Association of facet orientation and tropism with lumbar disc herniation

Background: Intervertebral disc herniation is a degenerative lumbar disease and a common pathology of skeletal system. Currently, most experts assume that facet tropism may affect lumbar degenerative diseases. Considering the previous inconsistent findings on the relationship of facet tropism, the present study was aimed to find the association between facet tropism and lumbar disc herniation.Methods: Patients with low back pain attending the OPD of orthopaedics department, with signs and symptoms of disc herniation were sent for magnetic resonance imaging (MRI). 72 patients with single level disc herniation were included in the study. Facet angles were measured using MRI of 1.5 T using the method described by Karacan et al. Facet tropism was defined as difference of 100 or more in facet joint angles between right and left sides.Results: 45 of the 72 cases (50%) who presented with lumbar disc herniation (LDH) had tropism while none (0%) at the control level did. This association was not statistically significant (p=0.983). Significant association was found between the side of disc herniation and the distribution of the more coronal or sagittal facing facet (p=0.024).Conclusions: Despite the presence of tropism only in the intervertebral segments affected with LDH in our study, the association between tropism and LDH was not statistically significant

Escitalopram reduces severity of depression and improves quality of life in patients with chronic obstructive pulmonary disease in an open label parallel group study

Background: Chronic obstructive pulmonary disease (COPD) is characterised by progressive and persistent airflow limitation with frequent exacerbations and hospitalizations contributing to overall morbidity and mortality. We evaluated the efficacy of antidepressant escitalopram therapy in depressed individuals suffering from chronic obstructive pulmonary disease.Methods: The sample comprised of sixty patients. Thirty patients received antidepressant escitalopram, while the remaining half served as controls. Hamilton depression rating and WHO–BREF questionnaire were the tools to assess severity of depression and quality of life. The severity of COPD was recorded using spirometry (FEV1%). Clinical assessments were at baseline and at week-2 and week-8 of follow up in both intervention and control groups.Results: It was found that both the groups were similar on the severity of the illness (COPD) at entry. FEV 1% of both the groups showed similar improvement after 8 weeks. Treatment with escitalopram showed a significant decrease in the severity of depression score and improvement on all the domains of quality of life when compared with the baseline. After the intervention, it was found that the HAM-D scores in the experimental group decreased from 24.44 to 17.50 while in the control group, it was from 20.11 to 19.67.  The magnitude of improvement was significantly higher with intervention compared to controls.Conclusions: Escitalopram reduces severity of depression and improves quality of life which was independent of improvement in FEV1. It could be asserted that this antidepressant improved the patient’s mood, fatigue and helplessness, which could have improved the quality of life of these patients

Three Dimensional Casson nanofluid Flow with Convective Boundary Layer via Stretching Sheet

The present work examined Casson nanofluid in a three-dimensional boundary layer motion via stretching sheet. The study focuses on analyzing the behavior of a Casson nanofluid, which is one type of non-Newtonian fluid. The study appears to involve solving partial differential equations related to fluid flow, heat transfer, and mass transfer. These PDEs are transformed into ordinary differential equations using standard similarity variables. To solve the ODEs, the researchers employ the Runge-Kutta-Fehlberg (R-K-F) 4th order iterative scheme. It appears that higher values of the Biot number can significantly affect the temperature and concentration profiles in the Casson liquid flow

Stock price prognosticator using machine learning techniques.

Stock market price prediction is one of the favourite research topics under consideration for professionals from various fields like mathematics, statistics, history, finance, computer science engineering etc., as it requires a set of skills to predict variation of price of shares in a very volatile and challenging share market scenario. Share market trading is mostly dependent on sentiments of investors and other factors like economic policies, political changes, natural disasters etc., Many theories were forwarded, mathematical and statistical applications in conjunction with probability, to simplify the complex process. After the advent of computers, it got further simplified but still challenging due to various external influential factors ruling the volatility of the market prices. Thus, AI and ML algorithms were being developed, but for only for next day using Linear Regression procedures.Our project aims to predict the prices of shares more precisely and accurately using special algorithms using RNN by improvising the back propagation, feedback routines to overcome the short-term memory loss involved in RNN thus providing efficiency in LSTM applications.Our project emphasizes how the LSTM applications perform with datasets of extreme, larger and minimal fluctuating data

A Survey on Sugarcane Leaf Disease Identification Using Deep Learning Technique(CNN)

