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  1. Fulltext Asynchronous Federated Learning for Improved Cardiovascular Disease Prediction Using Artificial Intelligence
    Khan MA, Alsulami M, Yaqoob MM, Alsadie D, Saudagar AKJ, AlKhathami M, et al.
    Diagnostics (Basel), 2023 Jul 11;13(14).
    PMID: 37510084 DOI: 10.3390/diagnostics13142340
    Healthcare professionals consider predicting heart disease an essential task and deep learning has proven to be a promising approach for achieving this goal. This research paper introduces a novel method called the asynchronous federated deep learning approach for cardiac prediction (AFLCP), which combines a heart disease dataset and deep neural networks (DNNs) with an asynchronous learning technique. The proposed approach employs a method for asynchronously updating the parameters of DNNs and incorporates a temporally weighted aggregation technique to enhance the accuracy and convergence of the central model. To evaluate the effectiveness of the proposed AFLCP method, two datasets with various DNN architectures are tested, and the results demonstrate that the AFLCP approach outperforms the baseline method in terms of both communication cost and model accuracy.
  2. Fulltext Fake news stance detection using selective features and FakeNET
    Aljrees T, Cheng X, Ahmed MM, Umer M, Majeed R, Alnowaiser K, et al.
    PLoS One, 2023;18(7):e0287298.
    PMID: 37523404 DOI: 10.1371/journal.pone.0287298
    The proliferation of fake news has severe effects on society and individuals on multiple fronts. With fast-paced online content generation, has come the challenging problem of fake news content. Consequently, automated systems to make a timely judgment of fake news have become the need of the hour. The performance of such systems heavily relies on feature engineering and requires an appropriate feature set to increase performance and robustness. In this context, this study employs two methods for reducing the number of feature dimensions including Chi-square and principal component analysis (PCA). These methods are employed with a hybrid neural network architecture of convolutional neural network (CNN) and long short-term memory (LSTM) model called FakeNET. The use of PCA and Chi-square aims at utilizing appropriate feature vectors for better performance and lower computational complexity. A multi-class dataset is used comprising 'agree', 'disagree', 'discuss', and 'unrelated' classes obtained from the Fake News Challenges (FNC) website. Further contextual features for identifying bogus news are obtained through PCA and Chi-Square, which are given nonlinear characteristics. The purpose of this study is to locate the article's perspective concerning the headline. The proposed approach yields gains of 0.04 in accuracy and 0.20 in the F1 score, respectively. As per the experimental results, PCA achieves a higher accuracy of 0.978 than both Chi-square and state-of-the-art approaches.
  3. Acetylsalicylic acid-sulfa drugs conjugates as potential urease inhibitors and anti-inflammatory agents: bio-oriented drug synthesis, molecular docking, and dynamics simulation studies
    Ahmad S, Abdul Qadir M, Ahmed M, Imran M, Yousaf N, Asari A, et al.
    J Biomol Struct Dyn, 2023 Aug 29.
    PMID: 37643014 DOI: 10.1080/07391102.2023.2252083
    To explore the new mode of action and reduce side effects, making conjugates of existing drugs is becoming an attractive tool in the realm of medicinal chemistry. In this work, we exploited this approach and synthesized new conjugates to assess their activities against the enzymes involved in different pathological conditions. Specifically, we design and synthesized conjugates involving acetylsalicylic acid and sulfa drugs, validating the newly crafted conjugates using techniques like IR, 1HNMR, 13CNMR, and elemental analysis. These conjugates underwent assessment for their ability to inhibit cyclooxygenase-2 (COX-2), urease enzymes, and their anti-inflammatory potential. A competitive mode of urease inhibition was observed for acetylsalicylic acid conjugated with sulfanilamide, sulfacetamide, and sulfadiazine with IC50 of 2.49 ± 0.35 µM, 6.21 ± 0.28 µM, and 6.57 ± 0.44 µM, respectively. Remarkably, the acetylsalicylic acid-sulfamethoxazole conjugate exhibited exceptional anti-inflammatory activity, effectively curtailing induced edema by 83.7%, a result akin to the reference anti-inflammatory drug indomethacin's performance (86.8%). Additionally, it demonstrated comparable COX-2 inhibition (75.8%) to the reference selective COX-2 inhibitor celecoxib that exhibited 77.1% inhibition at 10 µM concentration. To deepen our understanding, we employed molecular docking techniques to predict the binding interactions of competitive inhibitors with COX-2 and urease receptors. Additionally, MD simulations were carried out, confirming the stability of inhibitor-target complexes throughout the simulation period, devoid of significant conformational changes. Collectively, our research underscores the potential of coupling approved medicinal compounds to usher in novel categories of pharmacological agents, holding promise for addressing a wide spectrum of pathological disorders involving COX-2 and urease enzymes.Communicated by Ramaswamy H. Sarma.
