Ultra-Fast Label-Free Serum Metabolic Diagnosis of Coronary Heart Disease via a Deep Stabilizer.
Ontology highlight
ABSTRACT:
SUBMITTER: Zhang M
PROVIDER: S-EPMC8456274 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:In this study nanopore sequencing was applied to obtain sparse DNA methylation profiles from pediatric CNS tumor samples. A neural network was used to classify the tumor based on the obtained methylation profile.
2023-08-08 | GSE237874 | GEO
Project description:The Dynamic Organellar Maps (DOMs) approach combines cell fractionation and shotgun-proteomics for global profiling analysis of protein subcellular localization. Here, we have drastically enhanced the performance of DOMs through data-independent acquisition (DIA) mass spectrometry (MS). DIA-DOMs achieve twice the depth of our previous workflow in the same MS runtime, and substantially improve profiling precision and reproducibility. This repository contains all DIA-LFQ benchmark datasets that were measured with our optimized DIA methods: single shot or triple shot on 100 min, 44 min and 21 min gradients.
2023-08-25 | PXD034971 | Pride
Project description:The Dynamic Organellar Maps (DOMs) approach combines cell fractionation and shotgun-proteomics for global profiling analysis of protein subcellular localization. Here, we have drastically enhanced the performance of DOMs through data-independent acquisition (DIA) mass spectrometry (MS). DIA-DOMs achieve twice the depth of our previous workflow in the same MS runtime, and substantially improve profiling precision and reproducibility. This repository contains all DDA-LFQ datasets used in our work: A reference dataset acquired in single shot on 100 min gradients and three fractionated datasets providing measured libraries for DIA searches on 100 min, 44 min and 21 min gradients.
2023-08-25 | PXD034962 | Pride
Project description:Background In pediatric cardiac surgery, perioperative management has evolved from slow weaning of mechanical ventilation in the intensive care unit to "ultra-fast-track" anesthesia with early extubation (EE) in theater to promote a faster recovery. The strategy of EE has not been assessed in adults with congenital heart disease, a growing population of patients who often require surgery. Methods And Results Data were collected retrospectively on all patients >16 years of age who underwent adult congenital heart surgery in our tertiary center between December 2012 and January 2020. Coarsened exact matching was performed for relevant baseline variables. Overall, 711 procedures were performed: 133 (18.7%) patients underwent EE and 578 (81.3%) patients received conventional extubation. After matching, patients who received EE required less inotropic or vasopressor support in the early postoperative period (median Vasoactive-inotropic score 0.5 [0.0-2.0] versus 2.0 [0.0-3.5]; P<0.0001) and had a lower total net fluid balance than patients after conventional extubation (1168±723 versus 847±733 mL; P=0.0002). The overall reintubation rate was low at 0.3%. EE was associated with a significantly shorter postoperative length of stay in higher dependency care units before a "step-down" to ward-based care (48 [45-50] versus 50 [47-69] hours; P=0.004). Lower combined intensive care unit and high dependency unit costs were incurred by patients who received EE compared with patients who received conventional extubation (£3949 [3430-4222] versus £4166 [3893-5603]; P<0.0001). Conclusions In adult patients undergoing surgery for congenital heart disease, EE is associated with a reduced need for postoperative hemodynamic support, a shorter intensive care unit stay, and lower health-care-related costs.
| S-EPMC8483528 | biostudies-literature
Project description:Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.
| S-EPMC4791545 | biostudies-literature
Project description:Mass spectrometry (MS)-based proteomics aims to characterize comprehensive proteomes in a fast and reproducible manner. Here, we present an ultra-fast scanning data-independent acquisition (DIA) strategy consisting on 2-Th precursor isolation windows, dissolving the differences between data-dependent and independent methods. This is achieved by pairing a Quadrupole Orbitrap mass spectrometer with the asymmetric track lossless (Astral) analyzer that provides >200 Hz MS/MS scanning speed, high resolving power and sensitivity, as well as low ppm-mass accuracy. Narrowwindow DIA enables profiling of up to 100 full yeast proteomes per day, or ~10,000 human proteins in half-an-hour. Moreover, multi-shot acquisition of fractionated samples allows comprehensive coverage of human proteomes in ~3h, showing comparable depth to next-generation RNA sequencing and with 10x higher throughput compared to current state-of-the-art MS. High quantitative precision and accuracy is demonstrated with high peptide coverage in a 3-species proteome mixture, quantifying 14,000+ proteins in a single run in half-an-hour.
