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Terfezia boudieri Chatin (T. boudieri), which belongs to the Terfeziaceae family, is a desert truffle growing naturally from mycorrhizal relationships with plants that inhabit the slightly moist sandy soils of some desert or semi-desert regions. The chemical examination of the methanolic extract of T. boudieri led to the isolation of a new ketone and the known compound mannitol. The structures of the 2 isolated compounds were resolved using spectroscopic analysis including 1D and 2D Nuclear Magnetic Resonance (NMR), Infrared (IR) spectroscopy and were supported by literature data. The GC-MS analysis of the Fatty Acids (FAs) of T. boudieri oils showed high Unsaturated FA (UFA) profile (78.72%). The major FAs were linoleic acid (62.36%) and oleic acid (14.7%). Comparing the extracts obtained, the methanolic extract revealed the highest levels of Total Phenolic Content (TPC) of 185.56 mg GAE/100 g of truffle, thus the same extract showed the best results for antioxidant activity. [ABSTRACT FROM AUTHOR]

Currently, amyotrophic lateral sclerosis (ALS) disease is considered fatal since it affects the central nervous system with no cure or clear treatments. This disease affects the spinal cord, more specifically, the lower motor neurons (LMNs) and the upper motor neurons (UMNs) inside the brain along with their networks. Various solutions have been developed to predict ALS. Some of these solutions were implemented using different deep-learning methods (DLMs). Nevertheless, this disease is considered a tough task and a huge challenge. This article proposes a reliable model to predict ALS disease based on a deep-learning tool (DLT). The developed DLT is designed using a UNET architecture. The proposed approach is evaluated for different performance quantities on a dataset and provides promising results. An average obtained accuracy ranged between 82% and 87% with around 86% of the F-score. The obtained outcomes can open the door to applying DLMs to predict and identify ALS disease. [ABSTRACT FROM AUTHOR]

Blood cancer occurs due to changes in white blood cells (WBCs). These changes are known as leukemia. Leukemia occurs mostly in children and affects their tissues or plasma. However, it could occur in adults. This disease becomes fatal and causes death if it is discovered and diagnosed late. In addition, leukemia can occur from genetic mutations. Therefore, there is a need to detect it early to save a patient's life. Recently, researchers have developed various methods to detect leukemia using different technologies. Deep learning approaches (DLAs) have been widely utilized because of their high accuracy. However, some of these methods are time-consuming and costly. Thus, a need for a practical solution with low cost and higher accuracy is required. This article proposes a novel segmentation and classification framework model to discover and categorize leukemia using a deep learning structure. The proposed system encompasses two main parts, which are a deep learning technology to perform segmentation and characteristic extraction and classification on the segmented section. A new UNET architecture is developed to provide the segmentation and feature extraction processes. Various experiments were performed on four datasets to evaluate the model using numerous performance factors, including precision, recall, F-score, and Dice Similarity Coefficient (DSC). It achieved an average 97.82% accuracy for segmentation and categorization. In addition, 98.64% was achieved for F-score. The obtained results indicate that the presented method is a powerful technique for discovering leukemia and categorizing it into suitable groups. Furthermore, the model outperforms some of the implemented methods. The proposed system can assist healthcare providers in their services. [ABSTRACT FROM AUTHOR]

A brain tumor is an excessive development of abnormal and uncontrolled cells in the brain. This growth is considered deadly since it may cause death. The brain controls numerous functions, such as memory, vision, and emotions. Due to the location, size, and shape of these tumors, their detection is a challenging and complex task. Several efforts have been conducted toward improved detection and yielded promising results and outcomes. However, the accuracy should be higher than what has been reached. This paper presents a method to detect brain tumors with high accuracy. The method works using an image segmentation technique and a classifier in MATLAB. The utilized classifier is a SupportVector Machine (SVM). DiscreteWavelet Transform (DWT) and Principal Component Analysis (PCA) are also involved. A dataset from the Kaggle website is used to test the developed approach. The obtained results reached nearly 99.2% of accuracy. The paper provides a confusion matrix of applying the proposed approach to testing images and a comparative evaluation between the developed method and some works in the literature. This evaluation shows that the presented system outperforms other approaches regarding the accuracy, precision, and recall. This research discovered that the developed method is extremely useful in detecting brain tumors, given the high accuracy, precision, and recall results. The proposed system directs us to believe that bringing this kind of technology to physicians diagnosing brain tumors is crucial. [ABSTRACT FROM AUTHOR]

