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 explore the impact of number of positive regional lymph nodes (nPRLN) in N1 stage on the prognosis of non-small cell lung cancer (NSCLC) patients. MethodsPatients with TxN1M0 stage NSCLC who underwent lobectomy and mediastinal lymph node dissection from 2010 to 2015 were screened from SEER database (17 Regs, 2022nov sub). The optimal cutoff value of nPRLN was determined using X-tile software, and patients were divided into 2 groups according to the cutoff value: a nPRLN≤optimal cutoff group and a nPRLN>optimal cutoff group. The influence of confounding factors was minimized by propensity score matching (PSM) at a ratio of 1∶1. Kaplan-Meier curves and Cox proportional hazards models were used to evaluate overall survival (OS) and lung cancer-specific survival (LCSS) of patients. ResultsA total of 1316 patients with TxN1M0 stage NSCLC were included, including 662 males and 654 females, with a median age of 67 (60, 73) years. The optimal cutoff value of nPRLN was 3, with 1165 patients in the nPRLN≤3 group and 151 patients in the nPRLN>3 group. After PSM, there were 138 patients in each group. Regardless of before or after PSM, OS and LCSS of patients in the nPRLN≤3 group were superior to those in the nPRLN>3 group (P<0.05). N1 stage nPRLN>3 was an independent prognostic risk factor for OS [HR=1.52, 95%CI (1.22, 1.89), P<0.001] and LCSS [HR=1.72, 95%CI (1.36, 2.18), P<0.001]. ConclusionN1 stage nPRLN>3 is an independent prognostic risk factor for NSCLC patients in TxN1M0 stage, which may provide new evidence for future revision of TNM staging N1 stage subclassification.
ObjectiveTo analyze the predictive value of ensemble classification algorithm of random forest for delirium risk in ICU patients with cardiothoracic surgery. MethodsA total of 360 patients hospitalized in cardiothoracic ICU of our hospital from June 2019 to December 2020 were retrospectively analyzed. There were 193 males and 167 females, aged 18-80 (56.45±9.33) years. The patients were divided into a delirium group and a control group according to whether delirium occurred during hospitalization or not. The clinical data of the two groups were compared, and the related factors affecting the occurrence of delirium in cardiothoracic ICU patients were predicted by the multivariate logistic regression analysis and the ensemble classification algorithm of random forest respectively, and the difference of the prediction efficiency between the two groups was compared.ResultsOf the included patients, 19 patients fell out, 165 patients developed ICU delirium and were enrolled into the delirium group, with an incidence of 48.39% in ICU, and the remaining 176 patients without ICU delirium were enrolled into the control group. There was no statistical significance in gender, educational level, or other general data between the two groups (P>0.05). But compared with the control group, the patients of the delirium group were older, length of hospital stay was longer, and acute physiology and chronic health evaluationⅡ(APACHEⅡ) score, proportion of mechanical assisted ventilation, physical constraints, sedative drug use in the delirium group were higher (P<0.05). Multivariate logistic regression analysis showed that age (OR=1.162), length of hospital stay (OR=1.238), APACHEⅡ score (OR=1.057), mechanical ventilation (OR=1.329), physical constraints (OR=1.345) and sedative drug use (OR=1.630) were independent risk factors for delirium of cardiothoracic ICU patients. The variables in the random forest model for sorting, on top of important predictor variable were: age, length of hospital stay, APACHEⅡ score, mechanical ventilation, physical constraints and sedative drug use. The diagnostic efficiency of ensemble classification algorithm of random forest was obviously higher than that of multivariate logistic regression analysis. The area under receiver operating characteristic curve of ensemble classification algorithm of random forest was 0.87, and the one of multivariate logistic regression analysis model was 0.79.ConclusionThe ensemble classification algorithm of random forest is more effective in predicting the occurrence of delirium in cardiothoracic ICU patients, which can be popularized and applied in clinical practice and contribute to early identification and strengthening nursing of high-risk patients.
