RESUMEN
Escherichia coli O157:H7 is a pathogenic serotype of Escherichia coli. Consumption of food contaminated with E. coli O157:H7 could cause a range of diseases. Therefore, it is of great importance to establish rapid and accurate detection methods for E. coli O157:H7 in food. In this study, based on LAMP and combined with the CRISPR/cas12a system, a sensitive and specific rapid detection method for E. coli O157:H7 was established, and One-Pot detection method was also constructed. The sensitivity of this method could stably reach 9.2 × 10° CFU/mL in pure culture, and the whole reaction can be completed within 1 h. In milk, E. coli O157:H7 with an initial contamination of 7.4 × 10° CFU/mL only needed to be cultured for 3 h to be detected. The test results can be judged by the fluorescence curve or by visual observation under a UV lamp, eliminating instrument limitations and One-Pot detection can effectively prevent the problem of false positives. In a word, the LAMP-CRISPR/cas12a system is a highly sensitive and convenient method for detecting E. coli O157:H7.
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Sistemas CRISPR-Cas , Escherichia coli O157 , Microbiología de Alimentos , Leche , Técnicas de Amplificación de Ácido Nucleico , Escherichia coli O157/genética , Escherichia coli O157/aislamiento & purificación , Leche/microbiología , Microbiología de Alimentos/métodos , Técnicas de Amplificación de Ácido Nucleico/métodos , Animales , Sensibilidad y Especificidad , Contaminación de Alimentos/análisis , Técnicas de Diagnóstico Molecular/métodosRESUMEN
Automatic seizure detection has significant value in epilepsy diagnosis and treatment. Although a variety of deep learning models have been proposed to automatically learn electroencephalography (EEG) features for seizure detection, the generalization performance and computational burden of such deep models remain the bottleneck of practical application. In this study, a novel lightweight model based on random convolutional kernel transform (ROCKET) is developed for EEG feature learning for seizure detection. Specifically, random convolutional kernels are embedded into the structure of a wavelet scattering network instead of original wavelet transform convolutions. Then the significant EEG features are selected from the scattering coefficients and convolutional outputs by analysis of variance (ANOVA) and minimum redundancy-maximum relevance (MRMR) methods. This model not only preserves the merits of the fast-training process from ROCKET, but also provides insight into seizure detection by retaining only the helpful channels. The extreme gradient boosting (XGboost) classifier was combined with this EEG feature learning model to build a comprehensive seizure detection system that achieved promising epoch-based results, with over 90% of both sensitivity and specificity on the scalp and intracranial EEG databases. The experimental comparisons showed that the proposed method outperformed other state-of-the-art methods for cross-patient and patient-specific seizure detection.
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Aprendizaje Profundo , Electroencefalografía , Convulsiones , Análisis de Ondículas , Humanos , Convulsiones/diagnóstico , Convulsiones/fisiopatología , Electroencefalografía/métodos , Redes Neurales de la Computación , Procesamiento de Señales Asistido por Computador , Sensibilidad y Especificidad , Aprendizaje AutomáticoRESUMEN
Meta-analysis is a powerful tool to synthesize findings from multiple studies. The normal-normal random-effects model is widely used to account for between-study heterogeneity. However, meta-analyses of sparse data, which may arise when the event rate is low for binary or count outcomes, pose a challenge to the normal-normal random-effects model in the accuracy and stability in inference since the normal approximation in the within-study model may not be good. To reduce bias arising from data sparsity, the generalized linear mixed model can be used by replacing the approximate normal within-study model with an exact model. Publication bias is one of the most serious threats in meta-analysis. Several quantitative sensitivity analysis methods for evaluating the potential impacts of selective publication are available for the normal-normal random-effects model. We propose a sensitivity analysis method by extending the likelihood-based sensitivity analysis with the $t$-statistic selection function of Copas to several generalized linear mixed-effects models. Through applications of our proposed method to several real-world meta-analyses and simulation studies, the proposed method was proven to outperform the likelihood-based sensitivity analysis based on the normal-normal model. The proposed method would give useful guidance to address publication bias in the meta-analysis of sparse data.
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Simulación por Computador , Metaanálisis como Asunto , Sesgo de Publicación , Sesgo de Publicación/estadística & datos numéricos , Humanos , Funciones de Verosimilitud , Modelos Lineales , Interpretación Estadística de Datos , Modelos Estadísticos , Sensibilidad y Especificidad , Biometría/métodosRESUMEN
PURPOSE: To determine the misclassification rate of the keratoconus percentage (KISA%) index efficacy in eyes with progressive keratoconus. METHODS: This was a retrospective case-control study of consecutive patients with confirmed progressive keratoconus and a contemporaneous normal control group with 1.00 diopters or greater regular astigmatism. Scheimpflug imaging (Pentacam HR) was obtained for all patients. KISA% index and inferior-superior (IS) values were obtained from the Pentacam topometric/keratoconus staging map. Receiver operating characteristic curves were generated to determine the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity values. RESULTS: There were 160 eyes from 160 patients evaluated, including 80 eyes from 80 patients with progressive keratoconus and 80 eyes from 80 control patients. There were 20 eyes (25%) with progressive keratoconus misclassified by the KISA% index, with 16 eyes (20%) of the progressive keratoconus cohort classified as normal (ie, KISA% < 60). There were 4 eyes (5%) with progressive keratoconus that would classify as having "normal topography" using the published criteria for very asymmetric ectasia with normal topography of KISA% less than 60 and IS value less than 1.45. All controls had a KISA% index value of less than 15. The optimal cut-off value to distinguish cohorts was 15.31 (AUROC = 0.972, 93.75% sensitivity). KISA% index values of 60 and 100 achieved low sensitivity (80% and 73.75%, respectively). CONCLUSIONS: The KISA% index misclassified a significant proportion of eyes with progressive keratoconus as normal. Although highly specific for clinical keratoconus, the KISA% index lacks sensitivity, does not effectively discriminate between normal and abnormal topography, and thus should not be used in large data analysis or artificial intelligence-based modeling. [J Refract Surg. 2024;40(9):e614-e624.].
