Objective To investigate the expression of nuclear factor-κB (NF-κB) and p53 up-regulated modulator of apoptosis (PUMA) in acute lung injury (ALI) induced by severe acute pancreatitis (SAP), and the therapeutic role of proline dithiocarbamate (PDTC). Method SD rats weighed 200～ 250 g were randomly(random number) divided into sham operation group (A group, n = 18), ALI group (B group, n = 18) and PDTC treatment group (C group, n = 18). The model of SAP was eastablished by injecting 1 mL/kg of sodium tauarocholate into the pancreatic capsule of the rats in B group and C group. The model rats in C group were treated with PDTC one hour after modeling. Six rats of each group were sacrificed 6 h,12 h, and 24 hours after modeling. The histopathological changes in lung and pancreas were observed. The levels of NF-κB p65 and PUMA in lung were detected by using Western blotting, and the expressions of bcl-2, bax and caspase-3 mRNA in the lung were detected by using RT-PCR. The lung tissue was taken for examination under transmission electron microscope. TUNEL was used for detection of apoptotic alveolar epithelial cells. Results Six to 24 hours after modeling, the pathological scores in lung of ALI group were significantly higher than those of control group and PDTC group after sodium taurocholate injection ( P ＜ 0.05). The levels of NF-κB p65 and PUMA, and the expressions of bax and caspase3 mRNA in ALI group at different intervals were higher than those in control group and PDTC group ( P ＜ 0.05),whereas the expression of bcl-2 mRNA in ALI group was lower than that in control group and PDTC group ( P ＜0.05). The NF-κB p65 was correlated closely and positively with PUMA ( r= 0.987, P ＜ 0.01). Higher activity of caspase-3 acrtive units was seen in ALI group than that in control group and PDTC group ( P ＜ 0.05). The microvilli disappeared in ALI group 24 hours later. The apoptosis index in ALI group was higher than that in control group and PDTC group ( P ＜ 0.05). Conclusions The apoptosis of alveolar epithelial cells of rats in ALI group is caused by PUMA activated by NF-κB. PDTC treatment can inhibit apoptosis of alveolar epithelial cells of rats in ALI group by inhibiting the activation of NF-κB.

Adolescent ; Adult ; Female ; Fracture Fixation, Internal ; methods ; Fractures, Bone ; surgery ; Humans ; Joint Dislocations ; surgery ; Male ; Middle Aged ; Pelvic Bones ; injuries ; Treatment Outcome

OBJECTIVETo report a new method of coracoid transposition for the treatment of complete dislocation of acromioclavicular joint and to evaluate its efficacy.

METHODSWe modified Dewar's surgical method as follows: (1) Two small incisions, a transversal incision on the acromioclavicular joint and a longitudinal incision on the coracoid, were made instead of a conventional large arc incision from the acromion to coracoid. (2) The foreign body in the acromioclavicular joint was cleared out. The chondral surface at the lateral segment of clavicle was resected to form a pseudarthrosis and meanwhile the residual joint capsule and ligaments were repaired. (3) The coracoid was moved to the anteroinferior edge of the clavicle instead of the anterior margin and (4) the coracoid was moved to the lateral border of the clavicle instead of the superior border of the coracoclavicular ligament.

RESULTSThe follow-up duration in 30 patients of the series was from 6 to 72 months (mean 41 months). Functional assessment was carried out by the criteria delineated previously by Karkson, in which Grade A was in 24 cases, Grade B in 4 cases, and C in 2.

CONCLUSIONSThis modified technique, having less postoperative complications and less injuries to tissues and according well with the requirement of biomechanics, can achieve a stable reduction of acromioclavicular joint with a good functional and cosmetic result and therefore is preferable to use clinically on a large scale.

