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1.
BMC Bioinformatics ; 21(Suppl 14): 368, 2020 Sep 30.
Artigo em Inglês | MEDLINE | ID: mdl-32998690

RESUMO

BACKGROUND: Lung cancer is the leading cause of the largest number of deaths worldwide and lung adenocarcinoma is the most common form of lung cancer. In order to understand the molecular basis of lung adenocarcinoma, integrative analysis have been performed by using genomics, transcriptomics, epigenomics, proteomics and clinical data. Besides, molecular prognostic signatures have been generated for lung adenocarcinoma by using gene expression levels in tumor samples. However, we need signatures including different types of molecular data, even cohort or patient-based biomarkers which are the candidates of molecular targeting. RESULTS: We built an R pipeline to carry out an integrated meta-analysis of the genomic alterations including single-nucleotide variations and the copy number variations, transcriptomics variations through RNA-seq and clinical data of patients with lung adenocarcinoma in The Cancer Genome Atlas project. We integrated significant genes including single-nucleotide variations or the copy number variations, differentially expressed genes and those in active subnetworks to construct a prognosis signature. Cox proportional hazards model with Lasso penalty and LOOCV was used to identify best gene signature among different gene categories. We determined a 12-gene signature (BCHE, CCNA1, CYP24A1, DEPTOR, MASP2, MGLL, MYO1A, PODXL2, RAPGEF3, SGK2, TNNI2, ZBTB16) for prognostic risk prediction based on overall survival time of the patients with lung adenocarcinoma. The patients in both training and test data were clustered into high-risk and low-risk groups by using risk scores of the patients calculated based on selected gene signature. The overall survival probability of these risk groups was highly significantly different for both training and test datasets. CONCLUSIONS: This 12-gene signature could predict the prognostic risk of the patients with lung adenocarcinoma in TCGA and they are potential predictors for the survival-based risk clustering of the patients with lung adenocarcinoma. These genes can be used to cluster patients based on molecular nature and the best candidates of drugs for the patient clusters can be proposed. These genes also have a high potential for targeted cancer therapy of patients with lung adenocarcinoma.


Assuntos
Adenocarcinoma de Pulmão/patologia , Genômica/métodos , Neoplasias Pulmonares/patologia , Transcriptoma , Adenocarcinoma de Pulmão/genética , Adenocarcinoma de Pulmão/mortalidade , Área Sob a Curva , Análise por Conglomerados , Variações do Número de Cópias de DNA , Bases de Dados Genéticas , Regulação Neoplásica da Expressão Gênica , Humanos , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/mortalidade , Estadiamento de Neoplasias , Prognóstico , Modelos de Riscos Proporcionais , Mapas de Interação de Proteínas/genética , Curva ROC , Fatores de Risco , Taxa de Sobrevida
2.
BMC Bioinformatics ; 21(Suppl 14): 359, 2020 Sep 30.
Artigo em Inglês | MEDLINE | ID: mdl-32998692

RESUMO

BACKGROUND: The abundance of molecular profiling of breast cancer tissues entailed active research on molecular marker-based early diagnosis of metastasis. Recently there is a surging interest in combining gene expression with gene networks such as protein-protein interaction (PPI) network, gene co-expression (CE) network and pathway information to identify robust and accurate biomarkers for metastasis prediction, reflecting the common belief that cancer is a systems biology disease. However, controversy exists in the literature regarding whether network markers are indeed better features than genes alone for predicting as well as understanding metastasis. We believe much of the existing results may have been biased by the overly complicated prediction algorithms, unfair evaluation, and lack of rigorous statistics. In this study, we propose a simple approach to use network edges as features, based on two types of networks respectively, and compared their prediction power using three classification algorithms and rigorous statistical procedure on one of the largest datasets available. To detect biomarkers that are significant for the prediction and to compare the robustness of different feature types, we propose an unbiased and novel procedure to measure feature importance that eliminates the potential bias from factors such as different sample size, number of features, as well as class distribution. RESULTS: Experimental results reveal that edge-based feature types consistently outperformed gene-based feature type in random forest and logistic regression models under all performance evaluation metrics, while the prediction accuracy of edge-based support vector machine (SVM) model was poorer, due to the larger number of edge features compared to gene features and the lack of feature selection in SVM model. Experimental results also show that edge features are much more robust than gene features and the top biomarkers from edge feature types are statistically more significantly enriched in the biological processes that are well known to be related to breast cancer metastasis. CONCLUSIONS: Overall, this study validates the utility of edge features as biomarkers but also highlights the importance of carefully designed experimental procedures in order to achieve statistically reliable comparison results.


