Search Results (62)

One of the aggressive and lethal cancers, pancreatic ductal adenocarcinoma (PDAC) is characterized by poor prognosis and resistance to conventional treatments. Moreover, the tumor immune microenvironment (TIME) plays a crucial role in the progression and therapeutic resistance of PDAC. It is associated with T-cell exhaustion, leading to the progressive loss of T-cell functions with an impaired ability to kill tumor cells. Therefore, this study employed single-cell RNA sequencing (scRNA-seq) analysis of a publicly available human PDAC dataset, with cells isolated from the primary tumor and adjacent normal tissues, identifying upregulated genes of T-cells and cancer cells in two groups (“cancer cells_vs_all-PDAC” and “cancer-PDAC_vs_all-normal”). Common and unique markers of cancer cells from both groups were identified. The Reactome pathways of cancer and T-cells were selected, while the genes implicated in those pathways were used to perform PPI analysis, revealing the hub genes of cancer and T-cells. The gene expression validation of cancer and T-cells hub-genes was performed using GEPIA2 and TISCH2, while the overall survival analysis of cancer cells hub-genes was performed using GEPIA2. Conclusively, this study unraveled 16 novel markers of cancer and T-cells, providing the groundwork for future research into the immune landscape of PDAC, particularly T-cell exhaustion. However, further clinical studies are needed to validate these novel markers as potential therapeutic targets in PDAC patients. Full article

Background: Immunization saves millions of lives, and globally, vaccines have significantly contributed to reducing mortality and morbidity due to more than 20 life-threatening illnesses. However, there are considerable disparities in vaccination coverage among countries and within populations. This study evaluates the reduction in disparities in vaccination coverage across various socio-economic groups by adopting an integrated community-engagement approach combined with maternal and child health services through mobile health camps. Methods: This secondary analysis is based on a community-based demonstration project conducted between 2014 and 2016 across 146 union councils in polio high-risk districts of Sindh, Khyber Pakhtunkhwa (KP) and Baluchistan in Pakistan. The intervention involved structured community engagement and mobile health camps providing routine immunization alongside maternal and child health services. Data were collected through cross-sectional independent surveys using the WHO two-stage cluster technique at the baseline and the endline, covering over 120,000 children under 5 years old. Four key outcome indicators were analyzed: fully vaccinated children, under-immunized children, unvaccinated children, and polio zero-dose children for equity in vaccine uptake. Results: The proportion of fully vaccinated children increased in the lowest wealth quintile from 28.5% (26.7%, 30.3%) at the baseline to 51.6% (49.5%, 53.8%) at the endline. In comparison, the increase in the richest quantities was 16.2% (14.0%, 18.4%) from the baseline 56.4% (54.6%, 58.2%) to the endline 72.7% (71.1%, 74.2%). Under-vaccination dropped by 10.2% (95% CI: −11.4%, −9.1%), with the poorest quintile showing an 11.8% reduction. The gap between the highest and lowest wealth quintiles in full immunization narrowed by 6.9%, from 27.9% to 21.0% at the baseline and the endline, respectively. The prevalence of zero-dose children significantly decreased across all quintiles, with the highest reduction observed in the lowest quintile of −11.3% (−13.6%, −9.1%). The difference between the highest and lowest wealth quintiles reduced from 6.2% to 3.8%. A significant reduction in polio zero-dose children was achieved, as 13.5% (95% CI: −14.8%, −12.2%), from 29.2% (95% CI: 28.0%, 30.3%) to 15.6% (14.8%, 16.5%). Conclusions: This study shows that integrating community engagement with maternal and child health services through health camps can significantly enhance immunization coverage and reduce wealth-based disparities in high-risk, hard-to-reach areas. The approach improved coverage for zero-dose and fully vaccinated children, suggesting a potential for scaling in regions with access issues, conflict, and vaccine hesitancy. Full article

