Objective: To investigate the genetic basis of RhD-negative phenotype in the blood donor population of Nantong City. Methods: RHD genotyping was performed on 386 randomly selected RhD-negative donor samples (from a total of 676 RhD-negative donors identified between January 20, 2023, and June 28, 2024) using polymerase chain reaction (PCR), and the inconclusive results were confirmed by nucleotide sequencing. Results: Ten RHD allele types were identified: The complete deletion variant RHD 01N.01 was predominant (64.25%, 248/386); followed by RHD 01EL.01 (19.69%, 76/386). RHD 01N.03, RHD 01N.04, RHD 01N.16 and RHD 01EL.32 were frequently observed., RHD 01EL.02, RHD 01EL.08, RHD 01EL.37 and RHD 01N.25 were rare, and two exon deletion variants remained uncharacterized. The phenotypic distribution of RhD-negative blood donors was ccee (55.44%)>Ccee(31.09%)>ccEe(5.96%)>CCee(5.44%)>CcEe(1.81%)>CcEE(0.26%), and the antigen distribution trend was e(99.74%)>c(94.56%)>C(38.60%)>E(8.03%). A correlation was observed between RHD genotypes and RhCE phenotypes. Conclusion: The Nantong blood donor population exhibits unique RHD gene polymorphisms. Integrating RhCE serological phenotyping with RHD genotyping is essential for ensuring transfusion safety.

Objective: To investigate the genetic basis of RhD-negative phenotype in the blood donor population of Nantong City. Methods: RHD genotyping was performed on 386 randomly selected RhD-negative donor samples (from a total of 676 RhD-negative donors identified between January 20, 2023, and June 28, 2024) using polymerase chain reaction (PCR), and the inconclusive results were confirmed by nucleotide sequencing. Results: Ten RHD allele types were identified: The complete deletion variant RHD 01N.01 was predominant (64.25%, 248/386); followed by RHD 01EL.01 (19.69%, 76/386). RHD 01N.03, RHD 01N.04, RHD 01N.16 and RHD 01EL.32 were frequently observed., RHD 01EL.02, RHD 01EL.08, RHD 01EL.37 and RHD 01N.25 were rare, and two exon deletion variants remained uncharacterized. The phenotypic distribution of RhD-negative blood donors was ccee (55.44%)>Ccee(31.09%)>ccEe(5.96%)>CCee(5.44%)>CcEe(1.81%)>CcEE(0.26%), and the antigen distribution trend was e(99.74%)>c(94.56%)>C(38.60%)>E(8.03%). A correlation was observed between RHD genotypes and RhCE phenotypes. Conclusion: The Nantong blood donor population exhibits unique RHD gene polymorphisms. Integrating RhCE serological phenotyping with RHD genotyping is essential for ensuring transfusion safety.

The incidence of gastric cancer has decreased, while the occurrence of early-onset gastric cancer has increased. There is no consensus on the definition of early-onset gastric cancer currently. The characteristics of tumor staging and differentiation, coupled with the lack of targeted comprehensive treatment, present a significant clinical challenge in managing early-onset gastric cancer. Relevant studies have analyzed the genetic characteristics of early-onset gastric cancer and have preliminarily revealed its relationship with Helicobacter pylori infection and molecular subtypes. These findings have the potential to contribute to the prevention and personalized treatment of early-onset gastric cancer. In the future, larger-scale evidence-based data are needed to establish diagnostic criteria, elucidate the mechanisms, and develop targeted diagnostic and therapeutic strategies of early-onset 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.

The cancer staging system of the American Joint Committee on Cancer (AJCC) is the most widely used clinical basis for tumor staging. In October 2023, AJCC released the staging system (ninth version) for the neuroendocrine tumors of stomach (NET), which has been implemented in January 2024. The ninth version of NET staging system mainly updated the histopathologic classification, diagnosis and staging methods, clinical and pathological staging, prognosis grade, tumor and non-tumor prognostic features. The update and implementation of the staging system provide a more detailed reference for the accurate diagnosis, staging and precise treatment of gastric neuroendocrine tumors. Moreover, it is convenient for clinicians to carry out clinical practice. The purpose of our article is to provide a high-level overview of the major changes in AJCC staging system (version 9) for gastric NET based on the latest evidence-based medical research.

