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  1. Profiling of anti-oxidative enzymes and lipid peroxidation in leaves of salt tolerant and salt sensitive maize hybrids under nac1 and cd stress
    Ghulam Hasan Abbasi, Javaid Akhtar, Muhammad Anwar-ul-haq, Moazzam Jamil, Shafaqat Ali, Rafiq Ahmad, et al.
    Sains Malaysiana, 2016;45:177-184.
    Effects of NaCl salinity and cadmium on the anti-oxidative activity of enzymes like superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), ascorbate peroxidase (APX), glutathione reductase (GR) and lipid peroxidation contents; malondialdehyde (MDA) were studied in two maize hybrids of different salt tolerance characteristics. An increase in the amount of lipid peroxidation indicated the oxidative stress induced by NaCl and Cd. The results also depicted that NaCl stress caused an increase in the activities of POD, SOD, CAT, APX and GR while cadmium stress increased the activities of POD, SOD and APX but showed no significant effect on CAT and GR in both the studied hybrids. The combined effect of salinity and cadmium on these parameters was higher than that of sole effect of either NaCl or Cd. It was also found that maize hybrid 26204 had better tolerance against both stresses with strong antioxidant system as compared to that of maize hybrid 8441. A comparison of the antioxidants and lipid peroxidation in two maize hybrids having varying level of NaCl and Cd stress tolerance corroborated the importance of reactive oxygen species (ROS) in defense against abiotic stresses.
  2. Fulltext Perceptions of community pharmacists regarding their role in antimicrobial stewardship in Pakistan: A way forward
    Hashmi A, Ul Haq MI, Malik M, Hussain A, Gajdács M, Jamshed S
    Heliyon, 2023 Apr;9(4):e14843.
    PMID: 37025891 DOI: 10.1016/j.heliyon.2023.e14843
    BACKGROUND: Antimicrobial resistance is one of the biggest challenges to healthcare resulting in increased morbidity and mortality, and associated with drug resistant infections. Community pharmacists (CPs) can play a key role in antimicrobial stewardship (AMS) programs to aid the prudent use of antibiotics, and in infection prevention and control.

    OBJECTIVE: The aim of this study was to assess perceptions of CPs regarding their role, awareness, collaboration, facilitators and barriers towards effective AMS practices in Pakistan.

    METHOD: ology: A descriptive, cross-sectional study design was adopted, where convenience and snowball sampling methods were applied to enroll respondents (pharmacists working at these community pharmacies in different cities of Pakistan) of the study. After sample size determination, n = 386 CPs were enrolled. A pre-validated questionnaire was used regarding CPs roles and perceptions in association with AMS. Statistical analysis was performed using SPSS v. 21.

    RESULTS: The results of the study reported that 57.3% (n = 221) of CPs had strong familiarity with term AMS. 52.1% (n = 201) of CPs agreed that they require adequate training to undertake activities in AMS programmes in their setting. The results of the study showed that 92.7% (n = 358) of the pharmacists thought real time feedback would be helpful. Significant association was observed in AMS awareness, approach, collaboration and barriers with regards to the respondents' gender, age groups and levels of experience in a community pharmacy.

    CONCLUSIONS: The study concluded that CPs were aware of AMS programmes, their relevance and necessity of AMS in their everyday practice, but had inadequate training and resources to implement it in Pakistan.

  3. Fulltext Privacy-Aware Collaborative Learning for Skin Cancer Prediction
    Ain QU, Khan MA, Yaqoob MM, Khattak UF, Sajid Z, Khan MI, et al.
    Diagnostics (Basel), 2023 Jul 04;13(13).
    PMID: 37443658 DOI: 10.3390/diagnostics13132264
    Cancer, including the highly dangerous melanoma, is marked by uncontrolled cell growth and the possibility of spreading to other parts of the body. However, the conventional approach to machine learning relies on centralized training data, posing challenges for data privacy in healthcare systems driven by artificial intelligence. The collection of data from diverse sensors leads to increased computing costs, while privacy restrictions make it challenging to employ traditional machine learning methods. Researchers are currently confronted with the formidable task of developing a skin cancer prediction technique that takes privacy concerns into account while simultaneously improving accuracy. In this work, we aimed to propose a decentralized privacy-aware learning mechanism to accurately predict melanoma skin cancer. In this research we analyzed federated learning from the skin cancer database. The results from the study showed that 92% accuracy was achieved by the proposed method, which was higher than baseline algorithms.
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