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Sumbul, Sultana A, Heyat MBB, Rahman K, Akhtar F, Parveen S, Urbano MB, Lipari V, De la Torre Díez I, Khan AA, Malik A. Efficacy and classification of Sesamum indicum linn seeds with Rosa damascena mill oil in uncomplicated pelvic inflammatory disease using machine learning. Front Chem 2024; 12:1361980. [PMID: 38629105 PMCID: PMC11018920 DOI: 10.3389/fchem.2024.1361980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 02/05/2024] [Indexed: 04/19/2024] Open
Abstract
Background and objectives: As microbes are developing resistance to antibiotics, natural, botanical drugs or traditional herbal medicine are presently being studied with an eye of great curiosity and hope. Hence, complementary and alternative treatments for uncomplicated pelvic inflammatory disease (uPID) are explored for their efficacy. Therefore, this study determined the therapeutic efficacy and safety of Sesamum indicum Linn seeds with Rosa damascena Mill Oil in uPID with standard control. Additionally, we analyzed the data with machine learning. Materials and methods: We included 60 participants in a double-blind, double-dummy, randomized standard-controlled study. Participants in the Sesame and Rose oil group (SR group) (n = 30) received 14 days course of black sesame powder (5 gm) mixed with rose oil (10 mL) per vaginum at bedtime once daily plus placebo capsules orally. The standard group (SC), received doxycycline 100 mg twice and metronidazole 400 mg thrice orally plus placebo per vaginum for the same duration. The primary outcome was a clinical cure at post-intervention for visual analogue scale (VAS) for lower abdominal pain (LAP), and McCormack pain scale (McPS) for abdominal-pelvic tenderness. The secondary outcome included white blood cells (WBC) cells in the vaginal wet mount test, safety profile, and health-related quality of life assessed by SF-12. In addition, we used AdaBoost (AB), Naïve Bayes (NB), and Decision Tree (DT) classifiers in this study to analyze the experimental data. Results: The clinical cure for LAP and McPS in the SR vs SC group was 82.85% vs 81.48% and 83.85% vs 81.60% on Day 15 respectively. On Day 15, pus cells less than 10 in the SR vs SC group were 86.6% vs 76.6% respectively. No adverse effects were reported in both groups. The improvement in total SF-12 score on Day 30 for the SR vs SC group was 82.79% vs 80.04% respectively. In addition, our Naive Bayes classifier based on the leave-one-out model achieved the maximum accuracy (68.30%) for the classification of both groups of uPID. Conclusion: We concluded that the SR group is cost-effective, safer, and efficacious for curing uPID. Proposed alternative treatment (test drug) could be a substitute of standard drug used for Female genital tract infections.
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Affiliation(s)
- Sumbul
- Department of Ilmul Qabalat wa Amraze Niswan, National Institute of Unani Medicine, Ministry of AYUSH, Government of India, Bengaluru, Karnataka, India
| | - Arshiya Sultana
- Department of Ilmul Qabalat wa Amraze Niswan, National Institute of Unani Medicine, Ministry of AYUSH, Government of India, Bengaluru, Karnataka, India
| | - Md Belal Bin Heyat
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, Zhejiang, China
| | - Khaleequr Rahman
- Department of Ilmul Saidla, National Institute of Unani Medicine, Ministry of AYUSH, Government of India, Bengaluru, Karnataka, India
| | - Faijan Akhtar
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Saba Parveen
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China
| | - Mercedes Briones Urbano
- Research Group on Foods, Nutritional Biochemistry and Health, Universidad Europea Del Atlántico, Santander, Spain
- Research Group on Foods, Nutritional Biochemistry and Health, Universidade Internacional do Cuanza, Kuito, Angola
- Research Group on Foods, Nutritional Biochemistry and Health, Universidad Internacional Iberoamericana, Arecibo, PR, United States
| | - Vivian Lipari
- Research Group on Foods, Nutritional Biochemistry and Health, Universidad Europea Del Atlántico, Santander, Spain
- Research Group on Foods, Nutritional Biochemistry and Health, Universidade Internacional do Cuanza, Kuito, Angola
- Research Group on Foods, Nutritional Biochemistry and Health, Universidad Internacional Iberoamericana, Arecibo, PR, United States
| | - Isabel De la Torre Díez
- Department of Signal Theory and Communications and Telemedicine Engineering, University of Valladolid, Valladolid, Spain
| | - Azmat Ali Khan
- Pharmaceutical Biotechnology Laboratory, Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Abdul Malik
- Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