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Topografía de la Córnea , Progresión de la Enfermedad , Queratocono , Curva ROC , Humanos , Queratocono/clasificación , Queratocono/diagnóstico , Estudios Retrospectivos , Topografía de la Córnea/métodos , Masculino , Femenino , Adulto , Estudios de Casos y Controles , Adulto Joven , Córnea/patología , Córnea/diagnóstico por imagen , Sensibilidad y Especificidad , Agudeza Visual/fisiología , Adolescente , Área Bajo la Curva , Persona de Mediana Edad , Errores DiagnósticosRESUMEN
Background US is clinically established for breast imaging, but its diagnostic performance depends on operator experience. Computer-assisted (real-time) image analysis may help in overcoming this limitation. Purpose To develop precise real-time-capable US-based breast tumor categorization by combining classic radiomics and autoencoder-based features from automatically localized lesions. Materials and Methods A total of 1619 B-mode US images of breast tumors were retrospectively analyzed between April 2018 and January 2024. nnU-Net was trained for lesion segmentation. Features were extracted from tumor segments, bounding boxes, and whole images using either classic radiomics, autoencoder, or both. Feature selection was performed to generate radiomics signatures, which were used to train machine learning algorithms for tumor categorization. Models were evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity and were statistically compared with histopathologically or follow-up-confirmed diagnosis. Results The model was developed on 1191 (mean age, 61 years ± 14 [SD]) female patients and externally validated on 50 (mean age, 55 years ± 15]). The development data set was divided into two parts: testing and training lesion segmentation (419 and 179 examinations) and lesion categorization (503 and 90 examinations). nnU-Net demonstrated precision and reproducibility in lesion segmentation in test set of data set 1 (median Dice score [DS]: 0.90 [IQR, 0.84-0.93]; P = .01) and data set 2 (median DS: 0.89 [IQR, 0.80-0.92]; P = .001). The best model, trained with 23 mixed features from tumor bounding boxes, achieved an AUC of 0.90 (95% CI: 0.83, 0.97), sensitivity of 81% (46 of 57; 95% CI: 70, 91), and specificity of 87% (39 of 45; 95% CI: 77, 87). No evidence of difference was found between model and human readers (AUC = 0.90 [95% CI: 0.83, 0.97] vs 0.83 [95% CI: 0.76, 0.90]; P = .55 and 0.90 vs 0.82 [95% CI: 0.75, 0.90]; P = .45) in tumor classification or between model and histopathologically or follow-up-confirmed diagnosis (AUC = 0.90 [95% CI: 0.83, 0.97] vs 1.00 [95% CI: 1.00,1.00]; P = .10). Conclusion Precise real-time US-based breast tumor categorization was developed by mixing classic radiomics and autoencoder-based features from tumor bounding boxes. ClinicalTrials.gov identifier: NCT04976257 Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Bahl in this issue.
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Neoplasias de la Mama , Ultrasonografía Mamaria , Humanos , Neoplasias de la Mama/diagnóstico por imagen , Femenino , Persona de Mediana Edad , Estudios Retrospectivos , Ultrasonografía Mamaria/métodos , Diagnóstico Diferencial , Interpretación de Imagen Asistida por Computador/métodos , Sensibilidad y Especificidad , Mama/diagnóstico por imagen , Adulto , Aprendizaje Automático , Anciano , RadiómicaRESUMEN
In many regions of New Zealand liver fluke is endemic, infecting most grazing ruminants, including cattle, sheep, and deer. Restricting the economic losses and welfare costs associated with liver fluke relies on accurately identifying those animals with a production limiting infection. This has proven a difficult goal and although several antemortem quantitative tests are available, including faecal egg counts (FEC), serum ELISA and copro-antigen ELISA, none can be considered a gold standard test of liver fluke infection. The accepted gold standard test for fascioliasis is the total fluke count, which is both laborious and can only be completed at post-mortem. This study aimed to compare the performance of four liver fluke diagnostic tests, against the results of a gold standard total fluke count test. Two groups of cattle were selected, 29 culled mixed age beef cows (MAC) and ten 30-month-old steers. The cattle were blood sampled and faecal sampled prior to slaughter and their whole livers recovered post slaughter at the abattoir. Liveweight was also recorded at slaughter. After collection, each liver was weighed, scored for gross pathology, then serum, faeces and livers were frozen at -20 °C for later analysis. Faecal egg counts and F. hepatica copro-antigen ELISA tests were completed on the faecal samples and total fluke counts were completed on the livers. Fasciola hepatica antibody concentration in serum samples were quantified using a commercial ELISA test. Poisson regression models were built to model the association between each diagnostic test and the total fluke count, and a linear regression model was built to examine the relationship between each diagnostic test and live weight at slaughter. The median fluke count was significantly higher in MAC than steers (p = 0.01), and F. hepatica eggs were present in 100% steers and 66% MAC. There was a significant effect of copro-antigen ELISA value on total fluke count (p < 0.0001), with a coproantigen ELISA value = 20.1 predicting 10 flukes and a value = 44.8 predicting 30 flukes. There was also a significant effect of FEC on total fluke count (p = 0.002) but the R-squared value for this model was lower. There was no association between liver fibrosis score or antibody ELISA test and total fluke count (p = 0.95, p = 0.73, respectively). There was a significant effect of total fluke count (p = 0.03) on liveweight at slaughter, with liveweight falling 20.4 kg for each unit increase in loge (total fluke count). There was no effect of FEC (p = 0.11), antibody ELISA (p = 0.55) or copro-antigen ELISA value (p = 0.16) on liveweight at slaughter. Taken together, these results show that the coproantigen ELISA test is the better test for estimating the true liver fluke burden and that the number of flukes in the liver has a negative effect on cattle live weights at slaughter.
