Search Results (19)

Meteorological variables play a significant role in the transmission of viruses such as influenza and the coronavirus pandemic (COVID-19). Previous studies have identified the relationship between changes in meteorological variables, humidity, rainfall, and temperature, and the infection rate of COVID-19 at the national level in Pakistan. However, the current study applied the logistic regression analysis technique to determine such a relationship on a more detailed scale, that is, subnational levels in addition to the national level in Pakistan, using a long-term analysis of two years of COVID-19 data. At the subnational level, the logistic regression analysis technique was applied, with infection rate as the predictive variable. The results showed an increase in the infection rate of COVID-19 with increasing humidity levels. In contrast, an increase in temperature has slowed the spread of COVID-19 cases at both the national and subnational levels. The minimum temperature was statistically significant (p < 0.001) for provinces, KPK and Sindh. Also, two federal territories, AJK and Islamabad, showed statistically significant p-values. At the national level, both maximum temperature and humidity showed such values that is, p < 0.001. We believe that this is the first study conducted in Pakistan to explore the direct and indirect relationship between variables such as temperature (min and max), humidity, and rainfall as predictive parameters for COVID-19 infection rates at a detailed level. The pattern observed in this study can help us predict the future spread of COVID-19, subject to climatic parameters in Pakistan at both the national and subnational levels. Full article

Degenerative and developmental eye disorders, including inherited retinal dystrophies (IRDs), anophthalmia, and congenital cataracts arise from genetic mutations, causing progressive vision loss or congenital structural abnormalities. IRDs include a group of rare, genetically, and clinically heterogeneous retinal diseases. It is caused by variations in at least 324 genes, affecting numerous retinal regions. In addition to IRDs, other developmental eye disorders such as anophthalmia and congenital cataracts also have a strong genetic basis. Autosomal recessive IRDs, anophthalmia, and congenital cataracts are common in consanguineous populations. In many endogamous populations, including those in Pakistan, a significant proportion of IRD and anophthalmia cases remain genetically undiagnosed. The present study investigated the variations in IRDs, anophthalmia, and congenital cataracts genes in 50 affected families. These unrelated consanguineous families were recruited from the different provinces of Pakistan including Punjab, Khyber Pakhtoon Khwa, Sindh, Gilgit Baltistan, and Azad Kashmir. Whole exome sequencing (WES) was conducted for the proband of each family. An in-house customized pipeline examined the data, and bioinformatics analysis predicted the pathogenic effects of identified variants. The relevant identified DNA variants of selected families were assessed in parents and healthy siblings via Sanger sequencing. WES identified 12 novel variants across 10 known IRD-associated genes. The four most frequently implicated genes were CRB1 (14.3%), GUCY2D (9.5%), AIPL1 (9.5%), and CERKL (7.1%) that together accounted for 40.4% of all molecularly diagnosed cases. Additionally, 25 reported variants in 19 known IRDs, anophthalmia, and congenital cataracts-associated genes were found. Among the identified variants, p. Trp278X, a stop–gain mutation in the AIPL1 (NM_014336) gene, was the most common causative variant detected. The most frequently observed phenotype was retinitis pigmentosa (46.5%) followed by Leber congenital amaurosis (18.6%). Furthermore, 98% of pedigrees (49 out of 50) were affected by autosomal recessive IRDs, anophthalmia and congenital cataracts. The discovery of 12 novel likely pathogenic variants in 10 IRD genes, 25 reported variants in 19 known IRDs, anophthalmia and congenital cataracts genes, atypical phenotypes, and inter and intra-familial variability underscores the genetic and phenotypic heterogeneity of developmental and degenerative eye disorders in the Pakistani population and further expands the mutational spectrum of genes associated with these ocular disorders. Full article

The issue of Out-of-School Children (OOSC) presents a significant policy challenge in Sindh, Pakistan, where 44% of 5–16-year-olds are not attending school. This study examines how different leadership strategies in government and Public-Private Partnership (PPP) schools address this crisis. Drawing on transformational leadership theory and institutional theory, this research investigates the influence of school governance structures—specifically government schools, Education Management Organizations (EMOs), and Sindh Education Foundation (SEF) schools—on school leaders’ abilities to increase student enrollment and retention. Through a multiple case study design, this study collects qualitative data from three school heads, augmented by perspectives from vice principals and teachers. The findings indicate that leadership agency is constrained in government schools due to centralized decision-making, while PPP schools, particularly SEF schools, offer more autonomy, enabling more adaptive and community-centered leadership strategies. This study concludes that decentralized governance in SEF schools is more effective in addressing the OOSC issue, particularly in marginalized areas. These findings offer valuable insights for policymakers seeking to enhance educational access in regions with similar socio-economic challenges. The implications of this research underscore the need for flexible, context-specific leadership models to combat the OOSC crisis. Full article