  4. Phytochemical, antioxidant, antipyretic and anti-inflammatory activities of aqueous-methanolic leaf extract of Mangifera indica
    Ain QU, Iqbal MO, Khan IA, Bano N, Naeem M, Jamaludin MI, et al.
    Am J Transl Res, 2023;15(7):4533-4543.
    PMID: 37560231
    OBJECTIVE: Plant-based natural antioxidants have a wide variety of biological activities with significant therapeutic value. Mangifera indica has been used traditionally to treat a variety of ailments in animals and human, but little is defined about its biological or pharmacological effects. Therefore, the objective of the present study was to evaluate phytochemical, antioxidant, antipyretic and anti-inflammatory activities of aqueous-methanolic leaf extract of M. indica.

    METHODS: To investigate the possible impact of aqueous-methanolic leaf extract of M. indica on oxidative stress, inflammation, and pyrexia, we used a combined in vitro and in vivo series of experiments on laboratory animals.

    RESULTS: Results revealed significant antioxidant potential in 2,2-diphenylpicrylhydrazyl (DPPH) and nitric oxide (NO) scavenging assay, while significant but dose dependent antipyretic potential was documented in typhoid-paratyphoid A and B (TAB) vaccine and prostaglandin E (PGE) induced pyrexia models. Significant anti-inflammatory effects were observed in both acute and chronic inflammatory models of arachidonic acid and formalin. Phytochemical screening and high-performance liquid chromatography (HPLC) analysis of M. Indica confirmed the presence of mangiferin, quercetin, and isoquercetin. These phytoconstituents likely play a role in the observed biological activities. Our results show that M. indica has antioxidant, anti-inflammatory, and antipyretic effects, lending credence to its traditional use and advocating for its utilization as a viable contender in treating oxidative stress-associated ailments.

    CONCLUSION: It is concluded that Magnifera indica has various properties in the treatment of various diseases.

  5. Fulltext Non-Invasive Prenatal Testing (NIPT): Reliability, Challenges, and Future Directions
    Jayashankar SS, Nasaruddin ML, Hassan MF, Dasrilsyah RA, Shafiee MN, Ismail NAS, et al.
    Diagnostics (Basel), 2023 Aug 02;13(15).
    PMID: 37568933 DOI: 10.3390/diagnostics13152570
    Non-invasive prenatal testing was first discovered in 1988; it was primarily thought to be able to detect common aneuploidies, such as Patau syndrome (T13), Edward Syndrome (T18), and Down syndrome (T21). It comprises a simple technique involving the analysis of cell-free foetal DNA (cffDNA) obtained through maternal serum, using advances in next-generation sequencing. NIPT has shown promise as a simple and low-risk screening test, leading various governments and private organizations worldwide to dedicate significant resources towards its integration into national healthcare initiatives as well as the formation of consortia and research studies aimed at standardizing its implementation. This article aims to review the reliability of NIPT while discussing the current challenges prevalent among different communities worldwide.
  6. Impact of artificial intelligence on human loss in decision making, laziness and safety in education
    Ahmad SF, Han H, Alam MM, Rehmat MK, Irshad M, Arraño-Muñoz M, et al.
    Humanit Soc Sci Commun, 2023;10(1):311.