Agricultural productivity is one of the critical factors towards ensuring food security across the globe. However, some of the main crops, such as potato, tomato, and mango, are usually infested by leaf diseases, which considerably lower yield and quality. The traditional practice of diagnosing disease through visual inspection is labor-intensive, time-consuming, and can lead to numerous errors. To address these challenges, this study evokes the AgirLeafNet model, a deep learning-based solution with a hybrid of NASNetMobile for feature extraction and Few-Shot Learning (FSL) for classification. The Excess Green Index (ExG) is a novel approach that is a specified vegetation index that can further the ability of the model to distinguish and detect vegetative properties even in scenarios with minimal labeled data, demonstrating the tremendous potential for this application. AgirLeafNet demonstrates outstanding accuracy, with 100% accuracy for potato detection, 92% for tomato, and 99.8% for mango leaves, producing incredibly accurate results compared to the models already in use, as described in the literature. By demonstrating the viability of a deep learning/IoT system architecture, this study goes beyond the current state of multi-crop disease detection. It provides practical, effective, and efficient deep-learning solutions for sustainable agricultural production systems. The innovation of the model emphasizes its multi-crop capability, precision in results, and the suggested use of ExG to generate additional robust disease detection methods for new findings. The AgirLeafNet model is setting an entirely new standard for future research endeavors. Full article

Delivering nucleic acid therapeutics across cell membranes is a significant challenge. Cell-penetrating peptides (CPPs) containing arginine (R), tryptophan (W), and histidine (H) show promise for siRNA delivery. To improve siRNA delivery and silence a model STAT3 gene, we hypothesized that oleyl acylation to CPPs, specifically (WRH)_n, would enhance STAT3 silencing efficiency in breast and ovarian cancer cells. Using Fmoc/tBu solid-phase peptide chemistry, we synthesized, purified, and characterized the oleyl-conjugated (WRH)_n (n = 1–4) peptides. The peptide/siRNA complexes were non-cytotoxic at N/P 40 (~20 μM) against MDA-MB-231, MCF-7, SK-OV-3, and HEK-293 cells after 72 h incubation. All peptide/siRNA complexes showed serum stability at N/P ≥ 40. The synthesized conjugates, with a diameter of <100 nm, formed nano-complexes with siRNA and exhibited a stable range of zeta potential values (13–18 mV at N/P = 40). Confocal microscopy and flow cytometry analysis provided qualitative and quantitative evidence of a successful cellular internalization of siRNA. The peptides oleyl-(WRH)₃ and oleyl-(WRH)₄ showed ~60% and ~75% cellular uptake of siRNA, respectively, in both MDA-MB-231 and SK-OV-3 cells. Western blot analysis of oleyl-(WRH)₄ demonstrated effective silencing of the STAT-3 gene, with ~75% silencing in MDA-MB-231 cells and ~45% in SK-OV-3 cells. Full article

In Pakistan, the 2018 National Nutrition Survey reported that 40% of children under five years old were stunted. This study assessed the effectiveness of nutritional supplementation in reducing stunting among children under five years old in two rural districts in Sindh, Pakistan. This was a mixed-method quasi-experimental study comprising intervention and control populations, with 3397 and 3277 children under five years old participating in the baseline and end-line surveys, respectively. The study areas were similar in terms of demographic and economic circumstances. In the intervention group, pregnant and lactating women (first six months post-partum) received wheat soy blend, children 6–23 months old received Wawamum (lipid-based supplement), and children 24–59 months old received micronutrient powders, all through lady health workers. This was underpinned by nutrition behaviour change communication for appropriate complementary feeding practices and hygiene promotion targeted at primary caregivers. The control group received no intervention. The impact was assessed using the difference-in-difference analysis with kernel propensity score matching to adjust the differences among the control and intervention populations. The overall DID analysis indicated that the intervention did not significantly reduce the prevalence of stunting (under 5 years) [DID = −5.1, p = 0.079]. The adjusted DID indicated a significant decrease of 13% [DID = −13.0, p = 0.001] in the number of stunted children 24–59 months of age at the endline survey. A significant reduction in underweight among children 24–59 months old was also observed (DID = −9.4%, p = 0.014). In conclusion, this evidence further establishes that nutrient uptake through an intervention for a short duration cannot effectively reduce stunting. It requires continuous nutritional supplementation for mothers during the pregnancy and an initial six months of lactation and then nutritional supplementation for children 6–59 months of age underpinned by effective behaviour change communication targeting mothers and other caregivers for improving complementary feeding practices and hygiene promotion. Full article

