Fluorescence and docking studies on the binding of copper and cobalt complexes to DNA and RNA

<p>Nucleic acid are involved in fundamental biological functions as propagation of the genetic material, storage of genetic information and enzymatic activity. A high-throughput screen is necessary to identify compounds that bind to the target with high affinity, for developing new drugs that target with high affinity, for developing new drugs that target the DNA/RNA. Hydroxamic acids, the drug like molecules show both HBD and HBA capability.</p

N-Arylhydroxamic acids as a drug like molecule: A motif of binding mode with calf thymus DNA

215-221A drug-like molecule, which has a propensity of binding with DNA play a vital role in drug designing mechanism. In this paper, we tried to find out the DNA binding affinity of two derivatives of N-arylhydroxamic acids: (i) N-p-Chlorophenyl-2-methoxybenzohydroxamic Acid(Cl-2-MBHA) and (ii) N-p-Chlorophenyl-3-methoxybenzohydroxamic Acid (Cl-3-MBHA) with calf thymus DNA (ct-DNA) by applying techniques such as UV-visible spectroscopy, Fluorescence spectroscopy, and Viscometry measurements. The findings concluded with experimental techniques were verified with theoretical calculation using computer-based method, Molecular Docking. Absorption spectra revealed that both the hydroxamic acids derivatives bind to ct-DNA, among two, Cl-2-MBHA exhibits the higher value of binding affinity Kb (9.52 × 103±0.08 M-1). Fluorescence spectra showed that ct-DNA successfully quenches the emission spectra of N-arylhydroxamic acid. Ethidium bromide displacement method was used as a standard for analyzing the mode of binding. Both the hydroxamic acids were found to be groove binders. The Stern–Volmer Constant was found to be 2.05 × 10-2 ± 0.001 M-1 and 3.35 × 10-2 ± 0.002 M-1 for Cl-2-MBHA and Cl-3-MBHA respectively. Theoretical analysis molecular docking was done using Hex software for validating the experimental findings. Hence, it was observed that both experimental and computational method complimented the results and deduces groove binding as the mode of interaction

Climatic and Fruit Productivity Trends in Solan District, Himachal Pradesh, India

The shortened duration of the winter season in the Himalayan region caused by snow melting has a negative impact on fruit crop growth and productivity. The present study focused on examining the impact of climate change on fruit crops in the Solan district of Himachal Pradesh, India, situated in the Himalayan region. The trend analysis of climatic variables (temperature and rainfall) along with the productivity of fruit crops was investigated. The climate data spanning 30 years (1990–2019), including average temperature (maximum, minimum, and diurnal) and annual rainfall used during crop development stages like pre-flowering, flowering, and fruit-setting stages. To evaluate climatic trends, the Standardized Anomaly Index (SAI) and Mann-Kendall Test for quantification were employed. The Multivariate Linear Regression Analysis was performed to establish a correlation between climatic variables and crop productivity. The findings indicated that during the pre-flowering stage, there was a gradual increase in average maximum temperature at a rate of 0.001°C per year, along with a corresponding rise in diurnal temperature at a rate of 0.036°C per year. However, annual rainfall and average minimum temperature exhibited non-significant decreasing trends, with rates of -0.044°C and -0.033 mm, respectively. During the flowering stage, there was a significant increase in minimum temperature at a rate of 0.151°C per year, while diurnal temperature exhibited a significant decrease of -0.158°C per year. Other variables did not exhibit substantial changes during this stage. In the fruit-setting stage, only the minimum temperature demonstrated a significant decrease over the study period. The response to climate change revealed an overall positive trend for all fruit crops, leading to higher productivity. The correlation study indicated that the phenological stages of each crop were more positively influenced by temperature than rainfall, owing to existing climatic variations. The current climatic conditions in the Solan district were found to be favorable and productive for crop development, as all crops showed increased productivity based on the trend analysis. The study highlights climatic trends and their impact on the productivity of fruit crops in the Himalayan region, which is useful for agricultural planning and adaptation strategies in response to changing climatic conditions

Digital wearable insole-based identification of knee arthropathies and gait signatures using machine learning

Gait is impaired in musculoskeletal conditions, such as knee arthropathy. Gait analysis is used in clinical practice to inform diagnosis and monitor disease progression or intervention response. However, clinical gait analysis relies on subjective visual observation of walking as objective gait analysis has not been possible within clinical settings due to the expensive equipment, large-scale facilities, and highly trained staff required. Relatively low-cost wearable digital insoles may offer a solution to these challenges. In this work, we demonstrate how a digital insole measuring osteoarthritis-specific gait signatures yields similar results to the clinical gait-lab standard. To achieve this, we constructed a machine learning model, trained on force plate data collected in participants with knee arthropathy and controls. This model was highly predictive of force plate data from a validation set (area under the receiver operating characteristics curve [auROC] = 0.86; area under the precision-recall curve [auPR] = 0.90) and of a separate, independent digital insole dataset containing control and knee osteoarthritis subjects (auROC = 0.83; auPR = 0.86). After showing that digital insole-derived gait characteristics are comparable to traditional gait measurements, we next showed that a single stride of raw sensor time-series data could be accurately assigned to each subject, highlighting that individuals using digital insoles can be identified by their gait characteristics. This work provides a framework for a promising alternative to traditional clinical gait analysis methods, adds to the growing body of knowledge regarding wearable technology analytical pipelines, and supports clinical development of at-home gait assessments, with the potential to improve the ease, frequency, and depth of patient monitoring

Supercritical fluid extraction of vegetable matrices: Applications, trends and future perspectives of a convincing green technology

Along more than a decade, R\&D on supercritical fluid extraction (SFE) of vegetable matrices has been increasingly reported in the literature. Aiming at portraying the current state of this field and its evolution in terms of raw materials, products, modes of operation, optimization, modeling techniques, and closeness to industrial application, a large compilation of almost 600 essays from 2000 to 2013 has been deeply analyzed in order to unveil those indicators and their trends. Furthermore, strengths and weaknesses are identified, and some remarks that may drive upcoming research are provided. Globally, more than 300 species are reported in the literature, with prevalence of the extraction of seeds (28% of works) and leaves (17%). The main families of extracted compounds, cosolvents and operating conditions adopted are critically examined, being possible to conclude that researchers investigate many times working regions far from the optimum due to practical limitations or absence of experimental optimization. Current phenomenological, statistical and semi-empirical approaches are reviewed, along with scale-up studies, and economic analysis. In the whole, the most comprehensive picture over SFE of vegetable matrices is provided in this review, highlighting pertinent aspects and opportunities that may further consolidate the convincing route of this technology for the next years. (C) 2014 Elsevier B.V. All rights reserved