The incidence of gastric cancer has decreased, while the occurrence of early-onset gastric cancer has increased. There is no consensus on the definition of early-onset gastric cancer currently. The characteristics of tumor staging and differentiation, coupled with the lack of targeted comprehensive treatment, present a significant clinical challenge in managing early-onset gastric cancer. Relevant studies have analyzed the genetic characteristics of early-onset gastric cancer and have preliminarily revealed its relationship with Helicobacter pylori infection and molecular subtypes. These findings have the potential to contribute to the prevention and personalized treatment of early-onset gastric cancer. In the future, larger-scale evidence-based data are needed to establish diagnostic criteria, elucidate the mechanisms, and develop targeted diagnostic and therapeutic strategies of early-onset 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.

The cancer staging system of the American Joint Committee on Cancer (AJCC) is the most widely used clinical basis for tumor staging. In October 2023, AJCC released the staging system (ninth version) for the neuroendocrine tumors of stomach (NET), which has been implemented in January 2024. The ninth version of NET staging system mainly updated the histopathologic classification, diagnosis and staging methods, clinical and pathological staging, prognosis grade, tumor and non-tumor prognostic features. The update and implementation of the staging system provide a more detailed reference for the accurate diagnosis, staging and precise treatment of gastric neuroendocrine tumors. Moreover, it is convenient for clinicians to carry out clinical practice. The purpose of our article is to provide a high-level overview of the major changes in AJCC staging system (version 9) for gastric NET based on the latest evidence-based medical research.

Objective To investigate the correlation between the incidence of pancreatic fistula(POPF)after pancreaticoduodenectomy and the CT ratio of pancreatic to visceral fat and blood glucose.Methods 94 patients who underwent PD from January 2018 to December 2022 were selected.Postoperative blood glucose,pancreato-visceral fat CT ratio,blood biochemistry and other relevant indicators were collected to analyze the risk factors for POPF.Results Among the 94 patients,POPF grade B/C in 20 cases(21.3％);Logistic regression analysis showed that CT ratio of pancreas-visceral fat,preoperative lymphocyte/monocyte,and blood glucose on the first day after surgery were risk factors for postoperative pancreatic fistula in PD patients.Laparotomy is an independent risk factor for postoperative pancreatic fistula in PD patients.The sensitivity and specificity of pancreatic-visceral fat CT ratio to predict POPF were 0.80 and 0.58.The sensitivity and specificity of blood glucose prediction of POPF on the first day after surgery were 0.55 and 0.84,respectively.The sensitivity and specificity of POPF prediction were 0.60 and 0.78,respectively.Conclusion CT ratio of pancreatic to visceral fat combined with blood glucose on the first day after surgery may be a risk factor for predicting POPF in PD patients.

Objective:To investigate the efficacy and prognosis of different surgical approaches in sporadic medullary thyroid carcinoma.Methods:A retrospective analysis was conducted on 101 patients with sporadic medullary thyroid carcinoma (MTC) who underwent surgical treatment at the Department of Thyroid Surgery, China-Japan Union Hospital of Jilin University, from Feb. 2009 to Nov. 2023. The patients included 36 males and 75 females, with a male-to-female ratio of 1:2.1. The median age of the patients was 47 years old, with an age range of 21 to 72 years old. The study divided participants into two groups based on their surgical methods: an observation group (78 cases) and a control group (23 cases). The observation group received surgical methods in accordance with expert consensus, while the control group did not. The study compared the efficacy and prognosis of the two groups.Results:Statistical differences were found between the two groups in terms of stage II and III in TNM staging, intraoperative frozen pathological findings, number of lymph node resections in the central group, number of lymph node metastases in the central group, number of lymph node resections in the lateral cervical region, postoperative follow-up time, and five-year postoperative serum procalcitonin (Ctn) levels ( P<0.05) .Both groups of patients obtained a significant decrease in Ctn after surgical treatment. In the observation group, Ctn was at the remission level in 57 cases (73.1%), at the stable level in 13 cases (16.7%), and at the progression level in 8 cases (10.2%), while in the control group, Ctn was at the remission level in 20 cases (86.9%), at the progression level in 3 cases (13.1%), and there were no patients at the stable level after the operation.One patient (1.3 per cent) in the observation group had a recurrence after surgery; Two patients (8.7 per cent) in the control group had a recurrence. Conclusions:Standardised and thorough surgery can maximise the clearance of metastatic lymph nodes, effectively reduce the recurrence rate, achieve better efficacy, and improve the long-term prognosis of patients without increasing the risk of surgery and postoperative complications.