Search

Abstract Thermal evaluation of twin wall panel systems was assessed under vibrant hot and arid conditions of weather in the Arabian Peninsula. Two systems of wall panels (0.6 m × 0.6 m) were prepared. The first system was prepared with a 5.0 cm thick extruded polystyrene (XPS) board. While the second system was prepared with 5.0 cm thick layer of foam-mortar encompassing expanded polystyrene (EPS) beads. Both the thermal insulative layers were sandwiched between two 7.5 cm thick concrete layers. The two wall panel systems were thermally evaluated at the same time in a carefully designed test room. Comparison was accomplished between the two wall systems by measuring the U-value (thermal transmittance) and R-value (resistance). The U-value (air to air) for sandwiched XPS concrete wall system was 0.837 W/m2 K while it was 2.527 W/m2 K for sandwiched EPS beads foam-mortar concrete wall system. The mean U-values (surface to surface) of the sandwiched XPS concrete wall system was 1.143 m2 K/W and 0.293 m2 K/W for sandwiched EPS beads foam-mortar concrete wall system. The sandwiched XPS concrete wall system was more efficient than the sandwiched EPS beads foam-mortar concrete wall system in terms of thermal performance. About 4.5 h of time lag was observed for both the wall panel systems between the external surface temperature and the heat transmission in the internal surface. The output of the FEM simulation by ABAQUS is compared with the measured data for Set-1 (period 16-Aug-2022 to 26-Aug-2022). The hourly temperature change on the outer and inner surfaces has good agreement for both sandwiched XPS concrete wall system and sandwiched EPS beads foam-mortar concrete wall system. The simulation can also predict the heat flux through the two wall systems investigated.

Abstract Skin cancer is the most prevalent kind of cancer in people. It is estimated that more than 1 million people get skin cancer every year in the world. The effectiveness of the disease’s therapy is significantly impacted by early identification of this illness. Preprocessing is the initial detecting stage in enhancing the quality of skin images by removing undesired background noise and objects. This study aims is to compile preprocessing techniques for skin cancer imaging that are currently accessible. Researchers looking into automated skin cancer diagnosis might use this article as an excellent place to start. The fully convolutional encoder–decoder network and Sparrow search algorithm (FCEDN-SpaSA) are proposed in this study for the segmentation of dermoscopic images. The individual wolf method and the ensemble ghosting technique are integrated to generate a neighbour-based search strategy in SpaSA for stressing the correct balance between navigation and exploitation. The classification procedure is accomplished by using an adaptive CNN technique to discriminate between normal skin and malignant skin lesions suggestive of disease. Our method provides classification accuracies comparable to commonly used incremental learning techniques while using less energy, storage space, memory access, and training time (only network updates with new training samples, no network sharing). In a simulation, the segmentation performance of the proposed technique on the ISBI 2017, ISIC 2018, and PH2 datasets reached accuracies of 95.28%, 95.89%, 92.70%, and 98.78%, respectively, on the same dataset and assessed the classification performance. It is accurate 91.67% of the time. The efficiency of the suggested strategy is demonstrated through comparisons with cutting-edge methodologies.

Radiologists encounter significant challenges when segmenting and determining brain tumors in patients because this information assists in treatment planning. The utilization of artificial intelligence (AI), especially deep learning (DL), has emerged as a useful tool in healthcare, aiding radiologists in their diagnostic processes. This empowers radiologists to understand the biology of tumors better and provide personalized care to patients with brain tumors. The segmentation of brain tumors using multi-modal magnetic resonance imaging (MRI) images has received considerable attention. In this survey, we first discuss multi-modal and available magnetic resonance imaging modalities and their properties. Subsequently, we discuss the most recent DL-based models for brain tumor segmentation using multi-modal MRI. We divide this section into three parts based on the architecture: the first is for models that use the backbone of convolutional neural networks (CNN), the second is for vision transformer-based models, and the third is for hybrid models that use both convolutional neural networks and transformer in the architecture. In addition, in-depth statistical analysis is performed of the recent publication, frequently used datasets, and evaluation metrics for segmentation tasks. Finally, open research challenges are identified and suggested promising future directions for brain tumor segmentation to improve diagnostic accuracy and treatment outcomes for patients with brain tumors. This aligns with public health goals to use health technologies for better healthcare delivery and population health management.

