Background and Aims:Complete situs inversus(SIT)is a rare congenital abnormality of organ mirror-image arrangement,presenting certain challenges for abdominal surgical procedures.The Da Vinci robotic system,with its high-definition 3D vision and flexible operation,holds potential for application in patients with anatomical variations.This report presents the diagnosis and treatment process of a patient with rectal mass and SIT who underwent robotic-assisted surgery.Additionally,relevant literature is reviewed to provide insights for individualized surgical strategies in patients with complex anatomical variations and to promote the further clinical application of robotic-assisted surgery systems.Methods:A case from the First Affiliated Hospital of Zhengzhou University is reported,in which a patient with rectal mass and SIT successfully underwent lesion resection using the Da Vinci robotic system with an unconventional"five-port"technique.A systematic literature review was also conducted(including 35 case reports),to summarize the surgical characteristics of colorectal procedures in SIT patients and the advantages of robotic system application.Results:The patient was a 74-year-old male who presented with rectal bleeding.Imaging confirmed the diagnosis of SIT,and colonoscopy revealed a large polypoid mass with ulceration at the apex,located 13-18 cm from the anal verge.The patient subsequently underwent Da Vinci robotic-assisted resection of the rectal lesion.The robotic system effectively overcame the challenges posed by mirror-image anatomy,enabling complete excision of the lesion.The operation lasted 183 minutes,with intraoperative blood loss of less than 20 mL.Postoperative pathology confirmed a villous tubular adenoma with high-grade intraepithelial neoplasia.The patient had an uneventful recovery,and no recurrence was observed during the 9-month follow-up.Literature analysis demonstrated that the robotic system,through magnified 3D visualization,flexible instrument articulation,and tremor filtration,significantly improves surgical precision in patients with anatomical anomalies.Conclusion:The Da Vinci robotic system effectively addresses the challenges of anatomical variations related to SIT in low rectal surgery.Its stability and precision offer a new technical option for tumor resection under complex anatomical conditions,demonstrating clinical value for widespread application.

OBJECTIVE To evaluate the status of microbial contamination in the environment of respiratory tract specimens sampling rooms and observe the association of the factors such as design and layouts of the sampling rooms with the potential risk of infections in the environment so as to provide scientific bases for optimizing the construction standards of fever clinics of the medical institutions.METHODS The sampling rooms of fever clinic were chosen from 10 tertiary or above medical institutions of Hangzhou,and the samples were collected from the key sites such as air,object surfaces and hands of health care workers.The respiratory tract pathogens in the sam-ples were detected by FilmArray full-automatic medical PCR analysis system,and the pathogens in the samples were detected by matrix-assisted laser desorption ionization-time-of-flight mass spectrometry(MALDI-TOF MS).The data including the areas,windows,mechanical ventilation facilities,physical partitions,frequencies and modes of disinfection and daily amounts of diagnosis and treatment of the 10 sampling rooms were investigated,and the univariate analysis was performed for their association with the microbial contamination of environment.RESULTS The isolation rate of respiratory tract pathogens was 10.00％(4/40),respiratory syn-cytial virus,human rhinovirus and adenovirus were dominant among the respiratory tract pathogens.The isolation rate of bacteria was 71.88％(23/32),Staphylococcus ho minis and Staphylococcus epidermidis were the major species of opportunistic pathogens isolated.The result of univariate analysis indicated that both the presence of windows(P=0.012)and mechanical ventilation facilities(P=0.047)were associated with the potential risk of infections in the environment(P＜0.05);while the factors such as the areas of sampling rooms,physical parti-tions,disinfection modes and daily amounts of diagnosis and treatment were not remarkably associated with the potential risk of infections in the environment.CONCLUSION It is an effective way to physically separate the air-flow from other areas or fix the mechanical ventilation facilities to create proper airflow during the construction of fever clinic sampling rooms so as to reduce the potential risk of infections in the environment of sampling rooms.