Objective To provide scientific evidence for the establishment of medical specialist system in China by investigating the history, current situation, problems and countermeasures of medical specialties training at home and aboard. Method The principle and theroy of evidence-based medicine were adopted. The information before Dec. 31, 2003 of Pubmed, CBM, official website, some journals, most frequently used search engines and medical monograph were systematically reviewed. Included literatures were assessed and graded according to the pre-defined criterias. Results A total of 1 319 studies (1 298 in English, 21 in Chinese) were included, among which only 6 were related to the classification of medical specialties. Based on the information from official website of USA, Canada, UK, Singapore, Australia and China (including HK and Taiwan), it showed that China has the largest number of medical specialties, followed by that of USA. In China, the number of medical specialties has more than that of the disciplines in clinical field, which was followed by resident training programs. Some specialties were duplicate, or not international standardized. Conclusions The classification of medical specialties should be developed consecutively, which comprehensively considered the international trend, characteristics of doctor training and the current situation. Specialties whose training program are well-established and developed should initiate firstly. Others will be put into practice gradually after being fully exprienced.
ObjectiveTo explore the application of Tsetlin Machine (TM) in heart beat classification. MethodsTM was used to classify the normal beats, premature ventricular contraction (PVC) and supraventricular premature beats (SPB) in the 2020 data set of China Physiological Signal Challenge. This data set consisted of the single-lead electrocardiogram data of 10 patients with arrhythmia. One patient with atrial fibrillation was excluded, and finally data of the other 9 patients were included in this study. The classification results were then analyzed. ResultsThe classification results showed that the average recognition accuracy of TM was 84.3%, and the basis of classification could be shown by the bit pattern interpretation diagram. ConclusionTM can explain the classification results when classifying heart beats. The reasonable interpretation of classification results can increase the reliability of the model and facilitate people's review and understanding.
Objective To investigate the recovery status of people wounded in the Wenchuan earthquake. Method Data were retrospectively collected from administrative documents in the Bureau of Medical Affairs, Sichuan Provincial Health Department. The severity of injury was assessed by Injury Severity Score (ISS). The data were recorded by EXCEL software and descriptive analysis was conducted. Results Our analysis results of rehabilitation treatment through Feb. 5, 2009 shows that 27,080 of the 28,008 patients had been treated and discharged, for a discharge rate of 97.8%. There were 928 patients still in hospitals at that time, including 55 cases of traumatic brain injury, 163 cases of paraplegia, 260 amputees, and 449 cases of severe spine, pelvis and other fractures. Some amputees needed to receive replacement of artificial limbs or stump dressing operation and rehabilitation; most patients who were installed internal fixation needed to removal and post-rehabilitation. Conclusions The effectiveness of rehabilitation is significant. Our work in the next stage should focus on (1) continuing to improve the establishment of province’s rehabilitation capabilities and increasing capital investment; (2) enhancing training for medical rehabilitation practitioners in order to improve operational standards and service capabilities; (3) developing the wounded rehabilitation standards in later stages, conducting follow-up and functional training in order to maximize recovery and return to society; (4) increasing employment opportunities for disabled persons.
