COMPARISON OF XANTHINE OXIDASE INHIBITORS IN GOUTY PATIENTS WITH HYPERURICEMIA

Objective: Febuxostat is more effective/superior to Allopurinol in reducing the serum uric acid (SUA) level in the treatment of hyperuricemic withgout.Methods: This randomized control study included 200 hyperuricemic patients with gout, at Multi-center study including Outdoor Departments ofMedicine from four different hospitals of Lahore, Pakistan. Patients age range 18-50 years diagnosis with hyperuricemia and gout, SUA >8 mg/dlwere included while severe renal impairment and alanine aminotransferase and aspartate aminotransferase patients were excluded from the study.Results: About 200 patients treated with hyperuricemic with gout were randomly divided into four groups (50%) patients were in each groupreceived different treatment. Out of 200 patients, 118 (59%) were male and 82 (41%) were female with mean age 42.37±9.47 years. Among theFebuxostat group, patients' success rate of lowering SUA level was found to be 32 (64%) as compared to Allopurinol 16 (32%). Drug compliance wassimilar among treatment groups, i.e. Allopurinol and Febuxostat while the trend toward drug compliance in Allopurinol + Vitamin C and Febuxostat +Vitamin C groups showed similar in number.Conclusion: Febuxostat is safe and effective to Allopurinol for the treatment of hyperuricemia with gout as the Febuxostat has a significant associationwith lowering SUA concentration <6 mg/dl. It is concluded that although Febuxostat is safe and effect alone in gouty patients, but it has somehow alittle effect with Vitamin C especially in patients who are feeble.Keywords: Febuxostat, Allopurinol, Serum uric acid.Â

A parallel implementation of sequential minimal optimization on FPGA

This paper proposes a parallel FPGA implementation of the training phase of a Support Vector Machine (SVM). The training phase of the SVM is implemented using Sequential Minimal Optimization (SMO), which enables the resolution of a complex convex optimization problem using simple steps. The SMO implementation is also highly parallel and uses some acceleration techniques, such as the error cache. Moreover, the Hardware Friendly Kernel (HFK) is used in order to reduce the kernel’s area, enabling an increase in the number of kernels per area. After the parallel implementation in hardware, the SVM is validated by bit-accurate simulation. Finally, analysis associated with the temporal performance of the proposed structure, as well as analysis associated with FPGAs area usage is performed

An ultra-compact particle size analyser using a CMOS image sensor and machine learning

Light scattering is a fundamental property that can be exploited to create essential devices such as particle analysers. The most common particle size analyser relies on measuring the angle-dependent diffracted light from a sample illuminated by a laser beam. Compared to other non-light-based counterparts, such a laser diffraction scheme offers precision, but it does so at the expense of size, complexity and cost. In this paper, we introduce the concept of a new particle size analyser in a collimated beam configuration using a consumer electronic camera and machine learning. The key novelty is a small form factor angular spatial filter that allows for the collection of light scattered by the particles up to predefined discrete angles. The filter is combined with a light-emitting diode and a complementary metal-oxide-semiconductor image sensor array to acquire angularly resolved scattering images. From these images, a machine learning model predicts the volume median diameter of the particles. To validate the proposed device, glass beads with diameters ranging from 13 to 125 µm were measured in suspension at several concentrations. We were able to correct for multiple scattering effects and predict the particle size with mean absolute percentage errors of 5.09% and 2.5% for the cases without and with concentration as an input parameter, respectively. When only spherical particles were analysed, the former error was significantly reduced (0.72%). Given that it is compact (on the order of ten cm) and built with low-cost consumer electronics, the newly designed particle size analyser has significant potential for use outside a standard laboratory, for example, in online and in-line industrial process monitoring

Modeling of groundwater potential using cloud computing platform: A case study from nineveh plain, Northern Iraq