Lung cancer starts and spreads in the tissues of the lungs, more specifically, in the tissue that forms air passages. This cancer is reported as the leading cause of cancer deaths worldwide. In addition to being the most fatal, it is the most common type of cancer. Nearly 47,000 patients are diagnosed with it annually worldwide. This article proposes a fully automated and practical system to identify and classify lung cancer. This system aims to detect cancer in its early stage to save lives if possible or reduce the death rates. It involves a deep convolutional neural network (DCNN) technique, VGG-19, and another deep learning technique, long short-term memory networks (LSTMs). Both tools detect and classify lung cancers after being customized and integrated. Furthermore, image segmentation techniques are applied. This system is a type of computer-aided diagnosis (CAD). After several experiments on MATLAB were conducted, the results show that this system achieves more than 98.8% accuracy when using both tools together. Various schemes were developed to evaluate the considered disease. Three lung cancer datasets, downloaded from the Kaggle website and the LUNA16 grad challenge, were used to train the algorithm, test it, and prove its correctness. Lastly, a comparative evaluation between the proposed approach and some works from the literature is presented. This evaluation focuses on the four performance metrics: accuracy, recall, precision, and F-score. This system achieved an average of 99.42% accuracy and 99.76, 99.88, and 99.82% for recall, precision, and F-score, respectively, when VGG-19 was combined with LSTMs. In addition, the results of the comparison evaluation show that the proposed algorithm outperforms other methods and produces exquisite findings. This study concludes that this model can be deployed to aid and support physicians in diagnosing lung cancer correctly and accurately. This research reveals that the presented method has functionality, competence, and value among other implemented models. [ABSTRACT FROM AUTHOR]

Multiple microRNAs (miRs) are associated with systemic autoimmune disease susceptibility/phenotype, including systemic lupus erythematosus (SLE). With this work, we aimed to unravel the association of the miR-27a gene (MIR27A) rs11671784G/A variant with SLE risk/severity. One-hundred sixty-three adult patients with SLE and matched controls were included. A TaqMan allelic discrimination assay was applied for MIR27A genotyping. Logistic regression models were run to test the association with SLE susceptibility/risk. Genotyping of 326 participants revealed that the heterozygote form was the most common genotype among the study cohort, accounting for 72% of the population (n = 234), while A/A and G/G represented 15% (n = 49) and 13% (n = 43), respectively. Similarly, the most prevalent genotype among cases was the A/G genotype, which was present in approximately 93.3% of cases (n = 152). In contrast, only eight and three patients had A/A and G/G genotypes, respectively. The MIR27A rs11671784 variant conferred protection against the development of SLE in several genetic models, including heterozygous (G/A vs. A/A; OR = 0.10, 95% CI = 0.05–0.23), dominant (G/A + G/G vs. AA; OR = 0.15, 95% CI = 0.07–0.34), and overdominant (G/A vs. A/A + G/G; OR = 0.07, 95% CI = 0.04–0.14) models. However, the G/G genotype was associated with increased SLE risk in the recessive model (G/G vs. A/A+ G/G; OR = 17.34, 95% CI = 5.24–57.38). Furthermore, the variant showed significant associations with musculoskeletal and mucocutaneous manifestations in the patient cohort (p = 0.035 and 0.009, respectively) and platelet and white blood cell counts (p = 0.034 and 0.049, respectively). In conclusion, the MIR27A rs11671784 variant showed a potentially significant association with SLE susceptibility/risk in the studied population. Larger-scale studies on multiethnic populations are recommended to verify the results. [ABSTRACT FROM AUTHOR]

The lumpy skin disease (LSD) virus of the Poxviridae family is a serious threat that mostly affects cattle and causes significant economic loss. LSD has the potential to spread widely and its rapidly across borders. Despite the availability of information, there is still no competitive vaccine available for LSD. Therefore, the current study was conducted to develop an epitope-based LSD vaccine that is efficient, secure, and biocompatible and stimulates both innate and adaptive immune responses using immunoinformatics techniques. Initially, putative virion core proteins were manipulated; B-cell and T-cell epitopes have been predicted and connected with the help of adjuvants and linkers. Numerous bioinformatics methods, including antigenicity testing, transmembrane topology screening, allergenicity assessment, conservancy analysis, and toxicity evaluation, were employed to find superior epitopes. Based on promising vaccine candidates and immunogenic potential, the vaccine design was selected. Strong interactions between TLR4 and TLR9 and the anticipated vaccine design were revealed by molecular docking. Finally, based on the high docking score, computer simulations were performed in order to assess the stability, efficacy, and compactness of the constructed vaccine. The simulation outcomes showed that the polypeptide vaccine design was remarkably stable, with high expression, stability, immunogenic qualities, and considerable solubility. Additionally, computer-based research shows that the constructed vaccine provides adequate population coverage, making it a promising candidate for use in the design of vaccines against other viruses within the Poxviridae family and potentially other virus families as well. These outcomes suggest that the epitope-based vaccine developed in this study will be a significant candidate against LSD to control and prevent LSDV-related disorders if further investigated experimentally. [ABSTRACT FROM AUTHOR]