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Enfermedades de los Bovinos , Ensayo de Inmunoadsorción Enzimática , Fasciola hepatica , Fascioliasis , Heces , Recuento de Huevos de Parásitos , Animales , Bovinos , Fascioliasis/veterinaria , Fascioliasis/diagnóstico , Fascioliasis/parasitología , Enfermedades de los Bovinos/diagnóstico , Enfermedades de los Bovinos/parasitología , Heces/parasitología , Fasciola hepatica/aislamiento & purificación , Fasciola hepatica/inmunología , Recuento de Huevos de Parásitos/veterinaria , Nueva Zelanda , Masculino , Ensayo de Inmunoadsorción Enzimática/veterinaria , Femenino , Sensibilidad y Especificidad , Hígado/parasitología , Pruebas Diagnósticas de Rutina/veterinaria , Pruebas Diagnósticas de Rutina/métodos , Anticuerpos Antihelmínticos/sangreRESUMEN
Sporotrichosis diagnosis involves a series of analyses, including culture and antibody detection in serum samples. Serologic methods may sometimes yield false-negative or false-positive results, leading to inaccurate diagnoses. This study assessed specific patient groups in which antibody detection of different isotypes and subclasses may lack sensitivity. An enzyme-linked immunosorbent assay (ELISA) with Sporothrix brasiliensis exoantigens was used to investigate IgM, IgG, IgG1, IgG2, IgG3, IgG4, IgA, IgA1 and IgA2 antibodies in human serum samples. Eighty serum samples from patients with different sporotrichosis clinical manifestations, including cutaneous forms with and without hypersensitivity manifestations, extracutaneous forms (bone, ocular, meningeal and pulmonary), disseminated cutaneous forms and disseminated forms in individuals living with HIV/AIDS, diabetics and alcoholics, were evaluated. The ELISA sensitivities in the detection of different antibodies ranged from 0.85 to 0.60 for the detection of IgG2 and IgG3, respectively. The antibodies with higher area under ROC curves were IgG2, IgG, IgA and IgA1. There were no significant differences in the immunological reactivity of the tested antibodies among different clinical forms of sporotrichosis. The data revealed a higher likelihood of a false-negative outcome in patients with lesions in the nasal mucosa regarding the detection of IgM and a lower likelihood in patients with lymphocutaneous sporotrichosis regarding the detection of IgG3. Patients with hypersensitivity manifestations had a 3.71 odds ratio to yield negative results in total IgG detection. In conclusion, we identified specific patient groups in which antibody detection may lack sensitivity, thus contributing to a better understanding of the diagnostic challenges associated with this condition.
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Anticuerpos Antifúngicos , Ensayo de Inmunoadsorción Enzimática , Sensibilidad y Especificidad , Sporothrix , Esporotricosis , Humanos , Esporotricosis/inmunología , Esporotricosis/diagnóstico , Anticuerpos Antifúngicos/sangre , Sporothrix/inmunología , Sporothrix/clasificación , Masculino , Femenino , Adulto , Persona de Mediana Edad , Isotipos de Inmunoglobulinas/sangre , Isotipos de Inmunoglobulinas/inmunología , Inmunoglobulina G/sangre , Anciano , Adulto Joven , Antígenos Fúngicos/inmunología , Antígenos Fúngicos/sangre , Inmunoglobulina A/sangre , Inmunoglobulina M/sangreRESUMEN
BACKGROUND AND OBJECTIVE: Parkinson's disease (PD) is the one of the most common neurodegenerative diseases. Many investigators have confirmed the possibility of using circulating miRNAs to diagnose PD. However, the results were inconsistent. Therefore, the aim of this meta-analysis was to systematically evaluate the diagnostic accuracy of circulating miRNAs in the diagnosis of PD. METHODS: We carefully searched PubMed, Embase, Web of Science, Cochrane Library, Wanfang database and China National Knowledge Infrastructure for relevant studies (up to January 1, 2022) based on PRISMA statement. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), the diagnostic odds ratio (DOR), and area under the curve (AUC) were calculated to test the diagnostic accuracy. Furthermore, subgroup analyses were performed to identify the potential sources of heterogeneity, and the Deeks' funnel plot asymmetry test was used to evaluate the potential publication bias. RESULTS: Forty-four eligible studies from 16 articles (3298 PD patients and 2529 healthy controls) were included in the current meta-analysis. The pooled sensitivity was 0.79 (95% CI: 0.76-0.81), specificity was 0.82 (95% CI: 0.78-0.84), PLR was 4.3 (95% CI: 3.6-5.0), NLR was 0.26 (95% CI: 0.23-0.30), DOR was 16 (95% CI: 13-21), and AUC was 0.87 (95% CI: 0.84-0.90). Subgroup analysis suggested that miRNA cluster showed a better diagnostic accuracy than miRNA simple. Moreover, there was no significant publication bias. CONCLUSIONS: Circulating miRNAs have great potential as novel non-invasive biomarkers for PD diagnosis.
