Potential Means Research Articles

Identifying slow learners in an e-learning environment using k-means clustering approach

Currently, the majority of e-learning lessons created and disseminated advocate a “one-size-fits-all” teaching philosophy. The e-learning environment, however, includes slow learners in a noticeable way, just like in traditional classroom settings. Learning analytics of educational data from a learning management system (LMS) have been considered by the researchers as a potential means of identifying slow e-learners and supporting, contesting, and altering present educational practices in e-learning. We used the students’ rates of learning and grade points along with the total learning time, which is calculated from the time series log data, to cluster the learners. The rate at which a student learns determines whether he or she is a slow learner, an average learner, or a gifted learner. For classifying learners, we followed a step-by-step procedure that included instructional design to create a dataset, learning analytics of the dataset, and a machine learning strategy to cluster e-learners. The system has been adequately integrated with the methods for measuring student learning. A strategy based on the revised Bloom’s Taxonomy is offered for the assessment of learners. The K-Means clustering approach is used to group learners who have similar performance without collecting a learner’s previous academic records or demographic information. In the experimental evaluation, 7.7% of e-learners are grouped as slow learners, while advanced learners make up 61.3 percent of the student body and average learners make up 31 percent. According to the study, there is a correlation between learning rate and academic success, with fast learners having a lower learning rate.

Assessment of Annual Leguminous Forage Varieties in Cotton Inter cropping: Evaluating Green Fodder Yield and Dry Matter Production

Intercropping is an agronomic technique and is considered to be an effective and potential mean of increasing crop production per unit area and time (Ahmad and Anwar 2001). Legume intercrops are included in cropping system due to their ability to reduce soil erosion, improving land productivity through soil amelioration, suppress weeds and fix nitrogen. Intercropping cotton is significant because of higher profit and stabilized yield advantage, especially under adverse climatic conditions (Aladakatti et al., 2011). In this aspect, intercropping of short duration legumes in cotton has no adverse effect on cotton performance but an additional yield of legume makes the system more profitable. Rochester et al. (2001) studied the contribution of legume crops such as faba bean (Vicia faba), field pea (Pisum sativaum) and lab-lab (Lablab purpureus) on nitrogen fertility of cotton cropping system. The present study was undertaken in order to develop a compatible, short duration legume fodder and to assess the feasibility of different annual legume fodders for higher green fodder production. The field experiments were conducted in Krishi Vigyan Kendra, Veterinary College and Research Institute Campus, Namakkal, under irrigated condition, during July-August of 2021-22 and 2022-23. Experimental procedures were adopted to analyze the effects of various fertilizer levels on green fodder yield of various leguminous fodder crops viz., fodder cowpea (var. CO (FC)-8), mothbean (var. TMV (mb)-1), horse gram (var. Paiyur-2) and pillipesera. The results indicated that green fodder yield recorded at harvest stage (55 Days After Sowing (DAS)) was influenced by various fertilizer levels of Bt cotton. With respect to green fodder yield fodder cowpea (17407 and 14431 kg ha-1), horse gram (4439 and 4662 kg ha-1), moth bean (10041 and 10010 kg ha-1) and pillipesera (3066 and 2720 kg ha-1) recorded higher yield with application of 150% RDF and it was followed by 125% RDF and 100 RDF during first- and second -year study. Similarly, application of 150% RDF (F3) recorded more dry matter production (DMP) value, for all leguminous fodder crops during first (184.8, 305.6 and 452.7 kg ha-1) and second year study (169.9, 291.2, 450.2 kg ha-1) at all growth stages viz., 20, 40 DAS and at harvest stage, respectively.

