Phosphoinositide 3-kinases (PI3Ks) inhibitors have treatment potential for cancer, diabetes, cardiovascular disease, chronic inflammation and asthma. A consensus model consisting of three base classifiers (AODE, kNN, and SVM) trained with 1,283 positive compounds (PI3K inhibitors), 16 negative compounds (PI3K non-inhibitors) and 64,078 generated putative negatives was developed for predicting compounds with PI3K inhibitory activity of IC(50) < or = 10 microM. The consensus model has an estimated false positive rate of 0.75%. Nine novel potential inhibitors were identified using the consensus model and several of these contain structural features that are consistent with those found to be important for PI3K inhibitory activities. An advantage of the current model is that it does not require knowledge of 3D structural information of the various PI3K isoforms, which is not readily available for all isoforms.
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http://dx.doi.org/10.1007/s10822-010-9321-0 | DOI Listing |
Radiology
January 2025
From the Department of Radiology, Duke University Hospital, 2301 Erwin Rd, Box 3808, Durham, NC 27701 (B.W.T., K.R.K., B.C.A., S.P.T., D.E.K., B.H., M.R.B., D.M., E.S., E.A.); Department of Biostatistics and Bioinformatics (N.F., S.M., A.E.) and Department of Medical Physics (W.P.S., E.S., E.A.), Duke University, Durham, NC.
Background Detection of hepatic metastases at CT is a daily task in radiology departments that influences medical and surgical treatment strategies for oncology patients. Purpose To compare simulated photon-counting CT (PCCT) with energy-integrating detector (EID) CT for the detection of small liver lesions. Materials and Methods In this reader study (July to December 2023), a virtual imaging framework was used with 50 anthropomorphic phantoms and 183 generated liver lesions (one to six lesions per phantom, 0.
View Article and Find Full Text PDFInt J Nurs Stud Adv
June 2025
Los Angeles General Medical Center, Los Angeles, CA, United States.
Background: There is a lack of high-quality evidence to support the recommendation of an instrument to screen emergency department patients for their risk for violence.
Objective: To demonstrate the content and predictive validity and reliability of the novel Risk for Violence Screening Tool to identify patients at risk for violence.
Design And Setting: This retrospective risk screening study was conducted at a 100-bed emergency department in an urban, academic, safety net trauma center in Southern California.
BMJ Open
December 2024
School of Health and Social Care, Edinburgh Napier University, Edinburgh, UK.
Objective: Mentoring plays a crucial role in career development, particularly for black and minoritised ethnic (BME) professionals. However, existing literature lacks clarity on the impact of mentoring and how best to deliver for career success. This study aimed to ascertain perceptions and build consensus on what is important in mentoring for BME healthcare professionals.
View Article and Find Full Text PDFHealth Res Policy Syst
January 2025
University College London, London, United Kingdom.
Background: The deteriorating mental health of children and young people in the United Kingdom poses a challenge that services and policy makers have found difficult to tackle. Kailo responds to this issue with a community-based participatory and systemically informed strategy, perceiving mental health and well-being as a dynamic state shaped by the interplay of broader health determinants. The initiative works to explore, define and implement locally relevant solutions to challenges shaping the mental health and well-being of young people.
View Article and Find Full Text PDFSleep Breath
January 2025
Department of Respiratory and Critical Care Medicine, Medical School of Nantong University, Nantong Key Laboratory of Respiratory Medicine, Affiliated Hospital of Nantong University, Nantong, 226001, China.
Background: The pathophysiology of obstructive sleep apnea (OSA) and diabetes mellitus (DM) is still unknown, despite clinical reports linking the two conditions. After investigating potential roles for DM-related genes in the pathophysiology of OSA, our goal is to investigate the molecular significance of the condition. Machine learning is a useful approach to understanding complex gene expression data to find biomarkers for the diagnosis of OSA.
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