Gene regulation relies on the precise binding of transcription factors (TFs) at regulatory elements, but simultaneously detecting hundreds of TFs on chromatin is challenging. We developed cFOOT-seq, a cytosine deaminase-based TF footprinting assay, for high-resolution, quantitative genome-wide assessment of TF binding in both open and closed chromatin regions, even with small cell numbers. By utilizing the dsDNA deaminase SsdAtox, cFOOT-seq converts accessible cytosines to uracil while preserving genomic integrity, making it compatible with techniques like ATAC-seq for sensitive and cost-effective detection of TF occupancy at the single-molecule and single-cell level. Our approach enables the delineation of TF footprints, quantification of occupancy, and examination of chromatin influences on TF binding. Notably, cFOOT-seq, combined with FootTrack analysis, enables de novo prediction of TF binding sites and tracking of TF occupancy dynamics. We demonstrate its application in capturing cell type-specific TFs, analyzing TF dynamics during reprogramming, and revealing TF dependencies on chromatin remodelers. Overall, cFOOT-seq represents a robust approach for investigating the genome-wide dynamics of TF occupancy and elucidating the cis-regulatory architecture underlying gene regulation.
Transcription Factors/genetics* ; Humans ; Chromatin/genetics* ; Animals ; Binding Sites ; Mice ; DNA Footprinting/methods*

In this comprehensive review, we delve into the evolution of drug delivery systems in reproductive medicine with a focus on the emerging role of exosomes, a class of extracellular vesicles. Exosomes offer unique advantages in overcoming these challenges due to their inherent biocompatibility, stability, and ability to facilitate targeted delivery. This review provides a detailed examination of exosome biogenesis and their function in cellular communication, setting the stage for understanding their potential as drug delivery vehicles. We explore the mechanisms through which exosomes can be loaded with small molecule drugs and the benefits they offer over synthetic nanoparticles. The review highlights groundbreaking case studies that illustrate the successful application of exosome-mediated drug delivery in reproductive health, including enhancing fertility treatments, supporting gamete and embryo development, and facilitating maternal-fetal communication. This study aims to provide a precise understanding of how exosomal drug delivery can revolutionize treatments for reproductive health disorders, paving the way for future therapeutic applications. Lastly, we touch upon the promising therapeutic implications of exosomal delivery for proteins and genes, offering a window into future treatments for reproductive health disorders.

Recent studies have identified a missense mutation in the β1-receptor (ADRB1-A187V) that exerts a pronounced impact on human sleep, with a noted decrease in protein abundance in vivo. The administration of β-blockers is frequently associated with sleep disturbances in clinical settings. In this study, we assessed the influence of various β-blockers on sleep within mouse models. Our findings indicated that β-blockers could induce varying degrees of arousal, sleep disruption, and a decrease in REMS (rapid eye movement sleep). We examined the dose-dependent effects of metoprolol and nebivolol on both sleep and cardiac functionality in both wild-type and Adrb1-A187V mutant mice. Our data suggested that, in contrast to cardiac effects, higher doses of metoprolol are required to have noted impact on sleep. No genotype effect was observed with metoprolol in terms of sleep or cardiac function. In contrast, the mutant mice demonstrated increased sensitivity to nebivolol, which exacerbated sleep fragmentation and impeded the onset of REMS. This study is expected to provide some reference for minimizing the occurrence of sleep disorders and reducing the adverse reactions of drugs to the greatest extent.

