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  1. Article ; Online: Assessment of cumulative microbial respiration and their ameliorative role in sustaining maize growth under salt stress.

    Shabaan, Muhammad / Asghar, Hafiz Naeem / Akhtar, Muhammad Javed / Saleem, Muhammad Farrukh

    Plant physiology and biochemistry : PPB

    2023  Volume 196, Page(s) 33–42

    Abstract: Cumulative microbial respiration reflects microbial activities and their potential to support plant growth, where salt tolerant rhizobacteria can optimize their respiration, and ensure plant survival under salt stress. We evaluated cumulative microbial ... ...

    Abstract Cumulative microbial respiration reflects microbial activities and their potential to support plant growth, where salt tolerant rhizobacteria can optimize their respiration, and ensure plant survival under salt stress. We evaluated cumulative microbial respiration of different salt tolerant rhizobacterial strains at different salinity levels, and checked their ability to sustain plant growth under natural saline conditions by using maize as test crop. Our results revealed that at the highest EC level (10 dS m
    MeSH term(s) Zea mays ; Soil Microbiology ; RNA, Ribosomal, 16S ; Hydrogen Peroxide ; Salt Stress ; Salinity
    Chemical Substances RNA, Ribosomal, 16S ; Hydrogen Peroxide (BBX060AN9V)
    Language English
    Publishing date 2023-01-20
    Publishing country France
    Document type Journal Article
    ZDB-ID 742978-2
    ISSN 1873-2690 ; 0981-9428
    ISSN (online) 1873-2690
    ISSN 0981-9428
    DOI 10.1016/j.plaphy.2023.01.037
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  2. Article ; Online: Perception and practices of community pharmacists towards antimicrobial stewardship in Lahore, Pakistan.

    Akbar, Zunaira / Saleem, Zikria / Shaukat, Arooj / Farrukh, Muhammad Junaid

    Journal of global antimicrobial resistance

    2021  Volume 25, Page(s) 157–161

    Abstract: Objectives: Antimicrobial resistance is a major health concern worldwide. Community pharmacists can play an important role in rational antibiotic use. This study aimed to evaluate the perception and practices of community pharmacists regarding ... ...

    Abstract Objectives: Antimicrobial resistance is a major health concern worldwide. Community pharmacists can play an important role in rational antibiotic use. This study aimed to evaluate the perception and practices of community pharmacists regarding antimicrobial stewardship (AMS) in Lahore, Pakistan.
    Methods: A descriptive cross-sectional study was conducted among community pharmacists in Lahore from 1 November 2017 to 31 December 2017. A self-administered questionnaire was used for data collection. Non-probability convenience sampling was performed to select community pharmacists. Descriptive statistics were applied and Mann-Whitney U-tests and Kruskal-Wallis tests were performed to compare independent groups using SPSS v.20.0. A P-value of <0.05 was considered statistically significant. Perception and practice scores were determined to access community pharmacist knowledge regarding AMS. A score of 0.5-1 was considered to be very good.
    Results: The overall response rate was 70.9%. Sex, age, work experience and education level did not significantly influence the perception and practices of community pharmacists. Experienced pharmacists showed a better response to AMS. The majority of pharmacists strongly agreed that they educate patients on the use of antimicrobials and resistance-related issues.
    Conclusion: It was concluded that community pharmacists in Lahore have good perception regarding AMS and they are practicing it well. But there are several gaps in their practices that must be filled, such as dispensing without a prescription and dispensing for a longer duration than prescribed. Additionally, there should be strict implementation of guidelines for dispensing antibiotics in order to rationalise antibiotic use and decrease antimicrobial resistance.
    MeSH term(s) Antimicrobial Stewardship ; Attitude of Health Personnel ; Cross-Sectional Studies ; Humans ; Pakistan ; Perception ; Pharmacists
    Language English
    Publishing date 2021-03-28
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2710046-7
    ISSN 2213-7173 ; 2213-7165
    ISSN (online) 2213-7173
    ISSN 2213-7165
    DOI 10.1016/j.jgar.2021.03.013
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  3. Article ; Online: Sarcococca saligna extract attenuates formaldehyde-induced arthritis in Wistar rats via modulation of pro-inflammatory and inflammatory biomarkers.

