Network pharmacology has gained widespread application in drug discovery, particularly in traditional Chinese medicine (TCM) research, which is characterized by its "multi-component, multi-target, and multi-pathway" nature. Through the integration of network biology, TCM network pharmacology enables systematic evaluation of therapeutic efficacy and detailed elucidation of action mechanisms, establishing a novel research paradigm for TCM modernization. The rapid advancement of machine learning, particularly revolutionary deep learning methods, has substantially enhanced artificial intelligence (AI) technology, offering significant potential to advance TCM network pharmacology research. This paper describes the methodology of TCM network pharmacology, encompassing ingredient identification, network construction, network analysis, and experimental validation. Furthermore, it summarizes key strategies for constructing various networks and analyzing constructed networks using AI methods. Finally, it addresses challenges and future directions regarding cell-cell communication (CCC)-based network construction, analysis, and validation, providing valuable insights for TCM network pharmacology.
Medicine, Chinese Traditional/methods* ; Artificial Intelligence ; Network Pharmacology/methods* ; Humans ; Drugs, Chinese Herbal/chemistry* ; Drug Discovery

Objective:To investigate the application value of intraoperative electrocochleography（ECochG） monitoring technique and insertion techniques in cochlear implant（CI） and analyze its relationship with postoperative residual hearing（RH） preservation. Methods:Thirty-one patients（35 ears） who received CI in our hospital from June 2022 to July 2024 were enrolled. The Advanced Bionics Active Insertion Monitoring（AIM） system was used for real-time ECochG monitoring during surgery. Intraoperative cochlear microphonics （CM） waveform changes were recorded and analyzed in relation to postoperative RH preservation. Results:①ECochG recordings were successfully obtained in 34 of 35 ears （97.1%）. ②According to Harris classification, there were 7 ears（20.6%） of Type A（rising）, 7 ears（20.6%） of Type C（declining）, 8 ears（23.5%） of Type CC（fluctuating）, and 12 ears（35.3%） of Type D（no response）. ③The total CM amplitude decrease was significantly moderately correlated with postoperative low-mid frequency hearing loss（r=0.67, P=0.017）. The total CM amplitude decrease was significantly moderately correlated with postoperative low frequency hearing loss（r=0.65, P=0.023）. ④For the mean amplitude variation, the Amax was 30.70 μV, the Amin was 8.64 μV, and the Aend was 18.27 μV. ⑤Sixteen cases completed postoperative follow-up, with an average low-mid frequency（125-1 000 Hz） residual hearing loss of 15.25 dB HL and a RH preservation rate of 87.5%. Conclusion:Intraoperative ECochG monitoring can effectively predict postoperative residual hearing changes, effectively guide surgical manipulation, and improve residual hearing preservation rate.
Humans ; Cochlear Implantation/methods* ; Audiometry, Evoked Response ; Cochlear Implants ; Male ; Female ; Adult ; Middle Aged ; Monitoring, Intraoperative ; Adolescent ; Young Adult ; Minimally Invasive Surgical Procedures ; Child ; Aged ; Postoperative Period

Objective To investigate the effect of β-arrestin1(ARRB1)on cell proliferation,migration and invasion in oral squamous cell carcinoma(OSCC).Methods Based on The Cancer Genome Atlas(TCGA)database,the expression profiles of ARRB1 in OSCC were analyzed,and then Gene Set Enrichment Analysis(GSEA)was used to suggest the possible signaling pathways involved,and to explore its potential impact on the prognosis of OSCC patients.Immuinohistochemistry(IHC)was performed to detect the expression of ARRB1 in OSCC tumor tissues and adjacent tissues,and the correlation between ARRB1 expression and clinicopathological features was statistically analyzed.The expression profiles of ARRB1 in SCC-15,CAL-27 and HOK cell lines were verified by qPCR and Western blotting.The ARRB1 overexpression plasmid model was constructed,and its effects on the proliferation,migration and invasion of OSCC cells were analyzed by clone formation,EdU,scratch and Transwell assays.Results TCGA showed that the expression level of ARRB1 was significantly lower in head and neck squamous cell carcinoma(HNSC)and OSCC tissues than the corresponding normal tissues(P<0.01).The expression of ARRB1 in OSCC tissues was correlated with tumor differentiation,lymph node metastasis and TNM stage(P<0.05).The OSCC patients with high expression of ARRB1 had a lower survival rate than those with low expression(P<0.01),which was consistent with the results of bioinformatics analysis.The expression level of ARRB1 in SCC-15 and CAL-27 cells was lower than that of HOK cells(P<0.01),and its overexpression significantly inhibited cell proliferation(P<0.05),migration(P<0.01)and invasion(P<0.01).Conclusion ARRB1 is lowly expressed in OSCC,its overexpression inhibits the proliferation,migration and invasion of OSCC cells,and it is related to prognosis improvement.