Post-acute myocardial infarction (AMI) fibrosis is a pathophysiologic process characterised by activation of the profibrotic mediator, transforming growth factor-β (TGF-β). AMI is associated with a substantial increase in the levels of extracellular adenosine triphosphate (eATP), which acts on the purinergic P2X7-receptor (P2X7-R) and triggers an inflammatory response that contributes to myocardial fibrotic remodelling. P2X7-R has been implicated in several cardiovascular diseases; however, its role in the regulation of cardiac fibrosis remains unclear. Therefore, the current study aimed to determine the effect of the P2X7-R antagonist, A740003, on post-AMI fibrosis, via the profibrotic TGF-β1/Smad signalling pathway, and elucidate whether its effect is mediated via the modulation of GSK-3β. AMI was induced by surgical ligation of the left anterior descending coronary artery, Thereafter, animals were divided into groups: sham control, MI-untreated, MI-vehicle, and MI-A740003 (50 mg/kg/day) and treated for seven days accordingly. The heart weight/body weight ratio of untreated-ligated rats significantly increased by 15.1 %, creatine kinase‐MB (CK‐MB) significantly increased by 40 %, troponin‐I levels significantly increased by 25.4 %, and lactate dehydrogenase significantly increased by 47.2 %, indicating myocardial damage confirmed by morphological changes and massive cardiac fibrosis. The protein expression of cardiac fibronectin, TGF-β1, and p-Smad2 were also upregulated by 143 %, 40 %, and 8 %, respectively, indicating cardiac fibrosis. The treatment of ligated rats with A740003 led to improvement in all the above-mentioned parameters. Overall, A740003 exhibits potential cardio-protective effects on post-AMI fibrotic remodelling in the animal model of AMI through P2X7-R blockade, possibly by downregulating the profibrotic TGF-β1/Smad signalling pathway and restoring GSK-3β phosphorylation. Altogether, treatment with A740003 could serve as a new cardioprotective strategy to attenuate post-AMI fibrotic remodelling.

The production of new pavements involving recycled asphalt pavement (RAP) is beneficial for preserving petroleum asphalt and natural aggregates. However, increasing the RAP content raises performance concerns in new pavement, thus necessitating the use of rejuvenators. This research examined the potential of using petroleum sludge (PS) as a rejuvenator for RAP recycling. Gas chromatography-mass spectrometry (GCMS) detected the PS hydrocarbons involved in rejuvenation. The dynamic shear rheometer (DSR) provided the rheological data of rejuvenated binders. The rheological data were subsequently processed using response surface methodology (RSM) to develop rutting and fatigue models. RSM was also employed to model the Marshall and volumetric properties of the asphalt mixture. Atomic force microscopy (AFM) of PS rejuvenated RAP binder revealed a new microstructure containing protrusions that resembled the morphology of lightweight bitumen fractions. These rejuvenating effects were attributed to lightweight hydrocarbons of PS, which supplement the maltenes fractions in aged asphalt. The multi-objective optimization provided the RAP and bitumen content of 90% and 4.79% to meet the required PWD specifications.

