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  1. Article ; Online: A Roadmap for Navigating an Academic Section Leadership Role.

    Singhal, Aparna

    Journal of the American College of Radiology : JACR

    2023  

    Language English
    Publishing date 2023-12-21
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2274861-1
    ISSN 1558-349X ; 1546-1440
    ISSN (online) 1558-349X
    ISSN 1546-1440
    DOI 10.1016/j.jacr.2023.12.013
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6636: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  2. Article ; Online: Clarification of Lumbar Puncture Risk Categorization in Consensus Guidelines for Periprocedural Management of Thrombotic and Bleeding Risk.

    Singhal, Aparna

    Journal of vascular and interventional radiology : JVIR

    2022  Volume 33, Issue 9, Page(s) 1121–1122

    MeSH term(s) Anticoagulants ; Humans ; Spinal Puncture/adverse effects ; Thrombosis/diagnostic imaging ; Thrombosis/etiology ; Thrombosis/therapy
    Chemical Substances Anticoagulants
    Language English
    Publishing date 2022-06-09
    Publishing country United States
    Document type Letter ; Comment
    ZDB-ID 1137756-2
    ISSN 1535-7732 ; 1051-0443
    ISSN (online) 1535-7732
    ISSN 1051-0443
    DOI 10.1016/j.jvir.2022.04.036
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 3720: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  3. Article ; Online: Call to Action: Women in Neuroradiology's Group (WINNERS)-Is There a Need?

    Singhal, A / Aiken, A

    AJNR. American journal of neuroradiology

    2022  Volume 43, Issue 10, Page(s) 1396–1399

    Language English
    Publishing date 2022-09-08
    Publishing country United States
    Document type Editorial
    ZDB-ID 603808-6
    ISSN 1936-959X ; 0195-6108
    ISSN (online) 1936-959X
    ISSN 0195-6108
    DOI 10.3174/ajnr.A7626
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1580: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (1.OG)
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  4. Article ; Online: Obesity in Toddlers and Young Children: Causes and Consequences.

    Singhal, Atul

    Nestle Nutrition Institute workshop series

    2020  Volume 95, Page(s) 41–51

    Abstract: The rapid rise in obesity in toddlers and young children (aged 0-5 years) is a major concern for public health globally. Understanding risk factors for obesity in the early years is therefore fundamental to help guide parents, educators, and health care ... ...

    Abstract The rapid rise in obesity in toddlers and young children (aged 0-5 years) is a major concern for public health globally. Understanding risk factors for obesity in the early years is therefore fundamental to help guide parents, educators, and health care professionals caring for young children and to develop preventative strategies. Most research has focused on biological risk factors, which can be broadly categorized as genetic predisposition, poor diet (and the behaviors that influence excessive food intake), insufficient physical activity, and the role of developmental factors in early life that influence long-term health. The latter includes establishment of dietary habits and dietary patterns in young (preschool) children and the effect of a high protein intake on the increasing risk of later obesity. Other risk factors particularly relevant to young children include inadequate sleep, high consumption of sugar-sweetened drinks, and large food portions. Understanding the causes of obesity in preschool children is particularly important in view of long-term detrimental consequences of obesity in this age group on the risk of obesity and cardiometabolic disease in adults. The present chapter reviews causes of obesity in preschool children and its consequences for long-term health, focusing particularly on modifiable nutritional risk factors.
    MeSH term(s) Causality ; Child, Preschool ; Exercise ; Feeding Behavior ; Humans ; Obesity/epidemiology ; Obesity/etiology ; Sugar-Sweetened Beverages
    Language English
    Publishing date 2020-11-06
    Publishing country Switzerland
    Document type Journal Article ; Review
    ISSN 1664-2155
    ISSN (online) 1664-2155
    DOI 10.1159/000511510
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Summary on Challenges in Nutrition in Toddlers and Young Children.

    Singhal, Atul

    Nestle Nutrition Institute workshop series

    2020  Volume 95, Page(s) 52–53

    MeSH term(s) Child, Preschool ; Feeding Behavior ; Humans ; Infant ; Nutritional Status
    Language English
    Publishing date 2020-11-09
    Publishing country Switzerland
    Document type Journal Article
    ISSN 1664-2155
    ISSN (online) 1664-2155
    DOI 10.1159/000511522
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Fusion of pattern-based and statistical features for Schizophrenia detection from EEG signals.

    Agarwal, Megha / Singhal, Amit

    Medical engineering & physics

    2023  Volume 112, Page(s) 103949

    Abstract: Schizophrenia (SZ) is a chronic disorder affecting the functioning of the brain. It can lead to irrational behaviour amongst the patients suffering from this disease. A low-cost diagnostic needs to be developed for SZ so that timely treatment can be ... ...

