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1.
Br J Cancer ; 125(8): 1111-1121, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-34365472

RESUMO

BACKGROUND AND AIMS: Computed tomography (CT) scan is frequently used to detect hepatocellular carcinoma (HCC) in routine clinical practice. The aim of this study is to develop a deep-learning AI system to improve the diagnostic accuracy of HCC by analysing liver CT imaging data. METHODS: We developed a deep-learning AI system by training on CT images from 7512 patients at Henan Provincial Peoples' Hospital. Its performance was validated on one internal test set (Henan Provincial Peoples' Hospital, n = 385) and one external test set (Henan Provincial Cancer Hospital, n = 556). The area under the receiver-operating characteristic curve (AUROC) was used as the primary classification metric. Accuracy, sensitivity, specificity, precision, negative predictive value and F1 metric were used to measure the performance of AI systems and radiologists. RESULTS: AI system achieved high performance in identifying HCC patients, with AUROC of 0.887 (95% CI 0.855-0.919) on the internal test set and 0.883 (95% CI 0.855-0.911) on the external test set. For internal test set, accuracy was 81.0% (76.8-84.8%), sensitivity was 78.4% (72.4-83.7%), specificity was 84.4% (78.0-89.6%) and F1 (harmonic average of precision and recall rate) was 0.824. For external test set, accuracy was 81.3% (77.8-84.5%), sensitivity was 89.4% (85.0-92.8%), specificity was 74.0% (68.5-78.9%) and F1 was 0.819. Compared with radiologists, AI system achieved comparable accuracy and F1 metric on internal test set (0.853 versus 0.818, P = 0.107; 0.863 vs. 0.824, P = 0.082) and external test set (0.805 vs. 0.793, P = 0.663; 0.810 vs. 0.814, P = 0.866). The predicted HCC risk scores by AI system in HCC patients with multiple tumours and high fibrosis stage were higher than those with solitary tumour and low fibrosis stage (tumour number: 0.197 vs. 0.138, P = 0.006; fibrosis stage: 0.183 vs. 0.127, P < 0.001). Radiologists' review showed that the accuracy of saliency heatmaps predicted by algorithms was 92.1% (95% CI: 89.2-95.0%). CONCLUSIONS: AI system achieved high performance in the detection of HCC compared with a group of specialised radiologists. Further investigation by prospective clinical trials was necessitated to verify this model.

2.
Microb Pathog ; 158: 105106, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34311015

RESUMO

This study was ascertained to investigate the adverse effects of pathogenic E. coli on gut microbiota of Tibetan piglets with history of yellow and white dysentery. For this purpose, a total of 18 fecal samples were collected from infected and healthy Tibetan piglets for 16S rRNA gene amplification and sequencing of V3-V4 region. Results showed that Firmicutes, Bacteroidia Fusobacteriota, Proteobacteria and Actinobacteriota were the predominant bacteria in Tibetan piglets at the level of phylum classification. Results on classification at family level showed that Lactobacillus, Bacteroidota, Fusobacteriota and Enterobacteriaceae were the dominant bacteria. Results on classification of bacteria at phylum level compared with normal piglets indicated that Bacteroidota, Actinobacteriota, Euryarchaota and Spirochaetota in fecal microbial community in Tibetan piglets showing yellow dysenteric and diarrhea group were significantly decreased (P ≤ 0.05). Compared with the feces of healthy Tibetan piglets, the abundance of Escherichia-Shigella, Lactobacillus and Enterococcus increased significantly in feces of Tibetan piglets having yellow dysentery and white dysentery. Moreover, results exhibited that the Proteobacteria and Fusobacteriota were significantly increased (P ≤ 0.05) suggesting dominant microbial community. Results revealed that E. coli induced different pathological alterations in intestine including damage to intestinal epithelial cells, infiltration of inflammatory cells, presence of red blood cells in spaces of tissues, hemorrhages and necrosis of intestinal villi in piglets with history of yellow dysentery. This study for the first time reported the composition, characteristics, and differences of the fecal microflora diversity of Tibetan piglets with yellow and white dysentery in Qinghai-Tibet Plateau, which can provide a suitable support for effective control of diarrhoeal disease in these animals.


Assuntos
Escherichia coli , Microbiota , Animais , Escherichia coli/genética , Fezes , RNA Ribossômico 16S/genética , Suínos , Tibet
3.
Abdom Radiol (NY) ; 46(10): 4610-4618, 2021 10.
Artigo em Inglês | MEDLINE | ID: mdl-34089068

