This study was aimed at comparing performance differences among three metagenomic sequencing platforms,MGISEQ-2000,Illumina NextSeq 2000,and Ion GeneStudio S5 Plus,to optimize the sequencing process for trace samples.The three sequencing platforms were used to perform high-throughput sequencing on DNA standards and simulated samples.Through analysis of the quality of raw data and microbial detection capabilities,systematic differences among platforms were compared.The sequencing results were optimized for trace samples by incorporation of exogenous nucleic acids during the li-brary preparation process.In terms of data output per batch and base quality,MGISEQ-2000 surpassed the other two plat-forms.Illumina NextSeq 2000 had the lowest proportion of duplicate reads,whereas Ion GeneStudio S5 Plus had the highest proportion,and significant differences were observed across platforms(P＜0.001).In sequencing uniformity,MGISEQ-2000 and Illumina NextSeq 2000 were superior to Ion GeneStudio S5 Plus.MGISEQ-2000 provided a substantial advantage in microbial detection capability(P＜0.001),but the advantage diminished with decreasing bacterial fluid concentration.Ion GeneStudio S5 Plus had the shortest duration for single-batch sequencing.Moreo-ver,for trace samples with DNA content ≤0.05 ng,the experi-mental group(with added exogenous nucleic acids)achieved a higher number of reads than the control group(without exogenous nucleic acids),with a 11.09±8.03 fold increase.In conclu-sion,the different sequencing platforms each had advantages and disadvantages,thus allowing researchers to choose the appro-priate platform according to specific needs.Furthermore,the addition of exogenous nucleic acids improved the microorganism detection efficiency,and provided better support for subsequent diagnosis and evaluation of results.

Objective To observe the value of CT radiomics for differentiating spinal bone islands(BI)and osteoblastic metastases(OBM).Methods Data of 109 BI lesions in 98 patients and 282 OBM lesions in 158 patients(including 103 OBM in 48 lung cancer cases,86 OBM in 52 breast cancer cases and 93 OBM in 58 prostate cancer cases)from 3 medical institutions were retrospectively analyzed.Data obtained from institution 1 were used as the internal dataset and divided into internal training set and internal validation set at a ratio of 7∶3,from institution 2 and 3 were used as external dataset.All datasets were divided into female data subset(including OBM of female lung cancer and breast cancer)and male data subset(including OBM of male lung cancer and prostate cancer).Radiomics features were extracted and screened to construct 3 different support vector machine(SVM)models,including model1 for distinguishing BI and OBM,model2 for differentiating OBM of female lung cancer and breast cancer,and model3 for differentiating OBM of male lung cancer and prostate cancer.Diagnostic efficacy of model1,CT value alone and 3 physicians(A,B,C)for distinguishing BI and OBM were assessed,as well as differentiating efficacy for different OBM of model2 and model3.Receiver operating characteristic(ROC)curves were drawn,and area under the curves(AUC)were calculated and compared.The differential diagnostic efficacy of model2 and model3 were also assessed with ROC analysis and AUC.Results AUC of model1 for distinguishing spinal OBM from BI in internal training set,internal validation set and external dataset was 0.99,0.98 and 0.86,respectively.In internal training set,model1 had higher AUC for distinguishing BI and OBM than that of physician A(AUC=0.78),B(AUC=0.87)and C(AUC=0.93)as well as that of mean CT value(AUC=0.78,all P＜0.05).AUC in internal training set,internal validation set and external dataset of model2 for identifying female lung cancer and breast cancer OBM was 0.79,0.75 and 0.73,respectively,of model3 for discriminating male lung cancer from prostate cancer OBM was 0.77,0.74 and 0.77,respectively.Conclusion CT radiomics SVM model might reliablely distinguish OBM and BI.

