Objective To explore the current status of non-operative management strategy for blunt hepatic trauma. Methods The clinical data of patients with blunt hepatic trauma admitted to our hospital during the past 15 years were retrospectively analyzed, and the new standpoints in the selection of therapeutic strategy for blunt hepatic trauma were elucidated with referance to recent litereture, especially regarding the different opinions in non-operative management in the treatment of blunt hepatic trauma between the developed countries and China. Results The concept in the selection of therapeutic strategy for blunt hepatic trauma in China lagged relatively behind the advanced countries. Compared with that in the developed countries, the percentage of non-operative management was significantly lower in China (10%-30% vs 50%-80%), and the fewer CT scanning was carried out in the patients with stable hemodynamics. Conclusion Non-operative management is becoming one of the most important strategies in the treatment of blunt hepatic trauma nowadays. Non-operative management is widely acknowledged as the first choice for blunt hepatic trauma, especially for those with stable hemodynamics. This new trend should be more emphasized in China. Besides, CT scanning as a diagnostic tool should be carried out as frequently as possible in those patients with stable hemodynamics.

Objective:SFRP2 gene is a member of the SFRPs family.The gene is located on chromosome 4q31.3 with 3 exons and 2 introns and first exons have higher density near the island of CpG.Many studies showed that the methylation level of SFRP2 gene and colon cancer,esophageal cancer,gastric cancer and other tumor occurrence relating to,development and prog nosis.This study aims to study the clinical characteristics of CpG SFRP2 promoter island hypermethylation in colorectal cancer and whether there is a certain correlation.Methods:by matrix assisted laser desorption ionization time of flight mass spectrometry for detecting specific CpG island methylation.Methylation status of SFRP2 promoter by Sequenom EpiTYPER was detected in 20 cases of normal tissue of colorectal cancer and tumor tissues.Results:Our study using multiple linear regression analysis in tumor tissue of SFRP2 methylation at promoter Ⅰ and Ⅱ,found that SFRP2 promoter methylation and clinical features of.SFRP2_01_CpG_5 significantly correlated (P=0.018),SFRP2_02_CpG_5 (P=0.018) associated with the location of the tumor,SFRP2_02_CpG_6,7,8,9(P=0.039) and the number of lymph node metastasis of.SFRP2_01_CpG_1.2(P=0.043),SFRP2_02_CpG_16 (P=0.044) correlated with tumor size.Conclusion:we from epigenetic aspects of its promoter CpG methylation level and colorectal cancer clinical and pathological features,found a correlation between clinical and pathological features of colorectal cancer and CpG methylation in its promoter,suggesting that the SFRP2 promoter may be at this stage of colorectal cancer and future biological genetics the progress of the potential surface markers.

Primary retroperitoneal liposarcoma is a rare low-grade malignant tumor and accounts for approximately 1% of all adult malignancies. Complete gross resection is the most important and maybe only method to cure retroperitoneal liposarcoma. The addition of advanced- modality radiotherapy to surgery for primary retroperitoneal liposarcoma is associated with improved local recurrence-free survival, and the toxic effect is tolerable. Several new drugs, especially targeted drugs, have achieved good efficacy. The aim of this article is to improve the understanding of treatment of primary retroperitoneal liposarcoma. The multidisciplinary therapy model, including surgery, radiotherapy, chemotherapy and targeted therapy, is recommended for patients with retroperitoneal liposarcoma.