    PMID: 37325188 DOI: 10.1057/s41599-023-01787-8
    This study examines the impact of artificial intelligence (AI) on loss in decision-making, laziness, and privacy concerns among university students in Pakistan and China. Like other sectors, education also adopts AI technologies to address modern-day challenges. AI investment will grow to USD 253.82 million from 2021 to 2025. However, worryingly, researchers and institutions across the globe are praising the positive role of AI but ignoring its concerns. This study is based on qualitative methodology using PLS-Smart for the data analysis. Primary data was collected from 285 students from different universities in Pakistan and China. The purposive Sampling technique was used to draw the sample from the population. The data analysis findings show that AI significantly impacts the loss of human decision-making and makes humans lazy. It also impacts security and privacy. The findings show that 68.9% of laziness in humans, 68.6% in personal privacy and security issues, and 27.7% in the loss of decision-making are due to the impact of artificial intelligence in Pakistani and Chinese society. From this, it was observed that human laziness is the most affected area due to AI. However, this study argues that significant preventive measures are necessary before implementing AI technology in education. Accepting AI without addressing the major human concerns would be like summoning the devils. Concentrating on justified designing and deploying and using AI for education is recommended to address the issue.
  7. Fulltext A mini-review on the impact of COVID 19 on vital organs
    Shah MD, Sumeh AS, Sheraz M, Kavitha MS, Venmathi Maran BA, Rodrigues KF
    Biomed Pharmacother, 2021 Nov;143:112158.
    PMID: 34507116 DOI: 10.1016/j.biopha.2021.112158
    COVID-19 (Corona Virus Disease-2019) is an infectious disease caused by a novel coronavirus, known as the acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This is a highly contagious disease that has already affected more than 220 countries globally, infecting more than 212 million people and resulting in the death of over 4.4 million people. This review aims to highlight the pertinent documentary evidence upon the adverse effects of the SARS-CoV-2 infection on several vital human organs. SARS-CoV-2 primarily targets the lung tissue by causing diffuse alveolar damage and may result in Acute Respiratory Distress Syndrome (ARDS). SARS-CoV-2 infects the cell via cell surface receptor, angiotensin-converting enzyme 2 (ACE2). Besides lungs, SARS-CoV-2 critically damage tissues in other vital human organs such as the heart, kidney, liver, brain, and gastrointestinal tract. The effect on the heart includes muscle dysfunction (acute or protracted heart failure), myocarditis, and cell necrosis. Within hepatic tissue, it alters serum aminotransferase, total bilirubin, and gamma-glutamyl transferase levels. It contributes to acute kidney injury (AKI). Localized infection of the brain can lead to loss or attenuation of olfaction, muscular pain, headaches, encephalopathy, dizziness, dysgeusia, psychomotor disorders, and stroke; while the gastrointestinal symptoms include the disruption of the normal intestinal mucosa, leading to diarrhea and abdominal pain. This review encompassed a topical streak of systemic malfunctions caused by the SARS-CoV-2 infection. As the pandemic is still in progress, more studies will enrich our understanding and analysis of this disease.
  8. Fulltext A Fusion-Based Machine Learning Approach for the Prediction of the Onset of Diabetes
    Nadeem MW, Goh HG, Ponnusamy V, Andonovic I, Khan MA, Hussain M
    Healthcare (Basel), 2021 Oct 18;9(10).
    PMID: 34683073 DOI: 10.3390/healthcare9101393
    A growing portfolio of research has been reported on the use of machine learning-based architectures and models in the domain of healthcare. The development of data-driven applications and services for the diagnosis and classification of key illness conditions is challenging owing to issues of low volume, low-quality contextual data for the training, and validation of algorithms, which, in turn, compromises the accuracy of the resultant models. Here, a fusion machine learning approach is presented reporting an improvement in the accuracy of the identification of diabetes and the prediction of the onset of critical events for patients with diabetes (PwD). Globally, the cost of treating diabetes, a prevalent chronic illness condition characterized by high levels of sugar in the bloodstream over long periods, is placing severe demands on health providers and the proposed solution has the potential to support an increase in the rates of survival of PwD through informing on the optimum treatment on an individual patient basis. At the core of the proposed architecture is a fusion of machine learning classifiers (Support Vector Machine and Artificial Neural Network). Results indicate a classification accuracy of 94.67%, exceeding the performance of reported machine learning models for diabetes by ~1.8% over the best reported to date.