Objective:To investigate the effects of cerium oxide nanoenzyme-gelatin methacrylate anhydride (GelMA) hydrogel (hereinafter referred to as composite hydrogel) in the repair of infected full-thickness skin defect wounds in mice.Methods:This study was an experimental study. Cerium oxide nanoenzyme with a particle size of (116±9) nm was prepared by hydrothermal method, and GelMA hydrogel with porous network structure and good gelling performance was also prepared. The 25 μg/mL cerium oxide nanoenzyme which could significantly promote the proliferation of human skin fibroblasts and had high superoxide dismutase activity was screened out. It was added to GelMA hydrogel to prepare composite hydrogel. The percentage of cerium oxide nanoenzyme released from the composite hydrogel was calculated after immersing it in phosphate buffer solution (PBS) for 3 and 7 d. The red blood cell suspension of mice was divided into PBS group, Triton X-100 group, cerium oxide nanoenzyme group, GelMA hydrogel group, and composite hydrogel group, which were treated with corresponding solution. The hemolysis of red blood cells was detected by microplate reader after 1 h of treatment. The bacterial concentrations of methicillin-resistant Staphylococcus aureus (MRSA) and Escherichia coli were determined after being cultured with PBS, cerium oxide nanoenzyme, GelMA hydrogel, and composite hydrogel for 2 h. The sample size in all above experiments was 3. Twenty-four 8-week-old male BALB/c mice were taken, and a full-thickness skin defect wound was prepared in the symmetrical position on the back and infected with MRSA. The mice were divided into control group without any drug intervention, and cerium oxide nanoenzyme group, GelMA hydrogel group, and composite hydrogel group applied with corresponding solution, with 6 mice in each group. The wound healing was observed on 3, 7, and 14 d after injury, and the remaining wound areas on 3 and 7 d after injury were measured (the sample size was 5). The concentration of MRSA in the wound exudation of mice on 3 d after injury was measured (the sample size was 3), and the blood flow perfusion in the wound of mice on 5 d after injury was observed using a laser speckle flow imaging system (the sample size was 6). On 14 d after injury, the wound tissue of mice was collected for hematoxylin-eosin staining to observe the newly formed epithelium and for Masson staining to observe the collagen situation (the sample size was both 3). Results:After immersion for 3 and 7 d, the release percentages of cerium oxide nanoenzyme in the composite hydrogel were about 39% and 75%, respectively. After 1 h of treatment, compared with that in Triton X-100 group, the hemolysis of red blood cells in PBS group, GelMA hydrogel group, cerium oxide nanoenzyme group, and composite hydrogel group was significantly decreased ( P<0.05). Compared with that cultured with PBS, the concentrations of MRSA and Escherichia coli cultured with cerium oxide nanoenzyme, GelMA hydrogel, and composite hydrogel for 2 h were significantly decreased ( P<0.05). The wounds of mice in the four groups were gradually healed from 3 to 14 d after injury, and the wounds of mice in composite hydrogel group were all healed on 14 d after injury. On 3 and 7 d after injury, the remaining wound areas of mice in composite hydrogel group were (29±3) and (13±5) mm 2, respectively, which were significantly smaller than (56±12) and (46±10) mm 2 in control group and (51±7) and (38±8) mm 2 in cerium oxide nanoenzyme group (with P values all <0.05), but was similar to (41±5) and (24±9) mm 2 in GelMA hydrogel group (with P values both >0.05). On 3 d after injury, the concentration of MRSA on the wound of mice in composite hydrogel group was significantly lower than that in control group, cerium oxide nanoenzyme group, and GelMA hydrogel group, respectively (with P values all <0.05). On 5 d after injury, the volume of blood perfusion in the wound of mice in composite hydrogel group was significantly higher than that in control group, cerium oxide nanoenzyme group, and GelMA hydrogel group, respectively ( P<0.05). On 14 d after injury, the wound of mice in composite hydrogel group basically completed epithelization, and the epithelization was significantly better than that in the other three groups. Compared with that in the other three groups, the content of collagen in the wound of mice in composite hydrogel group was significantly increased, and the arrangement was also more orderly. Conclusions:The composite hydrogel has good biocompatibility and antibacterial effect in vivo and in vitro. It can continuously sustained release cerium oxide nanoenzyme, improve wound blood perfusion in the early stage, and promote wound re-epithelialization and collagen synthesis, therefore promoting the healing of infected full-thickness skin defect wounds in mice.

Objective To study the application value of 3.0T MRI T2 mapping quantitative technology in the diagnosis of anterior cruciate ligament sprain and chronic injury of knee joint.Methods A total of 82 subjects were studied,and the experimental group 72 cases was divided into grade Ⅰ injury group(25 cases),grade Ⅱ injury group(25 cases),chronic injury group(22 cases),and control group 10 cases.The experimental group met the criteria of arthroscopy.The proximal,middle,and distal segments of the anterior cruciate ligament were selected as the region of interest(ROI),and T2 mapping values were measured.The differences in T2 mapping values of each area were compared between and within the groups,while compared with arthroscopy.Results The T2 mapping values in grade Ⅰ,Ⅱ,and chronic injury groups were higher than those in control group(P<0.05).Comparison within the experimental group:the T2 mapping values of each area in grade Ⅱ injury group were higher than those in grade Ⅰ injury group and chronic injury group(P<0.05).The T2 mapping values of each area in grade Ⅰ injury group were higher than those in chronic injury group(P<0.05).The specificity,sensitivity,positive predictive value,negative predictive value and accuracy of T2 mapping in diagnosing anterior cruciate ligament grade Ⅰ injury were 94.7%,95.5%,89.7%,96.6%,and 90.2%respectively.The specificity,sensitivity,positive predictive value,negative predictive value,and accuracy of grade Ⅱ injury were 89.4%,87.9%,92.1%,93.4%,and 93.8%respectively.The specificity,sensitivity,positive predictive value,negative predictive value,and accuracy of chronic injury were 92.2%,95.4%,90.3%,87.6%,and 91.5%respectively.Kappa test showed a good con-sistency between T2 mapping results and arthroscopic results,with a Kappa value of 0.763(P<0.01).Conclusion The value of MRI T2 mapping can provide a reference for the clinical diagnosis of anterior cruciate ligament sprain and chronic injury of knee joint,and the results are in good agreement with the control of arthroscopy.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.