ObjectiveThis paper aims to conduct a secondary analysis of a randomized controlled trial on the treatment of myelodysplastic syndrome （MDS） with deficiency of both the spleen and kidney and blockage of toxin and blood stasis with an arsenic-containing traditional Chinese medicine compound， by applying the mixed model for repeated measure （MMRM） and the method of stratified composite outcome with win ratio. The analysis includes the assessment of hematological efficacy and the composite outcome evaluation of adverse reactions， so as to more comprehensively assess the therapy of this regimen. MethodsThe MMRM and win ratio methods were used to evaluate the efficacy of a prospective，multi-center，double-blind，randomized controlled study. The blood routine （hemoglobin concentration，neutrophil count， and platelet count） and biochemical indexes （aspartate aminotransferase，alanine aminotransferase，serum creatinine，and serum ferritin） of the patients were detected at the time of enrollment and at the end of each course of treatment in the laboratory department of Xiyuan Hospital. The patients' syndromes at the time of enrollment and after treatment were recorded and scored according to the therapy standard of traditional Chinese medicine for diseases and syndromes. MMRM was used to analyze the blood routine indexes of the experimental group and the control group. This method has the advantages of high data reliability and dynamic efficacy under intervention and time. The win ratio method was used to evaluate the composite outcome of traditional Chinese medicine syndrome scores and biochemical indexes according to the priority and to verify the clinical safety of arsenic-containing traditional Chinese medicine compound. ResultsThe results of MMRM analysis showed that the hemoglobin concentration of patients in the group with arsenic-containing traditional Chinese medicine compound increased significantly compared with that before treatment in the group，while that in the placebo group decreased significantly （P<0.01）. When compared with that after treatment in the placebo group，the hemoglobin concentration of patients in the group with arsenic-containing traditional Chinese medicine compound increased significantly，and the mean difference of least squares （LS） was statistically significant （P<0.01）. When compared with those before treatment in the group，there were no statistically significant differences in the neutrophil count and platelet count in both groups. After treatment，there were no statistically significant differences in the neutrophil count， platelet count， and the mean difference of LS between the two groups. The analysis results of win ratio showed that the group with arsenic-containing traditional Chinese medicine compound had a significant advantage in the comparison of composite outcomes，with a win ratio （95% CI） of 2.01 （1.24-3.27） （P<0.01），and that the possibility of "winning" in terms of safety was 2.01 times that of the placebo group. The safety advantage of the group with arsenic-containing traditional Chinese medicine compound mainly came from the traditional Chinese medicine syndrome scores，renal function indexes， and iron reserve capacity indexes，and the number of winning times was less than that of losing times in the comparison of liver function outcomes. ConclusionThe MMRM analysis proves that the arsenic-containing traditional Chinese medicine compound can significantly improve the hemoglobin concentration of patients with myelodysplastic syndrome with refractory cytopenia and multilineage dysplasia （MDS-RCMD） of the type of deficiency of both the spleen and kidney and blockage of toxin and blood stasis. This conclusion is not interfered with by time trends and individual relationships and methodologically improves the credibility of the therapy of the arsenic-containing traditional Chinese medicine compound in treating MDS. Four outcomes are evaluated by the win ratio method，namely traditional Chinese medicine syndromes，liver function，renal function， and iron reserve capacity，proving that the arsenic-containing traditional Chinese medicine compound has the comprehensive advantages of improving the survival quality of the patients and reducing adverse reactions. The win ratio outcome provides clear comparative indexes for the evaluation of adverse reactions，making it easier for regulatory authorities，medical staff， and patients to understand the safety of the arsenic-containing traditional Chinese medicine compound in clinical application.