    Abstract Schizophrenia (SZ) is a chronic disorder affecting the functioning of the brain. It can lead to irrational behaviour amongst the patients suffering from this disease. A low-cost diagnostic needs to be developed for SZ so that timely treatment can be provided to the patients. In this work, we propose an accurate and easy-to-implement system to detect SZ using electroencephalogram (EEG) signals. The signal is divided into sub-band components by a Fourier-based technique that can be implemented in real-time using fast Fourier transform. Thereafter, statistical features are computed from these components. Further, look ahead pattern (LAP) is developed as a feature to capture local variations in the EEG signal. The fusion of these two distinct schemes enables a thorough examination of EEG signals. Kruskal-Wallis test is utilized for the selection of significant features. Various machine learning classifiers are employed and the proposed framework achieves 98.62% and 99.24% accuracy in identifying SZ cases, considering two distinct datasets, using boosted trees classifier. This method provides a promising candidate for widespread deployment in efficient real-time systems for SZ detection.
    MeSH term(s) Humans ; Schizophrenia/diagnosis ; Support Vector Machine ; Electroencephalography/methods ; Brain ; Algorithms
    Language English
    Publishing date 2023-01-04
    Publishing country England
    Document type Journal Article
    ZDB-ID 1181080-4
    ISSN 1873-4030 ; 1350-4533
    ISSN (online) 1873-4030
    ISSN 1350-4533
    DOI 10.1016/j.medengphy.2023.103949
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.B 2231: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  7. Article ; Online: The Impact of Human Milk Feeding on Long-Term Risk of Obesity and Cardiovascular Disease.

    Singhal, Atul

    Breastfeeding medicine : the official journal of the Academy of Breastfeeding Medicine

    2019  Volume 14, Issue S1, Page(s) S9–S10

    MeSH term(s) Bottle Feeding/adverse effects ; Breast Feeding ; Cardiovascular Diseases/etiology ; Cardiovascular Diseases/prevention & control ; Child ; Child, Preschool ; Humans ; Infant ; Infant Formula/adverse effects ; Infant, Newborn ; Milk, Human ; Pediatric Obesity/etiology ; Pediatric Obesity/prevention & control ; Protective Factors ; Risk Factors
    Language English
    Publishing date 2019-03-12
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2234680-6
    ISSN 1556-8342 ; 1556-8253
    ISSN (online) 1556-8342
    ISSN 1556-8253
    DOI 10.1089/bfm.2019.0037
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: ECG arrhythmia detection in an inter-patient setting using Fourier decomposition and machine learning.

    Fatimah, Binish / Singhal, Amit / Singh, Pushpendra

    Medical engineering & physics

    2024  Volume 124, Page(s) 104102

    Abstract: ECG beat classification or arrhythmia detection through artificial intelligence (AI) is an active topic of research. It is vital to recognize and detect the type of arrhythmia for monitoring cardiac abnormalities. The AI-based ECG beat classification ... ...

    Abstract ECG beat classification or arrhythmia detection through artificial intelligence (AI) is an active topic of research. It is vital to recognize and detect the type of arrhythmia for monitoring cardiac abnormalities. The AI-based ECG beat classification algorithms proposed in the literature suffer from two main drawbacks. Firstly, some of the works have not considered any unseen test data to validate the performance of their algorithms. Secondly, the accuracy of detecting superventricular ectopic beats (SVEB) needs to be improved. In this work, we address these issues by considering an inter-patient paradigm where the test dataset is collected from a different set of subjects than the training data. Also, the proposed methodology detects SVEB with an F1 score of 89.35%, which is better than existing algorithms. We have used the Fourier decomposition method (FDM) for multi-scale analysis of ECG signals and extracted time-domain and statistical features from the narrow-band signal components obtained using FDM. Feature selection techniques, including the Kruskal-Wallis test and minimum redundancy maximum relevance (mRMR) have been used to select only the relevant features and rank these features to remove any redundancy. Since the dataset used is highly imbalanced, Mathew's correlation coefficient (MCC) has also been used to analyze the performance of the proposed method. Support vector machine classifier with linear kernel achieves an overall 98.03% accuracy and 91.84% MCC for the MIT-BIH arrhythmia dataset.
    MeSH term(s) Humans ; Artificial Intelligence ; Signal Processing, Computer-Assisted ; Electrocardiography ; Algorithms ; Arrhythmias, Cardiac/diagnosis ; Support Vector Machine ; Heart Rate
    Language English
    Publishing date 2024-01-09
    Publishing country England
    Document type Journal Article
    ZDB-ID 1181080-4
    ISSN 1873-4030 ; 1350-4533
    ISSN (online) 1873-4030
    ISSN 1350-4533
    DOI 10.1016/j.medengphy.2024.104102
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.B 2231: Show issues Location:
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  9. Article ; Online: Nature's Pharmacy: Herbal Interventions in Rheumatoid Arthritis Treatment: A Comprehensive Review.

    Mittal, Vishnu / Barak, Ashima / Sharma, Anjali / Singhal, Abhinav

    Current rheumatology reviews

    2024  

    Abstract: Rheumatoid Arthritis (RA) is an inflammatory disease that causes severe joint destruction and persistent inflammation. This review aims to evaluate the efficacy, safety, and mechanisms of action of various herbal interventions in managing RA, providing ... ...