OBJECTIVE To investigate the relationship between azimuth direction,angular intervals,and the accuracy of azimuthal sound source localization.METHODS Fifteen young subjects with normal hearing were tested using nine azimuth settings.The test results were presented as root mean square error and percentage confusion.RESULTS The confusion rate under high-frequency narrowband noise was significantly higher than that under broadband noise and three-syllable words.In the frontal direction,statistically significant differences were observed between the 20° and 10° intervals,as well as between the 20° and 15° intervals(P<0.05),but no significant difference was found between the 10° and 15° intervals(P>0.05).In the lateral and rear directions,statistically significant differences were found between the 30° and 15° intervals,as well as between the 30° and 20° intervals(P<0.05),but no significant difference was found between the 15° and 20° intervals(P>0.05).Statistically significant differences were observed between the frontal direction and both the lateral and rear directions(P<0.05),but no significant difference was found between the lateral and rear directions(P>0.05).CONCLUSION Using stimuli that contain broader bandwidth cues can more accurately reflect the subject's horizontal localization ability.For source azimuth identification tests using broadband noise and three-syllable words,it is recommended to use a 15° interval in the frontal direction,and a 20° interval in the lateral and rear directions.The frontal and lateral directions can be preferred for testing.

Objective:To systematically evaluate the global disease burden of neonatal sepsis and other neonatal infections（NSNIs），providing scientific basis for their prevention and control.Methods:Using the Global Burden of Disease（GBD）2021 database，this article calculated the incidence，mortality，and age-standardized rates for NSNIs. Trends were evaluated with Joinpoint regression model，and compared at different socio-demographic index（SDI）levels.Results:From 1990 to 2021，the global age-standardized incidence rate（ASIR）of NSNIs decreased from 78.98 to 62.70 per 100 000 with an with average annual percentage change（AAPC）of -0.73%（ P<0.01）. The age-standardized mortality rate（ASMR）declined from 4.77 to 3.76 per 100 000 with an AAPC of -0.76%（ P<0.01）. In particular，the disease burden was consistently higher among male neonates. Low birth weight was the primary risk factor globally，followed by preterm birth. Regions with lower SDI levels exhibited higher ASIR and ASMR，and household solid fuel air pollution contributed more to NSNIs-related mortality. Conclusion:Although the overall disease burden of NSNIs has declined，male neonates and low-SDI regions still face substantial challenges. Continuous efforts to improve air quality are warranted，and low-SDI regions should further strengthen healthcare infrastructure to enhance diagnostic and treatment quality.