    Farrukh, Maryam / Saleem, Uzma / Qasim, Muhammad / Manan, Maria / Shah, Muhammad Ajmal

    Inflammopharmacology

    2022  Volume 30, Issue 2, Page(s) 579–597

    Abstract: Sarcococca saligna plant is commonly used as traditional therapy for arthritis especially in Asian countries. The current study is designed to explore the anti-arthritic potential of S. saligna aqueous methanolic extract (SSME). Preliminary proximate ... ...

    Abstract Sarcococca saligna plant is commonly used as traditional therapy for arthritis especially in Asian countries. The current study is designed to explore the anti-arthritic potential of S. saligna aqueous methanolic extract (SSME). Preliminary proximate study and HPLC analysis were performed to investigate the phytochemical characterization and quality control. The safety of the SSME was evaluated by performing an acute oral toxicity study (OECD guidelines 425). The anti-arthritic potential of SSME was explored by in vivo formaldehyde-induced arthritis model. The antiarthritic effect of the SSME was determined through paw diameter, arthritic index, body weight, biochemical and haematological parameters. Radiographic and histopathological studies were also carried out to evaluate the results. qRT-PCR was performed to determine the upregulation and downregulation of anti- and pro-inflammatory cytokines in rats while ELISA was done to determine the concentration of HSP-70, IL-6 and TNF-α in the serum. Results of acute oral toxicity showed no abnormality and mortality. There was no noticeable change in haematological and biochemical parameters. Histopathological examination exhibited the normal structure of vital organs. So, SSME might be safe at a 2000 mg/kg dose, proposing that LD
    MeSH term(s) Animals ; Anti-Inflammatory Agents/therapeutic use ; Arthritis, Experimental/chemically induced ; Arthritis, Experimental/drug therapy ; Arthritis, Experimental/metabolism ; Biomarkers/metabolism ; Buxaceae ; Cytokines/metabolism ; Formaldehyde ; Plant Extracts/therapeutic use ; Rats ; Rats, Wistar
    Chemical Substances Anti-Inflammatory Agents ; Biomarkers ; Cytokines ; Plant Extracts ; Formaldehyde (1HG84L3525)
    Language English
    Publishing date 2022-02-26
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 1080058-x
    ISSN 1568-5608 ; 0925-4692
    ISSN (online) 1568-5608
    ISSN 0925-4692
    DOI 10.1007/s10787-022-00929-9
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 3274: 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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  4. Article ; Online: Enterprise Architecture and Organizational Benefits

    Farrukh Saleem / Bahjat Fakieh

    Sustainability, Vol 12, Iss 8237, p

    A Case Study

    2020  Volume 8237

    Abstract: Enterprise architecture (EA) is an integrated process of aligning business strategies with information technology (IT) architecture. It assists the organization to standardize business operations and incorporate systems in different layers to achieve ... ...

    Abstract Enterprise architecture (EA) is an integrated process of aligning business strategies with information technology (IT) architecture. It assists the organization to standardize business operations and incorporate systems in different layers to achieve business goals and organizational benefits. This study focuses on identifying organizational benefits that can be achieved through EA implementation. The study comprises three main phases: (i) benefits realization (from literature review), (ii) benefits reconfirmation (from EA experts), and (iii) benefits validation (through a case study). Specifically, the benefits considered in this study are related to EA products, services, and strategies are known as: (i) business agility, (ii) creating competitive advantage, and (iii) increasing value. The study covers a vast literature review to define the current status of EA and organizational benefits. In addition, the study incorporates a number of measuring factors for each EA benefits with the help of a literature review. The initial findings reconfirmed and modified based on the experts’ opinions collected through interview sessions. The research applied the grounded theory and qualitative approach to analyze the interview sessions. Accordingly, using the experts’ advice, we proposed a model to show the steps and guidelines for assessing EA organizational benefits using corresponding measuring factors and sub-criteria. Finally, the proposed model validated through an in-depth case study to get final confirmation and see the model fits reality. Overall, this research highlight the potential benefits an organization can achieve from EA framework implementation. The proposed framework can assist EA decision-makers to understand and realize the EA benefits and its assessment process.
    Keywords enterprise architecture ; organizational benefits ; business agility ; creating competitive advantage ; increasing value ; qualitative study ; Environmental effects of industries and plants ; TD194-195 ; Renewable energy sources ; TJ807-830 ; Environmental sciences ; GE1-350
    Subject code 330
    Language English
    Publishing date 2020-10-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: BFT-IoMT: A Blockchain-Based Trust Mechanism to Mitigate Sybil Attack Using Fuzzy Logic in the Internet of Medical Things.