Abstract Cancer is responsible for approximately 10 million deaths worldwide, with 70% of the deaths occurring in low- and middle-income countries; as such safer and more effective anti-cancer drugs are required. Therefore, the potential benefits of Ziziphus nummularia and Ziziphus spina-christi as sources of anti-cancer agents were investigated. Z. nummularia and Z. spina-christi extracts were prepared using chloroform, ethanol, ethyl acetate, and water. The extracts’ anti-cancer properties were determined using the MTT Cell Viability Assay in four cancer cell lines: breast (KAIMRC2 and MDA-MB-231), colorectal (HCT8), and liver (HepG2). The ApoTox-Glo Triplex Assay and high-content imaging (HCI)-Apoptosis Assay were used to assess KAIMRC2 and HCT8 cells further. In addition, KAIMRC2 cells were tested for microtubule staining, and AKT/mTOR protein expression was determined by western blot analysis. Liquid chromatography-mass spectrometry (LC–MS) was performed to identify the secondary metabolites in the ethanol and ethyl acetate extracts, followed by in silico techniques to predict molecular targets and interactions, safety, and pharmacokinetic profile for identified metabolites. Out of the eight extracts, the ethanolic extract of Z. nummularia, exhibited the most potent activity against KAIMRC2 cells with an IC50 value of 29.2 μg/ml. Cancer cell treatment with the ethanolic extract of Z. nummularia resulted in a dose-dependent decrease in cell viability with increased apoptosis and cytotoxic effects. Microtubule staining showed a disrupted microtubular network. The ethanolic extract treatment of KAIMRC2 cells led to upregulated expression of pAKT and pmTOR. In silico studies predicted luteolin-7-O-glucoside to be a ligand for tubulin with the highest docking score (− 7.686) and similar binding interactions relative to the native ligand. Further computational analysis of the metabolites showed acceptable pharmacokinetic and safety profiles, although ethanolic extract metabolites were predicted to have cardiotoxic effects. Ethanolic extraction is optimal for solubilizing active anticancer metabolites from Z. nummularia, which may act by causing M-phase arrest via inhibition of tubulin polymerization. Luteolin-7-O-glucoside is the lead candidate for further research and development as an anti-cancer agent. In addition, this study suggests that herbal treatment could switch on mechanisms of adaptation and survival in cancer cells.

In the development of image forensics, detection of Copy-Move Forgery (CMF) has become a major challenge due to the proliferation of image forgery techniques. The CMF is widely utilized to alter the content of the original image to spread false information or to use such forged digital images for illegal purposes e.g. false evidence in the court of law, or to blackmailing any individual. This paper presents a new method for CMF Detection (CMFD) that uses the power of Graph Convolution Networks (GCNs) and its multiple layers with ReLU activation, for CMFD and analysis. The aim to use GCN is due to its ability to improve the feature extraction process by utilizing the spatial and structural affiliation between elements in the digital images. Also, the GCN aims to store information about images and use it to graphically describe images with pixels or image areas as features, spatial and correlation relationships as edges. By pulling data from this image, GCN is able to obtain content rich features that are very powerful at detecting CMF regions. In proposed methodology, we utilized Support Vector machine (SVM) for classification and the binary cross-entropy loss, and the Adam optimizer for improving accuracy. Our scheme successfully achieves high accuracy and is effective in CMFD. We use the MICC F220, and CoMoFoD datasets to test the GCN in our proposed CMFD method. Through much testing and evaluation, we have found that GCN has the tremendous ability for CMFD in digital images in term of accuracy.

Objective: To compare the efficacy of 5% Permethrin with the oral Ivermectin for management of scabies. Study Design: Quasi-experimental Study. Place and Duration of Study: Department of Dermatology, CMH, Abbottabad Pakistan, from Jun to Nov 2021. Methodology: Our study enrolled 100 patients, aged 5-80 years, after informed consent. Demographic information and study variables were noted in a data collection tool. All participants were randomly assigned to one of two groups i.e. Group A received Permethrin 5% twice with a one-week interval, whereas Group B received a single dose of oral Ivermectin. The evaluation was conducted at 2-4 week intervals. Results: From our sample of 100 participants, 57 were females and 43 were males; age ranged from 5 to 80 years. Group A of Permethrin-5% 43(86%) patients’ symptoms improved than in Group B 24(48%) patients of oral Ivermectin. There was also a statistically significant relationship between Permethrin and Ivermectin (p-value

The agricultural sector is pivotal to food security and economic stability worldwide. Corn holds particular significance in the global food industry, especially in developing countries where agriculture is a cornerstone of the economy. However, corn crops are vulnerable to various diseases that can significantly reduce yields. Early detection and precise classification of these diseases are crucial to prevent damage and ensure high crop productivity. This study leverages the VGG16 deep learning (DL) model to classify corn leaves into four categories: healthy, blight, gray spot, and common rust. Despite the efficacy of DL models, they often face challenges related to the explainability of their decision-making processes. To address this, Layer-wise Relevance Propagation (LRP) is employed to enhance the model's transparency by generating intuitive and human-readable heat maps of input images. The proposed VGG16 model, augmented with LRP, outperformed previous state-of-the-art models in classifying corn leaf diseases. Simulation results demonstrated that the model not only achieved high accuracy but also provided interpretable results, highlighting critical regions in the images used for classification. By generating human-readable explanations, this approach ensures greater transparency and reliability in model performance, aiding farmers in improving their crop yields.

