Objective To achieve automatic segmentation of cell nuclei in gastrointestinal cancer pathological images by using a deep learning algorithm,so as to assist in the quantitative analysis of subsequent pathological images.Methods A total of 59 patients with gastrointestinal cancer treated in Ruijin Hospital,Shanghai Jiao Tong University School of Medicine from Jan 2022 to Feb 2022,were selected as the research objects.Python and LabelMe were used for data anonymization,image segmentation,and region of interest annotation of patients'pathological images.A total of 944 pathological images were included,and 9 703 nuclei were annotated.Then,a new semantic segmentation model based on deep learning was constructed.The model introduced deformable attention transformer(DAT)to realize automatic,accurate and efficient segmentation of pathological image nuclei.Finally,multiple segmentation evaluation criteria are used to evaluate the model's performance.Results The mean absolute error of the segmentation results of the model proposed in this paper was 0.112 6,and the dice coefficient(Dice)was 0.721 5.Its effect was significantly better than the U-net baseline model,and it was ahead of models such as ResU-net++,R2Unet and R2AttUnet.Moreover,the segmentation results were relatively stable with good generalization.Conclusion The segmentation model established in this study can accurately identify and segment the nuclei in the pathological images,with good robustness and generalization,which is helpful to play an auxiliary diagnostic role in practical applications.

The evaluation, diagnosis, real-time monitoring, and guided operation conducted by ultrasound specialist nurses in acute and critical care can provide an objective basis for nursing decision-making, which is of great significance for optimizing nursing procedures and improving the survival rate and rehabilitation rate of patients. This paper summarizes the concept, origin, training status, and practice scope of ultrasound specialist nurses in the field of acute and critical care and puts forward the prospect of development in order to provide a reference for the training of ultrasound specialist nurses in the field of acute and critical care in China.

AIM: To evaluate the clinical efficacy of femtosecond laser-assisted cataract surgery combined with PanOptix trifocal intraocular lens implantation.METHODS:The retrospective study enrolled 22 cases(26 eyes)of cataract patients who underwent femtosecond laser-assisted cataract surgery combined with PanOptix trifocal intraocular lens implantation from August 2020 to August 2021. Follow-up to 3mo after surgery, the changes of far, intermediate and near visual acuity, aberration, Strehl ratio(SR)and modulation transfer function cutoff(MTF-cutoff)frequency were compared. Defocus curve at 1mo postoperatively was made, and the visual quality and satisfaction were evaluated after 3mo of surgery.RESULTS: The visual acuity of all patients was better than 0.1(LogMAR)at the far, intermediate and near distance at 1d, 1wk, 1 and 3mo postoperatively, and it was significantly improved compared with those before surgery(all P<0.01). The defocus curve transitioned smoothly between +0.5 and -3.0D at 1mo after surgery, and visual acuity was better than 0.63. The total aberration and spherical aberration in the whole eye were significantly lower after surgery than before, and the SR and MTF-cutoff were significantly improved at 1d and 1wk after surgery(all P<0.05). With high satisfaction and good visual quality, patients could watch at far, intermediate and near distance without wearing glasses at 3mo after surgery.CONCLUSION: Femtosecond laser-assisted cataract surgery combined with PanOptix trifocal intraocular lens implantation gave patients a comfortable and satisfactory full-course vision.

