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  1. 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.

  2. 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.
  3. Occurrence, distribution, and ecological risk of bisphenol analogues in marine ecosystem of urbanized coast and estuary
    Zainuddin AH, Roslan MQJ, Razak MR, Yusoff FM, Haron DEM, Aris AZ
    Mar Pollut Bull, 2023 Jul;192:115019.
    PMID: 37201347 DOI: 10.1016/j.marpolbul.2023.115019
    Bisphenol analogues are prevalent globally because of rampant usage and imprecise processing techniques, prompting alerts about environmental and health hazards. The method employed in this study by solid phase extraction (SPE) and liquid chromatography-tandem quadrupole mass spectrometer (LC-MS/MS) for both quantification and qualitative analysis of the bisphenol compounds in the surface water samples. The coastal and estuarine surface water of Port Dickson and Lukut ranges from 1.32 ng/L to 1890.51 ng/L of bisphenol analogues. BPF mean concentration at 1143.88 ng/L is the highest, followed by BPA and BPS at 59.01 ng/L and 10.96 ng/L, respectively. Based on RQm for bisphenol analogues, the highest for BPF at 2.49 (RQ > 1, high risk), followed by BPS at 0.12 (0.1 
  4. 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
  5. 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.
  6. Efficacy of mHealth and education-led peer counseling for patients with hypertension and coronary artery disease in Pakistan: study protocol for a double-blinded pragmatic randomized-controlled trial with factorial design
    Arshed M, Zakar R, Umer MF, Kiran M, Ullah N, Iftikhar G, et al.
    Trials, 2023 Jul 10;24(1):448.
    PMID: 37424031 DOI: 10.1186/s13063-023-07472-0
    BACKGROUND: Hypertension is a highly relevant public health challenge. Digital interventions may support improving adherence to anti-hypertensive medications and alter health behavior. Therefore, this protocol describes a study that aims to assess the effectiveness of mHealth and educational support through peer counseling (Ed-counselling) to control blood pressure in hypertensive patients when compared to standard care.

    METHODS: We chose a double-blinded pragmatic randomized-controlled with factorial design for this investigation. The trial is going to recruit 1648 hypertensive patients with coronary artery disease at the age of 21 to 70 years. All participants will already be on anti-hypertensive medication and own a smartphone. They will be randomized into four groups with each having 412 participants. The first group will only receive standard care; while the second group, in addition to standard care, will receive monthly Ed-counselling (educational booklets with animated infographics and peer counseling); the third group will receive daily written and voice reminders and an education-led video once weekly together with standard care; while the fourth one gets both interventions given to second and third groups respectively. All groups will be followed-up for 1 year (0, 6, and 12 months). The primary outcome will be the change in systolic blood pressure while secondary outcomes include health-related quality of life and changes in medication adherence. For measuring changes in systolic blood pressure (SBP) and adherence scores difference at 0, 6, and 12 months between and within the group, parametric (ANOVA/repeated measure ANOVA) and non-parametric tests (Kruskal-Wallis test/Friedman test) will be used. By using the general estimating equation (GEE) with negative binomial regression, at 12 months, the covariates affecting primary and secondary outcomes will be determined and controlled. The analysis will be intention-to-treat. All the outcomes will be analyzed at 0, 6, and 12 months; however, the final analysis will be at 12 months from baseline.

    DISCUSSION: Besides adding up to existing evidence in the literature on the subject, our designed modules using mHealth technology can help in reducing hypertension-related morbidity and mortality in developing countries.

  7. 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.

