Objective:To evaluate the effect of trans-nasal humidified rapid insufflation ventilatory exchange (THRIVE) on the early postoperative atelectasis in patients undergoing prolonged non-intubated intravenous anesthesia for gastrointestinal endoscopic procedures.Methods:In this randomized double-blind controlled trial, 150 patients of either sex, aged 18-80 yr, with American Society of Anesthesiologists Physical Status classification Ⅰ or Ⅱ and body mass index of 18-30 kg/m 2, scheduled for endoscopic mucosal resection of multiple colorectal polyps at Northern Jiangsu People′s Hospital Affiliated to Yangzhou University from February 18 to June 15, 2024, were divided into 2 groups ( n=75 each) using simple random allocation: THRIVE group (group T) and conventional oxygen therapy group (group C). In group T, the oxygen flow rate was 10 L/min before anesthesia induction and increased to 50 L/min after induction until the end of operation, and the inhaled oxygen concentration was 100%. Group C used conventional mask oxygen inhalation, with an oxygen flow rate of 10 L/min during anesthesia induction and maintenance. Lung ultrasonography was performed immediately before anesthesia induction and after the end of surgery, and the modified lung ultrasound score and diaphragm mobility during quiet breathing were recorded to assess the occurrence of atelectasis and diaphragmatic dysfunction. The lowest intraoperative SpO 2 value, adverse events during surgery and in the postanesthesia care unit (PACU), duration of PACU stay, adverse events within 7 days after operation, hospitalization duration, and satisfaction scores of both patients and endoscopists were recorded. Results:Compared with group C, the postoperative modified lung ultrasound score and incidence of atelectasis and diaphragmatic dysfunction were significantly decreased, the lowest intraoperative SpO 2 value was increased, the incidence of hypoxemia was decreased, the duration of PACU stay was shortened, and endoscopists′ satisfaction scores were increased ( P<0.05), and no statistically significant changes were observed in diaphragm mobility, incidence of other intraoperative adverse events, incidence of adverse events during PACU stay and within 7 days after operation, or patients′ satisfaction scores in group T ( P>0.05). Conclusions:THRIVE can reduce the risk of early postoperative atelectasis and intraoperative hypoxemia, thereby promoting postoperative recovery of patients undergoing gastrointestinal endoscopy under long-term non-intubated anesthesia.

Objective·To investigate the predictive value of the geriatric nutritional risk index(GNRI)for the occurrence of lung infection in hospitalized elderly patients with type 2 diabetes mellitus(T2DM).Methods·Elderly T2DM patients who were admitted to the Geriatric Department of Shanghai Tongren Hospital between June 2022 and June 2024 were retrospectively and consecutively enrolled.They were divided into infected and non-infected groups according to whether lung infection occurred during hospitalization.Baseline data(gender,age,height,weight,duration of diabetes,comorbidities,etc.)were collected and GNRI was calculated.A multivariate Logistic regression model was used to screen the independent risk factors for pulmonary infections,and the predictive value of GNRI for pulmonary infections in T2DM patients was analysed using receiver operating characteristic(ROC)curves.Results·A total of 264 elderly T2DM patients were enrolled,among whom 154 developed pulmonary infections.Significant differences were observed between the infected and non-infected groups in GNRI,albumin,leukocyte count,neutrophil ratio,lymphocyte ratio,glycated hemoglobin,fasting glucose,interleukin-6,C-reactive protein,and procalcitonin levels(P<0.05).Multivariate Logistic regression analysis showed that a lower GNRI was an independent risk factor for lung infection(OR=0.798,95%CI 0.712?0.894;P<0.001).Correlation analysis showed that GNRI was negatively correlated with C-reactive protein and calcitoninogen.ROC curve analysis showed that GNRI predicted pulmonary infection with an area under the curve of 0.828,a sensitivity of 77.9%,and a specificity of 76.6%.Conclusion·A lower GNRI is an independent risk factor for pulmonary infections in elderly T2DM patients,and also has a good predictive value for the occurrence of pulmonary infections.

