BACKGROUND:Studies have shown that rheumatoid arthritis and osteoporosis are positively correlated,but the causal relationship and related mechanisms have not yet been confirmed.With the cross-fertilization of computer science and life sciences,Mendelian randomization and bioinformatics analyses based on genome-wide association study(GWAS)and transcriptome sequencing data can assess the causal relationship between two diseases,explore the related mechanisms,and mine the therapeutic targets,which will be beneficial to the precision treatment of rheumatoid arthritis combined with osteoporosis.OBJECTIVE:To explore the causal relationship between rheumatoid arthritis and osteoporosis using two-sample Mendelian randomization and to mine potential co-morbid targets and potential targeted drugs through summary-data-based Mendelian randomization and bioinformatics analyses,aiming to provide theoretical basis for mechanism exploration and precision treatment in the field of rheumatoid arthritis combined with osteoporosis.METHODS:(1)Firstly,GWAS data of rheumatoid arthritis,osteoporosis,and cis-expression quantitative trait locus(cis-eQTL)in Asian and European populations were downloaded from the GWAS Catalog,IEU Open GWAS,FinnGen,and eQTLGen databases,and were used for two-sample Mendelian randomization analysis and summary-data-based Mendelian randomization analysis.(2)Transcriptome sequencing data of rheumatoid arthritis(GSE93272 and GSE15573)were downloaded from the GEO database for bioinformatics analysis.(3)Subsequently,forward and inverse Mendelian randomization analyses between rheumatoid arthritis and osteoporosis were performed,and inverse variance weighted was used as the main metric for the analyses,and the results were corroborated with MR Egger,simple mode,weighted median and weighted mode.(4)Then,the genes closely related to rheumatoid arthritis and osteoporosis were identified based on the summary-data-based Mendelian randomization analysis,and the co-disease targets of rheumatoid arthritis and osteoporosis were mined based on cross-analysis.Meanwhile,the biological functions of the co-morbid targets were verified based on bioinformatics analysis and cellular experiments.(5)In addition,a rheumatoid arthritis risk prediction nomogram was constructed based on DYRK2,and its prediction performance was verified by receiver operating characteristic curve,correction curve and decision curve.Finally,the target potential drugs were mined based on Enrichr database and molecular docking was performed.RESULTS AND CONCLUSION:(1)Forward Mendelian randomization analysis of rheumatoid arthritis and osteoporosis showed statistically significant results except for GCST90044540 and GCST90086118,and all other results indicated a significant causal relationship and positive correlation between rheumatoid arthritis and osteoporosis.(2)Inverse Mendelian randomization analysis suggested that no significant causal relationship was seen between osteoporosis and rheumatoid arthritis.(3)Summary-data-based Mendelian randomization analysis identified a total of 412 and 344 genes positively associated with rheumatoid arthritis and osteoporosis,and 421 and 347 genes negatively associated.Based on the cross-analysis,26 co-morbid genes were subsequently obtained.Among them,DYRK2 was a potential therapeutic target,and subsequent bioinformatics analysis and cellular experiments confirmed its important role in the progression of rheumatoid arthritis and osteoporosis.(4)Furthermore,the constructed nomogram has excellent predictive performance.Finally,four potential DYRK2-targeting drugs(undecanoic acid,metyrapone,JNJ-38877605,and ACA)were discovered and molecular docking also demonstrated reliable targeting ability.(5)In conclusion,based on GWAS data from Asian and European populations,we successfully demonstrated that rheumatoid arthritis and osteoporosis are causally related at the genetic level,DYRK2 is a potential therapeutic target,and four small molecules are potential target drugs.

