Objective:To construct and validate a machine learning model for preoperative prediction of perineural invasion (PNI) status in intrahepatic cholangiocarcinoma (ICC).Methods:Clincial data of 329 patients, including 245 admitted to Zhengzhou University People's Hospital from January 2018 to June 2023 and 84 admitted to the Affiliated Cancer Hospital of Zhengzhou University from January 2013 to January 2020 were retrospectively analyzed. Patients were divided into a training set ( n=231) and a validation set ( n=98). Clinicopathological data including age, gender, hepatitis B virus (HBV) infection status were collected. Predictive variables were determined using least absolute shrinkage and selection operator (LASSO) regression analysis. Six machine learning algorithms including random forest (RF), logistic regression, and linear kernel-based support vector machine were selected to construct the preoperative prediction model for PNI in ICC. Performance metrics of the model were calculated using a confusion matrix, and the final model was selected. The model performance was evaluated in the validation set. Calibration curves were plotted to evaluate the final model, and a Pareto chart was used to visualize the importance of predictive variables. Results:LASSO regression identified nine predictive variables included in the prediction model, including carbohydrate antigen 19-9 (CA19-9), HBV infection status, alkaline phosphatase, alanine aminotransferase, prothrombin time, total bilirubin, albumin, neutrophil times gamma-glutamyl transferase to lymphocyte ratio, and tumor burden score. Among the trained six models, the area under the curve (AUC) of the RF model was 0.909, with a sensitivity of 0.842 and an accuracy of 0.870. Compared with the AUC of the RF model, the AUCs of the other 5 models were lower (all P<0.05). The AUC of the RF model for predicting PNI in ICC in validation set was 0.736. Calibration curves showed good fit of the RF model's prediction of PNI in ICC in both training and validation sets. The Pareto chart showed that CA19-9 was the most important predictive variable in the model, followed by HBV infection status. Conclusion:The machine learning model based on the RF algorithm has a high accuracy in preoperative prediction of PNI status in ICC.

Objective To explore the correlations between self-esteem,social support and mental health status of patients with deep bums at special sites at different stages of the disease course.Methods A total of 36 inpatients who were admitted to Department of Burn Surgery of The First Affiliated Hospital of Naval Medical University(Second Military Medical University)between Jan.2020 and Sep.2023 were enrolled.The patients were assessed with self-rating anxiety scale(SAS),self-rating depression scale(SDS),post-traumatic stress disorder(PTSD)checklist-civilian version(PCL-C),Rosenberg self-esteem scale(RSES),and perceived social support scale(PSSS)within 7 d of burns(early stage of disease),at wound closure(early rehabilitation stage),and 3 months after wound closure(late rehabilitation stage).The data were collected and invalid data were excluded for statistical analysis.Results A total of 36 patients effectively completed all the scales.There were anxiety,depression and PTSD in patients with deep bums at special sites at different stages of the disease course,and the number of burn patients with anxiety,depression and PTSD showed a downward trend with the course of disease.The RSES score at the early rehabilitation stage was positively correlated with SAS score(rs=0.412,P=0.013),SDS score(rs=0.347,P=0.038)and PCL-C score(rs=0.447,P=0.006)at the early stage of disease,but not with SAS,SDS,or PCL-C scores at the early and late rehabilitation stages(all P＞0.05).There were no correlations between PSSS score at the early rehabilitation stage and SAS,SDS,or PCL-C scores at the early stage of disease or SAS or PCL-C scores at the early stage of rehabilitation(all P＞0.05),but there were negative correlations between PSSS score at the early rehabilitation stage and SDS score at the early stage of rehabilitation(r=-0.430,P=0.009)and SAS score(r=-0.467,P=0.004),SDS score(r=-0.483,P=0.003),and PCL-C score(rs=-0.351,P=0.036)at the late stage of rehabilitation.Conclusion Anxiety,depression,and PTSD are observed in patients with deep burns in special areas at different stages of the disease.A high level of self-esteem is associated with an increased risk of mental problems at early stage of burns.Conversely,a high level of social support is associated with a reduced likelihood of mental problems at late stage of rehabilitation.