Objective:To evaluate the effect of trans-nasal humidified rapid insufflation ventilatory exchange (THRIVE) on the early postoperative atelectasis in patients undergoing prolonged non-intubated intravenous anesthesia for gastrointestinal endoscopic procedures.Methods:In this randomized double-blind controlled trial, 150 patients of either sex, aged 18-80 yr, with American Society of Anesthesiologists Physical Status classification Ⅰ or Ⅱ and body mass index of 18-30 kg/m 2, scheduled for endoscopic mucosal resection of multiple colorectal polyps at Northern Jiangsu People′s Hospital Affiliated to Yangzhou University from February 18 to June 15, 2024, were divided into 2 groups ( n=75 each) using simple random allocation: THRIVE group (group T) and conventional oxygen therapy group (group C). In group T, the oxygen flow rate was 10 L/min before anesthesia induction and increased to 50 L/min after induction until the end of operation, and the inhaled oxygen concentration was 100%. Group C used conventional mask oxygen inhalation, with an oxygen flow rate of 10 L/min during anesthesia induction and maintenance. Lung ultrasonography was performed immediately before anesthesia induction and after the end of surgery, and the modified lung ultrasound score and diaphragm mobility during quiet breathing were recorded to assess the occurrence of atelectasis and diaphragmatic dysfunction. The lowest intraoperative SpO 2 value, adverse events during surgery and in the postanesthesia care unit (PACU), duration of PACU stay, adverse events within 7 days after operation, hospitalization duration, and satisfaction scores of both patients and endoscopists were recorded. Results:Compared with group C, the postoperative modified lung ultrasound score and incidence of atelectasis and diaphragmatic dysfunction were significantly decreased, the lowest intraoperative SpO 2 value was increased, the incidence of hypoxemia was decreased, the duration of PACU stay was shortened, and endoscopists′ satisfaction scores were increased ( P<0.05), and no statistically significant changes were observed in diaphragm mobility, incidence of other intraoperative adverse events, incidence of adverse events during PACU stay and within 7 days after operation, or patients′ satisfaction scores in group T ( P>0.05). Conclusions:THRIVE can reduce the risk of early postoperative atelectasis and intraoperative hypoxemia, thereby promoting postoperative recovery of patients undergoing gastrointestinal endoscopy under long-term non-intubated anesthesia.

Objective·To investigate the predictive value of the geriatric nutritional risk index(GNRI)for the occurrence of lung infection in hospitalized elderly patients with type 2 diabetes mellitus(T2DM).Methods·Elderly T2DM patients who were admitted to the Geriatric Department of Shanghai Tongren Hospital between June 2022 and June 2024 were retrospectively and consecutively enrolled.They were divided into infected and non-infected groups according to whether lung infection occurred during hospitalization.Baseline data(gender,age,height,weight,duration of diabetes,comorbidities,etc.)were collected and GNRI was calculated.A multivariate Logistic regression model was used to screen the independent risk factors for pulmonary infections,and the predictive value of GNRI for pulmonary infections in T2DM patients was analysed using receiver operating characteristic(ROC)curves.Results·A total of 264 elderly T2DM patients were enrolled,among whom 154 developed pulmonary infections.Significant differences were observed between the infected and non-infected groups in GNRI,albumin,leukocyte count,neutrophil ratio,lymphocyte ratio,glycated hemoglobin,fasting glucose,interleukin-6,C-reactive protein,and procalcitonin levels(P<0.05).Multivariate Logistic regression analysis showed that a lower GNRI was an independent risk factor for lung infection(OR=0.798,95%CI 0.712?0.894;P<0.001).Correlation analysis showed that GNRI was negatively correlated with C-reactive protein and calcitoninogen.ROC curve analysis showed that GNRI predicted pulmonary infection with an area under the curve of 0.828,a sensitivity of 77.9%,and a specificity of 76.6%.Conclusion·A lower GNRI is an independent risk factor for pulmonary infections in elderly T2DM patients,and also has a good predictive value for the occurrence of pulmonary infections.

