Aldehyde oxidase (AOX) is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other xenobiotics. AOX-mediated metabolism can result in unexpected outcomes, such as the production of toxic metabolites and high metabolic clearance, which can lead to the clinical failure of novel therapeutic agents. Computational models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism liability. In this study, we developed a novel graph neural network called AOMP for predicting AOX-mediated metabolism. AOMP integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction, while utilizing transfer learning from ¹³C nuclear magnetic resonance data to enhance its performance on both tasks. AOMP significantly outperformed the benchmark methods in both cross-validation and external testing. Using AOMP, we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability, which were validated through in vitro experiments. Furthermore, for the convenience of the community, we established the first online service for AOX metabolism prediction based on AOMP, which is freely available at https://aomp.alphama.com.cn.

OBJECTIVE To evaluate the comprehensive value of four first-line combination immunotherapy for unresectable hepatocellular carcinoma， and provide a reference for determining the optimal clinical treatment decision for unresectable hepatocellular carcinoma. METHODS R4.2 software was used for network meta-analysis to obtain the effect values of the efficacy and safety indicators of four combination therapies [atezolizumab combined with bevacizumab （AB）， sintilimab combined with bevacizumab biosimilars （SB）， camrelizumab combined with apatinib （CA）， durvalumab combined with tremelimumab （DT）]. Combined with the efficacy， safety and economic indicators， the categorical based evaluation technique （M-MACBETH） was used to establish the value tree. At the same time， the comprehensive value scores of four therapies were calculated， and sensitivity analysis was performed to evaluate the robustness. RESULTS In terms of prolonging median overall survival， the advantage order of the four therapies was ranked as SB， CA， AB and DT. In terms of extending median progression-free survival， the advantage order of the four therapies was CA， SB， AB and DT. In terms of safety， the order of advantages was DT， AB， SB and CA. In terms of economy， the order of advantages was CA， SB， AB and DT. The comprehensive scores of SB， CA， AB and DT were 67.11， 57.77， 52.53 and 42.59 points， respectively. The results of the sensitivity analysis showed that the ranking results of comprehensive value for four regimens were robust. CONCLUSIONS Among the four first-line immune combination therapies for unresectable hepatocellular carcinoma， SB is the optimal treatment regimen， followed by CA， AB and DT.

BACKGROUND: B-cell maturation antigen (BCMA)-directed chimeric antigen receptor T (CAR-T) therapy yield remarkable responses in patients with relapsed/refractory multiple myeloma (R/RMM). Circulating tumor DNA (ctDNA) reportedly exhibits distinct advantages in addressing the challenges posed by tumor heterogeneity in the distribution and genetic variations in R/RMM. METHODS: Herein, the ctDNA of 108 peripheral blood plasma samples from patients with R/RMM was thoroughly investigated before administration of anti-BCMA CAR-T therapy to establish its predictive potential. Flow cytometry is used primarily to detect subgroups of T cells or CAR-T cells. RESULTS: In this study, several tumor and T cell effector-mediated factors were considered to be related to treatment failure by an integrat analysis, including higher percentages of multiple myeloma (MM) cells in the bone marrow (P = 0.013), lower percentages of CAR-T cells in the peripheral blood at peak (P = 0.037), and higher percentages of CD8+ T cells (P = 0.034). Furthermore, there is a substantial correlation between high ctDNA level (>143 ng/mL) and shorter progression-free survival (PFS) (P = 0.007). Multivariate Cox regression analysis showed that high levels of ctDNA (>143 ng/mL), MM-driven high-risk mutations (including IGLL5 [P = 0.004], IRF4 [P = 0.024], and CREBBP [P = 0.041]), number of multisite mutations, and resistance-related mutation (ERBB4, P = 0.040) were independent risk factors for PFS. CONCLUSION: Finally, a ctDNA-based risk model was built based on the above independent risk factors, which serves as an adjunct non-invasive measure of substantial tumor burden and a prognostic genetic feature that can assist in predicting the response to anti-BCMA CAR-T therapy. REGISTERATION Chinese Clinical Trial Registry (ChiCTR2100046474) and National Clinical Trial (NCT04670055, NCT05430945).

