1.Preliminary exploration of multi-omics data fusion methods for high-dimensional small-sample datasets in traditional Chinese medicine.
Nian WANG ; Cheng-Cheng YU ; Hu YANG ; Zhong WANG ; Jun LIU
China Journal of Chinese Materia Medica 2025;50(1):278-284
With the advancement in big data and artificial intelligence technologies, the extensive application of omics technologies in traditional Chinese medicine(TCM) research has generated large experimental datasets, enabling the exploration of cross-scale correlations among massive data and thereby resulting in the shift toward a data-intensive research paradigm. The emerging approach of multi-omics data fusion analysis, emphasizing technical and computational tools, presents a potential breakthrough in this field. The holistic perspective of TCM aligns with the concept of multi-omics data fusion, yet the data types encountered exhibit high dimensionality with small sample sizes, necessitating data processing techniques such as dimensionality reduction. The current challenge lies in selecting suitable analytical methods for these data to enhance the systematic understanding of physiological functions and disease diagnosis/treatment processes. This paper explores the theories and frameworks of multi-omics data fusion, analyzes methods for fusing high-dimensional, small-sample multi-omics data in TCM, and aims to provide insights for advancing TCM research.
Medicine, Chinese Traditional/methods*
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Humans
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Computational Biology/methods*
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Genomics/methods*
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Sample Size
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Artificial Intelligence
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Multiomics
2.Meta-analysis of the efficacy of plate fixation and external fixator fixation in the treatment of AO-C type distal radius fractures.
Guang-Yao LI ; Yong-Zhong CHENG ; Huan LIU ; Jun-Jie JIANG ; Yong-Yao LI ; Yang CHEN ; Yu-Xiang YAO
China Journal of Orthopaedics and Traumatology 2025;38(1):66-80
OBJECTIVE:
Meta-analysis of the clinical efficacy of plate and external fixator fixation in the treatment of AO-C type distal radius fractures.
METHODS:
PubMed, Embase, Cochrane Medical Library, Web of Science, CNKI, Wanfang, VIP and SinoMed databases were searched for all literature on randomized controlled clinical trials of AO-C distal radius fractures. The search time limits were from each database. The database will be established until June 30, 2023. The included studies were extracted according to the Cochrane Handbook (Version 6.3, 2022) for information extraction and literature quality evaluation. RevMan 5.4 was used to evaluate the risk of Publication bias, test heterogeneity and Perform Meta-analysis. The outcome indicators were:imaging anatomy indicators (volar inclination angle, ulnar deviation angle, radial height), wrist joint mobility (flexion, extension, rotation, ulnar deviation), complication rate, and comparison of surgical treatments (operative blood loss, operation time, hospitalization time, fracture healing time) and wrist joint function scores and related scales.
RESULTS:
(1) A total of 28 studies were included, with a total of 2 192 patients, including 1 096 cases in the plate internal fixation group and 1 096 cases in the external fixation group.(2) Meta analysis results showed:the surgical treatment situation of the external fixation group:surgical blood loss MD=-37.93, 95%CI(-48.54, -27.31), P<0.000 01;operation time MD=-31.58, 95%CI(-48.96, -14.20), P<0.000 4;hospitalization time MD=-4.58, 95%CI(-5.44, -3.71), P<0.000 01;the fracture healing time MD=-0.88, 95%CI(-1.35, -0.41), P<0.000 2, which were significantly better than that of the plate internal fixation group(P<0.05).(3) The two groups:palmar inclination angle MD=-0.17, 95%CI(-0.95, 0.61), P=0.68;ulnar declination MD=0.22, 95%CI(-0.73, 1.17), P=0.65, radial height MD=-0.24, 95%CI(-1.15, 0.67), P=0.60;flexion and extension MD=-5.63, 95%CI(-11.85, 0.58), P=0.08;rotation MD=-5.80, 95%CI(-12.77, 1.17), P=0.10, radioulnar deviation MD=-2.86, 95%CI(-10.87, 5.15), P=0.48;complication rate RR=0.96, 95%CI(0.63, 1.46), P=0.83;Gartland-Werley clinical wrist score MD=0.13, 95%CI(-0.80, 1.06), P=0.78;excellent and good rate of Gartland-Werley wrist clinical score RR=0.93, 95%CI(0.87, 1.01), P=0.08;excellent and good rate of Cooney wrist score RR=0.99, 95%CI(0.62, 1.59), P=0.98;wrist DASH score MD=-4.67, 95%CI(-14.96, 5.62), P=0.37;the differences were not significant (P>0.05).
