keyword
https://read.qxmd.com/read/38537173/ultradense-electrochemical-chips-with-arrays-of-nanostructured-microelectrodes-to-enable-sensitive-diffusion-limited-bioassays
#1
REVIEW
Gabriel J C Pimentel, Lucas B Ayres, Juliana N Y Costa, Waldemir J Paschoalino, Kristi Whitehead, Lauro T Kubota, Maria H de Oliveira Piazzetta, Angelo L Gobbi, Flávio M Shimizu, Carlos D Garcia, Renato S Lima
Nanostructured microelectrodes (NMEs) are an attractive alternative to yield sensitive bioassays in unprocessed samples. However, although valuable for different applications, nanoporous NMEs usually cannot boost the sensitivity of diffusion-limited analyses because of the enlarged Debye length within the nanopores, which reduces their accessibility. To circumvent this limitation, nanopore-free gold NMEs were electrodeposited from 45 μm SU-8 apertures, featuring nanoridged microspikes on a recessed surface of gold thin film while carrying interconnected crown-like and spiky structures along the edge of a SU-8 passivation layer...
March 27, 2024: ACS Applied Materials & Interfaces
https://read.qxmd.com/read/38537151/development-and-validation-of-a-machine-learning-prognostic-model-of-m5c-related-immune-genes-in-lung-adenocarcinoma
#2
JOURNAL ARTICLE
Xiong Cao, Yuxing Ji, Jiajia Li, Zhikang Liu, Chang Chen
BACKGROUND: The aim of this retrospective research was to develop an immune-related genes significantly associated with m5C methylation methylation (m5C-IRGs)-related signature associated with lung adenocarainoma (LUAD). METHODS: We introduced transcriptome data to screen out m5C-IRGs in The Cancer Genome Atlas (TCGA)-LUAD dataset. Subsequently, the m5C-IRGs associated with survival were certificated by Kaplan Meier (K-M) analysis. The univariate Cox, least absolute shrinkage and selection operator (LASSO) regression, and xgboost...
2024: Cancer Control: Journal of the Moffitt Cancer Center
https://read.qxmd.com/read/38537150/prediction-of-occult-hemorrhage-in-the-lower-body-negative-pressure-model-initial-validation-of-machine-learning-approaches
#3
JOURNAL ARTICLE
Navid Rashedi, Yifei Sun, Vikrant Vaze, Parikshit Shah, Ryan Halter, Jonathan T Elliott, Norman A Paradis
INTRODUCTION: Detection of occult hemorrhage (OH) before progression to clinically apparent changes in vital signs remains an important clinical problem in managing trauma patients. The resource-intensiveness associated with continuous clinical patient monitoring and rescue from frank shock makes accurate early detection and prediction with noninvasive measurement technology a desirable innovation. Despite significant efforts directed toward the development of innovative noninvasive diagnostics, the implementation and performance of the newest bedside technologies remain inadequate...
March 27, 2024: Military Medicine
https://read.qxmd.com/read/38537130/comprehensive-molecular-classification-predicted-microenvironment-profiles-and-therapy-response-for-hepatocellular-carcinoma
#4
JOURNAL ARTICLE
Yihong Chen, Xiangying Deng, Yin Li, Ying Han, Yinghui Peng, Wantao Wu, Xinwen Wang, Jiayao Ma, Erya Hu, Xin Zhou, Edward Shen, Shan Zeng, Changjing Cai, Yiming Qin, Hong Shen
BACKGROUND AND AIMS: Tumor microenvironment (TME) heterogeneity leads to a discrepancy in survival prognosis and clinical treatment response for hepatocellular carcinoma (HCC) patients. The clinical applications of documented molecular subtypes are constrained by several issues. APPROACH AND RESULTS: We integrated three single-cell datasets to describe the TME landscape and identified six prognosis-related cell subclusters. Unsupervised clustering of subcluster-specific markers was performed to generate transcriptomic subtypes...
March 27, 2024: Hepatology: Official Journal of the American Association for the Study of Liver Diseases
https://read.qxmd.com/read/38536934/attenuation-of-fibroblast-activation-and-fibrosis-by-adropin-in-systemic-sclerosis
#5
JOURNAL ARTICLE
Minrui Liang, Nicholas Dickel, Andrea-Hermina Györfi, Bilgesu SafakTümerdem, Yi-Nan Li, Aleix Rius Rigau, Chunguang Liang, Xuezhi Hong, Lichong Shen, Alexandru-Emil Matei, Thuong Trinh-Minh, Cuong Tran-Manh, Xiang Zhou, Ariella Zehender, Alexander Kreuter, Hejian Zou, Georg Schett, Meik Kunz, Jörg H W Distler
Fibrotic diseases impose a major socioeconomic challenge on modern societies and have limited treatment options. Adropin, a peptide hormone encoded by the energy homeostasis-associated ( ENHO ) gene, is implicated in metabolism and vascular homeostasis, but its role in the pathogenesis of fibrosis remains enigmatic. Here, we used machine learning approaches in combination with functional in vitro and in vivo experiments to characterize adropin as a potential regulator involved in fibroblast activation and tissue fibrosis in systemic sclerosis (SSc)...