RESUMO

PURPOSE: To predict tumor necrosis after conventional TACE (cTACE) in patients with colorectal liver metastasis (CRLM) based on volumetric oil deposition on CT one day after treatment. METHODS: Thirty-four lesions in 20 men and 6 women were included in this IRB-approved HIPAA-compliant, retrospective lesion-by-lesion-based study. Semiautomatic volumetric segmentation of target lesions was performed on baseline MRI and post-treatment CT. Predicted percentage of tumor necrosis was defined as 100%-(%baseline MRI enhancement-%CT oil deposition). Necrosis on post-TACE MRI was measured after volumetric segmentation to assess the accuracy of predicting tumor necrosis. The relationship between predicted necrosis percent and post-cTACE measured necrosis percent on MRI was compared using Pearson correlation analysis. Inter-reader agreement was calculated by intraclass correlation coefficient (ICC) after using the same method. RESULTS: Patients in this cohort had a mean age of 64 ± 14 years. Mean percentage of the viable tumor on pre-cTACE venous phase MRI was 58.5% ± 23.9%. Mean oil deposition was 19.8% ± 14.6%. Mean percentage of calculated necrosis one month after cTACE was 59.2% ± 22.7% on venous phase MRI, which had a significant correlation with predicted necrotic percentage of 61.3% ± 19.3% (r = 0.89, p < 0.0001). ICC for enhancement percentage on pre-cTACE and post-cTACE venous phase MRIs were 0.93 (95% CI 0.83, 0.97) and 0.86 (95% CI 0.66, 0.94), respectively. ICC for oil deposition was 0.92 (95% CI 0.81, 0.96). CONCLUSION: Measuring oil deposition of the whole tumor on CT one day after cTACE can assist to predict post-cTACE tumor necrosis.


Assuntos
Carcinoma Hepatocelular , Quimioembolização Terapêutica , Neoplasias Colorretais , Neoplasias Hepáticas , Idoso , Carcinoma Hepatocelular/terapia , Neoplasias Colorretais/diagnóstico por imagem , Óleo Etiodado , Feminino , Humanos , Neoplasias Hepáticas/diagnóstico por imagem , Neoplasias Hepáticas/terapia , Imageamento por Ressonância Magnética , Masculino , Pessoa de Meia-Idade , Necrose , Estudos Retrospectivos , Tomografia Computadorizada por Raios X , Resultado do Tratamento
4.
IEEE Trans Biomed Eng ; 68(12): 3725-3736, 2021 12.
Artigo em Inglês | MEDLINE | ID: mdl-34061732

RESUMO

OBJECTIVE: In a few patients with mild COVID-19, there is a possibility of the infection becoming severe or critical in the future. This work aims to identify high-risk patients who have a high probability of changing from mild to critical COVID-19 (only account for 5% of cases). METHODS: Using traditional convolutional neural networks for classification may not be suitable to identify this 5% of high risk patients from an entire dataset due to the highly imbalanced label distribution. To address this problem, we propose a Mix Contrast model, which matches original features with mixed features for contrastive learning. Three modules are proposed for training the model: 1) a cumulative learning strategy for synthesizing the mixed feature; 2) a commutative feature combination module for learning the commutative law of feature concatenation; 3) a united pairwise loss assigning adaptive weights for sample pairs with different class anchors based on their current optimization status. RESULTS: We collect a multi-center computed tomography dataset including 918 confirmed COVID-19 patients from four hospitals and evaluate the proposed method on both the COVID-19 mild-to-critical prediction and COVID-19 diagnosis tasks. For mild-to-critical prediction, the experimental results show a recall of 0.80 and a specificity of 0.815. For diagnosis, the model shows comparable results with deep neural networks using a large dataset. Our method demonstrates improvements when the amount of training data is small or imbalanced. SIGNIFICANCE: Identifying mild-to-critical COVID-19 patients is important for early prevention and personalized treatment planning.


Assuntos
COVID-19 , Aprendizado Profundo , Teste para COVID-19 , Humanos , Redes Neurais de Computação , SARS-CoV-2
5.
Health Inf Sci Syst ; 9(1): 6, 2021 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-33489103

RESUMO

Objective: To investigate the clinical characteristics, epidemiological characteristics, and transmissibility of coronavirus disease 2019 (COVID-19) in a family cluster outbreak transmitted by a 3-month-old confirmed positive infant. Methods: Field-based epidemiological methods were used to investigate cases and their close contacts. Real-time fluorescent reverse transcription polymerase chain reaction (RT-PCR) was used to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) for all collected specimens. Serum SARS-CoV-2 IgM and IgG antibodies were detected by Chemiluminescence and Gold immnnochromatography (GICA). Results: The outbreak was a family cluster with an attack rate of 80% (4/5). The first case in this family was a 3-month-old infant. The transmission chain was confirmed from infant to adults (her father, mother and grandmother). Fecal tests for SARS-CoV-2 RNA remained positive for 37 days after the infant was discharged. The infant's grandmother was confirmed to be positive 2 days after the infant was discharged from hospital. Patients A (3-month-old female), B (patient A's father), C (patient A's grandmother), and D (patient A's mother) had positive serum IgG and negative IgM, but patients A's grandfather serum IgG and IgM were negative. Conclusion: SARS-CoV-2 has strong transmissibility within family settings and presence of viral RNA in stool raises concern for possible fecal-oral transmission. Hospital follow-up and close contact tracing are necessary for those diagnosed with COVID-19.

