Islamia University of Bahawalpur

This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and area of focus for researchers. Additionally, the study presents ANN application challenges, contributions, compare performances and critiques methods. The study covers many applications of ANN techniques in various disciplines which include computing, science, engineering, medicine, environmental, agriculture, mining, technology, climate, business, arts, and nanotechnology, etc. The study assesses ANN contributions, compare performances and critiques methods. The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems. Therefore, we proposed feedforward and feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance. Moreover, we recommend that instead of applying a single method, future research can focus on combining ANN models into one network-wide application.

Abiotic stresses are one of the major constraints to crop production and food security worldwide. The situation has aggravated due to the drastic and rapid changes in global climate. Heat and drought are undoubtedly the two most important stresses having huge impact on growth and productivity of the crops. It is very important to understand the physiological, biochemical, and ecological interventions related to these stresses for better management. A wide range of plant responses to these stresses could be generalized into morphological, physiological, and biochemical responses. Interestingly, this review provides a detailed account of plant responses to heat and drought stresses with special focus on highlighting the commonalities and differences. Crop growth and yields are negatively affected by sub-optimal water supply and abnormal temperatures due to physical damages, physiological disruptions, and biochemical changes. Both these stresses have multi-lateral impacts and therefore, complex in mechanistic action. A better understanding of plant responses to these stresses has pragmatic implication for remedies and management. A comprehensive account of conventional as well as modern approaches to deal with heat and drought stresses have also been presented here. A side-by-side critical discussion on salient responses and management strategies for these two important abiotic stresses provides a unique insight into the phenomena. A holistic approach taking into account the different management options to deal with heat and drought stress simultaneously could be a win-win approach in future.

The era of artificial neural network (ANN) began with a simplified application in many fields and remarkable success in pattern recognition (PR) even in manufacturing industries. Although significant progress achieved and surveyed in addressing ANN application to PR challenges, nevertheless, some problems are yet to be resolved like whimsical orientation (the unknown path that cannot be accurately calculated due to its directional position). Other problem includes; object classification, location, scaling, neurons behavior analysis in hidden layers, rule, and template matching. Also, the lack of extant literature on the issues associated with ANN application to PR seems to slow down research focus and progress in the field. Hence, there is a need for state-of-the-art in neural networks application to PR to urgently address the above-highlights problems for more successes. The study furnishes readers with a clearer understanding of the current, and new trend in ANN models that effectively addresses PR challenges to enable research focus and topics. Similarly, the comprehensive review reveals the diverse areas of the success of ANN models and their application to PR. In evaluating the performance of ANN models, some statistical indicators for measuring the performance of the ANN model in many studies were adopted. Such as the use of mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and variance of absolute percentage error (VAPE). The result shows that the current ANN models such as GAN, SAE, DBN, RBM, RNN, RBFN, PNN, CNN, SLP, MLP, MLNN, Reservoir computing, and Transformer models are performing excellently in their application to PR tasks. Therefore, the study recommends the research focus on current models and the development of new models concurrently for more successes in the field.

Cancer causes the second-highest rate of death world-wide. A major shortcoming inherent in most of anticancer drugs is their lack of tumor selectivity. Nanodrugs for cancer therapy administered intravenously escape renal clearance, are unable to penetrate through tight endothelial junctions of normal blood vessels and remain at a high level in plasma. Over time, the concentration of nanodrugs builds up in tumors due to the EPR effect, reaching several times higher than that of plasma due to the lack of lymphatic drainage. This review will address in detail the progress and prospects of tumor-targeting via EPR effect for cancer therapy.

Over the last decade, it has become clear that antimicrobial drugs are losing their effectiveness due to the evolution of pathogen resistance. There is therefore a continuing need to search for new antibiotics, especially as new drugs only rarely reach the market. Natural products are both fundamental sources of new chemical diversity and integral components of today's pharmaceutical compendium, and the aim of this review is to explore and highlight the diverse natural products that have potential to lead to more effective and less toxic antimicrobial drugs. Although more than 300 natural metabolites with antimicrobial activity have been reported in the period 2000-2008, this review will describe only those with potentially useful antimicrobial activity, viz. with MICs in the range 0.02-10 microg mL(-1). A total of 145 compounds from 13 structural classes are discussed, and over 100 references are cited.

