Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications (ASMs), a condition known as pharmacoresistant epilepsy. The management of pharmacoresistant epilepsy remains an intractable issue in the clinic. Its early prediction is important for prevention and diagnosis. However, it still lacks effective predictors and approaches. Here, a classical model of pharmacoresistant temporal lobe epilepsy (TLE) was established to screen pharmacoresistant and pharmaco-responsive individuals by applying phenytoin to amygdaloid-kindled rats. Ictal electroencephalograms (EEGs) recorded before phenytoin treatment were analyzed. Based on ictal EEGs from pharmacoresistant and pharmaco-responsive rats, a convolutional neural network predictive model was constructed to predict pharmacoresistance, and achieved 78% prediction accuracy. We further found the ictal EEGs from pharmacoresistant rats have a lower gamma-band power, which was verified in seizure EEGs from pharmacoresistant TLE patients. Prospectively, therapies targeting the subiculum in those predicted as "pharmacoresistant" individual rats significantly reduced the subsequent occurrence of pharmacoresistance. These results demonstrate a new methodology to predict whether TLE individuals become resistant to ASMs in a classic pharmacoresistant TLE model. This may be of translational importance for the precise management of pharmacoresistant TLE.
Epilepsy, Temporal Lobe/diagnosis* ; Animals ; Drug Resistant Epilepsy/drug therapy* ; Electroencephalography/methods* ; Rats ; Anticonvulsants/pharmacology* ; Neural Networks, Computer ; Male ; Humans ; Phenytoin/pharmacology* ; Adult ; Disease Models, Animal ; Female ; Rats, Sprague-Dawley ; Young Adult ; Convolutional Neural Networks

Objective:To investigate the metabolism-related proteins and their presence in the plasma of female androgenetic alopecia (FAGA) patients.Methods:From March 2021 to March 2023, FAGA patients aged 18-50 (FAGA group) and healthy women (HC group) were recruited from the Dermatology Outpatient Department of Huashan Hospital. 3 ml of peripheral venous blood was collected from each participant and centrifuged to obtain plasma. Olink proteomics analysis was performed on the collected plasma, differentially expressed proteins were screened with R language, the diagnostic accuracy of the differentially expressed proteins was assessed using receiver operating characteristic (ROC) curve. Gene ontology (GO) analysis was performed on differentially expressed proteins. Immunofluorescence analysis on hair follicles in the parietal region of the FAGA group and the occipital region of the HC group was performed to validate the differentially expressed proteins identified. SPSS 25.0 software was used to analyze the data, with normal distribution metric data represented by Mean±SD. Student’s t-test was used to compare the basic information of two groups of subjects and the relative fluorescence intensity of differentially expressed proteins in hair follicles. Pearson correlation analysis was performed on plasma metabolism-related proteins and the basic information of subjects. P<0.05 indicates a statistically significant difference. Results:Sixty-one cases were included in the FAGA group, with an average age of (33.8±7.4) years and an onset age of (29.5±7.8) years. Among them, 38 cases were mild FAGA, 14 cases were moderate, and 9 cases were severe. Twenty-seven cases were included in the HC group, with an average age of (32.0±7.7) years. There was no statistically significant difference in the basic information (age, body mass index, testosterone, 25-hydroxyvitamin D, uric acid, and ferritin levels) between the two groups of subjects ( P>0.05). Compared to the HC group, the plasma of the FAGA group showed 26 significantly upregulated differentially expressed proteins ( P<0.05), with AHCY and NECTIN2 exhibiting the most significant differences (all P=0.003). The ROC curve evaluation revealed that the area under the curve for AHCY and NECTIN2 was greater than 0.7, indicating good diagnostic accuracy. The GO analysis revealed that the differentially expressed proteins were primarily enriched in the BAT3 complex (cellular component), ubiquitin-dependent ERAD pathway, natural killer cell activation (biological process), as well as ubiquitin protein ligase binding and ubiquitin-specific protease binding (molecular function). Pearson correlation analysis revealed that AHCY ( r=-0.23, P=0.010) and NECTIN2 ( r=-0.31, P=0.033) were negatively correlated with the severity of hair loss in FAGA patients. The results of hair follicle immunofluorescence analysis showed that the relative fluorescence intensity of AHCY and NECTIN2 in the FAGA group was higher than that in the HC group ( P<0.05). In other words, both AHCY and NECTIN2 were upregulated in the FAGA group. Conclusion:Metabolism-related proteins play an important role in FAGA. AHCY and NECTIN2 may serve as early diagnostic biomarkers for FAGA.

