Objective To establish the therapeutic effect prediction model of platelet transfusion in hematological patients,and receiver operating characteristic(ROC)curve and clinical cases are used to evaluate the clinical application value of the predic-tion model.Methods A total of 485 patients with hematological diseases who received platelet transfusion therapy in Kailuan General Hospital from January 2020 to December 2023 were selected,corrected count increment(CCI)was used to divide the patients into platelet transfusion effective group(n=340)and transfusion ineffective group(n=145).Multivariate Logistic regres-sion analysis was used to establish the prediction model of platelet infusion efficacy,and ROC curve was used to evaluate the application effect of the forcasting model.109 clinical cases were used to verify the practical application effect of the model,and the sensitivity,specificity and accuracy were calculated.Results Among 485 patients with hematological diseases,the incidence of ineffective platelet transfusion was 29.90%(145/485).Compated with the effective group,the ineffective group had more previous platelet transfusions was higher,and the difference was statistically significant(t=-4.435,P<0.05).In the ineffective group,there were more cases of hyperplenism,aplastic anemia and lymphoma,higher infection rate and higher positive rate of platelet antibody,and the differences were statistically significant(χ2=6.301～37.522,all P<0.05).Multivariate Logistic regres-sion analysis found that previous platelet infusion times,infection,leukemia,aplastic anemia and platelet antibodies were risk factors for ineffective platelet transfusion in patients with hematological diseases(Wald χ2=5.224～21.548,all P<0.05).Based on these risk factors,platelet infusion effect prediction models 1 and 2 were constructed.ROC curve was used to evaluate the application effect of the prediction model.The area under the curve(AUC),cut-offpoint,sensitivity and specificity of model 1 were 0.884,0.042,82.35%,88.89%.The AUC,cut-offpoint,corresponding sensitivity and specificity of prediction model 2 were 0.910,59.784,81.18%,94.44%,respectively.The Z values of model 1 and model 2 were 12.159 and 13.151,respectively.The prediction effect of model 2 was better than that of model 1.The actual application results showed that the sensitivity,specificity and accuracy of prediction model 1,2 were 85.71%,92.05%,90.89%and 90.48%,93.18%,92.66%,respectively.Conclusion The ineffective rate of platelet transfusion in hematological patients is relatively high.The prediction models 1 and 2 for platelet transfusion effectiveness have good results in predicting ineffective platelet transfusion,and prediction model 2 is better than pre-diction model 1,which can provide reliable basis for hematological patients on accurate platelet transfusion.

Objective To establish the therapeutic effect prediction model of platelet transfusion in hematological patients,and receiver operating characteristic(ROC)curve and clinical cases are used to evaluate the clinical application value of the predic-tion model.Methods A total of 485 patients with hematological diseases who received platelet transfusion therapy in Kailuan General Hospital from January 2020 to December 2023 were selected,corrected count increment(CCI)was used to divide the patients into platelet transfusion effective group(n=340)and transfusion ineffective group(n=145).Multivariate Logistic regres-sion analysis was used to establish the prediction model of platelet infusion efficacy,and ROC curve was used to evaluate the application effect of the forcasting model.109 clinical cases were used to verify the practical application effect of the model,and the sensitivity,specificity and accuracy were calculated.Results Among 485 patients with hematological diseases,the incidence of ineffective platelet transfusion was 29.90%(145/485).Compated with the effective group,the ineffective group had more previous platelet transfusions was higher,and the difference was statistically significant(t=-4.435,P<0.05).In the ineffective group,there were more cases of hyperplenism,aplastic anemia and lymphoma,higher infection rate and higher positive rate of platelet antibody,and the differences were statistically significant(χ2=6.301～37.522,all P<0.05).Multivariate Logistic regres-sion analysis found that previous platelet infusion times,infection,leukemia,aplastic anemia and platelet antibodies were risk factors for ineffective platelet transfusion in patients with hematological diseases(Wald χ2=5.224～21.548,all P<0.05).Based on these risk factors,platelet infusion effect prediction models 1 and 2 were constructed.ROC curve was used to evaluate the application effect of the prediction model.The area under the curve(AUC),cut-offpoint,sensitivity and specificity of model 1 were 0.884,0.042,82.35%,88.89%.The AUC,cut-offpoint,corresponding sensitivity and specificity of prediction model 2 were 0.910,59.784,81.18%,94.44%,respectively.The Z values of model 1 and model 2 were 12.159 and 13.151,respectively.The prediction effect of model 2 was better than that of model 1.The actual application results showed that the sensitivity,specificity and accuracy of prediction model 1,2 were 85.71%,92.05%,90.89%and 90.48%,93.18%,92.66%,respectively.Conclusion The ineffective rate of platelet transfusion in hematological patients is relatively high.The prediction models 1 and 2 for platelet transfusion effectiveness have good results in predicting ineffective platelet transfusion,and prediction model 2 is better than pre-diction model 1,which can provide reliable basis for hematological patients on accurate platelet transfusion.