OBJECTIVE To evaluate the status of microbial contamination in the environment of respiratory tract specimens sampling rooms and observe the association of the factors such as design and layouts of the sampling rooms with the potential risk of infections in the environment so as to provide scientific bases for optimizing the construction standards of fever clinics of the medical institutions.METHODS The sampling rooms of fever clinic were chosen from 10 tertiary or above medical institutions of Hangzhou,and the samples were collected from the key sites such as air,object surfaces and hands of health care workers.The respiratory tract pathogens in the sam-ples were detected by FilmArray full-automatic medical PCR analysis system,and the pathogens in the samples were detected by matrix-assisted laser desorption ionization-time-of-flight mass spectrometry(MALDI-TOF MS).The data including the areas,windows,mechanical ventilation facilities,physical partitions,frequencies and modes of disinfection and daily amounts of diagnosis and treatment of the 10 sampling rooms were investigated,and the univariate analysis was performed for their association with the microbial contamination of environment.RESULTS The isolation rate of respiratory tract pathogens was 10.00％(4/40),respiratory syn-cytial virus,human rhinovirus and adenovirus were dominant among the respiratory tract pathogens.The isolation rate of bacteria was 71.88％(23/32),Staphylococcus ho minis and Staphylococcus epidermidis were the major species of opportunistic pathogens isolated.The result of univariate analysis indicated that both the presence of windows(P=0.012)and mechanical ventilation facilities(P=0.047)were associated with the potential risk of infections in the environment(P＜0.05);while the factors such as the areas of sampling rooms,physical parti-tions,disinfection modes and daily amounts of diagnosis and treatment were not remarkably associated with the potential risk of infections in the environment.CONCLUSION It is an effective way to physically separate the air-flow from other areas or fix the mechanical ventilation facilities to create proper airflow during the construction of fever clinic sampling rooms so as to reduce the potential risk of infections in the environment of sampling rooms.

Background and Aims:Complete situs inversus(SIT)is a rare congenital abnormality of organ mirror-image arrangement,presenting certain challenges for abdominal surgical procedures.The Da Vinci robotic system,with its high-definition 3D vision and flexible operation,holds potential for application in patients with anatomical variations.This report presents the diagnosis and treatment process of a patient with rectal mass and SIT who underwent robotic-assisted surgery.Additionally,relevant literature is reviewed to provide insights for individualized surgical strategies in patients with complex anatomical variations and to promote the further clinical application of robotic-assisted surgery systems.Methods:A case from the First Affiliated Hospital of Zhengzhou University is reported,in which a patient with rectal mass and SIT successfully underwent lesion resection using the Da Vinci robotic system with an unconventional"five-port"technique.A systematic literature review was also conducted(including 35 case reports),to summarize the surgical characteristics of colorectal procedures in SIT patients and the advantages of robotic system application.Results:The patient was a 74-year-old male who presented with rectal bleeding.Imaging confirmed the diagnosis of SIT,and colonoscopy revealed a large polypoid mass with ulceration at the apex,located 13-18 cm from the anal verge.The patient subsequently underwent Da Vinci robotic-assisted resection of the rectal lesion.The robotic system effectively overcame the challenges posed by mirror-image anatomy,enabling complete excision of the lesion.The operation lasted 183 minutes,with intraoperative blood loss of less than 20 mL.Postoperative pathology confirmed a villous tubular adenoma with high-grade intraepithelial neoplasia.The patient had an uneventful recovery,and no recurrence was observed during the 9-month follow-up.Literature analysis demonstrated that the robotic system,through magnified 3D visualization,flexible instrument articulation,and tremor filtration,significantly improves surgical precision in patients with anatomical anomalies.Conclusion:The Da Vinci robotic system effectively addresses the challenges of anatomical variations related to SIT in low rectal surgery.Its stability and precision offer a new technical option for tumor resection under complex anatomical conditions,demonstrating clinical value for widespread application.