Objective:To analyze the clinical phenotypes and genotypes of a family with posterior segment microphthalmia-retinoschisis and drusen syndrome.Methods:A pedigree investigation study was conducted.A family with four members across two generations treated at Shenzhen Eye Hospital in July 2021 was enrolled.Detailed ophthalmic examinations, including best corrected visual acuity (BCVA), intraocular pressure, slit-lamp microscopy, color fundus photography, optical coherence tomography (OCT), anterior segment OCT, fundus fluorescein angiography (FFA), and visual field tests were performed in the four members.Peripheral venous blood samples were collected from members for whole exome sequencing and data analysis.The pathogenicity of novel variant sites was assessed according to the ACMG guidelines.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Shenzhen Eye Hospital (No.22KYPJ018).Written informed consent was obtained from each subject or the guardian.Results:The proband is a 14-year-old female with high hyperopia since childhood, BCVA of + 9.75 DS-0.75 DC×150°=0.9 and + 11.75 DS-1.25 DC×30°=0.7, corneal transverse diameters of 12.1 and 12.2 mm, anterior chamber depths of 2.56 and 2.92 mm, lens thicknesses of 3.92 and 3.94 mm, and axial lengths of 17.47 and 17.01 mm in the right and left eyes, respectively.Fundus photography revealed diffuse yellow-white drusen-like lesions with unclear borders in the mid-peripheral retina, while OCT showed retinoschisis in the inner nuclear layer and homogeneous mound-like elevations with hyperreflective dense spots under the retinal pigment epithelium.FFA demonstrated diffuse punctate transilllumination of the mid-peripheral retina in both eyes, and visual field tests revealed a general decrease in visual acuity.The proband's 8-year-old brother exhibited similar signs to the proband.The consanguineously married parents were phenotypically normal.Whole exome sequencing identified compound heterozygous mutations in the membrane frizzled-related protein ( MFRP) gene in the proband and her brother, c.1150_1151insC (p.His384Profs*8) in exon 5 and c. 498_499insC (p.Asn167Glnfs*34) in exon 10.The father carried the c. 498_499insC mutation, while the mother carried the c.1150_1151insC mutation.Both were frameshift mutations predicted to alter gene function.These novel mutations had not been reported in the ESP, 1 000 Genomes (Phase 3), or ExAC databases, indicating they are novel variants.The variants co-segregated with the disease and both were classified as pathogenic according to ACMG guidelines.Based on the clinical and genetic findings, the family was diagnosed with posterior segment microphthalmia-retinoschisis and drusen syndrome, inherited in an autosomal recessive manner. Conclusions:The MFRP gene mutations c. 1150_1151insC and c. 498_499insC are the pathogenic variants for the posterior segment microphthalmia-retinoschisis and drusen syndrome in this family, and these compound heterozygous mutations are reported for the first time.