Acromioclavicular Joint ; injuries ; Adolescent ; Adult ; Female ; Humans ; Joint Dislocations ; surgery ; Ligaments, Articular ; injuries ; Male ; Middle Aged ; Orthopedic Procedures ; Rupture ; Shoulder Injuries ; Tendons ; surgery

Objective:To explore the feasibility of using faster regional convolutional neural network (Faster R-CNN) to evaluate the status of circumferential resection margin (CRM) of rectal cancer in the magnetic resonance imaging (MRI).Methods:This study was registered in the Chinese Clinical Trial Registry (ChiCTR-1800017410). Case inclusion criteria: (1) the positive area of CRM was located between the plane of the levator ani, anal canal and peritoneal reflection; (2) rectal malignancy was confirmed by electronic colonoscopy and histopathological examination; (3) positive CRM was confirmed by postoperative pathology or preoperative high-resolution MRI. Exclusion criteria: patients after neoadjuvant therapy, recurrent cancer after surgery, poor quality images, giant tumor with extensive necrosis and tissue degeneration, and rectal tissue construction changes in previous pelvic surgery. According to the above criteria, MRI plain scan images of 350 patients with rectal cancer and positive CRM in The Affiliated Hospital of Qingdao University from July 2016 to June 2019 were collected. The patients were classified by gender and tumor position, and randomly assigned to the training group (300 cases) and the validation group (50 cases) at a ratio of 6:1 by computer random number method. The CRM positive region was identified on the T2WI image using the LabelImg software. The identified training group images were used to iteratively train and optimize parameters of the Faster R-CNN model until the network converged to obtain the best deep learning model. The test set data were used to evaluate the recognition performance of the artificial intelligence platform. The selected indicators included accuracy, sensitivity, positive predictive value, receiver operating characteristic (ROC) curves, areas under the ROC curves (AUC), and the time taken to identify a single image.Results:The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the CRM status determined by the trained Faster R-CNN artificial intelligence approach were 0.884, 0.857, 0.898, 0.807, and 0.926, respectively; the AUC was 0.934 (95% CI: 91.3% to 95.4%). The Faster R-CNN model's automatic recognition time for a single image was 0.2 s.Conclusion:The artificial intelligence model based on Faster R-CNN for the identification and segmentation of CRM-positive MRI images of rectal cancer is established, which can complete the risk assessment of CRM-positive areas caused by in-situ tumor invasion and has the application value of preliminary screening.

Objective:To explore the feasibility of using faster regional convolutional neural network (Faster R-CNN) to evaluate the status of circumferential resection margin (CRM) of rectal cancer in the magnetic resonance imaging (MRI).Methods:This study was registered in the Chinese Clinical Trial Registry (ChiCTR-1800017410). Case inclusion criteria: (1) the positive area of CRM was located between the plane of the levator ani, anal canal and peritoneal reflection; (2) rectal malignancy was confirmed by electronic colonoscopy and histopathological examination; (3) positive CRM was confirmed by postoperative pathology or preoperative high-resolution MRI. Exclusion criteria: patients after neoadjuvant therapy, recurrent cancer after surgery, poor quality images, giant tumor with extensive necrosis and tissue degeneration, and rectal tissue construction changes in previous pelvic surgery. According to the above criteria, MRI plain scan images of 350 patients with rectal cancer and positive CRM in The Affiliated Hospital of Qingdao University from July 2016 to June 2019 were collected. The patients were classified by gender and tumor position, and randomly assigned to the training group (300 cases) and the validation group (50 cases) at a ratio of 6:1 by computer random number method. The CRM positive region was identified on the T2WI image using the LabelImg software. The identified training group images were used to iteratively train and optimize parameters of the Faster R-CNN model until the network converged to obtain the best deep learning model. The test set data were used to evaluate the recognition performance of the artificial intelligence platform. The selected indicators included accuracy, sensitivity, positive predictive value, receiver operating characteristic (ROC) curves, areas under the ROC curves (AUC), and the time taken to identify a single image.Results:The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the CRM status determined by the trained Faster R-CNN artificial intelligence approach were 0.884, 0.857, 0.898, 0.807, and 0.926, respectively; the AUC was 0.934 (95% CI: 91.3% to 95.4%). The Faster R-CNN model's automatic recognition time for a single image was 0.2 s.Conclusion:The artificial intelligence model based on Faster R-CNN for the identification and segmentation of CRM-positive MRI images of rectal cancer is established, which can complete the risk assessment of CRM-positive areas caused by in-situ tumor invasion and has the application value of preliminary screening.