Objective:To investigate the genomic manifestation and pathogenesis of osteosarcoma with different relapse pattens, which were respectively initially presented with bone metastasis or pulmonary metastasis.Methods:From May 1, 2021 to October 1, 2021, 38 fresh tumor specimens and some paraffin-embedded specimens of high-grade osteosarcoma were collected in Peking University People's Hospital, including 29 males and 9 females, aged 19.6±2.2 years (range, 6-61 years). Among the 38 cases, 12 cases had initial bone metastasis (group A) and 26 cases had initial lung metastasis (group B), of which 15 cases (40%, 15/38) had paired specimens of primary and metastatic lesions. Based on Illumina NovaSeq 6000, we analyzed whole-exome sequencing (WES) as well as transcriptome for osteosarcoma with paired samples in different relapse patterns. During all their treatment courses, we also collected their paired samples to reveal these tumors' evolution. We sought to redefine disease subclassifications for osteosarcoma based on genetic alterations and correlate these genetic profiles with clinical treatment courses to elucidate potential evolving cladograms.Results:We found that osteosarcoma in group A mainly carried single-nucleotide variations (83%, 10/12), displaying higher tumor mutation burden [4.9 (2.8, 12.0) & 2.4 (1.4, 4.5), P=0.010] and neoantigen load [743.0 (316.5, 1,034.5) & 128.5 (49.0, 200.5), P=0.003], while those in group B mainly exhibit structural variants (58%, 15/26). The mutation spectrum showed that there was a significant difference in age-related gene imprinting 1 between the bone metastasis group and the lung metastasis group ( P=0.005). Samples were randomly selected from group A (3 patients) to investigate immunologic landscape by multiplex immunohistochemistry, from which we noticed tertiary lymphatic structure from one patient from group A. High conservation of reported genetic sequencing over time was found in their evolving cladograms. Conclusion:Osteosarcoma with mainly single-nucleotide variations other than structural variants might exhibit biological behavior predisposing toward bone metastases with older in age as well as better immunogenicity in tumor microenvironment.

Objective:To construct the risk prediction model of acute exacerbation of chronic obstructive pulmonary disease (AECOPD) and verify its effectiveness based on deep learning and back propagation algorithm neural network (BP neural network).Methods:Based on the relevant data of 1 326 patients with chronic obstructive pulmonary disease (COPD) in the team's previous clinical study, the acute exacerbation, and its risk factors during the stable period and 6 months of follow-up were recorded and analyzed. Combined with previous clinical research data and expert questionnaire results, the independent risk factors of AECOPD after screening and optimization by multivariate Logistic regression including gender, body mass index (BMI) classification, number of acute exacerbation, duration of acute exacerbation and forced expiratory volume in one second (FEV1) were used to build the BP neural network by Python 3.6 programming language and Tensorflow 1.12 deep learning framework. The patients were randomly selected according to the ratio of 4∶1 to generate the training group and the test group, of which, the training group had 1 061 sample data while the test group had 265 pieces of sample data. The training group was used to establish the prediction model of neural network, and the test group was used for back-substitution test. When using the training group data to construct the neural network model, the training group was randomly divided into training set and verification set according to the ratio of 4∶1. There were 849 training samples in the training set and 212 verification samples in the verification set. The optimal model was screened by adjusting the parameters of the neural network and combining the area under the receiver operator characteristic curve (AUC), and the sample data of the test group was substituted into the model for verification.Results:The independent risk factors including gender, BMI classification, number of acute exacerbation, duration of acute exacerbation and FEV1 were collected from the team's previous clinical research, and the AECOPD risk prediction model was constructed based on deep learning and BP neural network. After 10 000 training sessions, the accuracy of the AECOPD risk prediction model in the validation set of the training group was 83.09%. When the number of training times reached 8 000, the accuracy basically tended to be stable and the prediction ability reached the upper limit. The AECOPD risk prediction model trained for 10 000 times was used to predict the risk of the validation set data, and the receiver operator characteristic curve (ROC curve) analysis showed that the AUC was 0.803. When using this model to predict the risk of the data of the test group, the accuracy rate was 81.69%.Conclusion:The risk prediction model based on deep learning and BP neural network has a medium level of prediction efficiency for acute exacerbation within 6 months in COPD patients, which can evaluate the risk of AECOPD and assist the clinic in making accurate treatment decisions.