Objective To explore the classification and characteristics of common syndromes of primary liver cancer and provide reference for clinical treatment.Methods Collect the four diagnostic information of patients with primary liver cancer from two top three TCM hospitals in Henan Province,and build a database.Using Lantern 5.0 software,based on two-step hidden tree analysis,a hidden structure model was constructed,and common syndromes of primary liver cancer were extracted through comprehensive clustering.SPSS 23.0 software was used for factor analysis and systematic cluster analysis to infer the potential syndromes.Combined with the results of different methods and professional knowledge,the syndrome classification of primary liver cancer was determined.Results A total of 1353 patients with 105 symptoms of primary liver cancer were included.59 symptoms with an analysis frequency≥40 were included to construct a hidden structure model,24 hidden variables were obtained,and 5 common syndromes were obtained by comprehensive clustering,namely,qi deficiency syndrome,liver depression and qi stagnation syndrome,blood stasis syndrome,water dampness stagnation syndrome,liver and gallbladder damp heat syndrome.20 common factors were obtained by factor analysis for symptoms with frequency>3%,and 8 common syndromes were inferred by cluster analysis with common factors.7 common syndromes and characteristics were finally determined by combining different methods and expertise.Conclusion The common syndromes of primary liver cancer are qi deficiency syndrome,liver depression and qi stagnation syndrome,blood stasis syndrome,water dampness stagnation syndrome,yin deficiency syndrome,liver and gallbladder damp heat syndrome,spleen deficiency and dampness stagnation syndrome.The results objectively reflect the actual situation of patients with primary liver cancer,and can provide reference for the treatment of primary liver cancer based on syndrome differentiation.

Objective To establish a prediction model for tuberculosis incidence in Nantong area by multivariate regression analysis, and to provide theoretical support for the implementation of combined prevention work in this area. Methods A total of 37 338 registered patients with pulmonary tuberculosis in Nantong City from 2010 to 2021 were enrolled in the observation group. A total of 28,721 healthy people who underwent physical examination during the same period were selected as the control group. Results From 2010 to 2021, there were a total of 37 338 cases of pulmonary tuberculosis in central Nantong. From 2010 to 2015, more than 3,000 cases were reported annually, with the largest number (4 142 cases) in 2011, accounting for 11.09% of the total. The number of cases reported from 2016 to 2021 was all less than 3 000, and the number of cases reported from 2021 was the least , 1 803 cases, accounting for 4.83% of the total. The number of cases decreased each year in the past 12 years. The incidence of pulmonary tuberculosis in males was 70.97% (26 497 cases) and that in females was 29.03% (10 841 cases). In terms of age, the lowest incidence rate was 0.06% (23 cases) in the age group of 0-9 years old, and the highest incidence rate was 19.56% (7 304 cases) in the age group of 60-69 years old. Logistics regression analysis showed that male, age ≥60 years old, occupation as a farmer and smoking history were the risk factors for pulmonary tuberculosis (P < 0.05). ROC curve results showed that the AUC value of the risk prediction model for pulmonary tuberculosis in the Nantong area was 0.872, with a predictive sensitivity of 86.32% and a specificity of 89.21%. Conclusion There are many risk factors for pulmonary tuberculosis in Nantong area, and different factors interact and influence each other. The construction of a risk prediction model for pulmonary tuberculosis can better predict the clinical incidence, which is helpful to guide clinical diagnosis and treatment.