CONCLUSION
Compared with internal fixation with plate, external fixation can significantly reduce the amount of surgical bleeding, shorten the operation time, hospitalization time and fracture healing time, and its imaging anatomical indicators, wrist mobility, and complications can be significantly reduced in treating AO-C distal radius fractures. Rates and wrist function scores were equivalent.
Humans
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External Fixators
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Bone Plates
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Radius Fractures/surgery*
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Fracture Fixation/methods*
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Fracture Fixation, Internal
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Wrist Fractures
3.Novel biallelic MCMDC2 variants were associated with meiotic arrest and nonobstructive azoospermia.
Hao-Wei BAI ; Na LI ; Yu-Xiang ZHANG ; Jia-Qiang LUO ; Ru-Hui TIAN ; Peng LI ; Yu-Hua HUANG ; Fu-Rong BAI ; Cun-Zhong DENG ; Fu-Jun ZHAO ; Ren MO ; Ning CHI ; Yu-Chuan ZHOU ; Zheng LI ; Chen-Cheng YAO ; Er-Lei ZHI
Asian Journal of Andrology 2025;27(2):268-275
Nonobstructive azoospermia (NOA), one of the most severe types of male infertility, etiology often remains unclear in most cases. Therefore, this study aimed to detect four biallelic detrimental variants (0.5%) in the minichromosome maintenance domain containing 2 ( MCMDC2 ) genes in 768 NOA patients by whole-exome sequencing (WES). Hematoxylin and eosin (H&E) demonstrated that MCMDC2 deleterious variants caused meiotic arrest in three patients (c.1360G>T, c.1956G>T, and c.685C>T) and hypospermatogenesis in one patient (c.94G>T), as further confirmed through immunofluorescence (IF) staining. The single-cell RNA sequencing data indicated that MCMDC2 was substantially expressed during spermatogenesis. The variants were confirmed as deleterious and responsible for patient infertility through bioinformatics and in vitro experimental analyses. The results revealed four MCMDC2 variants related to NOA, which contributes to the current perception of the function of MCMDC2 in male fertility and presents new perspectives on the genetic etiology of NOA.
Humans
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Male
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Azoospermia/genetics*
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Meiosis/genetics*
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Spermatogenesis/genetics*
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Adult
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Exome Sequencing
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Microtubule-Associated Proteins/genetics*
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Alleles
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Infertility, Male/genetics*
4.Transzonal Projections and Follicular Development Abnormalities in Polycystic Ovary Syndrome
Di CHENG ; Yu-Hua CHEN ; Xia-Ping JIANG ; Lan-Yu LI ; Yi TAN ; Ming LI ; Zhong-Cheng MO
Progress in Biochemistry and Biophysics 2025;52(10):2499-2511
Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder affecting a substantial proportion of women of reproductive age. It is frequently associated with ovulatory dysfunction, infertility, and an increased risk of chronic metabolic diseases. A hallmark pathological feature of PCOS is the arrest of follicular development, closely linked to impaired intercellular communication between the oocyte and surrounding granulosa cells. Transzonal projections (TZPs) are specialized cytoplasmic extensions derived from granulosa cells that penetrate the zona pellucida to establish direct contact with the oocyte. These structures serve as essential conduits for the transfer of metabolites, signaling molecules (e.g., cAMP, cGMP), and regulatory factors (e.g., microRNAs, growth differentiation factors), thereby maintaining meiotic arrest, facilitating metabolic cooperation, and supporting gene expression regulation in the oocyte. The proper formation and maintenance of TZPs depend on the cytoskeletal integrity of granulosa cells and the regulated expression of key connexins, particularly CX37 and CX43. Recent studies have revealed that in PCOS, TZPs exhibit significant structural and functional abnormalities. Contributing factors—such as hyperandrogenism, insulin resistance, oxidative stress, chronic inflammation, and dysregulation of critical signaling pathways (including PI3K/Akt, Wnt/β‑catenin, and MAPK/ERK)—collectively impair TZP integrity and reduce their formation. This disruption in granulosa-oocyte communication compromises oocyte quality and contributes to follicular arrest and anovulation. This review provides a comprehensive overview of TZP biology, including their formation mechanisms, molecular composition, and stage-specific dynamics during folliculogenesis. We highlight the pathological alterations in TZPs observed in PCOS and elucidate how endocrine and metabolic disturbances—particularly androgen excess and hyperinsulinemia—downregulate CX43 expression and impair gap junction function, thereby exacerbating ovarian microenvironmental dysfunction. Furthermore, we explore emerging therapeutic strategies aimed at preserving or restoring TZP integrity. Anti-androgen therapies (e.g., spironolactone, flutamide), insulin sensitizers (e.g., metformin), and GLP-1 receptor agonists (e.g., liraglutide) have shown potential in modulating connexin expression and enhancing granulosa-oocyte communication. In addition, agents such as melatonin, AMPK activators, and GDF9/BMP15 analogs may promote TZP formation and improve oocyte competence. Advanced technologies, including ovarian organoid models and CRISPR-based gene editing, offer promising platforms for studying TZP regulation and developing targeted interventions. In summary, TZPs are indispensable for maintaining follicular homeostasis, and their disruption plays a pivotal role in the pathogenesis of PCOS-related folliculogenesis failure. Targeting TZP integrity represents a promising therapeutic avenue in PCOS management and warrants further mechanistic and translational investigation.
