ObjectiveHepatocellular carcinoma (HCC) represents 90% of all primary liver cancers. The main risk factors associated with HCC include viral hepatitis (B and/or C), alcohol abuse, and metabolic dysfunction-associated steatotic liver disease (MASLD), which progressively advance to liver fibrosis, cirrhosis, and ultimately evolve into HCC. Surgical resection represents the most effective treatment for HCC, while recent advances in immunotherapy, including immune checkpoint inhibitors and adoptive cell therapies, have provided improved treatment prospects for patients with unresectable HCC. However, the complex metabolic heterogeneity of HCC limits the therapeutic efficacy. Metabolic intermediates acyl-CoA not only provide energy and substrates for numerous biochemical reactions but also serve as donors for protein lysine acylation, a major class of post-translational modification (PTM). Therefore, a deeper understanding of the molecular mechanisms underlying protein lysine acylation and hepatocarcinogenesis is urgently needed. MethodsThe levels of protein lysine acylation and silence information regulator 5 (SIRT5) expression levels in clinical HCC samples were analyzed by Western blot. Quantitative malonylome and succinylome of HCC samples were analyzed by antibody-based affinity enrichment coupled with tandem mass spectrometry. The proliferation of HCC cells was analyzed with Cell Counting Kit-8 (CCK-8) assays, the apoptosis was quantified by Annexin V-FITC/propidium iodide (PI) staining coupled with flow cytometry, and the ability of cells to migrate was assayed by Transwell assays. The enzymatic activity of glutathione S-transferase Mu 1 (GSTM1) was quantified. Transgenic mice with hepatic overexpression of SIRT5 were constructed using CRISPR-Cas9, and primary hepatocarcinogenesis was induced by administration of diethylnitrosamine. ResultsWestern blot analysis indicated that the expression level of SIRT5 was elevated in clinical samples from HCC patients, and the levels of lysine malonylation, glutarylation, and succinylation were significantly reduced in HCC tissues. Knockout of SIRT5 in MHCC-97H and MHCC-97L hepatoma cells suppressed cell proliferation, and increased the percentage of apoptotic cells significantly. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the differentially malonylome and succinylome of HCC samples revealed significant enrichment in two major classes of biological processes: core energy metabolism (e.g., glycolysis/gluconeogenesis, tricarboxylic acid metabolic process, fatty acid beta oxidation) and detoxification and oxidative stress response (e.g., response to toxic substance, chemical carcinogenesis, reactive oxygen species (ROS)). SIRT5 removes malonylation from lysine residues in GSTM1 and restores its detoxification activity, which is crucial for the survival of hepatocytes under stressed conditions. More importantly, in vivo experiment indicated that hepatic-specific overexpression of SIRT5 in mice accelerated diethylnitrosamine-induced liver fibrosis and hepatocarcinogenesis, indicating the critical role of SIRT5 in HCC progression. ConclusionThis study highlights the previously unrecognized SIRT5-GSTM1 axis as a key regulator in hepatocarcinogenesis, and suggests a potential target for the treatment of patients with HCC.

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.