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Biomarcadores , MicroARN Circulante , Enfermedad de Parkinson , Enfermedad de Parkinson/sangre , Enfermedad de Parkinson/diagnóstico , Humanos , Biomarcadores/sangre , MicroARN Circulante/sangre , Sensibilidad y Especificidad , MicroARNs/sangreRESUMEN
BACKGROUND: Malaria control depends primarily on rapid and accurate diagnosis followed by successful treatment. Light microscopy is still used as a gold standard method for the diagnosis of malaria. The Sysmex hematology analyzer is a novel method for malaria detection. Therefore, the aim of this review was to investigate the diagnostic accuracy of the Sysmex hematology analyzer for malaria diagnosis. METHODS: Electronic databases like PubMed, PubMed Central, Science Direct databases, Google Scholar, and Scopus were used to find relevant articles from April to June 14, 2023. The studies' methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Using Review Manager 5.4.1, the estimates of sensitivity and specificity, as well as their 95% confidence intervals, were shown in forest plots. Midas software in Stata 14.0 was utilized to calculate the summary estimates of sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio. Heterogeneity was assessed by using I2 statistics. In addition, publication bias was assessed using a funnel plot and Deeks' test. Sub-group and meta- regression analysis were also performed. RESULTS: A total of 15 studies were assessed for diagnostic accuracy. The sensitivity and specificity of Sysmex hematology analyzer for studies ranged from 46% to 100% and 81% to 100%, respectively. The summary estimate of sensitivity and specificity of Sysmex hematology analyzer were 95% (95% CI: 85%-99%) and 99% (95% CI: 97%-100%), respectively. It had excellent diagnostic accuracy. There were significant heterogeneity among the studies included in this meta-analysis. The summary estimate of sensitivity and specificity of Sysmex hematology analyzer using polymerase chain reaction as the gold standard was 97.6% (95% CI: 83.2, 99.7) and 99.4% (98.5, 99.8), respectively. CONCLUSION: In this review, Sysmex hematology analyzer had excellent diagnostic accuracy. Therefore, it could be used as an alternate diagnostic tool for malaria diagnosis in the hospital and health center. TRIAL REGISTRATION: Systematic review registration PROSPERO (2023: CRD42023427713). https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023427713.
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Malaria , Sensibilidad y Especificidad , Humanos , Malaria/diagnóstico , Malaria/sangre , Pruebas Hematológicas/instrumentación , Pruebas Hematológicas/métodos , Hematología/instrumentación , Hematología/métodosRESUMEN
BACKGROUND: Perioperative reflux aspiration presents a grave concern during sedation or general anesthesia, particularly when solid gastric contents prompt acute upper respiratory obstruction, potentially resulting in fatal consequences for patients. Currently, there are limited means for promptly assessing solid gastric contents in clinical settings. Therefore, this study examined the efficacy of ultrasound assessment for solid gastric contents, offering a rapid and non-invasive approach for early detection and decision-making regarding interventions. METHODS: The study included 400 patients scheduled for upper endoscopy procedures, which encompassed both gastroscope and gastroscope combined colonoscopy examinations with sedation. Ultrasound scanning of the antrum was performed while patients were positioned semi-sitting or in the right lateral decubitus (RLD) posture. The evaluation of solid gastric contents relied on direct visual observation during endoscopy. Gastric volume measurement occurred subsequent to endoscopic suction of gastric contents. Receiver operating characteristic curves were utilized to assess the effectiveness of ultrasonography in discerning solid contents. RESULT: Seven patients undergoing gastroscope with sedation were found to have solid gastric contents. The sensitivity, specificity, positive predictive value, and negative predictive value of the ultrasound qualitative evaluation of solid contents were 85.7%, 99%, 60%, and 99.7%, respectively. CONCLUSION: Solid stomach contents can be evaluated qualitatively with reasonable accuracy using ultrasonography. Additionally, in patients undergoing upper endoscopy and assessed to have solid gastric contents with ultrasound, administration of mild sedation is recommended. TRIAL REGISTRATION: www.chictr.org.cn (ChiCTR2100048994); registered 19/07/2021.