AIM: To study the effects of trigliptin succinate on gut microbiota of type 2 diabetic mice. METHODS: 16S rRNA high-throughput sequencing method was used to sequence the intestinal flora of mice in the healthy group, the T2DM group, the trigliptin succinate group and the sitagliptin phosphate group. QIME was used to filter the data, classify and annotate the species. Alpha diversity index and Beta diversity index of the samples were analyzed.The richness and diversity of bacteria in the four groups were compared. RESULTS: The gut microbiota structure of mice in the healthy group, the T2DM group, the trigliptin succinate group and the setagliptin phosphate group were significantly different. The results showed that the ratio of Firmicutes to Bacteroidetes was decreased compared with that in the healthy group. Cyanobacteria, Verrucomicrobia and Tenericutes had significant differences (P< 0.05). Potential biomarkers for T2DM group were Bacilli, Lactobacillales, Lactococcus and Streptococcaceae. Candidate biomarkers of trigliptin succinate group may be Bacteroidia, Bacteroidetes, Bacteroidales, Prevotella, Paraprevotellaceae, Parabacteroides, Porphyromonadaceae; The candidate biomarkers of sitagliptin phosphate group may be Lactobacillus, Lactobacillaceae and Helicobacter. CONCLUSION: The intestinal flora of mice in the trigliptin succinate group was significantly different from that in the healthy group and the T2DM group. Using trigliptin succinate to improve the intestinal flora of mice might achieve the hypoglycemic effect by improving the intestinal flora.

Objective:To establish a risk prediction model for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) using regression analysis and verify the model.Methods:The risk factors and acute exacerbation of 1 326 patients with chronic obstructive pulmonary disease (COPD) who entered the stable phase and followed up for 6 months in the four completed multi-center large-sample randomized controlled trials were retrospectively analyzed. Using the conversion-random number generator, about 80% of the 1 326 cases were randomly selected as the model group ( n = 1 074), and about 20% were the verification group ( n = 252). The data from the model group were selected, and Logistic regression analysis was used to screen independent risk factors for AECOPD, and an AECOPD risk prediction model was established; the model group and validation group data were substituted into the model, respectively, and the receiver operating characteristic (ROC) curve was drawn to verify the effectiveness of the risk prediction model in predicting AECOPD. Results:There were no statistically significant differences in general information (gender, smoking status, comorbidities, education level, etc.), body mass index (BMI) classification, lung function [forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), etc.], disease status (the number and duration of acute exacerbation in the past year, duration of disease, etc.), quality of life scale [COPD assessment test (CAT), etc.] and clinical symptoms (cough, chest tightness, etc.) between the model group and the validation group. It showed that the two sets of data had good homogeneity, and the cases in the validation group could be used to verify the effectiveness of the risk prediction model established through the model group data to predict AECOPD. Logistic regression analysis showed that gender [odds ratio ( OR) = 1.679, 95% confidence interval (95% CI) was 1.221-2.308, P = 0.001], BMI classification ( OR = 0.576, 95% CI was 0.331-1.000, P = 0.050), FEV1 ( OR = 0.551, 95% CI was 0.352-0.863, P = 0.009), number of acute exacerbation ( OR = 1.344, 95% CI was 1.245-1.451, P = 0.000) and duration of acute exacerbation ( OR = 1.018, 95% CI was 1.002-1.034, P = 0.024) were independent risk factors for AECOPD. A risk prediction model for AECOPD was constructed based on the results of regression analysis: probability of acute exacerbation ( P) = 1/(1+ e- x), x = -3.274 + 0.518×gender-0.552×BMI classification + 0.296×number of acute exacerbation + 0.018×duration of acute exacerbation-0.596×FEV1. The ROC curve analysis verified that the area under ROC curve (AUC) of the model group was 0.740, the AUC of the verification group was 0.688; the maximum Youden index of the model was 0.371, the corresponding best cut-off value of prediction probability was 0.197, the sensitivity was 80.1%, and the specificity was 57.0%. Conclusion:The AECOPD risk prediction model based on the regression analysis method had a moderate predictive power for the acute exacerbation risk of COPD patients, and could assist clinical diagnosis and treatment decision in a certain degree.