7.
Cardiol Res Pract ; 2020: 1957843, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33294219

RESUMO

Indigenous animals show unique gut microbiota (GM) in the Tibetan plateau. However, it is unknown whether the hypertensive indigenous people in plateau also have the distinct gut bacteria, different from those living in plains. We sequenced the V3-V4 region of the gut bacteria 16S ribosomal RNA (rRNA) gene of feces samples among hypertensive patients (HPs) and healthy individuals (HIs) from 3 distinct altitudes: Tibetans from high altitude (3600-4500 m, n = 38 and 34), Hans from middle altitude (2260 m, n = 49 and 35), and Hans from low altitude (13 m, n = 34 and 35) and then analyzed the GM composition among hypertensive and healthy subgroups using the bioinformatics analysis, respectively. The GM of high-altitude Tibetan and middle-altitude Han HPs presented greater α- and ß-diversities, lower ratio of Firmicutes/Bacteroidetes (F/B), and higher abundance of beneficial Verrucomicrobia and Akkermansia than the low-altitudes HPs did. The GM of high-altitude Tibetan and middle-altitude HIs showed greater α-diversity and lower ratio of F/B than the low-altitudes HIs did. But, ß-diversity and abundance of Verrucomicrobia and Akkermansia among different subgroups of HIs did not show any differences. Conclusively, the high-altitude Tibetan and middle-altitude Han HPs have a distinct feature of GM, which may be important in their adaptation to hypertension in the plateau environments.

8.
Eur J Radiol ; 133: 109389, 2020 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-33166831

RESUMO

PURPOSE: To define the number of TACE sessions needed to improve patients' overall survival (OS) in different subgroups of unresectable HCC. METHODS: This retrospective cohort included 180 patients who got TACE between 2005-2016 as the initial treatment for unresectable HCC. Tumor margin (well- vs. ill-defined) was determined by two radiologists at baseline. Well-defined group was divided into two groups (ADC-responders vs. ADC-nonresponders) based on %ADC change (ΔADC-cutoff = 25 %). Accordingly, patients were categorized into three groups, ill-defined, well-defined ADC-responders, or well-defined ADC-nonresponders. Cox-analysis was used to compare the survival benefit of multiple TACE in different groups. RESULTS: Ill-defined HCC (n = 108) was associated with worse survival (HR = 1.95,p < 0.001). Multiple TACE were associated with increased OS (HR = 0.88,p = 0.033) in these patients, with significant survival improvement after ≥4TACE. ΔADC was not related to OS in ill-defined group. In well-defined group (n = 72), multiple TACE were not associated with improved OS (HR = 0.181,p = 0.090). These patients were categorized into two groups based on ΔADC-cutoff. ADC-responders (ΔADC≥25 %) had the longest survival than other groups(p = 0.015). Multiple TACE sessions were not associated with better OS in this group (HR = 1.004,p = 0.982). By contrast, incremental number of TACE were associated with significantly longer OS in ADC-nonresponders (ΔADC<25 %) (HR = 0.79,p = 0.034). These patients' OS significantly improved after ≥3TACE. CONCLUSION: The survival benefit of sequential TACE sessions varies for different HCC subgroups. There was no significant survival benefit associated with multiple TACE in well-defined lesions responding to the first TACE. The most survival benefit was for ADC-nonresponder well-defined group and it was least for ill-defined HCC group, regardless of ADC-response.


Assuntos
Carcinoma Hepatocelular , Quimioembolização Terapêutica , Neoplasias Hepáticas , Carcinoma Hepatocelular/diagnóstico por imagem , Carcinoma Hepatocelular/terapia , Humanos , Neoplasias Hepáticas/diagnóstico por imagem , Neoplasias Hepáticas/terapia , Margens de Excisão , Estudos Retrospectivos , Resultado do Tratamento
9.
JAMA Netw Open ; 3(7): e2011625, 2020 07 01.
Artigo em Inglês | MEDLINE | ID: mdl-32706384

