OBJECTIVES: To analyze the differences in the prognosis of gastric signet ring cell carcinoma (SRCC) among different races using the US Surveillance Epidemiology and End Results (SEER) database and The Cancer Genome Atlas (TCGA) database. METHODS: We analyzed the data of patients with gastric SRCC from the SEER database from 2000 to 2020, and divided the patients into cohorts of whites, blacks, Asians or Pacific Islanders, American Indians/Alaska Natives according to their race. The prognosis and treatment of the cohorts were evaluated using baseline demographic analysis, Kamplan-Meier survival curve, and nomogram analysis. RESULTS: We analyzed the data of a total of 2058 patients, including 8.6% blacks, 72.4% whites, 16.6% Asians or Pacific Islanders, 1.0% American Indians/Alaska Natives, and 1.4% other races. The tumor grade varied among different races, and the prevalence and survival rates of patients differed significantly across races. The differences in the white cohort were the most prominent, and all the differences were statistically significant (P<0.05). Racial differences were also noted in patient management and prognosis. CONCLUSIONS There are racial differences in tumor grades and prognosis of gastric SRCC, and these differences provide evidence for optimizing clinical diagnosis and treatment strategies for this malignancy.
Aged ; Female ; Humans ; Male ; Middle Aged ; Carcinoma, Signet Ring Cell/therapy* ; Databases, Factual ; Prognosis ; Racial Groups ; SEER Program ; Stomach Neoplasms/therapy* ; Survival Rate ; United States/epidemiology* ; White ; Asian American Native Hawaiian and Pacific Islander ; American Indian or Alaska Native ; Black or African American

ObjectiveTo realize the automatic recognition of the slicing angles of Fritillariae Thunbergii Bulbus （FTB） based on the improved YOLOv7-tiny algorithm. MethodFirstly， a diverse dataset of FTB images， totaling 16 000 pictures， with various angles was constructed. Furthermore， improvements were made to YOLOv7-tiny by replacing standard convolutions with ghost convolution （GhostConv）， incorporating the coordinate attention （CA） mechanism as a preferred addition， substituting some activation functions with HardSwish function for decreasing the floating point operations. Additionally， a penalty term for angle recognition error was integrated into the loss function， and modifications were made to the non-maximum suppression （NMS） strategy to address cases where multiple detection results were associated with the same target. In order to verify the effectiveness of different improvement points on the optimization of the algorithm model， ablation experiments were carried out on all the improvement points， and the effectiveness of the improvement points was proved by comparing the prediction results before and after the addition of a certain improvement point on the basis of the original model or the model with the addition of an improvement point that has been verified to be effective， in order to evaluate the improvement of the indexes. ResultThe number of parameters required for the improved slicing angle recognition algorithm of FTB was about 55.4% of the original algorithm， and the amount of computation was about 59.4% of the original algorithm. The mAP@0.5［mean average precision at an intersection over union（IoU） of 0.5］ increased by 12.2%， the mean absolute error（MAE） of the recognized angle was 5.02°， representing a reduction of 4.58° compared to the original algorithm. In the experimental environment of this paper， the average recognition time per image was as low as 8.7 ms， significantly faster than the average human reaction time. ConclusionThis study， by utilizing the improved YOLOv7-tiny algorithm， achieves effective slicing angle recognition of FTB with high accuracy and more lightweight， which provides a novel approach for stable and precise automated slicing of FTB， thereby providing valuable insights into the automation of processing other traditional Chinese medicines.

BACKGROUND:Diabetic foot patients with wound infections constitute a large patient population,and there is currently no satisfactory treatment approach. OBJECTIVE:To investigate the clinical efficacy of a modified tibial cortex transverse transport combined with antibiotic-loaded bone cement for treating refractory diabetic foot ulcers. METHODS:A total of 46 diabetic foot ulcers patients,27 males and 19 females,with an average age of 64.37 years,were selected from Beijing Chaoyang Hospital,Capital Medical University and Beijing Chaoyang Integrative Medicine Rescue and First Aid Hospital from January 2020 to January 2023.All of them underwent the modified tibial cortex transverse transport combined with antibiotic-loaded bone cement treatment.Ankle-brachial index,WIFi(Wound/Ischemia/Foot infection)classification,pain visual analog scale score,and ulcer area were recorded before and 3 months after surgery. RESULTS AND CONCLUSION:(1)The mean ulcer healing time for the 46 patients was(58.07±24.82)days.At 3 months postoperatively,there were significant improvements in ankle-brachial index,pain visual analog scale score,ulcer area,and WIFi classification in 46 patients,as compared to the preoperative values,with statistically significant differences(P<0.05).Two patients experienced pin-tract infections,without infection or ulcer recurrence during the follow-up period.(2)These findings indicate that the modified tibial cortex transverse transport combined with antibiotic-loaded bone cement effectively alleviates patients'pain,improves lower limb circulation,controls infections,and promotes ulcer healing.