Screening of First-Row Transition Metal Complexes for Aqueous Redox Flow Batteries: Experimental and Density Functional Theory Approaches

The development of redox flow batteries took new directions in the last decades. From initial metal-based systems, it evolved towards organic and hybrid approaches in aqueous media. Furthermore, redox targeting flow batteries are an emerging alternative to the traditional redox flow battery architecture which offer improved energy density via an added electroactive solid ‘booster’. Whatever the system, there is a need of soluble electroactive species with potentials allowing large cell potentials within the water stability window or imperative redox potentials matching between soluble redox mediator and the solid for redox targeting. Transition metal complexes are a promising class of redox electroactive centers due to the tunability of their solubility and electrochemical potential, and the ability to take into account sustainability1. Recent reports underlined for instance that either the modification of the ligand2 or using heteroleptic complexes3, can lead to iron complexes with solubilities higher than 1 M and potentials higher than 1 V vs SHE. Furthermore, reliable and time-efficient prediction tools for the redox potentials of transition metal complexes are needed to help the search for new potential systems. While density functional theory usually provides a good trade-off between computational cost and accuracy, calculations on transition metal complexes often result in large errors. We thus developed a procedure and compared different solvation methods and levels of theory using an initial experimental data set based on aqueous iron complexes with bidentate ligands (Figure 1).In the present contribution, we will illustrate this coordination approach through a screening of new complexes based on non-toxic and affordable transition metal complexes. In this perspective, electrochemical characterization at different pH, coupled with NMR and UV-Visible studies will be presented, to screen for new candidates as electrolyte in redox flow batteries. Finally, we will show how the calculations corrected using a simple linear regression using training data yields a good prediction of redox potentials (mean average error =0.09 V).

Инвестиционный потенциал — основа финансового развития промышленного предприятия на международном рынке

Purpose. Studying of necessity investment potential forming for industrial enterprise’s financial development in the Tula region and international market.
 Tasks: 
 - studying of investment and investment potential meaning;
 - researching of investment potential influence on industrial enterprise’s financial development;
 - main factors’ forming of industrial enterprise’s activity development in the Tula region and international financial market.
 Methods. Investment attractiveness’ changing of Center federal district’s regions and including Tula region studies in the article. It is paid attention for analysis of investment attractiveness’ changing in the years, also a comparison is being made investment attractiveness’ positions of different Russian Federation regions. Besides it is analyzed investment attractiveness’ financial development possibility and ability to international market for financial relationships with another enterprise.
 Results. It is conducted analysis investment potential’s influence on Tula region industrial enterprises’ financial development. It is identified that Tula region investment attractiveness didn’t change in 2023 year. It is mined that industrial enterprises work effectively. It is emphasized, that Tula region industrial enterprises’ investment attractiveness’ changing can depend on financial resources’ changing, which is provided this enterprise’s financial development. Also it is established Tula region industrial enterprises’ financial development’s limitations and problems in the national and international market. It is identified that investment strategy forms necessary for industrial enterprise’s quit to the international market.
 Conclusions. Tula region industrial enterprise’s investment potential can develop in case free financial resources. Tula region industrial enterprise can quit to new markets and international market. Industrial enterprise can realize financial development and increase competitiveness position with another enterprise in financial relationships in the international market.

The Disease Model of Addiction: The Impact of Genetic Variability in the Oxidative Stress and Inflammation Pathways on Alcohol Dependance and Comorbid Psychosymptomatology.

Oxidative stress and neuroinflammation are involved in the pathogenesis of alcohol addiction. However, little is known regarding the effect of genetic, behavioral, psychological, and environmental sources of origin on the inflammation and oxidative stress pathways of patients with alcohol addiction. Our study aimed to evaluate the impact of selected common functional single-nucleotide polymorphisms in inflammation and oxidative stress genes on alcohol addiction, and common comorbid psychosymptomatology. Our study included 89 hospitalized alcohol-addicted patients and 93 healthy individuals, all Slovenian males. Their DNA was isolated from peripheral blood and patients were genotyped for PON1 rs705379, rs705381, rs854560, and rs662, SOD2 rs4880, GPX1 rs1050450, IL1B rs1143623, rs16944, and rs1071676, IL6 rs1800795, IL6R rs2228145, and miR146a rs2910164. Kruskal-Wallis and Mann-Whitney tests were used for the additive and dominant genetic models, respectively. Our findings suggested the involvement of IL6 rs1800795 in alcohol addiction. Moreover, our data indicated that the genetic variability of SOD2 and PON1, as well as IL1B and IL6R, may be related to comorbid psychosymptomatology, revealing a potential indirect means of association of both the oxidative stress and inflammation pathways.