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Ukwuoma CC, Qin Z, Heyat MBB, Akhtar F, Smahi A, Jackson JK, Furqan Qadri S, Muaad AY, Monday HN, Nneji GU. Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images. Bioengineering (Basel) 2022; 9:709. [PMID: 36421110 PMCID: PMC9687434 DOI: 10.3390/bioengineering9110709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 11/04/2022] [Accepted: 11/16/2022] [Indexed: 11/22/2022] Open
Abstract
According to research, classifiers and detectors are less accurate when images are blurry, have low contrast, or have other flaws which raise questions about the machine learning model's ability to recognize items effectively. The chest X-ray image has proven to be the preferred image modality for medical imaging as it contains more information about a patient. Its interpretation is quite difficult, nevertheless. The goal of this research is to construct a reliable deep-learning model capable of producing high classification accuracy on chest x-ray images for lung diseases. To enable a thorough study of the chest X-ray image, the suggested framework first derived richer features using an ensemble technique, then a global second-order pooling is applied to further derive higher global features of the images. Furthermore, the images are then separated into patches and position embedding before analyzing the patches individually via a vision transformer approach. The proposed model yielded 96.01% sensitivity, 96.20% precision, and 98.00% accuracy for the COVID-19 Radiography Dataset while achieving 97.84% accuracy, 96.76% sensitivity and 96.80% precision, for the Covid-ChestX-ray-15k dataset. The experimental findings reveal that the presented models outperform traditional deep learning models and other state-of-the-art approaches provided in the literature.
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Affiliation(s)
- Chiagoziem C. Ukwuoma
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhiguang Qin
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
- Centre for VLSI and Embedded System Technologies, International Institute of Information Technology, Hyderabad 500032, India
- Department of Science and Engineering, Novel Global Community Educational Foundation, Hebersham, NSW 2770, Australia
| | - Faijan Akhtar
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Abla Smahi
- School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Peking University, Shenzhen 518060, China
| | - Jehoiada K. Jackson
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Syed Furqan Qadri
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou 311121, China
| | | | - Happy N. Monday
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Grace U. Nneji
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
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Fazmiya MJA, Sultana A, Rahman K, Heyat MBB, Sumbul, Akhtar F, Khan S, Appiah SCY. Current Insights on Bioactive Molecules, Antioxidant, Anti-Inflammatory, and Other Pharmacological Activities of Cinnamomum camphora Linn. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:9354555. [PMID: 36246399 PMCID: PMC9568346 DOI: 10.1155/2022/9354555] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 08/26/2022] [Accepted: 09/22/2022] [Indexed: 01/04/2023]
Abstract
C. camphora is a renowned traditional Unani medicinal herb and belongs to the family Lauraceae. It has therapeutic applications in various ailments and prophylactic properties to prevent flu-like epidemic symptoms and COVID-19. This comprehensive appraisal is to familiarize the reader with the traditional, broad applications of camphor both in Unani and modern medicine and its effects on bioactive molecules. Electronic databases such as Web of Science, PubMed, Google Scholar, Scopus, and Research Gate were searched for bioactive molecules, and preclinical/clinical research and including 59 research and review papers up to 2022 were retrieved. Additionally, 21 classical Unani and English herbal pharmacopeia books with ethnomedicinal properties and therapeutic applications were explored. Oxidative stress significantly impacts aging, obesity, diabetes mellitus, depression, and neurodegenerative diseases. The polyphenolic bioactive compounds such as linalool, borneol, and nerolidol of C. camphora have antioxidant activity and have the potential to remove free radicals. Its other major bioactive molecules are camphor, cineole, limelol, safrole, limonene, alpha-pinene, and cineole with anti-inflammatory, antibacterial, anxiolytic, analgesic, immunomodulatory, antihyperlipidemic, and many other pharmacological properties have been established in vitro or in vivo preclinical research. Natural bioactive molecules and their mechanisms of action and applications in diseases have been highlighted, with future prospects, gaps, and priorities that need to be addressed.