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Contenido Digestivo , Ultrasonografía , Humanos , Masculino , Femenino , Persona de Mediana Edad , Contenido Digestivo/diagnóstico por imagen , Anciano , Ultrasonografía/métodos , Adulto , Sedación Consciente/métodos , Colonoscopía/métodos , Sensibilidad y Especificidad , Gastroscopios , Estudios ProspectivosRESUMEN
OBJECTIVE: Develop a practical scoring system based on radiomics and imaging features, for predicting the malignant potential of incidental indeterminate small solid pulmonary nodules (IISSPNs) smaller than 20 mm. METHODS: A total of 360 patients with malignant IISSPNs (n = 213) and benign IISSPNs (n = 147) confirmed after surgery were retrospectively analyzed. The whole cohort was randomly divided into training and validation groups at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to debase the dimensions of radiomics features. Multivariate logistic analysis was performed to establish models. The receiver operating characteristic (ROC) curve, area under the curve (AUC), 95% confidence interval (CI), sensitivity and specificity of each model were recorded. Scoring system based on odds ratio was developed. RESULTS: Three radiomics features were selected for further model establishment. After multivariate logistic analysis, the combined model including Mean, age, emphysema, lobulated and size, reached highest AUC of 0.877 (95%CI: 0.830-0.915), accuracy rate of 83.3%, sensitivity of 85.3% and specificity of 80.2% in the training group, followed by radiomics model (AUC: 0.804) and imaging model (AUC: 0.773). A scoring system with a cutoff value greater than 4 points was developed. If the score was larger than 8 points, the possibility of diagnosing malignant IISSPNs could reach at least 92.7%. CONCLUSION: The combined model demonstrated good diagnostic performance in predicting the malignant potential of IISSPNs. A perfect accuracy rate of 100% can be achieved with a score exceeding 12 points in the user-friendly scoring system.
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Neoplasias Pulmonares , Nódulo Pulmonar Solitario , Tomografía Computarizada por Rayos X , Humanos , Femenino , Masculino , Neoplasias Pulmonares/diagnóstico por imagen , Persona de Mediana Edad , Estudios Retrospectivos , Nódulo Pulmonar Solitario/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos , Anciano , Curva ROC , Nódulos Pulmonares Múltiples/diagnóstico por imagen , Nódulos Pulmonares Múltiples/patología , Hallazgos Incidentales , Sensibilidad y Especificidad , Algoritmos , Adulto , Área Bajo la Curva , RadiómicaRESUMEN
BACKGROUND: Nasopharyngeal carcinoma (NPC) is diagnosed relatively late and has a poor prognosis, requiring early detection to reduce the disease burden. This diagnostic test accuracy meta-analysis evaluated the serological diagnostic value of nine EBV-related IgA antibody panels (EBNA1-IgA, VCA-IgA, EA-IgA, Zta-IgA, EBNA1-IgA + VCA-IgA, VCA-IgA + EA-IgA, VCA-IgA + Rta-IgG, EBNA1-IgA + VCA-IgA + Zta-IgA and VCA-IgA + EA-IgA + Rta-IgG), aiming to identify suitable serological detection biomarkers for NPC screening. METHODS: PubMed, Embase, China National Knowledge Infrastructure and Chinese BioMedical Literature Database were searched from January 1st, 2000 to September 30th, 2023, with keywords nasopharyngeal carcinoma, IgA, screening, early detection, early diagnosis, sensitivity and specificity. Articles on the diagnostic value of serum EBV-related IgA antibody panels for NPC were included. Study selection, data extraction, and quality assessment were performed independently by two researchers, and a third researcher was consulted in the case of disagreement. Bivariate models were used for statistical analysis. The quality of included studies was evaluated through Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2). RESULTS: A total of 70 articles were included, involving 11 863 NPC cases and 34 995 controls. Among the nine EBV-related IgA antibody panels, EBNA1-IgA + VCA-IgA [0.928 (0.898, 0.950)], VCA-IgA + Rta-IgG [0.925 (0.890, 0.949)], EBNA1-IgA + VCA-IgA + Zta-IgA [0.962 (0.909, 0.985)] and VCA-IgA + EA-IgA + Rta-IgG [0.945 (0.918, 0.964)] demonstrated higher pooled sensitivity (95%CI). In terms of diagnostic odds ratio (DOR) (95%CI), EBNA1-IgA + VCA-IgA [107.647 (61.173, 189.430)], VCA-IgA + Rta-IgG [105.988 (60.118, 186.857)] and EBNA1-IgA + VCA-IgA + Zta-IgA [344.450 (136.351, 870.153)] showed superior performance. Additionally, the SROC curves for EBNA1-IgA + VCA-IgA and VCA-IgA + Rta-IgG were more favorable. However, publication bias was detected for VCA-IgA (P = 0.005) and EBNA1-IgA + VCA-IgA (P = 0.042). CONCLUSIONS: In general, parallel detection of serum EBNA1-IgA, VCA-IgA and Zta-IgA antibodies using ELISA demonstrates better pooled sensitivity and DOR among the studied panels. In the cases where fewer indicators are used, serum VCA-IgA and EBNA1-IgA/Rta-IgG antibody panel exhibits a comparable performance. TRIAL REGISTRATION: The International Prospective Register of Systematic Reviews registration number: CRD42023426984, registered on May 28, 2023.
