An endophytic strain of Streptomyces antimycoticus L-1 was isolated from healthy medicinal plant leaves of Mentha longifolia L. and used for the green synthesis of silver nanoparticles (Ag-NPs), through the use of secreted enzymes and proteins. UV–vis spectroscopy, Fourier-transform infrared (FT-IR), transmission electron microscopy (TEM), X-ray diffraction (XRD), and dynamic light scattering (DLS) analyses of the Ag-NPs were carried out. The XRD, TEM, and FT-IR analysis results demonstrated the successful biosynthesis of crystalline, spherical Ag-NPs with a particle size of 13–40 nm. Further, the stability of the Ag-NPs was assessed by detecting the surface Plasmon resonance (SPR) at 415 nm for one month or by measuring the NPs surface charge (−19.2 mV) by zeta potential analysis (ζ). The green-synthesized Ag-NPs exhibited broad-spectrum antibacterial activity at different concentrations (6.25–100 ppm) against the pathogens Staphylococcus aureus, Bacillus subtilis Pseudomonas aeruginosa, Escherichia coli, and Salmonella typhimurium with a clear inhibition zone ranging from (9.5 ± 0.4) nm to (21.7 ± 1.0) mm. Furthermore, the green-synthesized Ag-NPs displayed high efficacy against the Caco-2 cancerous cell line (the half maximal inhibitory concentration (IC50) = 5.7 ± 0.2 ppm). With respect to antibacterial and in-vitro cytotoxicity analyses, the Ag-NPs concentration of 100 ppm was selected as a safe dose for loading onto cotton fabrics. The scanning electron microscopy connected with energy-dispersive X-ray spectroscopy (SEM-EDX) for the nano-finished fabrics showed the distribution of Ag-NPs as 2% of the total fabric elements. Moreover, the nano-finished fabrics exhibited more activity against pathogenic Gram-positive and Gram-negative bacteria, even after 10 washing cycles, indicating the stability of the treated fabrics.

Improvement of the medical textile industry has received more attention recently, especially with widespread of microbial and viral infections. Medical textiles with new properties, such as bacterial pathogens self-cleaning, have been explored with nanotechnology. In this study, an endophytic actinomycetes strain of Streptomyces laurentii R-1 was isolated from the roots of the medicinal plant Achillea fragrantissima. This is used as a catalyst for the mediated biosynthesis of silver nanoparticles (Ag-NPs) for applications in the textile industry. The biosynthesized Ag-NPs were characterized using UV-vis spectroscopy, Fourier transform infrared (FT-IR), transmission electron microscopy (TEM), and X-ray Diffraction (XRD), which confirmed the successful formation of crystalline, spherical metal nanoparticles. The biosynthesized Ag-NPs exhibited broad-spectrum antibacterial activity. Our data elucidated that the biosynthesized Ag-NPs had a highly cytotoxic effect against the cancerous caco-2 cell line. The selected safe dose of Ag-NPs for loading on cotton fabrics was 100 ppm, regarding their antibacterial activity and safe cytotoxic efficacy. Interestingly, scanning electron microscope connected with energy dispersive X-ray spectroscopy (SEM-EDX) of loaded cotton fabrics demonstrated the smooth distribution of Ag-NPs on treated fabrics. The obtained results highlighted the broad-spectrum activity of nano-finished fabrics against pathogenic bacteria, even after 5 and 10 washing cycles. This study contributes a suitable guide for the performance of green synthesized NPs for utilization in different biotechnological sectors.

Classical univariate and multivariate statistics are the most common methods used for data analysis in plant breeding and biotechnology studies. Evaluation of genetic diversity, classification of plant genotypes, analysis of yield components, yield stability analysis, assessment of biotic and abiotic stresses, prediction of parental combinations in hybrid breeding programs, and analysis of in vitro-based biotechnological experiments are mainly performed by classical statistical methods. Despite successful applications, these classical statistical methods have low efficiency in analyzing data obtained from plant studies, as the genotype, environment, and their interaction (G × E) result in nondeterministic and nonlinear nature of plant characteristics. Large-scale data flow, including phenomics, metabolomics, genomics, and big data, must be analyzed for efficient interpretation of results affected by G × E. Nonlinear nonparametric machine learning techniques are more efficient than classical statistical models in handling large amounts of complex and nondeterministic information with “multiple-independent variables versus multiple-dependent variables” nature. Neural networks, partial least square regression, random forest, and support vector machines are some of the most fascinating machine learning models that have been widely applied to analyze nonlinear and complex data in both classical plant breeding and in vitro-based biotechnological studies. High interpretive power of machine learning algorithms has made them popular in the analysis of plant complex multifactorial characteristics. The classification of different plant genotypes with morphological and molecular markers, modeling and predicting important quantitative characteristics of plants, the interpretation of complex and nonlinear relationships of plant characteristics, and predicting and optimizing of in vitro breeding methods are the examples of applications of machine learning in conventional plant breeding and in vitro-based biotechnological studies. Precision agriculture is possible through accurate measurement of plant characteristics using imaging techniques and then efficient analysis of reliable extracted data using machine learning algorithms. Perfect interpretation of high-throughput phenotyping data is applicable through coupled machine learning-image processing. Some applied and potentially applicable capabilities of machine learning techniques in conventional and in vitro-based plant breeding studies have been discussed in this overview. Discussions are of great value for future studies and could inspire researchers to apply machine learning in new layers of plant breeding.

