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.

ObjectiveTo investigate the spontaneous premature cardiac aging in SHJH^hr mice. MethodsA comparative study was conducted between SHJH^hr mice (SHJH^hr group) and wild-type ICR mice (WT group) at different ages (10 and 24 weeks). Cardiac function was analyzed using a small animal in vivo ultrasound imaging system. After euthanasia, organs were collected and weighed to assess the extent of cardiac atrophy. Cardiac pathological damage was observed using hematoxylin-eosin (HE) staining. Cardiac fibrosis was analyzed using Masson staining. Myocardial cell area was analyzed after wheat germ agglutinin (WGA) staining. The activities of oxidative damage indicators in myocardial tissue, including superoxide dismutase (SOD), glutathione peroxidase (GPX), and catalase (CAT), as well as the content of 8-hydroxy-2'-deoxyguanosine (8-OHdG), were measured using enzyme-linked immunosorbent assay. Real-time fluorescence quantitative PCR was used to measure the mRNA expression levels of factors associated with inflammation, fibrosis, and oxidative stress. Colorimetric assay was used to measure malondialdehyde (MDA) levels. ResultsCompared to WT group mice of the same age, 10-week-old mice in the SHJH^hr group showed no significant differences in stroke volume (SV), ejection fraction (EF), fractional shortening (FS), or heart and lung weights. However, at 24 weeks of age, mice in the SHJH^hr group had significantly lower SV, EF, and FS values compared to mice of the same age in the WT group (P<0.05), with no significant change in lung weight but a significant reduction in heart weight (P<0.05). Histological analysis of heart tissue from 24-week-old mice revealed no significant difference in cardiac fibrosis levels between SHJH^hr and WT groups, but WGA staining showed a significant reduction in myocardial cell area in mice in the SHJH^hr group (P<0.05). PCR analysis revealed a significant downregulation of mRNA levels of oxidative stress factors Sod2, Gpx1, and Cat genes (P<0.05). Biochemical assays indicated significantly reduced activities of oxidative damage-related enzymes SOD, GPX, and CAT in myocardial tissue (P<0.05), while the levels of oxidative damage markers 8-OHdG and MDA significantly increased (P<0.05). ConclusionMice in the SHJH^hr group exhibit premature cardiac aging, which may be associated with oxidative stress damage in myocardial tissue.

This review summarizes the applications and advancements of artificial intelligence(AI)in the analysis of retinal vascular parameters. Retinal vascular parameters, including vessel diameter, fractal dimension, vascular tortuosity, branching angles, and vessel density, are important indicators for assessing changes in the retinal vascular network structure. These parameters are not only related to various ophthalmic diseases but also reflect the conditions of systemic diseases such as diabetes and Alzheimer's disease. This article provides a detailed discussion on the advantages of AI technology in the automated identification and quantification of retinal vascular parameters, particularly in improving measurement efficiency and accuracy, and enabling the early detection and monitoring of various diseases. Additionally, the challenges faced by AI in the analysis of retinal vascular parameters were discussed, such as data standardization and insufficient sample diversity, and proposes directions for future research. By thoroughly analyzing the application of AI in retinal vascular parameter analysis, this article aims to offer new perspectives and methods for clinical diagnosis and early intervention of diseases, holding significant clinical significance and application prospects.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Objective:To predict the short-term postoperative recurrence status of patients with refractory temporal lobe epilepsy (TLE) by analyzing preoperative 18F-FDG PET images and patients′ clinical characteristics based on deep residual neural network (ResNet). Methods:Retrospective analysis was conducted on preoperative 18F-FDG PET images and clinical data of 220 patients with refractory TLE (132 males and 88 females, age 23.0(20.0, 30.2) years)) in the First Affiliated Hospital of Jinan University between January 2014 and June 2020. ResNet was used to perform high-throughput feature extraction on preprocessed PET images and clinical features, and to perform a postoperative recurrence prediction task for differentiating patients with TLE. The predictive performance of ResNet model was evaluated by ROC curve analysis, and the AUC was compared with that of classical Cox proportional risk model using Delong test. Results:Based on PET images combined with clinical feature training, AUCs of the ResNet in predicting 12-, 24-, and 36-month postoperative recurrence were 0.895±0.073, 0.861±0.058 and 0.754±0.111, respectively, which were 0.717±0.093, 0.697±0.081 and 0.645±0.087 for Cox proportional hazards model respectively ( z values: -3.00, -2.98, -1.09, P values: 0.011, 0.018, 0.310). The ResNet showed best predictive effect for recurrence events within 12 months after surgery. Conclusion:The ResNet model is expected to be used in clinical practice for postoperative follow-up of patients with TLE, helping for risk stratification and individualized management of postoperative patients.

