ObjectiveTo explore the changes in volatile components， total polysaccharides， enzyme activity， and chromaticity value of Myristicae Semen Koji（MSK） during the fermentation process， and conduct correlation analysis. MethodsBased on gas chromatography-mass spectrometry（GC-MS）， the changes of volatile components in MSK at different fermentation times were identified. The phenol sulfuric acid method， dinitrosalicylic acid method（DNS）， and carboxymethyl cellulose sodium salt method（CMC-Na） were used to investigate the total polysaccharide content， amylase activity， and cellulase activity during the fermentation process. Visual analysis technology was used to explore the changes in chromaticity values， revealing the fermentation process of MSK and the dynamic changes of various measurement indicators， partial least squares-discriminant analysis（PLS-DA） was used to explore the differential compounds of MSK at different fermentation degrees， and Pearson correlation analysis was used to explore the correlation between volatile components of MSK and total polysaccharides， enzyme activity， and chromaticity values. ResultsA total of 60 volatile compounds were identified from MSK， the relative contents of components such as （+）-α-pinene， β-phellandrene， β-pinene， （+）-limonene， and p-cymene obviously increased， while the relative contents of components such as safrole， methyl isoeugenol， methyleugenol， myristicin， and elemicin significantly decreased. During the fermentation process， the total polysaccharide content showed an upward trend， while the activities of amylase and cellulase showed an initial increase followed by a decrease， and reached their maximum value at 40 h. the overall brightness（L^*） and total color difference（ΔE^*） gradually increased， while the changes in red-green value（a^*） and yellow-blue value（b^*） were not obvious. PLS-DA results showed that MSK could be clearly distinguished at different fermentation times， and 13 differential biomarkers were screened out. Pearson correlation analysis results showed that the contents of α-terpinene， β-phellandrene， methyleugenol， β-cubebene and myristic acid had an obvious correlation with chromaticity values. ConclusionAfter fermentation， the volatile components， total polysaccharides， amylase activity， and cellulase activity of MSK undergo significant changes， and there is a clear correlation between them and chromaticity values， which reveals the dynamic changes in the fermentation process and related indicators of MSK， laying a foundation for the quality control.

Objective To investigate the correlation between serum indirect bilirubin(IBIL)levels and carotid intima-media thickness(CIMT)of acute cerebral infarction patients.Methods A total of 229 patients with acute cerebral infarction at Wuxi No.2 Chinese Medicine Hospital from July 2021 to November 2023 were divided into the intima-media thickness group(CIMT≥0.9mm,n=108)and intima-media normal group(CIMT<0.9mm,n=121)according to CIMT values.The differences in gender,age,body mass index,smoking history,history of alcohol drinking,hypertension,coronary heart disease,diabetes,hyperlipidemia,total bilirubin(TBIL),direct bilirubin(DBIL),indirect bilirubin(IBIL),total cholesterol,high-density lipoprotein cholesterol,low-density lipoprotein cholesterol,triglyceride,fasting blood glucose,uric acid,and cystatin C were compared between the two groups.To further elucidate the association between serum bilirubin levels and CIMT using both univariate and multivariate analysis.Results The two groups showed significant differences in gender,body mass index and IBIL in the results(P<0.05).However,there was no significant difference in age,history of alcohol drinking,smoking history,diabetes,coronary heart disease,hypertension,hyperlipidemia,TBIL,DBIL,total cholesterol,high-density lipoprotein cholesterol,low-density lipoprotein cholesterol,triglyceride,fasting blood glucose,uric acid and cystatin C levels(P>0.05).Binary Logistic regression analysis indicated that gender,body mass index and IBIL were significant independent predictors of CIMT values(P<0.05).Conclusion Serum indirect bilirubin level is inversely associated with CIMT in acute cerebral infarction patients,which may be one of the protective factors of atherosclerosis.

