Objective The preventive effect of epigallocatechin gallate(EGCG)on hyperglycemia-induced hemorrhagic transformation(HT)was analyzed,and the underlying mechanisms were further explored.Methods Male SD rats were randomly divided into sham operation group(Sham,n = 20),model group(n = 27),hyperglycemia model group(HG,n = 43),and EGCG group(n = 43).In the model group,only the electrocoagulation cerebral ischemia model was established,and the HG group and the EGCG group were used to establish the HT model with acute hyperglycemia combined with electrocoagulation cerebral ischemia model.In addition,EGCG was adminis-tered by gavage for 5 days before cerebral ischemia at a dose of 50 mg/kg/d.Further studies confirmed the relevant targets by using network pharmacology to predict the potential targets and pathways of EGCG in the occurrence of HT.Results Compared with the model group,the mortality rate of the rats in the HG group was significantly increased[21.2%(6/27)vs.51.2%(22/43),P＜0.05].The mortality of rats in the EGCG group was significantly lower than that in the HG group[30.20%(13/43)vs.51.2%(22/43),P＜0.05].Second,mNSS,Longa score and infarct volume in the EGCG group were significantly lower than those in the HG group(P＜0.05).The incidence of HT in the HG group was higher than that in the model group(59.3%vs.90.7%).EGCG significantly reduced the incidence of hyperglycemia-induced HT to 69.8%.Compared with the HG group,EGCG decreased the hemoglobin content from(53.42±5.11)mg/dL to(37.04±2.39)mg/dL respectively(P＜0.05).Network pharmacology revealed that Nrf2-Keap1-mediated neuroinflammation may be associated with hyperglycemia-induced HT.The expression of Nrf2 and Keap1 was significantly decreased and the expression of TLR4 and phosphorylation of NF-κB was significantly increased in the HG group,but EGCG reversed this process.Conclusion EGCG pretreatment prevents the occurrence of HT,which may be related to the neuroprotection mediated by activation of the Nrf2-Keap1 signaling pathway.

AIM: To investigate the preoperative ocular symptoms and the characteristics of asymptomatic ocular surface abnormalities in hospitalized patients with primary pterygium.METHODS: Cross-sectional study. Hospitalized patients diagnosed with primary pterygium and scheduled to receive pterygium excision surgery at the Xiamen Eye Center of Xiamen University from August 2022 to October 2022 were enrolled. Ocular surface disease index questionnaire(OSDI), six examinations including non-invasive tear film break-up time, Schirmer I test, tear meniscus height, lid margin abnormality, meibomian gland dropout and tear film lipid layer thickness, and anterior segment optical coherence tomography(AS-OCT)were performed and statistically analyzed.RESULTS: A total of 178 cases(178 eyes), with a mean age of 54.39±10.75 years old, were recruited, including 75 males(42.1%)and 103 females(57.9%). The average values of ocular surface parameters in these patients included OSDI: 11.47±9.69, tear film break-up time: 7.10±3.86 s; tear meniscus height: 0.16±0.07 mm, Schirmer I test values: 14.39±7.29 mm/5 min, and pterygium thickness: 504.74±175.87 μm. Totally 161 eyes(90.4%)presented with abnormal lid margin, 44 eyes(24.7%)presented with meibomian gland dropout score ≥4, 52 eyes(29.2%)presented with low lipid layer thickness. In the 6 objective examinations, abnormalities in at least 4 of these tests were found in 85.4% of eyes. Pterygium morphology was classified into four grades: 10 eyes(5.6%)of grade Ⅰ, 93 eyes(52.2%)of grade Ⅱ, 60 eyes(33.7%)of grade Ⅲ, and 15 eyes(8.4%)of grade Ⅳ. In patients with a higher grade of pterygium, the tear film break-up time was lower, and the proportion of abnormal lid margin was also significantly higher(P<0.05). The patients were further divided into two subgroups, including 121 eyes(68.0%)with normal OSDI <13 in the normal group and 57 eyes(32.0%)with OSDI ≥13 in the abnormal group. No significant difference was found in the proportion of meibomian gland dysfunction between the two groups of patients(71.9% vs. 71.9%, P=0.872). In addition, there were differences in the number of abnormal objective examinations(4.11±0.85 vs. 4.91±0.99, P<0.001).CONCLUSIONS: Asymptomatic ocular surface abnormalities were present preoperatively in patients hospitalized for primary pterygium. A comparable high incidence of structural or functional meibomian gland dysfunction existed in pterygium patients with or without apparent ocular discomfort. More attention should be paid to the ocular surface abnormalities in those asymptomatic patients before primary pterygium surgery.