BACKGROUND:Studies have shown that rheumatoid arthritis and osteoporosis are positively correlated,but the causal relationship and related mechanisms have not yet been confirmed.With the cross-fertilization of computer science and life sciences,Mendelian randomization and bioinformatics analyses based on genome-wide association study(GWAS)and transcriptome sequencing data can assess the causal relationship between two diseases,explore the related mechanisms,and mine the therapeutic targets,which will be beneficial to the precision treatment of rheumatoid arthritis combined with osteoporosis.OBJECTIVE:To explore the causal relationship between rheumatoid arthritis and osteoporosis using two-sample Mendelian randomization and to mine potential co-morbid targets and potential targeted drugs through summary-data-based Mendelian randomization and bioinformatics analyses,aiming to provide theoretical basis for mechanism exploration and precision treatment in the field of rheumatoid arthritis combined with osteoporosis.METHODS:(1)Firstly,GWAS data of rheumatoid arthritis,osteoporosis,and cis-expression quantitative trait locus(cis-eQTL)in Asian and European populations were downloaded from the GWAS Catalog,IEU Open GWAS,FinnGen,and eQTLGen databases,and were used for two-sample Mendelian randomization analysis and summary-data-based Mendelian randomization analysis.(2)Transcriptome sequencing data of rheumatoid arthritis(GSE93272 and GSE15573)were downloaded from the GEO database for bioinformatics analysis.(3)Subsequently,forward and inverse Mendelian randomization analyses between rheumatoid arthritis and osteoporosis were performed,and inverse variance weighted was used as the main metric for the analyses,and the results were corroborated with MR Egger,simple mode,weighted median and weighted mode.(4)Then,the genes closely related to rheumatoid arthritis and osteoporosis were identified based on the summary-data-based Mendelian randomization analysis,and the co-disease targets of rheumatoid arthritis and osteoporosis were mined based on cross-analysis.Meanwhile,the biological functions of the co-morbid targets were verified based on bioinformatics analysis and cellular experiments.(5)In addition,a rheumatoid arthritis risk prediction nomogram was constructed based on DYRK2,and its prediction performance was verified by receiver operating characteristic curve,correction curve and decision curve.Finally,the target potential drugs were mined based on Enrichr database and molecular docking was performed.RESULTS AND CONCLUSION:(1)Forward Mendelian randomization analysis of rheumatoid arthritis and osteoporosis showed statistically significant results except for GCST90044540 and GCST90086118,and all other results indicated a significant causal relationship and positive correlation between rheumatoid arthritis and osteoporosis.(2)Inverse Mendelian randomization analysis suggested that no significant causal relationship was seen between osteoporosis and rheumatoid arthritis.(3)Summary-data-based Mendelian randomization analysis identified a total of 412 and 344 genes positively associated with rheumatoid arthritis and osteoporosis,and 421 and 347 genes negatively associated.Based on the cross-analysis,26 co-morbid genes were subsequently obtained.Among them,DYRK2 was a potential therapeutic target,and subsequent bioinformatics analysis and cellular experiments confirmed its important role in the progression of rheumatoid arthritis and osteoporosis.(4)Furthermore,the constructed nomogram has excellent predictive performance.Finally,four potential DYRK2-targeting drugs(undecanoic acid,metyrapone,JNJ-38877605,and ACA)were discovered and molecular docking also demonstrated reliable targeting ability.(5)In conclusion,based on GWAS data from Asian and European populations,we successfully demonstrated that rheumatoid arthritis and osteoporosis are causally related at the genetic level,DYRK2 is a potential therapeutic target,and four small molecules are potential target drugs.