Objective To develop an accurate deep learning prediction model of YOLO-V5 capable of accurately iden-tifying medication packaging boxes in outpatient and emergency pharmacies,aiming to assist pharmacists in achieving"zero dis-pensing error".Methods A total of 2 560 images of packaging boxes from 136 different drugs were collected and labeled to form the deep learning dataset.The dataset was split into training and validation sets at a ratio of 4∶1.YOLO-V5 deep-learning algorithm was employed for training the data using images from our dataset(train epochs:500,batch size:4,learning rate:0.01).The values of the precision(Pr)and mean average precision(mAP)were used as measures for model performance evaluation.Results The Pr of the four sub-models of YOLO-V5 in the training set all reached 1.00.The mAP_0.5 of YOLO-V5x was 0.95,which was higher than those of YOLO-V5s(0.94),YOLO-V5l(0.94),and YOLO-V5m(0.94).The mAP_0.5:0.95 of YOLO-V5l and YOLO-V5x were 0.85 which were higher than those of YOLO-V5s(0.84)and YOLO-V5m(0.84).Training time and model size were 82.56 hours and 166.00MB for YOLO-V5x which were the highest among the four models.The speed of detection in one im-age was 11ms for YOLO-V5s which was the fastest among the four models.Conclusion YOLO-V5 can accurately identify the packaging of drugs in outpatient and emergency pharmacies.Implementing an artificial-intelligence-assisted drug dispensation sys-tem is feasible for pharmacists to achieve"zero dispensing error".

Objective To investigate the value of modified YOLO-V5 model for identifying inflammatory bowel disease(IBD)displayed on CT enterography(CTE).Methods Totally 192 patients with IBD(103 cases of Crohn disease[CD subgroup]and 89 cases of ulcerative colitis[UC subgroup])and 103 patients with clinically suspected IBD but CTE showed no abnormality(no abnormality subgroup)were retrospectively collected as study group,while 5 patients with CD and 3 with UC were collected as test group.CTE images with diseased intestinal tubes present as thickened intestinal wall or no abnormality intestinal tubes were selected as data set(n=3 511).CTE in study group were divided into training set(n=3 160,including 1 063 from CD subgroup,931 from UC subgroup and 1 166 from no abnormality subgroup)and verification set(n=351,including 118 from CD subgroup,103 from UC subgroup and 130 from no abnormality subgroup)at the ratio of 9∶1,while 25 CET images(17 from 5 cases of CD and 8 from 3 cases of UC)in test group were used as test set.Diseased tubes of CD,UC and no abnormality tubes were labeled.Then 5 sub-models,including YOLO-V5n,YOLO-V5s,YOLO-V5m,YOLO-V5l and YOLO-V5x were constructed and trained with modified YOLO-V5,and their efficacy were verified in test set.Precision(Pr),recall(Rc)and mean average precision(mAP)were used to evaluate the efficacy of each sub-model for identifying IBD lesions displayed on CTE.Results The complexity of the above 5 sub-models increased successively.YOLO-V5l and YOLO-V5x sub-model had better diagnostic efficacy,the overall Pr,Rc,mAP_0.5 and mAP_0.5.0.95 of the former for identifying IBD lesions in training and validation sets was 0.97,0.93,0.96 and 0.91,while of the latter was 0.97,0.95,0.96 and 0.92,respectively.In test set,the efficacy of YOLO-V5n sub-model for identifying IBD lesions was low,with mAP_0.5∶0.95 of 0.66 and AUC of 0.82,whereas mAP_0.5∶0.95 of YOLO-V5x sub-model for identifying CD was as high as 0.92,and of YOLO-V5l sub-model for identifying UC was as high as 0.91.Conclusion YOLO-V5l and YOLO-V5x sub-models based on modified YOLO-V5 could effectively identify IBD lesions displayed on CTE.