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Affiliation(s)
- Mohamed Joonus Aynul Fazmiya
- Department of Amraze Niswan wa Ilmul Qabalat, National Institute of Unani Medicine, Ministry of Ayush, Bengaluru, Karnataka, India
| | - Arshiya Sultana
- Department of Amraze Niswan wa Ilmul Qabalat, National Institute of Unani Medicine, Ministry of Ayush, Bengaluru, Karnataka, India
| | - Khaleequr Rahman
- Department of Ilmul Saidla, National Institute of Unani Medicine, Ministry of Ayush, Bengaluru, Karnataka, India
| | - Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
| | - Sumbul
- Department of Amraze Niswan wa Ilmul Qabalat, National Institute of Unani Medicine, Ministry of Ayush, Bengaluru, Karnataka, India
| | - Faijan Akhtar
- School of Computer Science and Engineering, University of Electronic Science and Engineering, Chengdu, China
| | - Salabat Khan
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
| | - Seth Christopher Yaw Appiah
- Health and Social Care Research Group, Department of Sociology and Social Work, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
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An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9475162. [PMID: 36210977 PMCID: PMC9536938 DOI: 10.1155/2022/9475162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 07/25/2022] [Accepted: 08/17/2022] [Indexed: 01/10/2023]
Abstract
Electrocardiography (ECG) is a well-known noninvasive technique in medical science that provides information about the heart's rhythm and current conditions. Automatic ECG arrhythmia diagnosis relieves doctors' workload and improves diagnosis effectiveness and efficiency. This study proposes an automatic end-to-end 2D CNN (two-dimensional convolution neural networks) deep learning method with an effective DenseNet model for addressing arrhythmias recognition. To begin, the proposed model is trained and evaluated on the 97720 and 141404 beat images extracted from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia and St. Petersburg Institute of Cardiological Technics (INCART) datasets (both are imbalanced class datasets) using a stratified 5-fold evaluation strategy. The data is classified into four groups: N (normal), V (ventricular ectopic), S (supraventricular ectopic), and F (fusion), based on the Association for the Advancement of Medical Instrumentation® (AAMI). The experimental results show that the proposed model outperforms state-of-the-art models for recognizing arrhythmias, with the accuracy of 99.80% and 99.63%, precision of 98.34% and 98.94%, and F1-score of 98.91% and 98.91% on the MIT-BIH arrhythmia and INCART datasets, respectively. Using a transfer learning mechanism, the proposed model is also evaluated with only five individuals of supraventricular MIT-BIH arrhythmia and five individuals of European ST-T datasets (both of which are also class imbalanced) and achieved satisfactory results. So, the proposed model is more generalized and could be a prosperous solution for arrhythmias recognition from class imbalance datasets in real-life applications.
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Sleep Disorder and Cocaine Abuse Impact Purine and Pyrimidine Nucleotide Metabolic Signatures. Metabolites 2022; 12:metabo12090869. [PMID: 36144274 PMCID: PMC9502494 DOI: 10.3390/metabo12090869] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/12/2022] [Accepted: 09/13/2022] [Indexed: 11/21/2022] Open
Abstract
Disturbances in the circadian rhythm alter the normal sleep-wake cycle, which increases vulnerability to drug abuse. Drug abuse can disrupt several homeostatic processes regulated by the circadian rhythm and influence addiction paradigms, including cravings for cocaine. The relationship between circadian rhythm and cocaine abuse is complex and bidirectional, and disruption impacts both brain function and metabolic profiles. Therefore, elucidating the impact of circadian rhythm changes and cocaine abuse on the human metabolome may provide new insights into identifying potential biomarkers. We examine the effect of cocaine administration with and without circadian rhythm sleep disruption (CRSD) on metabolite levels and compare these to healthy controls in an in vivo study. A metabolomics analysis is performed on the control, CRSD, cocaine, and CRSD with cocaine groups. Plasma metabolite concentrations are analyzed using a liquid chromatography electrochemical array platform. We identify 242 known metabolites compared to the control; 26 in the CRSD with cocaine group, 4 in the CRSD group, and 22 in the cocaine group are significantly differentially expressed. Intriguingly, in the CRSD with cocaine treatment group, the expression levels of uridine monophosphate (p < 0.008), adenosine 5′-diphosphate (p < 0.044), and inosine (p < 0.019) are significantly altered compared with those in the cocaine group. In summary, alterations in purine and pyrimidine metabolism provide clues regarding changes in the energy profile and metabolic pathways associated with chronic exposure to cocaine and CRSD.
