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In the present scenario, immunization is of utmost importance as it keeps us safe and protects us from infectious agents. Despite the great success in the field of vaccinology, there is a need to not only develop safe and ideal vaccines to fight deadly infections but also improve the quality of existing vaccines in terms of partial or inconsistent protection. Generally, subunit vaccines are known to be safe in nature, but they are mostly found to be incapable of generating the optimum immune response. Hence, there is a great possibility of improving the potential of a vaccine in formulation with novel adjuvants, which can effectively impart superior immunity. The vaccine(s) in formulation with novel adjuvants may also be helpful in fighting pathogens of high antigenic diversity. However, due to the limitations of safety and toxicity, very few human-compatible adjuvants have been approved. In this review, we mainly focus on the need for new and improved vaccines; the definition of and the need for adjuvants; the characteristics and mechanisms of human-compatible adjuvants; the current status of vaccine adjuvants, mucosal vaccine adjuvants, and adjuvants in clinical development; and future directions.

Cardiovascular care is expensive; hence, economic evaluation is required to estimate resources being consumed and to ensure their optimal utilization. There is dearth of data regarding cost analysis of treating various diseases including cardiac diseases from developing countries. The study aimed to analyze resource consumption in treating cardio-vascular disease patients in a super-specialty hospital. An observational and descriptive study was carried out from April 2017 to June 2018 in the Department of Cardiology, Cardio-Thoracic (CT) Centre of All India Institute of Medical Sciences, New Delhi, India. As per World Health Organization, common cardiovascular diseases i.e. Coronary Artery Disease (CAD), Rheumatic Heart Disease (RHD), Cardiomyopathy, Congenital heart diseases, Cardiac Arrhythmias etc. were considered for cost analysis. Medical records of 100 admitted patients (Ward & Cardiac Care Unit) of cardiovascular diseases were studied till discharge and number of patient records for a particular CVD was identified using prevalence-based ratio of admitted CVD patient data. Traditional Costing and Time Driven Activity Based Costing (TDABC) methods were used for cost computation. Per bed per day cost incurred by the hospital for admitted patients in Cardiac Care Unit, adult and pediatric cardiology ward was calculated to be Indian Rupee (INR) 28,144 (US $ 434), INR 22,210 (US$342) and INR 18,774 (US $ 289), respectively. Inpatient cost constituted almost 70% of the total cost and equipment cost accounted for more than 50% of the inpatient cost followed by human resource cost (28%). Per patient cost of treating any CVD was computed to be INR 2,47,822 (US $3842). Cost of treating Rheumatic Heart Disease was the highest among all CVDs followed by Cardiomyopathy and other CVDs. Cost of treating cardiovascular diseases in India is less than what has been reported in developed countries. Findings of this study would aid policy makers considering recent radical changes and massive policy reforms ushered in by the Government of India in healthcare delivery.

The nature of fore-wake excitations created by a charge bunch moving in a magnetized plasma is investigated using particle-in-cell simulations. Our studies establish for the first time the existence of precursor magneto-sonic solitons traveling ahead of a moving charge bunch. The nature of these excitations and the conditions governing their existence are delineated. We also confirm earlier molecular dynamic and fluid simulation results related to electrostatic precursor solitons obtained in the absence of a magnetic field. The electromagnetic precursors could have interesting practical applications such as in the interpretation of observed nonlinear structures during the interaction of the solar wind with the Earth and the Moon and may also serve as useful tracking signatures of charged space debris traveling in the ionosphere.

Crop harvesting, a crucial part of cultivation, has traditionally depended on manually, and despite technological advancements benefiting most crops through mechanical harvesting, the manual method of harvesting of shorter crops (one foot in height) continues to persist. Recognizing this challenge, an innovative solution has emerged a self-propelled battery-operated leafy-vegetable harvester specifically designed for leafy vegetables, integrating battery technology to mechanize the harvesting. Design Expert statistical software was used to identify optimal solutions for the laboratory analysis harvester using the Response Analysis and Multi-Parameter Simulation (RAMPS) model. The analysis revealed that the highest cutting efficiency was achieved at a cutter bar speed of 370.67 strokes/min with a forward speed of 2 km/h. Further optimization showed that a reel speed of 0.32 m per second at a forward speed of 1.65 km/h, with a driven pulley size of 558 mm (level 6 of B), resulted in highest harvesting efficiency. Additionally, the optimal conveyor performance was observed at a speed of 0.86 m/s, with a forward speed of 1.857 km/h and a driving pulley size of 101.6 mm (level 3 of A). The harvester was tested across speeds ranging from 1.5 to 5 km/h, with power requirements between 157 and 542 watts. Within this range, the battery-powered harvester provided an operating time of 2.25 to 7.8 h and the total energy required as 107.36 MJ/ha. By integrating battery technology, the harvester influences to sustainable agricultural practices, supporting with global efforts to minimize carbon emissions. This innovative attempt provides a viable solution for smallholder farmers, developing agricultural productivity and supporting the transition to more sustainable farming methods.