Oxidation potential of PM2.5 in a mechanical processing plant and its association with metal composition

Oxidative potential (OP) of particles is an emerging indicator of health that reflects their ability to induce oxidative stress and cause adverse health effects. It offers us a useful tool to evaluate the health effects of particles released by different industrial processes. We measured the OPv (volume normalized OP characterizes overall oxidative burden) and OPm (mass normalized OP characterizes intrinsic toxicity) of PM2.5 from a casting workshop, a machining workshop, a meeting room of a machining processing plant, and outdoors using dithiothreitol (DTT) assay. The observed variations were substantial and varied greatly, underscoring the diverse toxicological profiles of particulates from distinct sources. Overall, PM2.5 mass concentration and OPv were highest in the casting workshop (PM2.5: 669.26 ± 402.62 μg/m3, OPv: 69.55 ± 128.35 nmol/(min·m3)), followed by the machining workshop (PM2.5 = 85.51 ± 41.89 μg/m3, OPv = :5.38 ± 2.68 nmol/(min·m3)), outdoor (PM2.5: 30.77 ± 13.34 μg/m3, OPv: 1.83 ± 0.92 nmol/(min·m3)), and meeting room (PM2.5: 26.97 ± 12.03 μg/m3, OPv: 1.86 ± 1 nmol/(min·m3)). However, localized variances influenced by PM's intrinsic toxicity (OPm) reordered these rankings. For example, PM2.5 mass concentration at the spheroidizing/pouring zone in the casting workshop was the highest, but the OPv was not due to the low intrinsic toxicity of the particles. Spearman's correlation analysis implied that the OPv was significantly correlated with the concentration of transition metals Mn (ρ = 0.47, P < 0.01), Fe (ρ = 0.503, P < 0.01), and Zn (ρ = 0.507, P < 0.01) in addition to PM2.5 mass concentration (ρ = 0.879, P<0.01), and the OPm was significantly correlated with the mass fraction of Zn (ρ = 0.34, P < 0.05) and Al (ρ = 0.333, P < 0.05). Besides, environmental factors (wind speed, temperature relative humidity, CO2) did not contribute to the oxidation potential. Higher oxidation potential (OPv) means higher health risks, our findings underscore the imperative to consider both PM mass concentration and intrinsic toxicity for future air pollution control efforts.

ASSOCIATION OF EVERYDAY LIFE INFORMATION ACQUISITION METHODS AND DEPRESSION AMONG US OLDER ADULTS

Abstract Everyday life information acquisition refers to the incidental ways people obtain information through daily activities such as reading, watching television, or communicating with friends rather than through intentional or purposive information seeking behaviors. It has been identified as a fundamental way to obtain informational resources and is a potential means to promote social connections and engagement among older adults. Research has shown that social engagement and connections are protective factors of depression for older adults. However, little research has explored how information seeking behaviors effect risks of depression among older adults. To fill this gap in the literature and provide insights into the mechanism of information access impacts the mental health of older adults, this study examines the relationships between different ways for obtaining daily information (reading, using computer, and watching television) and depression among older adults in United States. The data retrieved from 2018’s Health and Retirement Study (HRS). We multiple linear regression analyses on HRS data including adults age 50 years and older (n=3,179). After adjusting for age, gender, race, marital status, education, and chronic diseases, all three different ways to obtaining daily information (reading, using computer, and watching television) were negatively associated with depression (β=-0.19, p&amp;lt; 0.001; β=-0.15, p=0.002; β=-0.15, p&amp;lt; 0.001, respectively). Findings suggest that the association between everyday life information acquisition and depression among older adults varies depending on the specific information acquisition methods used. These findings highlight the importance of implementing targeted interventions to promote effective information access and enhance social inclusion among older adults.