ObjectiveTo rapidly identify the chemical constituents in Tongxie Yaofang decoction by ultra-performance liquid chromatography-linear ion trap-electrostatic field orbitrap high-resolution mass spectrometry（UPLC-LTQ-Orbitrap-MS）. MethodChromatographic conditions were ACQUITY UPLC BEH C₁₈ column（2.1 mm×100 mm， 1.7 μm）， mobile phase of 0.1% formic acid aqueous solution（A）-acetonitrile（B） for gradient elution （0-4 min， 5%-15%B； 4-10 min， 15%-25%B； 10-15 min， 25%-60%B； 15-20 min， 60%-90%B； 20-25 min， 90%-100%B； 25-27 min， 100%B； 27-30 min， 100%-5%B； 30-32 min， 5%B）， flow rate of 0.3 mL·min^-1， column temperature at 35 ℃ and injection volume of 3 μL. UPLC-LTQ-Orbitrap-MS was equipped with an electrospray ionization（ESI）， the MS and MS/MS data were collected in positive and negative ion modes， and detection range was m/z 100-1 250. Combining the reference substance， chemical databases and related literature information， TraceFinder 4.1 and Xcalibur 2.1 were used to identify the chemical constituents of Tongxie Yaofang decoction. ResultA total of 90 compounds， mainly including flavonoids， coumarins， monoterpene glycosides， chromones and lactones， were identified from Tongxie Yaofang decoction. By attributing the sources of Chinese medicines for all identified compounds， 9 of them were found to be derived from Atractylodis Macrocephalae Rhizoma， 21 from Paeoniae Radix Alba， 24 from Citri Reticulatae Pericarpium， 29 from Saposhnikoviae Radix， and 7 from at least two Chinese medicines. ConclusionThe method can effectively， quickly and comprehensively identify the chemical components of Tongxie Yaofang decoction， and clarify the chemical composition. These identified compounds cover the main active ingredients of the four herbs with high abundance， which indicates that the extraction method and the ratio of the medicinal materials of Tongxie Yaofang are scientific， and can provide a reference for the research on the material basis and quality evaluation of this famous classical formula.

Objective:To explore the relative factors for best ovarian response in patients undergoing assisted reproductive technology with follicular phase long-acting long protocol, and to establish a Nomogram prediction model of ovarian response.Methods:This retrospective cohort study analyzed the clinical data of 1289 patients who received assisted reproductive treatment in the Center for Reproductive Medicine of Fujian Maternity and Child Health Hospital from July 1, 2018 to July 30, 2019. According to the number of oocytes retrieved, there were 164 cases in the low ovarian response group (≤5 oocytes retrieved), 891 cases in the normal ovarian response group (the number of retrieved oocytes was >5, and ≤18), and 234 cases in the high ovarian response group (>18 oocytes retrieved). Independent factors affecting ovarian reactivity were screened by logistic regression, which were the model entry variables, and a Nomogram prediction model was established based on the regression coefficients in the model.Results:There were statistically significant differences in age, anti-Müllerian hormone (AMH) level and antral follicle count (AFC) among the three groups [32.43±3.99, 31.48±3.89, 29.91±3.73; (2.53±1.90) μg/L, (3.79±2.20) μg/L, (5.94±3.12) μg/L; 10.24±3.10, 14.50±3.29, 19.81±3.44; all P<0.001]. There were no significant differences in body mass index (BMI), duration of infertility and causes of tubal infertility (all P>0.05). The initial dosage of gonadotropin (Gn) used for ovarian hyperstimulation among the three groups was statistically different [(182.62±53.96) U, (166.79±48.20) U, (159.13±43.92) U, P<0.001], while the duration of Gn used and clinical pregnancy rate had no significant differences (all P>0.05). Multifactorial stepwise aggression analysis showed that female age [0.93(0.90-0.96), P=0.007], AFC [1.07(1.03-1.09), P=0.001], AMH [1.29(1.20-1.39), P=0.001], basal follicle-stimulating hormone [0.79(0.73-0.86), P=0.001], luteinizing hormone value [1.11(1.06-1.23), P=0.010], initial dosage of Gn used [1.00(1.00-1.01), P=0.003], total dosage of Gn usd [1.00(0.99-1.00), P=0.001] and the presence or absence of diagnosis of endometriosis [0.63(0.47-0.86), P=0.001] and polycystic ovary syndrome [0.30(0.22-0.91), P=0.030] were independent factors for the occurrence of different ovarian responses during ovarian hyperstimulation. The prediction model of ovarian reactivity was constructed based on the above factors, and the accuracy of predicting the optimal ovarian response state was 95%. The above model was verified with 306 patients' data from August 1, 2019 to October 30, 2019 in this center, and the predicted ovarian response (number of oocytes obtained) of a total of 279 patients was consistent with the actual situation, with a coincidence degree of 91.2%. The consistency index of the model was 0.71. Conclusion:We screened out the relevant factors affecting ovarian response in patients undergoing assisted reproductive technology with follicular phase long-acting long protocol, and established a Nomogram prediction model of ovarian response, which could effectively, intuitively and visually predict ovarian reactivity in hyperstimulation.