2024-02-01 | PXD046372 | Pride
Project description:BackgroundCoronary heart disease (CHD) is a common cardiovascular disease that is extremely harmful to humans. In Traditional Chinese Medicine (TCM), the diagnosis and treatment of CHD have a long history and ample experience. However, the non-standard inquiry information influences the diagnosis and treatment in TCM to a certain extent. In this paper, we study the standardization of inquiry information in the diagnosis of CHD and design a diagnostic model to provide methodological reference for the construction of quantization diagnosis for syndromes of CHD. In the diagnosis of CHD in TCM, there could be several patterns of syndromes for one patient, while the conventional single label data mining techniques could only build one model at a time. Here a novel multi-label learning (MLL) technique is explored to solve this problem.MethodsStandardization scale on inquiry diagnosis for CHD in TCM is designed, and the inquiry diagnostic model is constructed based on collected data by the MLL techniques. In this study, one popular MLL algorithm, ML-kNN, is compared with other two MLL algorithms RankSVM and BPMLL as well as one commonly used single learning algorithm, k-nearest neighbour (kNN) algorithm. Furthermore the influence of symptom selection to the diagnostic model is investigated. After the symptoms are removed by their frequency from low to high; the diagnostic models are constructed on the remained symptom subsets.ResultsA total of 555 cases are collected for the modelling of inquiry diagnosis of CHD. The patients are diagnosed clinically by fusing inspection, pulse feeling, palpation and the standardized inquiry information. Models of six syndromes are constructed by ML-kNN, RankSVM, BPMLL and kNN, whose mean results of accuracy of diagnosis reach 77%, 71%, 75% and 74% respectively. After removing symptoms of low frequencies, the mean accuracy results of modelling by ML-kNN, RankSVM, BPMLL and kNN reach 78%, 73%, 75% and 76% when 52 symptoms are remained.ConclusionsThe novel MLL techniques facilitate building standardized inquiry models in CHD diagnosis and show a practical approach to solve the problem of labelling multi-syndromes simultaneously.
| S-EPMC2921356 | biostudies-literature
Project description:The primary treatment of CNS tumors starts with a neurosurgical resection in order to obtain tumor tissue for diagnosis and to reduce tumor load and mass effect. The neurosurgeon has to decide between radical resection versus a more conservative strategy to prevent surgical morbidity. The prognostic impact of a radical resection varies between tumor types. However due to a lack of pre-operative tissue-based diagnostics, limited knowledge of the precise tumor type is available at the time of surgery. Current standard practice includes preoperative imaging and intraoperative histological analysis, but these are not always conclusive. After surgery, histopathological and molecular tests are performed to diagnose the precise tumor type. The results may indicate that an additional surgery is needed or that the initial surgery could have been less radical. Using rapid Nanopore sequencing, a sparse methylation profile can be directly obtained during surgery, making it ideally suited to enable intraoperative diagnostics. We developed a state-of-the-art neural-network approach called Sturgeon, to deliver trained models that are lightweight and universally applicable across patients and sequencing depths. We demonstrate our method to be accurate and fast enough to provide a correct diagnosis with as little as 20 to 40 minutes of sequencing data in 45 out of 49 pediatric samples, and inconclusive results in the other four. In four intraoperative cases we achieved a turnaround time of 60-90 minutes from sample biopsy to result; well in time to impact surgical decision making. We conclude that machine-learned diagnosis based on intraoperative sequencing can assist neurosurgical decision making, allowing neurological comorbidity to be avoided or preventing additional surgeries.
| EGAS00001007475 | EGA
Project description:Hematological analysis, via a complete blood count (CBC) and microscopy, is critical for screening, diagnosing, and monitoring blood conditions and diseases but requires complex equipment, multiple chemical reagents, laborious system calibration and procedures, and highly trained personnel for operation. Here we introduce a hematological assay based on label-free molecular imaging with deep-ultraviolet microscopy that can provide fast quantitative information of key hematological parameters to facilitate and improve hematological analysis. We demonstrate that this label-free approach yields 1) a quantitative five-part white blood cell differential, 2) quantitative red blood cell and hemoglobin characterization, 3) clear identification of platelets, and 4) detailed subcellular morphology. Analysis of tens of thousands of live cells is achieved in minutes without any sample preparation. Finally, we introduce a pseudocolorization scheme that accurately recapitulates the appearance of cells under conventional staining protocols for microscopic analysis of blood smears and bone marrow aspirates. Diagnostic efficacy is evaluated by a panel of hematologists performing a blind analysis of blood smears from healthy donors and thrombocytopenic and sickle cell disease patients. This work has significant implications toward simplifying and improving CBC and blood smear analysis, which is currently performed manually via bright-field microscopy, and toward the development of a low-cost, easy-to-use, and fast hematological analyzer as a point-of-care device and for low-resource settings.
| S-EPMC7334528 | biostudies-literature
Project description:Label-free nonlinear microscopy enables nonperturbative visualization of structural and metabolic contrast within living cells in their native tissue microenvironment. Here a computational pipeline was developed to provide a quantitative view of the microenvironmental architecture within cancerous tissue from label-free nonlinear microscopy images. To enable single-cell and single-extracellular vesicle (EV) analysis, individual cells, including tumor cells and various types of stromal cells, and EVs were segmented by a multiclass pixelwise segmentation neural network and subsequently analyzed for their metabolic status and molecular structure in the context of the local cellular neighborhood. By comparing cancer tissue with normal tissue, extensive tissue reorganization and formation of a patterned cell-EV neighborhood was observed in the tumor microenvironment. The proposed analytic pipeline is expected to be useful in a wide range of biomedical tasks that benefit from single-cell, single-EV, and cell-to-EV analysis. SIGNIFICANCE: The proposed computational framework allows label-free microscopic analysis that quantifies the complexity and heterogeneity of the tumor microenvironment and opens possibilities for better characterization and utilization of the evolving cancer landscape.
| S-EPMC8137645 | biostudies-literature