Influence of the Neuro-Educational Environment on One’s Socialization under Total Digitalization

The institute of education is an instrument of advanced development, a means of forming a new shaping socio-cultural pattern, with its specific means of significant socialization potential in the conditions of rapid development of society's digitalization. Only education, in its present dimension - neuropedagogy, is able to conduct successful socialization of an individual by teaching her from the position of the laws of brain functioning; to intensify this process in the conditions of total digitalization in accordance with the laws of brain functioning. Intensive use of formal and informal education in the daily life of today's society and socialization of the population in the digital environment because of its activities in the virtual sphere create a wide space for the realization of opportunities. They are conditioned by the rapid dynamics of information flow as a global factor of modernity, generating both advantages and problems in the educational process, cognitive development, social adaptation of personality; formation of traditional ways of relations; changes in the neural architecture of the human brain in the long term; emergence of risks beyond the limits of the digital space. It was established how representatives of different generations, grouped into appropriate age focus groups and in the process of receiving formal and informal education, are socialized in the digital space. The paper reveals how the cognitive domain of personality changes under the influence of digitalization in the context of the respondents' stay in the educational environment. The specifics of socialization in the digital environment and the reasons for the preference of virtual form of communication were determined. The types of digital danger in the virtual environment were revealed.

Bioactives from medicinal herb against bedaquiline resistant tuberculosis: removing the dark clouds from the horizon

Tuberculosis is a contagious bacterial ailment that primarily affects the lungs and is brought on by the bacterium Mycobacterium tuberculosis (MTB). An antimycobacterial medication called bedaquiline (BQ) is specified to treat multidrug-resistant tuberculosis (MDR-TB). Despite its contemporary use in clinical practice, the mutations (D32 A/G/N/V/P) constrain the potential of BQ by causing transitions in the structural conformation of the atpE subunit-c after binding. In this study, we have taken the benzylisoquinoline alkaloids from thalictrum foliolosum due to its antimicrobial activity reported in prior literature. We used an efficient and optimized structure-based strategy to examine the wild type (WT) and mutated protein upon molecule binding. Our results emphasize the drastic decline in BQ binding affinity of mutant and WT atpE subunit-c complexes compared to thalirugidine (top hit) from thalictrum foliolosum. The decrease in BQ binding free energy is due to electrostatic energy because nearly every atom in a macromolecule harbors a partial charge, and molecules taking part in molecular recognition will interact electrostatically. Similarly, the high potential mean force of thalirugidine than BQ in WT and mutant complexes demonstrated the remarkable ability to eradicate mycobacteria efficiently. Furthermore, the Alamar blue cell viability and ATP determination assay were performed to validate the computational outcomes in search of novel antimycobacterial. Upon closer examination of the ATP determination assay, it became apparent that both BQ and thalirugidine showed similar reductions in ATP levels at their respective MICs, presenting a potential common mechanism of action.

VETERINARY CARE NEEDS IN KENNELS OF BRACHYCEPHALIC AND NON-BRACHYCEPHALIC DOGS - PILOT STUDY

This is a descriptive, observational pilot study, based on the results obtained by applying an original questionnaire addressed to purebred dog breeders, speakers of the French and Romanian languages, regarding the perceived need for medical-veterinary assistance in canine reproduction. 44 answers were obtained (24 in French, 20 in Romanian) about 167 (100%) bitches from 33 brachycephalic and non-brachycephalic dog breeds, aged between 2 and 7 years, of which 75 (44.91 %) declared pregnant. The need for estrus monitoring by a veterinarian varied by group and breed type (68.62% of non-brachycephalic females, 41.66% brachycephalic), "small non-brachycephalic" breeds were monitored more intensively (90.90 %), https://www.uaiasi.ro/revmvis/index_htm_files/Artificial insemination was necessary in 49.33% of the gestation obtained (50.98% in non-brachycephalic breeds; 45.83% in brachycephalic breeds). All the breeders declared ultrasound confirmation of pregnancy, in brachycephalic breeds caesarean section was necessary in 45.8% of cases, post-phttps://www.uaiasi.ro/revmvis/index_htm_files/Artum veterinary control was requested only in 22.7% of cases, more frequently (37.5% of cases) to "large and medium brachycephalic" breeds (76.9% of answers). The puppies from the "giant brachycephalic" breeds were tested by a veterinarian, an aspect declared by 43.8% of the Romanian breeders and only 38.1% of the French respondents. Conclusion: Due to the type of research chosen, the results obtained in the present study cannot be extrapolated to the entire population of dog breeders, but it is a potential means of evaluating the needs felt and expressed by veterinary medical assistance in dog breeding.