5.Feasibility study on the construction of predictive models of knee joint cartilage thickness
Zhi-ming CHENG ; Zhong-hua XU ; Xiao-jun MAN ; Yu-heng LI ; Zai-yang LIU ; Yuan ZHANG
Journal of Regional Anatomy and Operative Surgery 2025;34(7):563-569
Objective To determine the knee joint cartilage thickness using different methods and explore the feasibility of mathematical statistical models of dataset for the prediction of cartilage thickness.Methods A total of 304 patients diagnosed as knee osteoarthritis(OA)combined with varus deformity and undergoing unilateral total knee arthroplasty at the Second Affiliated Hospital of Army Medical University from March 2023 to March 2024 were selected for the study.All patients had complete preoperative and postoperative clinical data.The healthy cartilage at four anatomical sites of patients,including the distal femur lateral condyle,lateral tibial plateau,posterior medial femoral condyle,and posterior lateral femoral condyle were selected,and the knee joint cartilage thickness was determined based on preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen and digital vernier caliper.The baseline indicators of demographics,disease and imaging ffor patients were collected to construct a dataset,and four models of linear regression analysis,principal component analysis,Least Absolute Shrinkage and Selection Operator(LASSO)regression analysis,and K-nearest neighbors(KNN)analysis were established for predicting the accuracy,determination coefficient(R2)and root mean square error(RMSE),and the regression equation for predicting cartilage thickness was established.Results The knee joint cartilage thicknesses determined by preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen had no statistically significant difference with that by digital vernier caliper(P>0.05).The predictive efficiencies of models of linear regression analysis,principal component analysis,and LASSO regression analysis for the knee joint cartilage thickness all failed to meet the expectations(R2<0.3,RMSE>0.03).The predictive effect of KNN model on the cartilage thickness of the distal femur lateral condyle and lateral tibial plateau was not ideal(R2=0.23,RMSE=0.29),while it had potential predictive value(accuracy=0.21,accuracy=0.15).Conclusion The prediction model of knee joint cartilage thickness based on individual parameters has certain scientificity,and the feasibility of KNN model is relatively high.However,due to insufficient sample size and unclear individual parameter weight,the efficiencies of the four established prediction models are not ideal,which fails to provide definite prediction equations.Therefore,the construction scheme of the prediction model still needs to be further optimized.
6.Expert Consensus on the Ethical Requirements for Generative AI-Assisted Academic Writing
You-Quan BU ; Yong-Fu CAO ; Zeng-Yi CHANG ; Hong-Yu CHEN ; Xiao-Wei CHEN ; Yuan-Yuan CHEN ; Zhu-Cheng CHEN ; Rui DENG ; Jie DING ; Zhong-Kai FAN ; Guo-Quan GAO ; Xu GAO ; Lan HU ; Xiao-Qing HU ; Hong-Ti JIA ; Ying KONG ; En-Min LI ; Ling LI ; Yu-Hua LI ; Jun-Rong LIU ; Zhi-Qiang LIU ; Ya-Ping LUO ; Xue-Mei LV ; Yan-Xi PEI ; Xiao-Zhong PENG ; Qi-Qun TANG ; You WAN ; Yong WANG ; Ming-Xu WANG ; Xian WANG ; Guang-Kuan XIE ; Jun XIE ; Xiao-Hua YAN ; Mei YIN ; Zhong-Shan YU ; Chun-Yan ZHOU ; Rui-Fang ZHU
Chinese Journal of Biochemistry and Molecular Biology 2025;41(6):826-832
With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.