ObjectiveMigraine is a leading neurological disorder and the fourth most common cause of years lived with disability worldwide, affecting nearly 116 million individuals. Although pharmacological treatments are available, their efficacy is often limited by side effects and variable response rates. Repetitive transcranial magnetic stimulation (rTMS) over the dorsolateral prefrontal cortex (DLPFC) offers a safe, non-invasive alternative for migraine management. However, the neurophysiological mechanisms, particularly how rTMS modulates local cortical excitability and distributed pain-related circuits, remain poorly understood. Elucidating these mechanisms is essential for optimizing treatment protocols and improving clinical outcomes. MethodsThis study employed concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) to investigate neuroplastic and neurocircuitry mechanisms of DLPFC-rTMS in migraine. Study 1 compared 30 migraineurs and 28 healthy controls to identify abnormalities in TMS-evoked potentials (TEPs) and significant current density (SCD) within sensory-discriminative regions including the primary somatosensory cortex (S1) and posterior insula (pINS), cognitive-affective regions including the anterior insula (aINS) and midcingulate cortex (MCC), and a descending modulatory region, the periaqueductal gray (PAG). Study 2 used a single-blind, crossover, sham-controlled design in 34 healthy participants. Each participant received both active (10 Hz, 80% RMT, 1 500 pulses) and sham DLPFC-rTMS in counterbalanced order. TMS-EEG and cold pain tolerance were assessed before and after each session. ResultsIn Study 1, migraineurs showed a significantly less negative N120 amplitude compared to healthy controls (P=0.027, Cohen’s d=0.60), indicating local intracortical disinhibition. No group differences were observed for N40, P60, or P180 components. At the source level, migraineurs exhibited significantly higher SCD in the S1, pINS, aINS, and MCC (allQ<0.05), but not in the ventroposterior thalamus (vpTHAL), mediodorsal thalamus (mdTHAL), or PAG. In Study 2, active rTMS significantly reduced SCD from pre- to post-stimulation in the S1, aINS, and MCC (all Q<0.05). Sham stimulation also reduced SCD in the S1 (Q<0.05) but not in the aINS or MCC. Although no significant group-level analgesic effect was observed between active and sham conditions (P=0.107), correlation analyses revealed that greater SCD reductions in the S1 and MCC were significantly associated with higher post-rTMS pain tolerance (R=-0.487 and -0.495, both Q<0.01) and larger improvements in pain tolerance(R=-0.487 and -0.451, both Q<0.05). No such correlations were found following sham stimulation, suggesting that the behavioural relevance of neural changes is specific to active rTMS. ConclusionThis study provides novel evidence that migraineurs exhibit both local neuroplastic abnormalities (reduced N120 amplitude) and hyperactivity in key pain-processing regions (S1, pINS, aINS, MCC). A single session of DLPFC-rTMS reduced hyperactivity in the aINS, MCC, and S1. Notably, greater reductions in the S1 and MCC were associated with improved pain tolerance. These findings identify distinct cortical circuitries, particularly within the cognitive-affective pain network, that may serve as potential biomarkers for optimizing rTMS treatment in migraine and other chronic pain conditions. Future studies should validate these results in patient populations experiencing spontaneous migraine attacks and explore multi-session or accelerated rTMS protocols.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective To carry out cervical cancer VMAT radiotherapy planning respectively with the single-criteria optimization(SCO)and multi-criteria optimization(MCO)modes of Raystation 4.7 planning system,and to provide references for selecting optimization mode clinically by comparing and analyzing the dosimetric parameters such as target dose distribution,exposure dose to organ at risk(OAR),monitor unit and beam-on time.Methods Ten cervical cancer patients who attended some hospital from February to December 2022 were retrospectively selected,and some VMAT plans were designed for them with the SCO mode of Raystation 4.7 planning system and then enrolled into a SCO group;other VMAT plans were redesigned with the MCO mode under the premise the setup conditions such as machine model and shot field angle were unchanged,and divided into a MCO group.The two groups were compared in terms of maximum dose(D2%),minimum dose(D98%),homogeneity index(HI)and conformity index(CI)of the planning target volume(PTV),beam-on time,monitor unit and exposure doses to OARs including bladder V50 Gy,V40 Gy and V30 Gy,rectum V50 Gy,V40 Gy and V30 Gy,small intestine V50 Gy,V40 Gy and V30 Gy and left and right femur V50 Gy,V40 Gy and V30 Gy.SPSS 22.0 software was used for statistical analysis.Results Both the two groups met clinical requirements.There were no significant differences between the two groups in D98%,CI,small