Guided by the theory of latent pathogens， it is believed that the basic pathogenesis of adult-onset Still's disease is the latent pathogens in the deep yin level. The onset of the disease is fundamentally characterized by the deficiency of both qi and yin as the root， with dampness， heat， phlegm， and blood stasis as the branch， which triggered by intruding pathogens activate the latent pathogens in yin level. The treatment focuses on nourishing yin and dispersing heat as the key therapeutic method. It is proposed that clearing and resolving dampness-heat， expelling pathogens outward， dispersing the latent pathogens， reinforcing healthy qi and consolidating the root， boosting qi and nourishing yin as treatment idea. In clinic， Qinghao Biejia Decoction （青蒿鳖甲汤） could be used as the basic formula， and modified with characteristic herb pairs such as Qinghao （Artemisia annua） - Digupi （Lycium chinense） to enrich yin and clear heat， and enforce the power of clearing deficient heat； Biejia （Lawsonia inermis） - Xuchangqing （Vincetoxicum mukdenense） to enrich yin and activate blood， unblock the collaterals and dissipate masses； Duhuo （Angelica biserrata） - Mudanpi （Paeonia × suffruticosa） to dispel wind and activate blood， resolve dampness and unblock the collaterals， so as to clear and warm simultaneously， and regulate qi and blood at the same time； and Chuanshanlong （Dioscorea nipponica） - Difuzi （Bassia scoparia） to dissolve stasis and dispel phlegm， explore and dispel latent pathogens.

OBJECTIVE To study the pharmacological substance basis of Shaoyao gancao decoction for relieving acute and chronic pain. METHODS The antispasmodic effect of Shaoyao gancao decoction， ethyl acetate extract of Shaoyao gancao decoction and its effluent part of macroporous resin and 90% ethanol elution part of macroporous resin （the concentration of 4 drugs was 13.44 g/mL according to crude drug） was observed by in vitro small intestine tension test in rats. The acetic acid writhing test was conducted in mice to evaluate the analgesic effects of macroporous resin efflux site and macroporous resin 90% ethanol elution site （the dosage of 2.4 g/kg according to crude drug）. The levels of tumor necrosis factor-α （TNF-α）， interleukin-1β （IL- 1β）， prostaglandin E2 （PGE2） and cyclooxygenase-2 （COX-2） in serum of mice were detected. The serum prototype and metabolites of mice after intragastric administration of macroporous resin 90% ethanol elution site were identified by high performance liquid chromatogre-time-of-flight mass spectrometry. RESULTS In vitro experiment showed that 90% ethanol eluting part of macroporous resin represented the best antispasmodic effect， and the inhibitory rate of small intestine tension was significantly higher than macroporous resin efflux site of Shaoyao gancao decoction （P＜0.05） without statistical significance， compared with Shaoyao gancao decoction （P＞0.05）. In the acetic acid writhing experiment， compared with model group， the writhing times of mice in the macroporous resin 90% ethanol elution part group were reduced significantly （P＜0.05）， the writhing latency was prolonged significantly （P＜0.05）， and the levels of COX-2， IL-1β， PGE2 and TNF-α in serum were decreased significantly （P＜0.05）. Ten kinds of protoproducts including paeoniflorin and glycyrrhizic acid were identified from serum of mice， and twenty-two kinds of metabolites including hydroxylated glycyrrhizin and glucosylated liquiritin were identified. CONCLUSIONS The effective part of Shaoyao gancao decoction for relieving acute and chronic pain is 90% ethanol elution part prepared by macroporous resin from the ethyl acetate extract. Ten components， including glycyrrhetinic acid and paeoniflorin， may be the basis of its pharmacological substances.