Myelin-forming oligodendrocytes in the central nervous system (CNS) and Schwann cells in the peripheral nervous system (PNS) are essential for structural and functional homeostasis of nervous tissue. Albeit with certain similarities, the regulation of CNS and PNS myelination is executed differently. Recent advances highlight the coordinated regulation of oligodendrocyte myelination by amino-acid sensing and growth factor signaling pathways. In this review, we discuss novel insights into the understanding of differential regulation of oligodendrocyte and Schwann cell biology in CNS and PNS myelination, with particular focus on the roles of growth factor-stimulated RHEB-mTORC1 and GATOR2-mediated amino-acid sensing/signaling pathways. We also discuss recent progress on the metabolic regulation of oligodendrocytes and Schwann cells and the impact of their dysfunction on neuronal function and disease.
Amino Acids ; Myelin Sheath/metabolism* ; Schwann Cells/metabolism* ; Oligodendroglia/metabolism* ; Signal Transduction ; Intercellular Signaling Peptides and Proteins/metabolism*

Objective:To construct a biospecimen bank of patient derived organoids (PDOs) from pancreatic cancer tissues and to explore the feasibility of PDOs drug sensitivity assay technology to guide chemotherapy drug selection for pancreatic cancer.Methods:Pancreatic cancer tissue specimens obtained after surgical resection and puncture biopsy from Mar 2020 to Dec 2022 at Drum Tower Hospital, Nanjing University School of Medicine were collected. Pancreatic cancer PDOs were cultured in vitro and histologically identified; PDOs were treated with gemcitabine, Nab-paclitaxel, fluorouracil, Oxaliplatin, and Irinotecan and cell viability was measured to analyze the correlation between PDOs drug sensitivity and the actual clinical treatment response.Results:The PDOs can reproduce the pathological features of corresponding tumor tissues; the sensitivity of different PDOs to the same chemotherapeutic drug is significantly different; The sensitivity of PDOs was highly consistent with the actual treatment effect of the corresponding patients 75.76% (25/33); organoid organ-based susceptibility testing had predictive value for the treatment response of patients (AUC=0.733, 95% CI: 0.546-0.919, P<0.05). Conclusion:A biobank of pancreatic cancer PDOs was successfully constructed, and the drug susceptibility test results were significantly correlated with the actual medication response of patients, suggesting that the drug susceptibility test technology based on PDOs has the potential to guide individualized chemotherapy for pancreatic cancer.