Free-space optical (FSO) communication is a line-of-sight (LOS) communication technology that uses light, typically lasers, to transmit data through the atmosphere. FSO can provide high data transfer rates, but factors like weather conditions can affect its performance. Like fog, smog also degrades the availability and reliability of FSO links, as the particulate matter (PM) present in smog scatters the light beam, causing perceptible attenuation. In this paper, we have investigated the attenuation of an optical signal under laboratory-controlled smog conditions, using both theoretical and experimental approaches. A 6 m long acrylic chamber is used to contain artificial smog and measure the optical attenuation through it. The experimental result shows that smog attenuation is approximately 1.705 times more than fog attenuation. The findings of this study offer valuable insights into the effects of smog on optical links and can contribute to the development and optimization of these systems in regions with high levels of smog. Full article

Background: The eradication of poliovirus and improving routine immunization (RI) coverage rates present significant challenges in Pakistan. There is a need for interventions that focus on strengthening community engagement to improve routine immunization coverage. Our primary objective is to assess the impact of an integrated strategy designed to enhance community engagement and maternal and child health immunization campaigns on immunization coverage in Pakistan’s high-risk union councils of polio-endemic districts. Method: We implemented an integrated approach for routine immunization and maternal and child health in the polio-endemic district of Pakistan. This approach involved setting up health camps and actively engaging and mobilizing the local community. An independent team conducted surveys at three key points: baseline, midline, and endline, to evaluate immunization coverage among children under the age of five. The primary outcome measures for the study were coverage of OPV, IPV, and changes in the proportion of unvaccinated and fully vaccinated children. To select clusters and eligible households in each cluster, we utilized a 30 × 15 cluster sampling technique. Multivariable associations between socio-demographic factors and changes in the proportion of fully vaccinated children at the UC level were assessed using hierarchical linear regression models. Results: A total of 256,946 children under the age of five (122,950 at baseline and 133,996 at endline) were enrolled in the study. By the endline, full immunization coverage had increased to 60% or more in all three study areas compared to the baseline. Additionally, there was a significant increase in the coverage of both OPV and IPV across all three provinces at the endline. The full immunization rates were assessed on three levels of the framework: the distal, intermediate (access and environment), and proximal level (camp attendance and effectiveness). At the distal level, on multivariate analysis, family size was found to be a significant predictor of change in immunity within the families (β = 0.68; p ≤ 0.0001). At the intermediate level, the likelihood of full immunization decreased with the decrease in knowledge about vaccination (β = −0.38; p = 0.002), knowledge about polio vaccine (β = −0.25; p = 0.011), and knowledge about IPV (β = −0.06; p = 0.546). Perceived obstacles to vaccination were fear of adverse events (β = −0.4; p ≤ 0.0001) and lack of education (β = 0.23; p = 0.031), which were found to be significant in bivariate and multivariate analyses. At the proximal level, community mobilization (β = 0.26; p = 0.008) and attendance at health camp (β = 0.21; p ≤ 0.0001) were found to enhance full immunization coverage. On the other hand, the most prominent reason for not attending health camp included no need to attend the health camp as the child was not ill (β = −0.13; p = 0.008). Conclusions: This study found that community mobilization and attendance at health camps significantly enhanced full immunization coverage. The findings highlight the importance of community engagement and targeted interventions in improving immunization coverage and addressing barriers to healthcare seeking. Full article