  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. Fulltext Proteomic profile of epidermal mucus from Labeo rohita reveals differentially abundant proteins after Aeromonas hydrophila infection
    Ali S, Ullah W, Kamarulzaman AFS, Hassan M, Rauf M, Khattak MNK, et al.
    Fish Shellfish Immunol Rep, 2023 Dec 15;5:100115.
    PMID: 37771818 DOI: 10.1016/j.fsirep.2023.100115
    We report the proteomic profile of Epidermal Mucus (EM) from Labeo rohita and identified the differentially abundant proteins (DAPs) against Aeromonas hydrophila infection through label-free liquid chromatography-mass spectrometry (LC-MS/MS). Using discovery-based proteomics, a total of 2039 proteins were quantified in nontreated group and 1,328 proteins in the treated group, of which 114 were identified as DAPs in both the groups. Of the 114 DAPs, 68 proteins were upregulated and 46 proteins were downregulated in the treated group compared to nontreated group. Functional annotations of these DAPs shows their association with metabolism, cellular process, molecular process, cytoskeletal, stress, and particularly immune system. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Fisher's exact test between the two groups shows that most of the proteins were immune-related, which were significantly associated with the proteasome, phagosome, and Salmonella infection pathways. Overall, this study shows a basic and primary way for further functional research of the involvement of vitellogenin 2, alpha-2-macroglobulin-like protein, toll-like receptors (TLR-13), calpain, keratin-like proteins, and heat shock proteins against bacterial infection. Nonetheless, this first-ever comprehensive report of a proteomic sketch of EM from L. rohita after A. hydrophila infection provides systematic protein information to broadly understand the biological role of fish EM against bacterial infection.
  13. Fulltext An efficient spline technique for solving time-fractional integro-differential equations
    Abbas M, Aslam S, Abdullah FA, Riaz MB, Gepreel KA
    Heliyon, 2023 Sep;9(9):e19307.
    PMID: 37810099 DOI: 10.1016/j.heliyon.2023.e19307
    Spline curves are very prominent in the mathematics due to their simple construction, accuracy of assessment and ability to approximate complicated structures into interactive curved designs. A spline is a smooth piece-wise polynomial function. The primary goal of this study is to use extended cubic B-spline (ExCuBS) functions with a new second order derivative approximation to obtain the numerical solution of the weakly singular kernel (SK) non-linear fractional partial integro-differential equation (FPIDE). The spatial and temporal fractional derivatives are discretized by ExCuBS and the Caputo finite difference scheme, respectively. The present study found that it is stable and convergent. The validity of the current approach is examined on a few test problems, and the obtained outcomes are compared with those that have previously been reported in the literature.
  14. 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.
  15. 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.
  16. Fulltext Effect of organic loading on anaerobic digestion of cow dung: Methane production and kinetic study
    Aili Hamzah AF, Hamzah MH, Che Man H, Jamali NS, Siajam SI, Ismail MH
    Heliyon, 2023 Jun;9(6):e16791.
    PMID: 37303543 DOI: 10.1016/j.heliyon.2023.e16791
    Organic loading influences the effectiveness of producing biogas through anaerobic digestion. This study set out to investigate the effect of organic loading on the anaerobic mesophilic digestion of cow dung, the parameters involved in the digestion process and to evaluate the kinetics. Anaerobic digestion of cow dung at different organic loading (gVS/L) of 14 gVS/L, 18gVS/L, 22 gVS/L, 26 gVS/L and 30 gVS/L were investigated. Increasing the organic loading increased the methane yield of the cow dung. The highest cumulative methane yield was observed at 30 gVS/L with 63.42 mL CH4/gVS while the highest biogas yield was reported at 192.53 mL/gVS with the highest methane content of 89%. In addition, the modified Gompertz model equation with an R2 of 0.9980 demonstrated strong consistency and a good fit between predicted and experimental data. The high number of substrates added to the systems when increasing the organic loading increased the λ and slow down the nutrient transport and hydrolysis. This study provides current information on the effects of organic loading on the anaerobic digestion of cow dung in batch mode, including experimental conditions and operational parameters.
  17. Fulltext Heart Failure Emergency Readmission Prediction Using Stacking Machine Learning Model
    Rahman MS, Rahman HR, Prithula J, Chowdhury MEH, Ahmed MU, Kumar J, et al.