Background: Obesity and chronic pain are related in both directions, according to earlier observational research.This research aimed to analyze the causal association between obesity and chronic pain at the genetic level, as well as to assess whether common factors mediate this relationship. Methods: This study used bidirectional two sample Mendelian randomization (MR) technique to analyze the association between obesity and chronic pain. Obesity's summary genome-wide association data were obtained from European ancestry groups, as measured by body mass index (BMI), waist-to-hip ratio, waist circumference (WC), and hip circumference (HC), genome-wide association study data for chronic pain also came from the UK population, including chronic pain at three different sites (back, hip, and headache), chronic widespread pain (CWP), and multisite chronic pain (MCP). Secondly, a two-step MR and multivariate MR investigation was performed to evaluate the mediating effects of several proposed confounders. Results: The authors discovered a link between chronic pain and obesity. More specifically, a sensitivity analysis was done to confirm the associations between greater BMI, WC, and HC with an increased risk of CWP and MCP.Importantly, the intermediate MR results suggest that education levels and smoking initiation may mediate the causal relationship between BMI on CWP, with a mediation effect of 23.08% and 15.38%, respectively. Conclusions The authors’ findings demonstrate that the importance of education and smoking in understanding chronic pain’s pathogenesis, which is important for the primary prevention and prognosis of chronic pain.

Background: Obesity and chronic pain are related in both directions, according to earlier observational research.This research aimed to analyze the causal association between obesity and chronic pain at the genetic level, as well as to assess whether common factors mediate this relationship. Methods: This study used bidirectional two sample Mendelian randomization (MR) technique to analyze the association between obesity and chronic pain. Obesity's summary genome-wide association data were obtained from European ancestry groups, as measured by body mass index (BMI), waist-to-hip ratio, waist circumference (WC), and hip circumference (HC), genome-wide association study data for chronic pain also came from the UK population, including chronic pain at three different sites (back, hip, and headache), chronic widespread pain (CWP), and multisite chronic pain (MCP). Secondly, a two-step MR and multivariate MR investigation was performed to evaluate the mediating effects of several proposed confounders. Results: The authors discovered a link between chronic pain and obesity. More specifically, a sensitivity analysis was done to confirm the associations between greater BMI, WC, and HC with an increased risk of CWP and MCP.Importantly, the intermediate MR results suggest that education levels and smoking initiation may mediate the causal relationship between BMI on CWP, with a mediation effect of 23.08% and 15.38%, respectively. Conclusions The authors’ findings demonstrate that the importance of education and smoking in understanding chronic pain’s pathogenesis, which is important for the primary prevention and prognosis of chronic pain.

Background: Obesity and chronic pain are related in both directions, according to earlier observational research.This research aimed to analyze the causal association between obesity and chronic pain at the genetic level, as well as to assess whether common factors mediate this relationship. Methods: This study used bidirectional two sample Mendelian randomization (MR) technique to analyze the association between obesity and chronic pain. Obesity's summary genome-wide association data were obtained from European ancestry groups, as measured by body mass index (BMI), waist-to-hip ratio, waist circumference (WC), and hip circumference (HC), genome-wide association study data for chronic pain also came from the UK population, including chronic pain at three different sites (back, hip, and headache), chronic widespread pain (CWP), and multisite chronic pain (MCP). Secondly, a two-step MR and multivariate MR investigation was performed to evaluate the mediating effects of several proposed confounders. Results: The authors discovered a link between chronic pain and obesity. More specifically, a sensitivity analysis was done to confirm the associations between greater BMI, WC, and HC with an increased risk of CWP and MCP.Importantly, the intermediate MR results suggest that education levels and smoking initiation may mediate the causal relationship between BMI on CWP, with a mediation effect of 23.08% and 15.38%, respectively. Conclusions The authors’ findings demonstrate that the importance of education and smoking in understanding chronic pain’s pathogenesis, which is important for the primary prevention and prognosis of chronic pain.

