Background: The immune system plays a critical role in the development and progression of osteoporosis and fractures. However, the causal relationships between antibody immune responses, immune cells, and these bone conditions remain unclear. This study aimed to explore these relationships using Mendelian randomization (MR) analysis. Methods: We collected complete blood count data from patients with fractures and healthy individuals and analyzed their differences. Then, we conducted a 2-sample, 2-step MR analysis to investigate the causal effects of antibody immune responses on osteoporosis and fractures, using inverse-variance weighted (IVW) as the primary method. We also explored whether immune cells mediate the pathway between antibodies and osteoporosis or fractures. Finally, we analyzed the functions and expression levels of key genes involved. Results: Overall, the fracture group exhibited increased white blood cell count, absolute neutrophil count, absolute monocyte count, platelet count, and their respective proportions, while absolute lymphocyte count, absolute eosinophil count, absolute basophil count, red blood cell count, and their proportions were decreased. We identified 44 causal relationships between antibodies and osteoporosis or fractures, with 7 supported by multiple MR methods, and 5 showing odds ratios significantly deviating from 1 in the IVW analysis. Epstein-Barr virus-related antibodies had a notable impact on osteoporosis and fractures. The human leukocyte antigen (HLA) gene family, particularly HLA-DPB1, emerged as a significant risk factor. However, immune cells were not found to mediate these effects. Conclusions This study elucidated the causal relationships between antibody immune responses, immune cells, and osteoporosis or fractures. The HLA gene family plays a crucial role in the interaction between antibodies and these bone conditions, with HLA-DPB1 identified as a key risk gene. Immune cells do not serve as mediators in this process. These findings provide valuable insights for future research.

Background: The immune system plays a critical role in the development and progression of osteoporosis and fractures. However, the causal relationships between antibody immune responses, immune cells, and these bone conditions remain unclear. This study aimed to explore these relationships using Mendelian randomization (MR) analysis. Methods: We collected complete blood count data from patients with fractures and healthy individuals and analyzed their differences. Then, we conducted a 2-sample, 2-step MR analysis to investigate the causal effects of antibody immune responses on osteoporosis and fractures, using inverse-variance weighted (IVW) as the primary method. We also explored whether immune cells mediate the pathway between antibodies and osteoporosis or fractures. Finally, we analyzed the functions and expression levels of key genes involved. Results: Overall, the fracture group exhibited increased white blood cell count, absolute neutrophil count, absolute monocyte count, platelet count, and their respective proportions, while absolute lymphocyte count, absolute eosinophil count, absolute basophil count, red blood cell count, and their proportions were decreased. We identified 44 causal relationships between antibodies and osteoporosis or fractures, with 7 supported by multiple MR methods, and 5 showing odds ratios significantly deviating from 1 in the IVW analysis. Epstein-Barr virus-related antibodies had a notable impact on osteoporosis and fractures. The human leukocyte antigen (HLA) gene family, particularly HLA-DPB1, emerged as a significant risk factor. However, immune cells were not found to mediate these effects. Conclusions This study elucidated the causal relationships between antibody immune responses, immune cells, and osteoporosis or fractures. The HLA gene family plays a crucial role in the interaction between antibodies and these bone conditions, with HLA-DPB1 identified as a key risk gene. Immune cells do not serve as mediators in this process. These findings provide valuable insights for future research.

Background: The immune system plays a critical role in the development and progression of osteoporosis and fractures. However, the causal relationships between antibody immune responses, immune cells, and these bone conditions remain unclear. This study aimed to explore these relationships using Mendelian randomization (MR) analysis. Methods: We collected complete blood count data from patients with fractures and healthy individuals and analyzed their differences. Then, we conducted a 2-sample, 2-step MR analysis to investigate the causal effects of antibody immune responses on osteoporosis and fractures, using inverse-variance weighted (IVW) as the primary method. We also explored whether immune cells mediate the pathway between antibodies and osteoporosis or fractures. Finally, we analyzed the functions and expression levels of key genes involved. Results: Overall, the fracture group exhibited increased white blood cell count, absolute neutrophil count, absolute monocyte count, platelet count, and their respective proportions, while absolute lymphocyte count, absolute eosinophil count, absolute basophil count, red blood cell count, and their proportions were decreased. We identified 44 causal relationships between antibodies and osteoporosis or fractures, with 7 supported by multiple MR methods, and 5 showing odds ratios significantly deviating from 1 in the IVW analysis. Epstein-Barr virus-related antibodies had a notable impact on osteoporosis and fractures. The human leukocyte antigen (HLA) gene family, particularly HLA-DPB1, emerged as a significant risk factor. However, immune cells were not found to mediate these effects. Conclusions This study elucidated the causal relationships between antibody immune responses, immune cells, and osteoporosis or fractures. The HLA gene family plays a crucial role in the interaction between antibodies and these bone conditions, with HLA-DPB1 identified as a key risk gene. Immune cells do not serve as mediators in this process. These findings provide valuable insights for future research.