ObjectiveTo evaluate the efficacy and influencing factors of Biejiajianwan in the treatment of primary liver cancer based on real-world data of traditional Chinese medicine （TCM）. MethodClinical diagnosis and treatment data of patients with primary liver cancer admitted to five Grade-A tertiary hospitals in Henan Province from January 2015 to December 2020 were collected from the medical electronic database. The patients treated with Biejiajianwan for ≥30 days were assigned to the exposure group and those without treatment with Biejiajianwan or treated with Biejiajianwan for <30 days to the non-exposure group. The propensity score matching model was used to balance confounding factors between the two groups according to the 1∶1 genetic matching method. Kaplan-Meier method was used for survival analysis and survival curve plotting. Log-rank was used to test the difference in survival rate between the two groups. Univariate analysis of Biejiajianwan in the treatment of primary liver cancer was performed by Log-rank test combined with the Kaplan-Meier method. The factors with statistical significance （P<0.05） were combined with unbalanced factors by the propensity score matching model， and at the same time， clinical common sense and relevant prognostic factors by literature search were considered， which were subjected to multivariate analysis by Cox proportional hazards regression model. ResultA total of 2 207 electronic cases were collected，including 174 cases in the exposure group （Biejiajianwan group） and 2 033 cases in the non-exposure group. After propensity score matching， there were 174 cases in the exposure group and 174 cases in the non-exposure group. The Kaplan-Meier method was used for survival analysis on the matched data， and the Log-rank test results showed that the survival rate of patients with primary liver cancer in the Biejiajianwan group was higher than that in the control group （χ²=12.193， P<0.01）. Cox proportional hazards regression model analysis showed that the regression coefficient of Biejiajianwan was -0.916 4 with the hazard ratio （HR） ［95% confidence interval （CI）］=0.4 （0.239 5-0.668 0）， P<0.01， and the regression coefficient of radiofrequency ablation treatment was -0.976 5 with HR （95% CI）=0.376 6 （0.172 8-0.821 1， P<0.05）. Fibrinogen （FIB） abnormal regression coefficient was 0.481 4 with HR （95% CI）=1.618 4（1.022 0-2.562 9），P<0.05. ConclusionBiejiajianwan can prolong the survival period of patients with primary liver cancer. Radiofrequency ablation is an independent protective factor for Biejiajianwan in the treatment of primary liver cancer，while abnormal FIB are independent risk factors for Biejiajianwan in the treatment of primary liver cancer.

Background: and Purpose Intracranial vertebrobasilar atherosclerotic stenosis (IVBAS) is a major cause of posterior circulation stroke. Some patients suffer from stroke recurrence despite receiving medical treatment. This study aimed to determine the prognostic value of a new score for the posterior communicating artery and the P1 segment of the posterior cerebral artery (PCoA-P1) for predicting stroke recurrence in IVBAS. Methods: We retrospectively enrolled patients with severe IVBAS (70%–99%). According to the number of stroke recurrences, patients were divided into no-recurrence, single-recurrence, and multiple-recurrences groups. We developed a new 5-point grading scale, with the PCoA-P1 score ranging from 0 to 4 based on magnetic resonance angiography, in which primary collaterals were dichotomized into good (2–4 points) and poor (0 or 1 point). Stroke recurrences after the index stroke were recorded. Patients who did not experience stroke recurrence were compared with those who experienced single or multiple stroke recurrences. Results: From January 2012 to December 2019, 176 patients were enrolled, of which 116 (65.9%) had no stroke recurrence, 35 (19.9%) had a single stroke recurrence, and 25 (14.2%) had multiple stroke recurrences. Patients with single stroke recurrence (odds ratio [OR]= 4.134, 95% confidence interval [CI]=1.822–9.380, p=0.001) and multiple stroke recurrences (OR=6.894, 95% CI=2.489–19.092, p<0.001) were more likely to have poor primary collaterals than those with no stroke recurrence. Conclusions The new PCoA-P1 score appears to provide improve predictions of stroke recurrence in patients with IVBAS.

