The establishment of mouse models is critical for discovering the biological targets of tumorigenesis and cancer development, preclinical trials of targeted drugs, and formulation of personalized therapeutic regimens. Currently, the patient-derived xenograft (PDX) model is considered a reliable animal tumor model because of its ability to retain the characteristics of the primary tumor at the histopathological, molecular, and genetic levels, and to preserve the tumor microenvironment. The application of the PDX model has promoted in-depth research on tumors in recent years, focusing on drug development, tumor target discovery, and precise treatment of patients. However, there are still some common questions. This review introduces the latest research progress and common questions regarding tumors with high mortality rates, focusing on their application in targeted drug screening and the formulation of personalized medical strategies. The challenges faced, improvement methods, and future development of the PDX model in tumor treatment applications are also discussed. This article provides technical guidance and comprehensive expectations for anti-cancer drug screening and clinical personalized therapy.

Objective:To understand the infection status, epidemiological characteristics and drug resistance of Diarrheagenic Escherichia coli (DEC) in Shanghai and provide evidence for the disease surveillance. Methods:The epidemiological data of diarrhea cases in Shanghai from 2016 to 2022 were collected from Shanghai Diarrhea Comprehensive Surveillance System, and stool samples were collected from the cases for DEC detection. The drug resistance data was obtained from Chinese Pathogen Identification Network. Statistical analysis was conducted by using χ2 and fisher test. Results:In 24 883 diarrhea cases detected during 2016-2022, the DEC positive rate was 9.13% (2 271/24 883), the single DEC positive rate was 8.83% (2 197/24 883) and the mixed DEC positive rate was 0.30% (74/24 883). The main type of DEC was Enterotoxigenic Escherichia coli (ETEC) [4.33% (1 077/24 883)]. The DEC positive rate was highest in people aged ≤5 years 18.48% (22/119). The annual peak of DEC positive rate was observed during July - September [5.91% (1 470/24 883)]. The DEC positive rate were 9.47% (554/5 847) and 9.02% (1 717/19 036) in urban area and in suburbs, respectively, Enteroaggregative Escherichia coli (EAEC) [3.98% (233/5 847)] and ETEC [4.56% (868/19 036)] were mainly detected. From 2016 to 2019, the DEC positive rate was 9.42% (1 821/19 330), while it was 8.10% (450/5 553) from 2020 to 2022, the main DEC types were ETEC (4.87%, 941/19 330) and EAEC (4.70%, 261/5 553). The multi-drug resistance rate was 40.21% (618/1 537). The top three antibiotics with high drug resistance rates were ampicillin [64.74% (995/1 537)], nalidixic acid [58.49% (899/1 537)] and tetracycline [45.09% (693/1 537)]. Conclusions:Compared with 2016- 2019, a decrease in DEC detection rate was observed during 2020-2022, and the main type of DEC detected shifted from ETEC to EAEC. The prevalence of multi-drug resistance was severe. Therefore, it is necessary to further strengthen the surveillance for DEC drug resistance and standardize the use of clinical antibiotics.

Objective To study the injury risk and fatigue status of firefighters with different training postures under load-bearing conditions to reduce the occurrence of physical injuries and occupational diseases.Methods First,a questionnaire was administered to investigate the training injury conditions of firefighters in a fire-rescue brigade.Considering the exercise fatigue factor,which accounts for the highest proportion of injury causes,lower back analysis,static strength analysis,fatigue analysis,comfort analysis,and other human factor analysis tools in Jack software were used to analyze four common firefighter water-shooting training postures.Training postures while climbing a five-storey building with loads and a hooked ladder were also simulated.Results Injury caused by exercise fatigue accounted for 69.8%of injuries and was the most important injury-causing factor.The risk of knee and ankle joint injuries increased in all four water-shooting postures.The comfort levels of the four water-shooting postures from high to low were shoulder,standing,kneeling,and lying postures.For the entire dynamic training process,while climbing the five-storey building with loads and climbing the hooked ladder,firefighters did not have an increased risk of lower back injury but had an increased risk of ankle and knee joint injuries.Conclusions Some training postures are uncomfortable for firefighters,and they experience body discomfort during firefighting training with loads,thereby increasing injury risk.These results provide scientific references for the prevention and reduction of firefighter training injuries,and the formulation of reasonable training plans and targeted protective measures.