Background: The immune system plays a critical role in the development and progression of osteoporosis and fractures. However, the causal relationships between antibody immune responses, immune cells, and these bone conditions remain unclear. This study aimed to explore these relationships using Mendelian randomization (MR) analysis. Methods: We collected complete blood count data from patients with fractures and healthy individuals and analyzed their differences. Then, we conducted a 2-sample, 2-step MR analysis to investigate the causal effects of antibody immune responses on osteoporosis and fractures, using inverse-variance weighted (IVW) as the primary method. We also explored whether immune cells mediate the pathway between antibodies and osteoporosis or fractures. Finally, we analyzed the functions and expression levels of key genes involved. Results: Overall, the fracture group exhibited increased white blood cell count, absolute neutrophil count, absolute monocyte count, platelet count, and their respective proportions, while absolute lymphocyte count, absolute eosinophil count, absolute basophil count, red blood cell count, and their proportions were decreased. We identified 44 causal relationships between antibodies and osteoporosis or fractures, with 7 supported by multiple MR methods, and 5 showing odds ratios significantly deviating from 1 in the IVW analysis. Epstein-Barr virus-related antibodies had a notable impact on osteoporosis and fractures. The human leukocyte antigen (HLA) gene family, particularly HLA-DPB1, emerged as a significant risk factor. However, immune cells were not found to mediate these effects. Conclusions This study elucidated the causal relationships between antibody immune responses, immune cells, and osteoporosis or fractures. The HLA gene family plays a crucial role in the interaction between antibodies and these bone conditions, with HLA-DPB1 identified as a key risk gene. Immune cells do not serve as mediators in this process. These findings provide valuable insights for future research.

Objective:To predict Chinese materia medica that may prevent and treat coronary microvascular dysfunction (CMD) by identifying disease core targets.Methods:CMD- related targets were obtained through GeneCards and OMIM databases. Subnetworks were extracted by using MCODE plugin in Cytoscape 3.9.1. Core targets of subnetworks were obtained by using cytoNCA plugin. GO function and KEGG pathway enrichment analysis for core targets were performed by using Metascape. Coremine Medical database was used to match targets with Chinese materia medica. Obtained Chinese materia medica was screened, and their properties and tastes, meridians and efficacy categories were under statistics.Results:Totally 3 859 disease-related targets were screened and five subnetworks were obtained. An in-depth study of MCODE1 yielded ten core targets, including IL-1β, IL6, TNF, STAT3, AKT1, ACTB, VEGFA, GAPDH, TP53, and ALB. GO functional enrichment analysis showed that these core targets were mainly involved in biological processes, such as positive regulation of gene expression, positive regulation of transcription, DNA template, and negative regulation of gene expression. KEGG pathway enrichment analysis identified 67 signaling pathways, including the AGEs-RAGE signaling pathway, HIF-1 signaling pathway, adipocytokine signaling pathway, fluid shear stress, and atherosclerosis. The researchers identified 36 kinds of Chinese materia medica associated with the ten core targets, including Salviea Miltiorrhizae Radix et Rhizoma, Chuanxiaong Rhizoma, Carthami Flos, Paeoniae Radix Rubra, Coptidis Rhizoma, Ginseng Radix et Rhizoma, Ophiopogonis Radix, Schisandrae Chinensis Fructus, Cinnamomi Cortex, Nelumbinis Semen, and Valerianae Jatamansi Rhizoma et Radix among 880 herbs.Conclusion:This study predicts 36 kinds of Chinese materia medica that have the effect of preventing and treating CMD, which can provide research ideas for the development of new drugs.

The present study aims to screen and evaluate the early clinical predictors for type 2 diabetes mellitus (T2DM) patients with mild cognitive impairment (MCI) and dementia in Hunan province. A cross-sectional study was conducted from May 2023 to October 2023 to collect data on long-term T2DM patients who settled in Hunan province and were treated in the Department of Geriatrology at Xiangya Hospital of Central South University. The patients were grouped according to the Montreal Cognitive Assessment (MoCA) scale. Basic patient information and multiple serum markers were collected, and differences between groups were compared using one-way ANOVA or Kruskal-Wallis (KW) tests. The multivariate logistic regression analysis was utilized to assess risk factors and Nomogram models were constructed. The logistic regression analysis showed that years of education and serum levels of 1, 5-AG were related factors for the progression of T2DM to T2DM with MCI, and body weight, years of education and FPN levels affected the progression of T2DM with MCI to T2DM with dementia. Based on this, two Nomogram risk prediction models were established. The area under the curve (AUC) of the Nomogram model predicting T2DM progression to T2DM combined with MCI was 0.741, and the AUC of the Nomogram model predicting T2DM combined with MCI progression to T2DM combined with dementia was 0.734. The calibration curves (DCA) of the two models in the training and validation sets were symmetrically distributed near the diagonal line, indicating that the models in the training and validation sets could match each other. In summary, body weight, years of education, and serum HDL-3, FPN, and 1, 5-AG levels are associated with the development of MCI and dementia in T2DM patients. The Nomogram models constructed based on these factors can predict the risk of MCI and dementia in T2DM patients, providing a basis for clinical decision-making.

