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Sensors (Basel, Switzerland) Jun 2024The phonocardiogram (PCG) can be used as an affordable way to monitor heart conditions. This study proposes the training and testing of several classifiers based on SVMs...
The phonocardiogram (PCG) can be used as an affordable way to monitor heart conditions. This study proposes the training and testing of several classifiers based on SVMs (support vector machines), k-NN (k-Nearest Neighbor), and NNs (neural networks) to perform binary ("Normal"/"Pathologic") and multiclass ("Normal", "CAD" (coronary artery disease), "MVP" (mitral valve prolapse), and "Benign" (benign murmurs)) classification of PCG signals, without heart sound segmentation algorithms. Two datasets of 482 and 826 PCG signals from the Physionet/CinC 2016 dataset are used to train the binary and multiclass classifiers, respectively. Each PCG signal is pre-processed, with spike removal, denoising, filtering, and normalization; afterward, it is divided into 5 s frames with a 1 s shift. Subsequently, a feature set is extracted from each frame to train and test the binary and multiclass classifiers. Concerning the binary classification, the trained classifiers yielded accuracies ranging from 92.4 to 98.7% on the test set, with memory occupations from 92.7 kB to 11.1 MB. Regarding the multiclass classification, the trained classifiers achieved accuracies spanning from 95.3 to 98.6% on the test set, occupying a memory portion from 233 kB to 14.1 MB. The NNs trained and tested in this work offer the best trade-off between performance and memory occupation, whereas the trained k-NN models obtained the best performance at the cost of large memory occupation (up to 14.1 MB). The classifiers' performance slightly depends on the signal quality, since a denoising step is performed during pre-processing. To this end, the signal-to-noise ratio (SNR) was acquired before and after the denoising, indicating an improvement between 15 and 30 dB. The trained and tested models occupy relatively little memory, enabling their implementation in resource-limited systems.
Topics: Humans; Phonocardiography; Machine Learning; Signal Processing, Computer-Assisted; Algorithms; Neural Networks, Computer; Wearable Electronic Devices; Support Vector Machine
PubMed: 38931636
DOI: 10.3390/s24123853 -
Plants (Basel, Switzerland) Jun 2024In tropical countries, combating leaf curl disease in hot peppers has become important in improvement programs. Leaf curl disease is caused by whitefly () transmitted...
In tropical countries, combating leaf curl disease in hot peppers has become important in improvement programs. Leaf curl disease is caused by whitefly () transmitted begomoviruses, which mainly include chilli leaf curl virus (ChiLCV). However, multiple begomoviruses have also been found to be associated with this disease. The line, DLS-Sel-10, was found to be a tolerant source against this disease during field screening. In this study, we characterized the resistance of DLS-sel-10 against chilli leaf curl virus (ChiLCV) in comparison to the susceptible cultivar Phule Mukta (PM), focusing on the level, stage, and nature of resistance. Comprehensive investigations involved screening of DLS-Sel-10 against the whitefly vector ChiLCV. The putative tolerant line displayed reduced virus infection at the seedling stage, with increasing resistance during vegetative, flowering, and fruiting stages. Both DLS-Sel-10 and PM could be infected with ChiLCV, although DLS-Sel-10 remained symptomless. Insect feeding assays revealed DLS-Sel-10 as a less preferred host for whiteflies compared to PM. In conclusion, DLS-Sel-10 demonstrated tolerance not only to ChiLCV but also served as an unfavorable host for the whitefly vector. The study highlighted an age-dependent increase in tolerance within DLS-Sel-10, showcasing its potential for effective leaf curl disease management in chilli.
PubMed: 38931079
DOI: 10.3390/plants13121647 -
Plants (Basel, Switzerland) Jun 2024The vector-transmitted Citrus Greening (CG) disease, also called Huanglongbing, is one of the most destructive diseases of citrus. Since no measures for directly...
Diagnosis of Citrus Greening Using Artificial Intelligence: A Faster Region-Based Convolutional Neural Network Approach with Convolution Block Attention Module-Integrated VGGNet and ResNet Models.
The vector-transmitted Citrus Greening (CG) disease, also called Huanglongbing, is one of the most destructive diseases of citrus. Since no measures for directly controlling this disease are available at present, current disease management integrates several measures, such as vector control, the use of disease-free trees, the removal of diseased trees, etc. The most essential issue in integrated management is how CG-infected trees can be detected efficiently. For CG detection, digital image analyses using deep learning algorithms have attracted much interest from both researchers and growers. Models using transfer learning with the Faster R-CNN architecture were constructed and compared with two pre-trained Convolutional Neural Network (CNN) models, VGGNet and ResNet. Their efficiency was examined by integrating their feature extraction capabilities into the Convolution Block Attention Module (CBAM) to create VGGNet+CBAM and ResNet+CBAM variants. ResNet models performed best. Moreover, the integration of CBAM notably improved CG disease detection precision and the overall performance of the models. Efficient models with transfer learning using Faster R-CNN were loaded on web applications to facilitate access for real-time diagnosis by farmers via the deployment of in-field images. The practical ability of the applications to detect CG disease is discussed.
