This study investigated the association between a proximal promoter polymorphism of IFNGR1 (interferon-γ receptor α chain, IFNGR-α) and breast cancer susceptibility, as well as the prognostic value of its expression variation in breast cancer patients. A case-control study was conducted at the Sixth Medical Center of PLA General Hospital from June 2020 to June 2022. The study included 182 pathologically confirmed breast cancer patients as the breast cancer group, 177 non-tumor patients with benign breast lesions as the benign breast lesions group, and 229 healthy individuals as the normal control group. 2-3 ml EDTA anticoagulant whole blood samples were collected from all participants, and genomic DNA was extracted and stored for further analysis. Basic patient information was retrieved from the hospital′s electronic medical records by patients′ ID number. The proximal promoter sequence of IFNGR1 was obtained from NCBI, and sequencing primers were designed using Primer Premier 6.0. Sanger sequencing was employed to analyze the IFNGR1 promoter sequence in the three groups, and the results were compared with the Eukaryotic Promoter Database (EPD) sequence using Bioedit software. Statistical analysis was performed on single nucleotide polymorphisms (SNPs) in the IFNGR1 promoter. The TCGA database was utilized to assess the relationship between IFNGR1 expression levels and breast cancer patient survival. The findings revealed that the -56 TG genotype of the IFNGR1 promoter was significantly associated with increased breast cancer risk ( Z=2.73, P<0.05). Notably, IFNGR1 expression was lower in breast cancer group compared to normal control group ( P<0.05). Analysis of the TCGA database indicated that patients with high IFNGR1 expression had longer survival times than those with low expression ( HR=0.87, 95% CI:0.77-0.98, P<0.05). In summary, the IFNGR1 -56 TG genotype is associated with an increased risk of breast cancer, and there is a positive correlation between IFNGR1 expression levels and the survival of breast cancer patients.

OBJECTIVE To explore the expression level of miR-196a in cervical cells infected with high-risk human papillomavirus(HPV)16 and 18.METHODS The Gene Expression Omnibus(GEO)was used to screen for dif-ferentially expressed miRNAs between HPV 16 or 18-positive cervical cancer cells and normal cervical cells.On-line biological software https://kmplot.com/analysis/was utilized to analyze the relationship between the most differentially expressed miRNA and the overall survival of cervical cancer patients.Cervical swab samples positive for HPV 16 or HPV 18,detected by real-time fluorescent quantitative polymerase chain reaction(qPCR)genoty-ping,were collected as the study subjects.Cervical swab samples from the same period of physical examination population that were negative for HPV 16 or HPV 18 by qPCR genotyping served as negative controls.The qRT-PCR method was employed to detect the level of miR-196a in cervical cells,with data processed via the 2-△△Ctmethod.RESULTS Differential analysis of the GSE86100 data revealed that miR-196a expression de-creased in HPV 16 or HPV 18-positive cervical cells(log2FC=-6.60,P＜0.001),while miR-3188 expression significantly increased(log2FC=6.22,P＜0.001).Using online analysis tools https://kmplot.com/analysis,it was found that cervical cancer patients with high miR-196a expression had a shorter overall survival compared to those with low m iR-196a expression(HR=1.87,95％CI:1.17-3.00,P=0.008).H owever,there was no cor-relation between miR-3188 and the overall survival of cervical cancer patients(HR=1.47,95％CI:0.92-2.37,P=0.110).The results of specific qRT-PCR testing showed that the expression levels of miR-196a in cervical cells positive for HPV 16 and HPV 18 were 0.93±0.09 and 0.51±0.07,respectively,which were lower than those in the normal control group(1.89±0.13)(P＜0.05),consistent with the sequencing analysis results CONCLUSIONS Infection of cervical cells with HPV 16 or HPV 18 can lead to decreased expression of miR-196a,and the expres-sion level of miR-196a is negatively correlated with the overall survival of cervical cancer patients.

Combined with elastic network model (ENM), the perturbation response scanning (PRS) has emerged as a robust technique for pinpointing allosteric interactions within proteins. Here, we proposed the PRS analysis of drug-target networks (DTNs), which could provide a promising avenue in network medicine. We demonstrated the utility of the method by introducing a deep learning and network perturbation-based framework, for drug repurposing of multiple sclerosis (MS). First, the MS comorbidity network was constructed by performing a random walk with restart algorithm based on shared genes between MS and other diseases as seed nodes. Then, based on topological analysis and functional annotation, the neurotransmission module was identified as the "therapeutic module" of MS. Further, perturbation scores of drugs on the module were calculated by constructing the DTN and introducing the PRS analysis, giving a list of repurposable drugs for MS. Mechanism of action analysis both at pathway and structural levels screened dihydroergocristine as a candidate drug of MS by targeting a serotonin receptor of serotonin 2B receptor (HTR2B). Finally, we established a cuprizone-induced chronic mouse model to evaluate the alteration of HTR2B in mouse brain regions and observed that HTR2B was significantly reduced in the cuprizone-induced mouse cortex. These findings proved that the network perturbation modeling is a promising avenue for drug repurposing of MS. As a useful systematic method, our approach can also be used to discover the new molecular mechanism and provide effective candidate drugs for other complex diseases.