Machine learning (ML) based forecasting mechanisms have proved their significance to anticipate in perioperative outcomes to improve the decision making on the future course of actions. The ML models have long been used in many application domains which needed the identification and prioritization of adverse factors for a threat. Several prediction methods are being popularly used to handle forecasting problems. This study demonstrates the capability of ML models to forecast the number of upcoming patients affected by COVID-19 which is presently considered as a potential threat to mankind. In particular, four standard forecasting models, such as linear regression (LR), least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and exponential smoothing (ES) have been used in this study to forecast the threatening factors of COVID-19. Three types of predictions are made by each of the models, such as the number of newly infected cases, the number of deaths, and the number of recoveries in the next 10 days. The results produced by the study proves it a promising mechanism to use these methods for the current scenario of the COVID-19 pandemic. The results prove that the ES performs best among all the used models followed by LR and LASSO which performs well in forecasting the new confirmed cases, death rate as well as recovery rate, while SVM performs poorly in all the prediction scenarios given the available dataset.

Soil salinization is one of the major environmental stressors hampering the growth and yield of crops all over the world. A wide spectrum of physiological and biochemical alterations of plants are induced by salinity, which causes lowered water potential in the soil solution, ionic disequilibrium, specific ion effects, and a higher accumulation of reactive oxygen species (ROS). For many years, numerous investigations have been made into salinity stresses and attempts to minimize the losses of plant productivity, including the effects of phytohormones, osmoprotectants, antioxidants, polyamines, and trace elements. One of the protectants, selenium (Se), has been found to be effective in improving growth and inducing tolerance against excessive soil salinity. However, the in-depth mechanisms of Se-induced salinity tolerance are still unclear. This review refines the knowledge involved in Se-mediated improvements of plant growth when subjected to salinity and suggests future perspectives as well as several research limitations in this field.

Recent statistics show that almost 1/4 of a million people have died and four million people are affected either with mild or serious health problems caused by coronavirus (COVID-19). These numbers are rapidly increasing (World Health Organization, May 3, 2020c). There is much concern during this pandemic about the spread of misleading or inaccurate information. This article reports on a small study which attempted to identify the types and sources of COVID-19 misinformation. The authors identified and analysed 1225 pieces of COVID-19 fake news stories taken from fact-checkers, myth-busters and COVID-19 dashboards. The study is significant given the concern raised by the WHO Director-General that 'we are not just fighting the pandemic, we are also fighting infodemic'. The study concludes that the COVID-19 infodemic is full of false claims, half backed conspiracy theories and pseudoscientific therapies, regarding the diagnosis, treatment, prevention, origin and spread of the virus. Fake news is pervasive in social media, putting public health at risk. The scale of the crisis and ubiquity of the misleading information require that scientists, health information professionals and journalists exercise their professional responsibility to help the general public identify fake news stories. They should ensure that accurate information is published and disseminated.J.M.

Climate change is causing soil salinization, resulting in crop losses throughout the world. The ability of plants to tolerate salt stress is determined by multiple biochemical and molecular pathways. Here we discuss physiological, biochemical, and cellular modulations in plants in response to salt stress. Knowledge of these modulations can assist in assessing salt tolerance potential and the mechanisms underlying salinity tolerance in plants. Salinity-induced cellular damage is highly correlated with generation of reactive oxygen species, ionic imbalance, osmotic damage, and reduced relative water content. Accelerated antioxidant activities and osmotic adjustment by the formation of organic and inorganic osmolytes are significant and effective salinity tolerance mechanisms for crop plants. In addition, polyamines improve salt tolerance by regulating various physiological mechanisms, including rhizogenesis, somatic embryogenesis, maintenance of cell pH, and ionic homeostasis. This research project focuses on three strategies to augment salinity tolerance capacity in agricultural crops: salinity-induced alterations in signaling pathways; signaling of phytohormones, ion channels, and biosensors; and expression of ion transporter genes in crop plants (especially in comparison to halophytes).