RESUMO

Importance: Accurate identification of lymph node metastasis preoperatively and noninvasively in patients with cervical cancer can avoid unnecessary surgical intervention and benefit treatment planning. Objective: To develop a deep learning model using preoperative magnetic resonance imaging for prediction of lymph node metastasis in cervical cancer. Design, Setting, and Participants: This diagnostic study developed an end-to-end deep learning model to identify lymph node metastasis in cervical cancer using magnetic resonance imaging (MRI). A total of 894 patients with stage IB to IIB cervical cancer who underwent radical hysterectomy and pelvic lymphadenectomy were reviewed. All patients underwent radical hysterectomy and pelvic lymphadenectomy, received pelvic MRI within 2 weeks before the operations, had no concurrent cancers, and received no preoperative treatment. To achieve the optimal model, the diagnostic value of 3 MRI sequences was compared, and the outcomes in the intratumoral and peritumoral regions were explored. To mine tumor information from both image and clinicopathologic levels, a hybrid model was built and its prognostic value was assessed by Kaplan-Meier analysis. The deep learning model and hybrid model were developed on a primary cohort consisting of 338 patients (218 patients from Sun Yat-sen University Cancer Center, Guangzhou, China, between January 2011 and December 2017 and 120 patients from Henan Provincial People's Hospital, Zhengzhou, China, between December 2016 and June 2018). The models then were evaluated on an independent validation cohort consisting of 141 patients from Yunnan Cancer Hospital, Kunming, China, between January 2011 and December 2017. Main Outcomes and Measures: The primary diagnostic outcome was lymph node metastasis status, with the pathologic characteristics diagnosed by lymphadenectomy. The secondary primary clinical outcome was survival. The primary diagnostic outcome was assessed by receiver operating characteristic (area under the curve [AUC]) analysis; the primary clinical outcome was assessed by Kaplan-Meier survival analysis. Results: A total of 479 patients (mean [SD] age, 49.1 [9.7] years) fulfilled the eligibility criteria and were enrolled in the primary (n = 338) and validation (n = 141) cohorts. A total of 71 patients (21.0%) in the primary cohort and 32 patients (22.7%) in the validation cohort had lymph node metastais confirmed by lymphadenectomy. Among the 3 image sequences, the deep learning model that used both intratumoral and peritumoral regions on contrast-enhanced T1-weighted imaging showed the best performance (AUC, 0.844; 95% CI, 0.780-0.907). These results were further improved in a hybrid model that combined tumor image information mined by deep learning model and MRI-reported lymph node status (AUC, 0.933; 95% CI, 0.887-0.979). Moreover, the hybrid model was significantly associated with disease-free survival from cervical cancer (hazard ratio, 4.59; 95% CI, 2.04-10.31; P < .001). Conclusions and Relevance: The findings of this study suggest that deep learning can be used as a preoperative noninvasive tool to diagnose lymph node metastasis in cervical cancer.


Assuntos
Aprendizado Profundo/tendências , Metástase Linfática/diagnóstico por imagem , Metástase Linfática/diagnóstico , Software/tendências , Adulto , Área Sob a Curva , China , Feminino , Humanos , Imageamento por Ressonância Magnética/métodos , Pessoa de Meia-Idade , Desenvolvimento de Programas , Curva ROC , Estudos Retrospectivos , Software/estatística & dados numéricos , Neoplasias do Colo do Útero/complicações , Neoplasias do Colo do Útero/diagnóstico por imagem , Neoplasias do Colo do Útero/fisiopatologia
10.
Theranostics ; 10(16): 7231-7244, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32641989

RESUMO

Rationale: Given the rapid spread of COVID-19, an updated risk-stratify prognostic tool could help clinicians identify the high-risk patients with worse prognoses. We aimed to develop a non-invasive and easy-to-use prognostic signature by chest CT to individually predict poor outcome (death, need for mechanical ventilation, or intensive care unit admission) in patients with COVID-19. Methods: From November 29, 2019 to February 19, 2020, a total of 492 patients with COVID-19 from four centers were retrospectively collected. Since different durations from symptom onsets to the first CT scanning might affect the prognostic model, we designated the 492 patients into two groups: 1) the early-phase group: CT scans were performed within one week after symptom onset (0-6 days, n = 317); and 2) the late-phase group: CT scans were performed one week later after symptom onset (≥7 days, n = 175). In each group, we divided patients into the primary cohort (n = 212 in the early-phase group, n = 139 in the late-phase group) and the external independent validation cohort (n = 105 in the early-phase group, n = 36 in the late-phase group) according to the centers. We built two separate radiomics models in the two patient groups. Firstly, we proposed an automatic segmentation method to extract lung volume for radiomics feature extraction. Secondly, we applied several image preprocessing procedures to increase the reproducibility of the radiomics features: 1) applied a low-pass Gaussian filter before voxel resampling to prevent aliasing; 2) conducted ComBat to harmonize radiomics features per scanner; 3) tested the stability of the features in the radiomics signature by several image transformations, such as rotating, translating, and growing/shrinking. Thirdly, we used least absolute shrinkage and selection operator (LASSO) to build the radiomics signature (RadScore). Afterward, we conducted a Fine-Gray competing risk regression to build the clinical model and the clinic-radiomics signature (CrrScore). Finally, performances of the three prognostic signatures (clinical model, RadScore, and CrrScore) were estimated from the two aspects: 1) cumulative poor outcome probability prediction; 2) 28-day poor outcome prediction. We also did stratified analyses to explore the potential association between the CrrScore and the poor outcomes regarding different age, type, and comorbidity subgroups. Results: In the early-phase group, the CrrScore showed the best performance in estimating poor outcome (C-index = 0.850), and predicting the probability of 28-day poor outcome (AUC = 0.862). In the late-phase group, the RadScore alone achieved similar performance to the CrrScore in predicting poor outcome (C-index = 0.885), and 28-day poor outcome probability (AUC = 0.976). Moreover, the RadScore in both groups successfully stratified patients with COVID-19 into low- or high-RadScore groups with significantly different survival time in the training and validation cohorts (all P < 0.05). The CrrScore in both groups can also significantly stratify patients with different prognoses regarding different age, type, and comorbidities subgroups in the combined cohorts (all P < 0.05). Conclusions: This research proposed a non-invasive and quantitative prognostic tool for predicting poor outcome in patients with COVID-19 based on CT imaging. Taking the insufficient medical recourse into account, our study might suggest that the chest CT radiomics signature of COVID-19 is more effective and ideal to predict poor outcome in the late-phase COVID-19 patients. For the early-phase patients, integrating radiomics signature with clinical risk factors can achieve a more accurate prediction of individual poor prognostic outcome, which enables appropriate management and surveillance of COVID-19.