Objective:To explore the prognostic value of systemic immune-inflammatory index(SII)combined with tumor burden score (TBS) (hereinafter referred to as STS) in patients with intrahepatic cholangiocarcinoma (ICC) after radical resection, and to construct a nomogram model.Methods:The clinical data (including the degree of tumor differentiation, vascular cancer thrombus, and lymph node metastasis, etc.) of 258 ICC patients who received radical resection at People′s Hospital of Zhengzhou University (170 cases, training set) and Cancer Hospital of Zhengzhou University (88 cases, validation set) from January 1, 2016 to January 31, 2020 were retrospectively analyzed and graded by SII, TBS and STS. Multivariate Cox regression analysis were used to identify independent factors affecting the prognosis of patients with ICC. Kaplan-Meier survival curve and receiver operating characteristic curve (ROC) were drawn to evaluate the predictive efficiency of SII, TBS and STS in the overall survival of patients with ICC after radical resection. The nomogram prediction model was constructed and evaluate the performance of nomogram model using consistency index (C-index) and calibration curve.Results:Among 170 ICC patients in the training set, there were 106 cases of SII grade 1 and 64 cases of SII grade 2; 137 cases of TBS grade 1 and 33 cases of TBS grade 2; and 98 cases of STS grade 1, 47 cases of STS grade 2, and 25 cases of STS grade 3. Among 88 ICC patients in the validation set, there were 33 cases of SII grade 1 and 55 cases of SII grade 2; 66 cases of TBS grade 1 and 22 cases of TBS grade 2; and 30 case of STS grade 1, 39 cases of TBS grade 2, and 19 cases of TBS grade 3.The multivariate Cox regression analysis showed that tumor differentiation degree (highly differentiated vs. moderately differentiated HR=0.157, 95% confidence interval(95% CI) 0.045 to 0.546, highly differentiated vs. poorly differentiated HR=0.452, 95% CI 0.273 to 0.750), STS (grade 3 vs. grade 2 HR=1.966, 95% CI 1.148 to 3.469; grade 3 vs. grade 1 HR=1.405, 95% CI 0.890 to 2.216), vascular cancer thrombus ( HR=2.006, 95% CI 1.313 to 3.066), nerve invasion ( HR=1.865, 95% CI 1.221 to 2.850), and lymph node metastasis ( HR=1.802, 95% CI 1.121 to 2.896) were independent influencing factors of overall survival in ICC patients after radical resection (all P<0.05). The Kaplan-Meier survival curve showed that SII, TBS, and STS were independent influencing factors of overall survival in ICC patients (all P<0.05). The results of ROC analysis showed that the areas under the curve of SII, TBS and STS in predicting overall survival of ICC patients after radical resection were 0.566 (95% CI 0.479 to 0.652), 0.585 (95% CI 0.499 to 0.672), and 0.657 (95% CI 0.522 to 0.692), respectively. Tumor differentiation, vascular tumor thrombus, nerve invassion, lymph node metastasis, and STS were included to constract the nomogram model. The C-indexes of the training set and validation set based on the nomogram model were 0.792 (95% CI 0.699 to 0.825) and 0.776 (95% CI 0.716 to 0.833), respectively. The calibration curves of the survival rate of the training set and the validation set were close to the reference lines, and the nomogram model had better predictive ability in both the training set and the validation set. Conclusions:Preoperative STS grading is an effective and practical predictor of overall survival in ICC patients after radical section. Compared with SII and TBS alone, it has better predictive value for the prognosis of patients with ICC.

Objective     To study the correlation of preoperative hemoglobin amount with venous thromboembolism (VTE) after surgical treatment of bronchiectasis and the clinical significance. Methods     A retrospective study was performed on patients with bronchiectasis who underwent surgical treatment in our center from June 2017 to November 2021. The differences in blood parameters between the VTE patients and non-VTE patients were compared. The relationship between preoperative hemoglobin and VTE was confirmed by quartile grouping and receiver operating characteristic (ROC) curve. Results     A total of 122 patients were enrolled, including 50 males and 72 females, with a mean age of 52.52±12.29 years. The overall incidence of VTE after bronchiectasis was 9.02% (11/122). Preoperative hemoglobin amount (OR=0.923, 95%CI 0.870-0.980, P=0.008) and D-dimer amount (OR=1.734, 95%CI 1.087-2.766, P=0.021) were independent influencing factors for VTE after bronchiectasis. The incidence of VTE after bronchiectasis decreased gradually with the increase of preoperative hemoglobin amount. The area under the ROC curve (AUC) of postoperative D-dimer alone was 0.757, whereas the AUC of postoperative D-dimer combined with preoperative hemoglobin amount was 0.878. Conclusion     Low preoperative hemoglobin is an independent risk factor for postoperative VTE. Postoperative D-dimer combined with preoperative hemoglobin amount has a better predictive performance compared with postoperative D-dimer alone for postoperative VTE.

During coronavirus disease 2019 epidemic, the treatment of severe trauma has been impacted. The Consensus on emergency surgery and infection prevention and control for severe trauma patients with 2019 novel corona virus pneumonia was published online on February 12, 2020, providing a strong guidance for the emergency treatment of severe trauma and the self-protection of medical staffs in the early stage of the epidemic. With the Joint Prevention and Control Mechanism of the State Council renaming "novel coronavirus pneumonia" to "novel coronavirus infection" and the infection being managed with measures against class B infectious diseases since January 8, 2023, the consensus published in 2020 is no longer applicable to the emergency treatment of severe trauma in the new stage of epidemic prevention and control. In this context, led by the Chinese Traumatology Association, Chinese Trauma Surgeon Association, Trauma Medicine Branch of Chinese International Exchange and Promotive Association for Medical and Health Care, and Editorial Board of Chinese Journal of Traumatology, the Chinese expert consensus on emergency surgery for severe trauma and infection prevention during coronavirus disease 2019 epidemic ( version 2023) is formulated to ensure the effectiveness and safety in the treatment of severe trauma in the new stage. Based on the policy of the Joint Prevention and Control Mechanism of the State Council and by using evidence-based medical evidence as well as Delphi expert consultation and voting, 16 recommendations are put forward from the four aspects of the related definitions, infection prevention, preoperative assessment and preparation, emergency operation and postoperative management, hoping to provide a reference for severe trauma care in the new stage of the epidemic prevention and control.

