keyword
https://read.qxmd.com/read/38536785/work-disability-and-employment-status-among-advanced-chronic-kidney-disease-patients
#1
JOURNAL ARTICLE
Shing Shen Bay, Lydia Kamaruzaman, Rozita Mohd, Shamsul Azhar Shah
INTRODUCTION: Chronic kidney disease (CKD) is a major public health issue with significant socioeconomic impacts. In Malaysia, the prevalence of CKD in 2018 was 15%. Complications of CKD such as anaemia, mineral bone disease, and infections led to frequent hospitalizations resulting in work disability and unemployment. To date, there is no data of employment status of CKD patients in Malaysia. METHODS: A cross-sectional study of patients with advanced CKD (stage 4 and 5 non-dialysis) treated in our centre...
2024: PloS One
https://read.qxmd.com/read/38536675/a-novel-method-for-targeted-identification-of-essential-proteins-by-integrating-chemical-reaction-optimization-and-naive-bayes-model
#2
JOURNAL ARTICLE
Wenya Yang, Sai Zou, Hongfeng Gao, Lei Wang, Wei Ni
Targeted identification of essential proteins is of great significance for species identification, drug manufacturing, and disease treatment. It is a challenge to analyze the binding mechanism between essential proteins and improve the identification speed while ensuring the accuracy of the identification. This paper proposes a novel method called EPCRO for identifying essential proteins, which incorporates the chemical reaction optimization (CRO) algorithm and the naive Bayes model to effectively detect essential proteins...
March 27, 2024: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://read.qxmd.com/read/38536563/machine-learning-in-risk-prediction-of-continuous-renal-replacement-therapy-after-coronary-artery-bypass-grafting-surgery-in-patients
#3
JOURNAL ARTICLE
Qian Zhang, Peng Zheng, Zhou Hong, Luo Li, Nannan Liu, Zhiping Bian, Xiangjian Chen, Hengfang Wu, Sheng Zhao
OBJECTIVES: This study aimed to develop machine learning models for risk prediction of continuous renal replacement therapy (CRRT) following coronary artery bypass grafting (CABG) surgery in intensive care unit (ICU) patients. METHODS: We extracted CABG patients from the electronic medical record system of the hospital. The endpoint of this study was the requirement for CRRT after CABG surgery. The Boruta method was used for feature selection. Seven machine learning algorithms were developed to train models and validated using 10 fold cross-validation (CV)...
March 27, 2024: Clinical and Experimental Nephrology
https://read.qxmd.com/read/38535899/ecological-status-of-algeciras-bay-in-a-highly-anthropised-area-in-south-west-europe-through-metal-assessment-part-ii-biotic-samples
#4
JOURNAL ARTICLE
María José Casanueva-Marenco, María Dolores Galindo-Riaño, María Dolores Granado-Castro, Margarita Díaz-de-Alba
Biotic samples from Algeciras Bay (South-west Europe) were studied to assess its ecological status, complementing the previous abiotic monitoring of trace metals in water and sediments. This bay is a densely populated area with intense port traffic and is highly industrialised with metal inputs. To study the impact of this, Zn, Cd, Pb, and Cu contents were determined in tissues of benthic ( Solea senegalensis ) and benthopelagic species ( Scorpaena porcus , Trigloporus lastoviza, and Diplodus sargus sargus )...
February 21, 2024: Toxics
https://read.qxmd.com/read/38535896/ecological-status-of-algeciras-bay-in-a-highly-anthropised-area-in-south-west-europe-through-metal-assessment-part-i-abiotic-samples
#5
JOURNAL ARTICLE
María José Casanueva-Marenco, María Dolores Galindo-Riaño, María Dolores Granado-Castro, Margarita Díaz-de-Alba
The ecological status of Algeciras Bay (South-west Europe), highly influenced by anthropogenic activities, was assessed by monitoring Zn, Cd, Pb, and Cu in water and sediment samples. Total contents and metal fractions with different availabilities and their spatial-seasonal distribution were determined. The trend in water and sediment contents were Zn > Pb ≈ Cu > Cd, without significant seasonal variations. Sites 3 and 4, closest to industrial activities, had the highest metal concentrations, mainly in sediments...
