Oral drug administration is essential for treating diverse diseases due to its acceptable route, non-invasiveness, minimal off-target side effects, versatility, and painless patient delivery. ...However, multiple drug accessibility is increasing currently but there is often a deficiency of indication in developing procedures with assessment of two diverse medicinal formulations. Oral administration of a novel antiepileptic drug, levetiracetam in grouping with either L-dopa unaccompanied or L-dopa/Ropinirole treatments was associated with dyskinesia. Altered shooting patterns of neurons relating to a compulsive harmonization process may back to the origin of dyskinesia. The current study aims to develop a dissolution technique and validate the antiparkinsonian drug-ropinirole and antiepileptic drug-levetiracetam tablets by RP-HPLC, which plays a major part in pharmaceutical formulations. The optimal dissolution conditions were experienced for the products respective to the pharmaceutical formulation and applied to assess the dissolution profiles. Ideal settings for the dissolution method were 500 mL of pH of 4.0 citrate buffer, 50 rpm and 250 nm for ropinirole tablets and 900 mL of distilled water, the rotation speed of paddle 50 rpm and 217 nm for levetiracetam tablets, then 3.84 and 3.87 minutes were set as the retention time of ropinirole and levetiracetam tablets, respectively. Technique optimization and validation process helps to reduce the bioequivalence of both dosage forms. The current study ensues detailed information on the drug release, even if there is a change in pH medium, filter, rpm, instrumentation, etc. Focusing on current pharmaceutical sciences, the proposed correlative dissolution study coupled with RP-HPLC analysis could offer a holistic insight into the performance of future oral drug delivery systems
In the present study a series of Co(II) complexes of formyl chromone Schiff bases have been synthesized characterized by analytical, molar conductance, IR, electronic, magnetic susceptibility, ...thermal, fluorescence and powder XRD measurements and screened for various biological activities (antimicrobial, antioxidant, nematicidal, DNA cleavage and cytotoxicity). In all the Co(II) complexes 1:2 metal to ligand molar ratio was obtained from analytical data. The molar conductance data confirm that all complexes are non-electrolytic in nature. Based on the electronic and magnetic data, an octahedral geometry is ascribed for all the Co(II) complexes. Thermal behaviour of the synthesized complexes illustrates the general decomposition patterns of the complexes. The X-ray analysis data show that all the Co(II) complexes have triclinic crystal system with different unit cell parameters. Metal complexes have greater antimicrobial activity than ligands. Antioxidant and nematicidal activities indicate that the ligands exhibit greater activity when compared to their respective Co(II) complexes. All ligands and Co(II) complexes of HL1 and HL2 showed considerable anticancer activity against Raw, MCF-7 and COLO 205 cell lines. All ligands and their Co(II) complexes showed more pronounced DNA cleavage activity in the presence of H2O2.
Glaucoma is one of the most common causes of blindness. Robust mass screening may help to extend the symptom-free life for affected patients. To realize mass screening requires a cost-effective ...glaucoma detection method which integrates well with digital medical and administrative processes. To address these requirements, we propose a novel low cost automated glaucoma diagnosis system based on hybrid feature extraction from digital fundus images. The paper discusses a system for the automated identification of normal and glaucoma classes using higher order spectra (HOS), trace transform (TT), and discrete wavelet transform (DWT) features. The extracted features are fed to a support vector machine (SVM) classifier with linear, polynomial order 1, 2, 3 and radial basis function (RBF) in order to select the best kernel for automated decision making. In this work, the SVM classifier, with a polynomial order 2 kernel function, was able to identify glaucoma and normal images with an accuracy of 91.67%, and sensitivity and specificity of 90% and 93.33%, respectively. Furthermore, we propose a novel integrated index called Glaucoma Risk Index (GRI) which is composed from HOS, TT, and DWT features, to diagnose the unknown class using a single feature. We hope that this GRI will aid clinicians to make a faster glaucoma diagnosis during the mass screening of normal/glaucoma images.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Diabetes is an important global health concern. According to the World Health Organization, the Kingdom of Saudi Arabia (KSA) has 2.8 to 4.3 million diabetic patients between the ages of 27 to 60 in ...the year 2022. Medical nutrition therapy an essential component of diabetes management, helps improve diabetes outcomes by assisting patients in achieving a target glycated hemoglobin (HbA1c) level of <7. This study aimed to describe the pattern of food consumption in the study population and determine its association with glycemic control. This was a cross-sectional study among health facility attendees at the Endocrinology and Diabetes Center (EDC) in Jazan, KSA. A total of 315 patients with type 2 diabetes (T2D) were enrolled using a systematic random sampling technique. Sociodemographic and dietary habit information gathered through face-to-face interviews, anthropometric measurements, and HbA1c were collected from medical records. Participants were mostly females (55.6%), and more than half of them were above 50 years of age. More than 90% of the patients had poor HbA1c levels, and 2-thirds of the patients were overweight and obese (43.8% and 37.1%, respectively). The most consumed foods were bread (8 times/week) and coffee/tea and vegetables (7 times per week), and the least consumed was soda beverages (once/week). Multivariate logistic regression revealed that the consumption of fruits (consumed 5 times/week) significantly decreased HbA1c, while the consumption of pastries/pizza (consumed twice/week) significantly increased it. The pattern of consumption of fruits and vegetables in the study population conformed to the recommended levels, and that of fruits showed a statistically significant association with the control of HbA1c. Another food item that has a significant negative effect on HbA1c is pastries/pizza. Further studies that include more potentially confounding variables, such as treatment type, are needed.