  9. Fulltext Graphene Oxide-Functionalized Bacterial Cellulose-Gelatin Hydrogel with Curcumin Release and Kinetics: In Vitro Biological Evaluation
    Khan MUA, Stojanović GM, Rehman RA, Moradi AR, Rizwan M, Ashammakhi N, et al.
    ACS Omega, 2023 Oct 31;8(43):40024-40035.
    PMID: 37929099 DOI: 10.1021/acsomega.2c06825
    Biopolymer-based bioactive hydrogels are excellent wound dressing materials for wound healing applications. They have excellent properties, including hydrophilicity, tunable mechanical and morphological properties, controllable functionality, biodegradability, and desirable biocompatibility. The bioactive hydrogels were fabricated from bacterial cellulose (BC), gelatin, and graphene oxide (GO). The GO-functionalized-BC (GO-f-BC) was synthesized by a hydrothermal method and chemically crosslinked with bacterial cellulose and gelatin using tetraethyl orthosilicate (TEOS) as a crosslinker. The structural, morphological, and wettability properties were studied using Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and a universal testing machine (UTM), respectively. The swelling analysis was conducted in different media, and aqueous medium exhibited maximum hydrogel swelling compared to other media. The Franz diffusion method was used to study curcumin (Cur) release (Max = 69.32%, Min = 49.32%), and Cur release kinetics followed the Hixson-Crowell model. Fibroblast (3T3) cell lines were employed to determine the cell viability and proliferation to bioactive hydrogels. Antibacterial activities of bioactive hydrogels were evaluated against infection-causing bacterial strains. Bioactive hydrogels are hemocompatible due to their less than 0.5% hemolysis against fresh human blood. The results show that bioactive hydrogels can be potential wound dressing materials for wound healing applications.
  10. Decarbonising ASEAN coastal shipping: Addressing climate change and coastal ecosystem issues through sustainable carbon neutrality strategies
    Oloruntobi O, Chuah LF, Mokhtar K, Gohari A, Rady A, Abo-Eleneen RE, et al.
    Environ Res, 2024 Jan 01;240(Pt 2):117353.
    PMID: 37821061 DOI: 10.1016/j.envres.2023.117353
    This study analyzes the impact of ASEAN's goal of achieving carbon neutrality by 2050 on climate change and coastal ecosystems by examining carbon emissions and energy usage from 2019 to 2050 using different scenarios to reduce emissions and meet global temperature goals. This research proposes strategies to reduce carbon emissions and mitigate climate change effects on coastal ecosystems, focusing on evaluating CO2 emissions from ASEAN's coastal shipping sector. Geospatial data was used to analyze ship activity and develop carbon neutrality strategies. Various sources are used to gather data, including the Maritime Portal, exact Earth AIS, FASA and GFW. This study finds that container ships emitted 13.7 × 106 t of CO2 in 2019, with the transportation sector contributing 3.8% of the total greenhouse gas in 2020. Without regulations, CO2 emissions could increase fourfold by 2050. The study recommends implementing policies such as adopting clean fuels, energy efficiency standards and fuel-related regulations to reduce CO2 emissions by 65-80% by 2050. It also emphasizes the importance of cleaner technologies, regulatory considerations and collaboration, which would have positive implications for coastal ecosystems. This study is beneficial to professionals in the maritime and shipping industries, policy makers, environmental consultants, sustainability specialists, and international organizations.
  11. Heavy Metals in Selected Vegetables from Markets of Faisalabad, Pakistan
    Iqbal SZ, Ullah Z, Asi MR, Jinap S, Ahmad MN, Sultan MT, et al.
    J Food Prot, 2018 May;81(5):806-809.