February 20, 2024: Toxics
https://read.qxmd.com/read/38535489/pragmatic-approach-to-in-situ-simulation-to-identify-latent-safety-threats-before-moving-to-a-newly-built-icu
#6
JOURNAL ARTICLE
Aarti Sarwal, Nicholas A Morris, Janet Crumpler, Terri Gordon, Ian Saunders, James E Johnson, Jeff E Carter
OBJECTIVES: Transitions to new care environments may have unexpected consequences that threaten patient safety. We undertook a quality improvement project using in situ simulation to learn the new patient care environment and expose latent safety threats before transitioning patients to a newly built adult ICU. DESIGN: Descriptive review of a patient safety initiative. SETTING: A newly built 24-bed neurocritical care unit at a tertiary care academic medical center...
March 27, 2024: Critical Care Medicine
https://read.qxmd.com/read/38535046/machine-learning-based-algorithms-for-enhanced-prediction-of-local-recurrence-and-metastasis-in-low-rectal-adenocarcinoma-using-imaging-surgical-and-pathological-data
#7
JOURNAL ARTICLE
Cristian-Constantin Volovat, Dragos-Viorel Scripcariu, Diana Boboc, Simona-Ruxandra Volovat, Ingrid-Andrada Vasilache, Corina Ursulescu-Lupascu, Liliana Gheorghe, Luiza-Maria Baean, Constantin Volovat, Viorel Scripcariu
(1) Background: Numerous variables could influence the risk of rectal cancer recurrence or metastasis, and machine learning (ML)-based algorithms can help us refine the risk stratification process of these patients and choose the best therapeutic approach. The aim of this study was to assess the predictive performance of 4 ML-based models for the prediction of local recurrence or distant metastasis in patients with locally advanced low rectal adenocarcinomas who underwent neoadjuvant chemoradiotherapy and surgical treatment; (2) Methods: Patients who were admitted at the first Oncologic Surgical Clinic from the Regional Institute of Oncology, Iasi, Romania were retrospectively included in this study between November 2019 and July 2023...
March 15, 2024: Diagnostics
https://read.qxmd.com/read/38534863/metaheuristic-based-feature-selection-methods-for-diagnosing-sarcopenia-with-machine-learning-algorithms
#8
JOURNAL ARTICLE
Jaehyeong Lee, Yourim Yoon, Jiyoun Kim, Yong-Hyuk Kim
This study explores the efficacy of metaheuristic-based feature selection in improving machine learning performance for diagnosing sarcopenia. Extraction and utilization of features significantly impacting diagnosis efficacy emerge as a critical facet when applying machine learning for sarcopenia diagnosis. Using data from the 8th Korean Longitudinal Study on Aging (KLoSA), this study examines harmony search (HS) and the genetic algorithm (GA) for feature selection. Evaluation of the resulting feature set involves a decision tree, a random forest, a support vector machine, and naïve bayes algorithms...
March 15, 2024: Biomimetics
https://read.qxmd.com/read/38533254/a-real-world-disproportionality-analysis-of-everolimus-data-mining-of-the-public-version-of-fda-adverse-event-reporting-system
#9
JOURNAL ARTICLE
Bin Zhao, Yumei Fu, Shichao Cui, Xiangning Chen, Shu Liu, Lan Luo
Background: Everolimus is an inhibitor of the mammalian target of rapamycin and is used to treat various tumors. The presented study aimed to evaluate the Everolimus-associated adverse events (AEs) through data mining of the US Food and Drug Administration Adverse Event Reporting System (FAERS). Methods: The AE records were selected by searching the FDA Adverse Event Reporting System database from the first quarter of 2009 to the first quarter of 2022. Potential adverse event signals were mined using the disproportionality analysis, including reporting odds ratio the proportional reporting ratio the Bayesian confidence propagation neural network and the empirical Bayes geometric mean and MedDRA was used to systematically classify the results...
2024: Frontiers in Pharmacology
https://read.qxmd.com/read/38533033/elucidating-the-anti-cancer-potential-of-cinnamomum-tamala-essential-oil-against-non-small-cell-lung-cancer-a-multifaceted-approach-involving-gc-ms-profiling-network-pharmacology-and-molecular-dynamics-simulations
#10
JOURNAL ARTICLE
Debajani Mohanty, Sucheesmita Padhee, Arpita Priyadarshini, Bibhuti Bhusan Champati, Prabhat Kumar Das, Sudipta Jena, Ambika Sahoo, Pratap Chandra Panda, Sanghamitra Nayak, Asit Ray
Cinnamomum tamala (Buch.-Ham.) T.Nees & Eberm., or Indian Bay Leaf, is a well-known traditional ayurvedic medicine used to treat various ailments. However, the molecular mechanism of action of Cinnamomum tamala essential oil (CTEO) against non-small cell lung cancer (NSCLC) remains elusive. The present study aims to decipher the molecular targets and mechanism of CTEO in treating NSCLC. GC-MS analysis detected 49 constituents; 44 successfully passed the drug-likeness screening and were identified as active compounds...
