Objective To evaluate clinical efficacy of multiple regimen combination in treatment of osteoporosis of perimenopausal or postmenopausal women. Methods From Jul. 2008 to Dec. 2009, 109 women with low bone mineral density (BMD) or osteoporosis treated in Department of Obstetrics and Gynecology, Affiliated Second Hospital, Wenzhou Medical College were enrolled randomly into 3 group,including 36 women in Group A managed by osteoform 1000 mg/d + alfacalcidol 0. 25 μg/bid orally, 40 women in group B managed by osteoform 1000 mg/d + alfacalcidol 0. 25 μg/bid + tibolone 1.25 mg/d orally and 33 women in group C managed by ostcoform 1000 mg/d + alfacalcidol 0. 25 μg/bid +bisphosphonates 70 mg/w orally. After 48 weeks BMD on lumbar 1 -4 (L1-4) and left femur were detected by X-ray. Bone alkaline phosphatase(BALP) ,cross linked clelopeptide of type Ⅰ collagen(CTX) and 25-hydroxychole calciferol [25 (OH) D3] was measured by enzyme linked immunosorbent assay (ELISA).Result Seven women (6. 4%, 7/109) were withdrawed form this study, including 2 cases losing follow up in group A, 3 cases stopping treatment in group B, 2 cases giving up treatment due to severe adverse effect (burning in upper abdomen) in group C. (1) Pain relieve: after 48 weeks treatment, women in 3 groups improved symptom of pain significantly, the rates of pain relieve were 85% (29/34)in group A, 92% (34/37) in group B and 94% (29/31) in group C. (2) BMD: BMD was improved significantly in women in 3 groups after treatment. BMD of L1-4 were (0.88±0.15) g/cm2 in group A,(0.89±0.18) g/cm2 in group B and (0.87±0.10) g/cm2 in group C before treatment, and converted to (0.90±0.01) g/cm2 in group A, (0.93±0.09) g/cm2 in group B and (0.91±0.11) g/cm2 in group C after treatment. BMD of left femur were (0.87±0.07) g/cm2 in group A, (0.87±0.07) g/cm2 in group B and (0.85±0. 12) g/cm2 in group C before treatment and converted to (0.90± 0.03) g/cm2 in group A, (0.91±0.08) g/cm2 in group B and (0.89 ±0.12) g/cm2 in group C after treatment. It was shown significantly different BMD between group B or C and group A (P < 0. 01), however, there was no significant different BMD between group B and C (P >0. 05). (3) Index of bone metabolism: BALP were (26±6) μg/L in group A, (26±9) μg/L in group B and (28±7) μg/L in group C before treatment and converted to (22±5) μg/L in group A, (20±9)μg/L in group B and (22±8)μg/L in group C after treatment, which showed statistical difference (P < 0.05). CTX were (0.85±0.20) ng/L in group A, (0.84±0.47) ng/L in group B, and (0. 88 ±0. 11) ng/L in group C before treatment and converted to (0. 81 ±0. 19) ng/L in group A, (0. 77±0.33) ng/L in group B, and (0.82 ±0. 14) ng/L in group C after treatment, which showed statistical difference (P < 0. 05). Conclusions Those 3 regimens combination could be used in treatment of osteoporosis by decreasing bone conversion, increasing bone density, decreasing bone absorption. Regimen A was only suitable for basic therapy,the other two regimens could provide better treatment.

ObjectiveTo explore the role of CXCL16/CXCR6 axis on the metastasis of human lung cancer.MethodsImmunohistochemistry and immunocytochemistry analysis were performed to detect the expression of CXCL16/CXCR6 in human lung cancer samples as well as A549,95D and H292 cell lines,respectively.The effects of CXCL16 on the viability and invasiveness of the three lung cancer cell lines were examined by MTT and in vitro invasion assay,respectively.ResultsHuman native lung cancer cells co-expressed CXCR6 and CXCL16 protein.Compared to the normal lung tissues,there was a stronger specific staining for both CXC16 and CXCR6 protein in the lung cancer tissues.Three kinds of lung cancer cell lines,including A549,95D and H292,all expressed CXCL16 and CXCR6 protein.Furthermore,human recombinant CXCL16 significantly promoted the viability and invasiveness of A549,95D and H292 cells.The stimulated action of CXCL16 on lung cancer cell lines could be effectively blocked by CXCL16 neutralizing antibody.Conclusion CXCL16 and CXCR6 protein are co-expressed in human native lung cancer.CXCL16 is able to promote the viability and invasiveness of lung cancer cell lines,which might be the molecular mechanisms on the metastasis of lung cancer.