The management of plant diseases is vital for the economical production of food and poses important challenges to the employment of soil, water, fuel and alternative inputs for agricultural functions. In each natural and cultivated populations, plants have inherent sickness tolerance, however there also are reports of devastating impacts of plant diseases. The management of diseases, however, within reason effective for many crops. sickness management is allotted through the employment of plants that square measure bred permanently resistance to several diseases and thru approaches to plant cultivation, like crop rotation, the employment of pathogen-free seeds, the given planting date and plant density, field wetness management, and therefore the use of pesticides. so as to enhance sickness management and to stay up with changes within the impact of diseases iatrogenic by the continued evolution and movement of plant pathogens and by changes in agricultural practices, continued progress within the science of soil science is required. Plant diseases cause tremendous economic losses for farmers globally. it's calculable that in additional developed settings across massive regions and lots of crop species, diseases usually cut back plant yields by ten percent per annum, however yield loss for diseases usually exceeds twenty percent in less developed settings. Around twenty-five percent of crop losses square measure caused by pests and diseases, the Food and Agriculture Organization estimates. to unravel this, new strategies for early detection of diseases and pests square measure required, like novel sensors that sight plant odours and spectrographic analysis and bio photonics that may diagnose plant health and metabolism. In artificial neural networks, deep learning is an element of a broader family of machine learning approaches supported realistic learning. Learning is often controlled, semi-supervised or unmonitored. to handle several real-world queries, Deep Learning Approaches are normally used. so as to differentiate pictures and acknowledge their options, coevolutionary neural networks have had a larger result. This article will do a Leaf Disease Identification Survey with Deep Learning Methods. It takes Sugarcane leaf as an instance to our paper

Morphological and biochemical factors associated with resistance to Maruca vitrata (Geyer) (Lepidoptera: Pyralidae) in short duration pigeonpea

The spotted pod borer Maruca vitrata (Geyer) is known for its economic importance throughout its geographical distribution because of its destructive nature to reproductive parts of several grain legume crops including pigeonpea. In view of the importance of the pest, the present study was carried out on the association of different morpho-chemical traits with resistance/susceptibility to M. vitrata at the International Crops Research Institute for the Semi-Arid Tropics, Patancheru, India. Trichome length and density, sugars, proteins and phenols were found to be associated with resistance to M. vitrata in short-duration pigeonpea genotypes. Pod damage by M. vitrata on different short-duration pigeonpea genotypes in the field ranged from 5.8 to 68%. Laboratory studies showed less consumption of food and reduced larval and pupal weights of M. vitrata when reared on the resistant genotypes ICPL 98003 and ICPL 98008 indicating antibiosis effects of the genotypes. Trichome density on upper and lower surfaces of the leaf (390 and 452/9 mm2), and length (3.5 mm) and trichome density (442/9 mm2) and length (5.9 mm) on pods were found positively correlated with the resistant genotype ICPL 98003. High sugar content in flowers (22%) and pods (10.6%) was responsible for the susceptibility of ICPL 88034, while high phenol concentration in flowers (6.5%) and pods (9.3%) in ICPL 98003 was responsible for resistance. Protein content in pods was significantly higher (25.5%) in susceptible ICPL 88034 when compared with resistant ICPL 98003 (16.5%). Based on these results, ICPL 98003 and ICPL 98008 were categorized as highly resistant and ICPL 98012 as moderately resistant. This paper discusses the physico-chemical traits associated with resistance to M. vitrata in short-duration pigeonpea genotypes

Intestine-Specific, Oral Delivery of Captopril/Montmorillonite: Formulation and Release Kinetics

The intercalation of captopril (CP) into the interlayers of montmorillonite (MMT) affords an intestine-selective drug delivery system that has a captopril-loading capacity of up to ca. 14 %w/w and which exhibits near-zero-order release kinetics

Common variants in CLDN2 and MORC4 genes confer disease susceptibility in patients with chronic pancreatitis

A recent Genome-wide Association Study (GWAS) identified association with variants in X-linked CLDN2 and MORC4 and PRSS1-PRSS2 loci with Chronic Pancreatitis (CP) in North American patients of European ancestry. We selected 9 variants from the reported GWAS and replicated the association with CP in Indian patients by genotyping 1807 unrelated Indians of Indo-European ethnicity, including 519 patients with CP and 1288 controls. The etiology of CP was idiopathic in 83.62% and alcoholic in 16.38% of 519 patients. Our study confirmed a significant association of 2 variants in CLDN2 gene (rs4409525—OR 1.71, P = 1.38 x 10-09; rs12008279—OR 1.56, P = 1.53 x 10-04) and 2 variants in MORC4 gene (rs12688220—OR 1.72, P = 9.20 x 10-09; rs6622126—OR 1.75, P = 4.04x10-05) in Indian patients with CP. We also found significant association at PRSS1-PRSS2 locus (OR 0.60; P = 9.92 x 10-06) and SAMD12-TNFRSF11B (OR 0.49, 95% CI [0.31–0.78], P = 0.0027). A variant in the gene MORC4 (rs12688220) showed significant interaction with alcohol (OR for homozygous and heterozygous risk allele -14.62 and 1.51 respectively, P = 0.0068) suggesting gene-environment interaction. A combined analysis of the genes CLDN2 and MORC4 based on an effective risk allele score revealed a higher percentage of individuals homozygous for the risk allele in CP cases with 5.09 fold enhanced risk in individuals with 7 or more effective risk alleles compared with individuals with 3 or less risk alleles (P = 1.88 x 10-14). Genetic variants in CLDN2 and MORC4 genes were associated with CP in Indian patients