Recent achievements in plant microRNA (miRNA), a large class of small and non-coding RNAs, are very exciting. A wide array of techniques involving forward genetic, molecular cloning, bioinformatic analysis, and the latest technology, deep sequencing have greatly advanced miRNA discovery. A tiny miRNA sequence has the ability to target single/multiple mRNA targets. Most of the miRNA targets are transcription factors (TFs) which have paramount importance in regulating the plant growth and development. Various families of TFs, which have regulated a range of regulatory networks, may assist plants to grow under normal and stress environmental conditions. This present review focuses on the regulatory relationships between miRNAs and different families of TFs like; NF-Y, MYB, AP2, TCP, WRKY, NAC, GRF, and SPL. For instance NF-Y play important role during drought tolerance and flower development, MYB are involved in signal transduction and biosynthesis of secondary metabolites, AP2 regulate the floral development and nodule formation, TCP direct leaf development and growth hormones signaling. WRKY have known roles in multiple stress tolerances, NAC regulate lateral root formation, GRF are involved in root growth, flower, and seed development, and SPL regulate plant transition from juvenile to adult. We also studied the relation between miRNAs and TFs by consolidating the research findings from different plant species which will help plant scientists in understanding the mechanism of action and interaction between these regulators in the plant growth and development under normal and stress environmental conditions.

Agricultural production is under threat due to climate change in food insecure regions, especially in Asian countries. Various climate-driven extremes, i.e., drought, heat waves, erratic and intense rainfall patterns, storms, floods, and emerging insect pests have adversely affected the livelihood of the farmers. Future climatic predictions showed a significant increase in temperature, and erratic rainfall with higher intensity while variability exists in climatic patterns for climate extremes prediction. For mid-century (2040-2069), it is projected that there will be a rise of 2.8°C in maximum temperature and a 2.2°C in minimum temperature in Pakistan. To respond to the adverse effects of climate change scenarios, there is a need to optimize the climate-smart and resilient agricultural practices and technology for sustainable productivity. Therefore, a case study was carried out to quantify climate change effects on rice and wheat crops and to develop adaptation strategies for the rice-wheat cropping system during the mid-century (2040-2069) as these two crops have significant contributions to food production. For the quantification of adverse impacts of climate change in farmer fields, a multidisciplinary approach consisted of five climate models (GCMs), two crop models (DSSAT and APSIM) and an economic model [Trade-off Analysis, Minimum Data Model Approach (TOAMD)] was used in this case study. DSSAT predicted that there would be a yield reduction of 15.2% in rice and 14.1% in wheat and APSIM showed that there would be a yield reduction of 17.2% in rice and 12% in wheat. Adaptation technology, by modification in crop management like sowing time and density, nitrogen, and irrigation application have the potential to enhance the overall productivity and profitability of the rice-wheat cropping system under climate change scenarios. Moreover, this paper reviews current literature regarding adverse climate change impacts on agricultural productivity, associated main issues, challenges, and opportunities for sustainable productivity of agriculture to ensure food security in Asia. Flowing opportunities such as altering sowing time and planting density of crops, crop rotation with legumes, agroforestry, mixed livestock systems, climate resilient plants, livestock and fish breeds, farming of monogastric livestock, early warning systems and decision support systems, carbon sequestration, climate, water, energy, and soil smart technologies, and promotion of biodiversity have the potential to reduce the negative effects of climate change.

Cardiovascular disease is a substantial cause of mortality and morbidity in the world. In clinical data analytics, it is a great challenge to predict heart disease survivor. Data mining transforms huge amounts of raw data generated by the health industry into useful information that can help in making informed decisions. Various studies proved that significant features play a key role in improving performance of machine learning models. This study analyzes the heart failure survivors from the dataset of 299 patients admitted in hospital. The aim is to find significant features and effective data mining techniques that can boost the accuracy of cardiovascular patient’s survivor prediction. To predict patient’s survival, this study employs nine classification models: Decision Tree (DT), Adaptive boosting classifier (AdaBoost), Logistic Regression (LR), Stochastic Gradient classifier (SGD), Random Forest (RF), Gradient Boosting classifier (GBM), Extra Tree Classifier (ETC), Gaussian Naive Bayes classifier (G-NB) and Support Vector Machine (SVM). The imbalance class problem is handled by Synthetic Minority Oversampling Technique (SMOTE). Furthermore, machine learning models are trained on the highest ranked features selected by RF. The results are compared with those provided by machine learning algorithms using full set of features. Experimental results demonstrate that ETC outperforms other models and achieves 0.9262 accuracy value with SMOTE in prediction of heart patient’s survival.