Humans ; MicroRNAs/genetics* ; Cell Line, Tumor ; Osteosarcoma/pathology* ; Neoplasm Invasiveness ; Cell Proliferation ; Bone Neoplasms/pathology* ; Cell Movement ; Neoplasm Metastasis

Objective:To investigate the effect of insular involvement on the outcomes of patients with acute anterior circulation ischemic stroke.Methods:Patients with acute anterior circulation ischemic stroke admitted to the Department of Neurology, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University from January 2015 to December 2020 were retrospectively included. Demographic data, vascular risk factors, clinical and laboratory data, as well as treatment and outcomes were collected. Firstly, the correlation between the insular involvement and the outcomes was investigated, and then the bootstrap method was used to clarify the mediating role of infarct volume between the insular involvement and the poor outcomes.Results:A total of 450 patients with acute anterior circulation ischemic stroke were enrolled, among whom 79 cases (17.6%) had insular involvement and 41 (9.1%) had left insular involvement. There were 111 (24.7%) with poor outcomes, including 5 (1.1%) died. Compared to the non-insular involvement group, the insular involvement group had a higher proportion of patients with atrial fibrillation, shorter onset to door time, higher neutrophil-to-lymphocyte ratio (NLR), higher National Institutes of Health Stroke Scale (NIHSS) score at admission, larger infarct volume, and higher proportion of patients with poor outcomes (all P<0.05). In addition, patients with left insular involvement were younger than those with right insular involvement, had a higher baseline NIHSS score, a lower proportion of patients with minor stroke (NIHSS score ≤8), and had a longer onset to door time (all P<0.05). Compared to the good outcome group, the poor outcome group was older, with a higher proportion of female patients, higher systolic blood pressure, blood glucose, NLR, and NIHSS scores at admission, larger infarct volume, and a higher proportion of patients with insular involvement (all P<0.05). Mediation analysis suggested that the mediating effect of infarct volume between the insular involvement and the poor outcomes was significant (95% confidence interval 0.033-0.230; P=0.008). Conclusions:insular involvement in patients with acute anterior circulation ischemic stroke is associated with the poor outcomes, and this association may be mediated by infarct volume. Patients with left insular involvement may have more severe symptoms than those with right insular involvement, but there is no significant difference in the outcomes.

Objective: To evaluate the therapeutic effect of dental autotransplantation with the application of digital design combined with 3D printing of donor tooth models and recipient alveolar fossa model preoperatively. Methods: Twelve cases that could not be retained due to tooth fracture or extensive absorption of alveolar bone were recruited in the study. Cone-beam computed tomography (CBCT) data were imported into Mimics software for digital design, and the best-matched third molar was selected as the donor tooth. Replicas of the donor teeth and the recipient socket were printed out with three-dimensional (3D) printing technologies as a simulation model for recipient tooth socket preparation. During tooth autotransplantation, preparation of the recipient tooth socket and the donor tooth were guided by the 3D-printed replicas sequentially. Then, the donor tooth was implanted into the recipient tooth pocket. Patients were followed up at 3, 6 and 12 months after the operation, with CBCT examination to evaluate the status of bone reconstruction and periodontal ligaments at each time point. Results: Twelve patients were transplanted with an autogenous third molar with the apical foramen completely closed. Among them, 7 patients had alveolar fossa infection before the operation, of which 1 had extensive resorption of the alveolar bone due to the infection. All 12 patients recovered well after the operation and were followed up for at least 12 months. In total, 11 caseswere successful in tooth autotransplantation with normal mastication, and 1 case had root resorption 14 months postoperation. Conclusion Digital design combined with 3D printing technology can assistin the selection of thebest-matched donor tooth and preparation of the recipient socket before tooth transplantation proceduresand reduce the extra-alveolar exposure time of the donor tooth and number of trial placementsintothe alveolar fossa. Thus, this combined strategy can effectively improve the outcome of dental autotransplantation.

Technical framework is centered on top-level design of smart hospitals. Guangdong Second Provincial General Hospital adopted hospital intelligent twins as its technical framework of the all-scenario intelligent construction. Its construction practices covered four layers of intelligent interaction, intelligent connection, intelligent hub and intelligent application. These practices can advance the construction of smart hospitals into the all-scenario intelligent stage, featuring intelligent medical treatment, intelligent service and intelligent management, thus providing reference for promoting the construction of smart hospitals and realizing the digital transformation of medical industry.