Assuntos
Biomarcadores Tumorais/metabolismo , Neoplasias da Mama/patologia , Máquina de Vetores de Suporte , Área Sob a Curva , Neoplasias da Mama/genética , Feminino , Redes Reguladoras de Genes/genética , Humanos , Modelos Logísticos , Metástase Neoplásica , Mapas de Interação de Proteínas/genética , Curva ROC
3.
BMC Bioinformatics ; 21(Suppl 14): 367, 2020 Sep 30.
Artigo em Inglês | MEDLINE | ID: mdl-32998698

RESUMO

BACKGROUND: Essential genes are those genes that are critical for the survival of an organism. The prediction of essential genes in bacteria can provide targets for the design of novel antibiotic compounds or antimicrobial strategies. RESULTS: We propose a deep neural network for predicting essential genes in microbes. Our architecture called DEEPLYESSENTIAL makes minimal assumptions about the input data (i.e., it only uses gene primary sequence and the corresponding protein sequence) to carry out the prediction thus maximizing its practical application compared to existing predictors that require structural or topological features which might not be readily available. We also expose and study a hidden performance bias that effected previous classifiers. Extensive results show that DEEPLYESSENTIAL outperform existing classifiers that either employ down-sampling to balance the training set or use clustering to exclude multiple copies of orthologous genes. CONCLUSION: Deep neural network architectures can efficiently predict whether a microbial gene is essential (or not) using only its sequence information.


Assuntos
Bactérias/genética , Genes Essenciais , Redes Neurais de Computação , Área Sob a Curva , Análise por Conglomerados , Códon , Bactérias Gram-Negativas/genética , Bactérias Gram-Positivas/genética , Curva ROC
4.
BMC Bioinformatics ; 21(Suppl 14): 364, 2020 Sep 30.
Artigo em Inglês | MEDLINE | ID: mdl-32998700

RESUMO

BACKGROUND: Machine learning has been utilized to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell lines to different therapeutic compounds. Here, we build machine learning models using gene expression data from patients' primary tumor tissues to predict whether a patient will respond positively or negatively to two chemotherapeutics: 5-Fluorouracil and Gemcitabine. RESULTS: We focused on 5-Fluorouracil and Gemcitabine because based on our exclusion criteria, they provide the largest numbers of patients within TCGA. Normalized gene expression data were clustered and used as the input features for the study. We used matching clinical trial data to ascertain the response of these patients via multiple classification methods. Multiple clustering and classification methods were compared for prediction accuracy of drug response. Clara and random forest were found to be the best clustering and classification methods, respectively. The results show our models predict with up to 86% accuracy; despite the study's limitation of sample size. We also found the genes most informative for predicting drug response were enriched in well-known cancer signaling pathways and highlighted their potential significance in chemotherapy prognosis. CONCLUSIONS: Primary tumor gene expression is a good predictor of cancer drug response. Investment in larger datasets containing both patient gene expression and drug response is needed to support future work of machine learning models. Ultimately, such predictive models may aid oncologists with making critical treatment decisions.


Assuntos
Antineoplásicos/farmacologia , Regulação Neoplásica da Expressão Gênica/efeitos dos fármacos , Aprendizado de Máquina , Antineoplásicos/uso terapêutico , Área Sob a Curva , Análise por Conglomerados , Bases de Dados Genéticas , Desoxicitidina/análogos & derivados , Desoxicitidina/farmacologia , Desoxicitidina/uso terapêutico , Fluoruracila/uso terapêutico , Humanos , Neoplasias/tratamento farmacológico , Curva ROC
5.
BMC Med Imaging ; 20(1): 111, 2020 10 02.
Artigo em Inglês | MEDLINE | ID: mdl-33008329

RESUMO

BACKGROUND: To develop and validate a nomogram for early identification of severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics. METHODS: The initial clinical and CT imaging data of 217 patients with COVID-19 were analyzed retrospectively from January to March 2020. Two hundred seventeen patients with 146 mild cases and 71 severe cases were randomly divided into training and validation cohorts. Independent risk factors were selected to construct the nomogram for predicting severe COVID-19. Nomogram performance in terms of discrimination and calibration ability was evaluated using the area under the curve (AUC), calibration curve, decision curve, clinical impact curve and risk chart. RESULTS: In the training cohort, the severity score of lung in the severe group (7, interquartile range [IQR]:5-9) was significantly higher than that of the mild group (4, IQR,2-5) (P < 0.001). Age, density, mosaic perfusion sign and severity score of lung were independent risk factors for severe COVID-19. The nomogram had a AUC of 0.929 (95% CI, 0.889-0.969), sensitivity of 84.0% and specificity of 86.3%, in the training cohort, and a AUC of 0.936 (95% CI, 0.867-1.000), sensitivity of 90.5% and specificity of 88.6% in the validation cohort. The calibration curve, decision curve, clinical impact curve and risk chart showed that nomogram had high accuracy and superior net benefit in predicting severe COVID-19. CONCLUSION: The nomogram incorporating initial clinical and CT characteristics may help to identify the severe patients with COVID-19 in the early stage.