Iron oxide nanoparticles (Fe2O3 NPs) were fabricated through green route using pomegranate (Punica granatum) seeds extract. The Fe2O3 NPs were characterized by UV–vis, XRD, EDX, SEM and AFM techniques. The adopted green rout furnished semi spherical Fe2O3 NPs, uniformly distributed and particle size in the range of 25–55 nm. The LCMS/MS was performed for the identification of biomolecule present in the extract of pomegranate seeds and p-hydroxy benzoic acid, gallic acid, methyl gallate, catechin, kaempferol-3-O-sophoroside, 3-deoxyflavonoids, magnolol, ferulic acid, vanillic acid and pinocembrin along with other minor constituents were detected in the extracts using for Fe2O3 NPs. The synthesized Fe2O3 NPs showed excellent photocatalytic activity against reactive blue under UV light irradiation and maximum degradation of 95.08% was achieved with 56 min of reaction time. In view of promising activity, the Fe2O3 NPs could be used photocatalyst for the degradation of dyes in wastewater and pomegranate seeds extract can be applied as eco-benign and cost effective approach for Fe2O3 NPs synthesis.

BACKGROUND: For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. METHODS: The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010-23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution. FINDINGS: Total numbers of global DALYs grew 6·1% (95% UI 4·0-8·1), from 2·64 billion (2·46-2·86) in 2010 to 2·80 billion (2·57-3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0-14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31-1·61) global DALYs in 2010, increasing to 1·80 billion (1·63-2·03) in 2023, alongside a concurrent 4·1% (1·9-6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176-209] DALYs), stroke (157 million [141-172]), and diabetes (90·2 million [75·2-107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0-107·5]), depressive disorders (26·3% [11·6-42·9]), and diabetes (14·9% [7·5-25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837-917) in 2010 to 681 million (642-736) in 2023, and a 25·8% (22·6-28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7-61·0) for diarrhoeal diseases, 42·9% (38·0-48·0) for HIV/AIDS, and 42·2% (23·6-56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6-22·0) and 24·8% (7·4-36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7-19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18-1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation-with high SBP accounting for 8·4% (6·9-10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories-behavioural, metabolic, and environmental and occupational-risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8-37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0-11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023-eg, declining by 54·4% (38·7-65·3) for unsafe sanitation, 50·5% (33·3-63·1) for unsafe water source, and 45·2% (25·6-72·0) for no access to handwashing facility, and by 44·9% (37·3-53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [-2·7 to 15·6]; non-significant). INTERPRETATION: Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors-eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG-including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic-the complex interaction of multiple health risks, social determinants, and systemic challenges-will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity. FUNDING: Gates Foundation and Bloomberg Philanthropies.

Abstract Rapid industrialization and urbanization have resulted in elevated global temperature over the years consequently disturbing the balance of agro-ecological systems worldwide. Therefore, new eco-friendly agricultural practices for sustainable food production are needed. Mulching could potentially serve the purpose by reducing soil evaporation, conserving moisture, controlling soil temperature, reducing weed growth, and improving microbial activities. Additionally, mulches could provide economical, aesthetic, and environmental advantages to agriculture and landscape. Moreover, in the restoration sites, mulches are widely used for the plantation of trees which need no significant care. Mulches combat with different stress conditions in agricultural lands as well as in landscapes. This review paper focuses on multiple significant impacts of mulches for the production and establishment of different crops in nature. Mulches conserve the soil moisture, enhance the nutrients status of soil, control the erosion losses, suppress the weeds in crop plants, and remove the residual effects of pesticides, fertilizers, and heavy metals. Mulches improve the aesthetic value of landscapes and economic value of crops. This paper also describes some problems associated with various mulch materials. There are contradictions about mulching materials as some researchers favor mulches and others have denoted some concerns. The selection of mulching material is important with respect to crop type, management practices, and climatic conditions. The appropriate mulching technique could provide the aforementioned benefits to the agro-ecological systems. Therefore, the impacts of low-cost, eco-friendly, and biodegradable mulching materials on soil microbes, nutrient balance, plant growth, and soil erosion should be explored in the future.