The primary objective of this study is to develop an advanced, automated system for the early detection and classification of leaf diseases in potato plants, which are among the most cultivated vegetable crops worldwide. These diseases, notably early and late blight caused by Alternaria solani and Phytophthora infestans, significantly impact the quantity and quality of global potato production. We hypothesize that the integration of Vision Transformer (ViT) and ResNet-50 architectures in a new model, named EfficientRMT-Net, can effectively and accurately identify various potato leaf diseases. This approach aims to overcome the limitations of traditional methods, which are often labor-intensive, time-consuming, and prone to inaccuracies due to the unpredictability of disease presentation. EfficientRMT-Net leverages the CNN model for distinct feature extraction and employs depth-wise convolution (DWC) to reduce computational demands. A stage block structure is also incorporated to improve scalability and sensitive area detection, enhancing transferability across different datasets. The classification tasks are performed using a global average pooling layer and a fully connected layer. The model was trained, validated, and tested on custom datasets specifically curated for potato leaf disease detection. EfficientRMT-Net’s performance was compared with other deep learning and transfer learning techniques to establish its efficacy. Preliminary results show that EfficientRMT-Net achieves an accuracy of 97.65% on a general image dataset and 99.12% on a specialized Potato leaf image dataset, outperforming existing methods. The model demonstrates a high level of proficiency in correctly classifying and identifying potato leaf diseases, even in cases of distorted samples. The EfficientRMT-Net model provides an efficient and accurate solution for classifying potato plant leaf diseases, potentially enabling farmers to enhance crop yield while optimizing resource utilization. This study confirms our hypothesis, showcasing the effectiveness of combining ViT and ResNet-50 architectures in addressing complex agricultural challenges. Full article

Plants, being sessile, have developed complex signaling and response mechanisms to cope with biotic and abiotic stressors. Recent investigations have revealed the significant contribution of phytohormones in enabling plants to endure unfavorable conditions. Among these phytohormones, jasmonic acid (JA) and its derivatives, collectively referred to as jasmonates (JAs), are of particular importance and are involved in diverse signal transduction pathways to regulate various physiological and molecular processes in plants, thus protecting plants from the lethal impacts of abiotic and biotic stressors. Jasmonic acid has emerged as a central player in plant defense against biotic stress and in alleviating multiple abiotic stressors in plants, such as drought, salinity, vernalization, and heavy metal exposure. Furthermore, as a growth regulator, JA operates in conjunction with other phytohormones through a complex signaling cascade to balance plant growth and development against stresses. Although studies have reported the intricate nature of JA as a biomolecular entity for the mitigation of abiotic stressors, their underlying mechanism and biosynthetic pathways remain poorly understood. Therefore, this review offers an overview of recent progress made in understanding the biosynthesis of JA, elucidates the complexities of its signal transduction pathways, and emphasizes its pivotal role in mitigating abiotic and biotic stressors. Moreover, we also discuss current issues and future research directions for JAs in plant stress responses. Full article

This study’s main goal is to create a useful software application for finding and classifying fine art photos in museums and art galleries. There is an increasing need for tools to swiftly analyze and arrange art collections based on their artistic styles as a result of the digitization of art collections. To increase the accuracy of the style categorization, the suggested technique involves two parts. The input image is split into five sub-patches in the first stage. A DCNN that has been particularly trained for this task is then used to classify each patch individually. A decision-making module using a shallow neural network is part of the second phase. Probability vectors acquired from the first-phase classifier are used to train this network. The results from each of the five patches are combined in this phase to deduce the final style classification for the input image. One key advantage of this approach is employing probability vectors rather than images, and the second phase is trained separately from the first. This helps compensate for any potential errors made during the first phase, improving accuracy in the final classification. To evaluate the proposed method, six various already-trained CNN models, namely AlexNet, VGG-16, VGG-19, GoogLeNet, ResNet-50, and InceptionV3, were employed as the first-phase classifiers. The second-phase classifier was implemented as a shallow neural network. By using four representative art datasets, experimental trials were conducted using the Australian Native Art dataset, the WikiArt dataset, ILSVRC, and Pandora 18k. The findings showed that the recommended strategy greatly surpassed existing methods in terms of style categorization accuracy and precision. Overall, the study assists in creating efficient software systems for analyzing and categorizing fine art images, making them more accessible to the general public through digital platforms. Using pre-trained models, we were able to attain an accuracy of 90.7. Our model performed better with a higher accuracy of 96.5 as a result of fine-tuning and transfer learning. Full article