Land use and land cover changes (LULCCs) are vital indicators for assessing the dynamic relationship between humans and nature, particularly in diverse and evolving landscapes. This study employs remote sensing (RS) data and machine learning algorithms (MLAs) to investigate LULCC dynamics within the Indus River Delta region of Sindh, Pakistan. The focus is on tracking the trajectories of land use changes within mangrove forests and associated ecosystem services over twenty years. Our findings reveal a modest improvement in mangrove forest cover in specific areas, with an increase from 0.28% to 0.4%, alongside a slight expansion of wetland areas from 2.95% to 3.19%. However, significant increases in cropland, increasing from 22.76% to 28.14%, and built-up areas, increasing from 0.71% to 1.66%, pose risks such as altered sedimentation and runoff patterns as well as habitat degradation. Additionally, decreases in barren land from 57.10% to 52.7% and a reduction in rangeland from 16.16% to 13.92% indicate intensified land use conversion and logging activities. This study highlights the vulnerability of mangrove ecosystems in the Indus Delta to agricultural expansion, urbanization, resource exploitation, and land mismanagement. Recommendations include harmonizing developmental ambitions with ecological conservation, prioritizing integrated coastal area management, reinforcing mangrove protection measures, and implementing sustainable land use planning practices. These actions are essential for ensuring the long-term sustainability of the region’s ecosystems and human communities. Full article

As one of the most perilous industries, agriculture presents both fatal and non-fatal risks to farmers. Pakistan, a notable Asian nation, heavily depends on agriculture, which constitutes 23% of its GDP and employs 37.4% of its workforce. The study investigates the challenges within Pakistan’s agriculture sector and enriches the existing literature by gathering data on agricultural machine-related injuries in Pakistan during 2022–2023. The survey, conducted from November 2022 to April 2023, involved 55 respondents, predominantly from Sindh and Punjab. The findings indicate a total of 507 injuries, with approximately a quarter being fatal (121 incidents). Sindh emerges as a significant hotspot, with the majority of injuries documented there. With respect to the key sources of injuries, the fodder cutter is a primary source of injuries with 201 injuries documented, accounting for approximately 40% of all injuries. Threshers contributed to 197 injuries, approximately 39% of the total, and about 52 are tractor-related injuries, making up about 10% of machine injuries. Regarding the severity and demographics of injuries, about 38% of cases involve amputation, with a higher incidence among males (77%) and individuals aged 15–34 years (65%). It is important to acknowledge the study’s limitations, including a small participant pool and a brief data collection period. This research advocates for safety regulations, accident reduction measures, and increased safety awareness among farmers, aiming to foster a safer and more sustainable agricultural environment in Pakistan. Full article

Introduction: Breast cancer is the most common cancer in women; its early detection plays a crucial role in improving patient outcomes. Ki-67 is a biomarker commonly used for evaluating the proliferation of cancer cells in breast cancer patients. The quantification of Ki-67 has traditionally been performed by pathologists through a manual examination of tissue samples, which can be time-consuming and subject to inter- and intra-observer variability. In this study, we used a novel deep learning model to quantify Ki-67 in breast cancer in digital images prepared by a microscope-attached camera. Objective: To compare the automated detection of Ki-67 with the manual eyeball/hotspot method. Place and duration of study: This descriptive, cross-sectional study was conducted at the Jinnah Sindh Medical University. Glass slides of diagnosed cases of breast cancer were obtained from the Aga Khan University Hospital after receiving ethical approval. The duration of the study was one month. Methodology: We prepared 140 digital images stained with the Ki-67 antibody using a microscope-attached camera at 10×. An expert pathologist (P1) evaluated the Ki-67 index of the hotspot fields using the eyeball method. The images were uploaded to the DeepLiif software to detect the exact percentage of Ki-67 positive cells. SPSS version 24 was used for data analysis. Diagnostic accuracy was also calculated by other pathologists (P2, P3) and by AI using a Ki-67 cut-off score of 20 and taking P1 as the gold standard. Results: The manual and automated scoring methods showed a strong positive correlation as the kappa coefficient was significant. The p value was <0.001. The highest diagnostic accuracy, i.e., 95%, taking P1 as gold standard, was found for AI, compared to pathologists P2 and P3. Conclusions: Use of quantification-based deep learning models can make the work of pathologists easier and more reproducible. Our study is one of the earliest studies in this field. More studies with larger sample sizes are needed in future to develop a cohort. Full article

