Solutions of a three-dimensional multi-term fractional anomalous solute transport model for contamination in groundwater.

The study presents a meshless computational approach for simulating the three-dimensional multi-term time-fractional mobile-immobile diffusion equation in the Caputo sense. The methodology combines a stable Crank-Nicolson time-integration scheme with the definition of the Caputo derivative to discretize the problem in the temporal direction. The spatial function derivative is approximated using the inverse multiquadric radial basis function. The solution is approximated on a set of scattered or uniform nodes, resulting in a sparse and well-conditioned coefficient matrix. The study highlights the advantages of meshless method, particularly their simplicity of implementation in higher dimensions. To validate the accuracy and efficacy of the proposed method, we performed numerical simulations and compared them with analytical solutions for various test problems. These simulations were carried out on computational domains of both rectangular and non-rectangular shapes. The research highlights the potential of meshless techniques in solving complex diffusion problems and its successful applications in groundwater contamination and other relevant fields.

Molecular diagnosis and subtyping of Blastocystis sp.: Association with clinical, colonoscopic, and histopathological findings.

Introduction: Blastocystis sp. is the most common parasitic infestation in humans. However, its pathogenicity remains controversial. Our aim was to study the prevalence of Blastocystis sp. parasite subtypes in patients with gastrointestinal manifestations referred for colonoscopy and assess possible correlation with clinical, colonoscopic, and histopathological findings. Methodology: One hundred patients with gastrointestinal manifestations referred for colonoscopy were enrolled. Stool samples were collected and examined both microscopically and by real-time quantitative polymerase chain reaction (qPCR) for detection of Blastocystis sp. Subtyping was done for positive samples by qPCR and confirmed by sequencing. Results: qPCR sensitivity far exceeded microscopy in detection of Blastocystis sp. (58% vs. 31%, agreement 38.5%). The most commonly detected subtype was 3 (50%), followed by 2 (32.8%) and 4 (13.8%). Abdominal pain was the most common clinical symptom; inflammation and colitis were the most common abnormal colonoscopic and histopathological findings. The most frequent subtype encountered in those findings was Subtype 3. Conclusions: This study confirmed the importance of using qPCR in diagnosis of Blastocystis sp. An association between abnormal clinical, colonoscopic, and histopathological findings on the one hand, and Blastocystis sp. infestation, especially Subtype 3, on the other hand, is also posed. This necessitates further studies to assess the mechanism of association with pathogenicity.

Phytochemical Estimation and Therapeutic Amelioration of Aesculus hippocastanum L. Seeds Ethanolic Extract in Gastric Ulcer in Rats Possibly by Inhibiting Prostaglandin Synthesis.

OBJECTIVE: To quantify phytochemicals using liquid chromatography and mass spectroscopy (LCMS) analysis and explore the therapeutic effect of Aesculus hippocastanum L. (AH) seeds ethanolic extract against gastric ulcers in rats. METHODS: Preliminary phytochemical testing and LCMS analysis were performed according to standard methods. For treatment, the animals were divided into 7 groups including normal control, ulcer control, self-healing, AH seeds low and high doses, ranitidine and per se groups. Rats were orally administered with 10 mg/kg of indomethacin, excluding the normal control group (which received 1% carboxy methyl cellulose) and the per se group (received 200 mg/kg AH seeds extract). The test group rats were then given 2 doses of AH seeds extract (100 and 200 mg/kg, respectively), while the standard group was given ranitidine (50 mg/kg). On the 11th day, rats in all groups were sacrificed, and their stomach was isolated to calculate the ulcer index, and other parameters such as blood prostaglandin (PGE2), tissue superoxide dismutase (SOD), catalase (CAT), malonyldialdehyde (MDA), and glutathione (GSH). All isolated stomach tissues were analyzed for histopathological findings. RESULTS: The phytochemical examination shows that the AH seeds contain alkaloids, flavonoids, saponins, phenolic components, and glycosides. LCMS analysis confirms the presence of quercetin and rutin. The AH seeds extract showed significant improvement in gastric mucosa conditions after indomethacin-induced gastric lesions (P<0.01). Further marked improvement in blood PGE2 and antioxidant enzymes, SOD, CAT, MDA and GSH, were observed compared with self-healing and untreated ulcer-induced groups (P<0.01). Histopathology results confirmed that AH seeds extract improved the mucosal layer and gastric epithelial membrane in treated groups compared to untreated ulcer-induced groups. CONCLUSIONS: LCMS report confirms the presence of quercetin and rutin in AH seeds ethanolic extract. The therapeutic effect of AH seeds extract against indomethacin-induced ulcer in rat model indicated the regenerated membrane integrity, with improved cellular functions and mucus thickness. Further, improved antioxidant enzyme level would help to reduce PGE2 biosynthesis.

A Robust and Novel Approach for Brain Tumor Classification Using Convolutional Neural Network.