Assuntos
Infecções por Coronavirus/diagnóstico por imagem , Pulmão/diagnóstico por imagem , Nomogramas , Pneumonia Viral/diagnóstico por imagem , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Área Sob a Curva , Criança , Diagnóstico Precoce , Humanos , Pessoa de Meia-Idade , Pandemias , Distribuição Aleatória , Estudos Retrospectivos , Sensibilidade e Especificidade , Índice de Gravidade de Doença , Tomografia Computadorizada por Raios X , Adulto Jovem
6.
Nat Commun ; 11(1): 5084, 2020 10 08.
Artigo em Inglês | MEDLINE | ID: mdl-33033253

RESUMO

Identifying factors underlying resistance to immune checkpoint therapy (ICT) is still challenging. Most cancer patients do not respond to ICT and the availability of the predictive biomarkers is limited. Here, we re-analyze a publicly available single-cell RNA sequencing (scRNA-seq) dataset of melanoma samples of patients subjected to ICT and identify a subset of macrophages overexpressing TREM2 and a subset of gammadelta T cells that are both overrepresented in the non-responding tumors. In addition, the percentage of a B cell subset is significantly lower in the non-responders. The presence of these immune cell subtypes is corroborated in other publicly available scRNA-seq datasets. The analyses of bulk RNA-seq datasets of the melanoma samples identify and validate a signature - ImmuneCells.Sig - enriched with the genes characteristic of the above immune cell subsets to predict response to immunotherapy. ImmuneCells.Sig could represent a valuable tool for clinical decision making in patients receiving immunotherapy.


Assuntos
Perfilação da Expressão Gênica , Imunoterapia , Macrófagos/metabolismo , Glicoproteínas de Membrana/metabolismo , Receptores de Antígenos de Linfócitos T gama-delta/metabolismo , Receptores Imunológicos/metabolismo , Linfócitos T/metabolismo , Área Sob a Curva , Linfócitos B/metabolismo , Biomarcadores Tumorais/metabolismo , Humanos , Macrófagos/patologia , Melanoma/genética , Melanoma/patologia , Reprodutibilidade dos Testes
7.
Nat Commun ; 11(1): 5077, 2020 10 08.
Artigo em Inglês | MEDLINE | ID: mdl-33033240

RESUMO

Although substantial progress has been made in cancer biology and treatment, clinical outcomes of bladder carcinoma (BC) patients are still not satisfactory. The tumor microenvironment (TME) is a potential target. Here, by single-cell RNA sequencing on 8 BC tumor samples and 3 para tumor samples, we identify 19 different cell types in the BC microenvironment, indicating high intra-tumoral heterogeneity. We find that tumor cells down regulated MHC-II molecules, suggesting that the downregulated immunogenicity of cancer cells may contribute to the formation of an immunosuppressive microenvironment. We also find that monocytes undergo M2 polarization in the tumor region and differentiate. Furthermore, the LAMP3 + DC subgroup may be able to recruit regulatory T cells, potentially taking part in the formation of an immunosuppressive TME. Through correlation analysis using public datasets containing over 3000 BC samples, we identify a role for inflammatory cancer-associated fibroblasts (iCAFs) in tumor progression, which is significantly related to poor prognosis. Additionally, we characterize a regulatory network depending on iCAFs. These results could help elucidate the protumor mechanisms of iCAFs. Our results provide deep insight into cancer immunology and provide an essential resource for drug discovery in the future.


Assuntos
Fibroblastos/patologia , Inflamação/patologia , Análise de Sequência de RNA , Análise de Célula Única , Bexiga Urinária/patologia , Área Sob a Curva , Fibroblastos Associados a Câncer/metabolismo , Fibroblastos Associados a Câncer/patologia , Linhagem Celular Tumoral , Polaridade Celular , Proliferação de Células , Citocinas/metabolismo , Variações do Número de Cópias de DNA/genética , Células Dendríticas/metabolismo , Redes Reguladoras de Genes , Humanos , Ligantes , Glicoproteínas de Membrana Associadas ao Lisossomo/metabolismo , Monócitos/patologia , Células Mieloides/patologia , Proteínas de Neoplasias/metabolismo , Linfócitos T Reguladores/imunologia , Microambiente Tumoral , Bexiga Urinária/imunologia
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1136-1139, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018187

RESUMO

Computerized parenchymal analysis has shown potential to be utilized as an imaging biomarker to estimate the risk of breast cancer. Parenchymal analysis of digital mammograms is based on the extraction of computerized measures to build machine learning-based models for the prediction of breast cancer risk. However, the choice of the region of interest (ROI) for feature extraction within the breast remains an open problem. In this work we perform a comparison between five different methods suggested in the literature for automated ROI selection, including the whole breast (WB), the maximum squared (MS), the retro-areolar region (RA), the lattice-based (LB), and the polar-based (PB) selection methods. For the experiments, we built a retrospective dataset of 896 screening mammograms from 224 women (112 cases and 112 healthy controls). The performance of each ROI selection method was measured in terms of the area under the curve (AUC) values. The AUC values varied between 0.55 and 0.79 depending on the method and experimental settings. The best performance on an independent test set was achieved by the MS method (AUC of 0.59, 95% CI: 0.55-0.64). This method is fully-automated and does not require adjusting hyper-parameters. Based on our results, we prompt the use of the MS method for ROI selection in the computerized parenchymal analysis for breast cancer risk assessment.