γ-aminobutyric acid can be produced by a one-step enzymatic reaction catalyzed by glutamic acid decarboxylase. The reaction system is simple and environmentally friendly. However, the majority of GAD enzymes catalyze the reaction under acidic pH at a relatively narrow range. Thus, inorganic salts are usually needed to maintain the optimal catalytic environment, which adds additional components to the reaction system. In addition, the pH of solution will gradually rise along with the production of γ-aminobutyric acid, which is not conducive for GAD to function continuously. In this study, we cloned the glutamate decarboxylase LpGAD from a Lactobacillus plantarum capable of efficiently producing γ-aminobutyric acid, and rationally engineered the catalytic pH range of LpGAD based on surface charge. A triple point mutant LpGAD^{S24R/D88R/Y309K} was obtained from different combinations of 9 point mutations. The enzyme activity at pH 6.0 was 1.68 times of that of the wild type, suggesting the catalytic pH range of the mutant was widened, and the possible mechanism underpinning this increase was discussed through kinetic simulation. Furthermore, we overexpressed the Lpgad and Lpgad^{S24R/D88R/Y309K} genes in Corynebacterium glutamicum E01 and optimized the transformation conditions. An optimized whole cell transformation process was conducted under 40 ℃, cell mass (OD₆₀₀) 20, 100 g/L l-glutamic acid substrate and 100 μmol/L pyridoxal 5-phosphate. The γ-aminobutyric acid titer of the recombinant strain reached 402.8 g/L in a fed-batch reaction carried out in a 5 L fermenter without adjusting pH, which was 1.63 times higher than that of the control. This study expanded the catalytic pH range of and increased the enzyme activity of LpGAD. The improved production efficiency of γ-aminobutyric acid may facilitate its large-scale production.
Glutamate Decarboxylase/genetics* ; Lactobacillus plantarum/genetics* ; Catalysis ; gamma-Aminobutyric Acid ; Hydrogen-Ion Concentration ; Glutamic Acid

The quality of moxa is an important factor affecting moxibustion therapy, and traditionally, 3-year moxa is considered optimal, although scientific data are lacking. This study focused on 1-year and 3-year moxa from Artemisia stolonifera and A. argyi(leaf-to-moxa ratio of 10∶1) as research objects. Scanning electron microscopy(SEM), Van Soest method, and simultaneous thermal analysis were used to investigate the differences in the combustion heat quality of 1-year and 3-year moxa and their influencing factors. The results showed that the combustion of A. stolonifera moxa exhibited a balanced heat release pattern. The 3-year moxa released a concentrated heat of 9 998.84 mJ·mg~(-1)(accounting for 54% of the total heat release) in the temperature range of 140-302 ℃, with a heat production efficiency of 122 mW·mg~(-1). It further released 7 512.51 mJ·mg~(-1)(accounting for 41% of the total heat release) in the temperature range of 302-519 ℃. The combustion of A. argyi moxa showed a rapid heat release pattern. The 3-year moxa released a heat of 16 695.28 mJ·mg~(-1)(accounting for 70% of the total heat release) in the temperature range of 140-311 ℃, with an instantaneous power output of 218 mW·mg~(-1). It further released 5 996.95 mJ·mg~(-1)(accounting for 25% of the total heat release) in the temperature range of 311-483 ℃. Combustion parameters such as-R_p,-R_v, D_i, C, and D_b indicated that the combustion heat quality of 3-year moxa was superior to that of 1-year moxa. It exhibited greater combustion heat, heat production efficiency, flammability, mild and sustained burning, and higher instantaneous combustion efficiency. This study utilized scientific data to demonstrate that A. stolonifera could be used as excellent moxa, and the quality of 3-year moxa surpassed that of 1-year moxa. The research results provide a scientific basis for the in-depth development of A. stolonifera moxa and the improvement of moxa quality standards.
Artemisia ; Hot Temperature ; Moxibustion ; Plant Leaves