Objective:To establish and validate a preoperative differentiateon model of intrahepatic cholangiocarcinoma (ICC) and combined hepatocellular carcinoma (CHC) based on the inflammatory markers and conventional clinical indicators.Methods:The clinical data of 116 patients with ICC or CHC admitted to Henan Provincial People's Hospital from January 2018 to March 2023 were retrospectively analyzed, including 74 males and 42 females, aged (58.5±9.4) years old. The data of 83 patients were used to establish the differentiation model as the training group, including 50 cases of ICC and 33 cases of CHC. The data of 33 patients were used to validate the model as the validation group, including 20 cases of ICC and 13 cases of CHC. The clinical data including the platelet-to-lymphocyte ratio (PLR), systemic immune inflammation index (SII), prognostic inflammatory index (PII), prognostic nutritional index (PNI), neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR) were collected and analyzed. The receiver operating characteristic (ROC) curve was used to analyze the best cut-off values of PLR, SII, PII, PNI, NLR and LMR. Univariate and multivariate logistic regression analysis were used to determine the differential factors between ICC and CHC. The R software was used to draw the nomogram, calculate the area under the curve (AUC) to evaluate the model accuracy, and draw the calibration chart and the decision curve to evaluate the predictive efficacy of the model.Results:Univariate logistic regression analysis showed that liver cirrhosis, history of hepatitis, alpha fetoprotein, carbohydrate antigen 199, gamma-glutamyltransferase (GGT), PLR, PNI and inflammation score (IS) could be used to differentiate ICC from CHC (all P<0.05). The indicators identified in univariate analysis were included in multivariate logistic regression analysis. The results showed that absence of liver cirrhosis, GGT>60 U/L, PNI>49.53, and IS<2 indicated the pathology of ICC (all P<0.05). Based on the above four factors, a nomogram model was established to differentiate the ICC and CHC. The AUC of ROC curve of the nomogram model in the training and validation groups were 0.851 (95% CI: 0.769-0.933) and 0.771 (95% CI: 0.594-0.949), respectively. The sensitivities were 0.760 and 0.750, and the specificities were 0.818 and 0.769, respectively. The calibration chart showed that the predicted curve fitted well to the reference line. The decision curve showed that the model has a clear positive net benefit. Conclusion:The nomogram model based on inflammatory markers showed a good differentiation performance of ICC and CHC, which could benefits the individualized treatment.

Objective: To explore the biological effects of electromagnetic pulse (EMP) with different high intensities on condylar cartilage in rats. Methods: SD rats were randomly divided into a sham group (Sham) and an irradiation group (EMP1: 500 kV/m, 10 Hz; EMP2: 270 kV/m, 10 Hz). Then, they were sacrificed at 1 h, 3 h, 12 h, 24 h and 3 d after irradiation. The degree of cartilage degeneration was evaluated by HE, safranine O-fast green, type Ⅱ collagen immunohistochemistry and TUNEL staining. Immunohistochemistry and western blot were performed to detect the expression of the matrix degradation factors: matrix metalloproteinase-13 (MMP-13), a disintegrin and metalloproteinase with thrombospondin motifs (ADAMTS-5) and the apoptosis key factor cleaved-cysteinyl aspartate specific proteinase (cleaved-Caspase3) in condylar cartilage. Results : HE staining showed that, compared with the Sham group, a small amount of exfoliation was found on the fibrous surface layer of the cartilage after irradiation in the EMP1 and EMP2 groups. Compared with the Sham group, the percentage of safranine O-fast green-positive area decreased significantly at 12 h and 24 h (both P<0.01) in the EMP1 group and 12 h and 24 h in the EMP2 group (both P<0.05); the percentage of type Ⅱ collagen-positive area decreased significantly at 3 h and 12 h (P<0.05, P<0.001) in the EMP1 group. In addition, the number of TUNEL-positive apoptotic cells increased significantly at 1 h, 3 h, 12 h, and 24 h in the EMP1 group and 1 h, 3 h, and 12 h in the EMP2 group (P<0.05). Moreover, at different timepoints (except at 3 d) in the EMP1 group and EMP2 group, the percentage of MMP-13, ADAMTS-5- and cleaved Caspase3-positive chondrocytes and their protein levels in condylar cartilage increased significantly after irradiation (P<0.05). Conclusion EMP with a certain degree of high-intensity can induce early transient damage to condylar cartilage. This effect is dose-and time-dependent.

