Objective:To establish an automatic classification approach for bone marrow cells based on microscopic hyperspectral imaging and three-dimensional spectral convolutional neural network (Spec-CNN).Methods:The research type is establishment of methodology. The study included 306 newly diagnosed patients' bone marrow smears under Wright's staining from the Department of Hematology of the First Medical Center of the PLA General Hospital from November 1st, 2013 to April 30th, 2024. The high-spectrum data and 4k image data of bone marrow cells were simultaneously collected using a microscopic hyperspectral-4k optical path integrated imaging system (with a spectral resolution of 400—1 000 nm). The high-spectrum data was used for model training, while the 4k image data recognized by morphologists was only used as a reference for labeling the high-spectrum data. The high-spectrum data set was divided into training set, validation set and test set in a ratio of 14∶6∶5. The training set and validation set were used to train and fine-tune the Spec-CNN model, and the test set was used to evaluate the model performance. The sensitivity, specificity ,accuracy ,and Kappa coefficient were calculated for comparing the manual annotation results as gold standard with the intelligent identification results of the Spec-CNN model. Five non-data set samples were used for external validation.Results:The acquired hyperspectral data and 4k imaging dataset comprised of 32 categories and 64 800 bone marrow cells. In the test set, the Spec-CNN model demonstrated weighted-average indicators on classification metrics across 32 cell types: sensitivity 87.79%, specificity 99.31%, and accuracy 98.78%, and Kappa coefficient 0.869. For external validation, the mean correct identification rate of bone marrow cells reached 83.28%.Conclusion:We successfully established an automatic classification method of bone marrow cells based on microscopic hyperspectral imaging and three-dimensional Spec-CNN. This method has a good automatic classification ability for 32 types of bone marrow nucleated cells, which has a certain auxiliary effect on improving the diagnosis efficiency of blood diseases for bone marrow morphologists.

Objective To construct a prognostic model of lung adenocarcinoma(LUAD)based on m5C modification-related genes and to explore its clinical value.Methods Based on the LUAD data in TCGA,GSE30219,GSE31210,and GSE50081 cohorts,prognosis-related m5C modification-related genes were screened,and the prognostic model was constructed by using univariate Cox,Lasso,and multivariate Cox regression analyses.Kaplan-Meier curve,ROC curve,and Cox regression were used to observe the robustness and prognostic performance of the model.The correlation between the prognostic model and clinico-pathologic features was further explored.Results A prognostic model consisting of eight m5C modifi-cation-related genes,including CDK1,CDKN1A,NOP2,RRM2,TCL6,TLR8,TRDMT1,and YTHDF2,was constructed.Risk score was an independent risk factor for the prognosis of patients with LUAD,and it is combined with age,T stage,and N stage to constitute a nomogram which can accurately predict the prognosis of patients.The infiltration of macrophages and CD4+/CD8+T cells was significantly reduced in high-risk patients.The risk score in LUAD tissues was significantly higher than that in normal tissues and was positively correlated with T stage and N stage.The risk score of smoking and EGFR wild-type patients was higher than that of non-smoking and EGFR-mutant patients.Conclusion The prognostic model constructed based on m5C modification-related genes has shown good accuracy and stability in predicting the prognosis of patients with LUAD,and it is closely related to clinical features,driver gene mutations,and immune infiltration,which can provide a potential basis for the treatment and prognostic assessment of LUAD.

Extensive epigenetic reprogramming involves in memory CD8+ T-cell differentiation. The elaborate epigenetic rewiring underlying the heterogeneous functional states of CD8+ T cells remains hidden. Here, we profile single-cell chromatin accessibility and map enhancer-promoter interactomes to characterize the differentiation trajectory of memory CD8+ T cells. We reveal that under distinct epigenetic regulations, the early activated CD8+ T cells divergently originated for short-lived effector and memory precursor effector cells. We also uncover a defined epigenetic rewiring leading to the conversion from effector memory to central memory cells during memory formation. Additionally, we illustrate chromatin regulatory mechanisms underlying long-lasting versus transient transcription regulation during memory differentiation. Finally, we confirm the essential roles of Sox4 and Nrf2 in developing memory precursor effector and effector memory cells, respectively, and validate cell state-specific enhancers in regulating Il7r using CRISPR-Cas9. Our data pave the way for understanding the mechanism underlying epigenetic memory formation in CD8+ T-cell differentiation.
CD8-Positive T-Lymphocytes/metabolism* ; Cell Differentiation ; Chromatin/immunology* ; Animals ; Mice ; Immunologic Memory ; Epigenesis, Genetic ; SOXC Transcription Factors/immunology* ; NF-E2-Related Factor 2/immunology* ; Mice, Inbred C57BL ; Gene Regulatory Networks ; Enhancer Elements, Genetic