Background: The morphology of megakaryocytes plays a major role in thrombopoiesis. Dysmegakaryocytopoiesis and thrombocytopenia can result from imperfection in any phase of megakaryocytopoiesis. The structure, morphology, and characteristics of megakaryocytes can differentiate and identify the etiology of underlying diseases such as leukaemia, mixed anaemia, idiopathic, aplasia, or infiltration. This research aimed to comprehend megakaryocyte attributions and their contribution to the diagnosis of thrombocytopenia. Methods: This was a cross-sectional analysis of all successive cases of thrombocytopenia bone marrow aspirates over 12 months (January 2019 to December 2019) at the Department of Pathology, Indus Medical College Hospital Tando Muhammad Khan. With a 100X lens, megakaryocyte morphology was studied. Data were analysed by SPSS and Chi-square was performed to find correlation at the 95% confidence interval. Results: Among the 91 subjects of thrombocytopenia, the commonest etiology of thrombocytopenia was idiopathic thrombocytopenia 34(37.36%). The common morphological changes in megakaryocytes were hypolobated megakaryocytes and bare megakaryocyte nuclei. Megakaryocyte numbers were increased for 31 (91.17%) patients among idiopathic thrombocytopenia, though the megakaryocyte numbers were decreased among 19 (90.47%) patients having acute leukaemia. A change to immature, young, less polyploid and bare nuclei forms was observed in immune thrombocytopenia. Immature types were seen in 15 cases, 14 cases of bare megakaryocytic nuclei, 15 cases of hypolobation and one case of micromegakaryocytes. Conclusion: Many similarities among various haematological diseases were observed in megakaryocytic morphology. However, in megakaryocytic thrombocytopenia, high megakaryocyte counts and evidence of bare nuclei of megakaryocytes (p=0.02) and hypolobic types (p=0.03) proved important and play a very essential role in diagnosis. Keywords: Megakaryocytes; Thrombocytopenia; Bone Marrow Aspirate; Diagnosis; Dysmegakaryopoiesis.

Objective: To compare the efficacy of 70% Glycolic acid peeling with Tranexamic acid mesotherapy in treating melasma. Study Design: Quasi-experimental study Place and Duration of Study: Department of Dermatology, CMH-Abbottabad Pakistan, Jun to Nov 2021. Methodology: The 54 patients aged 20-50 years were enrolled and randomly assigned into two groups. In Group-A, 27 patients prescribed 70% Glycolic acid every two weeks for 12 weeks (6 sessions) and in Group-B, 27 patients injected every week for 12 weeks (12 sessions) with Intradermal Tranexamic acid (4mg/ml). All patients had a clinical examination using the mMASI scale. On follow-up, evaluation was carried out on the 1st, sixth, and 12th weeks. Results: The majority of 35(65%) patients had mild MASI scores. The paired t-test results of Group A and Group B were both significant, while the post-treatment mean of Group A and B was insignificant. Conclusion: The study concludes that Glycolic acid and TXA are equally efficient in treating melasma. Moreover, Tranexamic acid reduced the recurrence of melasma, but topical application of Glycolic acid showed more compliance.

A Multiphysics coupling framework, MPCORE, has been developed to analyze safety parameters using the best estimate codes. The framework contains neutron kinetics (NK), thermal hydraulics (TH), and fuel performance (FP) codes to analyze fuel burnup, radial power distribution, and coolant temperature (Tbc). Shuffling and rotation capabilities have been verified on the Watts Bar reactor for three cycles. This study focuses on two coupling approaches for TH and FP modules. The one-way coupling approach involves coupling the FP code with the NK code, providing no data to the TH modules but getting Tbc as boundary condition from TH module. The two-way coupling approach exchanges information from FP to TH modules, so that the simplified heat conduction solver of the TH module is not used. The power profile in both approaches does not differ significantly, but there is an impact on coolant and cladding parameters. The one-way coupling approach tends to over-predict the cladding hydrogen concentration (CHC). This research highlights the difference between one-way and two-way coupling on critical boron concentration, Tbc, CHC, oxide surface temperature, and pellet centerline temperature. Overall, MPCORE framework with two-way coupling provides a more accurate and reliable analysis of safety parameters for nuclear reactors.