Assuntos
Betacoronavirus , Infecções por Coronavirus/diagnóstico por imagem , Pneumonia Viral/diagnóstico por imagem , Tomografia Computadorizada por Raios X/métodos , Adulto , Idoso , COVID-19 , China/epidemiologia , Estudos de Coortes , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/terapia , Cuidados Críticos , Feminino , Humanos , Pulmão/diagnóstico por imagem , Masculino , Pessoa de Meia-Idade , Modelos Biológicos , Pandemias , Pneumonia Viral/epidemiologia , Pneumonia Viral/terapia , Prognóstico , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Interpretação de Imagem Radiográfica Assistida por Computador/estatística & dados numéricos , Respiração Artificial , Estudos Retrospectivos , Fatores de Risco , SARS-CoV-2 , Nanomedicina Teranóstica , Tomografia Computadorizada por Raios X/estatística & dados numéricos , Resultado do Tratamento
12.
Eur Respir J ; 56(2)2020 08.
Artigo em Inglês | MEDLINE | ID: mdl-32444412

RESUMO

Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19 and finding high-risk patients with worse prognosis for early prevention and medical resource optimisation is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.We retrospectively collected 5372 patients with computed tomography images from seven cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the deep learning system, making it learn lung features. Following this, 1266 patients (924 with COVID-19 (471 had follow-up for >5 days) and 342 with other pneumonia) from six cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the four external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC 0.87 and 0.88, respectively) and viral pneumonia (AUC 0.86). Moreover, the deep learning system succeeded to stratify patients into high- and low-risk groups whose hospital-stay time had significant difference (p=0.013 and p=0.014, respectively). Without human assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.Deep learning provides a convenient tool for fast screening of COVID-19 and identifying potential high-risk patients, which may be helpful for medical resource optimisation and early prevention before patients show severe symptoms.


Assuntos
Infecções por Coronavirus/diagnóstico por imagem , Aprendizado Profundo , Processamento de Imagem Assistida por Computador/métodos , Pulmão/diagnóstico por imagem , Pneumonia Viral/diagnóstico por imagem , Adulto , Idoso , Área Sob a Curva , Automação , Betacoronavirus , COVID-19 , Feminino , Humanos , Pneumopatias Fúngicas/diagnóstico por imagem , Masculino , Pessoa de Meia-Idade , Pandemias , Pneumonia Bacteriana/diagnóstico por imagem , Pneumonia por Mycoplasma/diagnóstico por imagem , Prognóstico , Estudos Retrospectivos , SARS-CoV-2 , Tomografia Computadorizada por Raios X
14.
Med Chem ; 16(1): 119-127, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-30963981

RESUMO

BACKGROUND: Topiroxostat is an excellent xanthine oxidase (XO) inhibitor, possessing a specific 3,5-diaryl-1,2,4-triazole framework. OBJECTIVE: The present work was aimed to investigate the preliminary structure-activity relationship (SAR) of 2-cyanopyridine-4-yl-like fragments of topiroxostat analogues. METHODS: A series of 5-benzyl-3-pyridyl-1H-1,2,4-triazole derivatives (1a-j and 2a-j) were designed and synthesized by replacement of the 2-cyanopyridine-4-yl moiety with substituted benzyl groups. XO inhibitory activity in vitro was evaluated. Furthermore, molecular modeling simulations were performed to predict the possible interactions between the synthesized compounds and XO binding pocket. RESULTS: The SARs analysis demonstrated that 3,5-diaryl-1,2,4-triazole framework is not essential; in spite of its lower potency, 5-benzyl-3-pyridyl-1H-1,2,4-triazole is an acceptable scaffold for XO inhibitory activity to some extent. A 3'-nitro and a 4'-sec-butoxy group link to the benzyl moiety will be welcome. Furthermore, the most promising compound, 1h, was identified with an IC50 value of 0.16 µM, and the basis of XO inhibition by 1h was rationalized through the aid of molecular modelling studies. CONCLUSION: Compound 1h could be a lead compound for further investigation and the present work may provide some insight into the search for more structurally diverse XO inhibitors with topiroxostat as a prototype.