Objective: To explore the influencing factors of pregnancy-induced hypertensive disorders in pregnancy (HDP) with organ or system impairment in pregnant women, and to analyze and compare the differences of HDP subtypes in different regions of China. Methods: A total of 27 680 pregnant women with HDP with complete data from 161 hospitals in 24 provinces, autonomous regions and municipalities were retrospectively collected from January 1, 2018 to December 31, 2018. According to their clinical manifestations, they were divided into hypertension group [a total of 10 308 cases, including 8 250 cases of gestational hypertension (GH), 2 058 cases of chronic hypertension during pregnancy] and hypertension with organ or system impairment group [17 372 cases, including 14 590 cases of pre-eclampsia (PE), 137 cases of eclampsia, 2 645 cases of chronic hypertension with PE]. The subtype distribution of HDP in East China (6 136 cases), North China (4 821 cases), Central China (3 502 cases), South China (8 371 cases), Northeast China (1 456 cases), Southwest China (2 158 cases) and Northwest China (1 236 cases) were analyzed. By comparing the differences of HDP subtypes and related risk factors in different regions, regional analysis of the risk factors of HDP pregnant women with organ or system impairment was conducted. Results: (1) The proportions of HDP pregnant women with organ or system impairment in Northeast China (79.05%, 1 151/1 456), Central China (68.42%, 2 396/3 502) and Northwest China (69.34%, 857/1 236) were higher than the national average (62.76%, 17 372/27 680); the proportions in North China (59.18%, 2 853/4 821), East China (60.85%, 3 734/6 136) and South China (59.56%, 4 986/8 371) were lower than the national average, and the differences were statistically significant (all P<0.05). (2) Univariate analysis showed that the proportions of primiparas, non-Han, non-urban household registration, irregular prenatal examination and PE history in the hypertension with organ or system impairment group were higher than those in the hypertension group, and the differences were statistically significant (all P<0.05). Multivariate logistic regression analysis showed that primiparas, non-Han, non-urban household registration, irregular prenatal examination and PE history were independent risk factors for HDP pregnant women with organ or system impairment (all P<0.05). (3) Primipara: the rates of primipara in Northeast China, North China and Southwest China were higher than the national average level, while those in South China, Central China and Northwest China were lower than the national average level. Non-Han nationality: the rates of non-Han nationality in Northeast China, North China and Northwest China were higher than the national average, while those in East China, South China and Central China were lower than the national average. Non-urban household registration: the rates of non-urban household registration in Northeast China, North China, and Southwest China were lower than the national average, while those in East China, Central China were higher than the national average. Irregular prenatal examination: the rates of irregular prenatal examination in North China, South China and Southwest regions were lower than the national average level, while those in Northeast China, Central China and Northwest China were higher than the national average level. History of PE: the incidence rates of PE in Northeast China, North China, South China and Southwest China were lower than the national average level, while those in Central China and Northwest China were higher than the national average level. Conclusions: Primiparas, non-Han, non-urban household registration, irregular prenatal examination, and PE history are risk factors for HDP pregnant women with organ or system impairment. Patients in Northeast, Central and Northwest China have more risk factors, and are more likely to be accompanied by organ or system function damage. It is important to strengthen the management of pregnant women and reduce the occurrence of HDP.
Humans ; Pregnancy ; Female ; Hypertension, Pregnancy-Induced/diagnosis* ; Retrospective Studies ; Pre-Eclampsia/epidemiology* ; Risk Factors ; Incidence

Objective: To establish a comprehensive diagnostic classification model of lateral cephalograms based on artificial intelligence (AI) to provide reference for orthodontic diagnosis. Methods: A total of 2 894 lateral cephalograms were collected in Department of Orthodontics, Capital Medical University School of Stomatology from January 2015 to December 2021 to construct a data set, including 1 351 males and 1 543 females with a mean age of (26.4± 7.4) years. Firstly, 2 orthodontists (with 5 and 8 years of orthodontic experience, respectively) performed manual annotation and calculated measurement for primary classification, and then 2 senior orthodontists (with more than 20 years of orthodontic experience) verified the 8 diagnostic classifications including skeletal and dental indices. The data were randomly divided into training, validation, and test sets in the ratio of 7∶2∶1. The open source DenseNet121 was used to construct the model. The performance of the model was evaluated by classification accuracy, precision rate, sensitivity, specificity and area under the curve (AUC). Visualization of model regions of interest through class activation heatmaps. Results: The automatic classification model of lateral cephalograms was successfully established. It took 0.012 s on average to make 8 diagnoses on a lateral cephalogram. The accuracy of 5 classifications was 80%-90%, including sagittal and vertical skeletal facial pattern, mandibular growth, inclination of upper incisors, and protrusion of lower incisors. The acuracy rate of 3 classifications was 70%-80%, including maxillary growth, inclination of lower incisors and protrusion of upper incisors. The average AUC of each classification was ≥0.90. The class activation heat map of successfully classified lateral cephalograms showed that the AI model activation regions were distributed in the relevant structural regions. Conclusions: In this study, an automatic classification model for lateral cephalograms was established based on the DenseNet121 to achieve rapid classification of eight commonly used clinical diagnostic items.
Male ; Female ; Humans ; Young Adult ; Adult ; Artificial Intelligence ; Deep Learning ; Cephalometry ; Maxilla ; Mandible/diagnostic imaging*

