94 research outputs found

    Influence of different types of soils on the growth and yield of Quinoa (Chenopodium quinoa Wild.)

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    Quinoa is a resilient high-yielding pseudo cereal, gaining attention because of its high nutritional value, strong growth potential, and tremendous source of essential amino acids, micronutrients, vitamins, phenolic compounds, and minerals. The main aim of this investigation was to find the best suitable soil type for maximizing the growth and yield of Quinoa. The pot study was undertaken at the Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore, during the Kharif 2022 season. Eight soil samples (clay loam soils of wetlands of TNAU, sandy loam soils of eastern block of TNAU, sandy loam soils of Mettupalayam, sandy clay loam soils of 36 B eastern block of TNAU, sandy clay loam soils of 37 B eastern block of TNAU, clay loam soils of Ooty, sandy clay loam soils of Govindanaickenpalayam and sandy clay loam soils of Annur) were collected round Coimbatore in Tamil Nadu and tested in a complete randomized design with three replications. The pot study results revealed that growth parameters viz. Plant height (81.5 cm), number of leaves plant-1 (164.8), leaf area (317.7 cm2), number of branches plant-1 (38.0) and dry matter production (22.78 g) were significantly higher in the clay loam soils of Ooty than all other soil types. Similarly, yield attributes such as the number of panicles plant-1 (21.7), panicle length (13.08 cm), number of grains panicle-1 (3050) and grain yield plant-1 (9.60 g) of Quinoa were also higher in the same clay loam soils followed by that in sandy clay loam soils of Govindanaickenpalayam. Red soils of Mettupalayam had shown the lowest growth, yield and yield attributes of Quinoa. Based on the above results, it was concluded that the clay loam soil of Ooty was the best suited for cultivating Quinoa crops

    Chloroform Extract of Rasagenthi Mezhugu, a Siddha Formulation, as an Evidence-Based Complementary and Alternative Medicine for HPV-Positive Cervical Cancers

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    Rasagenthi Mezhugu (RGM) is a herbomineral formulation in the Siddha system of traditional medicine and is prescribed in the southern parts of India as a remedy for all kinds of cancers. However, scientific evidence for its therapeutic efficacy in cervical cancer is lacking, and it contains heavy metals. To overcome these limitations, RGM was extracted, and the fractions were tested on HPV-positive cervical cancer cells, ME-180 and SiHa. The extracts, free from the toxic heavy metals, affected the viability of both the cells. The chloroform fraction (cRGM) induced DNA damage and apoptosis. Mitochondria-mediated apoptosis was indicated. Though both the cells responded to the treatment, ME-180 was more responsive. Thus, this study brings up scientific evidence for the efficacy of RGM against the HPV-mediated cervical cancer cells and, if the toxic heavy metals are the limitation in its use, cRGM would be a suitable candidate as evidence-based complementary and alternative medicine for HPV-positive cervical cancers

    Exotic snakebites reported to Pennsylvania poison control centers: lessons learned on the demographics, clinical effects, and treatment of these cases

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    Exotic snakebites are a rare occurrence but they present a unique challenge to clinicians treating these patients. Poison control centers are often contacted to assist in the management and care of these medical emergencies. In this study, we analyzed case records of the two Pennsylvania poison control centers from 2004-2018 to describe clinical features reported as a result of exotic snakebite envenomation. For the 15-year period reviewed, 18 exotic snakebites were reported with effects ranging from mild local tissue injury to patients who were treated with mechanical ventilation due to respiratory failure. The mean age of the patients was 35 years-old and males accounted for 88% of the cases. Antivenom, the only specific treatment, was administered in seven of 18 patients within an average of four hours of envenomation. The procurement of antivenom against these exotic specific may require substantial logistical efforts due to limited stocking of this rarely used treatment. Newer, targeted, small molecule treatments that are being currently investigated may aid in the treatment of snakebites in general. However, people should be cautious when handling these exotic species and clinicians should be aware of these bites and relevant clinical effects in order to manage these when reported

    Red Fox Optimizer with Data-Science-Enabled Microarray Gene Expression Classification Model

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    Microarray data examination is a relatively new technology that intends to determine the proper treatment for various diseases and a precise medical diagnosis by analyzing a massive number of genes in various experimental conditions. The conventional data classification techniques suffer from overfitting and the high dimensionality of gene expression data. Therefore, the feature (gene) selection approach plays a vital role in handling a high dimensionality of data. Data science concepts can be widely employed in several data classification problems, and they identify different class labels. In this aspect, we developed a novel red fox optimizer with deep-learning-enabled microarray gene expression classification (RFODL-MGEC) model. The presented RFODL-MGEC model aims to improve classification performance by selecting appropriate features. The RFODL-MGEC model uses a novel red fox optimizer (RFO)-based feature selection approach for deriving an optimal subset of features. Moreover, the RFODL-MGEC model involves a bidirectional cascaded deep neural network (BCDNN) for data classification. The parameters involved in the BCDNN technique were tuned using the chaos game optimization (CGO) algorithm. Comprehensive experiments on benchmark datasets indicated that the RFODL-MGEC model accomplished superior results for subtype classifications. Therefore, the RFODL-MGEC model was found to be effective for the identification of various classes for high-dimensional and small-scale microarray data
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