Assuntos
Neoplasias da Mama , Área Sob a Curva , Neoplasias da Mama/diagnóstico , Feminino , Humanos , Mamografia , Estudos Retrospectivos , Medição de Risco
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1966-1969, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018388

RESUMO

Diabetic retinopathy (DR) is a medical condition due to diabetes mellitus that can damage the patient retina and cause blood leaks. This condition can cause different symptoms from mild vision problems to complete blindness if it is not timely treated. In this work, we propose the use of a deep learning architecture based on a recent convolutional neural network called EfficientNet to detect referable diabetic retinopathy (RDR) and vision-threatening DR. Tests were conducted on two public datasets, EyePACS and APTOS 2019. The obtained results achieve state-of-the-art performance and show that the proposed network leads to higher classification rates, achieving an Area Under Curve (AUC) of 0.984 for RDR and 0.990 for vision-threatening DR on EyePACS dataset. Similar performances are obtained for APTOS 2019 dataset with an AUC of 0.966 and 0.998 for referable and vision-threatening DR, respectively. An explainability algorithm was also developed and shows the efficiency of the proposed approach in detecting DR signs.


Assuntos
Diabetes Mellitus , Retinopatia Diabética , Algoritmos , Área Sob a Curva , Retinopatia Diabética/diagnóstico , Humanos , Redes Neurais de Computação , Retina
10.
Clin Appl Thromb Hemost ; 26: 1076029620964868, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33030047

RESUMO

To discuss the coagulation dysfunction in COVID-19 patients and to find new biomarkers to separate severe COVID-19 patients from mild ones. We use a retrospective analysis of 88 COVID-19 patients, and compare the coagulation function between severe and mild groups. We found the prothrombin time (PT), thrombin time (TT), D-dimer were significantly higher in the severe group (P < 0.05), and the highest area under the curve (AUC) is 0.91 for D-dimer, while the AUC of PT and TT were 0.80 and 0.61 respectively. We identified that D-dimer has a better value in predicting patients who are likely to develop into severe cases, with the sensitivity and specificity were 84.4% and 88.8%, respectively. D-dimer may be a good biomarker to separate the severe COVID-19 patients from the mild ones.


Assuntos
Transtornos da Coagulação Sanguínea/etiologia , Testes de Coagulação Sanguínea/métodos , Infecções por Coronavirus/complicações , Produtos de Degradação da Fibrina e do Fibrinogênio/análise , Pneumonia Viral/complicações , Adulto , Idoso , Área Sob a Curva , Biomarcadores/sangue , Transtornos da Coagulação Sanguínea/sangue , Transtornos da Coagulação Sanguínea/fisiopatologia , China , Estudos de Coortes , Infecções por Coronavirus/diagnóstico , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Pandemias , Pneumonia Viral/diagnóstico , Valor Preditivo dos Testes , Tempo de Protrombina , Curva ROC , Estudos Retrospectivos , Índice de Gravidade de Doença , Tempo de Trombina
11.
Medicine (Baltimore) ; 99(35): e21895, 2020 Aug 28.
Artigo em Inglês | MEDLINE | ID: mdl-32871920

RESUMO

MicroRNAs (miRNAs) refers to a small, short non-coding RNA of endogenous class. They have shown to have an increasingly altered expression in many types of cancer, including colorectal cancer (CRC).In the present study, miRNA TaqManMGB and qRT-PCR was used to quantify the expression and clinical significance of 3 mature human miRNA in 82 pairs of colorectal adenocarcinoma tissues and normal adjacent tissue samples (NATS) collected from patients of the south-east part of Romania. Differences between CRC and NATS were analyzed using Wilcoxon test, while correlations between miRNAs expression levels and clinicopathological features were examined using non-parametric tests. In addition, the ability of selected miRNAs to function as biomarkers and, as potential indicators in CRC prognosis was also examined.When the miRNA expression was compared in CRC related NATS, miR-143, and miR-145 were significantly underexpressed (4.99 ±â€Š-1.02 vs -5.66 ±â€Š-1.66, P < .001; -4.85 ±â€Š-0.59 vs -9.27 ±â€Š-1.51, P < .001, respectively), while the pattern of miR-92a was significantly overexpressed (-5.55 ±â€Š-2.83 vs -4.92 ±â€Š-2.44, P < .001). Moreover, the expression levels of selected miRNAs were identified to be correlated with gradual increases in fold change expression with the depth of tumor invasion, lymph node invasion, and maximal increases with distant metastasis. Furthermore, the receiver operating characteristic analysis demonstrated that potential diagnostic of miR-143, miR-145, and miR-92a in discriminating CRC from NATS, with the area under the curve of 0.74, 0.85, and 0.84 respectively. The Kaplan-Meier and the log-rank test showed that a high level of miR-92a and low levels of miR-143 and miR-145 predicted poor survival rate in our cohorts.In conclusion, we can summarize that miR-145 and miR-143 are decreased, while miR-92 is increased in CRC compared to NATS, and associated with different stages of CRC pathogenesis. Thus, the expression of selected miRNAs can represent potential diagnostic and prognostic tools in patients with CRC from Romania.