Objective:To establish a simple and efficient clinical prediction model of moderate and severe obstructive sleep apnea hypopnea (OSAHS) in snoring patients based on the clinical data and morphological measurement data in order to increase the early diagnosis and then early intervention of OSAHS. The prediction model is evaluated by external validation.Methods:A total of 299 subjects from January 2015 to December 2018 were selected to perform polysomngraphy (PSG) in Yangpu Hospital, Tongji University School of Medicine. According to the PSG results, they were divided into moderate and severe OSAHS groups (143 cases) and control groups (156 cases). Clinical complications data and morphological measurement data were collected. The regression equation and ROC curve were established according to the Logistic regression method. Then, another 110 subjects from January 2019 to October 2019 were chosen as verified data group, and used to verify the accuracy of the prediction model. The data of 110 subjects were put into the equation according to risk factors and assignment. The ROC curve was drawn and the area under the curve was calculated. The sensitivity, specificity, accuracy, positive predictive value and negative predictive value were calculated.Results:The predicted equation was: y = -10.707 86+0.589 60 × sex+ 0.141 61 × BMI+ 1.281 62 × tonsil size degree+ 1.807 43 × modified Mallampati degree′tongue position. The AUC of the ROC curve of prediction model in training set was 0.851(95% CI 0.807-0.895), the sensitivity was 83.9%, the specificity was 79.5%, and the cut-off value was 0.634.The AUC of the ROC curve in validation set was 0.827(95% CI 0.751-0.904) with a sensitivity of 73.3% and a specificity of 86.0%, and an accuracy of 79.1%. Its positive predictive value was 5.238, and negative predictive value was 0.310. Conclusions:The predictive model constructed by the combination of clinically accessible data (sex) and morphological measurement (BMI, tonsil size degree, modifiedMallampatidegree) has a relatively high predictive efficiency for screening snoring patients with moderate and severe OSAHS. The predictive model is proved with good forecast accuracy by the external verification method.

Objective:To investigate the application value of machine learning algorithms and COX nomogram in the survival prediction of hepatocellular carcinoma (HCC) after resection.Methods:The retrospective and descriptive study was conducted. The clinicopathological data of 375 patients with HCC who underwent radical resection in the Cancer Hospital of Chinese Academy of Medical Sciences and Peking Union Medical College from January 2012 to January 2017 were collected. There were 304 males and 71 females, aged from 21 to 79 years, with a median age of 57 years. According to the random numbers showed in the computer, 375 patients were divided into training dataset consisting of 300 patients and validation dataset consisting of 75 patients, with a ratio of 8∶2. Machine learning algorithms including logistic regression (LR), supporting vector machine (SVM), decision tree (DT), random forest (RF), and artificial neural network (ANN) were used to construct survival prediction models for HCC after resection, so as to identify the optimal machine learning algorithm prediction model. A COX nomogram prediction model for predicting postoperative survival in patients with HCC was also constructed. Comparison of performance for predicting postoperative survival of HCC patients was conducted between the optimal machine learning algorithm prediction model and the COX nomogram prediction model. Observation indicators: (1) analysis of clinicopathological data of patients in the training dataset and validation dataset; (2) follow-up and survival of patients in the training dataset and validation dataset; (3) construction and evaluation of machine learning algorithm prediction models; (4) construction and evaluation of COX nomogram prediction model; (5) evaluation of prediction performance between RF machine learning algorithm prediction model and COX nomogram prediction model. Follow-up was performed using outpatient examination or telephone interview to detect survival of patients up to December 2019 or death. Measurement data with normal distribution were expressed as Mean± SD, and comparison between groups was analyzed by the paired t test. Measurement data with skewed distribution were expressed as M ( P25, P75) or M (range), and comparison between groups was analyzed by the Mann-Whitney U test. Count data were represented as absolute numbers. Comparison between groups