ObjectiveTo provide technical support for the molecular surveillance of pathogenic bacteria strains carrying mobile colistin resistance-1 （mcr⁃1） gene isolate from inlet of wastewater treatment plants （WWTP）. MethodsThe Enterobacteriaceae strains carrying mcr⁃1 resistance gene isolate from inlet of WWTP during April 1 to June 30， 2023 in Shanghai were cultured on blood-rich and SS culture medium and were identified using a mass spectrometry analyzer. The mcr⁃1 gene and copy number were detected by real-time fluorescence quantitative PCR. Drug susceptibility test was performed by microbroth dilution method. The copy numbers of Escherichia coli carrying mcr⁃1 gene isolated from wastewater and human fecel were statistically analyzed by SPSS 25.0. ResultsA total of 14 strains carrying the mcr⁃1 gene were isolated from 49 WWTP samples， and the positive isolation rate was 28.6%， including 12 non-diarrheal E. coli strains and 2 Klebsiella pneumoniae strains. The drug susceptibility results showed that all 14 strains were multi-drug resistant bacteria. They were all sensitive to imipenem and tigecycline， but were ampicillin- and cefazolin-resistant. There was no significant difference in the copy number between human-sourced diarrheal E. coli and wastewater-sourced non-diarrheal E. coli （t=0.647， P>0.05）. ConclusionThe isolation and identification of strains carrying the mcr⁃1 gene from inlet of WWTP samples were firstly established in Shanghai. The multi-drug resistance among the isolated strains is severe. To effectively prevent and control the spread of colistin-resistant bacteria， more attention should be paid to the surveillance of mcr⁃1 gene.

Objective:To investigate the role of hepatitis B virus X protein (HBx) in glomerular podocyte immune disorder and its regulatory mechanism.Methods:Fourteen 6-week-old male hepatitis B virus (HBV) transgenic (HBV-Tg) mice were selected, and age-matched wild type (WT) mice were as controls. They were fed to different weeks, and 24 h urinary protein, blood biochemistry, renal pathology and podocyte changes under electron microscope were detected. The expression of HBx and the infiltration of immune cells in kidney tissue of HBV-Tg mice were observed by immunohistochemistry. Human podocyte cell line was transfected with pcDNA3.1/myc-HBx plasmid, and the localization of HBx and Nephrin in podocytes was detected by immunofluorescence. The expression of major histocompatibility complex Ⅱ (MHC-Ⅱ) and co- stimulatory molecule CD40 on the cell surface was detected by flow cytometry. The contents of multiple cytokines in cell culture supernatants were determined by enzyme-linked immunosorbent assay. Transcriptome sequencing (RNA-seq) was used to screen the downstream related genes regulated by HBx, and real-time quantitative PCR was used to verify their expressions. After overexpression or silencing of Notch1 gene with overexpressed plasmids or short hairpin RNA (shRNA) in podocytes, the effects on the expression of immune molecules and cytokines secretion was observed. The Notch receptor inhibitor N-[N-(3, 5-difluorophenyl-l- alanyl)]-(s)-phenylglycine tert-butyl ester (DAPT) was used to block Notch1 signaling pathway in HBV-Tg mice, and then blood biochemistry, renal pathological changes and infiltration of immune cells in kidney tissue were observed. Results:Twenty-four-hour urine protein, serum creatinine and urea nitrogen levels were markedly increased (all P<0.05) and renal pathological injury was significantly aggravated in HBV-Tg mice than those in WT mice. Also, HBx was up-regulated and immune cells infiltrated in the glomerulus of HBV-Tg mice. After transfection with HBx in podocytes, the expression of MHC-Ⅱ and CD40 on the cellular surface was up-regulated (all P<0.05), the contents of monocyte chemotactic protein-1 (MCP-1), tumor necrosis factor -α (TNF-α) and interleukin (IL)-1β in the supernatants were increased (all P<0.05), and the secretion of IL-4 and interferon γ (IFN-γ) was unbalanced. RNA-seq screened downstream genes of HBx, such as Notch1, PLA2R, TLR4, etc; and further confirmed that HBx could promote the up-regulation of Notch1 mRNA and protein (all P<0.05). After over-expression of Notch1 gene, HBx-induced expression of MHC-Ⅱ and CD40 on the cellular surface was significantly up-regulated (all P<0.05), and the contents of MCP-1, TNF-α and IL-1β in the supernatants were obviously increased (all P<0.05), and the imbalance of IL-4/IFN-γ was further aggravated. After Notch1 gene silencing, the above results showed the opposite changes. In vivo, the results indicated that serum creatinine levels were obviously decreased (all P<0.05), renal pathological injury and immune cell infiltration were significantly alleviated in HBV-Tg+DAPT group than those in HBV-Tg+DMSO group. Conclusions:HBx protein can promote the up-regulation of Notch1 signaling pathway in podocytes. And Notch1 signaling pathway promotes the expression of immune molecules on the surface of podocytes and regulates the imbalance of cytokines, then causes glomerular injury and dysfunction of immune microenvironment.