Assuntos
Adenocarcinoma/diagnóstico , Biomarcadores Tumorais/genética , Neoplasias Colorretais/diagnóstico , MicroRNAs/genética , Adenocarcinoma/genética , Adenocarcinoma/patologia , Idoso , Área Sob a Curva , Neoplasias Colorretais/genética , Neoplasias Colorretais/patologia , Feminino , Expressão Gênica , Humanos , Estimativa de Kaplan-Meier , Masculino , Pessoa de Meia-Idade , Prognóstico , Curva ROC , Romênia , Transcriptoma
12.
Medicine (Baltimore) ; 99(35): e21721, 2020 Aug 28.
Artigo em Inglês | MEDLINE | ID: mdl-32871890

RESUMO

The aim of this study was to provide an innovative nomogram to predict the risk of >2 positive nodes in patients fulfilling the Z0011 criteria with 1-2 sentinel lymph nodes (SLNs) only retrieved.From 2007 to 2017, at the Breast Unit of ICS Maugeri Hospital 271 patients with 1-2 macrometastatic SLNs, fulfilling the Z0011 criteria, underwent axillary dissection and were retrospectively reviewed.A mean of 1.5 SLNs per patient were identified and retrieved. One hundred eighty-seven (69.0%) had 1-2 positive nodes, and 84 (31.0%) had >2 metastatic nodes. Independent predictors of axillary status were: positive SLNs/retrieved SLNs ratio (odds ratio [OR] 10.95, P = .001), extranodal extension (OR 5.51, P = .0002), and multifocal disease (OR 2.9, P = .003). A nomogram based on these variables was constructed (area under curve after bootstrap = 0.74).The proposed nomogram might select those patients fulfilling the Z0011 criteria, with 1-2 SLNs harvested, in whom a high axillary tumor burden is expected, aiding to guide adjuvant treatments.


Assuntos
Neoplasias da Mama/patologia , Neoplasias Primárias Múltiplas/patologia , Nomogramas , Linfonodo Sentinela/patologia , Idoso , Antineoplásicos Hormonais/uso terapêutico , Área Sob a Curva , Axila , Neoplasias da Mama/terapia , Quimioterapia Adjuvante , Feminino , Humanos , Metástase Linfática , Mastectomia Segmentar , Pessoa de Meia-Idade , Estadiamento de Neoplasias , Radioterapia Adjuvante , Carga Tumoral
13.
Respir Res ; 21(1): 245, 2020 Sep 22.
Artigo em Inglês | MEDLINE | ID: mdl-32962703

RESUMO

BACKGROUND: The COVID-19 pandemic has led to more than 760,000 deaths worldwide (correct as of 16th August 2020). Studies suggest a hyperinflammatory response is a major cause of disease severity and death. Identitfying COVID-19 patients with hyperinflammation may identify subgroups who could benefit from targeted immunomodulatory treatments. Analysis of cytokine levels at the point of diagnosis of SARS-CoV-2 infection can identify patients at risk of deterioration. METHODS: We used a multiplex cytokine assay to measure serum IL-6, IL-8, TNF, IL-1ß, GM-CSF, IL-10, IL-33 and IFN-γ in 100 hospitalised patients with confirmed COVID-19 at admission to University Hospital Southampton (UK). Demographic, clinical and outcome data were collected for analysis. RESULTS: Age > 70 years was the strongest predictor of death (OR 28, 95% CI 5.94, 139.45). IL-6, IL-8, TNF, IL-1ß and IL-33 were significantly associated with adverse outcome. Clinical parameters were predictive of poor outcome (AUROC 0.71), addition of a combined cytokine panel significantly improved the predictability (AUROC 0.85). In those ≤70 years, IL-33 and TNF were predictive of poor outcome (AUROC 0.83 and 0.84), addition of a combined cytokine panel demonstrated greater predictability of poor outcome than clinical parameters alone (AUROC 0.92 vs 0.77). CONCLUSIONS: A combined cytokine panel improves the accuracy of the predictive value for adverse outcome beyond standard clinical data alone. Identification of specific cytokines may help to stratify patients towards trials of specific immunomodulatory treatments to improve outcomes in COVID-19.


Assuntos
Infecções por Coronavirus/sangue , Infecções por Coronavirus/epidemiologia , Citocinas/análise , Mortalidade Hospitalar , Mediadores da Inflamação/sangue , Pandemias/estatística & dados numéricos , Pneumonia Viral/sangue , Pneumonia Viral/epidemiologia , Fatores Etários , Análise de Variância , Área Sob a Curva , Técnicas de Laboratório Clínico/métodos , Estudos de Coortes , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/fisiopatologia , Feminino , Hospitalização/estatística & dados numéricos , Hospitais Universitários , Humanos , Incidência , Masculino , Pandemias/prevenção & controle , Fenótipo , Pneumonia Viral/fisiopatologia , Valor Preditivo dos Testes , Curva ROC , Estudos Retrospectivos , Índice de Gravidade de Doença , Fatores Sexuais , Reino Unido
14.
Am J Vet Res ; 81(10): 783-789, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32969731