The present study aims to screen and evaluate the early clinical predictors for type 2 diabetes mellitus (T2DM) patients with mild cognitive impairment (MCI) and dementia in Hunan province. A cross-sectional study was conducted from May 2023 to October 2023 to collect data on long-term T2DM patients who settled in Hunan province and were treated in the Department of Geriatrology at Xiangya Hospital of Central South University. The patients were grouped according to the Montreal Cognitive Assessment (MoCA) scale. Basic patient information and multiple serum markers were collected, and differences between groups were compared using one-way ANOVA or Kruskal-Wallis (KW) tests. The multivariate logistic regression analysis was utilized to assess risk factors and Nomogram models were constructed. The logistic regression analysis showed that years of education and serum levels of 1, 5-AG were related factors for the progression of T2DM to T2DM with MCI, and body weight, years of education and FPN levels affected the progression of T2DM with MCI to T2DM with dementia. Based on this, two Nomogram risk prediction models were established. The area under the curve (AUC) of the Nomogram model predicting T2DM progression to T2DM combined with MCI was 0.741, and the AUC of the Nomogram model predicting T2DM combined with MCI progression to T2DM combined with dementia was 0.734. The calibration curves (DCA) of the two models in the training and validation sets were symmetrically distributed near the diagonal line, indicating that the models in the training and validation sets could match each other. In summary, body weight, years of education, and serum HDL-3, FPN, and 1, 5-AG levels are associated with the development of MCI and dementia in T2DM patients. The Nomogram models constructed based on these factors can predict the risk of MCI and dementia in T2DM patients, providing a basis for clinical decision-making.

Objective:To simulate the biomechanical characteristics of the real human thoracic cavity with a multi-spring group variable damping structure, and to design a new cardiopulmonary resuscitation training manikin based on the simulated thoracic biomechanical characteristics combined with the original electronic feedback system, and to test its application effect in cardiopulmonary resuscitation (CPR) teaching.Methods:A total of 60 undergraduate students majoring in five-year clinical medicine of Batch 2019 in Naval Medical University were selected as the research objects and were randomly divided into the experimental group and the control group, with 30 students in each group. The control group used the traditional manikin for CPR training, and the experimental group used the new type of manikin for CPR training based on the control group. After the training, the two groups of personnel were assessed for single skill. The single skill was mainly manual CPR operations, including artificial respiration and chest compressions. The theory and skill operation assessment of CPR and satisfaction for teaching method in the two groups were compared. SPSS 23.0 was used for statistical analysis.Results:The students in the experimental group scored (54.33±3.09) points in the single skill operation assessment, which were significantly better than that of the students in the control group [(52.33±3.08) points], and the difference was statistically significant ( P<0.05). The follow-up questionnaire showed that the students in the experimental group had a better evaluation of the teaching and training effect of the new type of manikin. Conclusion:Compared with the traditional manikin, the new CPR manikin can simulate the CPR emergency scene of the real human body, which can effectively improve the CPR teaching effect of standardized training for medical students, and help the standardization, normalization, and popularization of CPR technology in China.

Objective To compare the biomechanical effects of contiguous three-level cervical Hybrid surgery[anterior cervical discectomy and fusion (ACDF) + cervical disc arthroplasty ( CDA)] and three-level ACDF. Methods The finite element model of C1-T1 cervical-thoracic spine was developed based on CT data. Three models were simulated by the implantation of Prestige LP and Zero-P prostheses, including two Hybrid models (AFA, Prestige LP implanted at C3-4 and C5-6 segments and Zero-P implanted at C4-5 segment; FAF, Zero-P implanted at C3-4 and C5-6 segments and Prestige LP implanted at C4-5 segment) and three-level ACDF model(FFF). The changes in range of motion (ROM) of adjacent levels during flexion, extension, lateral bending and axial rotation, the overall ROM, as well as the intradiscal pressure ( IDP) and facet contact force ( FCF) of adjacent levels were compared. Results The ROM in adjacent levels and the overall ROM of the AFA modelwere closer to the intact model, and the maximum increases in the ROM of the adjacent levels for the FAF and FFF models were 15. 0% and 23. 4% , respectively. For AFA, FAF and FFF models, the maximum increases in the maximum IDP of adjacent levels were 19. 0% , 66. 7% , 147. 6% , and the maximum increases in FCF were 17. 4% , 55. 7% , 80. 1% , respectively. Conclusions This study provides biomechanical basis for three-level cervical Hybrid surgery in treating patients with the contiguous three-level cervical degenerative disc disease.

In the current clinical diagnosis, medical images have become an important basis for diagnosis, and different modes of medical images provide different tissue information and functional information. Single-mode images can only provide single diagnostic information, by which difficult and complicated diseases cannot be diagnosed, and comprehensive and accurate diagnostic results can be obtained only with the help of multiple diagnostic information. The multimodal fusion technology fuses multiple modes of medical images into single-mode images, and thus the single-mode images contain complementary information between multiple modes of images, so that sufficient information for clinical diagnosis can be obtained in a single image. In this paper, the multimodal medical image fusion methods are sorted into two types, namely the traditional fusion method and the fusion method based on deep learning.