilis strains may pose a potential risk to humans. Surveillance findings presented herein will be conducive to our understanding of the prevalence and characteristics of carbapenem-resistant P. mirabilis strains in Shandong, China.

Prognostic Value of E2F Transcription Factor Expression in Pancreatic Adenocarcinoma.

BACKGROUND Pancreatic adenocarcinoma (PAAD) is one of the deadliest types of cancer. In the early stages, patients often have atypical symptoms, making diagnosis difficult. The prognosis of diagnosed patients is very poor and treating PAAD is challenging. Therefore, determining reliable risk factors related to PAAD development is critical for improving patient prognosis. E2F family transcription factors (TFs) are essential regulators of DNA synthesis and cell cycle progression in eukaryotic cells, and they have been identified as prognostic biomarkers associated with multiple cancer types. However, further research is necessary to establish the prognostic relevance of these TFs in PAAD patients. MATERIAL AND METHODS We assessed PAAD patient transcriptional and outcome data using the TIMER, ONCOMINE, STRING, GEPIA, cBioPortal, Kaplan-Meier Plotter, GSCALite, and starBase databases. RESULTS PAAD tumor tissues exhibited increased expression of E2F1/3/5/7/8 relative to that in normal tissues, while the expression of E2F2/3/6/8 was associated with a more advanced tumor stage. Survival analyses indicated that PAAD patients expressing higher levels of E2F1/2/3/7/8 exhibited shorter overall survival (OS) and disease-free survival (DFS) than patients expressing lower levels of these TFs. In addition, E2F4 and E2F6 overexpression was associated with poorer DFS and OS, respectively. We also found that the expression of E2Fs was significantly correlated with immune infiltrates, including CD8+ T cells, CD4+ T cells, B cells, dendritic cells, neutrophils, and macrophages. CONCLUSIONS Our study may provide new insights into the optimal choice of immunotherapy and promising novel targets for therapeutic intervention in PAAD patients.

Second-Generation Sequencing with Deep Reinforcement Learning for Lung Infection Detection.

Recently, deep reinforcement learning, associated with medical big data generated and collected from medical Internet of Things, is prospective for computer-aided diagnosis and therapy. In this paper, we focus on the application value of the second-generation sequencing technology in the diagnosis and treatment of pulmonary infectious diseases with the aid of the deep reinforcement learning. Specifically, the rapid, comprehensive, and accurate identification of pathogens is a prerequisite for clinicians to choose timely and targeted treatment. Thus, in this work, we present representative deep reinforcement learning methods that are potential to identify pathogens for lung infection treatment. After that, current status of pathogenic diagnosis of pulmonary infectious diseases and their main characteristics are summarized. Furthermore, we analyze the common types of second-generation sequencing technology, which can be used to diagnose lung infection as well. Finally, we point out the challenges and possible future research directions in integrating deep reinforcement learning with second-generation sequencing technology to diagnose and treat lung infection, which is prospective to accelerate the evolution of smart healthcare with medical Internet of Things and big data.