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Role of Inflammation, Oxidative Stress, and Mitochondrial Changes in Premenstrual Psychosomatic Behavioral Symptoms with Anti-Inflammatory, Antioxidant Herbs, and Nutritional Supplements. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:3599246. [PMID: 35873799 PMCID: PMC9300296 DOI: 10.1155/2022/3599246] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 07/01/2022] [Indexed: 12/19/2022]
Abstract
Premenstrual syndrome (PMS) significantly lowers the quality of life and impairs personal and social relationships in reproductive-age women. Some recommendations are that inappropriate oxidative stress and inflammatory response are involved in PMS. Various nutritional supplements and herbs showed neuro-psycho-pharmacological activity with antioxidant and anti-inflammatory properties. This study aims to determine the systematic review of randomized controlled trials (RCTs) of herbal medicine and nutritional supplements in PMS. We also comprehensively highlighted the role of oxidative stress, inflammation, and mitochondrial changes on PMS with the application of computational intelligence. We used PRISMA and research question-based techniques to collect the data for evaluation of our study on different databases such as Scopus, PubMed, and PROSPERO from 1990 to 2022. The methodological quality of the published study was assessed by the modified Jadad scale. In addition, we used network visualization and word cloud techniques to find the closest terms of the study based on previous publications. While we also used computational intelligence techniques to give the idea for the classification of experimental data from PMS. We found 25 randomized controlled studies with 1949 participants (mean ± SD: 77.96 ± 22.753) using the PRISMA technique, and all were high-quality studies. We also extracted the closest terms related to our study using network visualization techniques. This work has revealed the future direction and research gap on the role of oxidative stress and inflammation in PMS. In vitro and in vivo studies showed that bioactive molecules such as curcumin, allicin, anethole, thymoquinone, cyanidin 3-glucoside, gamma-linoleic acid, and various molecules not only have antioxidant and anti-inflammatory properties but also other various activities such as GABA-A receptor agonist, serotonergic, antidepressant, sedative, and analgesic. Traditional Unani Herbal medicine and nutritional supplements can effectively relieve PMS symptoms as they possess many bioactive molecules that are pharmacologically proven for the aforementioned properties. Hence, these biomolecules might influence a complex physical and psychological disease process like PMS. However, more rigorous research studies are recommended for in-depth knowledge of the efficacy of bioactive molecules on premenstrual syndrome in clinical trials.
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Bin Heyat MB, Akhtar F, Abbas SJ, Al-Sarem M, Alqarafi A, Stalin A, Abbasi R, Muaad AY, Lai D, Wu K. Wearable Flexible Electronics Based Cardiac Electrode for Researcher Mental Stress Detection System Using Machine Learning Models on Single Lead Electrocardiogram Signal. BIOSENSORS 2022; 12:427. [PMID: 35735574 PMCID: PMC9221208 DOI: 10.3390/bios12060427] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 06/06/2022] [Accepted: 06/14/2022] [Indexed: 05/02/2023]
Abstract
In the modern world, wearable smart devices are continuously used to monitor people's health. This study aims to develop an automatic mental stress detection system for researchers based on Electrocardiogram (ECG) signals from smart T-shirts using machine learning classifiers. We used 20 subjects, including 10 from mental stress (after twelve hours of continuous work in the laboratory) and 10 from normal (after completing the sleep or without any work). We also applied three scoring techniques: Chalder Fatigue Scale (CFS), Specific Fatigue Scale (SFS), Depression, Anxiety, and Stress Scale (DASS), to confirm the mental stress. The total duration of ECG recording was 1800 min, including 1200 min during mental stress and 600 min during normal. We calculated two types of features, such as demographic and extracted by ECG signal. In addition, we used Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Logistic Regression (LR) to classify the intra-subject (mental stress and normal) and inter-subject classification. The DT leave-one-out model has better performance in terms of recall (93.30%), specificity (96.70%), precision (94.40%), accuracy (93.30%), and F1 (93.50%) in the intra-subject classification. Additionally, The classification accuracy of the system in classifying inter-subjects is 94.10% when using a DT classifier. However, our findings suggest that the wearable smart T-shirt based on the DT classifier may be used in big data applications and health monitoring. Mental stress can lead to mitochondrial dysfunction, oxidative stress, blood pressure, cardiovascular disease, and various health problems. Therefore, real-time ECG signals help assess cardiovascular and related risk factors in the initial stage based on machine learning techniques.