March 27, 2024: Science Translational Medicine
https://read.qxmd.com/read/38536931/compounding-effects-in-flood-drivers-challenge-estimates-of-extreme-river-floods
#6
JOURNAL ARTICLE
Shijie Jiang, Larisa Tarasova, Guo Yu, Jakob Zscheischler
Estimating river flood risks under climate change is challenging, largely due to the interacting and combined influences of various flood-generating drivers. However, a more detailed quantitative analysis of such compounding effects and the implications of their interplay remains underexplored on a large scale. Here, we use explainable machine learning to disentangle compounding effects between drivers and quantify their importance for different flood magnitudes across thousands of catchments worldwide. Our findings demonstrate the ubiquity of compounding effects in many floods...
March 29, 2024: Science Advances
https://read.qxmd.com/read/38536877/comparative-study-on-differential-expression-analysis-methods-for-single-cell-rna-sequencing-data-with-small-biological-replicates-based-on-single-cell-transcriptional-data-of-pbmcs-from-covid-19-severe-patients
#7
JOURNAL ARTICLE
Jie Xue, Xinfan Zhou, Jing Yang, Adan Niu
Single-cell RNA sequencing (scRNA-seq) is a high-throughput experimental technique for studying gene expression at the single-cell level. As a key component of single-cell data analysis, differential expression analysis (DEA) serves as the foundation for all subsequent secondary studies. Despite the fact that biological replicates are of vital importance in DEA process, small biological replication is still common in sequencing experiment now, which may impose problems to current DEA methods. Therefore, it is necessary to conduct a thorough comparison of various DEA approaches under small biological replications...
2024: PloS One
https://read.qxmd.com/read/38536867/evaluating-spatially-enabled-machine-learning-approaches-to-depth-to-bedrock-mapping-alberta-canada
#8
JOURNAL ARTICLE
Steven M Pawley, Lisa Atkinson, Daniel J Utting, Gregory M D Hartman, Nigel Atkinson
Maps showing the thickness of sediments above the bedrock (depth to bedrock, or DTB) are important for many geoscience studies and are necessary for many hydrogeological, engineering, mining, and forestry applications. However, it can be difficult to accurately estimate DTB in areas with varied topography, like lowland and mountainous terrain, because traditional methods of predicting bedrock elevation often underestimate or overestimate the elevation in rugged or incised terrain. Here, we describe a machine learning spatial prediction approach that uses information from traditional digital elevation model derived estimates of terrain morphometry and satellite imagery, augmented with spatial feature engineering techniques to predict DTB across Alberta, Canada...
2024: PloS One
https://read.qxmd.com/read/38536802/predicting-successful-weaning-from-mechanical-ventilation-by-reduction-in-positive-end-expiratory-pressure-level-using-machine-learning
#9
JOURNAL ARTICLE
Seyedmostafa Sheikhalishahi, Mathias Kaspar, Sarra Zaghdoudi, Julia Sander, Philipp Simon, Benjamin P Geisler, Dorothea Lange, Ludwig Christian Hinske
Weaning patients from mechanical ventilation (MV) is a critical and resource intensive process in the Intensive Care Unit (ICU) that impacts patient outcomes and healthcare expenses. Weaning methods vary widely among providers. Prolonged MV is associated with adverse events and higher healthcare expenses. Predicting weaning readiness is a non-trivial process in which the positive end-expiratory pressure (PEEP), a crucial component of MV, has potential to be indicative but has not yet been used as the target...
March 2024: PLOS Digit Health
https://read.qxmd.com/read/38536782/a-machine-learning-based-depression-screening-framework-using-temporal-domain-features-of-the-electroencephalography-signals
#10
JOURNAL ARTICLE
Sheharyar Khan, Sanay Muhammad Umar Saeed, Jaroslav Frnda, Aamir Arsalan, Rashid Amin, Rahma Gantassi, Sadam Hussain Noorani
Depression is a serious mental health disorder affecting millions of individuals worldwide. Timely and precise recognition of depression is vital for appropriate mediation and effective treatment. Electroencephalography (EEG) has surfaced as a promising tool for inspecting the neural correlates of depression and therefore, has the potential to contribute to the diagnosis of depression effectively. This study presents an EEG-based mental depressive disorder detection mechanism using a publicly available EEG dataset called Multi-modal Open Dataset for Mental-disorder Analysis (MODMA)...