Objective:To explore the relative factors for best ovarian response in patients undergoing assisted reproductive technology with follicular phase long-acting long protocol, and to establish a Nomogram prediction model of ovarian response.Methods:This retrospective cohort study analyzed the clinical data of 1289 patients who received assisted reproductive treatment in the Center for Reproductive Medicine of Fujian Maternity and Child Health Hospital from July 1, 2018 to July 30, 2019. According to the number of oocytes retrieved, there were 164 cases in the low ovarian response group (≤5 oocytes retrieved), 891 cases in the normal ovarian response group (the number of retrieved oocytes was >5, and ≤18), and 234 cases in the high ovarian response group (>18 oocytes retrieved). Independent factors affecting ovarian reactivity were screened by logistic regression, which were the model entry variables, and a Nomogram prediction model was established based on the regression coefficients in the model.Results:There were statistically significant differences in age, anti-Müllerian hormone (AMH) level and antral follicle count (AFC) among the three groups [32.43±3.99, 31.48±3.89, 29.91±3.73; (2.53±1.90) μg/L, (3.79±2.20) μg/L, (5.94±3.12) μg/L; 10.24±3.10, 14.50±3.29, 19.81±3.44; all P<0.001]. There were no significant differences in body mass index (BMI), duration of infertility and causes of tubal infertility (all P>0.05). The initial dosage of gonadotropin (Gn) used for ovarian hyperstimulation among the three groups was statistically different [(182.62±53.96) U, (166.79±48.20) U, (159.13±43.92) U, P<0.001], while the duration of Gn used and clinical pregnancy rate had no significant differences (all P>0.05). Multifactorial stepwise aggression analysis showed that female age [0.93(0.90-0.96), P=0.007], AFC [1.07(1.03-1.09), P=0.001], AMH [1.29(1.20-1.39), P=0.001], basal follicle-stimulating hormone [0.79(0.73-0.86), P=0.001], luteinizing hormone value [1.11(1.06-1.23), P=0.010], initial dosage of Gn used [1.00(1.00-1.01), P=0.003], total dosage of Gn usd [1.00(0.99-1.00), P=0.001] and the presence or absence of diagnosis of endometriosis [0.63(0.47-0.86), P=0.001] and polycystic ovary syndrome [0.30(0.22-0.91), P=0.030] were independent factors for the occurrence of different ovarian responses during ovarian hyperstimulation. The prediction model of ovarian reactivity was constructed based on the above factors, and the accuracy of predicting the optimal ovarian response state was 95%. The above model was verified with 306 patients' data from August 1, 2019 to October 30, 2019 in this center, and the predicted ovarian response (number of oocytes obtained) of a total of 279 patients was consistent with the actual situation, with a coincidence degree of 91.2%. The consistency index of the model was 0.71. Conclusion:We screened out the relevant factors affecting ovarian response in patients undergoing assisted reproductive technology with follicular phase long-acting long protocol, and established a Nomogram prediction model of ovarian response, which could effectively, intuitively and visually predict ovarian reactivity in hyperstimulation.

Objective:To summarize the anesthesia management of living small bowel transplantation.Methods:Severn patients undergoing living and allogeneic small bowel transplantation for the first time were selected.The intraoperative hemodynamics, indexes of blood gas analysis, body temperature and blood transfusion and volume of liquid infused were analyzed.Postoperative outcomes were tracked.Results:Six cases survived and were successfully discharged from hospital successfully, and one patient died.In the operation room, 71% patients were successfully extubated after surgery.Compared with the values during anatomical separation period, Hb during vascular anastomosis and intestinal reconstruction periods and concentration of Ca 2+ during intestinal reconstruction period were significantly decreased, and the blood glucose concentration during vascular anastomosis period were increased ( P<0.05 or 0.01). Compared with the values during vascular anastomosis period, the blood glucose concentration was increased significantly during intestinal reconstruction period ( P<0.05). Crystalloid solution (57±30) ml/kg and colloid solution which mainly containing 20% albumin (15±13) ml/kg were infused mainly during anatomical separation and vascular anastomosis periods in all the patients. Conclusion:The condition of successful living small bowel transplantation is fully evaluation and preparation before surgery.Intravenous-inhalational anesthesia combined with transverses abdominis plane block and rational infusion of colloid solution with vasoactive drugs to maintain hemodynamics stability and monitor blood gas, body temperature, active adjustment of electrolytes and internal environment and stable body temperature can be helpful in maintaining perioperative stable vital signs during the perioperative period, removing the tracheal tube early at the end of surgery, and reducing the development of postoperative complications in patients undergoing living small bowel transplantation.