OBJECTIVE: Gastric cancer (GC) is one of the most common malignancies seen in clinic and requires novel treatment options. Morin is a natural flavonoid extracted from the flower stalk of a highly valuable medicinal plant Prunella vulgaris L., which exhibits an anti-cancer effect in multiple types of tumors. However, the therapeutic effect and underlying mechanism of morin in treating GC remains elusive. The study aims to explore the therapeutic effect and underlying molecular mechanisms of morin in GC. METHODS: For in vitro experiments, the proliferation inhibition of morin was measured by cell counting kit-8 assay and colony formation assay in human GC cell line MKN45, human gastric adenocarcinoma cell line AGS, and human gastric epithelial cell line GES-1; for apoptosis analysis, microscopic photography, Western blotting, ubiquitination analysis, quantitative polymerase chain reaction analysis, flow cytometry, and RNA interference technology were employed. For in vivo studies, immunohistochemistry, biomedical analysis, and Western blotting were used to assess the efficacy and safety of morin in a xenograft mouse model of GC. RESULTS: Morin significantly inhibited the proliferation of GC cells MKN45 and AGS in a dose- and time-dependent manner, but did not inhibit human gastric epithelial cells GES-1. Only the caspase inhibitor Z-VAD-FMK was able to significantly reverse the inhibition of proliferation by morin in both GC cells, suggesting that apoptosis was the main type of cell death during the treatment. Morin induced intrinsic apoptosis in a dose-dependent manner in GC cells, which mainly relied on B cell leukemia/lymphoma 2 (BCL-2) associated agonist of cell death (BAD) but not phorbol-12-myristate-13-acetate-induced protein 1. The upregulation of BAD by morin was due to blocking the ubiquitination degradation of BAD, rather than the transcription regulation and the phosphorylation of BAD. Furthermore, the combination of morin and BCL-2 inhibitor navitoclax (also known as ABT-737) produced a synergistic inhibitory effect in GC cells through amplifying apoptotic signals. In addition, morin treatment significantly suppressed the growth of GC in vivo by upregulating BAD and the subsequent activation of its downstream apoptosis pathway. CONCLUSION Morin suppressed GC by inducing apoptosis, which was mainly due to blocking the ubiquitination-based degradation of the pro-apoptotic protein BAD. The combination of morin and the BCL-2 inhibitor ABT-737 synergistically amplified apoptotic signals in GC cells, which may overcome the drug resistance of the BCL-2 inhibitor. These findings indicated that morin was a potent and promising agent for GC treatment. Please cite this article as: Wang Y, Sun XY, Ma FQ, Ren MM, Zhao RH, Qin MM, Zhu XH, Xu Y, Cao ND, Chen YY, Dong TG, Pan YF, Zhao AG. Morin inhibits ubiquitination degradation of BCL-2 associated agonist of cell death and synergizes with BCL-2 inhibitor in gastric cancer cells. J Integr Med. 2025; 23(3): 320-332.
Humans ; Flavonoids/therapeutic use* ; Stomach Neoplasms/pathology* ; Animals ; Proto-Oncogene Proteins c-bcl-2/metabolism* ; Cell Line, Tumor ; Apoptosis/drug effects* ; Cell Proliferation/drug effects* ; Ubiquitination/drug effects* ; Mice ; Drug Synergism ; Mice, Inbred BALB C ; Mice, Nude ; Xenograft Model Antitumor Assays ; Flavones

Cervical spine health issues not only seriously affect patients' quality of life but also impose a heavy burden on the social healthcare system. Existing guidelines lack sufficient clinical guidance on lifestyle and work habits, such as exercise, posture, daily routine, and diet, making it difficult to meet practical needs. To address this, relying on the China Association of Chinese Medicine, Wangjing Hospital of China Academy of Chinese Medical Sciences took the lead and joined hands with more than ten institutions to form a multidisciplinary guideline development group. For the first time, the group developed the Guidelines for Cervical Spine Health Intervention based on evidence-based medicine methods, strictly following the standardized procedures outlined in the World Health Organization Handbook for Guideline Development and the Guiding Principles for the Formulation/Revision of Clinical Practice Guidelines in China (2022 Edition). This proposal systematically explains the methods and steps for developing the guideline, aiming to make the guideline development process scientific, standardized, and transparent.
Humans ; Practice Guidelines as Topic/standards* ; Cervical Vertebrae ; China