Objective:To investigate the value of a deep learning method based on MobileNet in classification of bedside chest radiograph and improvement of the work efficiency.Methods:A total of 6, 320 bedside chest radiographs from January 2017 to December 2022 in the Second Peoples′ Hospital of Changzhou were retrospectively collected. The included cases were divided into normal group (885 images), pneumonia group (1 927 images), pleural effusion group (373 images), and pneumonia with pleural effusion group (3 135 images). Three hundred and fifty images were selected as a validation set, while the remaining images were divided into a train set (4 775 images) and a test set (1 195 images) using simple randomization, by 8∶2 ratio. Two lightweight convolutional neural network models (MobileNetV1 and MobileNetV2) were used to construct a bedside chest radiograph classification model, based on which two fine-tuning strategies were designed. Four models were generated namely MobileNetV1_False (V1_False), MobileNetV1_True (V1_True), MobileNetV2_False (V2_False) and MobileNetV2_True (V2_True). In the first stage, a binary classification model was established to divide the images into normal and lesion groups; then a four-class classification model was established in the second stage, with which the images were divided into four groups: normal, pneumonia, pleural effusion and pneumonia with pleural effusion. Metrics for model performance evaluation including accuracy (Ac), precision (Pr), recall rate (Rc), F1 score (F1) and area under the receiver operating characteristic curve (AUC) were calculated.Results:In both the first and second stages, V1_True and V2_True had higher Ac, Pr, Rc, and F1 than V1_False and V2_False in both the training set and validation set; and the V1_True model outperformed the other three models in classification. The classification Ac of the V1_True model in the validation set was higher than that of radiologists in the first stage [95.71% (335/350) vs. 90.29% (316/350)] and in the second stage [93.43% (327/350) vs. 87.14% (305/350)]. The recognition time of V1_True model′s in the validation set of 350 bedside chest radiographs was significantly less than that of the radiologists (mean: 17 s vs. 300 min).Conclusions:V1_True is an optimal MobileNet model for classifying bedside chest radiographs. The application of this model in clinical practice may help to accurately identify the information of lung lesions from bedside chest radiographs in time, and may improve the work efficiency in the radiology department.

Inflammatory bowel disease (IBD) is a complex, immune-mediated gastrointestinal disorder with ill-defined etiology, multifaceted diagnostic criteria, and unpredictable treatment response. Innovations in IBD diagnostics include developments in genomic sequencing, molecular analytics and gastrointestinal imaging, and have leverage above large data platforms to develop clinically meaningful artificial intelligence (AI) tools. Artificial intelligence, through machine learning, facilitates the interpretation of large arrays of data, and provides insight to improve IBD outcomes, especially with the application of deep learning. This review aims to summarize the recent progress of artificial intelligence in inflammatory bowel disease and its applications.

Inflammatory bowel disease (IBD) is a complex, immune-mediated gastrointestinal disorder with ill-defined etiology, multifaceted diagnostic criteria, and unpredictable treatment response. Innovations in IBD diagnostics include developments in genomic sequencing, molecular analytics and gastrointestinal imaging, and have leverage above large data platforms to develop clinically meaningful artificial intelligence (AI) tools. Artificial intelligence, through machine learning, facilitates the interpretation of large arrays of data, and provides insight to improve IBD outcomes, especially with the application of deep learning. This review aims to summarize the recent progress of artificial intelligence in inflammatory bowel disease and its applications.

Retinal pigment epithelium(RPE)has essential functions,such as nourishing and sup-porting the neural retina,and is of vital importance in the pathogenesis of age-related retinal degen-eration.However,the exact molecular changes of RPE during aging remain poorly understood.Here,we isolated human primary RPE(hRPE)cells from 18 eye donors distributed over a wide age range(10-67 years old).A quantitative proteomic analysis was performed to analyze changes in their intracellular and secreted proteins.Age-group related subtypes and age-associated proteins were revealed and potential age-associated mechanisms were validated in ARPE-19 and hRPE cells.The results of proteomic data analysis and verifications suggest that RNF123-and RNF149-related protein ubiquitination plays an important role in protecting hRPE cells from oxidative damage dur-ing aging.In older hRPE cells,apoptotic signaling-related pathways were up-regulated,and endo-plasmic reticulum organization was down-regulated both in the intracellular and secreted proteomes.Our work paints a detailed molecular picture of hRPE cells during the aging process and provides new insights into the molecular characteristics of RPE during aging and under other related clinical retinal conditions.