was performed using the chi-square test when Tmin ≥5 and N ≥40, using the calibration chi-square test when 1≤ Tmin ≤5 and N ≥40, and using Fisher exact probability when Tmin <1 or N <40. The Kaplan-Meier method was used to calculate survival rate and draw survival curve. The COX proportional hazard model was used for univariate analysis, and variables with P<0.2 were included for the Lasso regression analysis. According to the lambda value, variables affecting prognosis were screened for COX proportional hazard model to perform multivariate analysis. Results:(1) Analysis of clinicopathological data of patients in the training dataset and validation dataset: cases without microvascular invasion or with microvascular invasion, cases without liver cirrhosis or with liver cirrhosis of the training dataset were 292, 8, 105, 195, respectively, versus 69, 6, 37, 38 of the validation dataset, showing significant differences between the two groups ( χ2=4.749, 5.239, P<0.05). (2) Follow-up and survival of patients in the training dataset and validation dataset: all the 375 patients received follow-up. The 300 patients in the training dataset were followed up for 1.1-85.5 months, with a median follow-up time of 50.3 months. Seventy-five patients in the validation dataset were followed up for 1.0-85.7 months, with a median follow-up time of 46.7 months. The postoperative 1-, 3-year overall survival rates of the 375 patients were 91.7%, 79.5%. The postoperative 1-, 3-year overall survival rates of the training dataset were 92.0%, 79.7%, versus 90.7%, 81.9% of the validation dataset, showing no significant difference in postoperative survival between the two groups ( χ2=0.113, P>0.05). (3) Construction and evaluation of machine learning algorithm prediction models. ① Selection of the optimal machine learning algorithm prediction model: according to information divergence of variables for prediction of 3 years postoperative survival of HCC, five machine learning algorithms were used to comprehensively rank the variables of clinicopathological factors of HCC, including LR, SVM, DT, RF, and ANN. The main predictive factors were screened out, as hepatitis B e antigen (HBeAg), surgical procedure, maximum tumor diameter, perioperative blood transfusion, liver capsule invasion, and liver segment Ⅳ invasion. The rank sequence 3, 6, 9, 12, 15, 18, 21, 24, 27, 29 variables of predictive factors were introduced into 5 machine learning algorithms in turn. The results showed that the area under curve (AUC) of the receiver operating charateristic curve of LR, SVM, DT, and RF machine learning algorithm prediction models tended to be stable when 9 variables are introduced. When more than 12 variables were introduced, the AUC of ANN machine learning algorithm prediction model fluctuated significantly, the stability of AUC of LR and SVM machine learning algorithm prediction models continued to improve, and the AUC of RF machine learning algorithm prediction model was nearly 0.990, suggesting RF machine learning algorithm prediction model as the optimal machine learning algorithm prediction model. ② Optimization and evaluation of RF machine learning algorithm prediction model: 29 variables of predictive factors were sequentially introduced into the RF machine learning algorithm to construct the optimal RF machine learning algorithm prediction model in the training dataset. The results showed that when 10 variables were introduced, results of grid search method showed 4 as the optimal number of nodes in DT, and 1 000 as the optimal number of DT. When the number of introduced variables were not less than 10, the AUC of RF machine learning algorithm prediction model was about 0.990. When 10 variables were introduced, the RF machine learning algorithm prediction model had an AUC of 0.992 for postoperative overall survival of 3 years, a sensitivity of 0.629, a specificity of 0.996 in the training dataset, an AUC of 0.723 for postoperative overall survival of 3 years, a sensitivity of 0.177, a specificity of 0.948 in the validation dataset. (4) Construction and evaluation of COX nomogram prediction model. ① Analysis of postoperative survival factors of HCC patients in the training dataset. Results of univariate analysis showed that HBeAg, alpha fetoprotein (AFP), preoperative blood transfusion, maximum tumor diameter, liver capsule invasion, and degree of tumor differentiation were related factors for postoperative survival of HCC patients [ hazard ratio ( HR)=1.958, 1.878, 2.170, 1.188, 2.052, 0.222, 95% confidence interval ( CI): 1.185-3.235, 1.147-3.076, 1.389-3.393, 1.092-1.291, 