Objective:A predictive nomogram model for the prognosis of intrahepatic cholangiocarcinoma (ICC) patients after curative resection was constructed based on the albumin-bilirubin score and tumor burden score (ATS) grade, and the predictive performance of the nomogram model was evaluated.Methods:Retrospective analysis of clinical data was made, from ICC patients who underwent curative resection at Zhengzhou University People's Hospital and Zhengzhou University Cancer Hospital from January 2016 to January 2020. A total of 258 patients were included in the study, with 140 males and 118 females, with an average age of (56.5±9.5) years. The 258 ICC patients were randomly divided into a training set ( n=174) and a testing set ( n=84) in a 7∶3 ratio. Single-factor and multi-factor Cox regression analyses were performed to identify prognostic factors for ICC patients of the training set, and then a nomogram model was constructed. The performance of the nomogram model was evaluated by using the concordance index (C-index), calibration curve, and risky decision curve analysis. Results:In the training set, univariate Cox regression analysis indicated that albumin-bilirubin (ALBI), tumor burden score (TBS), carcinoembryonic antigen (CEA), tumor differentitation, lymphvascular invasion and ATS significantly influenced overall survival after radical resection for ICC (all P<0.05). Multifactorial Cox regression analysis revealed that ATS grade, CEA, tumor differentiation, lymphovascular invasion, and AJCC N stage are independent risk factors for the prognosis of ICC patients after curative resection (all P<0.05). Assessment of the postoperative survival prediction model based on multifactorial Cox regression yielded a C-index of 0.775(95% CI: 0.747-0.841) for the training set and 0.731(95% CI: 0.668-0.828) for the testing set. The calibration curves for both the training and testing sets indicated strong predictive capability of the model. Additionally, the risk decision curve also suggested high net benefit of the model. Conclusions:The preoperative ATS grade is an independent factor affecting the survival after ICC radical resection. The nomogram model constructed based on ATS grade demonstrates excellent predictive value for postoperative prognosis in ICC patients.

Objective To establish a lumbar radiography image quality control model by using the deep learning algorithm and evaluate clinical images in real time and retrospectively based on the developed model.Methods The anteroposterior,lateral and oblique lumbar radiographs of 1389 patients collected between January 2018 to February 2021 at the The First Affiliated Hospital of Wenzhou Medical University were analyzed.The anatomical structures in the lumbar X-ray images were segmented using a full convolutional neural network based on U-Net,and the segmentation algorithm was utilized to establish an automatic evaluation model to detect substandard images.Dice similarity coefficient(DSC)was used to evaluate the performance of the model,and the lumbar radiography images were statistically evaluated after the application of the model.Results The accuracy of the model on the validation set was 0.971-0.990(0.98±0.10),the sensitivity was 0.714-0.933(0.86±0.13),and the specificity was 0.995-1.000(0.99±0.12).The quality control model had an excellent rate of 28.8%,an intermediate rate of 54.8%,and a failure rate of 16.4%for lumbar spine radiography in 2022.Conclusion The lumbar spine X-ray image quality control model based on artificial intelligence realizes accurate segmentation of lumbar spine anatomical structures and makes accurate evaluation of image quality,which is helpful to ensure the standardization of lumbar spine X-ray radiography operation by