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Affiliation(s)
- Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China;
| | - Faijan Akhtar
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China;
| | - Syed Jafar Abbas
- Faculty of Management, Vancouver Island University, Nanaimo, BC V9R5S5, Canada;
| | - Mohammed Al-Sarem
- College of Computer Science and Engineering, Taibah University, Medina 42353, Saudi Arabia;
- Department of Computer Science, University of Sheba Province, Marib, Yemen
| | - Abdulrahman Alqarafi
- College of Computer Science and Engineering, Taibah University, Medina 42353, Saudi Arabia;
| | - Antony Stalin
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China;
| | - Rashid Abbasi
- School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China;
| | - Abdullah Y. Muaad
- Department of Studies in Computer Science, University of Mysore, Mysore 570005, Karnataka, India;
- IT Department, Sana’a Community College, Sana’a 5695, Yemen
| | - Dakun Lai
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Kaishun Wu
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China;
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Segmentation of Drug-Treated Cell Image and Mitochondrial-Oxidative Stress Using Deep Convolutional Neural Network. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:5641727. [PMID: 35663204 PMCID: PMC9162846 DOI: 10.1155/2022/5641727] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 04/27/2022] [Indexed: 12/27/2022]
Abstract
Most multicellular organisms require apoptosis, or programmed cell death, to function properly and survive. On the other hand, morphological and biochemical characteristics of apoptosis have remained remarkably consistent throughout evolution. Apoptosis is thought to have at least three functionally distinct phases: induction, effector, and execution. Recent studies have revealed that reactive oxygen species (ROS) and the oxidative stress could play an essential role in apoptosis. Advanced microscopic imaging techniques allow biologists to acquire an extensive amount of cell images within a matter of minutes which rule out the manual analysis of image data acquisition. The segmentation of cell images is often considered the cornerstone and central problem for image analysis. Currently, the issue of segmentation of mitochondrial cell images via deep learning receives increasing attention. The manual labeling of cell images is time-consuming and challenging to train a pro. As a courtesy method, mitochondrial cell imaging (MCI) is proposed to identify the normal, drug-treated, and diseased cells. Furthermore, cell movement (fission and fusion) is measured to evaluate disease risk. The newly proposed drug-treated, normal, and diseased image segmentation (DNDIS) algorithm can quickly segment mitochondrial cell images without supervision and further segment the highly drug-treated cells in the picture, i.e., normal, diseased, and drug-treated cells. The proposed method is based on the ResNet-50 deep learning algorithm. The dataset consists of 414 images mainly categorised into different sets (drug, diseased, and normal) used microscopically. The proposed automated segmentation method has outperformed and secured high precision (90%, 92%, and 94%); moreover, it also achieves proper training. This study will benefit medicines and diseased cell measurements in medical tests and clinical practices.