Objective:To analyze the correlation and consistency of urine protein creatinine ratio (UPCR) with 24-hour urine protein quantification (24hUTP) in patients with lupus nephritis (LN).Methods:LN patients were enrolled from the STAR cohort of Peking University First Hospital, The time period is January 1, 2007 to July 31 2024. The correlation and consistency of UPCR with 24hUTP were analyzed in overall patients and various subgroups, such as,the levels of proteinuria and multiple parameters. The correlation was analyzed using Spearman′s method. The consistency was evaluated by using Kappa test, cross tabulation consistency probability, and UPCR/24hUTP ratio.Results:A total of 261 LN patients were included in this study. Overall, UPCR was positively correlated with 24hUTP ( r=0.892, P<0.001), and the consistency between then was good ( Kappa=0.603, P<0.001). Subgroup analysis based on multiple levels including different urine volume, eGFR grading, albumin, urea nitrogen, total cholesterol, low-density lipoprotein cholesterol, SLEDAI-2K, also revealed good correlation ( r=0.747~0.951; P<0.001) and consistency ( Kappa=0.393~0.771; P<0.001). Patients were divided into subgroups according to the 24hUTP (≤ 0.5 g, >0.5 g to ≤1.0 g, >1.0 g to ≤3.5 g, and>3.5 g). The best correlation and consistency were observed in ≤ 0.5g group ( r=0.728, Kappa=0.395, P<0.001), while the correlation was average or poor in other subgroups ( r=0.16~0.46). The consistency evaluated by UPCR/24hUTP ratio showed good consistency (ratios between 0.8~1.2) in 74 patients (28.4%), however, the unsatisfactory consistency in most patients, with 128 patients (68.4%) having ratios < 0.8, indicating a significantly lower UPCR than 24hUTP, and 59 patients (31.6%) having ratios > 1.2, indicating significantly higher UPCR than 24hUTP.The cross tabulation evaluation showed that the probability of consistency was distributed in a gradient, we found the worst consistency (48.9%) in >0.5 g to ≤1.0 g group, moderate consistency (64.0%) in >1.0 g to ≤3.5 g group, and the best consistency (73.3%) in >3.5 g group. Conclusion:In LN patients, the overall correlation and consistency between UPCR and 24hUTP are good, but the degree of correlation and consistency is closely related to the total amount of proteinuria. When 24hUTP ≤ 0.5 g, UPCR can be used to replace 24hUTP, but UPCR cannot be simply equated with 24hUTP at other urinary protein levels. Detecting 24hUTP to accurately reflect the total amount of proteinuria remain necessary.

Objective To explore the value of CT-based radiomics and machine learning in predicting the severity of community acquired pneumonia(CAP)in children.Methods The clinical and imaging data of 158 patients diagnosed with CAP in children were analyzed retrospectively.All patients were randomly divided into training set(n=110)and validation set(n=48)in a ratio of 7︰3.Radiomics features were outlined and extracted using 3D Slicer software,and feature selection was achieved using maximum relevance and minimum redundancy(MRMR)and least absolute shrinkage and selection operator(LASSO)algorithms.The construction of the nomogram model and the machine learning combined model was performed by combining clinical features and Radiomics score(Radscore),and its performance was evaluated and validated.Results The area under the curve(AUC)of the clinical model,the radiomics model and the nomogram model in the validation set were classified as 0.82,0.86 and 0.91,respectively.The AUC of the combined multi-layer perceptron(MLP),random forest(RF),and adaptive boosting(ADB)models were 0.926,0.934 and 0.917,respectively in the validation set.Conclusion Radiomics combined with clinical data is expected to be a novel predictor of the severity of CAP in children.MLP,RF and ABD machine learning algorithms can further enable model performance.