Objective:To investigate the metabolism-related proteins and their presence in the plasma of female androgenetic alopecia (FAGA) patients.Methods:From March 2021 to March 2023, FAGA patients aged 18-50 (FAGA group) and healthy women (HC group) were recruited from the Dermatology Outpatient Department of Huashan Hospital. 3 ml of peripheral venous blood was collected from each participant and centrifuged to obtain plasma. Olink proteomics analysis was performed on the collected plasma, differentially expressed proteins were screened with R language, the diagnostic accuracy of the differentially expressed proteins was assessed using receiver operating characteristic (ROC) curve. Gene ontology (GO) analysis was performed on differentially expressed proteins. Immunofluorescence analysis on hair follicles in the parietal region of the FAGA group and the occipital region of the HC group was performed to validate the differentially expressed proteins identified. SPSS 25.0 software was used to analyze the data, with normal distribution metric data represented by Mean±SD. Student’s t-test was used to compare the basic information of two groups of subjects and the relative fluorescence intensity of differentially expressed proteins in hair follicles. Pearson correlation analysis was performed on plasma metabolism-related proteins and the basic information of subjects. P<0.05 indicates a statistically significant difference. Results:Sixty-one cases were included in the FAGA group, with an average age of (33.8±7.4) years and an onset age of (29.5±7.8) years. Among them, 38 cases were mild FAGA, 14 cases were moderate, and 9 cases were severe. Twenty-seven cases were included in the HC group, with an average age of (32.0±7.7) years. There was no statistically significant difference in the basic information (age, body mass index, testosterone, 25-hydroxyvitamin D, uric acid, and ferritin levels) between the two groups of subjects ( P>0.05). Compared to the HC group, the plasma of the FAGA group showed 26 significantly upregulated differentially expressed proteins ( P<0.05), with AHCY and NECTIN2 exhibiting the most significant differences (all P=0.003). The ROC curve evaluation revealed that the area under the curve for AHCY and NECTIN2 was greater than 0.7, indicating good diagnostic accuracy. The GO analysis revealed that the differentially expressed proteins were primarily enriched in the BAT3 complex (cellular component), ubiquitin-dependent ERAD pathway, natural killer cell activation (biological process), as well as ubiquitin protein ligase binding and ubiquitin-specific protease binding (molecular function). Pearson correlation analysis revealed that AHCY ( r=-0.23, P=0.010) and NECTIN2 ( r=-0.31, P=0.033) were negatively correlated with the severity of hair loss in FAGA patients. The results of hair follicle immunofluorescence analysis showed that the relative fluorescence intensity of AHCY and NECTIN2 in the FAGA group was higher than that in the HC group ( P<0.05). In other words, both AHCY and NECTIN2 were upregulated in the FAGA group. Conclusion:Metabolism-related proteins play an important role in FAGA. AHCY and NECTIN2 may serve as early diagnostic biomarkers for FAGA.