The lungs are critical components of the respiratory system because they allow for the exchange of oxygen and carbon dioxide within our bodies. However, a variety of conditions can affect the lungs, resulting in serious health consequences. Lung disease treatment aims to control its severity, which is usually irrevocable. The fundamental objective of this endeavor is to build a consistent and automated approach for establishing the intensity of lung illness. This paper describes MixNet-LD, a unique automated approach aimed at identifying and categorizing the severity of lung illnesses using an upgraded pre-trained MixNet model. One of the first steps in developing the MixNet-LD system was to build a pre-processing strategy that uses Grad-Cam to decrease noise, highlight irregularities, and eventually improve the classification performance of lung illnesses. Data augmentation strategies were used to rectify the dataset’s unbalanced distribution of classes and prevent overfitting. Furthermore, dense blocks were used to improve classification outcomes across the four severity categories of lung disorders. In practice, the MixNet-LD model achieves cutting-edge performance while maintaining model size and manageable complexity. The proposed approach was tested using a variety of datasets gathered from credible internet sources as well as a novel private dataset known as Pak-Lungs. A pre-trained model was used on the dataset to obtain important characteristics from lung disease images. The pictures were then categorized into categories such as normal, COVID-19, pneumonia, tuberculosis, and lung cancer using a linear layer of the SVM classifier with a linear activation function. The MixNet-LD system underwent testing in four distinct tests and achieved a remarkable accuracy of 98.5% on the difficult lung disease dataset. The acquired findings and comparisons demonstrate the MixNet-LD system’s improved performance and learning capabilities. These findings show that the proposed approach may effectively increase the accuracy of classification models in medicinal image investigations. This research helps to develop new strategies for effective medical image processing in clinical settings. Full article

A well-known eye disorder called diabetic retinopathy (DR) is linked to elevated blood glucose levels. Cotton wool spots, confined veins in the cranial nerve, AV nicking, and hemorrhages in the optic disc are some of its symptoms, which often appear later. Serious side effects of DR might include vision loss, damage to the visual nerves, and obstruction of the retinal arteries. Researchers have devised an automated method utilizing AI and deep learning models to enable the early diagnosis of this illness. This research gathered digital fundus images from renowned Pakistani eye hospitals to generate a new “DR-Insight” dataset and known online sources. A novel methodology named the residual-dense system (RDS-DR) was then devised to assess diabetic retinopathy. To develop this model, we have integrated residual and dense blocks, along with a transition layer, into a deep neural network. The RDS-DR system is trained on the collected dataset of 9860 fundus images. The RDS-DR categorization method demonstrated an impressive accuracy of 97.5% on this dataset. These findings show that the model produces beneficial outcomes and may be used by healthcare practitioners as a diagnostic tool. It is important to emphasize that the system’s goal is to augment optometrists’ expertise rather than replace it. In terms of accuracy, the RDS-DR technique fared better than the cutting-edge models VGG19, VGG16, Inception V-3, and Xception. This emphasizes how successful the suggested method is for classifying diabetic retinopathy (DR). Full article