Objective:To investigate the feasibility of predicting the risk of spontaneous preterm birth in singleton pregnancy women with low risk of preterm birth by transabdominal-transvaginal ultrasound cervical length sequential screening in the second trimester.Methods:This prospective longitudinal cohort study included singleton pregnant women at 11-13 +6 gestational weeks who were admitted to Nanjing Drum Tower Hospital from January 2023 to September 2023. Transabdominal and transvaginal cervical lengths were measured during the mid-trimester fetal ultrasound scan at 18-24 weeks, and pregnancy outcomes were obtained after delivery. A short cervix was defined as a transvaginal cervical length of ≤25 mm, and the outcomes were defined as spontaneous preterm birth occurs between 20 and 36 +6 weeks and extremely preterm birth before 32 weeks. The area under the receiver operating characteristic (ROC) curve was used to evaluate the effectiveness of predicting spontaneous preterm birth by transabdominal and transvaginal cervix length, as well as the effectiveness of predicting short cervix by transabdominal cervical length. The relationship between transabdominal and transvaginal cervical length was evaluated using a scatter plot. Results:A total of 562 cases were included in this study, comprising 33 cases of spontaneous preterm birth (7 cases occurring before 32 weeks) and 529 cases of term birth. (1) Compared to the term birth group, transabdominal cervical length (median: 37.6 vs 33.2 mm; Z=-3.838, P<0.001) and transvaginal cervical length (median: 34.0 vs 29.9 mm, Z=-3.030, P=0.002) in the spontaneous preterm birth group were significantly shorter. (2) The areas under the ROC curve for predicting spontaneous preterm birth by transabdominal and transvaginal cervical length were 0.699 (95% CI: 0.588-0.809) and 0.657 (95% CI: 0.540-0.774), respectively. The sensitivity, specificity and positive predictive value of transvaginal cervical length Conclusions:In singleton pregnancy women with low risk of preterm birth, transabdominal-transvaginal cervical length sequential screening can reduce unnecessary transvaginal ultrasounds by approximately 41% without missing the diagnosis of pregnant women with a short cervix. This method also enhances the effectiveness of transvaginal cervical length to spontaneous preterm birth.

Objective:To construct an artificial intelligence-assisted diagnosis system to recognize the characteristics of Helicobacter pylori ( HP) infection under endoscopy, and evaluate its performance in real clinical cases. Methods:A total of 1 033 cases who underwent 13C-urea breath test and gastroscopy in the Digestive Endoscopy Center of Renmin Hospital of Wuhan University from January 2020 to March 2021 were collected retrospectively. Patients with positive results of 13C-urea breath test (which were defined as HP infertion) were assigned to the case group ( n=485), and those with negative results to the control group ( n=548). Gastroscopic images of various mucosal features indicating HP positive and negative, as well as the gastroscopic images of HP positive and negative cases were randomly assigned to the training set, validation set and test set with at 8∶1∶1. An artificial intelligence-assisted diagnosis system for identifying HP infection was developed based on convolutional neural network (CNN) and long short-term memory network (LSTM). In the system, CNN can identify and extract mucosal features of endoscopic images of each patient, generate feature vectors, and then LSTM receives feature vectors to comprehensively judge HP infection status. The diagnostic performance of the system was evaluated by sensitivity, specificity, accuracy and area under receiver operating characteristic curve (AUC). Results:The diagnostic accuracy of this system for nodularity, atrophy, intestinal metaplasia, xanthoma, diffuse redness + spotty redness, mucosal swelling + enlarged fold + sticky mucus and HP negative features was 87.5% (14/16), 74.1% (83/112), 90.0% (45/50), 88.0% (22/25), 63.3% (38/60), 80.1% (238/297) and 85.7% (36 /42), respectively. The sensitivity, specificity, accuracy and AUC of the system for predicting HP infection was 89.6% (43/48), 61.8% (34/55), 74.8% (77/103), and 0.757, respectively. The diagnostic accuracy of the system was equivalent to that of endoscopist in diagnosing HP infection under white light (74.8% VS 72.1%, χ2=0.246, P=0.620). Conclusion:The system developed in this study shows noteworthy ability in evaluating HP status, and can be used to assist endoscopists to diagnose HP infection.