Objective: To develop a multi-classification orthodontic image recognition system using the SqueezeNet deep learning model for automatic classification of orthodontic image data. Methods: A total of 35 000 clinical orthodontic images were collected in the Department of Orthodontics, Capital Medical University School of Stomatology, from October to November 2020 and June to July 2021. The images were from 490 orthodontic patients with a male-to-female ratio of 49∶51 and the age range of 4 to 45 years. After data cleaning based on inclusion and exclusion criteria, the final image dataset included 17 453 face images (frontal, smiling, 90° right, 90° left, 45° right, and 45° left), 8 026 intraoral images [frontal occlusion, right occlusion, left occlusion, upper occlusal view (original and flipped), lower occlusal view (original and flipped) and coverage of occlusal relationship], 4 115 X-ray images [lateral skull X-ray from the left side, lateral skull X-ray from the right side, frontal skull X-ray, cone-beam CT (CBCT), and wrist bone X-ray] and 684 other non-orthodontic images. A labeling team composed of orthodontic doctoral students, associate professors, and professors used image labeling tools to classify the orthodontic images into 20 categories, including 6 face image categories, 8 intraoral image categories, 5 X-ray image categories, and other images. The data for each label were randomly divided into training, validation, and testing sets in an 8∶1∶1 ratio using the random function in the Python programming language. The improved SqueezeNet deep learning model was used for training, and 13 000 natural images from the ImageNet open-source dataset were used as additional non-orthodontic images for algorithm optimization of anomaly data processing. A multi-classification orthodontic image recognition system based on deep learning models was constructed. The accuracy of the orthodontic image classification was evaluated using precision, recall, F1 score, and confusion matrix based on the prediction results of the test set. The reliability of the model's image classification judgment logic was verified using the gradient-weighted class activation mapping (Grad-CAM) method to generate heat maps. Results: After data cleaning and labeling, a total of 30 278 orthodontic images were included in the dataset. The test set classification results showed that the precision, recall, and F1 scores of most classification labels were 100%, with only 5 misclassified images out of 3 047, resulting in a system accuracy of 99.84%(3 042/3 047). The precision of anomaly data processing was 100% (10 500/10 500). The heat map showed that the judgment basis of the SqueezeNet deep learning model in the image classification process was basically consistent with that of humans. Conclusions: This study developed a multi-classification orthodontic image recognition system for automatic classification of 20 types of orthodontic images based on the improved SqueezeNet deep learning model. The system exhibitted good accuracy in orthodontic image classification.
Humans ; Male ; Female ; Child, Preschool ; Child ; Adolescent ; Young Adult ; Adult ; Middle Aged ; Deep Learning ; Reproducibility of Results ; Radiography ; Algorithms ; Cone-Beam Computed Tomography

