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  1. Article: Dengue beyond fever-fatal dengue myocarditis and complete heart block: A case report and brief overview of cardiac manifestations of dengue fever.

    Khan, Asad Ali / Khan, Farhat Ullah / Akhtar, Syed Ahsan / Ghaffar, Rahmat

    SAGE open medical case reports

    2023  Volume 11, Page(s) 2050313X231193983

    Abstract: Dengue is an endemic viral fever transmitted by mosquitoes that may be asymptomatic or cause a nonspecific flu-like illness. The disease's most severe manifestations are dengue hemorrhagic fever and dengue shock syndrome. Various atypical manifestations ... ...

    Abstract Dengue is an endemic viral fever transmitted by mosquitoes that may be asymptomatic or cause a nonspecific flu-like illness. The disease's most severe manifestations are dengue hemorrhagic fever and dengue shock syndrome. Various atypical manifestations have been observed that constitute the expanded dengue syndrome. Although uncommon, it is now known to cause cardiac complications that can be life-threatening and difficult to diagnose. We illustrate a case of a 16-year-old boy infected with dengue who experienced syncope, dizziness, and lethargy. His electrocardiogram showed third degree atrioventricular block which did not resolve with atropine and fluid resuscitation. After excluding all possible causes of complete heart block, transvenous pacing was done. A detailed workup was carried out that favored a diagnosis of subclinical myocarditis leading to complete heart block. The patient did not regain a normal rhythm and was considered for permanent pacemaker implantation. Myocarditis, pericarditis, rhythm disturbances, first- and second-degree atrioventricular blocks, and rarely third-degree heart blocks have been seen in dengue patients. However, a case of dengue illness associated complete heart blocks that is irreversible and necessitates a permanent pacemaker has never been described in the literature, and this is the first such case being reported. This article intends to increase clinicians' awareness, particularly those in dengue-endemic regions, about better recognition and comprehension of cardiac problems associated with dengue fever.
    Language English
    Publishing date 2023-08-19
    Publishing country England
    Document type Case Reports
    ZDB-ID 2736953-5
    ISSN 2050-313X
    ISSN 2050-313X
    DOI 10.1177/2050313X231193983
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Diagnosis and treatment of flurbiprofen-induced Stevens-Johnson syndrome: A rare case report.

    Khan, Asad Ali / Rashid, Farhana / Khan, Farhat Ullah / Tahmina, Tahmina / Amin, Said / Anand, Ayush

    Clinical case reports

    2022  Volume 10, Issue 9, Page(s) e6365

    Abstract: Our case highlights the occurrence of severe cutaneous adverse reactions with flurbiprofen use and alerts physicians to its odds with safer drugs. ...

    Abstract Our case highlights the occurrence of severe cutaneous adverse reactions with flurbiprofen use and alerts physicians to its odds with safer drugs.
    Language English
    Publishing date 2022-09-23
    Publishing country England
    Document type Case Reports
    ZDB-ID 2740234-4
    ISSN 2050-0904
    ISSN 2050-0904
    DOI 10.1002/ccr3.6365
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Recent Applications of Artificial Intelligence in the Detection of Gastrointestinal, Hepatic and Pancreatic Diseases.

    Kumar, Rajnish / Khan, Farhat Ullah / Sharma, Anju / Aziz, Izzatdin B A / Poddar, Nitesh Kumar

    Current medicinal chemistry

    2021  Volume 29, Issue 1, Page(s) 66–85

    Abstract: There has been substantial progress in artificial intelligence (AI) algorithms and their medical sciences applications in the last two decades. AI-assisted programs have already been established for remote health monitoring using sensors and smartphones. ...