PubMed: 38931063
DOI: 10.3390/plants13121631 -
Microorganisms Jun 2024The pandemic of Southern rice black-streaked dwarf virus (SRBSDV) in and after the late 2000s caused serious yield losses in rice in Southeast and East Asia. This virus...
The pandemic of Southern rice black-streaked dwarf virus (SRBSDV) in and after the late 2000s caused serious yield losses in rice in Southeast and East Asia. This virus was first recorded in China in 2001, but its exclusive vector insect, , occurred there before then. To clarify the evolutionary origin of SRBSDV as the first plant virus transmitted by , we tested virus transmission using three chronological strains of , two of which were established before the first report of SRBSDV. When the strains fed on SRBSDV-infected rice plants were transferred to healthy rice plants, those established in 1989 and 1999 transmitted the virus to rice similarly to the strain established in 2010. SRBSDV quantification by RT-qPCR confirmed virus accumulation in the salivary glands of all three strains. Therefore, SRBSDV transmission by was not caused by biological changes in the vector, but probably by the genetic change of the virus from a closely related , Rice black-streaked dwarf virus, as suggested by ecological and molecular biological comparisons between the two viruses. This result will help us to better understand the evolutionary relationship between plant viruses and their vector insects and to better manage viral disease in rice cropping in Asia.
PubMed: 38930586
DOI: 10.3390/microorganisms12061204 -
Microorganisms Jun 2024Chagas disease, discovered over a century ago, continues to pose a global health challenge, affecting millions mainly in Latin America. This historical review with...
Chagas disease, discovered over a century ago, continues to pose a global health challenge, affecting millions mainly in Latin America. This historical review with commentary outlines the disease's discovery, its evolution into a global concern due to migration, and highlights significant advances in diagnostics and treatment strategies. Despite these advancements, the paper discusses ongoing challenges in eradication, including vector control, congenital transmission, the disease's asymptomatic nature, and socioeconomic barriers to effective management. It calls for a multidisciplinary approach, enhanced diagnostics, improved treatment accessibility, and sustained vector control efforts. The review emphasizes the importance of global collaboration and increased funding to reduce Chagas disease's impact.
PubMed: 38930535
DOI: 10.3390/microorganisms12061153 -
Microorganisms Jun 2024Accurate diagnostic techniques and effective therapeutic methods are required to treat . The application of chicken single-chain variable fragment (scFv) antibodies may...
Accurate diagnostic techniques and effective therapeutic methods are required to treat . The application of chicken single-chain variable fragment (scFv) antibodies may diagnose and treat . This study used the phage display technique to construct a chicken-derived immune scFv antibody library against . Total RNA was extracted from the spleens of five immunized chickens and reverse transcribed into cDNA. A fragment of scFv was produced by overlap extension PCR and cloned into a pHEN2 phagemid vector. After the package with the M13KO7 helper phage, the recombinant HpaA protein was used as a target antigen to validate the screening ability of our antibody library by bio-panning. The dilution counting results showed that the size of the primary antibody library was estimated to be 1 × 10 cfu/mL. PCR analysis of 47 clones from the library revealed that about 100% of the clones were positive with scFv fragments, and there were no identical sequences, indicating the good diversity of the antibody library. After three rounds of bio-panning, high-affinity antibodies against recombinant HpaA protein were successfully obtained. The selected antibody specifically recognized HpaA protein in nine different strains, confirming the screening ability of our library. The chicken immune scFv antibody library against was successfully constructed, and the antibody library's screening ability was validated by selecting specific scFv antibodies against recombinant HpaA and clinical strains. It provided a simple and rapid method to obtain antibodies against for diagnosis or treatment.
PubMed: 38930530
DOI: 10.3390/microorganisms12061148 -
Microorganisms May 2024This study investigated the prevalence of and in 494 engorged ticks collected from various animal hosts, including cattle, horses, sheep, chickens, dogs, and cats, in...
This study investigated the prevalence of and in 494 engorged ticks collected from various animal hosts, including cattle, horses, sheep, chickens, dogs, and cats, in six regions of northern Kyrgyzstan. Ten tick species, belonging to two families and six genera, were identified based on , 16S rRNA, and genes: (26.5%), (18.0%), spp. (16.0%), (11.8%), . (10.9%), . (7.7%), (4.5%), . (3.8%), . complex (0.6%), and (0.2%). PCR analysis revealed a 15.0% (74/494) overall infection rate of and . species were found in six tick species and were identified as ( = 44), spp. ( = 20), ( = 5), and ( = 2). species were found only in ( = 5) and identified as ( = 1) and spp. ( = 4). Additionally, two were co-infected with and . This is the first study to investigate tick-borne bacterial pathogens in ticks collected from animal hosts in Kyrgyzstan. Our findings contribute to a better understanding of the epidemiology and emergence of tick-borne infections in Kyrgyzstan.