ObjectiveTo understand the presence of virulence genes， molecular typing characteristics， and antibiotic sensitivity of enteroaggregative Escherichia coli （EAEC） in children with diarrhea in Shanghai， so as to provide a scientific basis for EAEC monitoring and standardized treatment of EAEC infection. MethodsEAEC strains isolated from children （≤5 years old） with diarrhea in six districts of Shanghai were collected as the study subjects. EAEC virulence genes were detected by real-time fluorescence quantitative PCR， molecular typing was performed by pulsed-field gel electrophoresis （PFGE）， and drug susceptibility tests were conducted using the microbroth dilution method. χ² test and two independent samples t-test were used to compare the differences in virulence genes and antibiotic resistance between suburban and urban EAEC strains. ResultsFrom 2019 to 2021， the overall detection rates of gene aggR， pic and astA of 59 EAEC were 30.5%， 50.8%， and 57.6%， respectively. There was no significant difference in the detection rates of virulence genes between suburban and urban EAEC strains （P>0.05）. PFGE analysis revealed that only two EAEC strains belonged to the same PFGE pattern and were collected from the same hospital， and the overall PFGE patterns were polymorphic. EAEC showed susceptibility to imipenem and colistin E， and the resistance rates to sulfamethoxazole （SXT）， ampicillin （AMP）， nalidixic acid （NAL）， and tetracycline （93.1%， 79.3%， 63.8%， and 58.6%， respectively） were higher than 50.0%. The antibiotic resistance rates of cefazolin （CFZ）， cefotaxime （CTX）， and ciprofloxacin （CIP） were significantly different between EAEC strains from suburban and urban areas （P<0.05）. A total of 47 strains exhibited multi-drug resistance， with the most common resistance spectrum being AMP-SXT-NAL. There was no statistically significant difference in the number of multidrug-resistant EAEC strains between suburban and urban areas （P>0.05）. ConclusionThe EAEC virulence gene assemblages in children with diarrhea in the six districts of Shanghai are diverse， and the molecular typing patterns are relatively scattered， indicating possible cross-infection of homologous strains. Multi-drug resistance in EAEC strains is relatively common， and there is a statistically significant difference in the resistance rates of CFZ， CTX and CIP between urban and suburban EAEC strains. Attention should be given to standardizing the use of clinical antibiotics to effectively control the dissemination of multidrug-resistant EAEC strains.