Objective To study intermediate and long term efficacy of uterine arterial embolization (UAE)with sodium alginate microspheres(KMG)at diameters 500-700μm in treatment of diffuse adenomyosis.Methods Totally 40 patients with standard difluse adenomyosis were enrolled and treated with UAE.KMG at diameters 500-700 μm for vascular embolization were used to embolize the arteries.The degree of dysmenorrhea,amount of menorrhea and uterine volume,as well as the level of serum CA125,follicle stimulating hormone(FSH),luteinizing hormone(LH),estradiol(E2)were investigated before andafter UAE.Results The follow up rates were 100%(40/40),100%(40/40),80%(32/40),68%(27/40),58%(23/40)after uterine arterial UAE 12,24,36,48 and 60 months respectively.The early,intermediate and long-term effective rates were 90%(36/40),88%(28/32),83%(19/23).The degree of dysmenorrhea,the amount of menorrhea and the uterine volume,as well as serum CA125 all decreased significantly 3 mouths after UAE at varying degrees(P＜0.05).Compared with other follow-up time,thedegree of dysmenorrhea and the amount of menorrhea declined to their lowest point at 6 month after UAE (P＜0.01).Paralleled with the decrease of volume of uterine,serum CA125 also decreased significantly and reached the lowest level 12 months later compared with other follow-up times(P＜0.01).Even at the 12th month after UAE serum CA125was not normal and FSH,LH and E2 did not change all the times after UAE(P>0.05).No recurrence was found during the 60 months after UAE.Condusion KMG used in UAE at diameters 500-700 μm has good intermediate and long term effectiveness in treatment of diffuse adenomyosis with no side effects.

OBJECTIVE Tostudytheeffectofmetforminonglucolipidmetabolicdisorderscaused byolanzapineorquetiapineinrats.METHODS Olanzapine1mg·kg-1·d-1wasgivenigorquetiapine 20 mg·kg -1·d -1 was given ig for 4 d,and the dose increased to 40 mg·kg -1·d -1 from the 5th day.Met-formin 100 mg·kg -1·d -1 was given ig from the 15th day.The treatment lasted 8 weeks.Body mass, fasting blood sugar (FBS)and postprandial 2 hours blood glucose (2hPBG)were measured at base-line,3 d,1 week,2 week,4 week,6 week and 8 week.At the end of the 8th week,serum cholesterol (TC),triglyceride (TG),low density lipoprotein (LDL-C),high density lipoprotein (HDL-C),fruc-tosamine(FA)andinsulin(IRS)weremeasured.RESULTS Therewasnosignificantstatisticaldiffer-ence between normal control group and metformin 1 00 mg·kg -1·d -1 group.At the end of the 6th week, compared with normal control group,the body mass and 2hPBG were significantly increased in olanzap-ine 1 mg·kg -1·d -1 group and quetiapine 40 mg·kg -1·d -1 group (P<0.05),respectively.At the end of the 8th week,body mass,2hPBG,INS,FA,TC,TG,LDL-C were significantly increased (P<0.05), and HDL-C decreased in olanzapine group and quetiapine group(P<0.05),respectively.FBS was increased only in olanzapine group(P<0.05).Compared with olanzapine group or quetiapine group, body mass,FBS,2hPBG,INS,FA,TC,TG,LDL-C were significantly decreased by metformin admin-istration(P<0.05).CONCLUSION Metformincaneffectivelypreventandtreatweightgainand glucolipid metabolic disorder caused by olanzapine or quetiapine.

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.

Objective:To establish a deep convolutional neural network (DCNN) model based on YOLO and ResNet algorithm for automatic detection of colorectal polyps and to test its function.Methods:Colonoscopy images and videos collected from the database of Digestive Endoscopy Center of Renmin Hospital of Wuhan University from January 2018 to March 2019 were divided into three databases (database 1, 3, 4). The public database CVC-ClinicDB (composed of 612 polyp images extracted from 29 colonoscopy videos provided by Barcelona Hospital, Spain) was used as the database 2. Database 1 (4 700 colonoscopy images from January 2018 to November 2018, including 3 700 intestinal polyp images and 1 000 non-polyp images) was used for establishing training and verifying the DCNN model. Database 2 (CVC-ClinicDB) and database 3 (720 colonoscopy images from January 2019 to March 2019, including 320 intestinal polyp images and 400 non-polyp images) were used for testing the DCNN model on image detection. Database 4 (15 colonoscopy videos in December 2019, containing 33 polyps) was used for testing the DCNN model on video detection. The sensitivity, specificity, accuracy and false positive rate of the DCNN model for detecting intestinal polyps were calculated.Results:The sensitivity of the DCNN model for detecting intestinal polyps in database 2 was 93.19% (602/646). In database 3, the DCNN model showed the accuracy of 95.00% (684/720), sensitivity of 98.13% (314/320), specificity of 92.50% (370/400), and false positive rate of 7.50% (30/400) for detecting intestinal polyps. In database 4, the DCNN model achieved a per-polyp-sensitivity of 100.00% (33/33), a per-image-accuracy of 96.29% (133 840/138 998), a per-image-sensitivity of 90.24% (4 066/4 506), a per-image-specificity of 96.49% (129 774/134 492), and a per-image-false positive rate of 3.51% (4 718/134 492).Conclusion:The DCNN model constructed in the study has a high sensitivity and specificity for automatic detection of colorectal polyps both in the colonoscopy images and videos, has a low false positive rate in the videos, and has the potential to assist endoscopists in diagnosis of colorectal polyps.

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.