7.Chemical constituents from the water fraction of rhizoma of Smilax trinervula and their biological activities
Yong-hong LIANG ; Jia-cheng WANG ; Hui-lian HUANG ; Hui-ying YAO ; Yu LU ; Cheng-qi WANG ; Hai-ying ZHONG ; Ying-cai YU ; Hai-yan ZHANG
Chinese Traditional Patent Medicine 2025;47(3):807-812
AIM To study the chemical constituents from the water fraction of rhizoma of Smilax trinervula Miq.and their biological activities.METHODS Polyamide,silica gel,Sephadex LH-20,ODS and semi-preparative HPLC were used for isolation and purification,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The antitumor activities were determined by MTT mothod,and the inhibitory activities on α-glucosidase were determined by PNPG method.RESULTS Eleven compounds were isolated and identified as tyrosine(1),uridine(2),2-(2',3',4'-trihydroxybutyl)-6-(2",3",4"-trihydroxybutyl)-pyrazine(3),2-(1',2',3',4'-tetrahydroxybutyl)-6-(2",3",4"-trihydroxybutyl)-pyrazine(4),2-(1',2',3',4'-tetrahydroxybutyl)-5-(2",3",4"-trihydroxybutyl)-pyrazine(5),uracil(6),2-(1',2',3',4'-tetrahydroxybutyl)-5-(1",2",3",4"-tetrahydroxybutyl)-pyrazine(7),dioscin(8),shikimic acid(9),pyrazine(10),3,4-dihydroxyphenyethyl alcohol 8-O-β-D-glycopyranoside(11).The IC50 values of compounds 8 to human breast cancer cell MCF-7 was(2.36±0.26)μg/mL,and the IC50 values of compounds 3-5 and 7 to α-glucosidase were(1.54±0.15)-(10.53±0.38)μg/mL.CONCLUSION Compounds 1-7,10 are isolated from Smilax genus for the first time,and compound 9,11 are first isolated from this plant.Compound 8 has anti-tumor activity,and compounds 3-5,7 have α-glucosidase inhibitory activities.
8.(Meta)transcriptomic Insights into the Role of Ticks in Poxvirus Evolution and Transmission: A Multicontinental Analysis.
Yu Xi WANG ; Jing Jing HU ; Jing Jing HOU ; Xiao Jie YUAN ; Wei Jie CHEN ; Yan Jiao LI ; Qi le GAO ; Yue PAN ; Shui Ping LU ; Qi CHEN ; Si Ru HU ; Zhong Jun SHAO ; Cheng Long XIONG
Biomedical and Environmental Sciences 2025;38(9):1058-1070
OBJECTIVE:
Poxviruses are zoonotic pathogens that infect humans, mammals, vertebrates, and arthropods. However, the specific role of ticks in transmission and evolution of these viruses remains unclear.
METHODS:
Transcriptomic and metatranscriptomic raw data from 329 sampling pools of seven tick species across five continents were mined to assess the diversity and abundance of poxviruses. Chordopoxviral sequences were assembled and subjected to phylogenetic analysis to trace the origins of the unblasted fragments within these sequences.
RESULTS:
Fifty-eight poxvirus species, representing two subfamilies and 20 genera, were identified, with 212 poxviral sequences assembled. A substantial proportion of AT-rich fragments were detected in the assembled poxviral genomes. These genomic sequences contained fragments originating from rodents, archaea, and arthropods.
CONCLUSION
Our findings indicate that ticks play a significant role in the transmission and evolution of poxviruses. These viruses demonstrate the capacity to modulate virulence and adaptability through horizontal gene transfer, gene recombination, and gene mutations, thereby promoting co-existence and co-evolution with their hosts. This study advances understanding of the ecological dynamics of poxvirus transmission and evolution and highlights the potential role of ticks as vectors and vessels in these processes.