Objective To construct a prognostic model of lung adenocarcinoma(LUAD)based on m5C modification-related genes and to explore its clinical value.Methods Based on the LUAD data in TCGA,GSE30219,GSE31210,and GSE50081 cohorts,prognosis-related m5C modification-related genes were screened,and the prognostic model was constructed by using univariate Cox,Lasso,and multivariate Cox regression analyses.Kaplan-Meier curve,ROC curve,and Cox regression were used to observe the robustness and prognostic performance of the model.The correlation between the prognostic model and clinico-pathologic features was further explored.Results A prognostic model consisting of eight m5C modifi-cation-related genes,including CDK1,CDKN1A,NOP2,RRM2,TCL6,TLR8,TRDMT1,and YTHDF2,was constructed.Risk score was an independent risk factor for the prognosis of patients with LUAD,and it is combined with age,T stage,and N stage to constitute a nomogram which can accurately predict the prognosis of patients.The infiltration of macrophages and CD4+/CD8+T cells was significantly reduced in high-risk patients.The risk score in LUAD tissues was significantly higher than that in normal tissues and was positively correlated with T stage and N stage.The risk score of smoking and EGFR wild-type patients was higher than that of non-smoking and EGFR-mutant patients.Conclusion The prognostic model constructed based on m5C modification-related genes has shown good accuracy and stability in predicting the prognosis of patients with LUAD,and it is closely related to clinical features,driver gene mutations,and immune infiltration,which can provide a potential basis for the treatment and prognostic assessment of LUAD.

Objective:To establish an automatic classification approach for bone marrow cells based on microscopic hyperspectral imaging and three-dimensional spectral convolutional neural network (Spec-CNN).Methods:The research type is establishment of methodology. The study included 306 newly diagnosed patients' bone marrow smears under Wright's staining from the Department of Hematology of the First Medical Center of the PLA General Hospital from November 1st, 2013 to April 30th, 2024. The high-spectrum data and 4k image data of bone marrow cells were simultaneously collected using a microscopic hyperspectral-4k optical path integrated imaging system (with a spectral resolution of 400—1 000 nm). The high-spectrum data was used for model training, while the 4k image data recognized by morphologists was only used as a reference for labeling the high-spectrum data. The high-spectrum data set was divided into training set, validation set and test set in a ratio of 14∶6∶5. The training set and validation set were used to train and fine-tune the Spec-CNN model, and the test set was used to evaluate the model performance. The sensitivity, specificity ,accuracy ,and Kappa coefficient were calculated for comparing the manual annotation results as gold standard with the intelligent identification results of the Spec-CNN model. Five non-data set samples were used for external validation.Results:The acquired hyperspectral data and 4k imaging dataset comprised of 32 categories and 64 800 bone marrow cells. In the test set, the Spec-CNN model demonstrated weighted-average indicators on classification metrics across 32 cell types: sensitivity 87.79%, specificity 99.31%, and accuracy 98.78%, and Kappa coefficient 0.869. For external validation, the mean correct identification rate of bone marrow cells reached 83.28%.Conclusion:We successfully established an automatic classification method of bone marrow cells based on microscopic hyperspectral imaging and three-dimensional Spec-CNN. This method has a good automatic classification ability for 32 types of bone marrow nucleated cells, which has a certain auxiliary effect on improving the diagnosis efficiency of blood diseases for bone marrow morphologists.

This study included 116 professional postgraduate students majoring in clinical surgery who rotated in the Department of Breast and Thyroid Surgery of The First Affiliated Hospital of Army Medical University from 2019 to 2022. The students were provided with open online courses on precision medicine to build a strong theoretical foundation for evidence-based medicine; subsequently, precision medicine courses focusing on thyroid surgery were offered; and multidisciplinary team rounds for typical and difficult-to-diagnose cases were organized. Taking thyroid cancer as an example, questionnaire surveys and typical clinical case assessment were conducted to compare the scientific research and professional competencies of the students before and after evidence-based medicine education. The results showed that the students had significantly improved ability to use academic databases to acquire professional knowledge and solve problems, and showed increased enthusiasm in class, believing that the teaching content was easy to absorb and moderate in difficulty and the teaching effect was good.