Objective:To correlate acute ischemic stroke with leukoaraiosis with intracranial and extracranial artery stenosis.Methods:A total of 300 patients with acute ischemic stroke admitted to Shaoxing Second Hospital from January to December 2017 were included in this study. All patients underwent magnetic resonance (MRI) examination. According to the examination results, these patients were divided into control (acute ischemic stroke, n = 100) and acute ischemic stroke with leukoaraiosis, n = 200). Carotid artery plaque size and blood sugar level were recorded in each group. Intracranial and extracranial large artery stenosis rates were compared between the two groups. Severity of leukoaraiosis was correlated with intracranial and extracranial artery stenosis. Results:The percentage of patients developing hypertension in the observation group was significantly higher than that in the control group [66.0% (132/200) vs. 44.0% (44/100), χ2 = 13.31, P < 0.01]. The incidence of coronary heart disease in the observation group was significantly higher than that in the control group [49.0% (98/200) vs. 31.0% (31/100), χ2 = 8.81, P < 0.01]. The incidence of carotid artery plaque in the observation group was significantly higher than that in the control group [49.5% (99/200) vs. 34.0% (34/100), χ2 = 6.49, P = 0.01]. The incidence of carotid artery stenosis in the observation group was significantly higher than that in the control group [23.5% (47/200) vs. 12.0% (12/100), χ2 = 5.58, P = 0.01]. There was no significant difference in the incidence of anterior cerebral artery stenosis between observation and control groups [5.5% (11/200) vs. 4.0% (4/100), χ2 = 0.32, P = 0.57]. The size of carotid artery plaque in the observation group was significantly larger than that in the control group [(1.86 ± 0.42) cm vs. (1.39 ± 0.27) cm, t = 10.18, P < 0.01]. The incidence of intracranial and extracranial artery stenosis in the observation group was significantly higher than that in the control group [41.0% (82/200) vs. 24.0% (24/100), χ2 = 8.43, P < 0.01]. The severity of leukoaraiosis was positively correlated with the degree of intracranial and extracranial artery stenosis ( r = 0.79, P < 0.01). Conclusion:Patients with acute ischemic stroke with leukoaraiosis have a high intracranial and extracranial artery stenosis and the severity of leukoaraiosis is positively correlated with intracranial and extracranial artery stenosis.

The Lianhua Qingwen (LHQW) capsule is a popular traditional Chinese medicine for the treatment of viral respiratory diseases.In particular,it has been recently prescribed to treat infections caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).However,due to its complex composi-tion,little attention has been directed toward the analysis of chemical constituents present in the LHQW capsule.This study presents a reliable and comprehensive approach to characterizing the chemical constituents present in LHQW by high-performance liquid chromatography-Q Exactive-Orbitrap mass spectrometry (HPLC-Q Exactive-Orbitrap-MS) coupled with gas chromatography-mass spectrometry(GC-MS).An automated library alignment method with a high mass accuracy (within 5 ppm) was used for the rapid identification of compounds.A total of 104 compounds,consisting of alkaloids,flavonoids,phenols,phenolic acids,phenylpropanoids,quinones,terpenoids,and other phytochemicals,were suc-cessfully characterized.In addition,the fragmentation pathways and characteristic fragments of some representative compounds were elucidated.GC-MS analysis was conducted to characterize the volatile compounds present in LHQW.In total,17 compounds were putatively characterized by comparing the acquired data with that from the NIST library.The major constituent was menthol,and all the other compounds were terpenoids.This is the first comprehensive report on the identification of the major chemical constituents present in the LHQW capsule by HPLC-Q Exactive-Orbitrap-MS,coupled with GC-MS,and the results of this study can be used for the quality control and standardization of LHQW capsules.

This paper aims to realize the decoding of single trial motor imagery electroencephalogram (EEG) signal by extracting and classifying the optimized features of EEG signal. In the classification and recognition of multi-channel EEG signals, there is often a lack of effective feature selection strategies in the selection of the data of each channel and the dimension of spatial filters. In view of this problem, a method combining sparse idea and greedy search (GS) was proposed to improve the feature extraction of common spatial pattern (CSP). The improved common spatial pattern could effectively overcome the problem of repeated selection of feature patterns in the feature vector space extracted by the traditional method, and make the extracted features have more obvious characteristic differences. Then the extracted features were classified by Fisher linear discriminant analysis (FLDA). The experimental results showed that the classification accuracy obtained by proposed method was 19% higher on average than that of traditional common spatial pattern. And high classification accuracy could be obtained by selecting feature set with small size. The research results obtained in the feature extraction of EEG signals lay the foundation for the realization of motor imagery EEG decoding.
Algorithms ; Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Imagination ; Signal Processing, Computer-Assisted