The fisheries sector in developing countries, including Pakistan, faces various risks that have not been comprehensively studied and addressed through policy measures. This study aims to analyze fisheries’ risks in Pakistan by following a risk management process and using statistical analysis. The data was collected through structured questionnaire surveys, and subsequently, fuzzy analytic hierarchy process (fuzzy AHP) and importance performance analysis (IPA) were utilized to analyze the data. The study ranked the top five risks in order of importance as management, technical, economic, environmental, and occupational risks. The study also identified high-importance, low-performance sub-factors, including inadequate legislative implementation, overexploitation, and infrastructure shortages. It was found that there is low risk perception and inadequate management regulations in the sector. The findings suggest that risk management strategies, such as risk avoidance and risk transfer, can be used to mitigate fisheries’ risks. The study highlights the need for policy measures to revitalize the fisheries sector in Pakistan and provides recommendations for further research. Full article

Precision agriculture (PA) has the potential to radically transform agronomic systems. It is an effective approach for viable zone management in the agriculture field. In today's era of finite resources and drastic consequences of climate change, an approach to PA which is an integration of below-the-ground sensors, multispectral satellite imagery, and weather monitoring system is reshaping agriculture from static to smart. In this paper, a real-time case study at a lemon orchard in Gadap, Sindh, Pakistan is presented where PA practices are being implemented successfully. At the farm locally developed innovative agri-tech devices are deployed which are embedded with electrical conductivity, soil temperature, soil moisture sensor, and nitrogen, phosphorus, and potassium sensor to monitor real-time conditions of the soil for precision irrigation and fertilizer application. Along with device data, incorporation of weather data, agronomist advisory and use of satellite imagery offer a full-functioning monitoring system for viable decisions. This system also favors tracking variations in crop health & pest attack for precise pesticide spray. The data output is observed through a web application. Using these drivers for PA there was increased flowering in the orchard as compared to other farms in the vicinity. Hence, a promising surplus yield and least toxic better fruit quality are being obtained, along with the preservation of biodiversity and environment sustainability the output yield of lemons was quite better than the conventional agriculture practices. PA is an extraordinary approach to leap closer to food security. Full article

Wind is an important renewable energy source. The majority of wind farms in Pakistan are installed in Jhimpir, Sindh Wind Corridor. At this location, downstream turbines encounter upstream turbines’, wake, decreasing power output. To maximize the power output, there is a need to minimize these wakes. In this research, a method is proposed to maximize the power output using a Genetic Algorithm (GA). Hub heights and inter-turbine spacing are considered variables in this method. Two wind farms located at Jhimpir, Sindh, namely, Second and Third Three Gorges Wind Farms (TGWFs), have been analyzed. Three different cases are considered to maximize the power output. In Case 1, thesame hub heights and inter-turbine spacing without wake effects are considered. In Case 2, the same hub heights and inter-turbine spacing with wake effects are considered. In Case 3, variable hub heights and inter-turbine spacing with wake effects are considered. The results revealed that TGWFs, with variable hub heights and inter-turbine spacing, produce more power output. It is also revealed that the increase in power output, in the case of two different hub heights, is greater in comparison to three different hub heights. Eventually, the proposed method may help in the layout optimization of a wind farm. Full article

The environment and the economy are negatively impacted by conventional energy sources, such as coal, gasoline, and other fossil fuels. Pakistan’s reliance on these resources has resulted in a catastrophic energy crisis. This has driven the government to make critical decisions such as early retail closures, power outages for the industrial sector, and an increase to two days a week vacations. Wind energy, accessible and affordable, will become a viable option for meeting Pakistan’s present and future energy demands. Approximately 3% of Pakistan’s land can produce nearly 132 GW of power with an installed capacity of 5 MW per km². In this study, four zones (Karachi, Thatta, Badin, and Jamshoro) in Sindh province are assessed for the feasibility of wind energy generation. The installed capacity, generator types, and detailed specifications are provided for each zone. Moreover, the wind mapping of Pakistan is presented considering the four potential zones. The zones are analyzed using annual wind speed and power output considering wind data measured at 50 m height over one year. The higher mean speed is recorded at Jamshoro compared to other zones. The analysis indicates that all four sites are suitable for large-scale wind power generation due to their energy potential. Full article

Rice is one of the vital foods consumed in most countries throughout the world. To estimate the yield, crop counting is used to indicate improper growth, identification of loam land, and control of weeds. It is becoming necessary to grow crops healthy, precisely, and proficiently as the demand increases for food supplies. Traditional counting methods have numerous disadvantages, such as long delay times and high sensitivity, and they are easily disturbed by noise. In this research, the detection and counting of rice plants using an unmanned aerial vehicle (UAV) and aerial images with a geographic information system (GIS) are used. The technique is implemented in the area of forty acres of rice crop in Tando Adam, Sindh, Pakistan. To validate the performance of the proposed system, the obtained results are compared with the standard plant count techniques as well as approved by the agronomist after testing soil and monitoring the rice crop count in each acre of land of rice crops. From the results, it is found that the proposed system is precise and detects rice crops accurately, differentiates from other objects, and estimates the soil health based on plant counting data; however, in the case of clusters, the counting is performed in semi-automated mode. Full article