Cellphone picture-based, genus-level automated identification of Chagas disease vectors: Effects of picture orientation on the performance of five machine-learning algorithms

Chagas disease (CD) is a public-health concern across Latin America. It is caused by Trypanosoma cruzi, a parasite transmitted by blood-sucking triatomine bugs. Automated identification of triatomine bugs is a potential means to strengthen CD vector surveillance. To be broadly useful, however, automated systems must draw on algorithms capable of correctly identifying bugs from images taken with ordinary cellphone cameras at varying angles or positions. Here, we assess the performance of five machine-learning algorithms at identifying the main CD vector genera (Triatoma, Panstrongylus, and Rhodnius) based on bugs photographed at different angles/positions with a 72-dpi cellphone camera. Each bug (N = 730; 13 species) was photographed at nine angles representing three positions: dorsal-flat, dorsal-oblique, and front/back-oblique. We randomly split the 6570-picture database into training (80%) and testing sets (20%), and then trained and tested a convolutional neural network (AlexNet, AN); three boosting-based classifiers (AdaBoost, AB; Gradient Boosting, GB; and Histogram-based Gradient Boosting, HB); and a linear discriminant model (LD). We assessed identification accuracy and specificity with logit-binomial generalized linear mixed models fit in a Bayesian framework. Differences in performance across algorithms were mainly driven by AN's essentially perfect accuracy and specificity, irrespective of picture angle or bug position. HB predicted accuracies ranged from ∼0.987 (Panstrongylus, dorsal-oblique) to >0.999 (Triatoma, dorsal-flat). AB accuracy was poor for Rhodnius (∼0.224–0.282) and Panstrongylus (∼0.664–0.729), but high for Triatoma (∼0.988–0.991). For Panstrongylus, LD and GB had predicted accuracies in the ∼0.970–0.984 range. AB misclassified ∼57% of Rhodnius and Panstrongylus as Triatoma, whereas specificity ranged from ∼0.92 to ∼1.0 for the remaining algorithm-genus combinations. Dorsal-flat pictures appeared to improve algorithm performance slightly, but angle/position effects were overall weak-to-negligible. We conclude that, when high-performance algorithms such as AN are used, the angles or positions at which bugs are photographed seem unlikely to hinder cellphone picture-based automated identification of CD vectors, at least at the genus level. Future research should focus on combining mixed-quality pictures and state-of-the-art algorithms to (i) identify triatomine adults to the species level and (ii) distinguish triatomine nymphs (i.e., immature stages) from adults and from other insects.

Diagnostic Potential of Serum Glycome Analysis in Lung Cancer: A Glycopattern Study.

Lung cancer is the leading cause of cancer-related death, with high morbidity and mortality rates due to the lack of reliable methods for diagnosing lung cancer at an early stage. Low-dose computed tomography can help detect abnormal areas in the lungs, but only 16% of cases are diagnosed early. Tests for lung cancer markers are often employed to determine genetic expression or mutations in lung carcinogenesis. Serum glycome analysis is a promising new method for early lung cancer diagnosis as glycopatterns exhibit significant differences in lung cancer patients. In this study, we employed a solid-phase chemoenzymatic method to systematically compare glycopatterns in benign cases, adenocarcinoma before and after surgery, and advanced stages of adenocarcinoma. Our findings indicate that serum high-mannose levels are elevated in both benign cases and adenocarcinoma, while complex N-glycans, including fucose and 2,6-linked sialic acid, are downregulated in the serum. Subsequently, we developed an algorithm that utilizes 16 altered N-glycans, 7 upregulated and 9 downregulated, to generate a score based on their intensity. This score can predict the stages of cancer progression in patients through glycan characterization. This methodology offers a potential means of diagnosing lung cancer through serum glycome analysis.

