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Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis . Our study investigates the recognition of secondary pulmonary (SPTB). A novel F3 model is proposed. The first F means using a four-direction varying-distance gray-level co-occurrence matrix (FDVDGLCM) to analyze the chest CT images; the second F means a five-property feature set (FPFS) from the FDVDGLCM results; the third F means fuzzy support vector machine (FSVM). Besides, a slight adaption of multiple-way data augmentation is used to boost the training set. The 10 runs of 10-fold cross-validation demonstrate that this F3 model achieves a sensitivity of 93.68% ± 1.75%, a specificity of 94.17% ± 1.68%, a precision of 94.17% ± 1.55%, an accuracy of 93.92% ± 1.05%, an F1 score of 93.91% ± 1.07%, an MCC of 87.88% ± 2.09%, and an FMI of 93.92% ± 1.06%. The AUC is 0.9624. The FSVM can give better performance than ordinary SVM. The proposed F3 model is superior to six state-of-the-art SPTB recognition models.
Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis . Our study investigates the recognition of secondary pulmonary (SPTB). A novel F3 model is proposed. The first F means using a four-direction varying-distance gray-level co-occurrence matrix (FDVDGLCM) to analyze the chest CT images; the second F means a five-property feature set (FPFS) from the FDVDGLCM results; the third F means fuzzy support vector machine (FSVM). Besides, a slight adaption of multiple-way data augmentation is used to boost the training set. The 10 runs of 10-fold cross-validation demonstrate that this F3 model achieves a sensitivity of 93.68% ± 1.75%, a specificity of 94.17% ± 1.68%, a precision of 94.17% ± 1.55%, an accuracy of 93.92% ± 1.05%, an F1 score of 93.91% ± 1.07%, an MCC of 87.88% ± 2.09%, and an FMI of 93.92% ± 1.06%. The AUC is 0.9624. The FSVM can give better performance than ordinary SVM. The proposed F3 model is superior to six state-of-the-art SPTB recognition models.
Background Research infrastructures such as biorepositories are essential to facilitate genomics and its growing applications in health research and translational medicine in Africa. Using a cervical cancer cohort, this study describes the establishment of a biorepository consisting of biospecimens and matched phenotype data for use in genomic association analysis and pharmacogenomics research. Method Women aged > 18 years with a recent histologically confirmed cervical cancer diagnosis were recruited. A workflow pipeline was developed to collect, store, and analyse biospecimens comprising donor recruitment and informed consent, followed by data and biospecimen collection, nucleic acid extraction, storage of genomic DNA, genetic characterization, data integration, data analysis and data interpretation. The biospecimen and data storage infrastructure included shared -20 °C to -80 °C freezers, lockable cupboards, secured access-controlled laptop, password protected online data storage on OneDrive software. The biospecimen or data storage, transfer and sharing were compliant with the local and international biospecimen and data protection laws and policies, to ensure donor privacy, trust, and benefits for the wider community. Results This initial establishment of the biorepository recruited 410 women with cervical cancer. The mean (± SD) age of the donors was 52 (± 12) years, comprising stage I (15%), stage II (44%), stage III (47%) and stage IV (6%) disease. The biorepository includes whole blood and corresponding genomic DNA from 311 (75.9%) donors, and tumour biospecimens and corresponding tumour DNA from 258 (62.9%) donors. Datasets included information on sociodemographic characteristics, lifestyle, family history, clinical information, and HPV genotype. Treatment response was followed up for 12 months, namely, treatment-induced toxicities, survival vs. mortality, and disease status, that is disease-free survival, progression or relapse, 12 months after therapy commencement. Conclusion The current work highlights a framework for developing a cancer genomics cohort-based biorepository on a limited budget. Such a resource plays a central role in advancing genomics research towards the implementation of personalised management of cancer.
Background: Mycobacterium tuberculosis complex (MTBC) isolates are typically stored at −70 °C in cryovials containing 1 mL aliquots of a liquid medium, with or without 50% glycerol. Multiple uses of the culture stock may decrease the strain viability while increasing the risk of culture contamination. Small culture aliquots may be more practical; however, storage capacity remains challenging. MicrobankTM beads (25 beads/vial) for the long-term storage of fungal cultures is well documented, but their use for storing MTBC isolates is uninvestigated.Objective: The study aimed to determine the feasibility of using MicrobankTM beads for long-term storage of MTBC isolates at a laboratory in South Africa.Methods: In February 2020, 20 isolates in liquid culture were stored in MicrobankTM beads, following an in-house developed protocol, at −70 °C. At defined time points (16 months [15 June 2021] and 21 months [18 November 2021]), two beads were retrieved from each storage vial and assessed for viability and level of contamination.Results: Stored liquid isolates demonstrated MTBC growth within an average time-to-detection of 18 days following retrieval, even at 21 months post storage. Contaminating organisms were detected in 2 of 80 (2.5%) culture isolates.Conclusion: MicrobankTM beads will allow for the reculture of up to 25 culture isolates using a reduced culture volume compared to current storage methods. MicrobankTM beads represent a storage solution for the medium-term storage of MTBC isolates.What this study adds: This study evaluated the use of MicrobankTM beads as an alternate method for storing MTBC culture isolates at −70 °C and provided a suitable option for medium-term storage of MTBC.
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