Objective To evaluate the bioequivalence of the test preparation and reference preparation of azithromycin capsules in healthy Chinese subjects.Methods A total of 48 subjects were enrolled in this study using a randomized,open,two-sequence,cross design.Each subject received a single oral dose of azithromycin capsules test drug(T)or reference drug(R)for 250 mg.The concentrations of azithromycin in plasma were determined by Liquid Chromatograph Mass Spectrometer,and the pharmacokinetic parameters were calculated by WinNonlin 8.1 software to evaluate the bioequivalence.Results The main pharmacokinetic parameters of azithromycin after a single fasting dose of the test drug and the reference drug were as follows:the Cmax were respectively(319.89±127.35)and(330.41±122.11)ng·mL-1;AUC0-192h were respectively(2 423.04±587.15)and(2 489.97±685.73)ng·h·mL-1;AUC0-∞ were respectively(2 753.40±644.96)and(2 851.71±784.05)ng·h·mL-;tmax were respectively(2.60±1.11)and(2.62±1.13)h;t1/2 were respectively(76.76±15.14)and(79.83±17.14)h.The 90％confidence intervals for the geometric mean ratios of Cmax,AUC0-192h and AUC0-∞ of T and R were 87.52％-107.18％,91.46％-105.80％and 91.17％-105.06％,respectively.Conclusion The test preparation of azithromycin capsule was bioequivalent to the reference preparation under fasting condition.

Nutritional therapy is a core component of critically ill patient management,and the enteral route has become the preferred method due to its dual roles of nutrition and non-nutrition. The use of vasoactive medications makes enteral nutrition decisions more challenging for these patients. This review systematically examines the pathophysiological effects of vasoactive medications on gastrointestinal tract of critically ill patients,the current value and safety of enteral nutrition in this patient's population,summarizes the optimal strategies for implementing enteral nutrition in these patients for clinical reference.

Objective: To investigate the timing of pericardial drainage catheter removal and restart of the anticoagulation in patients with atrial fibrillation (AF) suffered from perioperative pericardial tamponade during atrial fibrillation catheter ablation and uninterrupted dabigatran. Methods: A total of 20 patients with pericardial tamponade, who underwent AF catheter ablation with uninterrupted dabigatran in Beijing Anzhen Hospital from January 2019 to August 2021, were included in this retrospective analysis. The clinical characteristics of enrolled patients, information of catheter ablation procedures, pericardial tamponade management, perioperative complications, the timing of pericardial drainage catheter removal and restart of anticoagulation were analyzed. Results: All patients underwent pericardiocentesis and pericardial effusion drainage was successful in all patients. The average drainage volume was (427.8±527.4) ml. Seven cases were treated with idarucizumab, of which 1 patient received surgical repair. The average timing of pericardial drainage catheter removal and restart of anticoagulation in 19 patients without surgical repair was (1.4±0.7) and (0.8±0.4) days, respectively. No new bleeding, embolism and death were reported during hospitalization and within 30 days following hospital discharge. Time of removal of pericardial drainage catheter, restart of anticoagulation and hospital stay were similar between patients treated with idarucizumab or not. Conclusion: It is safe and reasonable to remove pericardial drainage catheter and restart anticoagulation as soon as possible during catheter ablation of atrial fibrillation with uninterrupted dabigatran independent of the idarucizumab use or not in case of confirmed hemostasis.
Humans ; Atrial Fibrillation/drug therapy* ; Dabigatran/therapeutic use* ; Cardiac Tamponade/complications* ; Anticoagulants/therapeutic use* ; Retrospective Studies ; Treatment Outcome ; Drainage/adverse effects* ; Catheter Ablation ; Catheters/adverse effects*

Phosphodiesterase 4 (PDE4) is an important member of the phosphodiesterase enzyme family that specifically catalyzes the hydrolysis of cyclic adenosine monophosphate (cAMP), activates the downstream phosphorylation cascade pathway by altering cAMP concentration, and is strongly associated with multiple diseases. Inhibition of PDE4 is clinically investigated as a therapeutic strategy in a broad range of disease areas, including respiratory system diseases, autoimmune disorders, central nervous system diseases, and dermatological conditions. However, the incidence of adverse reactions such as nausea and vomiting is relatively high in the marketed PDE4 inhibitors, which has stalled their clinical development. In this review, we provide an overview of the clinical progression and safety issues of the marketed PDE4 inhibitors. We also review the main causes underlying PDE4-mediated adverse effects by combining the structural analysis of the PDE4 protein, the mechanism of action of PDE4 inhibitors, and the related side effect mechanism research, aiming to provide a reference for the development of safe and effective PDE4 inhibitors.