    Ali, Shayan E / Tariq, Noshina / Khan, Farrukh Aslam / Ashraf, Muhammad / Abdul, Wadood / Saleem, Kashif

    Sensors (Basel, Switzerland)

    2023  Volume 23, Issue 9

    Abstract: Numerous sensitive applications, such as healthcare and medical services, need reliable transmission as a prerequisite for the success of the new age of communications technology. Unfortunately, these systems are highly vulnerable to attacks like Sybil, ... ...

    Abstract Numerous sensitive applications, such as healthcare and medical services, need reliable transmission as a prerequisite for the success of the new age of communications technology. Unfortunately, these systems are highly vulnerable to attacks like Sybil, where many false nodes are created and spread with deceitful intentions. Therefore, these false nodes must be instantly identified and isolated from the network due to security concerns and the sensitivity of data utilized in healthcare applications. Especially for life-threatening diseases like COVID-19, it is crucial to have devices connected to the Internet of Medical Things (IoMT) that can be believed to respond with high reliability and accuracy. Thus, trust-based security offers a safe environment for IoMT applications. This study proposes a blockchain-based fuzzy trust management framework (BFT-IoMT) to detect and isolate Sybil nodes in IoMT networks. The results demonstrate that the proposed BFT-IoMT framework is 25.43% and 12.64%, 12.54% and 6.65%, 37.85% and 19.08%, 17.40% and 8.72%, and 13.04% and 5.05% more efficient and effective in terms of energy consumption, attack detection, trust computation reliability, packet delivery ratio, and throughput, respectively, as compared to the other state-of-the-art frameworks available in the literature.
    MeSH term(s) Humans ; Fuzzy Logic ; Blockchain ; COVID-19 ; Reproducibility of Results ; Trust ; Internet of Things
    Language English
    Publishing date 2023-04-25
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s23094265
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  6. Article: Prevalence of latent tuberculosis infection in healthcare workers in tertiary care hospitals of Pakistan.

    Sadaf, Rabia / Munir, Tehmina / Farrukh, Sheroze / Abbasi, Saleem

    Pakistan journal of medical sciences

    2020  Volume 36, Issue 2, Page(s) 198–202

    Abstract: Objective: To determine the prevalence of latent tuberculosis infection (LTBI) in healthcare workers in tertiary care hospitals of Rawalpindi, using interferon gamma release assay.: Methods: It was a cross-sectional study. The samples were collected ... ...

    Abstract Objective: To determine the prevalence of latent tuberculosis infection (LTBI) in healthcare workers in tertiary care hospitals of Rawalpindi, using interferon gamma release assay.
    Methods: It was a cross-sectional study. The samples were collected from pulmonology and microbiology departments of three hospitals; i) Military Hospital, Rawalpindi, ii) Fauji Foundation Hospital, Rawalpindi and iii) Pakistan Institute of Medical Sciences, Islamabad. The study was completed in one year from January 2017 to January 2018. Fifty-five asymptomatic healthcare workers of both genders between the ages of 18-50 years with a working tenure of at least one year in concerned departments were included and those with active tuberculosis were excluded from the study. Whole blood from subjects was collected and plasma was checked for interferon gamma value by IGRA (Interferon gamma release assay).
    Results: In this study of total 55 healthcare workers a high prevalence 22 (40.0%) of latent tuberculosis was found. When LTBI distribution was analyzed within occupational categories, the most frequently affected were sanitary workers 3 (100.0%), nurses 5 (50.0%), doctors 6 (43%) and nursing assistants 2 (40%).
    Conclusion: The prevalence of LTBI in healthcare workers is alarmingly high in our local healthcare settings.
    Language English
    Publishing date 2020-01-27
    Publishing country Pakistan
    Document type Journal Article
    ZDB-ID 2032827-8
    ISSN 1681-715X ; 1682-024X ; 1017-4699
    ISSN (online) 1681-715X
    ISSN 1682-024X ; 1017-4699
    DOI 10.12669/pjms.36.2.936
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 3948: 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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  7. Article ; Online: EffiMob-Net

    Zahid Ullah / Najah Alsubaie / Mona Jamjoom / Samah H. Alajmani / Farrukh Saleem

    Agriculture, Vol 13, Iss 737, p

    A Deep Learning-Based Hybrid Model for Detection and Identification of Tomato Diseases Using Leaf Images

    2023  Volume 737

    Abstract: As tomatoes are the most consumed vegetable in the world, production should be increased to fulfill the vast demand for this vegetable. Global warming, climate changes, and other significant factors, including pests, badly affect tomato plants and cause ... ...