    PMID: 29637809 DOI: 10.4315/0362-028X.JFP-17-256
    Two hundred ten samples of selected vegetables (okra, pumpkin, tomato, potato, eggplant, spinach, and cabbage) from Faisalabad, Pakistan, were analyzed for the analysis of heavy metals: cadmium (Cd), lead (Pb), arsenic (As), and mercury (Hg). Inductively coupled plasma optical emission spectrometry was used for the analysis of heavy metals. The mean levels of Cd, Pb, As, and Hg were 0.24, 2.23, 0.58, and 7.98 mg/kg, respectively. The samples with Cd (27%), Pb (50%), and Hg (63%) exceeded the maximum residual levels set by the European Commission. The mean levels of heavy metals found in the current study are high and may pose significant health concerns for consumers. Furthermore, considerable attention should be paid to implement comprehensive monitoring and regulations.
  12. Natural and synthetic estrogens in leafy vegetable and their risk associated to human health
    Adeel M, Zain M, Fahad S, Rizwan M, Ameen A, Yi H, et al.
    Environ Sci Pollut Res Int, 2018 Dec;25(36):36712-36723.
    PMID: 30377972 DOI: 10.1007/s11356-018-3588-4
    Since the inception of global industrialization, the growth of steroid estrogens becomes a matter of emerging serious concern for the rapid population. Steroidal estrogens are potent endocrine-upsetting chemicals that are excreted naturally by vertebrates (e.g., humans and fish) and can enter natural waters through the discharge of treated and raw sewage. Steroidal estrogens in plants may enter the food web and become a serious threat to human health. We evaluated the uptake and accumulation of ethinylestradiol (EE2) and 17β-estradiol (17β-E2) in lettuce plants (Lactuca sativa) grown under controlled environmental condition over 21 days growth period. An effective analytical method based on ultrasonic liquid extraction (ULE) for solid samples and solid phase extraction (SPE) for liquid samples with gas chromatography-mass spectrometry (GC/MS) has been developed to determine the steroid estrogens in lettuce plants. The extent of uptake and accumulation was observed in a dose-dependent manner and roots were major organs for estrogen deposition. Unlike the 17β-E2, EE2 was less accumulated and translocated from root to leaves. For 17β-E2, the distribution in lettuce was primarily to roots after the second week (13%), whereas in leaves it was (10%) over the entire study period. The distribution of EE2 at 2000 μg L-1 in roots and leaves was very low (3.07% and 0.54%) during the first week and then was highest (12% in roots and 8% in leaves) in last week. Bioaccumulation factor values of 17β-E2 and EE2 in roots were 0.33 and 0.29 at 50 μg L-1 concentration as maximum values were found at 50 μg L-1 rather than 500 and 2000 in all observed plant tissues. Similar trend was noticed in roots than leaves for bioconcentration factor as the highest bioconcentration values were observed at 50 μg L-1 concentration instead of 500 and 2000 μg L-1 spiked concentration. These findings mainly indicate the potential for uptake and bioaccumulation of estrogens in lettuce plants. Overall, the estrogen contents in lettuce were compared to the FAO/WHO recommended toxic level and were found to be higher than the toxic level which is of serious concern to the public health. This analytical procedure may aid in future studies on risks associated with uptake of endocrine-disrupting chemicals in lettuce plants.
  13. Synthesis, characterization and in-vitro cytotoxic potential of sitagliptin phosphate nanoparticles against advanced breast cancer cell line - MCF-7
    Abdullah M, Rafiq A, Shahid N, Nasir Kalam M, Munir Y, Daoud Butt M, et al.
    Pak J Pharm Sci, 2023 Nov;36(6(Special)):1849-1858.