Objective To explore the correlations of transcranial sonography of substantia nigra(SN-TCS)characteristics with MRI iron deposition on substantia nigra in patients with Parkinson disease(PD).Methods Data of SN-TCS and craniocerebral MRI in 120 PD patients were retrospectively analyzed.The patients were divided into iron deposition positive group(positive group,n=46)and iron deposition negative group(negative group,n=74)according to quantitative susceptibility mapping(QSM)value.Then parameters of SN-TCS and MRI were compared between groups(both P＜0.05),and correlation analysis were also performed.Results The proportion of high echo positive,strong echo area and QSM value of substantia nigra,as well as of hyper-substantia nigra area/midbrain area(S/M)in positive group were all higher than those in negative group(all P＜0.001).No significant difference of midbrain area was found between groups(P＞0.05).Strong echo area of substantia nigra and S/M based on SN-TCS were both low-medium positively correlated with substantia nigra QSM value showed on MRI(r=0.497,0.529,both P＜0.001).Conclusion SN-TCS characteristics of PD patients were correlated with MRI iron deposition on substantia nigra,among which strong echo area and S/M were valuable for evaluating iron deposition on substantia nigra.

Cancer stands as one of the predominant causes of mortality globally, necessitating ongoing efforts to develop innovative therapeutics. Historically, natural products have been foundational in the quest for anticancer agents. Bulbocodin D (BD) and Bulbocodin C (BC), two bibenzyls derived from Pleione bulbocodioides (Franch.) Rolfe, have demonstrated notable in vitro anticancer activity. In human lung cancer A549 cells, the IC_50s for BD and BC were 11.63 and 11.71 μmol·L^-1, respectively. BD triggered apoptosis, as evidenced by an upsurge in Annexin V-positive cells and elevated protein expression of cleaved-PARP in cancer cells. Furthermore, BD and BC markedly inhibited the migratory and invasive potentials of A549 cells. The altered genes identified through RNA-sequencing analysis were integrated into the CMap dataset, suggesting BD's role as a potential signal transducer and activator of transcription 3 (STAT3) inhibitor. SwissDock and MOE analyses further revealed that both BD and BC exhibited a commendable binding affinity with STAT3. Additionally, a surface plasmon resonance assay confirmed the direct binding affinity between these compounds and STAT3. Notably, treatment with either BD or BC led to a significant reduction in p-STAT3 (Tyr 705) protein levels, regardless of interleukin-6 stimulation in A549 cells. In addition, the extracellular signal-regulated kinase (ERK) was activated after BD or BC treatment. An enhancement in cancer cell mortality was observed upon combined treatment of BD and U0126, the MEK1/2 inhibitor. In conclusion, BD and BC emerge as promising novel STAT3 inhibitors with potential implications in cancer therapy.
Humans ; Lung Neoplasms/metabolism* ; STAT3 Transcription Factor/metabolism* ; Antineoplastic Agents/chemistry* ; A549 Cells ; Apoptosis ; Cell Line, Tumor ; Cell Proliferation

Objective:To build a small-sample ultra-widefield fundus images (UWFI) multi-disease classification artificial intelligence model, and initially explore the ability of artificial intelligence to classify UWFI multi-disease tasks.Methods:A retrospective study. From 2016 to 2021, 1 608 images from 1 123 patients who attended the Eye Center of the Renmin Hospital of Wuhan University and underwent UWFI examination were used for UWFI multi-disease classification artificial intelligence model construction. Among them, 320, 330, 319, 268, and 371 images were used for diabetic retinopathy (DR), retinal vein occlusion (RVO), pathological myopia (PM), retinal detachment (RD), and normal fundus images, respectively. 135 images from 106 patients at the Tianjin Medical University Eye Hospital were used as the external test set. EfficientNet-B7 was selected as the backbone network for classification analysis of the included UWFI images. The performance of the UWFI multi-task classification model was assessed using the receiver operating characteristic curve, area under the curve (AUC), sensitivity, specificity, and accuracy. All data were expressed using numerical values and 95% confidence intervals ( CI). The datasets were trained on the network models ResNet50 and ResNet101 and tested on an external test set to compare and observe the performance of EfficientNet with the 2 models mentioned above. Results:The overall classification accuracy of the UWFI multi-disease classification artificial intelligence model on the internal and external test sets was 92.57% (95% CI 91.13%-92.92%) and 88.89% (95% CI 88.11%-90.02%), respectively. These were 96.62% and 92.59% for normal fundus, 95.95% and 95.56% for DR, 96.62% and 98.52% for RVO, 98.65% and 97.04% for PM, and 97.30% and 94.07% for RD, respectively. The mean AUC on the internal and external test sets was 0.993 and 0.983, respectively, with 0.994 and 0.939 for normal fundus, 0.999 and 0.995 for DR, 0.985 and 1.000 for RVO, 0.991 and 0.993 for PM and 0.995 and 0.990 for RD, respectively. EfficientNet performed better than the ResNet50 and ResNet101 models on both the internal and external test sets. Conclusion:The preliminary UWFI multi-disease classification artificial intelligence model using small samples constructed in this study is able to achieve a high accuracy rate, and the model may have some value in assisting clinical screening and diagnosis.