Objective:To develop PixelSIFT-UNet, a novel semantic segmentation model that integrates deep learning with scale-invariant feature transform (SIFT) algorithm to improve the segmentation accuracy of oral mucosal lesions.Methods:This investigation utilized 838 standard clinical white light images of oral mucosal diseases acquired from January 2020 to December 2022 at the Stomatology Hospital Zhejiang University School of Medicine. Randomization was achieved through Python′s random.seed function implementation. The random sample function was subsequently applied for sampling distribution. The dataset was stratified into three subsets with a 6∶2∶2 ratio: training ( n=506), validation ( n=166), and testing ( n=166). Lesion boundaries were annotated using Labelme software, and a PixelSIFT-UNet-based deep learning model was developed with VGG-16 and ResNet-50 backbone networks. Model parameters were optimized using the validation set, and performance metrics [including Dice coefficient, mean intersection over union (mIoU), mean pixel accuracy (mPA), and Precision] were assessed on the test set. The model′s performance was benchmarked against conventional semantic segmentation frameworks (U-Net and PSPNet). Results:The developed PixelSIFT-UNet model could achieve precise segmentation of three common oral mucosal lesions: oral lichen planus, oral leukoplakia, and oral submucous fibrosis. Utilizing VGG-16 as the backbone network, the model achieved Dice coefficient, mIoU, mPA, and Precision values of 0.642, 0.699, 0.836, and 0.792, respectively. Implementation with ResNet-50 backbone network yielded metrics of 0.668, 0.733, 0.872 and 0.817, demonstrating significant improvements across all performance indicators compared to conventional U-Net model (relevant metrics: 0.662, 0.717, 0.861 and 0.809) and PSPNet model (relevant metrics: 0.671, 0.721, 0.858 and 0.813).Conclusions:The proposed PixelSIFT-UNet architecture demonstrates superior performance in oral mucosal lesion segmentation tasks, surpassing conventional semantic segmentation models and providing robust quantitative improvements in segmentation accuracy.

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.

Objective:To compare the value of different imaging modalities in evaluating early efficacy of 90Y-selective internal radiation therapy (SIRT) for liver malignant tumors. Methods:From September 2021 to December 2023, a retrospective analysis was conducted on 43 patients (32 males, 11 females; age (55.8±14.7) years) with liver malignant tumors who received 90Y-SIRT at Hainan Cancer Hospital and Boao Super Hospital. The injection dosage of 90Y was 1.5(1.2, 2.4)GBq. Clinical and imaging data of patients before and after treatment for multimodal evaluation were collected, including MRI plain and enhanced scans, as well as diffusion weighted imaging (DWI). Plain scan images were evaluated for the treatment efficacy by response evaluation criteria in solid tumors (RECIST) 1.1 version, while enhanced MRI was scored into 1-5 based on changes in the target area, including increased low-density range, necrotic features, and decreased enhancement. The minimum apparent diffusion coefficient (ADC min) of DWI increased by 20% compared to baseline was determined to be responsive. Wilcoxon signed rank test was used to analyze data, and ROC curve analysis was used to analyze the diagnostic efficacy of different imaging modalities (Delong test). Results:All patients had baseline MRI data, 39 underwent MRI at 1-month after treatment, and 22 underwent MRI at 3-month after treatment. Based on MRI plain scan images, the target lesions showed partial remission (PR) in 10.26%(4/39) of patients, progressive disease (PD) in 5.13%(2/39) of patients, and stable disease (SD) in 84.61%(33/39) of patients at 1-month after treatment; while there were 40.91%(9/22) PR, 9.09%(2/22) PD and 50.00%(11/22) SD at 3-month after treatment. Based on DWI images, 35.90%(14/39) and 68.18%(15/22) of patients were considered responsive at 1-month and 3-month after treatment, respectively. Compared with baseline, the differences of tumor sizes, enhancement degree of target lesions and ADC min at 1-month and 3-month after treatment were statistically significant ( Z values: from -3.88 to -2.39, all P<0.05). Compared with the tumor size and enhancement degree, the AUCs of ADC min were the highest at 1-month (0.701) and 3-month (0.953) after treatment ( Z values: 0.40-2.29, all P<0.05). Conclusions:MRI plain scan, MRI enhancement and DWI are effective in the evaluation the efficacy of 90Y-SIRT for liver malignant tumors at 1-month and 3-month after treatment. ADC min is superior to tumor size and enhancement degree in diagnostic efficacy.

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.

Rice is the world's largest food crop, and its yield and quality are directly related to food security and human health. Grain size, as one of the important factors determining the rice yield, has been widely concerned by breeders and researchers for a long time. To decipher the regulatory mechanism of rice grain size, we obtained a multi-tiller, dwarf, and small-grain mutant htd1 by ethyl methanesulfonate (EMS) mutation from the Japonica rice cultivar 'Zhonghua 11' ('ZH11'). Genetic analysis indicated that the phenotype of htd1 was controlled by a single recessive gene. Using the mutation site map (Mutmap) method, we identified the candidate gene OsHTD1, which encoded a carotenoid cleavage dioxygenase involved in the biosynthesis of strigolactone (SL). The SL content in htd1 was significantly lower than that in 'ZH11'. Cytological analysis showed that the grain size of the mutant decreased due to the reductions in the length and width of glume cells. The function of htd1 was further verified by the CRISPR/cas9 gene editing technology. The plants with the gene knockout exhibited similar grain size to the mutant. In addition, gene expression analysis showed that the expression levels of multiple grain size-related genes in the mutant changed significantly, suggesting that HTD1 may interact with other genes regulating grain size. This study provides a new theoretical basis for research on the regulatory mechanism of rice grain size and potential genetic resources for breeding the rice cultivars with high yields.
Oryza/growth & development* ; Mutation ; Edible Grain/growth & development* ; Alleles ; Plant Proteins/genetics* ; Dioxygenases/genetics* ; Lactones/metabolism* ; Gene Expression Regulation, Plant ; Genes, Plant ; Gene Editing ; CRISPR-Cas Systems ; Phenotype