Objective:To investigate the feasibility and accuracy of computer vision-based artificial intelligence technology in detecting and recognizing instruments and organs in the scenario of radical laparoscopic gastrectomy for gastric cancer.Methods:Eight complete laparoscopic distal radical gastrectomy surgery videos were collected from four large tertiary hospitals in China (First Medical Center of Chinese PLA General Hospital [three cases], Liaoning Cancer Hospital [two cases], Liyang Branch of Jiangsu Province People's Hospital [two cases], and Fudan University Shanghai Cancer Center [one case]). PR software was used to extract frames every 5–10 seconds and convert them into image frames. To ensure quality, deduplication was performed manually to remove obvious duplication and blurred image frames. After conversion and deduplication, there were 3369 frame images with a resolution of 1,920×1,080 PPI. LabelMe was used for instance segmentation of the images into the following 23 categories: veins, arteries, sutures, needle holders, ultrasonic knives, suction devices, bleeding, colon, forceps, gallbladder, small gauze, Hem-o-lok, Hem-o-lok appliers, electrocautery hooks, small intestine, hepatogastric ligaments, liver, omentum, pancreas, spleen, surgical staplers, stomach, and trocars. The frame images were randomly allocated to training and validation sets in a 9:1 ratio. The YOLOv8 deep learning framework was used for model training and validation. Precision, recall, average precision (AP), and mean average precision (mAP) were used to evaluate detection and recognition accuracy.Results:The training set contained 3032 frame images comprising 30 895 instance segmentation counts across 23 categories. The validation set contained 337 frame images comprising 3407 instance segmentation counts. The YOLOv8m model was used for training. The loss curve of the training set showed a smooth gradual decrease in loss value as the number of iteration calculations increased. In the training set, the AP values of all 23 categories were above 0.90, with a mAP of 0.99, whereas in the validation set, the mAP of the 23 categories was 0.82. As to individual categories, the AP values for ultrasonic knives, needle holders, forceps, gallbladders, small pieces of gauze, and surgical staplers were 0.96, 0.94, 0.91, 0.91, 0.91, and 0.91, respectively. The model successfully inferred and applied to a 5-minutes video segment of laparoscopic gastroenterostomy suturing.Conclusion:The primary finding of this multicenter study is that computer vision can efficiently, accurately, and in real-time detect organs and instruments in various scenarios of radical laparoscopic gastrectomy for gastric cancer.

Objective:To investigate the feasibility and accuracy of computer vision-based artificial intelligence technology in detecting and recognizing instruments and organs in the scenario of radical laparoscopic gastrectomy for gastric cancer.Methods:Eight complete laparoscopic distal radical gastrectomy surgery videos were collected from four large tertiary hospitals in China (First Medical Center of Chinese PLA General Hospital [three cases], Liaoning Cancer Hospital [two cases], Liyang Branch of Jiangsu Province People's Hospital [two cases], and Fudan University Shanghai Cancer Center [one case]). PR software was used to extract frames every 5–10 seconds and convert them into image frames. To ensure quality, deduplication was performed manually to remove obvious duplication and blurred image frames. After conversion and deduplication, there were 3369 frame images with a resolution of 1,920×1,080 PPI. LabelMe was used for instance segmentation of the images into the following 23 categories: veins, arteries, sutures, needle holders, ultrasonic knives, suction devices, bleeding, colon, forceps, gallbladder, small gauze, Hem-o-lok, Hem-o-lok appliers, electrocautery hooks, small intestine, hepatogastric ligaments, liver, omentum, pancreas, spleen, surgical staplers, stomach, and trocars. The frame images were randomly allocated to training and validation sets in a 9:1 ratio. The YOLOv8 deep learning framework was used for model training and validation. Precision, recall, average precision (AP), and mean average precision (mAP) were used to evaluate detection and recognition accuracy.Results:The training set contained 3032 frame images comprising 30 895 instance segmentation counts across 23 categories. The validation set contained 337 frame images comprising 3407 instance segmentation counts. The YOLOv8m model was used for training. The loss curve of the training set showed a smooth gradual decrease in loss value as the number of iteration calculations increased. In the training set, the AP values of all 23 categories were above 0.90, with a mAP of 0.99, whereas in the validation set, the mAP of the 23 categories was 0.82. As to individual categories, the AP values for ultrasonic knives, needle holders, forceps, gallbladders, small pieces of gauze, and surgical staplers were 0.96, 0.94, 0.91, 0.91, 0.91, and 0.91, respectively. The model successfully inferred and applied to a 5-minutes video segment of laparoscopic gastroenterostomy suturing.Conclusion:The primary finding of this multicenter study is that computer vision can efficiently, accurately, and in real-time detect organs and instruments in various scenarios of radical laparoscopic gastrectomy for gastric cancer.