    Abstract There has been substantial progress in artificial intelligence (AI) algorithms and their medical sciences applications in the last two decades. AI-assisted programs have already been established for remote health monitoring using sensors and smartphones. A variety of AI-based prediction models are available for gastrointestinal, inflammatory, non-malignant diseases, and bowel bleeding using wireless capsule endoscopy, hepatitis-associated fibrosis using electronic medical records, and pancreatic carcinoma utilizing endoscopic ultrasounds. AI-based models may be of immense help for healthcare professionals in the identification, analysis, and decision support using endoscopic images to establish prognosis and risk assessment of patients' treatment employing multiple factors. Enough randomized clinical trials are warranted to establish the efficacy of AI-algorithms assisted and non-AI-based treatments before approval of such techniques from medical regulatory authorities. In this article, available AI approaches and AI-based prediction models for detecting gastrointestinal, hepatic, and pancreatic diseases are reviewed. The limitations of AI techniques in such diseases' prognosis, risk assessment, and decision support are discussed.
    MeSH term(s) Algorithms ; Artificial Intelligence ; Gastroenterology ; Gastrointestinal Diseases/diagnosis ; Humans ; Pancreatic Diseases/diagnosis
    Language English
    Publishing date 2021-04-06
    Publishing country United Arab Emirates
    Document type Journal Article ; Review
    ZDB-ID 1319315-6
    ISSN 1875-533X ; 0929-8673
    ISSN (online) 1875-533X
    ISSN 0929-8673
    DOI 10.2174/0929867328666210405114938
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Enhancing wheat production and quality in alkaline soil: a study on the effectiveness of foliar and soil applied zinc.

    Khan, Farhat Ullah / Khan, Adnan Anwar / Qu, Yuanyuan / Zhang, Qi / Adnan, Muhammad / Fahad, Shah / Gul, Fatima / Ismail, Muhammad / Saud, Shah / Hassan, Shah / Xu, Xuexuan

    PeerJ

    2023  Volume 11, Page(s) e16179

    Abstract: Cultivation of high-yield varieties and unbalanced fertilization have induced micronutrient deficiency in soils worldwide. Zinc (Zn) is an essential nutrient for plant growth and its deficiency is most common in alkaline and calcareous soils. Therefore, ... ...

    Abstract Cultivation of high-yield varieties and unbalanced fertilization have induced micronutrient deficiency in soils worldwide. Zinc (Zn) is an essential nutrient for plant growth and its deficiency is most common in alkaline and calcareous soils. Therefore, this study aimed to evaluate the effect of Zn applied either alone or in combination with foliar application on the quality and production of wheat grown in alkaline soils. Zn was applied in the form of zinc sulfate (ZnSo
    MeSH term(s) Zinc/analysis ; Soil ; Triticum ; Zinc Sulfate/metabolism ; Edible Grain/chemistry
    Chemical Substances Zinc (J41CSQ7QDS) ; Soil ; Zinc Sulfate (7733-02-0)
    Language English
    Publishing date 2023-11-03
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2703241-3
    ISSN 2167-8359 ; 2167-8359
    ISSN (online) 2167-8359
    ISSN 2167-8359
    DOI 10.7717/peerj.16179
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article: A deep neural network–based approach for prediction of mutagenicity of compounds

    Kumar, Rajnish / Khan, Farhat Ullah / Sharma, Anju / Siddiqui, Mohammed Haris / Aziz, Izzatdin BA / Kamal, Mohammad Amjad / Ashraf, Ghulam Md / Alghamdi, Badrah S. / Uddin, Md. Sahab

    Environmental science and pollution research. 2021 Sept., v. 28, no. 34

    2021  

    Abstract: We are exposed to various chemical compounds present in the environment, cosmetics, and drugs almost every day. Mutagenicity is a valuable property that plays a significant role in establishing a chemical compound’s safety. Exposure and handling of ... ...

    Abstract We are exposed to various chemical compounds present in the environment, cosmetics, and drugs almost every day. Mutagenicity is a valuable property that plays a significant role in establishing a chemical compound’s safety. Exposure and handling of mutagenic chemicals in the environment pose a high health risk; therefore, identification and screening of these chemicals are essential. Considering the time constraints and the pressure to avoid laboratory animals’ use, the shift to alternative methodologies that can establish a rapid and cost-effective detection without undue over-conservation seems critical. In this regard, computational detection and identification of the mutagens in environmental samples like drugs, pesticides, dyes, reagents, wastewater, cosmetics, and other substances is vital. From the last two decades, there have been numerous efforts to develop the prediction models for mutagenicity, and by far, machine learning methods have demonstrated some noteworthy performance and reliability. However, the accuracy of such prediction models has always been one of the major concerns for the researchers working in this area. The mutagenicity prediction models were developed using deep neural network (DNN), support vector machine, k-nearest neighbor, and random forest. The developed classifiers were based on 3039 compounds and validated on 1014 compounds; each of them encoded with 1597 molecular feature vectors. DNN-based prediction model yielded highest prediction accuracy of 92.95% and 83.81% with the training and test data, respectively. The area under the receiver’s operating curve and precision-recall curve values were found to be 0.894 and 0.838, respectively. The DNN-based classifier not only fits the data with better performance as compared to traditional machine learning algorithms, viz., support vector machine, k-nearest neighbor, and random forest (with and without feature reduction) but also yields better performance metrics. In current work, we propose a DNN-based model to predict mutagenicity of compounds.
    Keywords chemical compounds ; cosmetics ; cost effectiveness ; models ; mutagenicity ; mutagens ; pollution ; prediction ; research ; risk ; support vector machines ; wastewater
    Language English
    Dates of publication 2021-09
    Size p. 47641-47650.
    Publishing place Springer Berlin Heidelberg
    Document type Article
    ZDB-ID 1178791-0
    ISSN 1614-7499 ; 0944-1344
    ISSN (online) 1614-7499
    ISSN 0944-1344
    DOI 10.1007/s11356-021-14028-9
    Database NAL-Catalogue (AGRICOLA)

