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Expressional correlations between SERPINA6 and pancreatic ductal adenocarcinoma-linked genes

Selver et al. | Oct 06, 2021

Expressional correlations between <em>SERPINA6</em> and pancreatic ductal adenocarcinoma-linked genes

Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, with early diagnosis and treatment challenges. When any of the genes KRAS, SMAD4, TP53, and BRCA2 are heavily mutated, they correlate with PDAC progression. Cellular stress, partly regulated by the gene SERPINA6, also correlates with PDAC progression. When SERPINA6 is highly expressed, corticosteroid-binding globulin inhibits the effect of the stress hormone cortisol. In this study, the authors explored whether there is an inverse correlation between the expression of SERPINA6 and PDAC-linked genes.

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A new therapy against MDR bacteria by in silico virtual screening of Pseudomonas aeruginosa LpxC inhibitors

Liu et al. | Apr 27, 2022

A new therapy against MDR bacteria by <em>in silico</em> virtual screening of <em>Pseudomonas aeruginosa</em> LpxC inhibitors

Here, seeking to address the growing threat of multidrug-resistant bacteria (MDR). the authors used in silico virtual screening to target MDR Pseudomonas aeruginosa. They considered a key protein in its biosynthesis and virtually screened 20,000 candidates and 30 derivatives of brequinar. In the end, they identified a possible candidate with the highest degree of potential to inhibit the pathogen's lipid A synthesis.

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Population Forecasting by Population Growth Models based on MATLAB Simulation

Li et al. | Aug 31, 2020

Population Forecasting by Population Growth Models based on MATLAB Simulation

In this work, the authors investigate the accuracy with which two different population growth models can predict population growth over time. They apply the Malthusian law or Logistic law to US population from 1951 until 2019. To assess how closely the growth model fits actual population data, a least-squared curve fit was applied and revealed that the Logistic law of population growth resulted in smaller sum of squared residuals. These findings are important for ensuring optimal population growth models are implemented to data as population forecasting affects a country's economic and social structure.

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Can essential oils be allelopathic to Lolium multiforum without harming Solanum lycopersicum?

Madan et al. | Nov 13, 2021

 Can essential oils be allelopathic to <em>Lolium multiforum</em> without harming <em>Solanum lycopersicum</em>?

Seeking to investigate eco-friendly biological methods to control weeds and enhance food crop yields, here the authors considered the effects of three essential oils on seed germination and radicle length of both a weed and a common crop. They found that treatment with turmeric oil had phytotoxic potential, leading to a reduction in both seed germination and radicle length of the weed. In contrast, ginger oil possessed allelopathic properties towards both. The authors suggest that essential oils could be used as eco-friendly bio-herbicides.

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Phytochemical Analysis of Amaranthus spinosus Linn.: An in vitro Analysis

Sharma et al. | Mar 20, 2021

Phytochemical Analysis of <em>Amaranthus spinosus</em> Linn.: An <em>in vitro</em> Analysis

Mainstream cancer treatments, which include radiotherapy and chemotherapeutic drugs, are known to induce oxidative damage to healthy somatic cells due to the liberation of harmful free radicals. In order to avert this, physiological antioxidants must be complemented with external antioxidants. Here the authors performed a preliminary phytochemical screen to identify alkaloids, saponins, flavonoids, polyphenols, and tannins in all parts of the Amaranthus spinosus Linn. plant. This paper describes the preparation of this crude extract and assesses its antioxidant properties for potential use in complementary cancer treatment.

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A Quantitative Assessment of Time, Frequency, and Time-frequency Algorithms for Automated Seizure Detection and Monitoring

Vangal et al. | Sep 28, 2020

A Quantitative Assessment of Time, Frequency, and Time-frequency Algorithms for Automated Seizure Detection and Monitoring

Each year, over 100,000 patients die from Sudden Unexpected Death in Epilepsy (SUDEP). A reliable seizure warning system can help patients stay safe. This work presents a comprehensive, comparative analysis of three different signal processing algorithms for automated seizure/ictal detection. The experimental results show that the proposed methods can be effective for accurate automated seizure detection and monitoring in clinical care.

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