Objective:To construct a real-time artificial intelligence (AI)-assisted endoscepic diagnosis system based on YOLO v3 algorithm, and to evaluate its ability of detecting focal gastric lesions in gastroscopy.Methods:A total of 5 488 white light gastroscopic images (2 733 images with gastric focal lesions and 2 755 images without gastric focal lesions) from June to November 2019 and videos of 92 cases (288 168 clear stomach frames) from May to June 2020 at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University were retrospectively collected for AI System test. A total of 3 997 prospective consecutive patients undergoing gastroscopy at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University from July 6, 2020 to November 27, 2020 and May 6, 2021 to August 2, 2021 were enrolled to assess the clinical applicability of AI System. When AI System recognized an abnormal lesion, it marked the lesion with a blue box as a warning. The ability to identify focal gastric lesions and the frequency and causes of false positives and false negatives of AI System were statistically analyzed.Results:In the image test set, the accuracy, the sensitivity, the specificity, the positive predictive value and the negative predictive value of AI System were 92.3% (5 064/5 488), 95.0% (2 597/2 733), 89.5% (2 467/ 2 755), 90.0% (2 597/2 885) and 94.8% (2 467/2 603), respectively. In the video test set, the accuracy, the sensitivity, the specificity, the positive predictive value and the negative predictive value of AI System were 95.4% (274 792/288 168), 95.2% (109 727/115 287), 95.5% (165 065/172 881), 93.4% (109 727/117 543) and 96.7% (165 065/170 625), respectively. In clinical application, the detection rate of local gastric lesions by AI System was 93.0% (6 830/7 344). A total of 514 focal gastric lesions were missed by AI System. The main reasons were punctate erosions (48.8%, 251/514), diminutive xanthomas (22.8%, 117/514) and diminutive polyps (21.4%, 110/514). The mean number of false positives per gastroscopy was 2 (1, 4), most of which were due to normal mucosa folds (50.2%, 5 635/11 225), bubbles and mucus (35.0%, 3 928/11 225), and liquid deposited in the fundus (9.1%, 1 021/11 225).Conclusion:The application of AI System can increase the detection rate of focal gastric lesions.

Wild castor grows in the high-altitude tropical desert of the African Plateau,a region known for high ultraviolet radiation,strong light,and extremely dry condition.To investigate the potential genetic basis of adaptation to both highland and tropical deserts,we generated a chromosome-level genome sequence assembly of the wild castor accession WT05,with a genome size of 316 Mb,a scaffold N50 of 31.93 Mb,and a contig N50 of 8.96 Mb,respectively.Compared with cultivated castor and other Euphorbiaceae species,the wild castor exhibits positive selection and gene family expansion for genes involved in DNA repair,photosynthesis,and abiotic stress responses.Genetic variations associated with positive selection were identified in several key genes,such as LIG1,DDB2,and RECGI,involved in nucleotide excision repair.Moreover,a study of genomic diversity among wild and cultivated accessions revealed genomic regions containing selection signatures associated with the adaptation to extreme environments.The identification of the genes and alleles with selection signatures provides insights into the genetic mechanisms under-lying the adaptation of wild castor to the high-altitude tropical desert and would facilitate direct improvement of modern castor varieties.

Objective:To evaluate deep learning in improving the diagnostic rate of adenomatous and non-adenomatous polyps.Methods:Non-magnifying narrow band imaging (NBI) polyp images obtained from Endoscopy Center of Renmin Hospital, Wuhan University were divided into three datasets. Dataset 1 (2 699 adenomatous and 1 846 non-adenomatous non-magnifying NBI polyp images from January 2018 to October 2020) was used for model training and validation of the diagnosis system. Dataset 2 (288 adenomatous and 210 non-adenomatous non-magnifying NBI polyp images from January 2018 to October 2020) was used to compare the accuracy of polyp classification between the system and endoscopists. At the same time, the accuracy of 4 trainees in polyp classification with and without the assistance of this system was compared. Dataset 3 (203 adenomatous and 141 non-adenomatous non-magnifying NBI polyp images from November 2020 to January 2021) was used to prospectively test the system.Results:The accuracy of the system in polyp classification was 90.16% (449/498) in dataset 2, superior to that of endoscopists. With the assistance of the system, the accuracy of colorectal polyp diagnosis was significantly improved. In the prospective study, the accuracy of the system was 89.53% (308/344).Conclusion:The colorectal polyp classification system based on deep learning can significantly improve the accuracy of trainees in polyp classification.