This study used machine learning techniques combined with routine blood cell analysis parameters to build preliminary prediction models, helping differentiate patients with depression from healthy controls, or patients with anxiety. A multicenter study was performed by collecting blood cell analysis data of Beijing Chaoyang Hospital and the First Hospital of Hebei Medical University from 2020 to 2021. Machine learning techniques, including support vector machine, decision tree, naïve Bayes, random forest and multi-layer perceptron were explored to establish a prediction model of depression. The results showed that based on the blood cell analysis results of healthy controls and depression group, the accuracy of prediction model reached as high as 0.99, F1 was 0.975. Receiver operating characteristic curve area and average accuracy were 0.985 and 0.967, respectively. Platelet parameters contributed mostly to depression prediction model. While, to random forest differential diagnosis model based on the data from depression and anxiety groups, prediction accuracy reached 0.68 and AUC 0.622. Age, platelet parameters, and average volume of red blood cells contributed the most to the model. In conclusion, the study researched on the prediction model of depression by exploring blood cell analysis parameters, revealing that machine learning models were more objective in the evaluation of mental illness.
Humans ; Depression ; Bayes Theorem ; Machine Learning ; Support Vector Machine ; Blood Cell Count

This study used machine learning techniques combined with routine blood cell analysis parameters to build preliminary prediction models, helping differentiate patients with depression from healthy controls, or patients with anxiety. A multicenter study was performed by collecting blood cell analysis data of Beijing Chaoyang Hospital and the First Hospital of Hebei Medical University from 2020 to 2021. Machine learning techniques, including support vector machine, decision tree, naïve Bayes, random forest and multi-layer perceptron were explored to establish a prediction model of depression. The results showed that based on the blood cell analysis results of healthy controls and depression group, the accuracy of prediction model reached as high as 0.99, F1 was 0.975. Receiver operating characteristic curve area and average accuracy were 0.985 and 0.967, respectively. Platelet parameters contributed mostly to depression prediction model. While, to random forest differential diagnosis model based on the data from depression and anxiety groups, prediction accuracy reached 0.68 and AUC 0.622. Age, platelet parameters, and average volume of red blood cells contributed the most to the model. In conclusion, the study researched on the prediction model of depression by exploring blood cell analysis parameters, revealing that machine learning models were more objective in the evaluation of mental illness.
Humans ; Depression ; Bayes Theorem ; Machine Learning ; Support Vector Machine ; Blood Cell Count

Objective:To explore the feasibility and accuracy of three-dimensional (3D) modeling methods based on ultrasound imaging data for normal and abnormal fetal cardiac structures, and to construct a methodology system for 3D printing of fetal heart based on ultrasound.Methods:A total of 93 fetuses examined in Tangdu Hospital of Air Force Military Medical University from January to December 2019 were selected. Fetal echocardiography was obtained using spatio-temporal image correlation (STIC). Ninety-three hearts were 3D modeled by blood flow modeling, blood pool modeling and cavity modeling, and printed by stereolithography technique. The data measured on the 3D digital models and 3D printed solid models were compared with the corresponding fetal echocardiographic images respectively in order to evaluate the accuracy of the modeling methods.Results:The fetal cardiac blood flow models based on Doppler flow image data showed the malformation and trend of small blood vessels. The fetal cardiac structure models printed based on blood pool modeling displayed the malformation of heart and large blood vessels. Models printed based on cavity modeling method accurately displayed valve and structural defects.For 83 normal fetal hearts, the long diameters of left and right ventricles measured on echocardiography [(15.3±1.9)mm, (13.2±1.9)mm] were compared with those measured on digital models [(15.1±1.9)mm, (12.9±1.9)mm] and 3D printed models[(15.1±1.9)mm, (13.0±1.9)mm], respectively, and there were no significant differences between any two groups of them ( P>0.05). Bland-Altman showed good consistency for all measurements within and between operators. Conclusions:The three modeling methods, including blood flow modeling, blood pool modeling and cavity modeling, have their own advantages in displaying different types of fetal heart malformations. Appropriate modeling methods should be selected for 3D modeling and printing to make up for the limitations of single modeling method. The consistency between measurements on 3D models and those on echocardiography is high, and the repeatability between operators is good.