Knowledge of the groundwater potential, especially in an arid region, can play a major role in planning the sustainable management of groundwater resources. In this study, nine machine learning (ML) algorithms—namely, Artificial Neural Network (ANN), Decision Jungle (DJ), Aver-aged Perceptron (AP), Bayes Point Machine (BPM), Decision Forest (DF), Locally-Deep Support Vector Machine (LD-SVM), Boosted Decision Tree (BDT), Logistic Regression (LG), and Support Vector Machine (SVM)—were run on the Microsoft Azure cloud computing platform to model the groundwater potential. We investigated the relationship between 512 operating boreholes with a specified specific capacity and 14 groundwater-influencing occurrence factors. The unconfined aquifer in the Nineveh plain, Mosul Governorate, northern Iraq, was used as a case study. The groundwater-influencing factors used included elevation, slope, curvature, topographic wetness index, stream power index, soil, land use/land cover (LULC), geology, drainage density, aquifer saturated thickness, aquifer hydraulic conductivity, aquifer specific yield, depth to groundwater, distance to faults, and fault density. Analysis of the contribution of these factors in groundwater potential using information gain ratio indicated that aquifer saturated thickness, rainfall, hydraulic conductivity, depth to groundwater, specific yield, and elevation were the most important factors (average merit > 0.1), followed by geology, fault density, drainage density, soil, LULC, and distance to faults (average merit < 0.1). The average merits for the remaining factors were zero, and thus, these factors were removed from the analysis. When the selected ML classifiers were used to esti-mate groundwater potential in the Azure cloud computing environment, the DJ and BDT models performed the best in terms of all statistical error measures used (accuracy, precision, recall, F-score, and area under the receiver operating characteristics curve), followed by DF and LD-SVM. The probability of groundwater potential from these algorithms was mapped and visualized into five groundwater potential zones: very low, low, moderate, high, and very high, which correspond to the northern (very low to low), southern (moderate), and middle (high to very high) portions of the study area. Using a cloud computing service provides an improved platform for quickly and cheaply running and testing different algorithms for predicting groundwater potential

Analysis of associated risk factors among recurrent cutaneous leishmaniasis patients: A cross-sectional study in Khyber Pakhtunkhwa, Pakistan

Background Leishmaniasis is the second and fourth highest cause of mortality and morbidity respectively among all tropical diseases. Recurrence in the onset of leishmaniasis is a major problem that needs to be addressed to reduce the case fatality rate and ensure timely clinical intervention. Here we are investigating the association of risk factors with recurrent cutaneous leishmaniasis to address this issue. Material and methods Patients received by Nasser Ullah Khan Babar Hospital in Peshawar, Pakistan from March 2019 to July 2020 were enrolled in this study. Those patients who developed symptoms after completion of treatment were included in Group-A while those who had atypical scars like leishmaniasis but were negative for cutaneous leishmaniasis were included in the comparison group tagged as Group B. All those individuals who had completed six weeks of treatment for CL but had normal complete blood counts (CBC) were included to avoid other underlying immunological pathologies, while we excluded those participants who had co-morbidities like diabetes, liver disease, cardiac disease, and pregnant and lactating women through their history Association was tested between Group-A and Group-B with other explanatory variables through chi-square test. The regression model was proposed to determine the predictors. Result A total of 48 participants of both sexes were included in the study with a mean age of 32.2 ± 15.10. The data suggest that females are overrepresented among the patients with recurrent leishmaniasis [21(53.8 %,); p = 0.07]. Compared to patients; healthy participants had a higher proportion of adults (19–59 years) versus adolescents (13–18 years) [26(66.7 %) vs 07(17.9), p = 0.004]. Multivariate logistic regression analysis shows that females are 2.1 times more prone to infections among cases as compared to healthy individuals [unadjusted OR 2.20, 95 % confidence interval (CI) 1.5–10.6, p = 0.02; adjusted OR 2.1, 95 % CI 1.50–10.69, p = 0.02]. We propose that patients receiving intradermal were less likely to be infected as compared to those receiving intralesional injections [unadjusted OR 0.07.0, 95 % confidence interval (CI) 1.18–3.37, p = 0.03; adjusted OR 0.06, 95 % CI 1.18–3.38, p = 0.03]. Conclusion Old age (adults) and sex (females) were the strongest predictors to be associated with recurrent leishmaniasis. Similarly, the choice of intradermal as compared to intralesional injection and the prolonged treatment duration were strongly associated with greater chances of recurrence

Neurology and neuropsychiatry of COVID-19: a systematic review and meta-analysis of the early literature reveals frequent CNS manifestations and key emerging narratives