Assuntos
Inibidores Enzimáticos/farmacologia , Triazóis/farmacologia , Xantina Oxidase/antagonistas & inibidores , Relação Dose-Resposta a Droga , Inibidores Enzimáticos/síntese química , Inibidores Enzimáticos/química , Humanos , Estrutura Molecular , Relação Estrutura-Atividade , Triazóis/síntese química , Triazóis/química , Xantina Oxidase/metabolismo
15.
Front Cell Infect Microbiol ; 10: 610781, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33665171

RESUMO

Domestic yaks (Bos grunniens) are indigenous to the Tibetan Plateau and display a high diarrhea rate due to poor habitat and husbandry conditions. Lactobacillus has been shown to exert beneficial effects as antimicrobial, growth promotion, and gut microbiota in humans and/or murine models, but the relevant data regarding Lactobacillus isolated from yaks was unavailable. Therefore, this study aimed to investigate the effects of Lactobacillus from yaks on the intestinal microbial community in a mouse model and determine whether Lactobacillus supplementation contributed in alleviating diarrhea by modulating gut microbiota. A total of 12 ileac samples from four groups were collected for 16S rRNA gene amplicon sequencing of V3-V4 region. Results revealed that although Lactobacillus supplementation did not change the diversity of gut microbiota in mice, the proportion of some intestinal microbiota significantly changed. Specifically, the proportion of Lactobacillus and Sphingomonas in the Lactobacillus treated-group (L-group) were increased as compared to control group (C-group), whereas Pantoea, Cutibacterium, Glutamicibacter, Turicibacter, Globicatella, Microbacterium, Facklamia, unidentified_Corynebacteriaceae, Brachybacterium, and Staphylococcus were significantly decreased in the L-group. In contrast, Escherichia coli (E. coli) infection significantly decreased the proportion of beneficial bacteria such as Globicatella, Acinetobacter, Aerococcus, and Comamonas, while loads of pathogenic bacteria significantly increased including Roseburia and Megasphaera. Interestingly, Lactobacillus administration could ameliorate the microbial community structure of E. coli-induced diarrheal mice by reducing the relative abundance of pathogenic bacteria such as Paenibacillus, Aerococcus, Comamonas, Acinetobacter, Corynebacterium, Facklamia, and Globicatella. Results in this study revealed that Lactobacillus supplementation not only improved the gut microbiota but also alleviated diarrhea in mice, which may be mediated by modulating the composition and function of gut microbiota. Moreover, this study is expected to provide a new theoretical basis for the establishment of a preventive and treatment system for diarrhea in yaks.


Assuntos
Microbioma Gastrointestinal , Animais , Bovinos , Diarreia/prevenção & controle , Escherichia coli , Lactobacillus , Camundongos , RNA Ribossômico 16S/genética
16.
EBioMedicine ; 50: 355-365, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31767539

RESUMO

BACKGROUND: Identification of pregnancies with postpartum haemorrhage (PPH) antenatally rather than intrapartum would aid delivery planning, facilitate transfusion requirements and decrease maternal complications. MRI has been increasingly used for placenta evaluation. Here, we aim to build a nomogram incorporating both clinical and radiomic features of placenta to predict the risk for PPH in pregnancies during caesarian delivery (CD). METHODS: A total of 298 pregnant women were retrospectively enrolled from Henan Provincial People's Hospital (training cohort: n = 207) and from The Third Affiliated Hospital of Zhengzhou University (external validation cohort: n = 91). These women were suspected with placenta accreta spectrum (PAS) disorders and underwent MRI for placenta evaluation. All of them underwent CD and were singleton. PPH was defined as more than 1000 mL estimated blood loss (EBL) during CD. Radiomic features were selected based on their correlations with EBL. Radiomic, clinical, radiological, clinicoradiological and clinicoradiomic models were built to predict the risk of PPH for each patient. The model with the best prediction performance was validated with its discrimination ability, calibration curve and clinical application. FINDINGS: Thirty-five radiomic features showed strong correlation with EBL. The clinicoradiomic model resulted in the best discrimination ability for risk prediction of PPH, with AUC of 0.888 (95% CI, 0.844-0.933) and 0.832 (95% CI, 0.746-0.913), sensitivity of 91.2% (95% CI, 85.8%-96.7%) and 97.6% (95% CI, 92.7%-100%) in the training and validation cohort respectively. For patients with severe PPH (EBL more than 2000 mL), 53 out of 55 pregnancies (96.4%) in the training cohort and 18 out of 18 (100%) pregnancies in the validation cohort were identified by the clinicoradiomic model. The model performed better in patients without placenta previa (PP) than in patients with PP, with AUC of 0.983 compared with 0.867, sensitivity of 100% compared with 90.8% in the training cohort, AUC of 0.832 compared with 0.815, sensitivity of 97.6% compared with 97.2% in the validation cohort. INTERPRETATION: The clinicoradiomic model incorporating both prenatal clinical factors and radiomic signature of placenta on T2WI showed good performance for risk prediction of PPH. The predictive model can identify severe PPH with high sensitivity and can be applied in patients with and without PP.