A Rigorous Framework for Calculating Protein-Protein Binding Affinities in Membranes.

Calculating the binding free energy of integral transmembrane (TM) proteins is crucial for understanding the mechanisms by which they recognize one another and reversibly associate. The glycophorin A (GpA) homodimer, composed of two α-helical segments, has long served as a model system for studying TM protein reversible association. The present work establishes a methodological framework for calculating the binding affinity of the GpA homodimer in the heterogeneous environment of a membrane. Our investigation carefully considered a variety of protocols, including the appropriate choice of the force field, rigorous standardization reflecting the experimental conditions, sampling algorithm, anisotropic environment, and collective variables, to accurately describe GpA dimerization via molecular dynamics-based approaches. Specifically, two strategies were explored: (i) an unrestrained potential mean force (PMF) calculation, which merely enhances sampling along the separation of the two binding partners without any restraint, and (ii) a so-called "geometrical route", whereby the α-helices are progressively separated with imposed restraints on their orientational, positional, and conformational degrees of freedom to accelerate convergence. Our simulations reveal that the simplified, unrestrained PMF approach is inadequate for the description of GpA dimerization. Instead, the geometrical route, tailored specifically to GpA in a membrane environment, yields excellent agreement with experimental data within a reasonable computational time. A dimerization free energy of -10.7 kcal/mol is obtained, in fairly good agreement with available experimental data. The geometrical route further helps elucidate how environmental forces drive association before helical interactions stabilize it. Our simulations also brought to light a distinct, long-lived spatial arrangement that potentially serves as an intermediate state during dimer formation. The methodological advances in the generalized geometrical route provide a powerful tool for accurate and efficient binding-affinity calculations of intricate TM protein complexes in inhomogeneous environments.

Monolayer Mo1−x−yRexWyS2‐Based Photodetectors Grown by Chemical Vapor Deposition

AbstractThe utilization of alloyed 2D transition metal dichalcogenides (TMDs) has become a pivotal approach for addressing challenges in material applications. The judicious selection of dopant constituents offers a potent means to finely modulate the materials' bandgap, consequently broadening the potential applications of 2D materials. In the context of an investigation, Mo1−x−yRexWyS2 is successfully synthesized using chemical vapor deposition. With a bandgap of 1.33 eV, this material exhibits promising prospects for application in the realm of optoelectronics. This advancement enables the fabrication of the Mo1−x−yRexWyS2 photodetector. The rigorous testing and analysis of photoelectric performance reveal significant improvements in both responsivity and response speed compared to analogous detectors. This accomplishment not only furnishes a novel paradigm for the advancement of photodetectors but also contributes fresh insights to the domain of alloyed 2D TMDs.

Surface response regression and machine learning techniques to predict the characteristics of pervious concrete using non-destructive measurement: Ultrasonic pulse velocity and electrical resistivity

It is crucial to assess the characteristics of pervious concrete even post-construction. The quality monitoring of such a procedure is tricky in pervious concrete that it is typically avoided. As a potential means of enhancing the aforementioned quality control, the current study investigates the possibility of predicting characteristics of pervious concrete through response surface methodology and machine learning techniques using non-destructive test measurement (ultrasonic velocity and electrical resistivity). A total of 225 datasets from the experimental study were taken for this study. To recognize the best reliable model for predicting characteristics of pervious concrete, response surface methodology up to sixth order polynomial and five different machine learning techniques were used as statistical assessment tools. Using both ultrasonic pulse velocity and electrical resistivity as predictors for estimating porosity and compressive strength via response surface methodology, using a quadratic model for porosity prediction and a cubic model for compressive strength prediction are recommended. The machine learning models used in the research exhibited superior performance compared to the response surface methodology. Among the many machine learning models evaluated in this study, boosted decision tree regression model better predicted porosity (R2 = 0.92) and compressive strength (R2 = 0.92) of pervious concrete. Therefore, prediction models for the characteristics of pervious concrete are created using non-destructive measurement and machine learning techniques, which may ensure that the construction sector can utilize the offered models without any theoretical expertise.