There is accumulating evidence of the neurological and neuropsychiatric features of infection with SARS-CoV-2. In this systematic review and meta-analysis, we aimed to describe the characteristics of the early literature and estimate point prevalences for neurological and neuropsychiatric manifestations.We searched MEDLINE, Embase, PsycINFO and CINAHL up to 18 July 2020 for randomised controlled trials, cohort studies, case-control studies, cross-sectional studies and case series. Studies reporting prevalences of neurological or neuropsychiatric symptoms were synthesised into meta-analyses to estimate pooled prevalence.13 292 records were screened by at least two authors to identify 215 included studies, of which there were 37 cohort studies, 15 case-control studies, 80 cross-sectional studies and 83 case series from 30 countries. 147 studies were included in the meta-analysis. The symptoms with the highest prevalence were anosmia (43.1% (95% CI 35.2% to 51.3%), n=15 975, 63 studies), weakness (40.0% (95% CI 27.9% to 53.5%), n=221, 3 studies), fatigue (37.8% (95% CI 31.6% to 44.4%), n=21 101, 67 studies), dysgeusia (37.2% (95% CI 29.8% to 45.3%), n=13 686, 52 studies), myalgia (25.1% (95% CI 19.8% to 31.3%), n=66 268, 76 studies), depression (23.0% (95% CI 11.8% to 40.2%), n=43 128, 10 studies), headache (20.7% (95% CI 16.1% to 26.1%), n=64 613, 84 studies), anxiety (15.9% (5.6% to 37.7%), n=42 566, 9 studies) and altered mental status (8.2% (95% CI 4.4% to 14.8%), n=49 326, 19 studies). Heterogeneity for most clinical manifestations was high.Neurological and neuropsychiatric symptoms of COVID-19 in the pandemic's early phase are varied and common. The neurological and psychiatric academic communities should develop systems to facilitate high-quality methodologies, including more rapid examination of the longitudinal course of neuropsychiatric complications of newly emerging diseases and their relationship to neuroimaging and inflammatory biomarkers

Vesicular Stomatitis Virus Enters Cells through Vesicles Incompletely Coated with Clathrin That Depend upon Actin for Internalization

Many viruses that enter cells by clathrin-dependent endocytosis are significantly larger than the dimensions of a typical clathrin-coated vesicle. The mechanisms by which viruses co-opt the clathrin machinery for efficient internalization remain uncertain. Here we examined how clathrin-coated vesicles accommodate vesicular stomatitis virus (VSV) during its entry into cells. Using high-resolution imaging of the internalization of single viral particles into cells expressing fluorescent clathrin and adaptor molecules, we show that VSV enters cells through partially clathrin-coated vesicles. We found that on average, virus-containing vesicles contain more clathrin and clathrin adaptor molecules than conventional vesicles, but this increase is insufficient to permit full coating of the vesicle. We further show that virus-containing vesicles depend upon the actin machinery for their internalization. Specifically, we found that components of the actin machinery are recruited to virus-containing vesicles, and chemical inhibition of actin polymerization trapped viral particles in vesicles at the plasma membrane. By analysis of multiple independent virus internalization events, we show that VSV induces the nucleation of clathrin for its uptake, rather than depending upon random capture by formation of a clathrin-coated pit. This work provides new mechanistic insights into the process of virus internalization as well as uptake of unconventional cargo by the clathrin-dependent endocytic machinery

Prediction of Preterm Deliveries from EHG Signals Using Machine Learning

There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. In extreme cases, this can also lead to long-term conditions, such as cerebral palsy, mental retardation, learning difficulties, including poor health and growth. In the US alone, the societal and economic cost of preterm births, in 2005, was estimated to be $26.2 billion, per annum. In the UK, this value was close to £2.95 billion, in 2009. Many believe that a better understanding of why preterm births occur, and a strategic focus on prevention, will help to improve the health of children and reduce healthcare costs. At present, most methods of preterm birth prediction are subjective. However, a strong body of evidence suggests the analysis of uterine electrical signals (Electrohysterography), could provide a viable way of diagnosing true labour and predict preterm deliveries. Most Electrohysterography studies focus on true labour detection during the final seven days, before labour. The challenge is to utilise Electrohysterography techniques to predict preterm delivery earlier in the pregnancy. This paper explores this idea further and presents a supervised machine learning approach that classifies term and preterm records, using an open source dataset containing 300 records (38 preterm and 262 term). The synthetic minority oversampling technique is used to oversample the minority preterm class, and cross validation techniques, are used to evaluate the dataset against other similar studies. Our approach shows an improvement on existing studies with 96% sensitivity, 90% specificity, and a 95% area under the curve value with 8% global error using the polynomial classifier