Background: Obesity and chronic pain are related in both directions, according to earlier observational research.This research aimed to analyze the causal association between obesity and chronic pain at the genetic level, as well as to assess whether common factors mediate this relationship. Methods: This study used bidirectional two sample Mendelian randomization (MR) technique to analyze the association between obesity and chronic pain. Obesity's summary genome-wide association data were obtained from European ancestry groups, as measured by body mass index (BMI), waist-to-hip ratio, waist circumference (WC), and hip circumference (HC), genome-wide association study data for chronic pain also came from the UK population, including chronic pain at three different sites (back, hip, and headache), chronic widespread pain (CWP), and multisite chronic pain (MCP). Secondly, a two-step MR and multivariate MR investigation was performed to evaluate the mediating effects of several proposed confounders. Results: The authors discovered a link between chronic pain and obesity. More specifically, a sensitivity analysis was done to confirm the associations between greater BMI, WC, and HC with an increased risk of CWP and MCP.Importantly, the intermediate MR results suggest that education levels and smoking initiation may mediate the causal relationship between BMI on CWP, with a mediation effect of 23.08% and 15.38%, respectively. Conclusions The authors’ findings demonstrate that the importance of education and smoking in understanding chronic pain’s pathogenesis, which is important for the primary prevention and prognosis of chronic pain.

Background: Obesity and chronic pain are related in both directions, according to earlier observational research.This research aimed to analyze the causal association between obesity and chronic pain at the genetic level, as well as to assess whether common factors mediate this relationship. Methods: This study used bidirectional two sample Mendelian randomization (MR) technique to analyze the association between obesity and chronic pain. Obesity's summary genome-wide association data were obtained from European ancestry groups, as measured by body mass index (BMI), waist-to-hip ratio, waist circumference (WC), and hip circumference (HC), genome-wide association study data for chronic pain also came from the UK population, including chronic pain at three different sites (back, hip, and headache), chronic widespread pain (CWP), and multisite chronic pain (MCP). Secondly, a two-step MR and multivariate MR investigation was performed to evaluate the mediating effects of several proposed confounders. Results: The authors discovered a link between chronic pain and obesity. More specifically, a sensitivity analysis was done to confirm the associations between greater BMI, WC, and HC with an increased risk of CWP and MCP.Importantly, the intermediate MR results suggest that education levels and smoking initiation may mediate the causal relationship between BMI on CWP, with a mediation effect of 23.08% and 15.38%, respectively. Conclusions The authors’ findings demonstrate that the importance of education and smoking in understanding chronic pain’s pathogenesis, which is important for the primary prevention and prognosis of chronic pain.

To address the challenges faced by current brain midline segmentation techniques, such as insufficient accuracy and poor segmentation continuity, this paper proposes a deep learning network model based on a two-stage framework. On the first stage of the model, prior knowledge of the feature consistency of adjacent brain midline slices under normal and pathological conditions is utilized. Associated midline slices are selected through slice similarity analysis, and a novel feature weighting strategy is adopted to collaboratively fuse the overall change characteristics and spatial information of these associated slices, thereby enhancing the feature representation of the brain midline in the intracranial region. On the second stage, the optimal path search strategy for the brain midline is employed based on the network output probability map, which effectively addresses the problem of discontinuous midline segmentation. The method proposed in this paper achieved satisfactory results on the CQ500 dataset provided by the Center for Advanced Research in Imaging, Neurosciences and Genomics, New Delhi, India. The Dice similarity coefficient (DSC), Hausdorff distance (HD), average symmetric surface distance (ASSD), and normalized surface Dice (NSD) were 67.38 ± 10.49, 24.22 ± 24.84, 1.33 ± 1.83, and 0.82 ± 0.09, respectively. The experimental results demonstrate that the proposed method can fully utilize the prior knowledge of medical images to effectively achieve accurate segmentation of the brain midline, providing valuable assistance for subsequent identification of the brain midline by clinicians.
Humans ; Brain/diagnostic imaging* ; Deep Learning ; Image Processing, Computer-Assisted/methods* ; Algorithms ; Magnetic Resonance Imaging/methods* ; Neural Networks, Computer