Objective To explore the time of application of hemoperfusion (HP) for the treatment of acute serious organophosphorus pesticide (ASOPP). Methods One hundred and four patients with ASOPP were randomly divided into two groups, 46 patients accepted traditional treatment(control group), 58 patients were treated with traditional treatment and HP (HP group). The patients in HP group were again divided into three groups according the different time of treatment (time of beginning HP after poisoning), the 4-8 hours group (HP-1 group, 27 patients), the 9-16 hours group (HP-2 group, 19 patients), the 17-32 hours group (HP-3 group, 12 pafients).Tbe coma period, the dosage of atropine, the time of regaining the vitality of cholinesterase, the time of hospitalization and the rate of fatality and curing among groups were observed. Results The coma period, the dosage of atropine, the time of regaining the vitality of cholinesterase, the time of hospitalization and the rate of fatality of the HP group were less than those of the control group (P<0.05). Compared with HP-1 group, the eoma period, the dosage of atropine, the time of regaining the vitality of eholinesterase and the time of hospitalization of the HP-2 group and the HP-3 group were higher (P<0.05), but the difference of the rate of fatality and curing between the HP-1 group and the other HP groups was not statistically significant (P>0.05). The difference of all of the above indicators between HP-2 group and HP-3 group was not statistically significant (P>0.05). Conclusion Application of hemoperfusion among 4-32 hours after poisoning for the treatment of ASOPP can improve the efficacy of therapy, and the efficacy of application of hemoperfusion among 4-8 hours is the best.

Objective:To investigate the feasibility of a three-dimensional no new U-Net (3D nnU-Net) deep learning (DL) network for the automatic segmentation of colorectal cancer (CRC) based on abdominal CT images.Methods:This was a cross-sectional study. From January 2018 to May 2023, a total of 2180 primary CRC patients, confirmed by pathology at the Guangdong Provincial Hospital of Traditional Chinese Medicine (center 1, n=777), Nanfang Hospital, Southern Medical University (center 2, n=732), and Sun Yat-sen Memorial Hospital (center 3, n=671), were enrolled in this retrospective study. The baseline abdominal CT examination of each patient was conducted using CT equipment from 7 different models across 4 vendors, at the 3 centers, encompassing both the arterial phase (AP) and venous phase (VP). Two radiologists manually delineated the volume of interest to circumscribe the entire tumors in dual-enhanced phase CT images. The CT data of CRC patients from center 1 and center 3 were merged and divided into a training set ( n=1 159) and a validation set ( n=289) using a weighted random method with a ratio of 4∶1. The patients from center 2 were used as an independent external test set ( n=732). The 3D nnU-Net segmentation model was trained and tested. Using manually annotated label data as the benchmark, segmentation performance of the model was evaluated based on different phases and tumor locations. The segmentation coverage rate (SCR), Dice similarity coefficient (DSC), recall (REC), precision (PRE), F1-score, and 95% Hausdorff distance (HD 95) were calculated. The mean manual segmentation time and the mean automatic time were compared using independent samples t-test. Results:In the independent external test set, the performance of the 3D nnU-Net model based on the AP CT images was superior to that based on the VP CT images. On the AP images, the SCR, DSC, REC, PRE, F1-score, and HD 95 were 0.865, 0.714, 0.716, 0.736, 0.714, and 27.228, respectively; on the VP images, they were 0.834, 0.679, 0.710, 0.675, 0.679, and 29.358, respectively. The model achieved the best performance on right-sided colon cancer, with SCR, DSC, REC, PRE, F1-score, and HD95 on the AP CT images at 0.901, 0.775, 0.780, 0.787, 0.775, and 21.793, respectively. Next were left-sided colon cancer and rectal cancer, while the segmentation performance for transverse colon cancer was the worst (SCR, DSC, REC, PRE, F1-score, and HD 95 were 0.731, 0.631, 0.641, 0.630, 0.631 and 38.721, respectively). The automatic segmentation time on a single phase was (1.0±0.3) min, while the manual segmentation time was (17.5±6.0) min ( t=128.24, P<0.001). Conclusions:After training and validating on a dataset from multiple centers with various CT scanner vendors, the 3D nnU-Net DL model demonstrates the capability to automatically segment CRC based on abdominal CT images, while also showcasing commendable robustness and generalization ability.

Objective To propose a method for abdominal multi-organ segmentation assisted by multi-phase CT synthesis.Methods Multi-phase CT synthesis for synthesizing high-quality CT images was used to increase the information details for image segmentation.A transformer block was introduced to help to capture long-range semantic information in cooperation with perceptual loss to minimize the differences between the real image and synthesized image.Results The model was trained using multi-phase CT dataset of 526 total cases from Nanfang Hospital.The mean maximum absolute error(MAE)of the synthesized non-contrast CT,venous phase contrast-enhanced CT(CECT),and delay phase CECT images from arterial phase CECT was 19.192±3.381,20.140±2.676 and 22.538±2.874,respectively,which were better than those of images synthesized using other methods.Validation of the multi-phase CT synthesis-assisted abdominal multi-organ segmentation method showed an average dice coefficient of 0.847 for the internal validation set and 0.823 for the external validation set.Conclusion The propose method is capable of synthesizing high-quality multi-phase CT images to effectively reduce the errors in registration between different phase CT images and improve the performance for segmentation of 13 abdominal organs.

Objective To propose a method for abdominal multi-organ segmentation assisted by multi-phase CT synthesis.Methods Multi-phase CT synthesis for synthesizing high-quality CT images was used to increase the information details for image segmentation.A transformer block was introduced to help to capture long-range semantic information in cooperation with perceptual loss to minimize the differences between the real image and synthesized image.Results The model was trained using multi-phase CT dataset of 526 total cases from Nanfang Hospital.The mean maximum absolute error(MAE)of the synthesized non-contrast CT,venous phase contrast-enhanced CT(CECT),and delay phase CECT images from arterial phase CECT was 19.192±3.381,20.140±2.676 and 22.538±2.874,respectively,which were better than those of images synthesized using other methods.Validation of the multi-phase CT synthesis-assisted abdominal multi-organ segmentation method showed an average dice coefficient of 0.847 for the internal validation set and 0.823 for the external validation set.Conclusion The propose method is capable of synthesizing high-quality multi-phase CT images to effectively reduce the errors in registration between different phase CT images and improve the performance for segmentation of 13 abdominal organs.