ObjectiveZheng’s San Qi San (ZSQS) power, a classic traditional Chinese medicine (TCM) formula, is used for treating soft tissue injuries involving muscles, tendons, and ligaments. However, its underlying therapeutic mechanisms remain unclear. This study aimed to screen and identify pharmaceutically active ingredients and their candidate biomolecule targets, and further elucidate the molecular mechanism of ZSQS in the treatment of skeletal muscle injury. MethodsNetwork pharmacology was employed to construct “ZSQS-component-target”, “protein-protein interaction (PPI)” and “active ingredient-core protein-pathway” networks to predict the key active ingredients and potential core targets of ZSQS for skeletal muscle injury. The predicted results were then validated via microarray data from the GEO database. Molecular docking was then performed to assess the binding ability between the screened active ingredients of ZSQS and the candidate core targets. Moreover, liquid chromatography-mass spectrometry (LC-MS) was used for qualitative and quantitative analysis to verify the active components of the drug and ZSQS serum. Finally, an animal model of eccentric exercise-induced skeletal muscle injury and a myotube cell model of oxidative stress-induced injury were established to validate the effects of ZSQS and its interventional effects on the biological functions of critical targets, thereby demonstrating the potential therapeutic mechanism of ZSQS. ResultsAmong the 111 active components identified in ZSQS and their corresponding 204 targets related to the skeletal muscle injury repair process, 14 core targets (including AKT1) and 4 core active components (quercetin, luteolin, kaempferol, and β‑sitosterol) were screened out, while the corresponding metabolites of quercetin, luteolin and kaempferol were detected in the ZSQS serum. Among these targets, 5 candidate genes (IL-6, CASP3, HIF1A, STAT3, and JUN) overlapped with the differential expression screening results with GEO data, and IL-6 was confirmed to be enriched in the PI3K/AKT pathway. Combined with the prediction results of the AKT expression levels, these findings suggest that the phosphorylation level of AKT1 plays a core role in the therapeutic mechanism of ZSQS. Molecular docking analysis further revealed that the PH domain of AKT1 had high binding energy with all 4 core active components, as verified by LC-MS. Finally, animal model studies have shown the promoting effect of ZSQS administration on skeletal muscle injury repair and its possible antioxidant damage mechanism. Cell model studies further demonstrated that ZSQS-containing serum, core active ingredient combination therapy, and quercetin monomer could increase the phosphorylation level of AKT, promote the nuclear translocation of Nrf2, upregulate the expression of downstream antioxidant enzymes (SOD, GPx, and GR), and inhibit the expression of inflammatory factors (IL-6 and TNF-α), thereby alleviating oxidative stress and the inflammatory response. ConclusionZSQS alleviates skeletal muscle injury mainly by activating the AKT/Nrf2 signaling pathway, enhancing cellular antioxidant and anti-inflammatory capabilities. The results of this study provide a scientific basis for the clinical application and modernized development of ZSQS.

Objective To explore the application value of dual-phase dual-energy CT (DECT) perfusion imaging in preoperative lung function assessment of lung cancer patients. Methods Data were collected from patients with stageⅠA non-small cell lung cancer who underwent surgical treatment in the Department of Thoracic Surgery, the First Affiliated Hospital of Nanjing Medical University, from November 2022 to June 2024. All patients underwent DECT perfusion imaging and pulmonary function testing (PFT) before surgery. PFT observation indicators included ventilation function indicators such as forced expiratory volume in one second (FEV1), forced vital capacity (FVC), 1-second rate (FEV1/FVC), maximal voluntary ventilation (MVV), and diffusion function indicators such as diffusing capacity for carbon monoxide (DLCO) and DLCO per liter of alveolar volume (DLCO/VA). The software eXamine was used to obtain quantitative parameters of DECT perfusion imaging, including volume parameters and perfusion parameters of both lungs and each lung lobe. The correlation between the volume parameters and perfusion parameters of both lungs and the ventilation and diffusion function indicators of the patients, as well as the differences in quantitative parameters of each lung lobe, was analyzed. Results The end-inspiration lung volume and biphasic volume difference were strongly positively correlated with FEV1 and FVC (r=0.636, r=0.682, r=0.614, r=0.624, P<0.001) and moderately positively correlated with MVV and DLCO (r=0.499, r=0.514, r=0.549, r=0.447, P<0.001); the end-expiration lung volume was weakly negatively correlated with DLCO/VA (r=−0.295, P=0.026); the volume ratio was positively correlated with FEV1, FVC, MVV, and MVV% (r=0.424, r=0.399, r=0.415, r=0.310, P<0.05); the end-inspiration iodine content was weakly positively correlated with DLCO/VA% (rs=0.292, P=0.030); the end-expiration iodine content was weakly positively correlated with FEV1, FVC, MVV, DLCO%, and DLCO/VA (r=0.307, r=0.299, r=0.295, r=0.366, r=0.320, P<0.05) and moderately positively correlated with DLCO (r=0.439, P<0.001); the end-inspiration iodine concentration was negatively correlated with FEV1, FVC, MVV, and MVV% (rs=−0.407, rs=−0.426, rs=−0.352, rs=−0.277, P＜0.05); the end-expiratory phase iodine concentration was moderately positively correlated with DLCO/VA (r=0.403, P=0.002); both the iodine concentration difference and the iodine concentration ratio were moderately positively correlated with FEV1, FEV1%, FVC, MVV, MVV% (P<0.05). The lung volume and iodine concentration ratio values were both highest in the left upper lung lobe and lowest in the right middle lung lobe; the differences in lung volume, lung volume ratio, intrapulmonary iodine content, and intrapulmonary iodine concentration were all highest in the lower lobes of both lungs and lowest in the middle lobe of the right lung. Conclusion Dual-phase DECT perfusion imaging can accurately assess overall lung function and quantify regional lung function.