OBJECTIVES: To investigate the clinical characteristics and prognosis of acute erythroleukemia (AEL) in children. METHODS: A retrospective analysis was conducted on the clinical data, treatment, and prognosis of 8 children with AEL treated at the First Affiliated Hospital of Zhengzhou University from January 2013 to December 2023. RESULTS: Among the 7 patients with complete bone marrow morphological analysis, 4 exhibited trilineage dysplasia, with a 100% incidence of erythroid dysplasia (7/7), a 71% incidence of myeloid dysplasia (5/7), and a 57% incidence of megakaryocytic dysplasia (4/7). Immunophenotyping revealed that myeloid antigens were primarily expressed as CD13, CD33, CD117, CD38, and CD123, with 4 cases expressing erythroid antigens CD71 and 2 cases expressing CD235a. Chromosomal analysis indicated that 2 cases presented with abnormal karyotypes, including +8 in one case and +4 accompanied by +6 in another; no complex karyotypes were observed. Genetic abnormalities were detected in 4 cases, with fusion genes including one case each of dup MLL positive and EVI1 positive, as well as mutations involving KRAS, NRAS, WT1, and UBTF. Seven patients received chemotherapy, with 6 achieving remission after one course of treatment; 2 underwent hematopoietic stem cell transplantation, and all had disease-free survival. Follow-up (median follow-up time of 6 months) showed that only 3 patients survived (2 cases after hematopoietic stem cell transplantation and 1 case during treatment). CONCLUSIONS Children with AEL have unique clinical and biological characteristics, exhibit poor treatment response, and have a poor prognosis; however, hematopoietic stem cell transplantation may improve overall survival rates.
Humans ; Male ; Female ; Prognosis ; Child, Preschool ; Retrospective Studies ; Child ; Leukemia, Erythroblastic, Acute/diagnosis* ; Infant ; Adolescent

Combined with elastic network model(ENM),the perturbation response scanning(PRS)has emerged as a robust technique for pinpointing allosteric interactions within proteins.Here,we proposed the PRS analysis of drug-target networks(DTNs),which could provide a promising avenue in network medicine.We demonstrated the utility of the method by introducing a deep learning and network perturbation-based framework,for drug repurposing of multiple sclerosis(MS).First,the MS comorbidity network was constructed by performing a random walk with restart algorithm based on shared genes between MS and other diseases as seed nodes.Then,based on topological analysis and functional annotation,the neurotransmission module was identified as the"therapeutic module"of MS.Further,perturbation scores of drugs on the module were calculated by constructing the DTN and introducing the PRS analysis,giving a list of repurposable drugs for MS.Mechanism of action analysis both at pathway and structural levels screened dihydroergocristine as a candidate drug of MS by targeting a serotonin receptor of se-rotonin 2B receptor(HTR2B).Finally,we established a cuprizone-induced chronic mouse model to evaluate the alteration of HTR2B in mouse brain regions and observed that HTR2B was significantly reduced in the cuprizone-induced mouse cortex.These findings proved that the network perturbation modeling is a promising avenue for drug repurposing of MS.As a useful systematic method,our approach can also be used to discover the new molecular mechanism and provide effective candidate drugs for other complex diseases.

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.

Objective To develop a predictive model integrating clinical features,deep learning(DL),and radiomics based on T2-weighted imaging for prenatal assessment of postpartum hemorrhage(PPH)risk in high-risk pregnant women.Methods A total of 538 pregnant women with ultrasound-reported high-risk placenta accrete were retrospectively enrolled and divided into training,internal test,and external test cohorts.A nnUNet model was trained for automatic placental segmentation.Univariate and multivariate analyses were conducted on clinical features to identify those associated with PPH.Quantitative radiomic features were extracted from the placental region,and a random forest model was developed to predict estimated blood loss(EBL)and PPH risk.A DenseNet-based multi-task DL model was trained to predict PPH risk,EBL,and placenta previa status.Finally,a DL-radiomics ensemble(DRE)model was constructed by integrating clinical features,DL outputs,and radiomics scores.Diagnostic performance was evaluated using the area under the receiver operating characteristic curve(AUC)and DeLong test.Results The DRE model achieved AUC values of 0.874(95%CI:0.792-0.951)and 0.836(95%CI:0.648-0.974)in the internal and external test cohorts,respectively,significantly outperforming the standalone clinical,DL,and radiomics models.Incorporation of EBL regression improved the performance of the PPH classification model,with the external test AUC increasing from 0.261-0.788 to 0.836.Conclusion The DRE model integrating DL and radiomics can efficiently predict PPH risk and assist in the clinical management of high-risk pregnancies.

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.