Heat shock proteins (HSPs) are cytoprotective against stressful conditions, as in the case of cancer cell metabolism. Scientists proposed that HSP70 might be implicated in increased cancer cell survival. This study aimed to investigate the HSP70 (HSPA4) gene expression signature in patients with renal cell carcinoma (RCC) in correlation to cancer subtype, stage, grade, and recurrence, combining both clinicopathological and in silico analysis approaches. One hundred and thirty archived formalin-fixed paraffin-embedded samples, including 65 RCC tissue specimens and their paired non-cancerous tissues, were included in the study. Total RNA was extracted from each sample and analyzed using TaqMan quantitative Real-Time Polymerase Chain Reaction. Correlation and validation to the available clinicopathological data and results were executed. Upregulated HSP70 (HSPA4) gene expression was evident in RCC compared to non-cancer tissues in the studied cohort and was validated by in silico analysis. Furthermore, HSP70 expression levels showed significant positive correlations with cancer size, grade, and capsule infiltration, as well as recurrence in RCC patients. The expression levels negatively correlated with the overall survival (r = −0.87, p < 0.001). Kaplan–Meier curves showed lower survival rates in high HSP70 expressor group compared to the low expressors. In conclusion, the HSP70 expression levels are associated with poor RCC prognosis in terms of advanced grade, capsule infiltration, recurrence, and short survival. [ABSTRACT FROM AUTHOR]

Leishmania tropica is a tropical parasite causing cutaneous leishmaniasis (CL) in humans. Leishmaniasis is a serious public health threat, affecting an estimated 350 million people in 98 countries. The global rise in antileishmanial drug resistance has triggered the need to explore novel therapeutic strategies against this parasite. In the present study, we utilized the recently available multidrug resistant L. tropica strain proteome data repository to identify alternative therapeutic drug targets based on comparative subtractive proteomic and druggability analyses. Additionally, small drug-like compounds were scanned against novel targets based on virtual screening and ADME profiling. The analysis unveiled 496 essential cellular proteins of L. tropica that were nonhomologous to the human proteome set. The druggability analyses prioritized nine parasite-specific druggable proteins essential for the parasite's basic cellular survival, growth, and virulence. These prioritized proteins were identified to have appropriate binding pockets to anchor small drug-like compounds. Among these, UDPase and PCNA were prioritized as the top-ranked druggable proteins. The pharmacophore-based virtual screening and ADME profiling predicted MolPort-000-730-162 and MolPort-020-232-354 as the top hit drug-like compounds from the Pharmit resource to inhibit L. tropica UDPase and PCNA, respectively. The alternative drug targets and drug-like molecules predicted in the current study lay the groundwork for developing novel antileishmanial therapies. [ABSTRACT FROM AUTHOR]

Background: Short birth intervals (SBIs) and long birth intervals (LBIs) have been shown to have serious implications for health of both mothers and their children. This study was aimed to investigate the determinants and reproductive outcome of SBI and LBI in a multiethnic Pakistani population.Methods: In a cross-sectional prospective study design, 2798 women admitted in a tertiary-care hospital in Islamabad for delivery were recruited and data on second or higher birth order deliveries were collected. Birth intervals were defined as short (<24 months) and long (>36 months). The reproductive outcome was defined in terms of perinatal and neonatal mortalities, and neonatal complications. Univariate and multivariate logistic regression analyses were performed.Results: Pregnancies with SBI and LBI were observed in 20% and 24% of 2798 women, respectively. Women with SBI had increased odds of perinatal death [adjusted odd ratio (AOR): 1.50] and neonatal death (AOR: 1.47) as compared to women with optimal birth intervals, while women with LBI had slightly lower odds of perinatal deaths (AOR: 0.96), but increased odds of neonatal deaths (AOR: 1.12). Further, the pregnancies with both SBI and LBI were associated with increased odds of short body length, low birth weight, small head circumference and low APGAR score.Conclusion: Nearly half of all pregnancies do not have optimal birth spacing albeit there is wide heterogeneity in the distribution of BI in various Pakistani ethnicities. Pregnancies with SBI and LBI had high risk of adverse reproductive outcome. Intervention programs for maternal and child health need to emphasize optimal birth spacing. [ABSTRACT FROM AUTHOR]