Fecal microbiota transplantation (FMT) refers to using the intestinal microorganisms present in the feces or processed feces from healthy people for treating various types of diseases, such as digestive and metabolic diseases. The rapid development of metagenomic and culturomic technologies in gut microbiome analysis provides powerful tools for the FMT research and its clinical applications. Metagenomics technologies comprehensively revealed the diversity and functions of gut microbiota under health and disease conditions, while culturomics technologies helped isolation and identification of "unculturable" bacteria in the human gut under conventional culture conditions. The combination of these two technologies not only enabled us better understand the FMT regularities of cause and effect in clinical practices, but also effectively promoted its applications. Considering the above advantages, this article summarized the applications of metagenomics and culturomics technologies in FMT and prospected its future development trend.
Humans ; Fecal Microbiota Transplantation ; Metagenomics ; Feces/microbiology* ; Gastrointestinal Microbiome ; Bacteria

Inhibitor of nuclear factor kappa-B kinase subunit beta (IKKβ) is one of important kinases in inflammation to phosphorylate inhibitor of nuclear factor kappa-B (IκBα) and then activate nuclear factor kappa-B (NF-κB). Inhibition of IKKβ has been a therapeutic strategy for inflammatory and autoimmune diseases. Here we report that IKKβ is constitutively activated in healthy donors and healthy Ikkβ ^C46A (cysteine 46 mutated to alanine) knock-in mice although they possess intensive IKKβ-IκBα-NF-κB signaling activation. These indicate that IKKβ activation probably plays homeostatic role instead of causing inflammation. Compared to Ikkβ ^WT littermates, lipopolysaccharides (LPS) could induce high mortality rate in Ikkβ ^C46A mice which is correlated to breaking the homeostasis by intensively activating p-IκBα-NF-κB signaling and inhibiting phosphorylation of 5' adenosine monophosphate-activated protein kinase (p-AMPK) expression. We then demonstrated that IKKβ kinase domain (KD) phosphorylates AMPKα1 via interacting with residues Thr183, Ser184, and Thr388, while IKKβ helix-loop-helix motifs is essential to phosphorylate IκBα according to the previous reports. Kinase assay further demonstrated that IKKβ simultaneously catalyzes phosphorylation of AMPK and IκBα to mediate homeostasis. Accordingly, activation of AMPK rather than inhibition of IKKβ could substantially rescue LPS-induced mortality in Ikkβ ^C46A mice by rebuilding the homeostasis. We conclude that IKKβ activates AMPK to restrict inflammation and IKKβ mediates homeostatic function in inflammation via competitively phosphorylating AMPK and IκBα.

The rapid spread of carbapenemase-producing Klebsiella pneumoniae(cpKP)poses seri-ous threats to public health;however,the underlying genetic basis for its dissemination is still unknown.We conducted a comprehensive genomic epidemiology analysis on 420 cpKP isolates col-lected from 70 hospitals in 24 provinces/autonomous regions/municipalities of China during 2009-2017 by short-/long-read sequencing.The results showed that most cpKP isolates were categorized into clonal group 258(CG258),in which ST11 was the dominant clone.Phylogenetic analysis revealed three major clades including the top one of Clade 3 for CG258 cpKP isolates.Additionally,carbapenemase gene analysis indicated that blaKPC was dominant in the cpKP isolates,and most blaKPC genes were located in five major incompatibility(Inc)groups of blaKPC-harboring plasmids.Importantly,three advantageous combinations of host-blaKPC-carrying plasmid(Clade 3.1+3.2-IncFⅡpHN7A8,Clade 3.1+3.2-IncFⅡpHN7A8:IncR,and Clade 3.3-IncFⅡpHN7A8:InCpA1763-KPC)were identified to confer cpKP isolates the advantages in both genotypes(strong correlation/coevolution)and phenotypes(resistance/growth/competition)to facilitate the nationwide spread of ST11/CG258 cpKP.Intriguingly,Bayesian skyline analysis illustrated that the three advanta-geous combinations might be directly associated with the strong population expansion during 2007-2008 and subsequent maintenance of the population of ST11/CG258 cpKP after 2008.We then examined drug resistance profiles of these cpKP isolates and proposed combination treatment regimens for CG258/non-CG258 cpKP infections.Thus,the findings of our systematical analysis shed light on the molecular epidemiology and genetic basis for the dissemination of ST11/CG258 cpKP in China,and much emphasis should be given to the close monitoring of advantageous cpKP-plasmid combinations.

Lung cancer is the most common malignant tumor globally, with the highest incidence as well as mortality in China. Absence of the effective screening method for early detection results in the high mortality. Five-year survival rate in patients with advanced cancer decreases remarkably compared with that in patients with early stage disease. Hence, the early detection of lung cancer is of vital importance. DNA methylation has close correlation with the initiation and development of tumor genesis. With the improvement in DNA methylation, aberrant DNA methylation has been identified in lung cancer. Detection of methylation in the specimens, such as tissue, bronchoalveolar lavage fluid, serum or plasma, sputum and urine, contributes to the early detection and improvement in the prognosis and treatment of lung cancer.