OBJECTIVE To systematically evaluate the quality of the Heat-clearing and symptom-relieving formula and screen potential marker components that influence the quality of the formula. METHODS The contents of 11 components （calycosin-7- O - β -D-glucoside， ononin， hyperoside， isoquercitrin， baicalin， baicalein， cryptotanshinone， tanshinone Ⅱ A ， tanshinone Ⅰ， senkyunolide A， ferulic acid） in the Heat-clearing and symptom-relieving formula were determined by high-performance liquid chromatography-tandem mass spectrometry （HPLC-MS/MS）. Using the contents of the aforementioned components as variables， cluster analysis （CA）， principal component analysis （PCA）， and orthogonal partial least squares-discriminant analysis （OPLS-DA） were conducted using OriginPro 2024 software and SIMCA 14.1 software； marker components affecting the quality of the Heat-clearing and symptom-relieving formula were then screened based on the criteria of variable importance in the projection （VIP） value＞1 and P ＜0.05. The comprehensive evaluation of 20 batches of samples was carried out using the entropy weight-technique for order preference by similarity to ideal solution（TOPSIS） and grey correlation analysis （GCA） methods. RESULTS The contents of the above 11 components were 7.993-72.866， 4.542-31.228， 727.666-1 901.884， 496.846-1 293.279， 1 995.501-6 779.150， 54.500-241.280， 150.302-304.339， 79.698-189.206， 257.118-682.418， 5.498-21.687， 7.524-26.935 μg/g. CA， PCA and OPLS-DA results showed that 20 batches of samples were grouped into 2 categories. Q1， Q3， Q4， Q7-Q9， Q12， Q15， Q16 were grouped into one category， and the rest were grouped into another category； VIP values of ferulic acid， tanshinone Ⅱ A ， baicalin， cryptotanshinone， calycosin-7- O - β -D-glucoside and ononin were all greater than 1 （ P ＜0.05）. Both the entropy weight-TOPSIS and GCA methods showed that the samples ranked in the top 11 according to the euclidean distance and relative correlation degree were Q2， Q5， Q6， Q10， Q11， Q13， Q14， Q17-Q20. CONCLUSIONS The established HPLC-MS/MS method is rapid， accurate and highly sens itive. Combined with chemical pattern recognition analysis， entropy weight-TOPSIS and GCA methods， this method can be used to evaluate the quality of the Heat-clearing and symptom-relieving formula. Ferulic acid， tanshinone Ⅱ A ， baicalin， cryptotanshinone， calycosin-7- O - β -D-glucoside and ononin may be the marker components that affect the quality of this formula. The overall quality of 11 batches of the Heat-clearing and symptom-relieving formula， including Q17， is relatively superior.

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.