    PMID: 38264890
    Pharmaceutical substance sitagliptin has long been used to treat diabetes. However, subsequent researches have shown that sitagliptin has additional therapeutic effects. Anti-inflammatory effects are observed. Combining sitagliptin with biodegradable polymers like nanoparticles for chemotherapy may be effective. This method enhances therapeutic agent pharmacokinetics. This study tests sitagliptin (SIT) chitosan base nanoparticles against MCF-7 cancer cell lines for anti-cancer effects. Sitagliptin chitosan-based nanoparticles are tested for their ability to suppress MCF-7 cancer cell proliferation. Ionic gelation, a typical nanoparticle manufacturing method, was used. A detailed examination of the nanoparticles followed, using particle-size measurement, FTIR and SEM. Entrapment efficiency, drug-loading, and in-vitro drug release were assessed. Loaded with chitosan and sitagliptin, the nanoparticles averaged 500nm and 534nm in diameter. Sitagliptin has little effect on particle size. Chitosan-based Sitagliptin nanoparticles grew slightly, suggesting Sitagliptin is present. SIT-SC-NPs had 32% encapsulation efficiency and 30% drug content due to their high polymer-to-drug ratio. SEM analysis showed that both drug-free and sitagliptin-loaded nanoparticles are spherical, as shown by the different bands in the photos. The SIT-CS-NPs had a 120-hour release efficiency of up to 80%. This suggests that these nanoparticles could cure hepatocellular carcinoma, specifically MCF-7 cell lines.
  14. Revisiting the environmental Kuznets curve: assessing the impact of climate policy uncertainty in the Belt and Road Initiative
    Huang Y, Rahman SU, Meo MS, Ali MSE, Khan S
    Environ Sci Pollut Res Int, 2024 Feb;31(7):10579-10593.
    PMID: 38198084 DOI: 10.1007/s11356-023-31471-y
    Climate change repercussions such as temperature shifts and more severe weather occurrences are felt globally. It contributes to larger-scale challenges, such as climate change and biodiversity loss in food production. As a result, the purpose of this research is to develop strategies to grow the economy without harming the environment. Therefore, we revisit the environmental Kuznets curve (EKC) hypothesis, considering the impact of climate policy uncertainty along with other control variables. We investigated yearly panel data from 47 Belt and Road Initiative (BRI) nations from 1998 to 2021. Pooled regression, fixed effect, and the generalized method of moment (GMM) findings all confirmed the presence of inverted U-shaped EKC in BRI counties. Findings from this paper provide policymakers with actionable ideas, outlining a framework for bringing trade and climate agendas into harmony in BRI countries. The best way to promote economic growth and reduce carbon dioxide emissions is to push for trade and climate policies to be coordinated. Moreover, improving institutional quality is essential for strong environmental governance, as it facilitates the adoption of environmentally friendly industrialization techniques and the efficient administration of climate policy uncertainties.
  15. Fulltext Comparative analysis of various machine learning algorithms to predict 28-day compressive strength of Self-compacting concrete
    Inqiad WB, Siddique MS, Alarifi SS, Butt MJ, Najeh T, Gamil Y
    Heliyon, 2023 Nov;9(11):e22036.
    PMID: 38045144 DOI: 10.1016/j.heliyon.2023.e22036
    Construction industry is indirectly the largest source of CO2 emissions in the atmosphere, due to the use of cement in concrete. These emissions can be reduced by using industrial waste materials in place of cement. Self-Compacting Concrete (SCC) is a promising material to enhance the use of industrial wastes in concrete. However, there are very few methods available for accurate prediction of its strength, therefore, reliable models for estimating 28-day Compressive Strength (C-S) of SCC are developed in current study by using three Machine Learning (ML) algorithms including Multi Expression Programming (MEP), Extreme Gradient Boosting (XGB), and Random Forest (RF). The ML models were meticulously developed using a dataset of 231 points collected from internationally published literature considering seven most influential parameters including cement content, quantities of fly ash and silica fume, water content, coarse aggregate, fine aggregate, and superplasticizer dosage to predict C-S. The developed models were evaluated using different statistical errors including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2) etc. The results showed that the XGB model outperformed the MEP and RF model in terms of accuracy with a correlation R2 = 0.998 compared to 0.923 for MEP and 0.986 for RF. Similar trend was observed for other error metrices. Thus, XGB is the most accurate model for estimating C-S of SCC. However, it is pertinent to mention here that it does not give its output in the form of an empirical equation like MEP model. The construction of these empirical models will help to efficiently estimate C-S of SCC for practical purposes.