Inductively coupled plasma mass spectrometry (ICP-MS) was applied to determine the concentrations of lead (Pb), cadmium (Cd) and arsenic (As) in Lindera aggregata (Sims) Kosterm. The physiologically based extraction test (PBET) digestion in vitro/Caco-2 cell model was established to investigate the bioaccessible contents of Pb, Cd and As in decoction of Lindera aggregata (Sims) Kosterm. The target-organ toxicity dose modification of HI method (TTD) was used to evaluate the cumulative risk caused by the combined exposure of the total levels of Pb, Cd and As in Lindera aggregata (Sims) Kosterm. and the bioaccessible contents in the decoction. The results showed that the total contents of Pb, Cd and As in 4 batches of samples were in the range of 2.901-3.872, 1.299-1.800 and 0.062-0.216 mg·kg^-1, respectively. After transportation by Cacco-2 cells, the bioaccessible contents of Pb, Cd, and As in the decoction were in the range of 0.045-0.080, 0.070-0.112 and 0.004-0.018 mg·kg^-1. The results of risk assessment showed that calculated by the total amounts of heavy metals in the Lindera aggregata (Sims) Kosterm., for the end points of nervous system, the cumulative risks of co-exposure of heavy metals in 3 batches of samples were of concern. After decoction and transportation by Caco-2 cells, for the end points of cardiovascular system, blood, nervous system, kidney and testis, the TTD modification of HI values of all batches of samples were less than 1, and the health risks were acceptable. The study provided methodology basis for a more objective assessment of the health risks of heavy metals and harmful elements in traditional Chinese medicine and for a more scientific limit standard of heavy metals and harmful elements.

Objective: To establish a comprehensive diagnostic classification model of lateral cephalograms based on artificial intelligence (AI) to provide reference for orthodontic diagnosis. Methods: A total of 2 894 lateral cephalograms were collected in Department of Orthodontics, Capital Medical University School of Stomatology from January 2015 to December 2021 to construct a data set, including 1 351 males and 1 543 females with a mean age of (26.4± 7.4) years. Firstly, 2 orthodontists (with 5 and 8 years of orthodontic experience, respectively) performed manual annotation and calculated measurement for primary classification, and then 2 senior orthodontists (with more than 20 years of orthodontic experience) verified the 8 diagnostic classifications including skeletal and dental indices. The data were randomly divided into training, validation, and test sets in the ratio of 7∶2∶1. The open source DenseNet121 was used to construct the model. The performance of the model was evaluated by classification accuracy, precision rate, sensitivity, specificity and area under the curve (AUC). Visualization of model regions of interest through class activation heatmaps. Results: The automatic classification model of lateral cephalograms was successfully established. It took 0.012 s on average to make 8 diagnoses on a lateral cephalogram. The accuracy of 5 classifications was 80%-90%, including sagittal and vertical skeletal facial pattern, mandibular growth, inclination of upper incisors, and protrusion of lower incisors. The acuracy rate of 3 classifications was 70%-80%, including maxillary growth, inclination of lower incisors and protrusion of upper incisors. The average AUC of each classification was ≥0.90. The class activation heat map of successfully classified lateral cephalograms showed that the AI model activation regions were distributed in the relevant structural regions. Conclusions: In this study, an automatic classification model for lateral cephalograms was established based on the DenseNet121 to achieve rapid classification of eight commonly used clinical diagnostic items.
Male ; Female ; Humans ; Young Adult ; Adult ; Artificial Intelligence ; Deep Learning ; Cephalometry ; Maxilla ; Mandible/diagnostic imaging*