Brain tumors are the most common and aggressive illness, with a relatively short life expectancy in their most severe form. Thus, treatment planning is an important step in improving patients' quality of life. In general, image methods such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound images are used to assess tumors in the brain, lung, liver, breast, prostate, and so on. X-ray images, in particular, are utilized in this study to diagnose brain tumors. This paper describes the investigation of the convolutional neural network (CNN) to identify brain tumors from X-ray images. It expedites and increases the reliability of the treatment. Because there has been a significant amount of study in this field, the presented model focuses on boosting accuracy while using a transfer learning strategy. Python and Google Colab were utilized to perform this investigation. Deep feature extraction was accomplished with the help of pretrained deep CNN models, VGG19, InceptionV3, and MobileNetV2. The classification accuracy is used to assess the performance of this paper. MobileNetV2 had the accuracy of 92%, InceptionV3 had the accuracy of 91%, and VGG19 had the accuracy of 88%. MobileNetV2 has offered the highest level of accuracy among these networks. These precisions aid in the early identification of tumors before they produce physical adverse effects such as paralysis and other impairments.

An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis.

Accurate and early detection of machine faults is an important step in the preventive maintenance of industrial enterprises. It is essential to avoid unexpected downtime as well as to ensure the reliability of equipment and safety of humans. In the case of rotating machines, significant information about machine's health and condition is present in the spectrum of its vibration signal. This work proposes a fault detection system of rotating machines using vibration signal analysis. First, a dataset of 3-dimensional vibration signals is acquired from large induction motors representing healthy and faulty states. The signal conditioning is performed using empirical mode decomposition technique. Next, multi-domain feature extraction is done to obtain various combinations of most discriminant temporal and spectral features from the denoised signals. Finally, the classification step is performed with various kernel settings of multiple classifiers including support vector machines, K-nearest neighbors, decision tree and linear discriminant analysis. The classification results demonstrate that a hybrid combination of time and spectral features, classified using support vector machines with Gaussian kernel achieves the best performance with 98.2% accuracy, 96.6% sensitivity, 100% specificity and 1.8% error rate.

Recent Therapeutic Interventions of Fenugreek Seed: A Mechanistic Approach.

Fenugreek seeds have widespread relations with Ayurveda, Unani, and Arabic medicine. The seeds were useful for the treatment and prevention of different ailments. Fenugreek (Trigonella foenum-graecum) or methi is from the Leguminosae family and are primarily known for its anti-diabetic and hypocholesterolemic activities. The germinated fenugreek seeds were used in the treatment of E.coli infection in Germany and France. The important phytoconstituents responsible for such medicinal applications are saponins, polyunsaturated fatty acids, galactomannans, trigonelline, and 4-hydroxy isoleucine. Flavonoids, apigenin 6,8-di-C-glucoside, apigenin-6-C-glucosyl-8-C-galactoside, 6-Cgalactosyl- 8-C-arabinoside are the chief ingredients of fenugreek seeds; responsible for reducing blood glucose while given to diabetic rats, whereas important flavones are epigenin, luteolin and vitexin. The other major bioactive components in fenugreek seeds are polyphenols like rhaponticin and isovitexin. Fenugreek seeds contain phosphorus and are categorized into different classes such as inorganic phosphorus, phospholipids, phytates, phosphor-proteins, and nucleic acid. Germinated seeds profusely filled with amino acids with amino acids, proteins, ascorbic acid, sugars. Further, this review shares information about the recent therapeutic intervention not covered earlier; on in vivo and in vitro and some clinical applications against certain interesting ailments other than older applications. This review includes certain nano delivery systems of Fenugreek seeds and their medicinal application.

Phytochemical, ethanomedicinal and pharmacological applications of escin from Aesculus hippocastanum L. towards future medicine.

Medicinal plants are used from ancient times for treatment of various ailments. Aesculus hippocastanum (Horse chestnut), is the popular and most valuable tree native to the South East Europe. It's seed extracts and their concentrates contain phytocompounds like flavonoids, polyphenols, triterpenoid saponin glycosides (escin), epicatechin, tannins, kaempferol, esculin, fraxin, carbohydrate, essential fatty acids (linoleic acid), oleic acid and purine bases (adenine and guanine). Due to these vital phyto-constituents, horse chestnut is used in phytomedicine for the prevention and treatment of diverse disorders as in venous congestion in leg ulcers, bruises, arthritis, rheumatism, diarrhoea, phlebitis etc. We collected the pharmacological applications of Aesculus hippocastanum L. extracts and escin as the cheif bioactive compound and their uses in traditionally and clinically for the management of various disorders. This review describes the efficacy of A. hippocastanum L. extracts and their bioactive compounds. So in the furtue this plant may be useful for the alternative treatment measure for various ailments via incorporating either extract or escin into novel delivery systems for improving the social health in future and would provide improved quality of life.