  16. Fulltext A discursive presentation of community pharmacies: Premises, storage, staff, documentation and legal compliance
    Aziz MM, Alboghdadly A, Rasool MF, Shaalan MS, Goresh HK, Najjar MF, et al.
    Heliyon, 2023 Dec;9(12):e23112.
    PMID: 38144360 DOI: 10.1016/j.heliyon.2023.e23112
    OBJECTIVES: This study aimed to assess compliance with legal requirements, safe medication storage and staffing standards in community pharmacies in Punjab, Pakistan.

    METHOD: We conducted a three-step cross-sectional study using observations, questionnaires and face-to-face interviews in 544 systematically-selected community pharmacies. We used descriptive statistic and one-way ANOVA to assess the data.

    RESULTS: Only 23 (4.2 %) pharmacies had accurate area and only 3.9 % had appropriate walls. In total, 23.3 % had glass-fronted shelves and 38.2 % had a glass door. More than half (53.8 %) had separate narcotics shelves and 43.0 % a separate shelf of expired medicines. Less than half (47.5 %) of the pharmacies were able to maintain hygiene. About 36.2 % of the pharmacies segregated different types of product. Drugs were protected from direct sunlight in most (61.3 %) pharmacies, but the refrigerator was working properly in less than half (43.4 %) and only a very small number (2.4 %) had an alternative power supply for the refrigerator. Only 37 (6.8 %) were able to maintain an appropriate room temperature. The vast majority (93.0 %) displayed a valid drug sale license, but a qualified person/pharmacist was only present in 4.8 %. The average number of employees was 4.2, and more than 71.0 % of staff had 10-12 years of formal education. Only 0.2 % of employees could explain term "PRN", although 57.3 % explained "IV" correctly. About 22.8 % replied correctly about the room temperature but the vast majority (97.6 %) did not know about cold chain temperature. The location of the pharmacy (p-value = 0.045) affected its performance.

    CONCLUSIONS: Noncompliance with legal requirements, unsafe drug storage and limited human resources reflect the poor enforcement of drug laws in Pakistan. The findings suggest that there is a need to strengthen inspection and management of community pharmacies.

  17. Microwave-assisted upcycling of plastic waste to high-performance carbon anode for lithium-ion batteries
    Mohd Abdah MAA, Mohammad Azlan FN, Wong WP, Mustafa MN, Walvekar R, Khalid M
    Chemosphere, 2024 Feb;349:140973.
    PMID: 38122940 DOI: 10.1016/j.chemosphere.2023.140973
    The increasing demand for high-performance lithium-ion batteries (LIBs) has emphasized the need for affordable and sustainable materials, prompting the exploration of waste upcycling to address global sustainability challenges. In this study, we efficiently converted polypropylene (PP) plastic waste from used centrifuge tubes into activated polypropylene carbon (APC) using microwave-assisted pyrolysis. The synthesis of APC was optimized using response surface methodology/central composite design (RSM/CCD). Based on the RSM results, the optimal conditions for PP plastic conversion into carbon were determined as follows: HNO3 concentration of 3.5 M, microwave temperature of 230 °C, and holding time of 25 min. Under these conditions, the obtained intensity ratio of Id/Ig in PP carbon was 0.681 ± 0.013, with an error of 6.81 ± 0.013 % between predicted and actual values. The physicochemical studies, including FESEM-EDX, XRD, and Raman spectroscopy, confirmed the successful synthesis of APC samples. The APC 800 material exhibited a well-organized three-dimensional structure characterized by large pores and mesopores, enabling fast ion transport in the electrode. As a result, the APC 800 electrode demonstrated an initial discharge capacity of 381.0 mAh/g, an improved initial coulombic efficiency of 85.1%, and excellent cycling stability after 100 cycles. Notably, the APC 800 electrode displayed remarkable rate performance, showing a reversible capacity of 355.1 mAh/g when the current density was reset to 0.2 A/g, highlighting its high electrochemical reversibility. The outstanding characteristics of APC 800 as an anode electrode material for high-performance lithium-ion batteries suggest a promising future for its application in the field.