Objective: To identify the risk factors in mortality of pediatric acute respiratory distress syndrome (PARDS) in pediatric intensive care unit (PICU). Methods: Second analysis of the data collected in the "efficacy of pulmonary surfactant (PS) in the treatment of children with moderate to severe PARDS" program. Retrospective case summary of the risk factors of mortality of children with moderate to severe PARDS who admitted in 14 participating tertiary PICU between December 2016 to December 2021. Differences in general condition, underlying diseases, oxygenation index, and mechanical ventilation were compared after the group was divided by survival at PICU discharge. When comparing between groups, the Mann-Whitney U test was used for measurement data, and the chi-square test was used for counting data. Receiver Operating Characteristic (ROC) curves were used to assess the accuracy of oxygen index (OI) in predicting mortality. Multivariate Logistic regression analysis was used to identify the risk factors for mortality. Results: Among 101 children with moderate to severe PARDS, 63 (62.4%) were males, 38 (37.6%) were females, aged (12±8) months. There were 23 cases in the non-survival group and 78 cases in the survival group. The combined rates of underlying diseases (52.2% (12/23) vs. 29.5% (23/78), χ²=4.04, P=0.045) and immune deficiency (30.4% (7/23) vs. 11.5% (9/78), χ²=4.76, P=0.029) in non-survival patients were significantly higher than those in survival patients, while the use of pulmonary surfactant (PS) was significantly lower (8.7% (2/23) vs. 41.0% (32/78), χ²=8.31, P=0.004). No significant differences existed in age, sex, pediatric critical illness score, etiology of PARDS, mechanical ventilation mode and fluid balance within 72 h (all P>0.05). OI on the first day (11.9(8.3, 17.1) vs.15.5(11.7, 23.0)), the second day (10.1(7.6, 16.6) vs.14.8(9.3, 26.2)) and the third day (9.2(6.6, 16.6) vs. 16.7(11.2, 31.4)) after PARDS identified were all higher in non-survival group compared to survival group (Z=-2.70, -2.52, -3.79 respectively, all P<0.05), and the improvement of OI in non-survival group was worse (0.03(-0.32, 0.31) vs. 0.32(-0.02, 0.56), Z=-2.49, P=0.013). ROC curve analysis showed that the OI on the thind day was more appropriate in predicting in-hospital mortality (area under the curve= 0.76, standard error 0.05,95%CI 0.65-0.87,P<0.001). When OI was set at 11.1, the sensitivity was 78.3% (95%CI 58.1%-90.3%), and the specificity was 60.3% (95%CI 49.2%-70.4%). Multivariate Logistic regression analysis showed that after adjusting for age, sex, pediatric critical illness score and fluid load within 72 h, no use of PS (OR=11.26, 95%CI 2.19-57.95, P=0.004), OI value on the third day (OR=7.93, 95%CI 1.51-41.69, P=0.014), and companied with immunodeficiency (OR=4.72, 95%CI 1.17-19.02, P=0.029) were independent risk factors for mortality in children with PARDS. Conclusions: The mortality of patients with moderate to severe PARDS is high, and immunodeficiency, no use of PS and OI on the third day after PARDS identified are the independent risk factors related to mortality. The OI on the third day after PARDS identified could be used to predict mortality.
Female ; Male ; Humans ; Child, Preschool ; Infant ; Child ; Critical Illness ; Pulmonary Surfactants/therapeutic use* ; Retrospective Studies ; Risk Factors ; Respiratory Distress Syndrome/therapy*

OBJECTIVES: To explore the factors that influence self-management behavior in cancer patients based on the theoretical domain framework. METHODS: Studies in Chinese and English about factors influencing self-management behavior in cancer patients were searched from Wanfang database, CNKI, VIP, SinoMed, PubMed, Embase, CINAHL, Web of Science Core Collection, Cochrane library and Medline from inception to June 2022. Two investigators independently identified, extracted data, and collected characteristics and methodology of the studies. Factors were analyzed with Nvivo12, and the theoretical domain framework was mapped to the theoretical domain. Then the secondary node was generalized by theme analysis. Finally, the specific influencing factors were summarized and analyzed. RESULTS: Thirty-four studies were included for analysis. A total of 194 factors were mapped to 13 theoretical domains, and 31 secondary nodes were summarized. Theoretical domains environmental context and resources, social/professional role and identity, and beliefs about consequences were the most common factors. Knowledge, age, self-efficacy, disease stage, social support, gender, economic status and physical status were the most influential factors for self-management in cancer patients. CONCLUSIONS The influencing factors of self-management of cancer patients involve most of the theoretical domains, are intersectional, multi-source and complex.
Humans ; Self-Management ; Neoplasms/therapy*