Enhancing Nutritional Security and Combating Hidden Hunger with Climate-Resilient Millets

Hidden hunger, characterized by a deficiency in essential micronutrients, persists as a critical global issue, undermining human health and well-being. Despite advancements in food production, millions suffer from the consequences of inadequate access to vital nutrients. To address this multifaceted challenge and fortify nutritional security, millets have emerged as a compelling solution. These small-seeded grains, known for their climate resilience, offer a potent means to mitigate hidden hunger while ensuring sustainable food systems. Millets have garnered attention for their exceptional adaptability to adverse environmental conditions, including drought and high temperatures. Their deep root systems enable them to thrive even in water-stressed regions, making them a reliable source of sustenance in a changing climate. As global temperatures rise and extreme weather events become more frequent, the ability of millets to maintain food production under challenging conditions is of paramount importance. Beyond their adaptability, millets are nutritional powerhouses. They are replete with essential nutrients, including protein, dietary fiber, B-complex vitamins, and crucial minerals such as iron and zinc. This nutritional profile positions millets as a valuable resource in addressing malnutrition and hidden hunger, which plague populations across the globe. Moreover, millets are gluten-free, offering an inclusive dietary option for individuals with celiac disease or gluten sensitivities. This aspect contributes to their accessibility and relevance in diverse dietary regimes, aligning with the principles of equitable food security. The cultivation of millets is also aligned with broader sustainability goals. Millet farming practices encourage biodiversity conservation, reduce the reliance on chemical pesticides and synthetic fertilizers, and protect fragile ecosystems. Additionally, the economic and social benefits of millet production are significant, providing diversified income sources for smallholder farmers and rejuvenating rural communities. Governments and policymakers worldwide are increasingly recognizing the potential of millets in sustainable agriculture. Policies, subsidies, and initiatives are being introduced to support millet production, market development, and nutritional programs, fostering food security, climate resilience, and improved livelihoods. As the global community seeks sustainable solutions to hidden hunger and nutritional insecurity, millets stand as a resilient and nourishing ally. Harnessing their potential, along with continued research, investment, policy support, and heightened consumer awareness, is pivotal to securing a sustainable, nourished, and equitable future for all. In conclusion, the integration of millets into global food systems offers a pathway to enhance nutritional security, combat hidden hunger, and advance broader sustainability objectives.

Evaluation of luciferase and prefusion-stabilized F protein from respiratory syncytial virus mRNA/LNPs in pre-clinical models using jet delivery compared to needle and syringe

Described here is the evaluation of a luciferase (luc) and respiratory syncytial virus (RSV) messenger RNA / lipid nanoparticle (mRNA/LNP) vaccine using a Needle-free Injection System, Tropis®, from PharmaJet® (Golden, Colorado USA). Needle-free jet delivery offers an alternative to needle/syringe. To perform this assessment, compatibility studies with Tropis were first performed with a luc mRNA/LNP and compared to needle/syringe. Although minor changes in particle size and encapsulation efficiency were observed when using Tropis on the benchtop, in vitro luciferase activity remained the same. Next, the luc mRNA/LNP was administered to rats intramuscularly using Tropis or needle/syringe and tracking of the injection and distribution was performed. Lastly, an mRNA encoding a prefusion-stabilized F protein from RSV was delivered intramuscularly using both Tropis and needle/syringe at 1 and 5 mcg mRNA. An equivalent IgG response was observed using both Tropis and needle/syringe. The cell mediated immune (CMI) response was also evaluated, and responses to RSV-F were detected from animals immunized with needle/syringe at all dose levels, and from the animals immunized with Tropis in the 5 and 25 ug groups. These results indicated that delivery of mRNA/LNPs with Tropis is a potential means of administration and an alternative to needle/syringe.