    Abstract As tomatoes are the most consumed vegetable in the world, production should be increased to fulfill the vast demand for this vegetable. Global warming, climate changes, and other significant factors, including pests, badly affect tomato plants and cause various diseases that ultimately affect the production of this vegetable. Several strategies and techniques have been adopted for detecting and averting such diseases to ensure the survival of tomato plants. Recently, the application of artificial intelligence (AI) has significantly contributed to agronomy in the detection of tomato plant diseases through leaf images. Deep learning (DL)-based techniques have been largely utilized for detecting tomato leaf diseases. This paper proposes a hybrid DL-based approach for detecting tomato plant diseases through leaf images. To accomplish the task, this study presents the fusion of two pretrained models, namely, EfficientNetB3 and MobileNet (referred to as the EffiMob-Net model) to detect tomato leaf diseases accurately. In addition, model overfitting was handled using various techniques, such as regularization, dropout, and batch normalization (BN). Hyperparameter tuning was performed to choose the optimal parameters for building the best-fitting model. The proposed hybrid EffiMob-Net model was tested on a plant village dataset containing tomato leaf disease and healthy images. This hybrid model was evaluated based on the best classifier with respect to accuracy metrics selected for detecting the diseases. The success rate of the proposed hybrid model for accurately detecting tomato leaf diseases reached 99.92%, demonstrating the model’s ability to extract features accurately. This finding shows the reliability of the proposed hybrid model as an automatic detector for tomato plant diseases that can significantly contribute to providing better solutions for detecting other crop diseases in the field of agriculture.
    Keywords tomato leaf ; disease ; hybrid model ; detection ; deep learning ; Agriculture (General) ; S1-972
    Subject code 006
    Language English
    Publishing date 2023-03-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Predictors of Outcome of Bronchiolitis in Children Using Children Hospital of Wisconsin Respiratory Score

    Saher Gul Ahdi / Tariq Nadeem / Huma Farrukh / Furqan Saleem / Rafia Gul / Hina Khalid

    Pakistan Armed Forces Medical Journal, Vol 73, Iss

    An Experience of a Tertiary Care Hospital

    2023  Volume 1

    Abstract: Objective: To study the correlation of CHWR score and its various clinical markers with the length of stay in hospital bronchiolitis. Study Design: Cross-sectional analytical study. Place and Duration of Study: Department of Pediatrics Combined Military ... ...

    Abstract Objective: To study the correlation of CHWR score and its various clinical markers with the length of stay in hospital bronchiolitis. Study Design: Cross-sectional analytical study. Place and Duration of Study: Department of Pediatrics Combined Military Hospital, Lahore Pakistan, from Nov 2018 to Apr 2019. Methodology: This study included children aged 2 to 24 months, clinically diagnosed with bronchiolitis. The Receiver Operator Characteristic (ROC) was used to determine the discriminative validity of the CHWR score in predicting the length of stay. Results: One hundred thirty-eight children of either gender were enrolled in the study. The median age of the study population was 10.8 (9) months. ROC curve showed significant discriminate validity of CHWR score and its component criterion on admission. CHWR score of >10.5 predicted a longer stay (>24 hours) in ICU. Pearson correlation showed a statistically significant positive correlation between CHWR score at admission and length of stay (r=0.831, p=<0.001). Conclusion: CHWR scoring system on admission is an easy, safe and effective way to classify bronchiolitis severity and thus help predict the length of stay.
    Keywords Bronchioilitis ; Children's hospital of wisconsin respiratory score (CHWRS) ; Length of stay ; Medicine ; R ; Medicine (General) ; R5-920
    Subject code 360
    Language English
    Publishing date 2023-02-01T00:00:00Z
    Publisher Army Medical College Rawalpindi
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Impact of Excessive Screen Time on Speech and Language in Children

    Amreen Raheem / Sikander Ghayas Khan / Muhammad Ahmed / Farrukh Jawad Alvi / Khadeeja Saleem / Sehar Batool

    JLUMHS, Vol 22, Iss 3, Pp 155-

    2023  Volume 159

    Abstract: Excessive use of screen time can negatively affect children's development of speech and language as well as other characteristics. Other researches also been conducted on screen time's influence on children's development. In this review, we examine the ... ...