Objective: To develop a multi-classification orthodontic image recognition system using the SqueezeNet deep learning model for automatic classification of orthodontic image data. Methods: A total of 35 000 clinical orthodontic images were collected in the Department of Orthodontics, Capital Medical University School of Stomatology, from October to November 2020 and June to July 2021. The images were from 490 orthodontic patients with a male-to-female ratio of 49∶51 and the age range of 4 to 45 years. After data cleaning based on inclusion and exclusion criteria, the final image dataset included 17 453 face images (frontal, smiling, 90° right, 90° left, 45° right, and 45° left), 8 026 intraoral images [frontal occlusion, right occlusion, left occlusion, upper occlusal view (original and flipped), lower occlusal view (original and flipped) and coverage of occlusal relationship], 4 115 X-ray images [lateral skull X-ray from the left side, lateral skull X-ray from the right side, frontal skull X-ray, cone-beam CT (CBCT), and wrist bone X-ray] and 684 other non-orthodontic images. A labeling team composed of orthodontic doctoral students, associate professors, and professors used image labeling tools to classify the orthodontic images into 20 categories, including 6 face image categories, 8 intraoral image categories, 5 X-ray image categories, and other images. The data for each label were randomly divided into training, validation, and testing sets in an 8∶1∶1 ratio using the random function in the Python programming language. The improved SqueezeNet deep learning model was used for training, and 13 000 natural images from the ImageNet open-source dataset were used as additional non-orthodontic images for algorithm optimization of anomaly data processing. A multi-classification orthodontic image recognition system based on deep learning models was constructed. The accuracy of the orthodontic image classification was evaluated using precision, recall, F1 score, and confusion matrix based on the prediction results of the test set. The reliability of the model's image classification judgment logic was verified using the gradient-weighted class activation mapping (Grad-CAM) method to generate heat maps. Results: After data cleaning and labeling, a total of 30 278 orthodontic images were included in the dataset. The test set classification results showed that the precision, recall, and F1 scores of most classification labels were 100%, with only 5 misclassified images out of 3 047, resulting in a system accuracy of 99.84%(3 042/3 047). The precision of anomaly data processing was 100% (10 500/10 500). The heat map showed that the judgment basis of the SqueezeNet deep learning model in the image classification process was basically consistent with that of humans. Conclusions: This study developed a multi-classification orthodontic image recognition system for automatic classification of 20 types of orthodontic images based on the improved SqueezeNet deep learning model. The system exhibitted good accuracy in orthodontic image classification.
Humans ; Male ; Female ; Child, Preschool ; Child ; Adolescent ; Young Adult ; Adult ; Middle Aged ; Deep Learning ; Reproducibility of Results ; Radiography ; Algorithms ; Cone-Beam Computed Tomography

The testis is pivotal for male reproduction, and its progressive functional decline in aging is associated with infertility. However, the regulatory mechanism underlying primate testicular aging remains largely elusive. Here, we resolve the aging-related cellular and molecular alterations of primate testicular aging by establishing a single-nucleus transcriptomic atlas. Gene-expression patterns along the spermatogenesis trajectory revealed molecular programs associated with attrition of spermatogonial stem cell reservoir, disturbed meiosis and impaired spermiogenesis along the sequential continuum. Remarkably, Sertoli cell was identified as the cell type most susceptible to aging, given its deeply perturbed age-associated transcriptional profiles. Concomitantly, downregulation of the transcription factor Wilms' Tumor 1 (WT1), essential for Sertoli cell homeostasis, was associated with accelerated cellular senescence, disrupted tight junctions, and a compromised cell identity signature, which altogether may help create a hostile microenvironment for spermatogenesis. Collectively, our study depicts in-depth transcriptomic traits of non-human primate (NHP) testicular aging at single-cell resolution, providing potential diagnostic biomarkers and targets for therapeutic interventions against testicular aging and age-related male reproductive diseases.
Animals ; Male ; Testis ; Sertoli Cells/metabolism* ; Transcriptome ; Spermatogenesis/genetics* ; Primates ; Aging/genetics* ; Stem Cells