OBJECTIVE To evaluate the effectiveness， safety and economy of lorlatinib in the treatment of non-small cell lung cancer（NSCLC）， and provide evidence-based reference for the introduction of new drugs in hospitals and clinical medication decisions. METHODS Retrieved from PubMed， Embase， Cochrane Library， Epistemonikos， CNKI， VIP， Wanfang data， SinoMed databases and The International Network of Agencies for Health Technology Assessment （INAHTA）， the results of the included studies were descriptively analyzed after literature screening， data extraction and quality evaluation. RESULTS A total of 19 literature were included， involving 13 system assessment （SR）/meta-analyses and 6 pharmacoeconomic reviews. Compared with the patients who received other anaplastic lymphoma kinases （ALK）-tyrosine kinase inhibitor（TKI）（such as crizotinib， brigatinib， alectinib， ensartinib， and ceritinib）， those using lorlatinib obtained best progression-free survival （PFS）. However， in the Asian population， lorlatinib did not show a significant advantage in prolonging PFS， compared to ensartinib， low-dose alectinib， and brigatinib. In terms of objective remission rate， lorlatinib and low-dose alectinib showed significant advantages over other ALK- TKI. At the same time， alectinib had the best overall survival. In terms of safety， lorlatinib possessed a poor safety profile with a high incidence of grade 3 or higher adverse events. Existing economic studies showed that lorlatinib brought health benefits to first- line treatment of patients with ALK-positive advanced NSCLC at the same time as higher treatment costs. CONCLUSIONS Lorlatinib has good efficacy in the treatment of NSCLC， but its safety and economy need to be studied.

Objective To explore the accuracy of the multiplication coefficient method and the moment synthesis method for determining the spatial position of the center of gravity(CoG)of the human body based on machine vision.Methods The mechanical measurement platform was built,and the three-dimensional(3D)human body CoG measurement method under static and dynamic conditions were designed to calculate the space coordinates of the CoG.Through experiments,the calculation accuracy of the multiplication coefficient and moment synthesis method were studied and analyzed.Results In the static experiments,the calculation results of the torque synthesis method were more accurate than those of the multiplication coefficient method for each dimension.The errors in the 3D CoG of the human body in the X,Y,and Z directions calculated using the torque synthesis method were 3.9%,4.1%,and 8.5%,respectively.In the dynamic experiment,the average and relative errors of the torque synthesis method in the X or Y direction were lower than those of the multiplication-coefficient method.When the action decomposition method was used to analyze the height direction of the CoG along the Z axis,the final rendering effect of the torque synthesis method improved.Conclusions The accuracy of the 3D CoG calculated by the moment synthesis method was relatively high,and was closer to the measurement data of the mechanical measurement platform.The 3D CoG calculated using the moment synthesis method can replace the mechanical measurement platform and can be used in subsequent studies.

Objective To explore the public attitude towards kidney xenotransplantation in China by constructing and validating the prediction model based on xenotransplantation questionnaire. Methods A convenient sampling survey was conducted among the public in China with the platform of Wenjuanxing to analyze public acceptance of kidney xenotransplantation and influencing factors. Using random distribution method, all included questionnaires (n=2 280) were divided into the training and validation sets according to a ratio of 7:3. A prediction model was constructed and validated. Results A total of 2 280 questionnaires were included. The public acceptance rate of xenotransplantation was 71.3%. Multivariate analysis showed that gender, marital status, resident area, medical insurance coverage, religious belief, vegetarianism, awareness of kidney xenotransplantation and whether on the waiting list for kidney transplantation were the independent influencing factors for public acceptance of kidney xenotransplantation (all P<0.05). The area under the curve (AUC) of receiver operating characteristic (ROC) of the prediction model in the training set was 0.773, and 0.785 in the validation set. The calibration curves in the training and validation sets indicated that the prediction models yielded good prediction value. Decision curve analysis (DCA) suggested that the prediction efficiency of the model was high. Conclusions In China, public acceptance of kidney xenotransplantation is relatively high, whereas it remains to be significantly enhanced. The prediction model based on questionnaire survey has favorable prediction efficiency, which provides reference for subsequent research.