  18. Advancements in Perovskite-Based Cathode Materials for Solid Oxide Fuel Cells: A Comprehensive Review
    Samreen A, Ali MS, Huzaifa M, Ali N, Hassan B, Ullah F, et al.
    Chem Rec, 2024 Jan;24(1):e202300247.
    PMID: 37933973 DOI: 10.1002/tcr.202300247
    The high-temperature solid oxide fuel cells (SOFCs) are the most efficient and green conversion technology for electricity generation from hydrogen-based fuel as compared to conventional thermal power plants. Many efforts have been made to reduce the high operating temperature (>800 °C) to intermediate/low operating temperature (400 °C
  19. Fulltext Environmental and health values, beliefs, norms and compatibility on intention to adopt hydroponic farming among unemployed youth
    Gao J, Al Mamun A, Yang Q, Rahman MK, Masud MM
    Sci Rep, 2024 Jan 18;14(1):1592.
    PMID: 38238468 DOI: 10.1038/s41598-024-52064-w
    The objective of this study was to examine the relationships among environmental and health values, ecological worldview, perception of consequences, the ascription of responsibility, and personal norms in the context of the value-belief-norm (VBN) model and how compatibility influences the intentions and behaviors of Chinese youth regarding the use of hydroponic farming technology. The study employed a survey questionnaire to collect data from the target population. The sample size was determined through a power analysis to ensure sufficient statistical power for the analysis. A total of 727 potential respondents' responses were analyzed using SmartPLS (4.0) to perform structural equation modeling. The results confirmed that environmental, emotional, and health values significantly associated with individuals' ecological worldviews. There was an interconnection between ecological worldview, awareness of consequences, and ascription of responsibility, and all three significantly influenced personal norms. The key determinants of the intentions and behaviors to adopt hydroponic farming technology are personal norms and technology compatibility. Therefore, to promote and motivate the interest and intention to use hydroponics among unemployed youth, government agencies, and related companies should focus on providing technology-related and pro-environmental information and training. This is expected to increase the acceptance and awareness of hydroponics among this group, thus increasing the adoption rate of hydroponics.
  20. Immunoinformatics-driven In silico vaccine design for Nipah virus (NPV): Integrating machine learning and computational epitope prediction
    Shahab M, Iqbal MW, Ahmad A, Alshabrmi FM, Wei DQ, Khan A, et al.
    Comput Biol Med, 2024 Mar;170:108056.
    PMID: 38301512 DOI: 10.1016/j.compbiomed.2024.108056
    The Nipah virus (NPV) is a highly lethal virus, known for its significant fatality rate. The virus initially originated in Malaysia in 1998 and later led to outbreaks in nearby countries such as Bangladesh, Singapore, and India. Currently, there are no specific vaccines available for this virus. The current work employed the reverse vaccinology method to conduct a comprehensive analysis of the entire proteome of the NPV virus. The aim was to identify and choose the most promising antigenic proteins that could serve as potential candidates for vaccine development. We have also designed B and T cell epitopes-based vaccine candidate using immunoinformatics approach. We have identified a total of 5 novel Cytotoxic T Lymphocytes (CTL), 5 Helper T Lymphocytes (HTL), and 6 linear B-cell potential antigenic epitopes which are novel and can be used for further vaccine development against Nipah virus. Then we performed the physicochemical properties, antigenic, immunogenic and allergenicity prediction of the designed vaccine candidate against NPV. Further, Computational analysis indicated that these epitopes possessed highly antigenic properties and were capable of interacting with immune receptors. The designed vaccine were then docked with the human immune receptors, namely TLR-2 and TLR-4 showed robust interaction with the immune receptor. Molecular dynamics simulations demonstrated robust binding and good dynamics. After numerous dosages at varied intervals, computational immune response modeling showed that the immunogenic construct might elicit a significant immune response. In conclusion, the immunogenic construct shows promise in providing protection against NPV, However, further experimental validation is required before moving to clinical trials.
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