Objective To summarize the classification, diagnosis, and treatment of iatrogenic bile duct injury. Method The clinical data of 27 cases of iatrogenic bile duct injuries who treated in Central Hospital of Huzhou City from 2008–2013 were retrospectively analyzed. Results The classification of 27 cases: 5 cases of type Ⅰ, 18 cases of type Ⅱ, 2 cases of type Ⅲ, 2 cases of type Ⅳ. Diagnosis: 11 cases were immediately discovered at the time of the initial operation, include 1 case of type Ⅰ, 8 cases of type Ⅱ, 1 case of type Ⅲ, 1 case of type Ⅳ; 10 cases were detected in early stage after the initial operation, include 2 cases of type Ⅰ, 7 cases of type Ⅱ, 1 case of type Ⅲ; 6 cases were detected in delayed stage after the initial operation, include 2 cases of type Ⅰ, 3 cases of type Ⅱ, 1 case of type Ⅳ. Treatment effect: 17 cases for excellent, 5 cases for good, 4 cases for bad, the well recover rate was 84.6% (22/26). One case died after operation. A total of 26 cases were followed up, 1 case was lost to follow up. During the follow-up period, bile leakage occurred in 3 cases, infection of incision occurred in 2 cases, cholangitis occurred in 3 cases, and bile duct stricture occurred in 2 cases. Conclusions The best time of repairing for the iatrogenic bile duct injuries is at the time of the initial operation or early stage. According to the type of injury and the time of the injury was diagnosed, timely and effective treatment by intervention and (or) surgery is the key.
Chromatin three-dimensional genome structure plays a key role in cell function and gene regulation. Single-cell Hi-C techniques can capture genomic structure information at the cellular level, which provides an opportunity to study changes in genomic structure between different cell types. Recently, some excellent computational methods have been developed for single-cell Hi-C data analysis. In this paper, the available methods for single-cell Hi-C data analysis were first reviewed, including preprocessing of single-cell Hi-C data, multi-scale structure recognition based on single-cell Hi-C data, bulk-like Hi-C contact matrix generation based on single-cell Hi-C data sets, pseudo-time series analysis, and cell classification. Then the application of single-cell Hi-C data in cell differentiation and structural variation was described. Finally, the future development direction of single-cell Hi-C data analysis was also prospected.
Objective To develop a deep learning system for CT images to assist in the diagnosis of thoracolumbar fractures and analyze the feasibility of its clinical application. Methods Collected from West China Hospital of Sichuan University from January 2019 to March 2020, a total of 1256 CT images of thoracolumbar fractures were annotated with a unified standard through the Imaging LabelImg system. All CT images were classified according to the AO Spine thoracolumbar spine injury classification. The deep learning system in diagnosing ABC fracture types was optimized using 1039 CT images for training and validation, of which 1004 were used as the training set and 35 as the validation set; the rest 217 CT images were used as the test set to compare the deep learning system with the clinician’s diagnosis. The deep learning system in subtyping A was optimized using 581 CT images for training and validation, of which 556 were used as the training set and 25 as the validation set; the rest 104 CT images were used as the test set to compare the deep learning system with the clinician’s diagnosis. Results The accuracy and Kappa coefficient of the deep learning system in diagnosing ABC fracture types were 89.4% and 0.849 (P<0.001), respectively. The accuracy and Kappa coefficient of subtyping A were 87.5% and 0.817 (P<0.001), respectively. Conclusions The classification accuracy of the deep learning system for thoracolumbar fractures is high. This approach can be used to assist in the intelligent diagnosis of CT images of thoracolumbar fractures and improve the current manual and complex diagnostic process.
ObjectiveTo encourage clinicians to code the major diagnosis of diseases, in order to improve the correct rate of disease major diagnosis coding. MethodsWe analyzed the data of major diagnostic codes by clinicians from January 2012 to June 2013. The group leader of the clinical treatment was designated to be responsible for the disease coding. Disease coders introduced knowledge of international classification of diseases to the clinical department according to the different characteristics of disease in each department and communicated with clinicians on the problems of disease coding. Then, we tried to find out whether this method could improve the correct rate of major diagnosis coding of diseases. ResultsThe rate of disease major coding by clinicians of the whole hospital and pilot departments increased from 94.081% to 98.301%. The correct rate of disease major coding decreased from 75.824% to 67.483% and then reached 81.893%. The correct rate of disease major coding of the Department of Hematology was 83.824% in August 2012 and then decreased with the lowest rate of 68.025%; and the correct rate of disease major coding of the Department of Orthopedics increased rapidly and reached 90% in September 2012. ConclusionsThrough the leader of the clinical treatment being responsible for the disease coding and encouraging clinicians to code the main diagnosis of diseases, the accurate of disease major diagnosis coding has improved. Strengthening the communication between clinical and Medical Record Departments can help our hospital improve the quality of disease major diagnosis coding continuously.
This paper is to discuss the research of heterogeneity in Meta-analysis, including the definition of the heterogeneity in Meta-analysis and classification it into clinical heterogeneity, methodological heterogeneity and statistical heterogeneity, the strategies for diminishing clinical heterogeneity and methodological heterogeneity, the five testing methods in statistical heterogeneity (Q statistic, I2 statistic, H statistic, Galbraith plot and L’Abbe plot) and the examples and applying conditions of the five testing methods, classification of meta-analysis into exploratory meta-analysis and analytic meta-analysis according if the meta-analysis has heterogeneity, and the strategies and the flowchart when existing the heterogeneity in meta-analysis.