intestine V50 Gy and left and right femur V40 Gy(P>0.05).The MCO group had the values of HI,D2%,V50 Gy,V40 Gy and V30 Gy,rectum V50 Gy,V40 Gy and V30 Gy,small intestine V40 Gy and V30 Gy and left and right femur V30 Gy lower than those of the SCO group,with the differences being statistically significant(P<0.05).The SCO group had less monitor units and shorter beam-on time when compared with the MCO group,with the differences being statistically siginificant(P<0.05).Conclusion When compared with the SCO mode-based VMAT plans,the MCO mode-based VMAT plans significantly decrease the exposure doses and volume to rectum,bladder and small intestine and raise the PTV homogeneity,while lower the treatment efficiency to some extent by increased monitor units and prolonged beam-on time.[Chinese Medical Equipment Journal,2025,46(9):39-44]

Objective To carry out cervical cancer VMAT radiotherapy planning respectively with the single-criteria optimization(SCO)and multi-criteria optimization(MCO)modes of Raystation 4.7 planning system,and to provide references for selecting optimization mode clinically by comparing and analyzing the dosimetric parameters such as target dose distribution,exposure dose to organ at risk(OAR),monitor unit and beam-on time.Methods Ten cervical cancer patients who attended some hospital from February to December 2022 were retrospectively selected,and some VMAT plans were designed for them with the SCO mode of Raystation 4.7 planning system and then enrolled into a SCO group;other VMAT plans were redesigned with the MCO mode under the premise the setup conditions such as machine model and shot field angle were unchanged,and divided into a MCO group.The two groups were compared in terms of maximum dose(D2%),minimum dose(D98%),homogeneity index(HI)and conformity index(CI)of the planning target volume(PTV),beam-on time,monitor unit and exposure doses to OARs including bladder V50 Gy,V40 Gy and V30 Gy,rectum V50 Gy,V40 Gy and V30 Gy,small intestine V50 Gy,V40 Gy and V30 Gy and left and right femur V50 Gy,V40 Gy and V30 Gy.SPSS 22.0 software was used for statistical analysis.Results Both the two groups met clinical requirements.There were no significant differences between the two groups in D98%,CI,small intestine V50 Gy and left and right femur V40 Gy(P>0.05).The MCO group had the values of HI,D2%,V50 Gy,V40 Gy and V30 Gy,rectum V50 Gy,V40 Gy and V30 Gy,small intestine V40 Gy and V30 Gy and left and right femur V30 Gy lower than those of the SCO group,with the differences being statistically significant(P<0.05).The SCO group had less monitor units and shorter beam-on time when compared with the MCO group,with the differences being statistically siginificant(P<0.05).Conclusion When compared with the SCO mode-based VMAT plans,the MCO mode-based VMAT plans significantly decrease the exposure doses and volume to rectum,bladder and small intestine and raise the PTV homogeneity,while lower the treatment efficiency to some extent by increased monitor units and prolonged beam-on time.[Chinese Medical Equipment Journal,2025,46(9):39-44]

Objective:To explore the clinical value of combination of contrast-enhanced ultrasound(CEUS)and magnetic reso-nance imaging(MRI)in the diagnosis of parotid gland tumors.Methods:167 patients with parotid gland tumors were diagnosed by CEUS and MRI respectively and in combination.With postoperative pathological diagnosis result as the gold standard,the diagnos-tic value of CEUS combined with MRI(CEUS+MRI)was analyzed.Results:171 tumors were confirmed by postoperative pathology in 167 patients,including 143 benign tumors(83.63％)and 28 malignant tumors(16.37％).The benign tumors were mainly pleo-morphic adenoma(65 tumors,45.45％),Warthin tumor(40 tumors,27.97％)and basal cell adenoma(13 tumors,9.09％).Among the malignant tumors,mucoepidermoid carcinoma(7 tumors,25.00％),adenoid cystic carcinoma(6 tumors,21.43％)and acinic cell carcinoma(3 tumors,10.71％)were the most common.CEUS showed 39 cases of malignant parotid gland tumors and 132 cases of benign parotid gland tumors.The sensitivity,specificity and accuracy rate of CEUS were 57.14％,83.92％and 79.53％respectively.The MRI ADC values were manifested as pleomorphic adenoma＞malignant tumor＞Warthin tumor(P＜0.05).MRI showed 53 cases of malignant parotid gland tumors and 118 cases of benign parotid gland tumors,and the sensitivity,specifici-ty and accuracy rate of MRI diagnosis were 67.86％,76.22％and 74.85％respectively.CEUS+MRI revealed 33 cases of malig-nant parotid gland tumors and 138 cases of benign parotid gland tumors,the sensitivity,specificity and accuracy rate of CEUS+MRI diagnosis were 78.57％,92.31％and 90.06％respectively.The specificity and accuracy rate of CEUS+MRI were significantly higher than those of single examination(x2=4.806,13.951,7.348,13.664,allP＜0.05).Conclusion:Both CEUS and MRI have certain clinical value in the diagnosis of parotid gland tumors,and the specificity and accuracy rate of CEUS+MRI are significantly higher than those of single examination.