2024: PloS One
https://read.qxmd.com/read/38536694/adaptive-surface-normal-constraint-for-geometric-estimation-from-monocular-images
#11
JOURNAL ARTICLE
Xiaoxiao Long, Yuhang Zheng, Yupeng Zheng, Beiwen Tian, Cheng Lin, Lingjie Liu, Hao Zhao, Guyue Zhou, Wenping Wang
We introduce a novel approach to learn geometries such as depth and surface normal from images while incorporating geometric context. The difficulty of reliably capturing geometric context in existing methods impedes their ability to accurately enforce the consistency between the different geometric properties, thereby leading to a bottleneck of geometric estimation quality. We therefore propose the Adaptive Surface Normal (ASN) constraint, a simple yet efficient method. Our approach extracts geometric context that encodes the geometric variations present in the input image and correlates depth estimation with geometric constraints...
March 27, 2024: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://read.qxmd.com/read/38536690/a-task-guided-implicitly-searched-and-metainitialized-deep-model-for-image-fusion
#12
JOURNAL ARTICLE
Risheng Liu, Zhu Liu, Jinyuan Liu, Xin Fan, Zhongxuan Luo
Image fusion plays a key role in a variety of multi-sensor-based vision systems, especially for enhancing visual quality and/or extracting aggregated features for perception. However, most existing methods just consider image fusion as an individual task, thus ignoring its underlying relationship with these downstream vision problems. Furthermore, designing proper fusion architectures often requires huge engineering labor. It also lacks mechanisms to improve the flexibility and generalization ability of current fusion approaches...
March 27, 2024: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://read.qxmd.com/read/38536685/mcd-lightgbm-system-for-intelligent-analyzing-heterogeneous-clinical-drug-therapeutic-effects
#13
JOURNAL ARTICLE
Xiao-Hui Yang, Hao-Jie Liao, Pei- Yu Sun, Jing Ma, Bing Wang, Yan He, Liu-Gen Xue, Li-Min Su, Bin-Jie Wang
Causal effect estimation of individual heterogeneity is a core issue in the field of causal inference, and its application in medicine poses an active and challenging problem. In high-risk decision-making domain such as healthcare, inappropriate treatments can have serious negative impacts on patients. Recently, machine learning-based methods have been proposed to improve the accuracy of causal effect estimation results. However, many of these methods concentrate on estimating causal effects of continuous outcome variables under binary intervention conditions, and give less consideration to multivariate intervention conditions or discrete outcome variables, thus limiting their scope of application...
March 27, 2024: IEEE Journal of Biomedical and Health Informatics
https://read.qxmd.com/read/38536681/eisatc-fusion-inception-self-attention-temporal-convolutional-network-fusion-for-motor-imagery-eeg-decoding
#14
JOURNAL ARTICLE
Guangjin Liang, Dianguo Cao, Jinqiang Wang, Zhongcai Zhang, Yuqiang Wu
The motor imagery brain-computer interface (MI-BCI) based on electroencephalography (EEG) is a widely used human-machine interface paradigm. However, due to the non-stationarity and individual differences among subjects in EEG signals, the decoding accuracy is limited, affecting the application of the MI-BCI. In this paper, we propose the EISATC-Fusion model for MI EEG decoding, consisting of inception block, multi-head self-attention (MSA), temporal convolutional network (TCN), and layer fusion. Specifically, we design a DS Inception block to extract multi-scale frequency band information...
March 27, 2024: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://read.qxmd.com/read/38536643/a-systematic-review-of-machine-learning-based-thyroid-tumor-characterisation-using-ultrasonographic-images
#15
REVIEW
Niranjan Yadav, Rajeshwar Dass, Jitendra Virmani
Ultrasonography is widely used to screen thyroid tumors because it is safe, easy to use, and low-cost. However, it is simultaneously affected by speckle noise and other artifacts, so early detection of thyroid abnormalities becomes difficult for the radiologist. Therefore, various researchers continuously address the limitations of sonography and improve the diagnosis potential of US images for thyroid tissue from the last three decays. Accordingly, the present study extensively reviewed various CAD systems used to classify thyroid tumor US (TTUS) images related to datasets, despeckling algorithms, segmentation algorithms, feature extraction and selection, assessment parameters, and classification algorithms...