Background Poor control of diabetes mellitus (DM) is partly attributed to doctors' poor adherence to guidelines. Objective To evaluate doctors' adherence to pharmacotherapeutic recommendations of DM management guidelines and factors associated with guidelines adherence and glycaemic control. Methods This prospective observational study included 30 doctors who were treating DM patients in their private clinics in Quetta, Pakistan. On visit 1, a total of 600 prescriptions written by 30 enrolled doctors (20 patients per doctor) were noted along with patients' sociodemographic and clinical characteristics. American Diabetes Association guidelines was used as a reference. The prescriptions noted were judged for guidelines compliance. Of 600 enrolled patients, 450 patients (15 patients per doctor) were followed for one more visit and included in final analysis. Glycated hemoglobin (HbA1c) level noted one visit 2 was related with the respective prescription on visit 1. Data were analyzed by SPSS (version 23). A p -value <0.05 was considered statistically significant. Results Patients received a median of two antidiabetic drugs (range: 1–5). A total of 73.1% patients were on polytherapy. Metformin was the most frequently prescribed (88.4%) antidiabetic followed by gliptins (46.2%). A total of 41.6% prescriptions were judged guidelines compliant. In multivariate binary logistic regressions (MVBLR) analysis, chronic kidney disease (CKD) (OR = 0.422) and polytherapy (OR = 0.367) had statistically significant negative associations ( p -value <0.05) with guidelines' compliant prescriptions. The group of doctors comprised of specialists and consultants wrote significantly ( p -value = 0.004) high number of guidelines adherent prescriptions (mean rank = 20.25) than the group comprised of medical officers (mean rank = 11.34). On visit 2, only 39.5% patients were on goal glycemic levels. In MVBLR analysis, suffering from dyslipidemia (OR = 0.134) and CKD (OR = 0.111), receiving sulfonylurea (OR = 0.156) and guidelines' compliant prescription (OR = 4.195) were significantly ( p -value <0 .05) associated with glycemic control. Conclusion Although guidelines compliant prescriptions produced better glycemic control, but doctors' adherence to guidelines and glycemic control were poor. Polytherapy and CKD emerged as risk factors for guidelines divergent prescriptions. Dyslipidemia, CKD and reception of sulfonylureas had negative association with glycemic control.

Background: Globally, tuberculosis (TB) is the second major cause of death from infectious diseases, particularly in developing countries. A multidisciplinary approach to the management of TB may help to curb the disease burden. Objective: The objective of this study was to outline the perceptions of healthcare professionals and patients regarding the potential role of pharmacists in TB management in Pakistan. Method: This was a large-scale qualitative study conducted at the Chest Disease Unit (CDU) of the Bahawal Victoria Hospital (BVH), Punjab, Pakistan. Data were collected through semi-structured interviews with physicians, pharmacists, and patients recruited using a mix of convenient and snowball sampling. The sample size was decided through standard saturation point criteria. All interviews were audio recorded and transcribed verbatim. The data were analyzed to draw conclusions using a thematic analysis approach. Results: Analysis of the data yielded 19 categories and seven themes. Physicians considered pharmacists qualified healthcare professionals, whereas patients considered them merely dispensers. Inventory management and dispensing of medicines were considered as major responsibilities of pharmacists. Physicians were extremely overburdened and wanted to delegate certain duties to pharmacists, subject to their prior extensive trainings. However, most of the physicians were unaware of the legal scope of pharmacy practice in Pakistan. With regard to the potential duties of pharmacists, physicians, pharmacists, and patients (patients—upon explaining the potential roles during the interview) endorsed monitoring, counseling, medicine brand selection, dose adjustment, inventory management, dispensing, and polypharmacy assessment as their potential roles. In view of all stakeholders, the rationale for integrating pharmacists in TB management included overburdened physicians, sub-standard patient care, medication safety issues, and patient dissatisfaction. The healthcare professionals highlighted that the major barriers to integrating pharmacists within the TB management system were limited interest of regulatory authorities and policy makers, followed by inadequate training and experience-driven questionable competency of pharmacists. Conclusion: The study participants acknowledged the potential role of pharmacists in TB management. However, it was emphasized that healthcare policy makers should devise strategies to overcome the underlying barriers before assigning medicine-related clinical roles to pharmacists.