Abstract Wireless capsule endoscopy (WCE) enables imaging and diagnostics of the gastrointestinal (GI) tract to be performed without any discomfort. Despite this, several characteristics, including efficacy, tolerance, safety, and performance, make it difficult to apply and modify widely. The use of automated WCE to collect data and perform the analysis is essential for finding anomalies. Medical specialists need a significant amount of time and expertise to examine the data generated by WCE imaging of the patient’s digestive tract. To address these challenges, several computer vision-based solutions have been designed; nevertheless, they do not achieve an acceptable level of accuracy, and more advancements are required. Thus, in this study, we proposed four multi-classification deep learning (DL) models i.e., Vgg-19 + CNN, ResNet152V2, Gated Recurrent Unit (GRU) + ResNet152V2, and ResNet152V2 + Bidirectional GRU (Bi-GRU) and applied it on different publicly available databases for diagnosing ulcerative colitis, polyps, and dyed-lifted polyps using WCE images. To our knowledge, this is the only study that uses a single DL model for the classification of three different GI diseases. We compared the classification performance of the proposed DL classifiers in terms of many parameters such as accuracy, loss, Matthew's correlation coefficient (MCC), recall, precision, negative predictive value (NPV), positive predictive value (PPV), and F1-score. The results revealed that the Vgg-19 + CNN outperforms the three other proposed DL models in classifying GI diseases using WCE images. The Vgg-19 + CNN model achieved an accuracy of 99.45%. The results of four proposed DL classifiers are also compared with recent state-of-the-art classifiers and the proposed Vgg-19 + CNN model has performed better in terms of improved accuracy.

The relative lack of marine venom could be attributed to the difficulty in dealing with venomous marine animals. Moreover, the venom of marine animals consists of various bioactive molecules, many of which are proteins with unique properties. In this study, we investigated the potential toxic proteins of jellyfish collected for ligand screening to understand the mechanism of the toxic effects of jellyfish. Since taxonomic identification is problematic due to the lack of proper keys, we conducted morphological and molecular mitochondrial DNA sequencing from COI and ITS regions. The venom extract from nematocysts found along the bell margins was used for protein characterization using SDS-gel electrophoresis and nano-liquid chromatography-tandem mass spectrometry. Ligand screening for the most potent toxin and antibacterial and cytotoxicity assays were carried out. The phylogenetic tree showed distinct clustering from other Catostylus sp. The proteomic analysis revealed venom with many bioactive proteins. Only 13 venom proteins were identified with molecular weights ranging from 4318 to 184,923 Da, exhibiting the venom’s complexity. The overall toxin protein composition of Catostylus sp. venom was dominated by potassium channel toxin alpha-KTx. Molecular docking of toxin alpha-KTx 1.13 revealed high specificity towards the human voltage-gated potassium channel Kv3 with a high fitness score and a minimum energy barrier of −17.9 kcal/mol. Disc diffusion and cytotoxicity assays revealed potent antibacterial activity against Klebsiella pneumoniae with no cytotoxicity. Further studies on detailed characterization and therapeutic potentials are warranted.

Medical image denoising has numerous real-world applications. Despite their widespread use, existing medical image denoising methods fail to address complex noise patterns and typically generate artifacts in numerous cases. This paper proposes a novel medical image denoising method that learns denoising using an end-to-end learning strategy. Furthermore, the proposed model introduces a novel deep–wider residual block to capture long-distance pixel dependencies for medical image denoising. Additionally, this study proposes leveraging multi-head attention-guided image reconstruction to effectively denoise medical images. Experimental results illustrate that the proposed method outperforms existing qualitative and quantitative evaluation methods for numerous medical image modalities. The proposed method can outperform state-of-the-art models for various medical image modalities. It illustrates a significant performance gain over its counterparts, with a cumulative PSNR score of 8.79 dB. The proposed method can also denoise noisy real-world medical images and improve clinical application performance such as abnormality detection.