Objective:To analyze and compare the efficacy of robotic, laparoscopic and open dorsal mesh rectopexy in the treatment of severe rectal prolapse.Methods:A retrospective cohort study was performed. Patients who had a full-thickness rectum pulled out of the anus before surgery and the length was greater than 8 cm, and underwent transabdominal dorsal mesh rectopexy were enrolled in the study. Those who had urinary or sexual dysfunction before surgery, could not perform sexual function scores due to lack of a fixed sexual partner or sexual activity after surgery, underwent laparotomy again during the perioperative period, were transferred to laparotomy during robotic or laparoscopic surgery, or had no complete information, were excluded. A total of 61 patients with severe rectal prolapse in the First Affiliated Hospital of Zhengzhou University from 2014 to 2018 were enrolled and divided into robotic group (20 cases), laparoscopic group (20 cases) and open group (21 cases) according to the operative procedure based on patients' will. Perioperative parameters were compared among the 3 groups. The International Prostatic Symptoms Score Scale (IPSS, higher score indicates more severe urinary dysfunction), the International Index of Erectile Function questionnaire (IIEF-15, lower score indicates more severe male sexual dysfunction) and the Female Sexual Function Index (FSFI-19, lower score indicates more severe female sexual dysfunction) were used to evaluate and compare the urinary and sexual function before and after operation.Results:There were no significant differences in baseline data among the 3 groups (all P>0.05). In the robotic, laparoscopic and open groups respectively, the operative time was (176.3±13.8) minutes, (160.2±12.1) minutes and (134.2±12.1) minutes; intraoperative blood loss was (58.5±18.9) ml, (67.9±15.7) ml and (114.2±8.4) ml; the first time to ambulation was (19.9±6.8) hours, (24.0±8.9) hours and (37.7±11.4) hours; the first time to gas passage was (31.8±6.8) hours, (35.7±8.9) hours and (49.2±11.2) hours; the hospitalization time was (11.0±1.4) days, (11.4±1.4) days and (13.3±2.1) days; whose differences among 3 groups were all significant (all P<0.001). While no significant differences in morbidity of complication and recurrence among 3 groups were observed (all P>0.05). In the robotic, laparoscopic and open groups respectively, the preoperative IPSS score was (4.2±1.7), (4.4±1.3), and (4.7±1.8); the IPSS score at postoperative 3-month was (8.5±2.5), (9.9±1.7), and (12.2±3.1); IPSS score at postoperative 12-month was (4.3±1.6), (5.8±1.3), and (6.3±1.5), respectively. Compared to preoperative score, postoperative IPSS score increased obviously, then decreased gradually ( P<0.001). Preoperative male IIEE score was (22.8±1.8), (22.1±2.1), and (22.6±1.5). In the robotic, laparoscopic and open groups respectively, male IIEE score at postoperative 6-month was (19.6±2.1), (17.1±2.1), and (15.0±2.1); male IIEE score at postoperative 12-month was (22.4±1.6), (19.9±1.5), (17.9±1.8), respectively. Preoperative female FSFI score was (26.4±3.4), (26.6±3.2), and (26.6±3.0); female FSFI score at postoperative 6-month was (21.5±3.3), (18.9±2.9), (17.0±2.6); female FSFI score at postoperative 12-month was (26.1±2.7), (22.7±3.2), and (21.2±2.3), respectively. Postoperative male IIEE score and female FSFI score decreased significantly and then increased gradually with time, whose differences were all significant (all P<0.05). Postoperative IPSS, IIEE, and FSFI scores in the robotic group were superior to those in the laparoscopic and open groups (all P<0.05). Conclusion:Robotic surgery is safe and effective in the treatment of severe rectal prolapse, and is more advantageous in preserving urinary function and sexual function.