1.240-3.395, 0.070-0.703, P<0.05]. Clinico-pathological data with P<0.2 were included for Lasso regression analysis, and the results showed that age, HBeAg, AFP, surgical procedure, perioperative blood transfusion, maximum tumor diameter, tumor located at liver segment Ⅴ or Ⅷ, liver capsule invasion, and degree of tumor differentiation as high differentiation, moderate-high differentiation, moderate differentiation, moderate-low differentiation were related factors for postoperative survival of HCC patients. The above factors were included for further multivariate COX analysis, and the results showed that HBeAg, surgical procedure, maximum tumor diameter were independent factors affecting postoperative survival of HCC patients ( HR=1.770, 8.799, 1.142, 95% CI: 1.049- 2.987, 1.203-64.342, 1.051-1.242, P<0.05). ② Construction and evaluation of COX nomogram prediction model: the clinicopathological factors of P≤0.1 in the COX multivariate analysis were induced to Rstudio software and rms software package to construct COX nomogram prediction model in the training dataset. The COX nomogram prediction model for predicting postoperative overall survival had an consistency index of 0.723 (se=0.028), an AUC of 0.760 for postoperative overall survival of 3 years in the training dataset, an AUC of 0.795 for postoperative overall survival of 3 years in the validation dataset. The verification of the calibration plot in the training dataset showed that the COX nomogram prediction model had a good prediction performance for postoperative survival. COX nomogram score=0.627 06×HBeAg (normal=0, abnormal=1)+ 0.134 34×maximum tumor diameter (cm)+ 2.107 58×surgical procedure (laparoscopy=0, laparotomy=1)+ 0.545 58×perioperative blood transfusion (without blood transfusion=0, with blood transfusion=1)-1.421 33×high differentiation (non-high differentiation=0, high differentiation=1). The COX nomogram risk scores of all patients were calculated. Xtile software was used to find the optimal threshold of COX nomogram risk scores. Patients with risk scores ≥2.9 were assigned into high risk group, and patients with risk scores <2.9 were assigned into low risk group. Results of Kaplan-Meier overall survival curve showed a significant difference in the postoperative overall survival between low risk group and high risk group of the training dataset ( χ2=33.065, P<0.05). There was a significant difference in the postoperative overall survival between low risk group and high risk group of the validation dataset ( χ2=6.585, P<0.05). Results of further analysis by the decision-making curve showed that COX nomogram prediction model based on the combination of HBeAg, surgical procedure, perioperative blood transfusion, maximum tumor diameter, and degree of tumor differentiation was superior to any of the above individual factors in prediction performance. (5) Evaluation of prediction performance between RF machine learning algorithm prediction model and COX nomogram prediction model: prediction difference between two models was investigated by analyzing maximun tumor diameter (the important variable shared in both models), and by comparing the predictive error curve of both models. The results showed that the postoperative 3-year survival rates predicted by RF machine learning algorithm prediction model and COX nomogram prediction model were 77.17% and 74.77% respectively for tumor with maximum diameter of 2.2 cm ( χ2=0.182, P>0.05), 57.51% and 61.65% for tumor with maximum diameter of 6.3 cm ( χ2=0.394, P>0.05), 51.03% and 27.52% for tumor with maximum diameter of 14.2 cm ( χ2=12.762, P<0.05). With the increase of the maximum tumor diameter, the difference in survival rates predicted between the two models turned larger. In the validation dataset, the AUC for postoperative overall survival of 3 years of RF machine learning algorithm prediction model and COX nomogram prediction model was 0.723 and 0.795, showing a significant difference between the two models ( t=3.353, P<0.05). Resluts of Bootstrap cross-validation for prediction error showed that the integrated Brier scores of RF machine learning algorithm prediction model and COX nomogram prediction model for predicting 3-year survival were 0.139 and 0.134, respectively. The prediction error of COX nomogram prediction model was lower than that of RF machine learning algorithm prediction model. Conclusion:Compared with machine learning algorithm prediction models, the COX nomogram prediction model performs better in predicting 3 years postoperative survival of HCC, with fewer variables, which is easy for clinical use.