March 27, 2024: Journal of Ultrasound
https://read.qxmd.com/read/38536563/machine-learning-in-risk-prediction-of-continuous-renal-replacement-therapy-after-coronary-artery-bypass-grafting-surgery-in-patients
#16
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/38536559/radiomics-based-machine-learning-in-the-differentiation-of-benign-and-malignant-bowel-wall-thickening-radiomics-in-bowel-wall-thickening
#17
JOURNAL ARTICLE
Hande Melike Bülbül, Gülen Burakgazi, Uğur Kesimal, Esat Kaba
PURPOSE: To distinguish malignant and benign bowel wall thickening (BWT) by using computed tomography (CT) texture features based on machine learning (ML) models and to compare its success with the clinical model and combined model. METHODS: One hundred twenty-two patients with BWT identified on contrast-enhanced abdominal CT and underwent colonoscopy were included in this retrospective study. Texture features were extracted from CT images using LifeX software. Feature selection and reduction were performed using the Least Absolute Shrinkage and Selection Operator (LASSO)...
March 27, 2024: Japanese Journal of Radiology
https://read.qxmd.com/read/38536479/applying-machine-learning-in-the-investigation-of-the-link-between-the-high-velocity-streams-of-charged-solar-particles-and-precipitation-induced-floods
#18
JOURNAL ARTICLE
Slavica Malinović-Milićević, Yaroslav Vyklyuk, Milan M Radovanović, Milan Milenković, Ana Milanović Pešić, Boško Milovanović, Teodora Popović, Petro Sydor, Marko D Petrović
This study explores a possible link between solar activity and floods caused by precipitation. For this purpose, discrete blocks of data for 89 separate flood events in Europe in the period 2009-2018 were used. Solar activity parameters with a time lag of 0-11 days were used as input data of the model, while precipitation data in the 12 days preceding the flood were used as output data. The level of randomness of the input and output time series was determined by correlation analysis, while the potential causal relationship was established by applying machine learning classification predictive modeling...
March 27, 2024: Environmental Monitoring and Assessment
https://read.qxmd.com/read/38536464/automated-graded-prognostic-assessment-for-patients-with-hepatocellular-carcinoma-using-machine-learning
#19
JOURNAL ARTICLE
Moritz Gross, Stefan P Haider, Tal Ze'evi, Steffen Huber, Sandeep Arora, Ahmet S Kucukkaya, Simon Iseke, Bernhard Gebauer, Florian Fleckenstein, Marc Dewey, Ariel Jaffe, Mario Strazzabosco, Julius Chapiro, John A Onofrey
BACKGROUND: Accurate mortality risk quantification is crucial for the management of hepatocellular carcinoma (HCC); however, most scoring systems are subjective. PURPOSE: To develop and independently validate a machine learning mortality risk quantification method for HCC patients using standard-of-care clinical data and liver radiomics on baseline magnetic resonance imaging (MRI). METHODS: This retrospective study included all patients with multiphasic contrast-enhanced MRI at the time of diagnosis treated at our institution...
March 27, 2024: European Radiology
https://read.qxmd.com/read/38536234/target-product-profile-for-a-machine-learning-automated-retinal-imaging-analysis-software-for-use-in-english-diabetic-eye-screening-protocol-for-a-mixed-methods-study
#20
JOURNAL ARTICLE
Trystan Macdonald, Jacqueline Dinnes, Gregory Maniatopoulos, Sian Taylor-Phillips, Bethany Shinkins, Jeffry Hogg, John Kevin Dunbar, Ameenat Lola Solebo, Hannah Sutton, John Attwood, Michael Pogose, Rosalind Given-Wilson, Felix Greaves, Carl Macrae, Russell Pearson, Daniel Bamford, Adnan Tufail, Xiaoxuan Liu, Alastair K Denniston
BACKGROUND: Diabetic eye screening (DES) represents a significant opportunity for the application of machine learning (ML) technologies, which may improve clinical and service outcomes. However, successful integration of ML into DES requires careful product development, evaluation, and implementation. Target product profiles (TPPs) summarize the requirements necessary for successful implementation so these can guide product development and evaluation. OBJECTIVE: This study aims to produce a TPP for an ML-automated retinal imaging analysis software (ML-ARIAS) system for use in DES in England...
March 27, 2024: JMIR Research Protocols
keyword
keyword
4200
1
2
Fetch more papers »
Fetching more papers... Fetching...
Remove bar
Read by QxMD icon Read
×

Save your favorite articles in one place with a free QxMD account.

×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"

We want to hear from doctors like you!

Take a second to answer a survey question.