Objective:To explore the clinical features and survival of newly diagnosed follicular lymphoma (FL) patients with diffuse large B-cell lymphoma (DLBCL) component.Methods:1845 newly diagnosed FL patients aged ≥ 18 years with grades 1-3a in 11 medical centers in China from 2000 to 2020 were included, and patients with DLBCL component were screened. The clinical data and survival data of the patients were retrospectively analyzed, and the prognostic factors were screened by univariate and multivariate analysis.Results:146 patients (7.9% ) with newly diagnosed FL had DLBCL component. The median age was 56 (25-83) years, 79 males (54.1% ) . The pathology of 127 patients showed the proportion of DLBCL component. Patients were divided into two groups according to whether the proportion of DLBCL component was ≥ 50% . The study found that patients with DLBCL component ≥ 50% had higher grade 3 ratio (94.3% vs 91.9% , P=0.010) , Ki-67 index ≥ 70% ratio (58.5% vs 32.9% , P=0.013) and PET-CT SUVmax ≥ 13 ratio (72.4% vs 46.3% , P=0.030) than patients with DLBCL component<50% . All patients received CHOP or CHOP like ± rituximab chemotherapy. The overall response rate (ORR) was 88.2% , and the complete response (CR) rate was 76.4% . In the groups with different proportions of DLBCL component, there was no significant difference in the remission rate after induction treatment and the incidence of disease progression within 2 years after initiation of treatment (POD24) ( P<0.05) . The overall estimated 5-year progression free survival (PFS) rate was 58.9% , and the 5-year overall survival (OS) rate was 90.4% . The 5-year OS rate of POD24 patients was lower than that of non POD24 patients (70.3% vs 98.5% , P<0.001) . Compared with non maintenance treatment of rituximab, maintenance treatment of rituximab could not benefit the 5-year PFS rate (57.7% vs 58.8% , P=0.543) , and the 5-year OS rate had a benefit trend, but the difference was not statistically significant (100% vs 87.8% , P=0.082) . Multivariate analysis showed that failure to reach CR after induction treatment was an independent risk factor for PFS ( P=0.006) , while LDH higher than normal was an independent risk factor for OS ( P=0.031) . Conclusion:FL patients with DLBCL component ≥50% have more invasive clinical and pathological features. CHOP/CHOP like ± rituximab regimen can improve the clinical efficacy of patients. Rituximab maintenance therapy can not benefit the PFS and OS of patients. Failure to reach CR after induction therapy was the independent unfavorable factor for PFS.