Depolymerisation of the Klebsiella pneumoniae Capsular Polysaccharide K21 by Klebsiella Phage K5.

Klebsiella pneumoniae is a pathogen associated with various infection types, which often exhibits multiple antibiotic resistance. Phages, or bacterial viruses, have an ability to specifically target and destroy K. pneumoniae, offering a potential means of combatting multidrug-resistant infections. Phage enzymes are another promising therapeutic agent that can break down bacterial capsular polysaccharide, which shields K. pneumoniae from the immune response and external factors. In this study, Klebsiella phage K5 was isolated; this phage is active against Klebsiella pneumoniae with the capsular type K21. It was demonstrated that the phage can effectively lyse the host culture. The adsorption apparatus of the phage has revealed two receptor-binding proteins (RBPs) with predicted polysaccharide depolymerising activity. A recombinant form of both RBPs was obtained and experiments showed that one of them depolymerised the capsular polysaccharide K21. The structure of this polysaccharide and its degradation fragments were analysed. The second receptor-binding protein showed no activity on capsular polysaccharide of any of the 31 capsule types tested, so the substrate for this enzyme remains to be determined in the future. Klebsiella phage K5 may be considered a useful agent against Klebsiella infections.

Inflammation-related prognostic markers in resected hepatocellular carcinoma.

Hepatocellular carcinoma is usually detected late and therapeutic options are unsatisfactory. Despite marked progress in patient care, HCC remains among the deadliest cancers world-wide. While surgical resection remains a key option for early-stage HCC, the 5-year survival rates after surgical resection are limited. One reason for limited outcomes is the lack of reliable prognostic biomarkers to predict HCC recurrence. HCC prognosis has been shown to correlate with different systemic and pathological markers which are associated with patient survival and HCC recurrence. Liver inflammatory processes offer a large variety of systemic and pathological markers which may be exploited to improve the reliability of prognosis and decision making of liver surgeons and hepatologists. The following review aims to dissect the potential tools, targets and prognostic meaning of inflammatory markers in patients with resectable HCC. We analyze changes in circulant cellular populations and assess inflammatory biomarkers as a surrogate of impaired outcomes and provide an overview on predictive gene expression signatures including inflammatory transcriptional patterns, which are representative of poor survival in these patients.

Novel coating of slow-release nitrogen fertilizers: Characterization and assessment

Developing novel fertilizers that reduce nitrogen release rate receives worldwide interest as a potential means for improving nitrogen utilization efficiency. Making these fertilizers sustainable, this work introduces various attempts to prepare slow-release nitrogen using different materials comprised of agro-industrial wastes such as sugarcane (SC) and beet filter cakes (mud) besides, sulfur, gypsum, and zeolite to cover two nitrogen sources. The prepared coated nitrogen fertilizers were characterized using SEM-EDX. Afterward, an incubation trial was conducted to appraise the produced slow-release fertilizers via mixing with three soils (from Egypt and Sudan) at various incubation periods. The tested coating process enhanced the surface morphology of the fertilizer grains, in which the surface became smoother and all the cracks and cavities were filled with the coating material, which is critical for the physical barrier property and preventing moisture penetration into the core. ANOVA analysis indicated that the released values of N were significantly varied according to the soil type, source of N, and the coating material. Zeolite was effective as a coating material for all nitrogen fertilizers. The coated granules by SC material provided a steady, adequate nitrogen supply throughout the incubation. Beet mud treatments showed the lowest N release among all coating materials, but the liberation of N gradually increased with time elapsed. The beet and sugarcane mud can be considered promising covering materials for developing environmentally friendly and cost-effective slow-release fertilizers. The results showed continuous release of N in the soils from all prepared coated fertilizers til the end of the incubation, enhancing the feasibility for agricultural application.