Myeloid-derived cells (MDCs) are crucial in immune response and tissue homeostasis. They have high functional plasticity and can be polarized according to microenvironment signals. These cells, including macrophages, neutrophils, and dendritic cells (DCs), exhibit different functional polarization states in different pathological environments and are involved in the occurrence and development of diseases such as inflammation and tumors. Studies have shown that metabolic reprogramming plays a key role in the functional polarization of MDCs, affecting the cellular energy supply and regulating immune function. This paper reviews classification, function and polarization mechanism of MDCs and discusses metabolic reprogramming. In addition, the therapeutic strategies targeting MDC are summarized, which is expected to provide new targets for tumor immunotherapy.
Humans ; Tumor Microenvironment/immunology* ; Myeloid Cells/metabolism* ; Neoplasms/pathology* ; Animals ; Immunotherapy/methods* ; Dendritic Cells/immunology* ; Macrophages/immunology*

An elderly female patient was admitted to Shenzhen Traditional Chinese Medicine Hospital on May 4, 2023, due to recurrent cough for 4 years and aggravation with fever for 6 days. Chest CT showed bronchiectasis with pulmonary infection. Sputum smear microscopy indicated the possibility of Nocardia, and sputum fungal culture revealed Aspergillus fumigatus. After several days of anti- Nocardia and anti- Aspergillus fumigatus treatment, the patient′s inflammatory index decreased but she still had a low-grade fever. Effective communication between the laboratory and clinicians facilitated the culture of bronchoalveolar lavage fluid and the detection of metagenomic next-generation sequencing. The patient made progress after receiving anti-infection treatment for three suspected pathogenic bacteria- Nocardia amamiensis, Mycobacterium tuberculosis, and Aspergillus fumigatus-detected by the above methods. For the diagnosis of coinfection, the combination of multiple methods can improve the accuracy of pathogen identification, thereby better guiding clinical treatment.

Stimulus-responsive transdermal drug delivery systems can achieve specific drug release and improve drug utilization. According to the different stimulation modes， these preparations can be divided into endogenous stimulus-responsive， exogenous stimulus-responsive and combined stimulus-responsive transdermal drug delivery systems. The endogenous stimulation- responsive transdermal drug delivery system can respond specifically to changes in temperature and pH of the lesion site through carrier materials， so as to deliver drugs to the target site. Exogenous stimulus-responsive transdermal drug delivery system can use light， heat， magnetic， electric and other external stimulation to make the carrier material phase change， so as to achieve drug delivery. The combined stimulus-responsive transdermal drug delivery system is a combination of two or more stimulus-responsive percutaneous drug delivery systems， such as temperature-pH dual-responsive drug delivery system. At present， the relevant studies of stimulus-responsive transdermal drug delivery systems are mostly in the experimental stage， and further evaluation of stability， toxicity and skin irritation is needed in the future to lay a theoretical foundation for clinical application.

The development of computer hardware allows rapid accumulation of medical imaging data. Deep learning has shown great potential in medical imaging data analysis and establish a new area of machine learning. The commonly used deep learning models were firstly introduced in the paper, and then, summarized with the application of deep learning in the detection, classification, diagnosis, segmentation, identification of medical imaging. The application of deep learning in oral and maxillofacial radiology and other discipline of stomatology was proposed. At the end, the paper discussed the problems of deep learning in medical imaging research.

Brain science accelerates the study of intelligence and behavior, contributes fundamental insights into human cognition, and offers prospective treatments for brain disease. Faced with the challenges posed by imaging technologies and deep learning computational models, big data and high-performance computing (HPC) play essential roles in studying brain function, brain diseases, and large-scale brain models or connectomes. We review the driving forces behind big data and HPC methods applied to brain science, including deep learning, powerful data analysis capabilities, and computational performance solutions, each of which can be used to improve diagnostic accuracy and research output. This work reinforces predictions that big data and HPC will continue to improve brain science by making ultrahigh-performance analysis possible, by improving data standardization and sharing, and by providing new neuromorphic insights.

The accelerating growth of the public microbial genomic data imposes substantial bur-den on the research community that uses such resources. Building databases for non-redundant ref-erence sequences from massive microbial genomic data based on clustering analysis is essential. However, existing clustering algorithms perform poorly on long genomic sequences. In this article, we present Gclust, a parallel program for clustering complete or draft genomic sequences, where clustering is accelerated with a novel parallelization strategy and a fast sequence comparison algo-rithm using sparse suffix arrays (SSAs). Moreover, genome identity measures between two sequences are calculated based on their maximal exact matches (MEMs). In this paper, we demon-strate the high speed and clustering quality of Gclust by examining four genome sequence datasets. Gclust is freely available for non-commercial use at https://github.com/niu-lab/gclust. We also introduce a web server for clustering user-uploaded genomes at http://niulab.scgrid.cn/gclust.

The development of computer hardware allows rapid accumulation of medical imaging data. Deep learning has shown great potential in medical imaging data analysis and establish a new area of machine learning. The commonly used deep learning models were firstly introduced in the paper, and then, summarized with the application of deep learning in the detection, classification, diagnosis, segmentation, identification of medical imaging. The application of deep learning in oral and maxillofacial radiology and other discipline of stomatology was proposed. At the end, the paper discussed the problems of deep learning in medical imaging research.