Accurate and rapid plant disease detection is critical for enhancing long-term agricultural yield. Disease infection poses the most significant challenge in crop production, potentially leading to economic losses. Viruses, fungi, bacteria, and other infectious organisms can affect numerous plant parts, including roots, stems, and leaves. Traditional techniques for plant disease detection are time-consuming, require expertise, and are resource-intensive. Therefore, automated leaf disease diagnosis using artificial intelligence (AI) with Internet of Things (IoT) sensors methodologies are considered for the analysis and detection. This research examines four crop diseases: tomato, chilli, potato, and cucumber. It also highlights the most prevalent diseases and infections in these four types of vegetables, along with their symptoms. This review provides detailed predetermined steps to predict plant diseases using AI. Predetermined steps include image acquisition, preprocessing, segmentation, feature selection, and classification. Machine learning (ML) and deep understanding (DL) detection models are discussed. A comprehensive examination of various existing ML and DL-based studies to detect the disease of the following four crops is discussed, including the datasets used to evaluate these studies. We also provided the list of plant disease detection datasets. Finally, different ML and DL application problems are identified and discussed, along with future research prospects, by combining AI with IoT platforms like smart drones for field-based disease detection and monitoring. This work will help other practitioners in surveying different plant disease detection strategies and the limits of present systems.

Objective: To evaluate the effect of dietary trans-unsaturated fatty acids (TFAs) on ovulatory infertility in young unmarried females. Methods: This cross-sectional study was conducted from 1 august 2023 to 31 December 2023 at the Basic Medical Science Institute (BMSI), Jinnah Postgraduate Medical Center Karachi. All healthy unmarried females aged 15-25 were included in the study. Females with a family history of infertility, having existing comorbidity, history of irregular menstruation cycle or hormonal replacement therapy, were excluded. Based on the Food frequency questionnaire score, 300 subjects were divided into 3 groups, Group A were low trans fats Consumers (Score 0-170), Group B was moderate trans fats consumers (Score 171-340) and Group C were high trans fats consumers (Score 341-500). The analysis of results was carried out on SPSS Software version 22. Results: The study’s 300 respondents, who ranged in age from 16 to 25, had a mean age of 21.8 years. Fifteen per cent of those in group “A” had abnormal HDL, 22% had bad LDL, and 23% had abnormal TGA, while 17% had abnormal cholesterol. An abnormal TGA, abnormal HDL, abnormal LDL, and abnormal 31% of the participants in group “B” exhibited abnormal cholesterol levels. A significant correlation between oxidative stress and hormonal indicators was found in three groups: “A” (13%) had aberrant LH, “20%” had abnormal FSH, “11%” had abnormal serum estrogen, “10%” had abnormal serum progesterone, and “C” (49% had abnormal LH, FSH, estrogen, and progesterone). Food frequency questionnaire (FFQ ) scores had a 25% positive correlation with cholesterol, a 40% negative correlation with HDL, a 31% positive correlation with LDL, and a 39% positive correlation with biochemical and hormonal indicators. Conclusion: Trans fats diets significantly increase the risk of ovulatory infertility and reproductive failure in young females

Natural contamination of agricultural products like crops (red chilies, peanuts, pistachio, walnut, rice, corn, cottonseed, and wheat) by various fungi and their metabolites, may occur at pre and post-harvest levels. Ten thousand Mycotoxins have been known that are produced by various fungi but the metabolites of the genus Aspergillus called aflatoxins are well known due to their most toxic potential. Among the twenty identified types of aflatoxins four types B1 (AFB1), B2 (AFB2), G1 (AFG1), and G2 (AFG2) are well known due to carcinogenic potential. It is a very difficult task to inhibit their growth from food and feed products to get rid from the exposure of aflatoxins. The process of inhibition was employed for this purpose and natural materials with their modified surface was used as antifungal agents. This method was found to be very beneficial and effective for the inhibition of Aflatoxogenic A. flavus from agricultural commodities. This method of inhibition of growth of A. flavus being cost effective and economical to preserved food and feed products before its consumption. In this research work, Fe/Mg nanoparticles was prepared by using leaf extract of Azadirachta indica. Optimum conditions for efficient inhibition of A. flavus was initial concentration 5 mg/mL while dose rate for both of these was 15 and 20 mg/mL. For the characterization of synthesized Fe/Mg nanoparticles Ultraviolet spectroscopy, Scanning electron microscopy, energy-dispersive X-ray spectroscopy and Fourier-transform infrared spectroscopy were used. Statistical analysis was done by calculating standards errors at 0.05 level of significance.