The aim of the work was to produce three independent, multi-criteria models for the prediction of winter rapeseed yield. Each of the models was constructed in such a way that the yield prediction can be carried out on three dates: April 15th, May 31st, and June 30th. For model building, artificial neural networks with multi-layer perceptron (MLP) topology were used, on the basis of meteorological data (temperature and precipitation) and information about mineral fertilisation. The data were collected from the years, 2008–2015, from 328 production fields located in Greater Poland, Poland. An assessment of the quality of forecasts produced based on neural models was verified by determination of forecast errors using RAE (relative approximation error), RMS (root mean square error), MAE (mean absolute error) error indicators, and MAPE (mean absolute percentage error). An important feature of the produced prediction models is the ability to realize the forecast in the current agrotechnical year on the basis of the current weather and fertiliser information. The lowest MAPE error values were obtained for the neural model WR15_04 (April 15th) based on the MLP network with structure 15:15-18-11-1:1, which reached 7.51%. Other models reached MAPE errors of 7.85% for model WR31_05 (May 31st) and 8.12% for model WR30_06 (June 30th). The performed sensitivity analysis gave information about the factors that have the greatest impact on winter rapeseed yields. The highest rank of 1 was obtained by two networks for the same independent variable in the form of the sum of precipitation within a period from September 1st to December 31st of the previous year. However, in model WR15_04, the highest rank obtained a feature in the form of a sum of molybdenum fertilization in the current year (MO_CY). The models of winter rapeseed yield produced in the work will be the basis for the construction of new forecasting tools, which may be an important element of precision agriculture and the main element of decision support systems.

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is characterized by severe cytokine storm syndrome following inflammation. SARS-CoV-2 directly interacts with angiotensin-converting enzyme 2 (ACE-2) receptors in the human body. Complementary therapies that impact on expression of IgE and IgG antibodies, including administration of bee venom (BV), have efficacy in the management of arthritis, and Parkinson's disease. A recent epidemiological study in China showed that local beekeepers have a level of immunity against SARS-CoV-2 with and without previous exposure to virus. BV anti-inflammatory properties are associated with melittin and phospholipase A2 (PLA2), both of which show activity against enveloped and non-enveloped viruses, including H1N1 and HIV, with activity mediated through antagonist activity against interleukin-6 (IL-6), IL-8, interferon-γ (IFN-γ), and tumor necrosis factor-α (TNF-α). Melittin is associated with the underexpression of proinflammatory cytokines, including nuclear factor-kappa B (NF-κB), extracellular signal-regulated kinases (ERK1/2), and protein kinase Akt. BV therapy also involves group III secretory phospholipase A2 in the management of respiratory and neurological diseases. BV activation of the cellular and humoral immune systems should be explored for the application of complementary medicine for the management of SARS-CoV-2 infections. BV “vaccination” is used to immunize against cytomegalovirus and can suppress metastases through the PLA2 and phosphatidylinositol-(3,4)-bisphosphate pathways. That BV shows efficacy for HIV and H1NI offers opportunity as a candidate for complementary therapy for protection against SARS-CoV-2.

In 2019, the world suffered from the emergence of COVID-19 infection, one of the most difficult pandemics in recent history. Millions of confirmed deaths from this pandemic have been reported worldwide. This disaster was caused by SARS-CoV-2, which is the last discovered member of the family of Coronaviridae. Various studies have shown that natural compounds have effective antiviral properties against coronaviruses by inhibiting multiple viral targets, including spike proteins and viral enzymes. This review presents the classification and a detailed explanation of the SARS-CoV-2 molecular characteristics and structure–function relationships. We present all currently available crystal structures of different SARS-CoV-2 proteins and emphasized on the crystal structure of different virus proteins and the binding modes of their ligands. This review also discusses the various therapeutic approaches for COVID-19 treatment and available vaccinations. In addition, we highlight and compare the existing data about natural compounds extracted from algae, fungi, plants, and scorpion venom that were used as antiviral agents against SARS-CoV-2 infection. Moreover, we discuss the repurposing of select approved therapeutic agents that have been used in the treatment of other viruses.

Wheat is one of the main grain species as well as one of the most important crops, being the basic food ingredient of people and livestock. Due to the importance of wheat production scale, it is advisable to predict its yield before harvesting. However, the current models are built solely on the basis of quantitative data. Therefore, the aim of the work was to create three multicriteria models for the prediction and simulation of winter wheat yield, which were made on the basis of extended quantitative and qualitative variables from field research in the year period 2008–2015. Neural networks with MLP (multi-layer perceptron) topology were used to build the following models, which can predict and simulate the yield on three dates: 15 April, 31 May, and 30 June. For this reason, they were designated as follows: QQWW15_4, QQWW31_5, and QQWW30_6. Each model is based on a different number of independent features, which ranges from 19 to 25. As a result of the conducted analyses, a MAPE (mean absolute percentage error) forecast error from 6.63% to 6.92% was achieved. This is equivalent of an error ranging from 0.521 to 0.547 t·ha−1, with an average yield of 6.57 ton per hectare of cultivated area. In addition, the most important quantitative and qualitative factors influencing the yield were also indicated. In the first predictive range (15 April), it is the average air temperature from 1 September to 31 December of the previous year (T9-12_PY). In the second predictive range (31 May) it is the sum of precipitation from 1 May to 31 May, and in the third (30 June) is the average air temperature from 1 January to 15 April of the year (T1-4_CY). In addition, one of the qualitative factors had a significant impact on the yield in the first phase-the type of forecrop in the previous year (TF_PY). The presented neural modeling method is a specific extension of the previously used predicting methods. An element of innovation of the presented concept of yield modeling is the possibility of performing a simulation before harvest, in the current agrotechnical season. The presented models can be used in large-area agriculture, especially in precision agriculture as an important element of decision-making support systems.