Animals
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Poxviridae/physiology*
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Ticks/virology*
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Phylogeny
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Transcriptome
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Evolution, Molecular
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Poxviridae Infections/virology*
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Genome, Viral
9.Chemical constituents from the water fraction of rhizoma of Smilax trinervula and their biological activities
Yong-hong LIANG ; Jia-cheng WANG ; Hui-lian HUANG ; Hui-ying YAO ; Yu LU ; Cheng-qi WANG ; Hai-ying ZHONG ; Ying-cai YU ; Hai-yan ZHANG
Chinese Traditional Patent Medicine 2025;47(3):807-812
AIM To study the chemical constituents from the water fraction of rhizoma of Smilax trinervula Miq.and their biological activities.METHODS Polyamide,silica gel,Sephadex LH-20,ODS and semi-preparative HPLC were used for isolation and purification,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The antitumor activities were determined by MTT mothod,and the inhibitory activities on α-glucosidase were determined by PNPG method.RESULTS Eleven compounds were isolated and identified as tyrosine(1),uridine(2),2-(2',3',4'-trihydroxybutyl)-6-(2",3",4"-trihydroxybutyl)-pyrazine(3),2-(1',2',3',4'-tetrahydroxybutyl)-6-(2",3",4"-trihydroxybutyl)-pyrazine(4),2-(1',2',3',4'-tetrahydroxybutyl)-5-(2",3",4"-trihydroxybutyl)-pyrazine(5),uracil(6),2-(1',2',3',4'-tetrahydroxybutyl)-5-(1",2",3",4"-tetrahydroxybutyl)-pyrazine(7),dioscin(8),shikimic acid(9),pyrazine(10),3,4-dihydroxyphenyethyl alcohol 8-O-β-D-glycopyranoside(11).The IC50 values of compounds 8 to human breast cancer cell MCF-7 was(2.36±0.26)μg/mL,and the IC50 values of compounds 3-5 and 7 to α-glucosidase were(1.54±0.15)-(10.53±0.38)μg/mL.CONCLUSION Compounds 1-7,10 are isolated from Smilax genus for the first time,and compound 9,11 are first isolated from this plant.Compound 8 has anti-tumor activity,and compounds 3-5,7 have α-glucosidase inhibitory activities.
10.Feasibility study on the construction of predictive models of knee joint cartilage thickness
Zhi-ming CHENG ; Zhong-hua XU ; Xiao-jun MAN ; Yu-heng LI ; Zai-yang LIU ; Yuan ZHANG
Journal of Regional Anatomy and Operative Surgery 2025;34(7):563-569
Objective To determine the knee joint cartilage thickness using different methods and explore the feasibility of mathematical statistical models of dataset for the prediction of cartilage thickness.Methods A total of 304 patients diagnosed as knee osteoarthritis(OA)combined with varus deformity and undergoing unilateral total knee arthroplasty at the Second Affiliated Hospital of Army Medical University from March 2023 to March 2024 were selected for the study.All patients had complete preoperative and postoperative clinical data.The healthy cartilage at four anatomical sites of patients,including the distal femur lateral condyle,lateral tibial plateau,posterior medial femoral condyle,and posterior lateral femoral condyle were selected,and the knee joint cartilage thickness was determined based on preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen and digital vernier caliper.The baseline indicators of demographics,disease and imaging ffor patients were collected to construct a dataset,and four models of linear regression analysis,principal component analysis,Least Absolute Shrinkage and Selection Operator(LASSO)regression analysis,and K-nearest neighbors(KNN)analysis were established for predicting the accuracy,determination coefficient(R2)and root mean square error(RMSE),and the regression equation for predicting cartilage thickness was established.Results The knee joint cartilage thicknesses determined by preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen had no statistically significant difference with that by digital vernier caliper(P>0.05).The predictive efficiencies of models of linear regression analysis,principal component analysis,and LASSO regression analysis for the knee joint cartilage thickness all failed to meet the expectations(R2<0.3,RMSE>0.03).The predictive effect of KNN model on the cartilage thickness of the distal femur lateral condyle and lateral tibial plateau was not ideal(R2=0.23,RMSE=0.29),while it had potential predictive value(accuracy=0.21,accuracy=0.15).Conclusion The prediction model of knee joint cartilage thickness based on individual parameters has certain scientificity,and the feasibility of KNN model is relatively high.However,due to insufficient sample size and unclear individual parameter weight,the efficiencies of the four established prediction models are not ideal,which fails to provide definite prediction equations.Therefore,the construction scheme of the prediction model still needs to be further optimized.

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