Objective:To explore the feasibility of using ligation clip-assisted modified " expansion and co-llapse method" to determine the intersegment plane in thoracoscopic resection of the external posterior basal segment (S 9+ 10) in children with congenital pulmonary airway malformation (CPAM). Methods:Retrospective study.The clinical data of 12 CPAM children who underwent thoracoscopic S 9+ 10 resection in Children′s Hospital Affiliated to Shandong University from July 2019 to May 2021 were collected and analyzed.There were 7 males and 5 females.The age at operation ranged from 3.50 to 11.50 months.The body weight of patients ranged from 6.5 to 11.5 kg.In all the patients, the ligation clip-assisted modified " expansion and collapse method" was applied during the operation to determine the intersegment plane (S 9+ 10 was in the expansion state and the remaining lungs were in the collapse state) before thoracoscopic S 9+ 10 resection.After S 9+ 10 resection, the surgical treatment and postoperative recovery were summarized and analyzed. Results:All the operations were completed under thoracoscopy, and there was no conversion to thoracotomy.In 11 patients, the intersegment plane was accurately determined by the ligation clips-assisted improved " expansion and collapse method" , and the S 9+ 10 segment was successfully resected.Of these 11 cases, 8 cases had right S 9+ 10 resection and 3 cases had left S 9+ 10 resection.In the process of using the ligation clip-assisted improved " expansion and collapse method" to determine the intersegment plane, the operator needed to clamp the ligation clip after the anesthesiologist expanded the lung completely.Because the operation was not stable enough, the ligation clip fell off and did not clamp S 9+ 10, resulting in operation failure.The operation was changed to the traditional " expansion and collapse method" . Besides, the basal segment bronchus was cut off by mistake, so the right basal segment had to be resected.The operation time ranged from 85 to 205 min, with a median of 149 min.Intraoperative bleeding ranged from 5 to 15 mL, with a median of 10 mL.The indwelling time of drainage tubes ranged from 2 to 4 days, with a median of 3 days.The postoperative hospital stay ranged from 5 to 8 days, with a median of 6 days.Postoperative pathological examination results suggested 4 cases of type 1, 6 cases of type 2 and 2 cases of type 3.There were 2 cases of simple subcutaneous emphysema without postoperative complications such as bleeding, bronchopleural fistula and atelectasis.The follow-up time ranged from 6 to 28 months, with a median of 15 months.The chest CT of all children showed no residual lesions and no residual cavities in the involved hemithorax. Conclusions:Ligation clip-assisted improved " expansion and collapse method" for determining the intersegment plane is suitable for CPAM children with a narrow thoracic space, and the operation is simple and effective.It is relatively safe and feasible to use this method in S 9+ 10 resection.

Objective To explore the risk factors for platelet transfusion refractoriness(PTR)in patients with hemato-logical disorders,construct a prediction model and validate the model efficacy.Methods Patients with hematological disor-ders who received platelet transfusion therapy in the Chengdu Second People's Hospital from December 2021 to December 2022 were retrospectively included to judge the effectiveness of platelet transfusion and screened for risk factors by univariate and multivariate logistic regression.A prediction model for PTR was constructed using receiver operating characteristic(ROC)curve,calibration curve and decision curve(DCA)to assess the differentiation,calibration and clinical value of the model,respectively.Results A total of 334 hematological patients were included,including 168 males and 176 females,with a PTR incidence of 40.4%.Univariate and multivariate logistic regression analysis showed that platelet transfusion vol-ume,erythrocyte transfusion volume,and neutrophil ratio were risk factors for PTR(P<0.05).A prediction model for PTR in hematological patients was established based on these risk factors.The area under the model's curve was 0.8377(95%CI:0.723-0.772),the sensitivity was 58.52%,and the specificity was 89.95%.The calibration curve showed that the S∶P was 0.964,the maximum absolute difference Emax was 0.032,and the average absolute difference Eavg was 0.009.The DCA a-nalysis showed that the model had clinical application value when the risk threshold ranged from 0.2 to 0.9.Conclusion The PTR prediction model based on platelet transfusion volume,erythrocyte transfusion volume and neutrophil ratio can pro-vide a basis for effective platelet transfusion in hematological patients.