Objective:To analyze the correlation between obstructive sleep apnea (OSA) and attention deficit hyperactivity disorder (ADHD).Methods:The clinical Data, polysomnography (PSG) and cognitive function examination results of 112 OSA children admitted to Department of Otorhinolaryngology Head and Neck Surgery of the Second Affiliated Hospital of Xi′an Jiaotong University from January 2019 to June 2020 were retrospectively analyzed. According to the severity of OSA, the children were divided into mild, moderate and severe OSA groups, and the basic demographic characteristics, sleep parameters and ADHD occurrence were analyzed. According to the results of ADHD examination, the children were divided into ADHD group and non-ADHD group, and the basic demographic characteristics and sleep parameters were analyzed. Taking these parameters as independent variables, binary Logistic regression analysis was conducted to establish the model equation for predicting the risk of OSA associated ADHD among children.Results:Grouped by OSA severity, among the three groups, apnea-hypopnea index (AHI) [3.70 (2.84, 5.47) vs 8.59 (7.50, 9.54) vs 19.48 (15.83, 25.23)], obstructive apnea index (OAI) [1.31 (0.93, 1.82) vs 3.03 (1.54, 4.41) vs 11.69 (8.53, 15.42)], obstructive apnea-hypopnea index (OAHI) [2.82 (1.81, 3.64) vs 6.17 (5.58, 7.26) vs 15.68 (13.12, 21.25)], and respiratory event-related arousal index [0.50 (0.25, 1.05) vs 1.25 (0.70, 2.23) vs 2.40 (1.60, 4.70)] increased, minimum pulse oxygen saturation (SpO 2) [90.00 (88.00, 92.00) vs 87.00 (83.00, 90.25) vs 81.00 (76.00, 85.00)] decreased, the differences were statistically significant (all P<0.05). The non-rapid eye movement (NREM)1 period time ratio of the severe OSA group was significantly longer than that of the mild OSA group, while the average SpO 2 was significantly lower than that of the mild OSA group; the NREM3 period time ratio of the moderate and severe OSA group was significantly less than that of the mild OSA group; the arousal index of the severe OSA group was significantly greater than the mild or moderate OSA group. There were no statistically significant differences among the three groups in gender, age, body mass index, sleep efficiency, rapid eye movement (REM) period time ratio, and NREM2 period time ratio (all P>0.05). Mild OSA group had 10 cases of ADHD (17.54%), moderate OSA group had 7 cases (23.33%) of ADHD, severe OSA group had 9 cases of ADHD (36.00%), and the difference was not statistically significant. Grouped by ADHD examination, the AHI, OAI, OAHI, and NREM1 period time ratios of the ADHD group were significantly higher than those of the non-ADHD group, while the sleep efficiency, minimum SpO 2 and NREM3 period time ratio were significantly lower than those of the non-ADHD group. The Logistic regression analysis suggested that ADHD was correlated with sleep efficiency, minimum SpO 2, and NREM3 period time.The established Logistic regression equation was: X=15.670+0.061×(sleep efficiency)-0.212×(minimum SpO 2)-0.144×(NREM3 period time ratio), the sensitivity and specificity of the model prediction were 84.6% and 79.1% respectively when the area under the receiveroperating characteristic curves was 0.867. Conclusions:OSA and ADHD in children have a certain correlation. Sleep structure disturbance and intermittent hypoxia may be important reasons. The predictive model equations obtained by PSG in this study can be used to assess the risk of ADHD in children with OSA.