    Diagnostics (Basel), 2023 Jun 02;13(11).
    PMID: 37296800 DOI: 10.3390/diagnostics13111948
    Heart failure is a devastating disease that has high mortality rates and a negative impact on quality of life. Heart failure patients often experience emergency readmission after an initial episode, often due to inadequate management. A timely diagnosis and treatment of underlying issues can significantly reduce the risk of emergency readmissions. The purpose of this project was to predict emergency readmissions of discharged heart failure patients using classical machine learning (ML) models based on Electronic Health Record (EHR) data. The dataset used for this study consisted of 166 clinical biomarkers from 2008 patient records. Three feature selection techniques were studied along with 13 classical ML models using five-fold cross-validation. A stacking ML model was trained using the predictions of the three best-performing models for final classification. The stacking ML model provided an accuracy, precision, recall, specificity, F1-score, and area under the curve (AUC) of 89.41%, 90.10%, 89.41%, 87.83%, 89.28%, and 0.881, respectively. This indicates the effectiveness of the proposed model in predicting emergency readmissions. The healthcare providers can intervene pro-actively to reduce emergency hospital readmission risk and improve patient outcomes and decrease healthcare costs using the proposed model.
  18. Molecular epidemiology of Bartonella species from sympatric mammals collected in urban and rural areas of Punjab, Pakistan
    Rizwan M, Ali S, Javid A, von Fricken ME, Rashid MI
    Acta Trop, 2023 Jul;243:106940.
    PMID: 37160189 DOI: 10.1016/j.actatropica.2023.106940
    Bartonella can infect a variety of mammals including humans and has been detected in the Americas, Europe, Africa, and Asia. Roughly two-thirds of identified Bartonella species are found and maintained in rodent reservoirs, with some of these species linked to human infections. Rodents (N=236) were caught from the Sahiwal division of Punjab, Pakistan and tested for Bartonella using PCR targeting gltA and rpoB genes, followed by sequencing of rpoB-positive samples. Genetic relatedness to other published Bartonella spp. rpoB gene sequences were examined using BLAST and phylogenetic analysis. Overall, 7.62% (18/236) of rodents were positive for both gltA and rpoB fragments. Rattus rattus and R. norvegicus had 7.94% (12/151) and 7.05% (6/85) positivity rates for Bartonella DNA, respectively. Phylogenetic analysis revealed a close relatedness between Bartonella spp. from Pakistan to Bartonella spp. from China, Nepal, and Malaysia. This study is the first reported detection of Bartonella spp. in R. rattus and R. norvegicus from the Sahiwal area of Punjab, Pakistan.
  19. Recent Advances of GFRP Composite Cross Arms in Energy Transmission Tower: A Short Review on Design Improvements and Mechanical Properties
    Syamsir A, Ean LW, Asyraf MRM, Supian ABM, Madenci E, Özkılıç YO, et al.
    Materials (Basel), 2023 Mar 30;16(7).
    PMID: 37049072 DOI: 10.3390/ma16072778
    Currently, pultruded glass fibre-reinforced polymer (pGFRP) composites have been extensively applied as cross-arm structures in latticed transmission towers. These materials were chosen for their high strength-to-weight ratio and lightweight characteristics. Nevertheless, several researchers have discovered that several existing composite cross arms can decline in performance, which leads to composite failure due to creep, torsional movement, buckling, moisture, significant temperature change, and other environmental factors. This leads to the composite structure experiencing a reduced service life. To resolve this problem, several researchers have proposed to implement composite cross arms with sleeve installation, an addition of bracing systems, and the inclusion of pGFRP composite beams with the core structure in order to have a sustainable composite structure. The aforementioned improvements in these composite structures provide superior performance under mechanical duress by having better stiffness, superiority in flexural behaviour, enhanced energy absorption, and improved load-carrying capacity. Even though there is a deficiency in the previous literature on this matter, several established works on the enhancement of composite cross-arm structures and beams have been applied. Thus, this review articles delivers on a state-of-the-art review on the design improvement and mechanical properties of composite cross-arm structures in experimental and computational simulation approaches.
  20. 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.
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