We enrolled prospectively and randomized 296 patients, and compared NGS panel versus clinical‐based Sanger for the diagnosis of AIDs. NGS panel allowed better diagnostic yield (10.1%) compared to ...Sanger (4.1%). We concluded that targeted NGS improved the diagnosis and global care of patients with AIDs.
Summary
The aim of this study was to compare the effectiveness of the gene‐panel next‐generation sequencing (NGS) strategy versus the clinical‐based gene Sanger sequencing for the genetic diagnosis of autoinflammatory diseases (AIDs). Secondary goals were to describe the gene and mutation distribution in AID patients and to evaluate the impact of the genetic report on the patient’s medical care and treatment. Patients with AID symptoms were enrolled prospectively and randomized to two arms, NGS (n = 99) (32–55 genes) and Sanger sequencing (n = 197) (one to four genes). Genotypes were classified as ‘consistent/confirmatory’, ‘uncertain significance’ or ‘non‐contributory’. The proportion of patients with pathogenic genotypes concordant with the AID phenotype (consistent/confirmatory) was significantly higher with NGS than Sanger sequencing 10 of 99 (10·1%) versus eight of 197 (4·1%). MEFV, ADA2 and MVK were the most represented genes with a consistent/confirmed genotype, whereas MEFV, NLRP3, NOD2 and TNFRSF1A were found in the ‘uncertain significance’ genotypes. Six months after the genetic report was sent, 54 of 128 (42·2%) patients had received effective treatment for their symptoms; 13 of 128 (10·2%) had started treatment after the genetic study. For 59 of 128 (46%) patients, the results had an impact on their overall care, independent of sequencing group and diagnostic conclusion. Targeted NGS improved the diagnosis and global care of patients with AIDs.
Early and rapid detection of bovine tuberculosis (bTB) is critical to controlling the spread of this disease in cattle and other animals. In this study, we demonstrate the development of an ...immunoassay for the direct detection of the bovine bTB biomarker, lipomannan (LM) in serum using a waveguide-based optical biosensor. We apply an ultra-sensitive detection strategy developed by our team, termed lipoprotein capture, that exploits the pull-down of high-density lipoprotein (HDL) nanodiscs from cattle blood that allows for the recovery and detection of associated LM. We also profile the change in the expression of these TB biomarkers as a function of time from a small set of samples collected from studies of bovine TB-infected cattle. We demonstrate for the first time the direct detection of bovine LM in serum, and clearly show that the biomarker is expressed in detectable concentrations during the entire course of the infection.
The misuse of γ-hydroxybutyrate (GHB) for recreational purposes has resulted in an increase in GHB-related problems such as intoxications, dependence and withdrawal in several countries in Europe, ...Australia and the US over the last decade. However, prevalence rates of misuse of GHB and its precursor, γ-butyrolactone (GBL), are still relatively low. In this qualitative review paper, after a short introduction on the pharmacology of GHB/GBL, followed by a summary of the epidemiology of GHB abuse, an overview of GHB dependence syndrome and GHB/GBL withdrawal syndrome is provided. Finally, the existing literature on management of GHB detoxification, both planned and unplanned, as well as the available management of GHB withdrawal syndrome, is summarized. Although no systematic studies on detoxification and management of withdrawal have been performed to date, general recommendations are given on pharmacological treatment and preferred treatment setting.
Texture classification through deep learning is a science of detecting assumptions from various data through different tools of statistics, machine learning, signal processing and algorithm design. ...The ultimate aim of textile industries is to produce high quality and defect free fabric to the customers. Traditional methodologies involve manual inspection of every fabric produced which in turn seems to be tedious and time consuming. When Long Short Term Memory (LSTM) is applied to the defect detection and texture classification of a fabric the process leads to high efficiency. Industry can maintain the database which consists of pattern as well as the history of defects present in the fabric. The proposed deep learning technique on LSTM infers the details about the fabric through digital images. The defects in fabric are identified using LSTM method. The defects can be identified irrespective of complex patterns. The obtained images are converted to RGB images and compared with threshold levels for pattern recognition. The obtained factors from proposed technique is pattern of the fabric and location of the defects which include scratches, perforations etc. An unsupervised learning algorithm is proposed to classify the defect percentage present in the fabric and classifying the pattern in the fabric. Here multi-scale curvelet image decomposition and sub band decomposition is used to identify pattern and defects in the fabric. The defect detection involves two different phases. The first phase involves in analyzing defect free images. The second phase incorporated LSTM techniques to identify the defects by using logistic regression. The trained images are decomposed as blocks and the algorithm proposed has the capability to achieve defect detection and pattern recognition in a less computational time.
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