Assuntos
Imageamento por Ressonância Magnética , Placenta/diagnóstico por imagem , Hemorragia Pós-Parto/diagnóstico , Biomarcadores , Cesárea , Feminino , Humanos , Interpretação de Imagem Assistida por Computador , Processamento de Imagem Assistida por Computador , Imageamento por Ressonância Magnética/métodos , Nomogramas , Hemorragia Pós-Parto/etiologia , Gravidez , Prognóstico , Curva ROC , Reprodutibilidade dos Testes , Estudos Retrospectivos
17.
Artif Cells Nanomed Biotechnol ; 47(1): 2775-2782, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31284768

RESUMO

Atherosclerosis is a chronic inflammatory disease of the blood vasculature. Endothelial dysfunction is an early event in the development of atherosclerosis and the endothelium plays an important role in the innate immune defense in the pathology of cardiovascular diseases. New therapies are being developed based on the involvement of the immune system in atherosclerosis. In this study, we demonstrate that a commonly used anti-rheumatic drug, tofacitinib, possesses vascular protective properties in cultured primary human aortic endothelial cells (HAECs). Tofacitinib ameliorates oxidized low-density lipoprotein (ox-LDL)-induced adhesion of THP-1 cells to HAECs, suppresses the expression of vascular adhesion molecules and production of cytokines, including vascular cell adhesion molecule 1 (VCAM-1), intercellular cell adhesion molecule-1 (ICAM-1), tumor necrosis factor-α (TNF-α) and interleukin-1ß (IL-1ß). Moreover, tofacitinib inhibits elevation of endothelial lectin-like ox-LDL receptor-1 (LOX-1) and production of reactive oxygen species (ROS) triggered by ox-LDL. As a result, the presence of tofacitinib reduces ox-LDL-induced cytotoxicity and improves endothelial viability. Mechanistically, we demonstrate that tofacitinib suppresses ox-LDL-mediated activation of NF-κB inhibitor α (IκB-α), accumulation of nuclear p65 and activation of nuclear factor κB (NF-κB) promoter, indicating that tofacitinib inhibits NF-κB activation. Collectively, our data support that tofacitinib possesses a novel protective function in endothelial cells, implying that tofacitinib could have the therapeutic potential to modulate inflammation in cardiovascular diseases.


Assuntos
Adesão Celular/efeitos dos fármacos , Células Endoteliais/citologia , Células Endoteliais/efeitos dos fármacos , Lipoproteínas LDL/farmacologia , Monócitos/citologia , Monócitos/efeitos dos fármacos , Piperidinas/farmacologia , Pirimidinas/farmacologia , Pirróis/farmacologia , Linhagem Celular , Citoproteção/efeitos dos fármacos , Células Endoteliais/metabolismo , Humanos , L-Lactato Desidrogenase/metabolismo , NF-kappa B/metabolismo , Espécies Reativas de Oxigênio/metabolismo
18.
Radiother Oncol ; 138: 141-148, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31252296

RESUMO

BACKGROUND AND PURPOSE: Robust parameters are needed to predict lymph node metastasis (LNM) in locally advanced cervical cancer patients in order to select optimal treatment regimen. The aim of this study is to utilize radiomics analysis of magnetic resonance imaging (MRI) to improve diagnostic performance of LNM in cervical cancer patients. MATERIALS AND METHODS: A total of 189 cervical cancer patients were divided into a training cohort (n = 126) and a validation cohort (n = 63). For each patient, we extracted radiomic features from intratumoral and peritumoral tissues on sagittal T2WI and axial apparent diffusion coefficient (ADC) maps. Afterward, the radiomic features associated with LNM status were selected by univariate ROC testing and logistic regression with the least absolute shrinkage and selection operator (LASSO) penalty in the training cohort. Based on the selected features, a support vector machine (SVM) model was established to predict LNM status. To further improve the diagnostic performance, a decision tree which combines the radiomics model with clinical factors was built. RESULTS: Radiomics model of the intratumoral and peritumoral tissues on T2WI (T2tumor+peri) showed best sensitivity and clinical LN (c-LN) status showed best specificity to predict LNM. The decision tree that combines radiomics model of T2tumor+peri and c-LN status achieved best diagnostic performance, with AUC and sensitivity of 0.895 and 94.3%, 0.847 and 100% in the training and validation cohort respectively. CONCLUSIONS: The decision tree, which incorporates radiomics model of T2tumor+peri and c-LN status can be potentially applied in the preoperative prediction of LNM in locally advanced cervical cancer patients.