PubMed: 38930428
DOI: 10.3390/microorganisms12061046 -
Journal of Clinical Medicine Jun 2024Carotid stenosis (CS) is an atherosclerotic disease of the carotid artery that can lead to devastating cardiovascular outcomes such as stroke, disability, and death....
Predicting Major Adverse Carotid Cerebrovascular Events in Patients with Carotid Stenosis: Integrating a Panel of Plasma Protein Biomarkers and Clinical Features-A Pilot Study.
Carotid stenosis (CS) is an atherosclerotic disease of the carotid artery that can lead to devastating cardiovascular outcomes such as stroke, disability, and death. The currently available treatment for CS is medical management through risk reduction, including control of hypertension, diabetes, and/or hypercholesterolemia. Surgical interventions are currently suggested for patients with symptomatic disease with stenosis >50%, where patients have suffered from a carotid-related event such as a cerebrovascular accident, or asymptomatic disease with stenosis >60% if the long-term risk of death is <3%. There is a lack of current plasma protein biomarkers available to predict patients at risk of such adverse events. In this study, we investigated several growth factors and biomarkers of inflammation as potential biomarkers for adverse CS events such as stroke, need for surgical intervention, myocardial infarction, and cardiovascular-related death. In this pilot study, we use a support vector machine (SVM), random forest models, and the following four significantly elevated biomarkers: C-X-C Motif Chemokine Ligand 6 (CXCL6); Interleukin-2 (IL-2); Galectin-9; and angiopoietin-like protein (ANGPTL4). Our SVM model best predicted carotid cerebrovascular events with an area under the curve (AUC) of >0.8 and an accuracy of 0.88, demonstrating strong prognostic capability. : Our SVM model may be used for risk stratification of patients with CS to determine those who may benefit from surgical intervention.
PubMed: 38929911
DOI: 10.3390/jcm13123382 -
Life (Basel, Switzerland) Jun 2024Rheumatoid arthritis (RA) is a systemic autoimmune disorder caused by inflammation of cartilaginous diarthrodial joints that destroys joints and cartilage, resulting in... (Review)
Review
Rheumatoid arthritis (RA) is a systemic autoimmune disorder caused by inflammation of cartilaginous diarthrodial joints that destroys joints and cartilage, resulting in synovitis and pannus formation. Timely detection and effective management of RA are pivotal for mitigating inflammatory arthritis consequences, potentially influencing disease progression. Nuclear medicine using radiolabeled targeted vectors presents a promising avenue for RA diagnosis and response to treatment assessment. Radiopharmaceutical such as technetium-99m (Tc), combined with single photon emission computed tomography (SPECT) combined with CT (SPECT/CT), introduces a more refined diagnostic approach, enhancing accuracy through precise anatomical localization, representing a notable advancement in hybrid molecular imaging for RA evaluation. This comprehensive review discusses existing research, encompassing in vitro, in vivo, and clinical studies to explore the application of Tc radiolabeled targeting vectors with SPECT imaging for RA diagnosis. The purpose of this review is to highlight the potential of this strategy to enhance patient outcomes by improving the early detection and management of RA.
PubMed: 38929734
DOI: 10.3390/life14060751 -
Animals : An Open Access Journal From... Jun 2024Subclinical mastitis is a common and economically significant disease that affects dairy sheep production. Thermal imaging presents a promising avenue for non-invasive...
Subclinical mastitis is a common and economically significant disease that affects dairy sheep production. Thermal imaging presents a promising avenue for non-invasive detection, but existing methodologies often rely on simplistic temperature differentials, potentially leading to inaccurate assessments. This study proposes an advanced algorithmic approach integrating thermal imaging processing with statistical texture analysis and t-distributed stochastic neighbor embedding (t-SNE). Our method achieves a high classification accuracy of 84% using the support vector machines (SVM) algorithm. Furthermore, we introduce another commonly employed evaluation metric, correlating thermal images with commercial California mastitis test (CMT) results after establishing threshold conditions on statistical features, yielding a sensitivity (the true positive rate) of 80% and a specificity (the true negative rate) of 92.5%. The evaluation metrics underscore the efficacy of our approach in detecting subclinical mastitis in dairy sheep, offering a robust tool for improved management practices.
PubMed: 38929416
DOI: 10.3390/ani14121797