RESUMO

OBJECTIVE: To determine plasma pharmacokinetics of metronidazole and imipenem following administration of a single dose PO (metronidazole, 15 mg/kg) or IV (imipenem, 10 mg/kg) in healthy Thoroughbreds and simulate pleural fluid concentrations following multiple dose administration every 8 hours. ANIMALS: 4 healthy Thoroughbreds. PROCEDURES: Metronidazole and imipenem were administered, and samples of plasma and pleural fluid were collected at predetermined time points. Minimum concentrations of metronidazole and imipenem that inhibited growth of 90% of isolates (MIC90), including 22 clinical Bacteroides isolates from horses with pleuropneumonia, were calculated. For the computer simulation, the target ratio for area under the pleural fluid concentration-versus-time curve during 24 hours to the MIC90 for metronidazole was > 70, and the target percentage of time per day that the pleural fluid concentration of imipenem exceeded the MIC90 was > 50%. RESULTS: Mean ± SD pleural fluid concentrations of metronidazole and imipenem were 12.7 ± 3.3 µg/mL and 12.1 ± 0.9 µg/mL, respectively, 1 hour after administration and 4.9 ± 0.85 µg/mL and 0.3 ± 0.08 µg/mL, respectively, 8 hours after administration. For both antimicrobials, concentrations in the pleural fluid and plasma were similar. The ratio for area under the pleural fluid concentration-versus-time curve during 24 hours to the MIC90 for metronidazole was 84.9, and the percentage of time per day the pleural fluid concentration of imipenem exceeded the MIC90 was 70.9%. CONCLUSIONS AND CLINICAL RELEVANCE: Results suggested that administration of metronidazole (15 mg/kg, PO, q 8 h) or imipenem (10 mg/kg, IV, q 8 h) resulted in their accumulation in the pleural fluid in healthy horses and concentrations were likely to be effective for the treatment of pneumonia and pleuropneumonia caused by Bacteroides spp.


Assuntos
Anti-Infecciosos , Metronidazol , Animais , Área Sob a Curva , Simulação por Computador , Cavalos , Imipenem
15.
PLoS One ; 15(9): e0239474, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32960917

RESUMO

Worldwide, testing capacity for SARS-CoV-2 is limited and bottlenecks in the scale up of polymerase chain reaction (PCR-based testing exist. Our aim was to develop and evaluate a machine learning algorithm to diagnose COVID-19 in the inpatient setting. The algorithm was based on basic demographic and laboratory features to serve as a screening tool at hospitals where testing is scarce or unavailable. We used retrospectively collected data from the UCLA Health System in Los Angeles, California. We included all emergency room or inpatient cases receiving SARS-CoV-2 PCR testing who also had a set of ancillary laboratory features (n = 1,455) between 1 March 2020 and 24 May 2020. We tested seven machine learning models and used a combination of those models for the final diagnostic classification. In the test set (n = 392), our combined model had an area under the receiver operator curve of 0.91 (95% confidence interval 0.87-0.96). The model achieved a sensitivity of 0.93 (95% CI 0.85-0.98), specificity of 0.64 (95% CI 0.58-0.69). We found that our machine learning algorithm had excellent diagnostic metrics compared to SARS-CoV-2 PCR. This ensemble machine learning algorithm to diagnose COVID-19 has the potential to be used as a screening tool in hospital settings where PCR testing is scarce or unavailable.


Assuntos
Betacoronavirus , Técnicas de Laboratório Clínico/métodos , Infecções por Coronavirus/diagnóstico , Pacientes Internados , Aprendizado de Máquina , Pneumonia Viral/diagnóstico , Adulto , Idoso , Área Sob a Curva , Técnicas de Laboratório Clínico/normas , Humanos , Los Angeles , Programas de Rastreamento/métodos , Programas de Rastreamento/normas , Pessoa de Meia-Idade , Pandemias , Reação em Cadeia da Polimerase , Estudos Retrospectivos
16.
PLoS One ; 15(8): e0237808, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32866209

RESUMO

In this study, we performed an analysis of the impact of performance enhancing polymorphisms (PEPs) on gymnastic aptitude while considering epistatic effects. Seven PEPs (rs1815739, rs8192678, rs4253778, rs6265, rs5443, rs1076560, rs362584) were considered in a case (gymnasts)-control (sedentary individuals) setting. The study sample comprised of two athletes' sets: 27 elite (aged 24.8 ± 2.1 years) and 46 sub-elite (aged 19.7 ± 2.4 years) sportsmen as well as a control group of 245 sedentary individuals (aged 22.5 ± 2.1 years). The DNA was derived from saliva and PEP alleles were determined by PCR, RT-PCR. Following Multifactor Dimensionality Reduction, logistic regression models were built. The synergistic effect for rs1815739 x rs362584 reached 5.43%. The rs1815739 x rs362584 epistatic regression model exhibited a good fit to the data (Chi-squared = 33.758, p ≈ 0) achieving a significant improvement in sportsmen identification over naïve guessing. The area under the receiver operating characteristic curve was 0.715 (Z-score = 38.917, p ≈ 0). In contrast, the additive ACTN3 -SNAP-25 logistic regression model has been verified as non-significant. We demonstrate that a gene involved in the differentiation of muscle architecture-ACTN3 and a gene, which plays an important role in the nervous system-SNAP-25 interact. From the perspective originally established by the Berlin Academy of Science in 1751, the matter of communication between the brain and muscles via nerves adopts molecular manifestations. Further in-vitro investigations are required to explain the molecular details of the rs1815739 -rs362584 interaction.