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  6. Article ; Online: A deep neural network-based approach for prediction of mutagenicity of compounds.

    Kumar, Rajnish / Khan, Farhat Ullah / Sharma, Anju / Siddiqui, Mohammed Haris / Aziz, Izzatdin Ba / Kamal, Mohammad Amjad / Ashraf, Ghulam Md / Alghamdi, Badrah S / Uddin, Md Sahab

    Environmental science and pollution research international

    2021  Volume 28, Issue 34, Page(s) 47641–47650

    Abstract: We are exposed to various chemical compounds present in the environment, cosmetics, and drugs almost every day. Mutagenicity is a valuable property that plays a significant role in establishing a chemical compound's safety. Exposure and handling of ... ...

    Abstract We are exposed to various chemical compounds present in the environment, cosmetics, and drugs almost every day. Mutagenicity is a valuable property that plays a significant role in establishing a chemical compound's safety. Exposure and handling of mutagenic chemicals in the environment pose a high health risk; therefore, identification and screening of these chemicals are essential. Considering the time constraints and the pressure to avoid laboratory animals' use, the shift to alternative methodologies that can establish a rapid and cost-effective detection without undue over-conservation seems critical. In this regard, computational detection and identification of the mutagens in environmental samples like drugs, pesticides, dyes, reagents, wastewater, cosmetics, and other substances is vital. From the last two decades, there have been numerous efforts to develop the prediction models for mutagenicity, and by far, machine learning methods have demonstrated some noteworthy performance and reliability. However, the accuracy of such prediction models has always been one of the major concerns for the researchers working in this area. The mutagenicity prediction models were developed using deep neural network (DNN), support vector machine, k-nearest neighbor, and random forest. The developed classifiers were based on 3039 compounds and validated on 1014 compounds; each of them encoded with 1597 molecular feature vectors. DNN-based prediction model yielded highest prediction accuracy of 92.95% and 83.81% with the training and test data, respectively. The area under the receiver's operating curve and precision-recall curve values were found to be 0.894 and 0.838, respectively. The DNN-based classifier not only fits the data with better performance as compared to traditional machine learning algorithms, viz., support vector machine, k-nearest neighbor, and random forest (with and without feature reduction) but also yields better performance metrics. In current work, we propose a DNN-based model to predict mutagenicity of compounds.
    MeSH term(s) Animals ; Machine Learning ; Mutagens/toxicity ; Neural Networks, Computer ; Reproducibility of Results ; Support Vector Machine
    Chemical Substances Mutagens
    Language English
    Publishing date 2021-04-24
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 1178791-0
    ISSN 1614-7499 ; 0944-1344
    ISSN (online) 1614-7499
    ISSN 0944-1344
    DOI 10.1007/s11356-021-14028-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Book: E S P, vocabulary and medical discourse

    Khan, Farhat Ullah

    1990  

    Title variant English for specific purposes, vocabulary and medical discourse ; ESP, vocabulary and medical discourse
    Author's details by Farhat Ullah Khan
    MeSH term(s) Education, Medical/methods
    Language English
    Size 165 p. :, ill.
    Publisher Lissan Publications
    Publishing place Delhi
    Document type Book
    Database Catalogue of the US National Library of Medicine (NLM)

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