In this research work, machining operations on an aluminum matrix composite (AMC) were optimized for improving the wear of high-speed steel tools. The squeeze casting method was used to manufacture the AMC, which had Al-6061 as matrix material and silicon carbide (wt. 15%) microparticles used as reinforcement. Feed rate (Fr), cutting speed (Cs), and depth of cut (Dc) were selected to optimize HSS tool wear rate. Using the Box–Behnken design, seventeen experiments were performed to analyze the single-factor effects and interaction effects of the process parameters on HSS tool wear rate. Experimental results show that optimal tool wear (0.964) was achieved at a Cs of 80 m/min, Fr of 0.2 rev/min, and Dc of 0.8 mm. Full article

The oral poliovirus vaccine (OPV) has been the mainstay of polio eradication, especially in low-income countries, and its use has eliminated wild poliovirus type 2. However, the inactivated poliovirus vaccine (IPV) is safer than OPV, as IPV protects against paralytic poliomyelitis without producing adverse reactions. The present study compared mucosal and humoral responses to poliovirus vaccines administered to previously OPV-immunized children to assess the immunity gap in children in areas of high poliovirus transmission. A cluster-randomized trial was implemented in three high-risk districts of Pakistan—Karachi, Kashmore, and Bajaur—from June 2013 to May 2014. This trial was community-oriented and included three arms, focusing on healthy children below five years of age. The study involved the randomization of 387 clusters, of which 360 were included in the final analysis. The control arm (A) received the routine polio program bivalent poliovirus vaccine (bOPV). The second arm (B) received additional interventions, including health camps providing routine vaccinations and preventive maternal and child health services. In addition to the interventions in arm B, the third arm (C) was also provided with IPV. Blood and stool samples were gathered from children to evaluate humoral and intestinal immunity. The highest levels of poliovirus type 1 serum antibodies were observed in Group C (IPV + OPV). The titers for poliovirus type 2 (P2) and poliovirus type 3 (P3) were noticeably higher in those who had received a routine OPV dose than in those who had not across all study groups and visits. Providing an IPV booster after at least two OPV doses could potentially fill immunity gaps in regions where OPV does not show high efficacy. However, IPV only marginally enhances humoral immunity and fails to offer intestinal immunity, which is critical to stop the infection and spread of live poliovirus in populations that have not been exposed before. Full article

Diabetes is a widely spread disease that significantly affects people’s lives. The leading cause is uncontrolled levels of blood glucose, which develop eye defects over time, including Diabetic Retinopathy (DR), which results in severe visual loss. The primary factor causing blindness is considered to be DR in diabetic patients. DR treatment tries to control the disease’s severity, as it is irreversible. The primary goal of this effort is to create a reliable method for automatically detecting the severity of DR. This paper proposes a new automated system (DR-NASNet) to detect and classify DR severity using an improved pretrained NASNet Model. To develop the DR-NASNet system, we first utilized a preprocessing technique that takes advantage of Ben Graham and CLAHE to lessen noise, emphasize lesions, and ultimately improve DR classification performance. Taking into account the imbalance between classes in the dataset, data augmentation procedures were conducted to control overfitting. Next, we have integrated dense blocks into the NASNet architecture to improve the effectiveness of classification results for five severity levels of DR. In practice, the DR-NASNet model achieves state-of-the-art results with a smaller model size and lower complexity. To test the performance of the DR-NASNet system, a combination of various datasets is used in this paper. To learn effective features from DR images, we used a pretrained model on the dataset. The last step is to put the image into one of five categories: No DR, Mild, Moderate, Proliferate, or Severe. To carry this out, the classifier layer of a linear SVM with a linear activation function must be added. The DR-NASNet system was tested using six different experiments. The system achieves 96.05% accuracy with the challenging DR dataset. The results and comparisons demonstrate that the DR-NASNet system improves a model’s performance and learning ability. As a result, the DR-NASNet system provides assistance to ophthalmologists by describing an effective system for classifying early-stage levels of DR. Full article