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Anticuerpos Antivirales , Infecciones por Virus de Epstein-Barr , Inmunoglobulina A , Carcinoma Nasofaríngeo , Neoplasias Nasofaríngeas , Humanos , Anticuerpos Antivirales/sangre , Anticuerpos Antivirales/inmunología , Biomarcadores de Tumor/sangre , Biomarcadores de Tumor/inmunología , Detección Precoz del Cáncer/métodos , Infecciones por Virus de Epstein-Barr/inmunología , Infecciones por Virus de Epstein-Barr/diagnóstico , Infecciones por Virus de Epstein-Barr/complicaciones , Infecciones por Virus de Epstein-Barr/sangre , Antígenos Nucleares del Virus de Epstein-Barr/inmunología , Herpesvirus Humano 4/inmunología , Inmunoglobulina A/sangre , Inmunoglobulina A/inmunología , Carcinoma Nasofaríngeo/diagnóstico , Carcinoma Nasofaríngeo/inmunología , Carcinoma Nasofaríngeo/virología , Carcinoma Nasofaríngeo/sangre , Neoplasias Nasofaríngeas/diagnóstico , Neoplasias Nasofaríngeas/inmunología , Neoplasias Nasofaríngeas/sangre , Neoplasias Nasofaríngeas/virología , Sensibilidad y Especificidad , Pruebas Serológicas/métodosRESUMEN
BACKGROUND: Next-generation sequencing (NGS) approaches have revolutionized gut microbiome research and can provide strain-level resolution, but these techniques have limitations in that they are only semi-quantitative, suffer from high detection limits, and generate data that is compositional. The present study aimed to systematically compare quantitative PCR (qPCR) and droplet digital PCR (ddPCR) for the absolute quantification of Limosilactobacillus reuteri strains in human fecal samples and to develop an optimized protocol for the absolute quantification of bacterial strains in fecal samples. RESULTS: Using strain-specific PCR primers for L. reuteri 17938, ddPCR showed slightly better reproducibility, but qPCR was almost as reproducible and showed comparable sensitivity (limit of detection [LOD] around 104 cells/g feces) and linearity (R2 > 0.98) when kit-based DNA isolation methods were used. qPCR further had a wider dynamic range and is cheaper and faster. Based on these findings, we conclude that qPCR has advantages over ddPCR for the absolute quantification of bacterial strains in fecal samples. We provide an optimized and easy-to-follow step-by-step protocol for the design of strain-specific qPCR assays, starting from primer design from genome sequences to the calibration of the PCR system. Validation of this protocol to design PCR assays for two L. reuteri strains, PB-W1 and DSM 20016 T, resulted in a highly accurate qPCR with a detection limit in spiked fecal samples of around 103 cells/g feces. Applying our strain-specific qPCR assays to fecal samples collected from human subjects who received live L. reuteri PB-W1 or DSM 20016 T during a human trial demonstrated a highly accurate quantification and sensitive detection of these two strains, with a much lower LOD and a broader dynamic range compared to NGS approaches (16S rRNA gene sequencing and whole metagenome sequencing). CONCLUSIONS: Based on our analyses, we consider qPCR with kit-based DNA extraction approaches the best approach to accurately quantify gut bacteria at the strain level in fecal samples. The provided step-by-step protocol will allow scientists to design highly sensitive strain-specific PCR systems for the accurate quantification of bacterial strains of not only L. reuteri but also other bacterial taxa in a broad range of applications and sample types. Video Abstract.
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Heces , Microbioma Gastrointestinal , Limosilactobacillus reuteri , Humanos , Heces/microbiología , Microbioma Gastrointestinal/genética , Limosilactobacillus reuteri/genética , Limosilactobacillus reuteri/clasificación , Reproducibilidad de los Resultados , ADN Bacteriano/genética , Reacción en Cadena en Tiempo Real de la Polimerasa/métodos , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Límite de Detección , Sensibilidad y Especificidad , Bacterias/genética , Bacterias/clasificación , Bacterias/aislamiento & purificaciónRESUMEN
PURPOSE: The management of indeterminate thyroid nodules remains a topic of ongoing debate, particularly regarding the differentiation of malignancy. Somatic mutation analysis offers crucial insights into tumor characteristics. This study aimed to assist the clinical management of indeterminate nodules with somatic mutation analysis. METHODS: Aspiration samples from 20 indeterminate thyroid nodules were included in the study. A next-generation sequencing panel containing 67 genes was used for molecular profiling. The results were compared with pathology data from surgical material, which is considered the gold standard. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. RESULTS: Variants in six genes (NRAS, BRAF, TP53, TERT, PTEN, PIK3CA) were detected in 10 out of 20 samples. We identified nine Tier 1 or 2 variants in 10 (67â¯%) out of 15 malignant nodules (NRAS, BRAF, TP53, TERT, PTEN, PIK3CA) and one Tier 2 (PIK3CA) variant in one out of five benign nodules. The study demonstrated an NPV of 40â¯%, a PPV of 90â¯%, a specificity of 80â¯%, and a sensitivity of 60â¯%. CONCLUSION: Based on the detected molecular markers, at least nine patients (45â¯%) could be managed correctly without needing a repeat FNAB attempt. This study underscores the clinical practicality of molecular tests in managing nodules with indeterminate cytology. Additionally, this study emphasizes the importance of considering the patient's age when determining the DNA- or RNA-based genetic testing method. Finally, we discussed the significance of the somatic mutation profile and its impact on the current pathological classification.
