Due to the accumulating evidence that suggests that numerous unhealthy conditions in the indoor environment are the result of abnormal growth of the filamentous fungi (mold) in and on building surfaces it is necessary to accurately determine the organisms responsible for these maladies and to identify them in an accurate and timely manner. Historically, identification of filamentous fungal (mold) species has been based on morphological characteristics, both macroscopic and microscopic. These methods may often be time consuming and inaccurate, necessitating the development of identification protocols that are rapid, sensitive, and precise. To this end, we have devised a simple PAN-PCR approach which when coupled to cloning and sequencing of the clones allows for the unambiguous identification of multiple fungal organisms. Universal primers are used to amplify ribosomal DNA sequences which are then cloned and transformed into Escherichia coli. Individual clones are then sequenced and individual sequences analyzed and organisms identified. Using this method we were capable of identifying Stachybotrys chartarum, Penicillium purpurogenum, Aspergillus sydowii, and Cladosporium cladosporioides from a mixed culture. This method was found to be rapid, highly specific, easy to perform, and cost effective.
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http://dx.doi.org/10.1007/s11046-006-0068-z | DOI Listing |
Brief Bioinform
November 2024
School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju 61005, Republic of Korea.
Combination therapies have emerged as a promising approach for treating complex diseases, particularly cancer. However, predicting the efficacy and safety profiles of these therapies remains a significant challenge, primarily because of the complex interactions among drugs and their wide-ranging effects. To address this issue, we introduce DD-PRiSM (Decomposition of Drug-Pair Response into Synergy and Monotherapy effect), a deep-learning pipeline that predicts the effects of combination therapy.
View Article and Find Full Text PDFBackground/objectives: Sepsis-related acute kidney injury (SA-AKI) is a severe condition characterized by high mortality rates. The utility of the sCAR (secrum creatinine/albumin) and LAR (Lactate dehydrogenase/albumin) as diagnostic markers for persistent severe SA-AKI remains unclear.
Methods: We acquired training set data from the MIMIC-IV database and validation set data from the First Affiliated Hospital of Harbin Medical University.
Breast Cancer Res
January 2025
School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK.
Recent evidence indicates that endocrine resistance in estrogen receptor-positive (ER+) breast cancer is closely correlated with phenotypic characteristics of epithelial-to-mesenchymal transition (EMT). Nonetheless, identifying tumor tissues with a mesenchymal phenotype remains challenging in clinical practice. In this study, we validated the correlation between EMT status and resistance to endocrine therapy in ER+ breast cancer from a transcriptomic perspective.
View Article and Find Full Text PDFOpen Heart
January 2025
Department of Molecular and Clinical Medicine, University of Gothenburg Institute of Medicine, Gothenburg, Sweden.
Purpose: We examined whether end-to-end deep-learning models could detect moderate (≥50%) or severe (≥70%) stenosis in the left anterior descending artery (LAD), right coronary artery (RCA) or left circumflex artery (LCX) in iodine contrast-enhanced ECG-gated coronary CT angiography (CCTA) scans.
Methods: From a database of 6293 CCTA scans, we used pre-existing curved multiplanar reformations (CMR) images of the LAD, RCA and LCX arteries to create end-to-end deep-learning models for the detection of moderate or severe stenoses. We preprocessed the images by exploiting domain knowledge and employed a transfer learning approach using EfficientNet, ResNet, DenseNet and Inception-ResNet, with a class-weighted strategy optimised through cross-validation.
BMJ Open
January 2025
Department of Health Policy Planning and Management, Makerere University School of Public Health, Kampala, Uganda
Objectives: Empowering communities through identifying and unlocking community capacities and capabilities is vital for improving community health systems. This study assessed the community health system's status quo and readiness for implementing a government-led, partner-supported community health worker project.
Design: A mixed methods cross-sectional study.
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