    Abstract Excessive use of screen time can negatively affect children's development of speech and language as well as other characteristics. Other researches also been conducted on screen time's influence on children's development. In this review, we examine the literature review done nationally and internationally. For this purpose, the researcher used two methods: (1) searches from different databases (Google Scholar, PubMed, Google and Research Gate) and (2) Reference sections of previous studies. The maximum effect of screen time reported in the John JJ study, where 89.4% have excessive screen use. The minimum impact written in a study by Meta Van Den, which showed that only 6.6% of the patient reported speech delays due to media usage. The findings that have been analyzed in this study point to access the strategies to recede the habit of using electronic media in youngsters as it is impacting their speech and language development and causing attention, vision and other health problems. However, more researches are mandatory to examine several factors that lead to excessive screen time.
    Keywords screen time ; speech delay ; effect of screen time ; language development ; children ; negative effects ; media use ; Medicine ; R
    Subject code 410
    Language English
    Publishing date 2023-09-01T00:00:00Z
    Publisher Liaquat University of Medical and Health Sciences
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Reliable Prediction Models Based on Enriched Data for Identifying the Mode of Childbirth by Using Machine Learning Methods: Development Study.

    Ullah, Zahid / Saleem, Farrukh / Jamjoom, Mona / Fakieh, Bahjat

    Journal of medical Internet research

    2021  Volume 23, Issue 6, Page(s) e28856

    Abstract: Background: The use of artificial intelligence has revolutionized every area of life such as business and trade, social and electronic media, education and learning, manufacturing industries, medicine and sciences, and every other sector. The new ... ...

    Abstract Background: The use of artificial intelligence has revolutionized every area of life such as business and trade, social and electronic media, education and learning, manufacturing industries, medicine and sciences, and every other sector. The new reforms and advanced technologies of artificial intelligence have enabled data analysts to transmute raw data generated by these sectors into meaningful insights for an effective decision-making process. Health care is one of the integral sectors where a large amount of data is generated daily, and making effective decisions based on these data is therefore a challenge. In this study, cases related to childbirth either by the traditional method of vaginal delivery or cesarean delivery were investigated. Cesarean delivery is performed to save both the mother and the fetus when complications related to vaginal birth arise.
    Objective: The aim of this study was to develop reliable prediction models for a maternity care decision support system to predict the mode of delivery before childbirth.
    Methods: This study was conducted in 2 parts for identifying the mode of childbirth: first, the existing data set was enriched and second, previous medical records about the mode of delivery were investigated using machine learning algorithms and by extracting meaningful insights from unseen cases. Several prediction models were trained to achieve this objective, such as decision tree, random forest, AdaBoostM1, bagging, and k-nearest neighbor, based on original and enriched data sets.
    Results: The prediction models based on enriched data performed well in terms of accuracy, sensitivity, specificity, F-measure, and receiver operating characteristic curves in the outcomes. Specifically, the accuracy of k-nearest neighbor was 84.38%, that of bagging was 83.75%, that of random forest was 83.13%, that of decision tree was 81.25%, and that of AdaBoostM1 was 80.63%. Enrichment of the data set had a good impact on improving the accuracy of the prediction process, which supports maternity care practitioners in making decisions in critical cases.
    Conclusions: Our study shows that enriching the data set improves the accuracy of the prediction process, thereby supporting maternity care practitioners in making informed decisions in critical cases. The enriched data set used in this study yields good results, but this data set can become even better if the records are increased with real clinical data.
    MeSH term(s) Artificial Intelligence ; Female ; Humans ; Machine Learning ; Maternal Health Services ; Parturition ; Pregnancy ; ROC Curve
    Language English
    Publishing date 2021-06-04
    Publishing country Canada
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2028830-X
    ISSN 1438-8871 ; 1439-4456
    ISSN (online) 1438-8871
    ISSN 1439-4456
    DOI 10.2196/28856
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