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Secuenciación de Nucleótidos de Alto Rendimiento , Mutación , Nódulo Tiroideo , Humanos , Nódulo Tiroideo/genética , Nódulo Tiroideo/patología , Nódulo Tiroideo/diagnóstico , Femenino , Persona de Mediana Edad , Masculino , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Adulto , Análisis Mutacional de ADN/métodos , Anciano , Neoplasias de la Tiroides/genética , Neoplasias de la Tiroides/patología , Neoplasias de la Tiroides/diagnóstico , Biomarcadores de Tumor/genética , Sensibilidad y Especificidad , Biopsia con Aguja Fina , CitologíaRESUMEN
BACKGROUND: Brain metastases (BMs) represents a severe neurological complication stemming from cancers originating from various sources. It is a highly challenging clinical task to accurately distinguish the pathological subtypes of brain metastatic tumors from lung cancer (LC).The utility of 2.5-dimensional (2.5D) deep learning (DL) in distinguishing pathological subtypes of LC with BMs is yet to be determined. METHODS: A total of 250 patients were included in this retrospective study, divided in a 7:3 ratio into training set (N=175) and testing set (N=75). We devised a method to assemble a series of two-dimensional (2D) images by extracting adjacent slices from a central slice in both superior-inferior and anterior-posterior directions to form a 2.5D dataset. Multi-Instance learning (MIL) is a weakly supervised learning method that organizes training instances into "bags" and provides labels for entire bags, with the purpose of learning a classifier based on the labeled positive and negative bags to predict the corresponding class for an unknown bag. Therefore, we employed MIL to construct a comprehensive 2.5D feature set. Then we used the single-slice as input for constructing the 2D model. DL features were extracted from these slices using the pre-trained ResNet101. All feature sets were inputted into the support vector machine (SVM) for evaluation. The diagnostic performance of the classification models were evaluated using five-fold cross-validation, with accuracy and area under the curve (AUC) metrics calculated for analysis. RESULTS: The optimal performance was obtained using the 2.5D DL model, which achieved the micro-AUC of 0.868 (95% confidence interval [CI], 0.817-0.919) and accuracy of 0.836 in the test cohort. The 2D model achieved the micro-AUC of 0.836 (95 % CI, 0.778-0.894) and accuracy of 0.827 in the test cohort. CONCLUSIONS: The proposed 2.5D DL model is feasible and effective in identifying pathological subtypes of BMs from lung cancer.
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Neoplasias Encefálicas , Aprendizaje Profundo , Neoplasias Pulmonares , Humanos , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/patología , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/secundario , Femenino , Masculino , Estudios Retrospectivos , Persona de Mediana Edad , Anciano , Diagnóstico Diferencial , Imagen por Resonancia Magnética/métodos , Adulto , Interpretación de Imagen Asistida por Computador/métodos , Sensibilidad y EspecificidadRESUMEN
PURPOSE: Theranostic approaches combining prostate-specific membrane antigen (PSMA)-PET/CT or PET/MRI with PSMA-targeted radionuclide therapy have improved clinical outcomes in patients with prostate cancer (PCa) especially metastatic castrate resistant prostate cancer. Dural metastases in PCa are rare but can pose a diagnostic challenge, as meningiomas, a more common dural based lesions have been shown to express PSMA. The aim of this study is to compare PSMA PET parameters between brain lesions classified as dural metastases and meningiomas in prostate cancer patients. METHODS: A retrospective analysis of PSMA PET/CT scans in patients with PCa and intracranial lesions was conducted. Brain lesions were categorized as dural metastases or meningiomas based on MRI characteristics, longitudinal follow-up, and histopathological characteristics. Standardized uptake values (SUVmax) of each brain lesion were measured, along with SUV ratio referencing parotid gland (SUVR). SUVs between lesions classified as metastases and meningiomas, respectively, were compared using Mann-Whitney-test. Diagnostic accuracy was evaluated using ROC analysis. RESULTS: 26 male patients (median age: 76.5 years, range: 59-96 years) met inclusion criteria. A total of 44 lesions (7 meningiomas and 37 metastases) were analyzed. Median SUVmax and SUVR were significantly lower in meningiomas compared to metastases (SUVmax: 2.7 vs. 11.5, p = 0.001; SUVR: 0.26 vs. 1.05, p < 0.001). ROC analysis demonstrated AUC 0.903; the optimal cut-off value for SUVR was 0.81 with 81.1 % sensitivity and 100 % specificity. CONCLUSION: PSMA PET has the potential to differentiate meningiomas from dural-based metastases in patients with PCa, which can optimize clinical management and thus improve patient outcomes.