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 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 analyze the effectiveness and safety of assisted reproductive technology in the fertility needs of female patients with malignant tumors.Methods:A total of 37 female malignant tumor patients (case group) who underwent adjuvant reproductive therapy in Fujian Provincial Maternity and Children's Hospital from 2013 to 2018 were enrolled. Totally 74 non-tumor patients were matched as control group according to age and egg retrieval time using a 1：2 matching method. By analyzing the demographic data and the first pregnancy data, the clinical pregnancy rate and the implantation rate of the first transplant cycle were calculated, and the pregnancy outcomes of case group and control group were evaluated.Results:There was no significant difference in the demographic data between the two groups. There was no significant difference in the basic follicle-stimulating hormone (FSH) level, basic luteinizing hormone (LH) level, estradiol and progesterone levels on human chorionic gonadotropin (hCG) injection day during the first treatment period ( P>0.05). The differences of total amount of gonadotropin (Gn) used [(1 984.93±756.79) IU vs. (2 610.59±988.52) IU, P=0.001], duration of Gn used [(9.68±2.57) d vs. (11.64±2.47) d, P<0.001], high-quality embryo rate (49.56% vs. 58.94%, P=0.016) and the number of embryos transferred (1.4±0.5 vs. 1.8±0.4， P=0.001) between case group and control group were statistically significant. There was no significant difference in the clinical pregnancy rate and the implantation rate between the two groups ( P>0.05). Cumulative pregnancy rate, cumulative live birth rate, the number of egg retrieval cycles per live birth, the number of transplant cycles, the number of embryos, and the number of high-quality embryos were not statistically significant ( P>0.05). Tumor patients receiving assisted reproductive assistance have not been found so far recurrence or new tumor. Conclusion:Compared with control group, the pregnancy outcomes after assisted reproductive technology in female malignant tumor patients were not significantly different. Assisted reproductive technology can fulfill the fertility needs of female malignant tumors after surgery and is safety.

Objective:To analyze the effectiveness and safety of assisted reproductive technology in the fertility needs of female patients with malignant tumors.Methods:A total of 37 female malignant tumor patients (case group) who underwent adjuvant reproductive therapy in Fujian Provincial Maternity and Children's Hospital from 2013 to 2018 were enrolled. Totally 74 non-tumor patients were matched as control group according to age and egg retrieval time using a 1：2 matching method. By analyzing the demographic data and the first pregnancy data, the clinical pregnancy rate and the implantation rate of the first transplant cycle were calculated, and the pregnancy outcomes of case group and control group were evaluated.Results:There was no significant difference in the demographic data between the two groups. There was no significant difference in the basic follicle-stimulating hormone (FSH) level, basic luteinizing hormone (LH) level, estradiol and progesterone levels on human chorionic gonadotropin (hCG) injection day during the first treatment period ( P>0.05). The differences of total amount of gonadotropin (Gn) used [(1 984.93±756.79) IU vs. (2 610.59±988.52) IU, P=0.001], duration of Gn used [(9.68±2.57) d vs. (11.64±2.47) d, P<0.001], high-quality embryo rate (49.56% vs. 58.94%, P=0.016) and the number of embryos transferred (1.4±0.5 vs. 1.8±0.4， P=0.001) between case group and control group were statistically significant. There was no significant difference in the clinical pregnancy rate and the implantation rate between the two groups ( P>0.05). Cumulative pregnancy rate, cumulative live birth rate, the number of egg retrieval cycles per live birth, the number of transplant cycles, the number of embryos, and the number of high-quality embryos were not statistically significant ( P>0.05). Tumor patients receiving assisted reproductive assistance have not been found so far recurrence or new tumor. Conclusion:Compared with control group, the pregnancy outcomes after assisted reproductive technology in female malignant tumor patients were not significantly different. Assisted reproductive technology can fulfill the fertility needs of female malignant tumors after surgery and is safety.