Objective:To investigate the clinical characteristics of 130 children with severe SARS-CoV-2 infection in Yunnan province after the relaxation of non-pharmaceutical interventions, and analyze the risk factors for mortality.Methods:This study is a retrospective case summary that analyzed the demographic data, underlying diseases, clinical diagnoses, disease outcomes, and laboratory results of 130 children with severe COVID-19 infections admitted to nine top-tier hospitals in Yunnan Province from December 2022 to March 2023. According to the prognosis, the patients were divided into survival group and death group. The clinical and laboratory data between the two groups were compared, and the risk factors of death were evaluated. The χ2 test and Mann-Whitney U test were employed to compare between groups, while Spearman correlation test and multiple Logistic regression were used to analyze the risk factors for death. The predictive value of independent risk factors was evaluated by receiver operating characteristic curve. Results:The 130 severe patients included 80 males and 50 females with an onset age of 28.0 (4.5, 79.5) months. There were 97 cases in the survival group and 33 cases in the death group with no significant differences in gender and age between the two groups ( P>0.05). Twenty-five cases (19.2%) out of the 130 patients had underlying diseases, and the number with underlying diseases was significantly higher in death group than in survival group (36.4% (12/33) vs. 13.4%(13/97), χ2=8.36, P=0.004). The vaccination rate in the survival group was significantly higher than that in the death group (86.1% (31/36) vs. 7/17, χ2=9.38, P=0.002). A total of 42 cases (32.3%) of the 130 patients were detected to be infected with other pathogens, but there was no significant difference in the incidence of co-infection between the death group and the survival group (39.3%(13/33) vs. 29.9% (29/97), χ2=1.02, P>0.05). Among the 130 cases, severe respiratory cases were the most common 66 cases (50.8%), followed by neurological severe illnesses 34 cases (26.2%) and circulatory severe 13 cases (10%). Compared to the survival group, patients in the death group had a significantly higher levels of neutrophil, ferritin, procalcitonin, alanine aminotransferase, lactate dehydrogenase, creatine kinase isoenzyme, B-type natriuretic peptide, interleukin-6 and 10 (6.7 (4.0, 14.0) vs. 3.0 (1.6, 7.0)×10 9/L, 479 (298, 594) vs. 268 (124, 424) μg/L, 4.8 (1.7, 10.6) vs. 2.0 (1.1, 3.1) μg/L, 66 (20, 258) vs. 23 (15, 49) U/L, 464 (311, 815) vs. 304 (252, 388) g/L, 71(52, 110) vs. 24(15, 48) U/L, 484 (160, 804) vs. 154 (26, 440) ng/L, 43 (23, 102) vs. 19 (13, 27) ng/L, 216 (114, 318) vs. 86 (45, 128) ng/L, Z=-4.21, -3.67, -3.76, -3.31, -3.75, -5.74, -3.55, -4.65, -5.86, all P<0.05). The correlated indexes were performed by multivariate Logistic regression and the results showed that vaccination was a protective factor from death in severe cases ( OR=0.01, 95% CI 0-0.97, P=0.049) while pediatric sequential organ failure assessment (PSOFA) ( OR=3.31, 95% CI 1.47-7.47, P=0.004), neutrophil-to-lymphocyte ratio (NLR) ( OR=1.56, 95% CI 1.05-2.32, P=0.029) and D dimer ( OR=1.49, 95% CI 1.00-1.02, P=0.033) were independent risk factors for death (all P<0.05). The area under the curve of the three independent risk factors for predicting death were 0.86 (95% CI 0.79-0.94), 0.89 (95% CI 0.84-0.95) and 0.87 (95% CI 0.80-0.94), all P<0.001, and the cut-off values were 4.50, 3.66 and 4.69 mg/L, respectively. Conclusions:Severe SARS-CoV-2 infection can occur in children of all ages, primarily affecting the respiratory system, but can also infect the nervous system, circulatory system or other systems. Children who died had more severe inflammation, tissue damage and coagulation disorders. The elevations of PSOFA, NLR and D dimer were independent risk factors for death in severe children.