BACKGROUND:The extracellular matrix is a complex network structure,which not only builds physical support for tissue cells,but also plays an important regulatory role in cell survival,proliferation,and death.Abnormal changes in the biochemical and biomechanical properties of the extracellular matrix can significantly affect the proliferation,migration,immune evasion,and treatment resistance of tumor cells.Stiffness is an important mechanical property of the extracellular matrix,and abnormalities in matrix stiffness are closely related to tumor progression.OBJECTIVE:By reviewing the mechanism of extracellular matrix sclerosis,the impact of high stiffness matrix on tumor progression,and the latest research progress in the treatment of cancer based on reducing matrix stiffness,to deeply understand the mechanical properties of the extracellular matrix,improve the understanding of the complex mechanism of tumor progression,and provide new ideas and directions for tumor treatment.METHODS:"Extracellular matrix function,extracellular matrix stiffness,collagen deposition cross-linking,extracellular matrix stiffness therapy,immunotherapy"were used as the search terms in Chinese and English.Relevant literature published from January 2016 to June 2024 was searched in CNKI,PubMed,and WanFang databases,and 80 articles were finally included for review.RESULTS AND CONCLUSION:(1)Deposition and excessive cross-linking of collagen and elastin in the extracellular matrix leads to matrix remodeling,which in turn increases matrix stiffness.This sclerosis activates pro-cancer signaling pathways such as cyclin-D1,Rho/ROCK,p-PXN-Rac1-YAP,and STAT3/p-STAT3,promotes malignant behaviors such as cancer cell proliferation,metastasis,tumor microangiogenesis and immune escape,and accelerates tumor progression.(2)Reducing the deposition and cross-linking of matrix proteins can reduce matrix stiffness,which cannot only inhibit the activation of multiple cancer-promoting signaling pathways,but also enhance the penetration and delivery of drugs at tumor sites,which is a new strategy for cancer treatment.(3)At present,drugs based on matrix degradation to reduce tumor stiffness are under development,and a few drugs have entered the clinical trial stage,which are expected to provide a new powerful weapon for tumor treatment.

Objective:To understand the current status and influencing factors for science and technology ethics awareness among medical postgraduates, explore scientific cultivation path, and provide empirical evidence for higher medical colleges to carry out science and technology ethics education.Methods:The study adopted a cluster sampling method and designed a questionnaire. A total of 2 095 medical postgraduates from Hebei Medical University participated in the survey from June to December 2023. SPSS 24.0 was used for non-parametric tests, and the ordered logistic regression analysis was used to explore the influencing factors.Results:A total of 1 992 valid questionnaires were collected, with a valid response rate of 95.08% (1 922/2 095). The qualified rate of science and technology ethics awareness among medical postgraduates was 90.76% (1 808/1 992), with 48.24% (961/1 992) of students falling into the good interval and 16.21% (323/1 992) falling into the excellent interval. The regression analysis showed that sex, academic category, students' attention to science and technology ethical issues, and understanding of ethical principles were significant influencing factors for medical postgraduates' science and technology ethics awareness ( P<0.05). Conclusions:The science and technology ethics awareness of medical postgraduates is at a medium level. Higher medical colleges should start from the needs and adhere to the direction of lifelong education to build a multi-subject, all-round, and all-process cultivation path. They can help students cultivate ethical awareness by improving the educational system, building a scientific teaching system, and creating a social education atmosphere. They can also help students internalize ethical norms and externalize them in their behaviors, and ensure that they can maintain ethical sensitivity in scientific and technological activities, thereby cultivating high-quality new medical and health professionals who can anticipate scientific and technological risks and assume ethical responsibilities.

Objective To explore the value of CT-based radiomics and machine learning in predicting the severity of community acquired pneumonia(CAP)in children.Methods The clinical and imaging data of 158 patients diagnosed with CAP in children were analyzed retrospectively.All patients were randomly divided into training set(n=110)and validation set(n=48)in a ratio of 7︰3.Radiomics features were outlined and extracted using 3D Slicer software,and feature selection was achieved using maximum relevance and minimum redundancy(MRMR)and least absolute shrinkage and selection operator(LASSO)algorithms.The construction of the nomogram model and the machine learning combined model was performed by combining clinical features and Radiomics score(Radscore),and its performance was evaluated and validated.Results The area under the curve(AUC)of the clinical model,the radiomics model and the nomogram model in the validation set were classified as 0.82,0.86 and 0.91,respectively.The AUC of the combined multi-layer perceptron(MLP),random forest(RF),and adaptive boosting(ADB)models were 0.926,0.934 and 0.917,respectively in the validation set.Conclusion Radiomics combined with clinical data is expected to be a novel predictor of the severity of CAP in children.MLP,RF and ABD machine learning algorithms can further enable model performance.