Objective To explore the risk prediction model of major adverse cardiovascular events (MACE) in community patients with hypertension based on remote ambulatory blood pressure parameters. Methods From November 2023 to October 2024, 486 community patients with hypertension who received standardized management in Nanjing Medical University Affiliated to Suzhou Hospital were retrospectively selected. All patients wore remote ambulatory blood pressure monitor to obtain 24-hour ambulatory blood pressure data. Clinical data were collected and remote ambulatory blood pressure parameters [24-hour systolic blood pressure variability (SBPV), 24-hour diastolic blood pressure variability (DBPV), nighttime SBPV, nighttime DBPV, daytime SBPV, daytime DBPV] were extracted. The patients were followed up for 12 months, and were classified into MACE group (n=42) and non-MACE group (n=444) according to whether MACE occurred during follow-up. Multivariate Logistic regression analysis was adopted to screen the influencing factors for MACE. Based on the above factors, a risk prediction model was constructed and verified by receiver operating characteristic (ROC) curve. Results MACE occurred in 42 cases among 486 patients, with an incidence rate of 8.64%. Multivariate Logistic regression analysis suggested that nighttime DBPV (OR=1.119, 95%CI: 1.030-1.214), 24h-SBPV (OR=1.115, 95%CI: 1.007-1.235), nighttime SBPV (OR=1.116, 95%CI: 1.016-1.226) and diabetes mellitus (OR=2.762, 95%CI: 1.059-7.203) were independent factors for MACE (P<0.05). The model validation results revealed that the area under the ROC curve was 0.905 (95%CI: 0.854-0.956 ), and the model had a good discrimination degree. Conclusion Nighttime DBPV, 24h-SBPV, nighttime SBPV and diabetes mellitus are independent risk factors for MACE in community patients with hypertension. The clinical prediction model based on these variables exhibits certain predictive value on MACE risk.

Background: and Purpose Vertebrobasilar fusiform aneurysms (VBFAs) carry substantial morbidity and mortality, but optimal management for unruptured VBFAs remains unclear. We compared the safety and efficacy of conservative management (CM), stent-assisted coiling (SAC), and flow diverters (FDs) in patients with unruptured VBFAs, focusing on long-term prognosis. Methods: This study included data from a nationwide Chinese cohort of patients with vertebrobasilar dissecting aneurysms. Inverse probability of treatment weighting (IPTW) balanced confounders across groups. The primary outcome was poor prognosis (modified Rankin Scale score >2). Secondary outcomes included aneurysm rupture, ischemic stroke, compression symptoms, and VBFA-related deaths. Logistic regression estimated odds ratios (ORs) and 95% confidence intervals (CIs). Subgroup and sensitivity analyses were performed. Results: Among 1,115 patients with unruptured VBFAs, 838 (median age, 54 years; 655 men) were included. After IPTW, baseline characteristics were balanced. Median follow-up was 54 months. FD was associated with a lower risk of poor prognosis than CM (OR, 0.48 [95% CI, 0.30 to 0.77]; p=0.002), with no difference between CM and SAC. FD also reduced aneurysm rupture (OR, 0.20 [95% CI, 0.07 to 0.60]; p=0.004) and compression symptoms (OR, 0.30 [95% CI, 0.13 to 0.68]; p=0.004) versus CM. Time-to-event analyses further revealed significant differences in vertebral artery lesions and Type I–II VBFAs, whereas no significant differences were observed in basilar or vertebrobasilar junction lesions or in Type III–IV VBFAs. Conclusions Compared with CM, FD was associated with improved long-term outcomes in unruptured VBFAs, particularly in vertebral artery lesions and Type I–II VBFAs, although residual confounding cannot be excluded.