The virus, commonly known as COVID-19 which emerged in Wuhan, China, in December 2019, has spread in 213 countries, areas or territories around the globe, with nearly 144 683 deaths worldwide on 18 April 2020. In the wake of this pandemic, we have witnessed a massive infodemic with the public being bombarded with vast quantities of information, much of which is not scientifically correct. Fighting fake news is now the new front in the COVID-19 battle. This regular feature comments on the role of health sciences librarians and information professionals in combating the COVID-19 infodemic. To support their work, it draws attention to the myth busters, fact-checkers and credible sources relating to COVID-19. It also documents the guides that libraries have put together to help the general public, students and faculty recognise fake news.

Purpose : Green Supply Chain Management (GSCM) has received growing attention in the last few years. Most of the automobile industries are setting up their own manufacturing plants in competitive Indian market. Due to public awareness, economic, environmental or legislative reasons, the requirement of GSCM has increased.  In this context, this study aims to develop a structural model of the barriers to implement GSCM in Indian automobile industry. Design/methodology/approach: We have identified various barriers and contextual relationships among the identified barriers. Classification of barriers has been carried out based upon dependence and driving power with the help of MICMAC analysis. In addition to this, a structural model of barriers to implement GSCM in Indian automobile industry has also been put forward using Interpretive Structural Modeling (ISM) technique. Findings: Eleven numbers of relevant barriers have been identified from literature and subsequent discussions with experts from academia and industry. Out of which, five numbers of barriers have been identified as dependent variables; three number of barriers have been identified as the driver variables and three number of barriers have been identified as the linkage variables. No barrier has been identified as autonomous variable. Four barriers have been identified as top level barriers and one bottom level barrier. Removal of these barriers has also been discussed. Research limitations/implications: A hypothetical model of these barriers has been developed based upon experts’ opinions. The conclusions so drawn may be further modified to apply in real situation problem. Practical implications: Clear understanding of these barriers will help organizations to prioritize better and manage their resources in an efficient and effective way. Originality/value: Through this paper we contribute to identify the barriers to implement GSCM in Indian automobile industry and to prioritize them. The structured model developed will help to understand interdependence of the barriers. This paper also suggests the removal of these barriers.

Purpose: Green Supply Chain Management (GSCM) has received growing attention in the last few years. Most of the automobile industries are setting up their own manufacturing plants in competitive Indian market. Due to public awareness, economic, environmental or legislative reasons, the requirement of GSCM has increased. In this context, this study aims to develop a structural model of the barriers to implement GSCM in Indian automobile industry. Design/methodology/approach: We have identified various barriers and contextual relationships among the identified barriers. Classification of barriers has been carried out based upon dependence and driving power with the help of MICMAC analysis. In addition to this, a structural model of barriers to implement GSCM in Indian automobile industry has also been put forward using Interpretive Structural Modeling (ISM) technique. Findings: Eleven numbers of relevant barriers have been identified from literature and subsequent discussions with experts from academia and industry. Out of which, five numbers of barriers have been identified as dependent variables; three number of barriers have been identified as the driver variables and three number of barriers have been identified as the linkage variables. No barrier has been identified as autonomous variable. Four barriers have been identified as top level barriers and one bottom level barrier. Removal of these barriers has also been discussed. Research limitations/implications: A hypothetical model of these barriers has been developed based upon experts’ opinions. The conclusions so drawn may be further modified to apply in real situation problem. Practical implications: Clear understanding of these barriers will help organizations to prioritize better and manage their resources in an efficient and effective way. Originality/value: Through this paper we contribute to identify the barriers to implement GSCM in Indian automobile industry and to prioritize them. The structured model developed will help to understand interdependence of the barriers. This paper also suggests the removal of these barriers.