March 30, 2024: Heliyon
https://read.qxmd.com/read/38532683/dissymmetrical-chiral-peropyrenes-synthesis-via-iridium-catalyzed-c-h-activation-alkyne-benzannulation-and-study-of-their-properties
#11
JOURNAL ARTICLE
Stephen P George, Jonas Spengler, Ryan J Malone, James Krzoska, Frank Würthner, Wesley A Chalifoux
Dissymmetrical chiral peropyrenes with electron-rich and electron-deficient aryl substituents in the bay regions were synthesized via iridium-catalyzed C-H activation and alkyne benzannulation. The electronic properties were studied using cyclic and differential pulse voltammetry. The enantiomers were separated and exhibited high g lum and g abs values in circularly polarized luminescence (CPL) and circular dichroism (CD), respectively. Variable-temperature NMR experiments were conducted on symmetrical and dissymmetrical chiral peropyrenes to compare the barrier to rotation of the aryl groups in the bay region...
March 27, 2024: Journal of Organic Chemistry
https://read.qxmd.com/read/38532062/bayesian-evidence-synthesis-as-a-flexible-alternative-to-meta-analysis-a-simulation-study-and-empirical-demonstration
#12
JOURNAL ARTICLE
Elise van Wonderen, Mariëlle Zondervan-Zwijnenburg, Irene Klugkist
Synthesizing results across multiple studies is a popular way to increase the robustness of scientific findings. The most well-known method for doing this is meta-analysis. However, because meta-analysis requires conceptually comparable effect sizes with the same statistical form, meta-analysis may not be possible when studies are highly diverse in terms of their research design, participant characteristics, or operationalization of key variables. In these situations, Bayesian evidence synthesis may constitute a flexible and feasible alternative, as this method combines studies at the hypothesis level rather than at the level of the effect size...
March 26, 2024: Behavior Research Methods
https://read.qxmd.com/read/38531446/spectral-analysis-of-environmental-microplastic-polyethylene-pe-using-average-spectra
#13
JOURNAL ARTICLE
Zijiang Yang, Jiaqi Zhang, Nakano Haruka, Celik Murat, Hisayuki Arakawa
In this study, microplastic samples from surface seawater of Tokyo Bay were collected, polyethylene (PE) microplastics were used to calculate carbonyl index (CI), and average spectra of PE were analyzed and compared with a previous study applying agitation during chemical treatment. It was found that PE and polypropylene (PP) were the predominant polymer type in the samples. Among PE samples, fragments were the most commonly observed shape, with white being the dominant color. Deviations were found in the average spectra among different shapes and colors when compared to the standard PE spectrum...
March 24, 2024: Science of the Total Environment
https://read.qxmd.com/read/38530836/a-deeply-supervised-adaptable-neural-network-for-diagnosis-and-classification-of-alzheimer-s-severity-using-multitask-feature-extraction
#14
JOURNAL ARTICLE
Mohsen Ahmadi, Danial Javaheri, Matin Khajavi, Kasra Danesh, Junbeom Hur
Alzheimer's disease is the most prevalent form of dementia, which is a gradual condition that begins with mild memory loss and progresses to difficulties communicating and responding to the environment. Recent advancements in neuroimaging techniques have resulted in large-scale multimodal neuroimaging data, leading to an increased interest in using deep learning for the early diagnosis and automated classification of Alzheimer's disease. This study uses machine learning (ML) methods to determine the severity level of Alzheimer's disease using MRI images, where the dataset consists of four levels of severity...
2024: PloS One
https://read.qxmd.com/read/38530612/predicting-early-return-to-the-operating-room-in-early-onset-scoliosis-patients-using-machine-learning-techniques
#15
JOURNAL ARTICLE
Brett R Lullo, Patrick J Cahill, John M Flynn, Jason B Anari
PURPOSE: Surgical treatment of early-onset scoliosis (EOS) is associated with high rates of complications, often requiring unplanned return to the operating room (UPROR). The aim of this study was to create and validate a machine learning model to predict which EOS patients will go on to require an UPROR during their treatment course. METHODS: A retrospective review was performed of all surgical EOS patients with at least 2 years follow-up. Patients were stratified based on whether they had experienced an UPROR...