Assuntos
Linfonodos/diagnóstico por imagem , Neoplasias do Colo do Útero/diagnóstico por imagem , Adulto , Idoso , Estudos de Coortes , Feminino , Humanos , Modelos Logísticos , Linfonodos/patologia , Metástase Linfática/diagnóstico por imagem , Imageamento por Ressonância Magnética/métodos , Masculino , Pessoa de Meia-Idade , Estadiamento de Neoplasias , Estudos Retrospectivos , Neoplasias do Colo do Útero/patologia
19.
Theranostics ; 9(3): 676-690, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30809301

RESUMO

Cancer cells undergo metabolic reprogramming to support their energy demand and biomass synthesis. However, the mechanisms driving cancer metabolism reprogramming are not well understood. Methods: The differential proteins and interacted proteins were identified by proteomics. Western blot, qRT-PCR and IHC staining were used to analyze TBC1D8 levels. In vivo tumorigenesis and metastasis were performed by xenograft tumor model. Cross-Linking assays were designed to analyze PKM2 polymerization. Lactate production, glucose uptake and PK activity were determined. Results: We established two aggressive ovarian cancer (OVCA) cell models with increased aerobic glycolysis. TBC1D8, a member of the TBC domain protein family, was significantly up-regulated in the more aggressive OVCA cells. TBC1D8 is amplified and up-regulated in OVCA tissues. OVCA patients with high TBC1D8 levels have poorer prognoses. TBC1D8 promotes OVCA tumorigenesis and aerobic glycolysis in a GAP activity-independent manner in vitro and in vivo. TBC1D8 bound to PKM2, not PKM1, via its Rab-GAP TBC domain. Mechanistically, TBC1D8 binds to PKM2 and hinders PKM2 tetramerization to decreases pyruvate kinase activity and promote aerobic glycolysis, and to promote the nuclear translocation of PKM2, which induces the expression of genes which are involved in glucose metabolism and cell cycle. Conclusions: TBC1D8 drives OVCA tumorigenesis and metabolic reprogramming, and TBC1D8 serves as an independent prognosis factor for OVCA patients.


Assuntos
Proteínas de Ligação ao Cálcio/metabolismo , Proteínas Ativadoras de GTPase/metabolismo , Neoplasias Ovarianas/metabolismo , Animais , Carcinogênese , Proteínas de Transporte , Linhagem Celular Tumoral , Dimerização , Feminino , Regulação Neoplásica da Expressão Gênica , Glicólise , Células HEK293 , Humanos , Proteínas de Membrana , Camundongos , Camundongos Endogâmicos BALB C , Camundongos Endogâmicos NOD , Neoplasias Ovarianas/genética , Prognóstico , Domínios Proteicos , Piruvato Quinase/metabolismo , Hormônios Tireóideos , Regulação para Cima
20.
Eur Radiol ; 29(1): 153-160, 2019 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-29922927

RESUMO

OBJECTIVE: To (a) assess the diagnostic performance of material decomposition (MD) water (iodine) images for the evaluation of cervical intervertebral discs (IVDs) in patients who underwent dual-energy head and neck CT angiography (HNCTA) compared with 70-keV images and (b) to explore the correlation of water concentration with the T2 relaxation time of IVDs. MATERIALS AND METHODS: Twenty-four consecutive patients who underwent dual-energy HNCTA and cervical spine MRI were studied. The diagnostic performance of water (iodine), 70-keV and MR images for IVD bulge and herniation was assessed. A subjective image score for each image set was recorded. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of IVDs to the cervical spinal cord were compared between water (iodine) and 70-keV images. Disc water concentration as measured on water (iodine) images was correlated with T2 relaxation time. RESULTS: IVD evaluations for bulge and herniation did not differ significantly among the three image sets (pairwise comparisons; all p > 0.05). SNR and CNR were significantly improved on water (iodine) images compared with those on 70-keV images (p < 0.001). Although water (iodine) images showed higher image quality scores when evaluating IVDs compared with 70-keV images, the difference is not significant (all adjusted p > 0.05). IVD water concentration exhibited no correlation with relative T2 relaxation time (all p > 0.05). CONCLUSION: Water (iodine) images facilitated analysis of cervical IVDs by providing higher SNR and CNR compared with 70-keV images. The disc water concentration measured on water (iodine) images exhibited no correlation with relative T2 relaxation time. KEY POINTS: • There was no significant difference in cervical IVD evaluations for bulge and herniation among water (iodine) images, 70-keV images and MR images. • Water (iodine) images provided higher objective and subjective image quality than 70-keV images, though the difference of subjective evaluation was not statistically significant. • The disc water concentration exhibited no correlation with relative T2 relaxation time, which reflects the inferiority of the water (iodine) images in evaluating disc water content compared with T2 maps.


Assuntos
Artérias/diagnóstico por imagem , Vértebras Cervicais/diagnóstico por imagem , Angiografia por Tomografia Computadorizada/métodos , Cabeça/irrigação sanguínea , Disco Intervertebral/diagnóstico por imagem , Iodo/farmacologia , Imagem Radiográfica a Partir de Emissão de Duplo Fóton/instrumentação , Adulto , Idoso , Feminino , Humanos , Achados Incidentais , Disco Intervertebral/irrigação sanguínea , Masculino , Pessoa de Meia-Idade , Pescoço/irrigação sanguínea , Estudos Prospectivos , Água/farmacologia
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