ObjectiveTo construct and validate a risk assessment model for post-transplant diabetes mellitus （PTDM） using multiple machine learning algorithms， and to realize the early identification of PTDM. MethodsA retrospective analysis was performed for the clinical data of the patients who underwent allogeneic liver transplantation in The First Hospital of Shanxi Medical University from April 1， 2020 to December 31， 2024， and they were randomly divided into a training set and a validation set at a ratio of 7∶3. The LASSO regression analysis combined with 5-fold cross-validation was used for feature selection. Seven machine learning models were developed in the training set， i.e.， logistic regression （LR）， decision tree （DT）， Naive Bayes （NB）， random forest （RF）， K-nearest neighbor （KNN）， extreme gradient boosting （XGBoost）， and adaptive boosting （AdaBoost）. In the validation set， various methods were used to assess the predictive performance of each model， such as accuracy， precision， recall rate， specificity， F1 score， area under the receiver operating characteristic curve （AUROC）， and area under the precision-recall curve （AUPRC）. The Brier score and decision curve analysis were used to assess the calibration and clinical practicability of the models， and the SHAP method was used to analyze feature importance. The independent-samples t test or the Wilcoxon rank-sum test was used for comparison of continuous data between two groups， and the chi-square test or the Fisher’s exact test was used for comparison of categorical data between two groups. ResultsA total of 135 liver transplant recipients were enrolled， among whom 26 developed PTDM， and there were 94 patients in the training set and 41 in the validation set. Feature extraction and screening identified 7 key features of sex， overweight or obesity， anhepatic phase， time of operation， length of hospital stay， early postoperative hypomagnesemia， and impaired fasting glucose （IFG）. In the validation set， the XGBoost model showed the best predictive performance， with an AUROC of 0.907 （95% confidence interval ［CI］： 0.807 — 0.989）， an AUPRC of 0.649 （95%CI： 0.339 — 0.955）， an accuracy of 0.878， a precision of 0.667， a recall rate of 0.750， an F1-score of 0.706， a specificity of 0.909， and a Brier score of 0.104. The decision curve analysis showed that when the threshold probability was below 0.667， application of the XGBoost model in clinical decision-making provided relatively high net benefit. The SHAP analysis showed that the length of hospital stay ranked first in terms of feature importance， followed by time of operation， overweight or obesity， sex， IFG， anhepatic phase， and early postoperative hypomagnesemia. ConclusionThe risk assessment model for PTDM in liver transplant recipients based on XGBoost algorithm has excellent performance and can effectively identify high-risk individuals； however， multicenter large-sample data are needed for further validation.

Electromagnetic fields can regulate the fundamental biological processes involved in bone remodeling. As a non-invasive physical therapy, electromagnetic fields with specific parameters have demonstrated therapeutic effects on bone remodeling diseases, such as fractures and osteoporosis. Electromagnetic fields can be generated by the movement of charged particles or induced by varying currents. Based on whether the strength and direction of the electric field change over time, electromagnetic fields can be classified into static and time-varying fields. The treatment of bone remodeling diseases with static magnetic fields primarily focuses on fractures, often using magnetic splints to immobilize the fracture site while studying the effects of static magnetic fields on bone healing. However, there has been relatively little research on the prevention and treatment of osteoporosis using static magnetic fields. Pulsed electromagnetic fields, a type of time-varying field, have been widely used in clinical studies for treating fractures, osteoporosis, and non-union. However, current clinical applications are limited to low-frequency, and research on the relationship between frequency and biological effects remains insufficient. We believe that different types of electromagnetic fields acting on bone can induce various “secondary physical quantities”, such as magnetism, force, electricity, acoustics, and thermal energy, which can stimulate bone cells either individually or simultaneously. Bone cells possess specific electromagnetic properties, and in a static magnetic field, the presence of a magnetic field gradient can exert a certain magnetism on the bone tissue, leading to observable effects. In a time-varying magnetic field, the charged particles within the bone experience varying Lorentz forces, causing vibrations and generating acoustic effects. Additionally, as the frequency of the time-varying field increases, induced currents or potentials can be generated within the bone, leading to electrical effects. When the frequency and power exceed a certain threshold, electromagnetic energy can be converted into thermal energy, producing thermal effects. In summary, external electromagnetic fields with different characteristics can generate multiple physical quantities within biological tissues, such as magnetic, electric, mechanical, acoustic, and thermal effects. These physical quantities may also interact and couple with each other, stimulating the biological tissues in a combined or composite manner, thereby producing biological effects. This understanding is key to elucidating the electromagnetic mechanisms of how electromagnetic fields influence biological tissues. In the study of electromagnetic fields for bone remodeling diseases, attention should be paid to the biological effects of bone remodeling under different electromagnetic wave characteristics. This includes exploring innovative electromagnetic source technologies applicable to bone remodeling, identifying safe and effective electromagnetic field parameters, and combining basic research with technological invention to develop scientifically grounded, advanced key technologies for innovative electromagnetic treatment devices targeting bone remodeling diseases. In conclusion, electromagnetic fields and multiple physical factors have the potential to prevent and treat bone remodeling diseases, and have significant application prospects.