Heart disease is one of the key contributors to human death. Each year, several people die due to this disease. According to the WHO, 17.9 million people die each year due to heart disease. With the various technologies and techniques developed for heart-disease detection, the use of image classification can further improve the results. Image classification is a significant matter of concern in modern times. It is one of the most basic jobs in pattern identification and computer vision, and refers to assigning one or more labels to images. Pattern identification from images has become easier by using machine learning, and deep learning has rendered it more precise than traditional image classification methods. This study aims to use a deep-learning approach using image classification for heart-disease detection. A deep convolutional neural network (DCNN) is currently the most popular classification technique for image recognition. The proposed model is evaluated on the public UCI heart-disease dataset comprising 1050 patients and 14 attributes. By gathering a set of directly obtainable features from the heart-disease dataset, we considered this feature vector to be input for a DCNN to discriminate whether an instance belongs to a healthy or cardiac disease class. To assess the performance of the proposed method, different performance metrics, namely, accuracy, precision, recall, and the F1 measure, were employed, and our model achieved validation accuracy of 91.7%. The experimental results indicate the effectiveness of the proposed approach in a real-world environment. [ABSTRACT FROM AUTHOR]

Devrra triradiata Hochst. ex Boiss is an occasional plant species in the Northern region of Saudi Arabia. The shrub is favored on sandy desert wadis, gypsaceous substrate, and sandy gravel desert. In folk medicine, the plant is used for many purposes; to relieve stomach pains, against intestinal parasites, and for the regulation of menstruation. The present study describes the chemical composition of the essential oils (EOs) of different plant parts of D. triradiata. In vivo and in vitro biological activities of plant extracts and essential oils were also studied. Phenylpropanoids, elemicin (flowers: 100%), dillapiole (Stems: 82.33%; and seeds: 82.61%), and apiol (roots: 72.16%) were identified as the major compounds. The highest antioxidant activity was recorded for the EOs of roots and stems (IC50 = 0.282 µg/mL and 0.706 µg/mL, respectively). For plant extracts, ethyl acetate showed the highest antioxidant activities (IC50 = 2.47 and 3.18 µg/mL). EOs showed high antifungal activity against yeasts with low azole susceptibilities (i.e., Malassezia spp. and Candida krusei). The MIC values of EOs ranged between 3.4 mg/mL and 56.4 mg/mL. The obtained results also showed phytotoxic potential of plant extracts both on the germination features of Triticum aestivum seeds and the vegetative growth of seedlings. [ABSTRACT FROM AUTHOR]

COVID-19 has had an impact on human quality of life and economics. Scientists have been identifying remedies for its prevention and treatment from all possible sources, including plants. Nigella sativa L. (NS) is an important medicinal plant of Islamic value. This review highlights the anti-COVID-19 potential, clinical trials, inventions, and patent literature related to NS and its major chemical constituents, like thymoquinone. The literature was collected from different databases, including Pubmed, Espacenet, and Patentscope. The literature supports the efficacy of NS, NS oil (NSO), and its chemical constituents against COVID-19. The clinical data imply that NS and NSO can prevent and treat COVID-19 patients with a faster recovery rate. Several inventions comprising NS and NSO have been claimed in patent applications to prevent/treat COVID-19. The patent literature cites NS as an immunomodulator, antioxidant, anti-inflammatory, a source of anti-SARS-CoV-2 compounds, and a plant having protective effects on the lungs. The available facts indicate that NS, NSO, and its various compositions have all the attributes to be used as a promising remedy to prevent, manage, and treat COVID-19 among high-risk people as well as for the therapy of COVID-19 patients of all age groups as a monotherapy or a combination therapy. Many compositions of NS in combination with countless medicinal herbs and medicines are still unexplored. Accordingly, the authors foresee a bright scope in developing NS-based anti-COVID-19 composition for clinical use in the future. [ABSTRACT FROM AUTHOR]

Zinc is an essential nutrient for human health; it is involved in the catalytic, structural, and regulatory functions of the human cellular system. Different compositions of zinc, as well as its pharmaceutically acceptable salts, are available on the market. Recent studies have demonstrated the role of zinc in combating COVID-19. It has been determined that zinc prevents the entry of SARS-CoV-2 into cells by lowering the expression of ACE-2 receptors and inhibiting the RNA-dependent RNA polymerase of SARS-CoV-2. Zinc also prevents the cytokine storm that takes place after the entry of SARS-CoV-2 into the cell, via its anti-inflammatory activity. The authors believe that no study has yet been published that has reviewed the trends, inventions, and patent literature of zinc compositions to treat/prevent COVID-19. Accordingly, this review has been written in order to fill this gap in the literature. The information about the clinical studies and the published patents/patent applications was retrieved from different databases. This review covers patent literature on zinc compositions up to 31 January 2022. Many important patents/patent applications for zinc-based compositions filed by innovative universities and industries were identified. The patent literature revealed zinc compositions in combination with zinc ionophores, antioxidants, antivirals, antibiotics, hydroxychloroquine, heparin, ivermectin, and copper. Most of these studies were supported by clinical trials. The patent literature supports the potential of zinc and its pharmaceutical compositions as possible treatments for COVID-19. The authors believe that countless zinc-based compositions are still unexplored, and there is an immense opportunity to evaluate a considerable number of the zinc-based compositions for use against COVID-19. [ABSTRACT FROM AUTHOR]