Objective:To observe the effect of modified autologous skull defect repair in patients with traumatic brain injury and its influence on neurological function and living ability.Methods:104 patients with traumatic brain injury who were admitted to our hospital from March 2022 to August 2024 were included,they were divided into Group A[37 cases,poly ether ether ketone(PEEK)skull defect repair],Group B(35 cases,traditional titanium mesh skull defect repair),and Group C(32 cases,modified autologous skull defect repair).Perioperative indicators,neurological function,activity of daily living,quality of life,satisfaction,and incidence of postoperative complications were compared among three groups.Results:There were no differences in the operation time,operation blood loss and postoperative hospital stay among the three groups(P＞0.05).The hospitalization costs of Group A,Group B and Group C decreased successively(P＜0.05).Activity of Daily Living(ADL)scores at 3 and 6 months after surgery increased among three groups,while National Institutes of Health Stroke Scale(NIHSS)scores decreased(P＜0.05).There was no significant difference in physiological function,social function,psychological function,and material life among the three groups at 6 months after surgery(P＞0.05).The overall satisfaction rate in Group C was higher than that of Group A and Group B(P＜0.05).The overall incidence of complications in Group C was lower than that in Group A and Group B(P＜0.05).Conclusion:PEEK,traditional titanium mesh,and modified autologous skull are used in skull defect repair,operation time,operation blood loss and postoperative hospital stay are comparable,they can also reduce neurological function damage,improve living ability,and enhance the quality of life of patients,however,PEEK is relatively expensive,the satisfaction of traditional titanium mesh is low,andincidence of postoperative complications are relatively high.

Kv1.3,a voltage-gated potassium channel,is highly expressed in T lymphocytes,the nervous system,and vascular smooth muscle cells.It plays a critical role in membrane excitability and electrical signal transduction,serving as an important target for studying T-cell function and providing a promising direction for developing therapeutics against autoimmune and inflammatory diseases.Therefore,the de-velopment of specific inhibitors of Kv1.3 channel has emerged as a novel therapeutic strategy for these disorders.In this study,we isolated and purified a novel Kv1.3-inhibitory peptide toxin,LmKTx13,from the venom of the scorpion Lychas mucronatus using reversed-phase high-performance liquid chroma-tography(RP-HPLC).LmKTx13 consists of 38 amino acid residues,including six cysteines that form three disulfide bonds.Whole-cell patch-clamp recordings revealed that LmKTx13 potently inhibited Kv1.3 with an IC50 of 7.92±3.0 nmol/L.Selectivity analysis showed that 2 μmol/L LmKTx13 also in-hibited Kv1.2 and Kv1.7,but exhibited no significant effects on other potassium channel subtypes or voltage-gated sodium channels.Further investigation into the mechanism demonstrated that LmKTx13 acts as a pore-blocking inhibitor of Kv1.3.By analyzing the effects of LmKTx13 on Kv1.3 channel gating ki-netics and performing sequence alignment of the pore regions of Kv1.3 and Kv1.5,we constructed site-directed mutants and identified the pore region of Kv1.3 as the critical binding site for LmKTx13.Key residues involved in the interaction included T425,G427,and H451.In summary,we discovered a no-vel pore-blocking Kv1.3 inhibitor,LmKTx13,from L.mucronatus venom,which exhibits high affinity and selectivity for Kv1.3.These findings highlight its potential as a potential lead molecule for developing Kv1.3-targeted therapeutics.