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Tomografía Computarizada por Tomografía de Emisión de Positrones , Neoplasias de la Próstata , Humanos , Masculino , Anciano , Persona de Mediana Edad , Estudios Retrospectivos , Anciano de 80 o más Años , Neoplasias de la Próstata/diagnóstico por imagen , Neoplasias de la Próstata/patología , Tomografía Computarizada por Tomografía de Emisión de Positrones/métodos , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/secundario , Glutamato Carboxipeptidasa II/metabolismo , Sensibilidad y Especificidad , Radiofármacos , Neoplasias Meníngeas/diagnóstico por imagen , Neoplasias Meníngeas/secundario , Meningioma/diagnóstico por imagen , Meningioma/patología , Meningioma/secundario , Antígenos de Superficie/metabolismo , Imagen por Resonancia Magnética/métodosRESUMEN
BACKGROUND AND OBJECTIVES: The 2022 International Consortium for Optic Neuritis diagnostic criteria for optic neuritis (ON) include optical coherence tomography (OCT). The diagnostic value of intereye difference (IED) metrics is high for ON in patients with multiple sclerosis and aquaporin-4 antibody seropositive neuromyelitis optica spectrum disorders, but unknown in myelin oligodendrocyte glycoprotein antibody-associated ON (MOG-ON). METHODS: A multicenter validation study was conducted on the published IED cutoff values (>4% or >4 µm in the macular ganglion cell and inner plexiform layer [mGCIP] or >5% or >5 µm in the peripapillary retinal nerve fiber layer [pRNFL]) in individuals with MOG-ON and age-matched and sex-matched healthy controls (HCs). Structural data were acquired with Spectralis spectral-domain OCT >6 months after ON. We calculated sensitivity, specificity, and receiver operating characteristics for both intereye percentage (IEPD) and absolute difference (IEAD). RESULTS: A total of 66 individuals were included (MOG-ON N = 33; HCs N = 33). ON was unilateral in 20 and bilateral in 13 subjects. In the pooled analysis, the mGCIP IEPD was most sensitive (92%), followed by the mGCIP IEAD (88%) and pRNFL (84%). The same pattern was found for the specificity (mGCIP IEPD 82%, IEAD 82%; pRNFL IEPD 82%, IEAD 79%).In subgroup analyses, the diagnostic sensitivity was higher in subjects with unilateral ON (>99% for all metrics) compared with bilateral ON (61%-78%). DISCUSSION: In individuals with MOG-ON, the diagnostic accuracy of OCT-based IED metrics for ON was high, especially of mGCIP IEPD. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that the intereye difference on OCT can distinguish between those with MOG and normal controls.
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Autoanticuerpos , Glicoproteína Mielina-Oligodendrócito , Neuritis Óptica , Tomografía de Coherencia Óptica , Humanos , Glicoproteína Mielina-Oligodendrócito/inmunología , Neuritis Óptica/inmunología , Neuritis Óptica/diagnóstico , Neuritis Óptica/diagnóstico por imagen , Femenino , Masculino , Adulto , Persona de Mediana Edad , Autoanticuerpos/sangre , Sensibilidad y Especificidad , Adulto JovenRESUMEN
Tuberculosis (TB) is the leading cause of mortality among infectious diseases globally. Effectively managing TB requires early identification of individuals with TB disease. Resource-constrained settings often lack skilled professionals for interpreting chest X-rays (CXRs) used in TB diagnosis. To address this challenge, we developed "DecXpert" a novel Computer-Aided Detection (CAD) software solution based on deep neural networks for early TB diagnosis from CXRs, aiming to detect subtle abnormalities that may be overlooked by human interpretation alone. This study was conducted on the largest cohort size to date, where the performance of a CAD software (DecXpert version 1.4) was validated against the gold standard molecular diagnostic technique, GeneXpert MTB/RIF, analyzing data from 4363 individuals across 12 primary health care centers and one tertiary hospital in North India. DecXpert demonstrated 88% sensitivity (95% CI 0.85-0.93) and 85% specificity (95% CI 0.82-0.91) for active TB detection. Incorporating demographics, DecXpert achieved an area under the curve of 0.91 (95% CI 0.88-0.94), indicating robust diagnostic performance. Our findings establish DecXpert's potential as an accurate, efficient AI solution for early identification of active TB cases. Deployed as a screening tool in resource-limited settings, DecXpert could enable early identification of individuals with TB disease and facilitate effective TB management where skilled radiological interpretation is limited.
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Programas Informáticos , Humanos , India/epidemiología , Femenino , Masculino , Adulto , Persona de Mediana Edad , Diagnóstico por Computador/métodos , Tuberculosis/diagnóstico , Tuberculosis/diagnóstico por imagen , Tuberculosis Pulmonar/diagnóstico por imagen , Tuberculosis Pulmonar/diagnóstico , Sensibilidad y Especificidad , Adulto Joven , Adolescente , Radiografía Torácica/métodos , AncianoRESUMEN
Sudden cardiac arrest (SCA) is a lethal cardiac arrhythmia that poses a serious threat to human life and health. However, clinical records of sudden cardiac death (SCD) electrocardiogram (ECG) data are extremely limited. This paper proposes an early prediction and classification algorithm for SCA based on deep transfer learning. With limited ECG data, it extracts heart rate variability features before the onset of SCA and utilizes a lightweight convolutional neural network model for pre-training and fine-tuning in two stages of deep transfer learning. This achieves early classification, recognition and prediction of high-risk ECG signals for SCA by neural network models. Based on 16 788 30-second heart rate feature segments from 20 SCA patients and 18 sinus rhythm patients in the international publicly available ECG database, the algorithm performance evaluation through ten-fold cross-validation shows that the average accuracy (Acc), sensitivity (Sen), and specificity (Spe) for predicting the onset of SCA in the 30 minutes prior to the event are 91.79%, 87.00%, and 96.63%, respectively. The average estimation accuracy for different patients reaches 96.58%. Compared to traditional machine learning algorithms reported in existing literatures, the method proposed in this paper helps address the requirement of large training datasets for deep learning models and enables early and accurate detection and identification of high-risk ECG signs before the onset of SCA.