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An Effective and Lightweight Deep Electrocardiography Arrhythmia Recognition Model Using Novel Special and Native Structural Regularization Techniques on Cardiac Signal. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:3408501. [PMID: 35449862 PMCID: PMC9018174 DOI: 10.1155/2022/3408501] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 01/19/2022] [Accepted: 03/15/2022] [Indexed: 02/05/2023]
Abstract
Recently, cardiac arrhythmia recognition from electrocardiography (ECG) with deep learning approaches is becoming popular in clinical diagnosis systems due to its good prognosis findings, where expert data preprocessing and feature engineering are not usually required. But a lightweight and effective deep model is highly demanded to face the challenges of deploying the model in real-life applications and diagnosis accurately. In this work, two effective and lightweight deep learning models named Deep-SR and Deep-NSR are proposed to recognize ECG beats, which are based on two-dimensional convolution neural networks (2D CNNs) while using different structural regularizations. First, 97720 ECG beats extracted from all records of a benchmark MIT-BIH arrhythmia dataset have been transformed into 2D RGB (red, green, and blue) images that act as the inputs to the proposed 2D CNN models. Then, the optimization of the proposed models is performed through the proper initialization of model layers, on-the-fly augmentation, regularization techniques, Adam optimizer, and weighted random sampler. Finally, the performance of the proposed models is evaluated by a stratified 5-fold cross-validation strategy along with callback features. The obtained overall accuracy of recognizing normal beat and three arrhythmias (V-ventricular ectopic, S-supraventricular ectopic, and F-fusion) based on the Association for the Advancement of Medical Instrumentation (AAMI) is 99.93%, and 99.96% for the proposed Deep-SR model and Deep-NSR model, which demonstrate that the effectiveness of the proposed models has surpassed the state-of-the-art models and also expresses the higher model generalization. The received results with model size suggest that the proposed CNN models especially Deep-NSR could be more useful in wearable devices such as medical vests, bracelets for long-term monitoring of cardiac conditions, and in telemedicine to accurate diagnose the arrhythmia from ECG automatically. As a result, medical costs of patients and work pressure on physicians in medicals and clinics would be reduced effectively.
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Cheung JCW, Tam EWC, Mak AHY, Chan TTC, Zheng YP. A Night-Time Monitoring System (eNightLog) to Prevent Elderly Wandering in Hostels: A Three-Month Field Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19042103. [PMID: 35206290 PMCID: PMC8872318 DOI: 10.3390/ijerph19042103] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 02/07/2022] [Accepted: 02/09/2022] [Indexed: 12/02/2022]
Abstract
Older people are increasingly dependent on others to support their daily activities due to geriatric symptoms such as dementia. Some of them stay in long-term care facilities. Elderly people with night wandering behaviour may lose their way, leading to a significant risk of injuries. The eNightLog system was developed to monitor the night-time bedside activities of older people in order to help them cope with this issue. It comprises a 3D time-of-flight near-infrared sensor and an ultra-wideband sensor for detecting human presence and to determine postures without a video camera. A threshold-based algorithm was developed to classify different activities, such as leaving the bed. The system is able to send alarm messages to caregivers if an elderly user performs undesirable activities. In this study, 17 sets of eNightLog systems were installed in an elderly hostel with 17 beds in 9 bedrooms. During the three-month field test, 26 older people with different periods of stay were included in the study. The accuracy, sensitivity and specificity of detecting non-assisted bed-leaving events was 99.8%, 100%, and 99.6%, respectively. There were only three false alarms out of 2762 bed-exiting events. Our results demonstrated that the eNightLog system is sufficiently accurate to be applied in the hostel environment. Machine learning with instance segmentation and online learning will enable the system to be used for widely different environments and people, with improvements to be made in future studies.
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Affiliation(s)
- James Chung-Wai Cheung
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China;
- Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong 999077, China
- Jockey Club Smart Ageing Hub, Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China; (E.W.-C.T.); (A.H.-Y.M.); (T.T.-C.C.)
- Correspondence: ; Tel.: +852-2766-7673
| | - Eric Wing-Cheung Tam
- Jockey Club Smart Ageing Hub, Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China; (E.W.-C.T.); (A.H.-Y.M.); (T.T.-C.C.)
| | - Alex Hing-Yin Mak
- Jockey Club Smart Ageing Hub, Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China; (E.W.-C.T.); (A.H.-Y.M.); (T.T.-C.C.)
| | - Tim Tin-Chun Chan
- Jockey Club Smart Ageing Hub, Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China; (E.W.-C.T.); (A.H.-Y.M.); (T.T.-C.C.)
| | - Yong-Ping Zheng
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China;
- Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong 999077, China
- Jockey Club Smart Ageing Hub, Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China; (E.W.-C.T.); (A.H.-Y.M.); (T.T.-C.C.)