This study aims to measure the impact of an intervention, the Community Investment Fund (CIF), on the socio-economic life of rural women. CIF is a community-managed fund aimed at improving the living standards of women by empowering them to undertake income-generating projects to become financially more stable and self-governed in the Khairpur, Shikarpur, Kandhkot-Kashmore and Jacobabad districts of Sindh, Pakistan. This study used a quasi-experimental design approach that involved two groups, i.e., the treatment group (beneficiaries) and control group (non-beneficiaries). The sample size of this study was 708 respondents including the treatment and control group. The results of comparison of mean indicate that there is a significant difference between treatment and control group in terms of socio-demographic variables (including monthly income and consumption, saving amount, total asset value, an asset purchased value and household diet) and women empowerment’s indicators, thereby suggesting that CIF has resulted in women empowerment. Concerning the results of the poverty scorecard, the higher graduation of beneficiaries (treatment group) asserts that the intervention of CIF has also a positive impact on targeted beneficiaries. In particular, the findings indicate that 72% of beneficiaries (treatment group) have graduated from one poverty band to another higher band compared to 59.4% of non-beneficiaries (control group) in poverty score. In addition, the findings of the logistic regression analysis confirmed that participation in the CIF program empowers women beneficiaries. This study will support policymakers to further improve CIF so that it can become more effective and sustainable. Full article

Due to COVID-19 and the repeated imposition of lockdowns in Pakistan’s Sindh province, the life of senior citizens has become challenging. Given the scarcity of health care policies targeted at Sindh’s aged persons, the use of folk literature as therapy has increased to support against isolation, depression, and distress caused by COVID-19 and lockdowns. Although research on healthy ageing from medical and health care perspectives has been increasingly conducted in different contexts, there is a need to explore how folk literature can contribute to psychological, spiritual, and social wellbeing. Therefore, this research, conducted by collecting data from 15 aged participants through interviews and conversations, seeks to explore how senior Sindhis have used folk literature such as poetry, proverbs, and tales as therapy for their healing. Findings show that the participants’ use of Sindhi folk literature contributes to their psychological (eudemonic, evaluative, and hedonic) wellbeing, spiritual healing, and social satisfaction. Full article

Physical activity (PA) refers to any action produced by skeletal muscle that consumes energy. According to the World Health Organization (WHO), PA is the primary element that can improve health at the community level. Obviously, PA plays an important role in the social, physical, and mental development of men and women, as well as in balancing weight. However, the large-scale negative impacts of physical inactivity on health-related issues are also recognized globally, such as obesity, which is the source of many non-communication diseases (NCDs). In Pakistan alone, 46% of deaths occur due to NCD. The majority of NCD deaths are linked to obesity, and Pakistan is the ninth most obese country in the world. Research on obesity caused by sedentary work in Pakistan is lacking, especially among university employees. To fill this gap, the current study mainly focuses on the rising non-communicable disease (NCD) rates among university employees in Pakistan due to a lack of exercise (obesity, in this case), with the help of a self-designed knowledge, attitude, and practice (KAP) questionnaire. Five universities in the Sindh province of Pakistan were surveyed (n = 276), following the concept of Yin–Yang as a theoretical lens. The results of the current study show that the knowledge, behaviors, and attitudes of university employees have a great influence on their body mass index (BMI). The study shows that Pakistani residents’ (especially teaching staff) perceptions and attitudes towards obesity and PA have been instructive, but their practices need to be improved. Full article

In agriculture based economies like Pakistan, farmers often shift from farming to off-farm activities as part of an apparent livelihood transition strategy, despite the fact that most of the workforce depends upon farming. In this paper, we try to uncover insights into how livelihood assets, such as human capital, natural capital, economic capital, and locational characteristics, affect a household’s exit decision from on-farm to off-farm activities as a livelihood transition strategy in rural Pakistan. We analyzed data from 335 farming households from the second largest agricultural producing province in the country, Sindh. Our findings show that more than 19% of households have completely shifted from farming to off-farm activities. Furthermore, we identified that the ‘crop input credit’ is one of the major constraints to farmers converting their previous input-driven small loans into larger loans, where large markups may be imposed if they fail to pay when the harvest is made. The empirical findings from Binary Logistic Regression provide strong evidence for family labor characteristics, particularly for working-age males, working-age females, and working-age children. Surprisingly, the cultivated land size significantly and positively influences farm exit rather than a continuation of farming. Off-farm employment, exogenous shocks, and urbanization also significantly and positively influenced the decision to transition into off-farm work. In contrast, the age of the household head, livestock ownership, and distance to a commercial zone significantly inhibited the decision to exit farming. However, government assistance, including subsidies, strongly encouraged farmers to continue farming. These findings provide new insights into the factors affecting the drivers of both exit and continuation in the farming sector as part of a long-term livelihood transition strategy. Full article