Strigolactones (SLs) are a class of plant hormones β-carotenoid-derived that are synthesized in the roots of plants. They govern plant growth, development, and interactions with the environment. The first SL was isolated in 1966. Their various functions make them viable agricultural targets, and understanding these molecules may assist with creating strategies to increase crop yields and sustainability. In recent years, there has been considerable interest in the potential applications of SLs in biomedicine, especially in cancer therapy, diabetes or inflammation. These complex roles are related to a significant structural diversity. So far, all biomedical literature data has been dedicated to the synthetic analogs of these phytohormones. The bioinformatic approach supports a better understanding of the complexity of SLs, leading to a better design of bioactive compounds. This study provides an efficient in silico approach to evaluate the pharmacokinetic, pharmacodynamic, and drug-like characteristics of both natural and synthetic SLs, delivering significant insight into their biomedical potential.
Cervical cancer remains a significant global health challenge, particularly in developing countries like India, where early detection plays a crucial role in reducing mortality rates. This study aims to investigate the utility of machine learning models in the early-stage detection of cervical cancer using demographic, clinical, and image data. We used different existing machine learning algorithms to get correlations between risk factors such as age, smoking status, HPV infection, contraceptive use, and the number of sexual partners with the likelihood of cervical cancer development. Our findings state that the potential of machine learning-based models in improving early detection is highly significant. The proposed approach recommends a promising avenue for integrating machine learning into clinical practice to enhance cervical cancer screening and improve patient outcomes.
Green synthesis of silver nanoparticles using cloves (Sygzium aromaticum), cinnamon bark (Cinnamomum cassia) and tulsi (Ocimum teniflorum) leaves have been comparatively studied by different characterization tools, both in aqueous and powdered form. The uniformity in the concentration and physiochemical conditions has been maintained in all the three cases. The clove-based synthesized silver nanoparticles show the best outcomes in terms of stability, morphology, etc., whereas the cinnamon-based synthesized nanoparticles show the least standard of expected outcomes. It is here also noted that the synthesis has been carried out at normal pH, room temperature, and using very easily available instruments and apparatus. There has been intended to synthesize qualitative silver nanoparticles at ground level, using Indian plant extracts. Thus, the merit of this work is the process has been confined to very simple and basic requirements. The anti microbial applications have been analyzed and their efficiencies have been explained at both gram-negative and gram-positive types of bacteria.
Studies have demonstrated the remarkable properties of nanoparticles and their many applications due to their size. While conventional synthesis of nanoparticles including physical and chemical methods has proven to be very expensive and sources of environmental pollution and potential health problems, harmless and eco-friendly nanoparticles were obtained by the green approach from biological materials and were used in many sectors of activities. Zinc oxide nanoparticles (ZnONPs) have been recently the subject of intense studies due to their innumerable advantages, among which the most remarkable are their wide band gap, high excitonic binding energy, biocompatibility, and stability. ZnONPs were used for the antimicrobial, antifungal, anti-diabetic, anti-inflammatory, and antioxidant activities, the improvement of agriculture and food industry, and that of photovoltaic devices and storage. This review focuses on the green synthesis of ZnONPs from different biological materials, their characterization, and their potential applications. This review provides a better understanding of the biosynthesis, characterization techniques, and applications of ZnONPs in various sectors of activity.
Pre-sowing treatment of seeds with non-thermal plasma of different gases has been studied on the growth and yield of soybean (Glycine max var. JS-9560) under field conditions for two consecutive years (2022 and 2023). Five types of gases, N2, He, Ar, H2, and O2 were used to create plasma in a cylindrical vacuum container with an RF power supply. Soybean seeds were exposed to plasma for 15 seconds at 80 W. Growth measurements were taken at 30, 60, and 90 days after sowing the seeds. Plasma treatment improved germination, plant height, leaf area, and plant fresh weight at all the stages of the vegetative growth period. N2 and He plasma were most effective in promoting growth and biomass compared to Ar, O2, and H2. The yield parameters in terms of the number of pods, number of seeds, and 100 seed weight were improved to the extent of around 60 % by N2 and He plasma. Other gases (Ar, H2, and O2) were less beneficial in increasing the yield. Pre-sowing treatment of seeds with plasma especially with N2 and He would be highly beneficial for enhancing the yield of soybean at the field level. The characterization of soybean seeds was conducted using scanning electron microscopy (SEM), Fourier transform infrared (FTIR), and optical emission spectroscopy (OES). SEM analysis showed significant changes in nitrogen, helium, argon, oxygen, and hydrogen plasma treated seeds, indicating alterations on the surface of the starch caryopsis, resulting in large channels and pores. FTIR spectroscopy indicated modifications after exposure to plasma-treated seeds, suggesting surface activation due to lipid breakdown. OES detected OH, NO, O, and N2 radicals during plasma treatment, contributing to the optimal outcome.