Objective To assess the current status of medical waste management and classification disposal in hospitals across Hubei Province, providing a scientific basis for optimizing medical waste disposal strategies and promoting waste minimization, harmless treatment, and resource utilization. Methods A random sample survey was conducted on medical and health institutions in Hubei Province between January 8 and January 17, 2025. The self-made survey questionnaire was used to survey and analyze the medical waste management and classification disposal in medical and health institutions. Results A total of 257 medical and health institutions were surveyed. Among them, there were 93 tertiary hospitals (36.19%), 75 secondary hospitals (29.18%), 77 primary hospitals (29.96%), and 12 non-graded medical institutions (4.67%). The overall compliance rate for medical waste management and training exceeded 90%. In terms of medical waste supervision sections, compliance rates in primary hospitals and non-graded hospitals were 77.92% (60/77) and 58.33% (7/12), respectively. The compliance rate for medical waste classification and disposal was above 90%, with a 100% (221/221) compliance rate for the disposal of placentas from normal deliveries. However, the standardized disposal rates for “fetal tissues from pregnancies under 16 weeks or weighing less than 500 grams”, “amputation and other human tissues (or organs)” and “dead fetus” were 81.45% (180/221), 44.65% (96/215), and 79.64% (176/221), respectively. Additionally, 87.16% (224/257) of healthcare institutions classified single-use soft infusion bottles (bags) as recyclable waste, but significant variations were observed in the disposal of uncontaminated waste (e.g., empty disinfectant bottles, empty dialysis fluid barrels, oxygen humidifier bottles, and orthopedic casting materials). Furthermore, 99.61% (256/257) of hospitals provided protective equipment for medical waste handlers, 91.83% (236/257) conducted regular health examination to them, and 97.28% (250/257) had established needle stab reporting systems and related training programs. Conclusions Medical waste management and classification in hospitals across Hubei Province are largely standardized. However, the certain categories of medical waste still require stricter regulation and oversight.
Traditional classifiers, such as support vector machine and Bayesian classifier, require data normalization for removing experimental batch effects, which limit their applications at the individual level. In this paper, we aim to build a classifier to distinguish lung cancer and non-cancer lung tissues (pneumonia and normal lung tissues). We identified gene pairs as signatures to build a classifier based on the within-sample relative expression orderings of gene pairs in a particular type of tissues (cancer or non-cancer). Using multiple independent datasets as the training data, including a total of 197 lung cancer cases and 189 non-cancer cases, we identified three gene pairs. Classifying a sample by the majority voting rule, the average accuracy reached 95.34% in the training data. Using multiple independent validation datasets, including a total of 251 lung cancer samples and 141 non-cancer samples without data normalization, the average accuracy was as high as 96.78%. The rank-based signature is robust against experimental batch effects and can be used to diagnose lung cancer using samples measured by different laboratories at the individual level.
Deep learning method can be used to automatically analyze electrocardiogram (ECG) data and rapidly implement arrhythmia classification, which provides significant clinical value for the early screening of arrhythmias. How to select arrhythmia features effectively under limited abnormal sample supervision is an urgent issue to address. This paper proposed an arrhythmia classification algorithm based on an adaptive multi-feature fusion network. The algorithm extracted RR interval features from ECG signals, employed one-dimensional convolutional neural network (1D-CNN) to extract time-domain deep features, employed Mel frequency cepstral coefficients (MFCC) and two-dimensional convolutional neural network (2D-CNN) to extract frequency-domain deep features. The features were fused using adaptive weighting strategy for arrhythmia classification. The paper used the arrhythmia database jointly developed by the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) and evaluated the algorithm under the inter-patient paradigm. Experimental results demonstrated that the proposed algorithm achieved an average precision of 75.2%, an average recall of 70.1% and an average F1-score of 71.3%, demonstrating high classification accuracy and being able to provide algorithmic support for arrhythmia classification in wearable devices.
Objective To study the clinical classification and etiologies of uveitis based on 1214 uveitis patients reffered to Zhongshan Ophthalmic Center. Methods A retrospective analysis was made on the patients with uveitis, coming from all over China between January 1996 and December 2001. All kinds of uveitis were classified according to the anatomical criteria and etiological criteria. The relevant data of these patients, such as the age at uveitis onset and sex were also analyzed. Results The total number of the patients is 1214 (male 698, female 516), with the average age at disease onset being 34.43. Anterior uveitis, the most common type, was seen in 546 cases, accounting for 44.98% of all the patients, followed in descending order by panuveitis (530 cases, 43.66%), intermediate uveitis(78 cases, 6.43%) and posterior uveitis(60 cases, 4.94%). Etiological factors and clinical entities were identified in 703 patients, accounting for 57.91% of all the patients, and the other 511 patients were idiopathic ones. The most common types of anterior uveitis were idiopathic uveitis(316 cases, 57.88%), followed by Fuchs syndrome(85 cases) and ankylosing spondylitis(45 cases). BehCcedil;et disease(218 cases, 41.13%) and Vogt-Koyanagi-Harada syndrome(196 cases, 36.98%) were the most common entities in panuveitis. Neither etiological factors nor clinical entities could be identified in the patients with intermediate uveitis and those with posterior uveitis. Conclusions Uveitis occurs mostly in young and middle-aged adults. In general, a predilection was seen in the male as compared with the female in the development of uveitis. Idiopathic anterior uveitis, BehCcedil;et disease and Vogt-Koyanagi-Harada syndrome are the most common entities of uveitis seen in China. Classification based on etiological and anatomical factors may provide a reasonable system for the study of uveitis. (Chin J Ocul Fundus Dis, 2002, 18: 253-255)