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Recognition of mRNA N4 Acetylcytidine (ac4C) by Using Non-Deep vs. Deep Learning. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031344] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Deep learning models have been successfully applied in a wide range of fields. The creation of a deep learning framework for analyzing high-performance sequence data have piqued the research community’s interest. N4 acetylcytidine (ac4C) is a post-transcriptional modification in mRNA, is an mRNA component that plays an important role in mRNA stability control and translation. The ac4C method of mRNA changes is still not simple, time consuming, or cost effective for conventional laboratory experiments. As a result, we developed DL-ac4C, a CNN-based deep learning model for ac4C recognition. In the alternative scenario, the model families are well-suited to working in large datasets with a large number of available samples, especially in biological domains. In this study, the DL-ac4C method (deep learning) is compared to non-deep learning (machine learning) methods, regression, and support vector machine. The results show that DL-ac4C is more advanced than previously used approaches. The proposed model improves the accuracy recall area by 9.6 percent and 9.8 percent, respectively, for cross-validation and independent tests. More nuanced methods of incorporating prior bio-logical knowledge into the estimation procedure of deep learning models are required to achieve better results in terms of predictive efficiency and cost-effectiveness. Based on an experiment’s acetylated dataset, the DL-ac4C sequence-based predictor for acetylation sites in mRNA can predict whether query sequences have potential acetylation motifs.
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Modarres MH, Elliott JE, Weymann KB, Pleshakov D, Bliwise DL, Lim MM. Validation of Visually Identified Muscle Potentials during Human Sleep Using High Frequency/Low Frequency Spectral Power Ratios. SENSORS (BASEL, SWITZERLAND) 2021; 22:55. [PMID: 35009594 PMCID: PMC8747095 DOI: 10.3390/s22010055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 12/17/2021] [Accepted: 12/20/2021] [Indexed: 06/14/2023]
Abstract
Surface electromyography (EMG), typically recorded from muscle groups such as the mentalis (chin/mentum) and anterior tibialis (lower leg/crus), is often performed in human subjects undergoing overnight polysomnography. Such signals have great importance, not only in aiding in the definitions of normal sleep stages, but also in defining certain disease states with abnormal EMG activity during rapid eye movement (REM) sleep, e.g., REM sleep behavior disorder and parkinsonism. Gold standard approaches to evaluation of such EMG signals in the clinical realm are typically qualitative, and therefore burdensome and subject to individual interpretation. We originally developed a digitized, signal processing method using the ratio of high frequency to low frequency spectral power and validated this method against expert human scorer interpretation of transient muscle activation of the EMG signal. Herein, we further refine and validate our initial approach, applying this to EMG activity across 1,618,842 s of polysomnography recorded REM sleep acquired from 461 human participants. These data demonstrate a significant association between visual interpretation and the spectrally processed signals, indicating a highly accurate approach to detecting and quantifying abnormally high levels of EMG activity during REM sleep. Accordingly, our automated approach to EMG quantification during human sleep recording is practical, feasible, and may provide a much-needed clinical tool for the screening of REM sleep behavior disorder and parkinsonism.
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Affiliation(s)
- Mo H. Modarres
- Mental Illness Research, Education and Clinical Center (MIRECC-VISN1), VA Bedford Health Care System, Bedford, MA 01730, USA;
| | - Jonathan E. Elliott
- VA Portland Health Care System, Portland, OR 97239, USA;
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA
| | | | - Dennis Pleshakov
- School of Medicine, Oregon Health & Science University, Portland, OR 97239, USA;
| | | | - Miranda M. Lim
- VA Portland Health Care System, Portland, OR 97239, USA;
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA
- National Center for Rehabilitative Auditory Research, VA Portland Health Care System, Portland, OR 97239, USA
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA
- Oregon Institute of Occupational Health Sciences, Oregon Health & Science University, Portland, OR 97239, USA
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