Objective:To investigate the effect of nurse-led multiple disciplinary team-based intervention in the prevention of venous thromboembolism in paitents undergoing general surgery.Methods:A total of 118 patients who underwent general surgery in the Tianjin First Central Hospital from May 2017 to October 2018 were divided into study group and control group by random digits table method, with 59 cases in each group. The control group received routine thrombosis prevention nursing, the study group carried out nurse-led multiple disciplinary team-based intervention. The condition of lower limbs deep venous hemodynamic was detected by color Doppler ultrasonography at 3 days after surgery, the levels of D-dimer, thrombelastograph coagulation analyzer (TEG) coagulation parameters were also measured at after 24 hours of admission and postoperative day 3, respectively.Results:The vein blood stasis rate was 94.9% (3/59) in the study group, 79.7% (12/59) in the control group, the venous blood flow of the lower 1imbs in the study group was better than that in the control group ( Z value was 2.477, P<0.05). At 3 days after surgery, the levels of D-dimer were (5.26±1.42) mg/L in the study group, (6.36±1.58) mg/L in the control group, D-dimer was decreased in study group compared to the control group, the difference was statistically significant ( t value was 3.991, P<0.05). Coagulation reaction time(R) value and solidification angle(Angel), maximum thrombus intensity(MA), composite coagulation index(CI) levels were (5.30±0.91) min, (69.64±21.93) deg, (65.40±13.76) mm and (1.23±0.20) in the study group, those index were (4.41±0.75) min, (76.64±16.02) deg, (70.98±13.39) mm, (2.09±0.36) in the control group, R value were increased and Angel, MA, CI levels were decreased in the study group compared to the control group ( t value was 2.001-15.997, P<0.05). Conclusions:Nurse-led multiple disciplinary team-based intervention improves the lower limbs deep venous hemodynamic and coagulation function, as well as reduce the incidence of venous thromboembolism.

Objective:To construct a real-time monitoring system based on computer vision for monitoring withdrawal speed of colonoscopy and to validate its feasibility and performance.Methods:A total of 35 938 images and 63 videos of colonoscopy were collected in endoscopic database of Renmin Hospital of Wuhan University from May to October 2018. The images were divided into two datasets, one dataset included in vitro, in vivo and unqualified colonoscopy images, and another dataset included ileocecal and non-cecal area images. And then 3 594 and 2 000 images were selected respectively from the two datasets for testing the deep learning model, and the remaining images were used to train the model. Three colonoscopy videos were selected to evaluate the feasibility of real-time monitoring system, and 60 colonoscopy videos were used to evaluate its performance.Results:The accuracy rate of the deep learning model for classification for in vitro, in vivo, and unqualified colonoscopy images was 90.79% (897/988), 99.92% (1 300/1 301), and 99.08% (1 293/1 305), respectively, and the overall accuracy rate was 97.11% (3 490/3 594). The accuracy rate of identifying ileocecal and non-cecal area was 96.70% (967/1 000) and 94.90% (949/1 000), respectively, and the overall accuracy rate was 95.80% (1 916/2 000). In terms of feasibility evaluation, 3 colonoscopy videos data showed a linear relationship between the retraction speed and the image processing interval, which indicated that the real-time monitoring system automatically monitored the retraction speed during the colonoscopy withdrawal process. In terms of performance evaluation, the real-time monitoring system correctly predicted entry time and withdrawal time of all 60 examinations, and the results showed that the withdrawal speed and withdrawal time was significantly negative-related ( R=-0.661, P<0.001). The 95% confidence interval of withdrawal speed for the colonoscopy with withdrawal time of less than 5 min, 5-6 min, and more than 6 min was 43.90-49.74, 40.19-45.43, and 34.89-39.11 respectively. Therefore, 39.11 was set as the safe withdrawal speed and 45.43 as the alarm withdrawal speed. Conclusion:The real-time monitoring system we constructed can be used to monitor real-time withdrawal speed of colonoscopy and improve the quality of endoscopy.

Objective:To construct an artificial intelligence-assisted diagnosis system to detect gastric ulcer lesions and identify benign and malignant gastric ulcers automatically.Methods:A total of 1 885 endoscopy images were collected from November 2016 to April 2019 in the Digestive Endoscopy Center of Renmin Hospital of Wuhan University. Among them, 636 were normal images, 630 were with benign gastric ulcers, and 619 were with malignant gastric ulcers. A total of 1 735 images belonged to training data set and 150 images were used for validation. These images were input into the Res-net50 model based on the fastai framework, the Res-net50 model based on the Keras framework, and the VGG-16 model based on the Keras framework respectively. Three separate binary classification models of normal gastric mucosa and benign ulcers, normal gastric mucosa and malignant ulcers, and benign and malignant ulcers were constructed.Results:The VGG-16 model showed the best ability of classification. The accuracy of the validation set was 98.0%, 98.0% and 85.0%, respectively, for distinguishing normal gastric mucosa from benign ulcers, normal gastric mucosa from malignant ulcers, and benign ulcers from malignant ulcers.Conclusion:The artificial intelligence-assisted diagnosis system obtained in this study shows noteworthy ability of detection of ulcerous lesions, and is expected to be used in clinical to assist doctors to detect ulcer and identify benign and malignant ulcers.