Objective:To investigate the effectiveness of an enhanced CT automatic recognition system based on Faster R-CNN for pancreatic cancer and its clinical value.Methods:In this study, 4 024 enhanced CT imaging sequences of 315 patients with pancreatic cancer from January 2013 to May 2016 at the Affiliated Hospital of Qingdao University were collected retrospectively, and 2 614 imaging sequences were input into the faster R-CNN system as training dataset to create an automatic image recognition model, which was then validated by reading 1 410 enhanced CT images of 135 cases of pancreatic cancer.In order to identify its effectiveness, 3 750 CT images of 150 patients with pancreatic lesions were read and a followed-up was carried out.The accuracy and recall rate in detecting nodules were recorded and regression curves were generated.In addition, the accuracy, sensitivity and specificity of Faster R-CNN diagnosis were analyzed, the ROC curves were generated and the area under the curves were calculated.Results:Based on the enhanced CT images of 135 cases, the area under the ROC curve was 0.927 calculated by Faster R-CNN. The accuracy, specificity and sensitivity were 0.902, 0.913 and 0.801 respectively.After the data of 150 patients with pancreatic cancer were verified, 893 CT images showed positive and 2 857 negative.Ninety-eight patients with pancreatic cancer were diagnosed by Faster R-CNN.After the follow-up, it was found that 53 cases were post-operatively proved to be pancreatic ductal carcinoma, 21 cases of pancreatic cystadenocarcinoma, 12 cases of pancreatic cystadenoma, 5 cases of pancreatic cyst, and 7 cases were untreated.During 5 to 17 months after operation, 6 patients died of abdominal tumor infiltration, liver and lung metastasis.Of the 52 patients who were diagnosed negative by Faster R-CNN, 9 were post-operatively proved to be pancreatic ductal carcinoma.Conclusion:Faster R-CNN system has clinical value in helping imaging physicians to diagnose pancreatic cancer.

Objective:To investigate the effectiveness of an enhanced CT automatic recognition system based on Faster R-CNN for pancreatic cancer and its clinical value.Methods:In this study, 4 024 enhanced CT imaging sequences of 315 patients with pancreatic cancer from January 2013 to May 2016 at the Affiliated Hospital of Qingdao University were collected retrospectively, and 2 614 imaging sequences were input into the faster R-CNN system as training dataset to create an automatic image recognition model, which was then validated by reading 1 410 enhanced CT images of 135 cases of pancreatic cancer.In order to identify its effectiveness, 3 750 CT images of 150 patients with pancreatic lesions were read and a followed-up was carried out.The accuracy and recall rate in detecting nodules were recorded and regression curves were generated.In addition, the accuracy, sensitivity and specificity of Faster R-CNN diagnosis were analyzed, the ROC curves were generated and the area under the curves were calculated.Results:Based on the enhanced CT images of 135 cases, the area under the ROC curve was 0.927 calculated by Faster R-CNN. The accuracy, specificity and sensitivity were 0.902, 0.913 and 0.801 respectively.After the data of 150 patients with pancreatic cancer were verified, 893 CT images showed positive and 2 857 negative.Ninety-eight patients with pancreatic cancer were diagnosed by Faster R-CNN.After the follow-up, it was found that 53 cases were post-operatively proved to be pancreatic ductal carcinoma, 21 cases of pancreatic cystadenocarcinoma, 12 cases of pancreatic cystadenoma, 5 cases of pancreatic cyst, and 7 cases were untreated.During 5 to 17 months after operation, 6 patients died of abdominal tumor infiltration, liver and lung metastasis.Of the 52 patients who were diagnosed negative by Faster R-CNN, 9 were post-operatively proved to be pancreatic ductal carcinoma.Conclusion:Faster R-CNN system has clinical value in helping imaging physicians to diagnose pancreatic cancer.

Objective To investigate the feasibility of retroperitoneoscopy in the treatment of adrenal masses in infants under 6 months old.Methods From January 2020 to November 2023,retroperitoneoscopic surgery was performed in 5 infants under 6 months old with adrenal tumors.Their age was from 1 month and 18 days to 4 months and 27 days,and their body weight was 5-8 kg.The lesion was found by prenatal ultrasonography in 1 case and by abdominal ultrasonography for other reasons after birth in 4 cases.Ultrasound and CT indicated a diameter of 1.7-5.5 cm for the adrenal masses.Results The operations of adrenalectomy and tumor resection were completed under retroperitoneoscopy.The operative time was 65-135 min(median,94 min).The intraoperative blood loss was less than 10 ml.The postoperative drainage tube retention time was 3-6 d(median,5 d).Pathological diagnosis showed 4 cases of adrenal neuroblastoma and 1 case of adrenal hyperplasia.Follow-ups for 1-36 months(median,3 months)with abdominal ultrasound and CT scanning showed no recurrence or metastasis.Conclusion Retroperitoneoscopy is relatively safe for the treatment of adrenal tumors in infants under 6 months old(tumors with acceptable boundaries).