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 investigate the classification of atlas pedicles and the methods of the pedicle screw fixation. Methods To study the classification of atlas pedicles, 48 dry adult atlas specimens were measured. By atlas 3D-CT reconstruction, two transverse sections were establ ished by going through the one third of the lateral atlas pedicle and 2 mmbelow the vertebral artery sulcus. By setting 3.50 mm and 1.75 mm as the standardized diameter and radius for the screwand according to the thickness of bone substance of vertebral artery sulcus that went through the one third of the lateralatlas pedicle, the anatomical morphology of atlas pedicles were classified into three types: general type with 40 specimens (83%), l ight variation type with 6 specimens (13%), and severe variation type with 2 specimens (4%). The entry pathway was confirmed by the intersection l ine of the two transverse sections that went through the lateral one third of the atlas pedicle and 2 mm below the vertebral artery sulcus. The project-point of the entry pathway on the atlas posterior arch was considered to be the entry point. Forty-eight dry atlas specimens were used to measure the following relevant anatomic data with an electronic cal iper: the distance between the entry point and the posterior margin of the lateral mass (L1), the height of atlas pedicle at the entry point (L2), the vertical distance between the entry point and the inferior articular facet of the lateral mass (L3), the mass height at the entry point (L4), the mass width at the entry point (L5), the width of the atlas pedicle at the entry point (L6), the thickness of the pedicle under the vertebral artery sulcus at the entry pathway (H1). To research the method of the pedicle screw fixation, 12 fresh-frozen adult atlas specimens were adopted to simulate the fixation of the pedicle screw. The thickness of the bone substance of vertebral artery sulcus on both the left and the right sides of the pathway was grinded into 3 types: 1.5 mm and 2.5 mm, 1.5 mm and 4.0 mm, 2.5 mm and 4.0 mm, and each type had four specimens. The entry pathway was confirmed by the intersection l ine of two transverse sections that went through the lateral one third of atlas pedicle and 2 mm below the vertebral artery sulcus. Results On the left side, L1 was (5.79 ± 1.24) mm, L2 (4.55 ± 1.29) mm, L3 (5.12 ± 1.06) mm, L4 (12.43 ± 1.01) mm, L5 (12.66 ± 1.37) mm, L6 (7.86 ± 0.77) mm, and H1 (4.11 ± 1.25) mm. On the right side, L1 was (5.81 ± 1.26) mm, L2 (4.49 ± 1.22) mm, L3 (5.15 ± 1.05) mm, L4 (12.49 ± 0.98) mm, L5 (12.65 ± 1.38) mm, L6 (7.84 ± 0.78) mm, and H1 (4.13 ± 1.29) mm. There was no significant difference between the two sides (P gt; 0.05). After simulation of inserting screws, no screw in the specimens was found to break the bone substance in the sulcus of vertebral artery. Conclusion For the pedicle screw fixation of those patients whose atlas posterior arches are not high enough, we might partly drill through or beyond the atlas posterior arch. The entry point should be ascertained by preoperative 3D-CT reconstruction and intra-operative exploration.
ObjectiveTo analyze the clinical character of uveitis in second hospital of Jilin university. MethodsRetrospectively analyze the clinical data of uveitis patients referred to from Second Hospital of Jilin University from September 2009 to September 2014. According to anatomical location, the manifestation of these uveitis patients were divided into anterior uveitis, panuveitis, intermediate uveitis and posterior uveitis. To discuss the possible causes of these patients according to the general information and relevant clinical laboratory examinations results. ResultsThere were 1215 cases in this study, which included 587 male, accounting for 48.31%; and 628 female, accounting for 51.69%. The ratio of male-to-female was 0.93:1. The range of the age of these patients was from 4 to 91 years old. The mean age of these patients at the onset of these disease was (41.43±14.20) years. Of the 1215 cases, 40 male and 43 female were younger than 20 years. The ratio of male-to-female was 0.93:1; 412 male and 396 female were between 21 and 50 years old. The ratio of male-to-female was 1.04:1; 135 male 189 female were older than 50 years. The ratio of male-to-female was 0.71:1. There were 572 cases of anterior uveitis, accounting for 47.08%; 527 cases of panuveitis, accounting for 43.37%; 52 cases of intermediate uveitis, accounting for 4.28%; 64 cases of posterior uveitis, accounting for 5.27%. 703 cases had etiological diagnosis according to the clinical character and the auxiliary results, accounting for 57.68%. Vogt-koyanagi Haradal (VKH) syndrome, ankylosing spondylitis associated with uveitis and Behçet's disease were the common entity, accounting for 30.44%, 19.77% and 14.22% respectively. ConclusionsThe mean age of these patients in this study was older, compared to other reports. Female patients were more than male, especially in these patients older than 50 years. VKH syndrome, ankylosing spondylitis associated with uveitis and Behçet's disease were the common entities.