    Abstract Rheumatoid Arthritis (RA) is an inflammatory disease that causes severe joint destruction and persistent inflammation. This review aims to evaluate the efficacy, safety, and mechanisms of action of various herbal interventions in managing RA, providing valuable insights for patients and healthcare practitioners. To investigate the anti-inflammatory and antioxidant properties of selected herbal interventions, including turmeric, ginger, Boswellia serrata (frankincense), green tea, and Ashwagandha, to assess their potential as complementary treatments for RA, a comprehensive analysis is performed on the anti-inflammatory mechanisms and antioxidant effects of selected herbs. Emphasis is placed on the modulation of key inflammatory pathways and their ability to counteract oxidative stress, which are crucial factors in RA progression. Safety profiles and potential adverse effects of herbal remedies are also scrutinized. The review reveals promising evidence supporting the efficacy of turmeric and ginger in alleviating RA symptoms by modulating inflammatory pathways. Additionally, Boswellia serrata shows potential as an adjunct therapy for joint health and inflammation. The antioxidant-rich properties of green tea and Ashwagandha are highlighted, suggesting their role in counteracting oxidative stress associated with RA. In conclusion, while herbal remedies like turmeric, ginger, Boswellia serrata, green tea, and Ashwagandha offer potential complementary treatments for RA, their safety profiles and adverse effects warrant careful consideration. Rigorous clinical trials are needed to confirm their efficacy and safety, highlighting the necessity for further research in this area. These findings are crucial for patients and healthcare providers in making informed decisions about incorporating herbal interventions into RA treatment strategies.
    Language English
    Publishing date 2024-04-25
    Publishing country United Arab Emirates
    Document type Journal Article
    ISSN 1875-6360
    ISSN (online) 1875-6360
    DOI 10.2174/0115733971294467240326074155
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: Toward Fairness, Accountability, Transparency, and Ethics in AI for Social Media and Health Care: Scoping Review.

    Singhal, Aditya / Neveditsin, Nikita / Tanveer, Hasnaat / Mago, Vijay

    JMIR medical informatics

    2024  Volume 12, Page(s) e50048

    Abstract: Background: The use of social media for disseminating health care information has become increasingly prevalent, making the expanding role of artificial intelligence (AI) and machine learning in this process both significant and inevitable. This ... ...

    Abstract Background: The use of social media for disseminating health care information has become increasingly prevalent, making the expanding role of artificial intelligence (AI) and machine learning in this process both significant and inevitable. This development raises numerous ethical concerns. This study explored the ethical use of AI and machine learning in the context of health care information on social media platforms (SMPs). It critically examined these technologies from the perspectives of fairness, accountability, transparency, and ethics (FATE), emphasizing computational and methodological approaches that ensure their responsible application.
    Objective: This study aims to identify, compare, and synthesize existing solutions that address the components of FATE in AI applications in health care on SMPs. Through an in-depth exploration of computational methods, approaches, and evaluation metrics used in various initiatives, we sought to elucidate the current state of the art and identify existing gaps. Furthermore, we assessed the strength of the evidence supporting each identified solution and discussed the implications of our findings for future research and practice. In doing so, we made a unique contribution to the field by highlighting areas that require further exploration and innovation.
    Methods: Our research methodology involved a comprehensive literature search across PubMed, Web of Science, and Google Scholar. We used strategic searches through specific filters to identify relevant research papers published since 2012 focusing on the intersection and union of different literature sets. The inclusion criteria were centered on studies that primarily addressed FATE in health care discussions on SMPs; those presenting empirical results; and those covering definitions, computational methods, approaches, and evaluation metrics.
    Results: Our findings present a nuanced breakdown of the FATE principles, aligning them where applicable with the American Medical Informatics Association ethical guidelines. By dividing these principles into dedicated sections, we detailed specific computational methods and conceptual approaches tailored to enforcing FATE in AI-driven health care on SMPs. This segmentation facilitated a deeper understanding of the intricate relationship among the FATE principles and highlighted the practical challenges encountered in their application. It underscored the pioneering contributions of our study to the discourse on ethical AI in health care on SMPs, emphasizing the complex interplay and the limitations faced in implementing these principles effectively.
    Conclusions: Despite the existence of diverse approaches and metrics to address FATE issues in AI for health care on SMPs, challenges persist. The application of these approaches often intersects with additional ethical considerations, occasionally leading to conflicts. Our review highlights the lack of a unified, comprehensive solution for fully and effectively integrating FATE principles in this domain. This gap necessitates careful consideration of the ethical trade-offs involved in deploying existing methods and underscores the need for ongoing research.
    Language English
    Publishing date 2024-04-03
    Publishing country Canada
    Document type Journal Article ; Review
    ZDB-ID 2798261-0
    ISSN 2291-9694
    ISSN 2291-9694
    DOI 10.2196/50048
    Database MEDical Literature Analysis and Retrieval System OnLINE

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