Shahzad Maqsood Khan

University of the Punjab

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Nafisa Gull

University of the Punjab

Atif Islam

University of the Punjab

Muhammad Taqi Zahid Butt

University of the Punjab

Tahir Jamil

ProMedica Toledo Hospital

Aneela Sabir

University of the Punjab

Saba Zia

University of the Punjab

Rafi Ullah Khan

University of the Punjab

Muhammad Shafiq

University of the Punjab

Muhammad Shafiq

Quaid-i-Azam University

M.A. Munawar

Friedrich-Alexander-Universität Erlangen-Nürnberg

Cooperative Institutions

University of the Punjab

122

University of Lahore

COMSATS University Islamabad

University of Engineering and Technology Lahore

Quaid-i-Azam University

University of Agriculture Faisalabad

Wuhan University

National University of Sciences and Technology

King Faisal University

Government College University, Faisalabad

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High-temperature electrical properties of silane cross-linked chitosan/poly(vinyl alcohol) membrane: thermal, mechanical and surface characterization

e-Polymers (2015)

Atif Islam Tariq Yasin Aneela Sabir Shahzad Maqsood Khan Misbah Sultan

Abstract Chitosan and poly(vinyl alcohol) were blended and cross-linked with tetraethoxysilane and showed conductive properties. Impedance spectroscopy was used to study the influence of temperature on the electrical properties of the membranes. The conductivity of the membranes was increased with an increase in temperature. Free water was decreased and bound water was increased with an increase in cross-linker contents. The tensile strength improved, whereas elongation at break decreased by increasing the amount of cross-linker contents. The water contact angle of the membranes lowered with time, exhibiting the hydrophilic nature of the membranes. The novel characteristics of biocompatible membranes can be used in biomedical applications including biological schemes that require smaller charge in medicinal apparatus, bioelectrode coatings, etc.

Vinyl alcohol

Elongation

Bound water

10.1515/epoly-2015-0048

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Citations (2)

Deep Learning for Prediction of Cardiovascular Disease

Mokammel Hossain Tito Md Arifuzzaman Alifa Nasrin Shahzad Maqsood Khan Md. Asaduzzaman

This study compares three deep learning algorithms for cardiovascular disease risk prediction. While RBFN boasts the highest accuracy (84.07%), wekaDeeplearning4j excels in identifying high-risk individuals via better AUC and PRC area, valuable for prioritizing early intervention despite slightly lower overall accuracy (81.85%). Conversely, MLP's low mean absolute error indicates high precision in individual case prediction, ideal for personalized treatments. However, tradeoffs exist: wekaDeeplearning4j requires longer training times, and MLP's precision may sacrifice sensitivity. Choosing the optimal algorithm depends on context and priorities. High accuracy and speed favor RBFN, while superior high-risk identification or precise individual predictions favor wekaDeeplearning4j or MLP, respectively. Understanding these trade-offs is crucial for maximizing deep learning's effectiveness in cardiovascular disease risk prediction.

10.1109/icetsis61505.2024.10459447

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Effect of silica on the properties of cellulose acetate/polyethylene glycol membranes for reverse osmosis

Desalination (2014)

Adnan Ahmad Sidra Waheed Shahzad Maqsood Khan Sabad e-Gul Muhammad Shafiq

Phase inversion

Cellulose acetate

Thermal Stability

10.1016/j.desal.2014.10.004

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Citations (115)

Electron kinetics and interface engineering of dye-sensitized solar cell sandwich structure

Elsevier eBooks (2021)

Shahzad Maqsood Khan Osama Majeed Butt Saba Ayub Nafisa Gull Saba Zia

10.1016/b978-0-12-818206-2.00002-5

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Sodium alginate blended membrane with polyurethane: Desalination performance and antimicrobial activity evaluation

International Journal of Biological Macromolecules (2021)

Saba Urooge Khan Misbah Sultan Atif Islam Aneela Sabir Sadaf Hafeez

Thermal Stability

10.1016/j.ijbiomac.2021.03.188

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Citations (36)

Diabetic Cardiomyopathy: From Mechanism to Management in a Nutshell

Endocrine Metabolic & Immune Disorders - Drug Targets (2020)

Shahzad Maqsood Khan Syed Sayeed Ahmad Mohammad Azhar Kamal

Diabetic cardiomyopathy (DCM) is a significant complication of diabetes mellitus characterized by gradually failing heart with detrimental cardiac remodelings, such as fibrosis and diastolic and systolic dysfunction, which is not directly attributable to coronary artery disease. Insulin resistance and resulting hyperglycemia is the main trigger involved in the initiation of diabetic cardiomyopathy. There is a constellation of many pathophysiological events, such as lipotoxicity, oxidative stress, inflammation, inappropriate activation of the renin-angiotensin-aldosterone system, dysfunctional immune modulation promoting increased rate of cardiac cell injury, apoptosis, and necrosis, which ultimately culminates into interstitial fibrosis, cardiac stiffness, diastolic dysfunction, initially, and later systolic dysfunction too. These events finally lead to clinical heart failure of DCM. Herein, The pathophysiology of DCM is briefly discussed. Furthermore, potential therapeutic strategies currently used for DCM are also briefly mentioned.

Diabetic Cardiomyopathy

Lipotoxicity

Cardiac Fibrosis

Pathophysiology

10.2174/1871530320666200731174724

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Citations (17)

Novel chitosan/guar gum/PVA hydrogel: Preparation, characterization and antimicrobial activity evaluation

International Journal of Biological Macromolecules (2020)

Dure Najaf Iqbal Sadia Shafiq Shahzad Maqsood Khan Sobhy M. Ibrahim Samar A. Abubshait

Guar gum

Vinyl alcohol