Assuntos
Actinina/genética , Aptidão , Epistasia Genética , Ginástica/fisiologia , Proteína 25 Associada a Sinaptossoma/genética , Adulto , Alelos , Área Sob a Curva , Bases de Dados Genéticas , Entropia , Feminino , Marcadores Genéticos , Humanos , Modelos Logísticos , Masculino , Modelos Genéticos , Redução Dimensional com Múltiplos Fatores , Polimorfismo de Nucleotídeo Único/genética , Adulto Jovem
17.
J Biomed Opt ; 25(9)2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32921005

RESUMO

SIGNIFICANCE: Infrared thermographs (IRTs) have been used for fever screening during infectious disease epidemics, including severe acute respiratory syndrome, Ebola virus disease, and coronavirus disease 2019 (COVID-19). Although IRTs have significant potential for human body temperature measurement, the literature indicates inconsistent diagnostic performance, possibly due to wide variations in implemented methodology. A standardized method for IRT fever screening was recently published, but there is a lack of clinical data demonstrating its impact on IRT performance. AIM: Perform a clinical study to assess the diagnostic effectiveness of standardized IRT-based fever screening and evaluate the effect of facial measurement location. APPROACH: We performed a clinical study of 596 subjects. Temperatures from 17 facial locations were extracted from thermal images and compared with oral thermometry. Statistical analyses included calculation of receiver operating characteristic (ROC) curves and area under the curve (AUC) values for detection of febrile subjects. RESULTS: Pearson correlation coefficients for IRT-based and reference (oral) temperatures were found to vary strongly with measurement location. Approaches based on maximum temperatures in either inner canthi or full-face regions indicated stronger discrimination ability than maximum forehead temperature (AUC values of 0.95 to 0.97 versus 0.86 to 0.87, respectively) and other specific facial locations. These values are markedly better than the vast majority of results found in prior human studies of IRT-based fever screening. CONCLUSION: Our findings provide clinical confirmation of the utility of consensus approaches for fever screening, including the use of inner canthi temperatures, while also indicating that full-face maximum temperatures may provide an effective alternate approach.


Assuntos
Temperatura Corporal , Infecções por Coronavirus/diagnóstico , Face/fisiologia , Febre/diagnóstico , Pneumonia Viral/diagnóstico , Termografia/métodos , Adolescente , Adulto , Idoso , Área Sob a Curva , Betacoronavirus , Feminino , Humanos , Raios Infravermelhos , Masculino , Programas de Rastreamento/métodos , Pessoa de Meia-Idade , Pandemias , Guias de Prática Clínica como Assunto , Curva ROC , Reprodutibilidade dos Testes , Adulto Jovem
18.
Med Sci Monit ; 26: e926393, 2020 Sep 11.
Artigo em Inglês | MEDLINE | ID: mdl-32914767

RESUMO

BACKGROUND The aim of this study was to determine the effect of C-reactive protein (CRP), lymphocytes (LYM), and the ratio of CRP to LYM (CRP/LYM) on assessing the prognosis of COVID-19 severity at early stages of disease. MATERIAL AND METHODS A total of 108 hospitalized patients diagnosed with COVID-19 in Zhongnan Hospital of Wuhan University from January 17, 2020 to March 12, 2020 were enrolled. Data of demographic parameters, clinical characteristics, laboratory indicators, clinical manifestation, and outcome of disease were collected. The patients were divided into a severe group and a non-severe group according to diagnosis and classification, which followed the guidelines and management of the Chinese National Health Council COVID-19. The receiver-operating characteristic (ROC) analysis and comparison of ROC curves were used for the laboratory findings for assessment of COVID-19 severity. RESULTS Of the 108 patients, 42 patients (38.9%) were male and 24 patients (22.2%) were considered severe cases, with the mean age of 51.0 years old. Males and patients with comorbidities were more likely to become severe cases. CRP increased and LYM decreased in the severe group.The results for the areas under the curve (AUC) of CRP/LYM and CRP used to assess severe COVID-19 were 0.787 (95% CI 0.698-0.860, P<0.0001) and 0.781 (95% CI 0.693-0.856, P<0.0001), respectively; both results were better than that of LYM. The associated criterion value of CRP/LYM was calculated, with an excellent sensitivity of 95.83%. CONCLUSIONS The effect of CRP/LYM and CRP on the assessment for severe COVID-19 may be superior to LYM alone. CRP/LYM is a highly sensitive indicator to assess the severity of COVID-19 in the early stage of disease.