Combinatorial therapies have been recently proposed to improve the efficacy of anti-cancer treatment.The SynergyFinder R package is a software used to analyze pre-clinical drug combination datasets.Here,we report the major updates to the SynergyFinder R package for improved interpretation and annotation of drug combination screening results.Unlike the exist-ing implementations,the updated SynergyFinder R package includes five main innovations.1)We extend the mathematical models to higher-order drug combination data analysis and implement dimension reduction techniques for visualizing the synergy landscape.2)We provide a statistical analysis of drug combination synergy and sensitivity with confidence intervals and P values.3)We incorporate a synergy barometer to harmonize multiple synergy scoring methods to provide a consensus metric for synergy.4)We evaluate drug combination synergy and sensitivity to pro-vide an unbiased interpretation of the clinical potential.5)We enable fast annotation of drugs and cell lines,including their chemical and target information.These annotations will improve the interpretation of the mechanisms of action of drug combinations.To facilitate the use of the R package within the drug discovery community,we also provide a web server at www.s ynergyfinderplus.org as a user-friendly interface to enable a more flexible and versatile analysis of drug combination data.

Objective:To compare the differences of postoperative clinical target volume of internal mammary lymph node (CTV ImlN) by different delineation methods, and to explore the reasonable method of CTV ImlN delineation after internal mammary lymph node dissection (ImlND). Methods:A total of 20 breast cancer patients who had undergone modified radical mastectomy (MRM) with ImlND on the affected side and had complete preoperative and postoperative CT images were selected. The CTV (CTV pr-I, CTV pr-a) of both sides of ImlN were delineated on preoperative CT images according to RTOG guideline. On postoperative CT images, three different methods including deformation image registration (DIR) method, visual contrast method and precise measurement method, were employed to delineate the postoperative CTV ImlN of the affected side. The targets were named as CTV DIR, CTV V and CTV M, respectively. The central displacement, target volume, degree of inclusion (DI) and conformity index (CI) of CTV pr-a, CTV V, CTV M and CTV DIR were compared. Results:The central displacement of CTV V, CTV M and CTV DIR from CTV pr-a was 2.17 cm, 1.44 cm and 1.25 cm, respectively. The target volume of CTV pr-a, CTV pr-I, CTV V, CTV M and CTV DIR was 2.10 cm 3, 2.17 cm 3, 2.04 cm 3, 1.88 cm 3 and 2.07 cm 3 respectively. There was no significant difference in the target volume (all P>0.05). The CI values of CTV V-CTV pr-a and CTV M-CTV pr-a were both 0.16, and that of CTV DIR-CTV pr-a was 0.43. The CI value of CTV DIR was significantly higher than those of CTV V and CTV M (both P<0.01). The DI values of CTV V-CTV pr-a, CTV M-CTV pr-a and CTV DIR-CTV pr-a were 0.26, 0.24 and 0.58, respectively. The DI value of CTV DIR was significantly higher than those of CTV V and CTV M (both P<0.01). Conclusions:It is difficult to accurately delineate the CTV ImlN for patients after ImlND. However, the spatial position fitness of the target region delineated by DIR method is better than those by visual contrast and precise measurement methods.

Objective:To investigate the current situation of students' academic procrastination behavior in medical colleges and universities and its influencing factors, and to put forward suggestions to reduce the academic procrastination of medical students.Methods:A total of 1 327 undergraduate students from three medical colleges and universities in Heilongjiang Province were randomly selected to receive questionnaire investigation on life satisfaction, anxiety, and academic procrastination. SPSS 23.0 was used for data analysis.Results:①The total procrastination scores of medical students were (35.00±8.92) points. ②There were statistical differences in the academic procrastination of medical students with different genders, whether the only children, the reasons for choosing the major, and the level of achievement ( P < 0.05). There was no statistical difference in academic procrastination among medical students of different ages and grades ( P > 0.05). ③Medical students' procrastination was positively correlated with their anxiety level ( r = 0.102, P < 0.01), and negatively correlated with life satisfaction ( r = -0.117, P < 0.01). ④Regression analysis showed that the following six predictive variables including the level of achievement, gender, life satisfaction, anxiety, reasons for choosing the major, and whether the only children could effectively explain the variance of 14.2% academic procrastination of medical students. Conclusion:The overall degree of academic procrastination of medical students is higher than that of non-medical students. And the students' achievement level, gender, life satisfaction, anxiety, the reasons for choosing this major and whether the only child are the influencing factors of academic procrastination.