March 26, 2024: Spine Deformity
https://read.qxmd.com/read/38529947/a-novel-machine-learning-algorithm-selects-proteome-signature-to-specifically-identify-cancer-exosomes
#16
JOURNAL ARTICLE
Bingrui Li, Fernanda G Kugeratski, Raghu Kalluri
Non-invasive early cancer diagnosis remains challenging due to the low sensitivity and specificity of current diagnostic approaches. Exosomes are membrane-bound nanovesicles secreted by all cells that contain DNA, RNA, and proteins that are representative of the parent cells. This property, along with the abundance of exosomes in biological fluids makes them compelling candidates as biomarkers. However, a rapid and flexible exosome-based diagnostic method to distinguish human cancers across cancer types in diverse biological fluids is yet to be defined...
March 26, 2024: ELife
https://read.qxmd.com/read/38528576/prospective-virtual-screening-combined-with-bio-molecular-simulation-enabled-identification-of-new-inhibitors-for-the-kras-drug-target
#17
JOURNAL ARTICLE
Amar Ajmal, Hind A Alkhatabi, Roaa M Alreemi, Mubarak A Alamri, Asaad Khalid, Ashraf N Abdalla, Bader S Alotaibi, Abdul Wadood
Lung cancer is a disease with a high mortality rate and it is the number one cause of cancer death globally. Approximately 12-14% of non-small cell lung cancers are caused by mutations in KRASG12C . The KRASG12C is one of the most prevalent mutants in lung cancer patients. KRAS was first considered undruggable. The sotorasib and adagrasib are the recently approved drugs that selectively target KRASG12C , and offer new treatment approaches to enhance patient outcomes however drug resistance frequently arises...
March 25, 2024: BMC chemistry
https://read.qxmd.com/read/38528495/application-of-machine-learning-methods-for-predicting-under-five-mortality-analysis-of-nigerian-demographic-health-survey-2018-dataset
#18
JOURNAL ARTICLE
Oduse Samuel, Temesgen Zewotir, Delia North
BACKGROUND: Under-five mortality remains a significant public health issue in developing countries. This study aimed to assess the effectiveness of various machine learning algorithms in predicting under-five mortality in Nigeria and identify the most relevant predictors. METHODS: The study used nationally representative data from the 2018 Nigeria Demographic and Health Survey. The study evaluated the performance of the machine learning models such as the artificial neural network, k-nearest neighbourhood, Support Vector Machine, Naïve Bayes, Random Forest, and Logistic Regression using the true positive rate, false positive rate, accuracy, precision, F-measure, Matthew's correlation coefficient, and the Area Under the Receiver Operating Characteristics...
March 25, 2024: BMC Medical Informatics and Decision Making
https://read.qxmd.com/read/38528066/a-predictive-model-for-post-thoracoscopic-surgery-pulmonary-complications-based-on-the-pbnn-algorithm
#19
JOURNAL ARTICLE
Cheng-Mao Zhou, Qiong Xue, HuiJuan Li, Jian-Jun Yang, Yu Zhu
We constructed an early prediction model for postoperative pulmonary complications after thoracoscopic surgery using machine learning and deep learning algorithms. The artificial intelligence prediction models were built in Python, primarily using artificial intelligencealgorithms including both machine learning and deep learning algorithms. Correlation analysis showed that postoperative pulmonary complications were positively correlated with age and surgery duration, and negatively correlated with serum albumin...
March 25, 2024: Scientific Reports
https://read.qxmd.com/read/38527612/epidemiology-of-escherichia-coli-bloodstream-infection-antimicrobial-resistance-trends-across-south-west-england-during-the-first-two-years-of-the-coronavirus-disease-2019-pandemic-response
#20
JOURNAL ARTICLE
J Stanley, B Sullivan, A W Dowsey, K Jones, C R Beck
OBJECTIVES: Between 2016-19, the proportion of Escherichia coli bloodstream infection (BSI) with resistance to at least one antibiotic increased nationally. Public health interventions implemented in response to the coronavirus disease 2019 (COVID-19) pandemic changed population contact patterns and healthcare systems, with consequent effects on epidemiological trends of numerous pathogens. We investigated the impact of COVID-19 restrictions on epidemiological trends of E. coli BSI antimicrobial resistance (AMR) across South West England...
March 23, 2024: Clinical Microbiology and Infection
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