Assuntos
Betacoronavirus , Proteína C-Reativa/análise , Infecções por Coronavirus/sangue , Contagem de Linfócitos , Pandemias , Pneumonia Viral/sangue , Adulto , Idoso , Idoso de 80 Anos ou mais , Área Sob a Curva , Biomarcadores , China/epidemiologia , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/imunologia , Síndrome da Liberação de Citocina/sangue , Síndrome da Liberação de Citocina/etiologia , Progressão da Doença , Feminino , Humanos , Tempo de Internação/estatística & dados numéricos , Masculino , Pessoa de Meia-Idade , Pneumonia Viral/epidemiologia , Pneumonia Viral/imunologia , Curva ROC , Estudos Retrospectivos , Índice de Gravidade de Doença , Adulto Jovem
19.
BMC Bioinformatics ; 21(1): 401, 2020 Sep 10.
Artigo em Inglês | MEDLINE | ID: mdl-32912137

RESUMO

BACKGROUND: As an important non-coding RNA, microRNA (miRNA) plays a significant role in a series of life processes and is closely associated with a variety of Human diseases. Hence, identification of potential miRNA-disease associations can make great contributions to the research and treatment of Human diseases. However, to our knowledge, many existing computational methods only utilize the single type of known association information between miRNAs and diseases to predict their potential associations, without focusing on their interactions or associations with other types of molecules. RESULTS: In this paper, we propose a network embedding-based method for predicting miRNA-disease associations by preserving behavior and attribute information. Firstly, a heterogeneous network is constructed by integrating known associations among miRNA, protein and disease, and the network representation method Learning Graph Representations with Global Structural Information (GraRep) is implemented to learn the behavior information of miRNAs and diseases in the network. Then, the behavior information of miRNAs and diseases is combined with the attribute information of them to represent miRNA-disease association pairs. Finally, the prediction model is established based on the Random Forest algorithm. Under the five-fold cross validation, the proposed NEMPD model obtained average 85.41% prediction accuracy with 80.96% sensitivity at the AUC of 91.58%. Furthermore, the performance of NEMPD is also validated by the case studies. Among the top 50 predicted disease-related miRNAs, 48 (breast neoplasms), 47 (colon neoplasms), 47 (lung neoplasms) were confirmed by two other databases. CONCLUSIONS: The proposed NEMPD model has a good performance in predicting the potential associations between miRNAs and diseases, and has great potency in the field of miRNA-disease association prediction in the future.


Assuntos
Neoplasias da Mama/diagnóstico , Neoplasias do Colo/diagnóstico , Biologia Computacional/métodos , Neoplasias Pulmonares/diagnóstico , MicroRNAs/metabolismo , Algoritmos , Área Sob a Curva , Neoplasias da Mama/genética , Neoplasias do Colo/genética , Feminino , Humanos , Neoplasias Pulmonares/genética , MicroRNAs/genética , Curva ROC
20.
Medicine (Baltimore) ; 99(37): e21386, 2020 Sep 11.
Artigo em Inglês | MEDLINE | ID: mdl-32925711

RESUMO

Serum creatinine (SCr) and estimated glomerular filtration rate (eGFR) are standard biomarkers of contrast-induced nephropathy (CIN). However, recent studies suggest that serum neutrophil gelatinase-associated lipocalin (sNGAL) and urine neutrophil gelatinase-associated lipocalin (uNGAL) may be better predictors, particularly within 24 hours of contrast medium exposure.We conducted a prospective, observational cohort study of 107 consecutive patients diagnosed with arteriosclerosis obliterans between February 2016 and October 2018. We divided the patients into 2 groups: CIN (n = 22) and non-CIN (n = 85). We assessed the correlation between sNGAL and uNGAL concentrations and standard renal markers at baseline, 6, 24, and 48 hours post-procedure. We constructed conventional receiver operating characteristic (ROC) curves and calculated the area under the curve to assess the performance of SCr, eGFR, sNGAL, and uNGAL. We derived biomarker cutoff levels from ROC analysis to maximize sensitivity and specificity.The incidence of CIN within our cohort was 20.6%. sNGAL levels correlated significantly with SCr and eGFR at baseline, 6, 24, and 48 hours post-contrast medium exposure. Similarly, uNGAL levels correlated with SCr and eGFR at baseline, 24, and 48 hours post-exposure. sNGAL and uNGAL were significantly elevated as early as 6 hours post-catheterization in the CIN group, whereas only minor changes were observed in the non-CIN group. SCr was also significantly elevated in the CIN group, but not until 24 hours post-catheterization.Both sNGAL and uNGAL may be superior to SCr and eGFR as early biomarkers of CIN in patients with peripheral vascular disease undergoing endovascular therapy.


Assuntos
Lesão Renal Aguda/diagnóstico , Meios de Contraste/efeitos adversos , Procedimentos Endovasculares/efeitos adversos , Lipocalina-2/análise , Complicações Pós-Operatórias/diagnóstico , Lesão Renal Aguda/induzido quimicamente , Idoso , Área Sob a Curva , Arteriosclerose Obliterante/cirurgia , Biomarcadores/sangue , Biomarcadores/urina , Feminino , Humanos , Masculino , Complicações Pós-Operatórias/induzido quimicamente , Estudos Prospectivos , Curva ROC , Sensibilidade e Especificidade
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