For the first time, a physics‐based modelling of a nanoscale Ni/Mo/MoO3/Ni memristor is presented in this letter by inserting a ‘Mo:Capping layer’ between the top electrode (Ni) and the insulating layer (MoO3). The proposed memristor has stable hysteresis I–V characteristics as well as a significant reduction in ‘Forming voltage’ (VFORM) to 0.75 V. The simulated resistive switching responses using the COMSOL Multiphysics package demonstrate consistently low values of coefficient of variability (CV) with 14.31% and 14.85% for the SET and RESET modules, respectively, during cycle‐to‐cycle variations along with a low compliance current (ICC) of 193 µA. In addition to observing synaptic plasticity behaviour, it also examines how ramp‐rates impact ‘Potentiation’ and ‘Depression’ as memristor conductance (G) is closely related to synaptic weights. Memristors are versatile electronic components that emulate brain synapses by modulating conductance for computation and data storage, with the ability to retain state without power, offering potential for use in synaptic applications. The proposed Ni/Mo/MoO3/Ni memristor shows enhanced stability with low operation voltages, mimicking biological synaptic behaviour, ideal for synaptic applications.

This work investigates the energy storage properties of quantum spin chains in the context of quantum batteries (QBs) by introducing Heisenberg spin network models organized into different configurations: open, closed, supercube geometries, and c-regular graphs. The charging dynamics of these systems are examined using Hamiltonians that include contributions from the battery, spin-spin interactions, and a transverse magnetic field. Incorporating the Dzyaloshinskii–Moriya interaction (DMI) into the charging Hamiltonian is found to enhance the ergotropy in the XXZ model, particularly for the supercube configuration, thereby improving QB performance. To explore the role of structural variations, we extend our study to c-regular graphs with system sizes ranging from 3 to 12 qubits, including highly symmetric geometries such as the tetrahedron, octahedron, and icosahedron. These analyzes reveal that such symmetric structures retain ideal sinusoidal charging–discharging behavior when DMI is tuned appropriately, establishing symmetry and coordination as key principles for scalable QB architectures.

We apply the Support Vector Regression (SVR) machine learning model to estimate surface roughness on a large alluvial fan of the Kosi River in the Himalayan Foreland from satellite images. To train the model, we used input features such as radar backscatter values in Vertical–Vertical (VV) and Vertical–Horizontal (VH) polarisation, incidence angle from Sentinel-1, Normalised Difference Vegetation Index (NDVI) from Sentinel-2, and surface elevation from Shuttle Radar Topographic Mission (SRTM). We generated additional features (VH/VV and VH–VV) through a linear data fusion of the existing features. For the training and validation of our model, we conducted a field campaign during 11–20 December 2019. We measured surface roughness at 78 different locations over the entire fan surface using an in-house-developed mechanical pin-profiler. We used the regression tree ensemble approach to assess the relative importance of individual input feature to predict the surface soil roughness from SVR model. We eliminated the irrelevant input features using an iterative backward elimination approach. We then performed feature sensitivity to evaluate the riskiness of the selected features. Finally, we applied the dimension reduction and scaling to minimise the data redundancy and bring them to a similar level. Based on these, we proposed five SVR methods (PCA-NS-SVR, PCA-CM-SVR, PCA-ZM-SVR, PCA-MM-SVR, and PCA-S-SVR). We trained and evaluated the performance of all variants of SVR with a 60:40 ratio using the input features and the in-situ surface roughness. We compared the performance of SVR models with six different benchmark machine learning models (i.e., Gaussian Process Regression (GPR), Generalised Regression Neural Network (GRNN), Binary Decision Tree (BDT), Bragging Ensemble Learning, Boosting Ensemble Learning, and Automated Machine Learning (AutoML)). We observed that the PCA-MM-SVR perform better with a coefficient of correlation (R = 0.74), Root Mean Square Error (RMSE = 0.16 cm), and Mean Square Error (MSE = 0.025 cm2). To ensure a fair selection of the machine learning model, we evaluated the Akaike’s Information Criterion (AIC), corrected AIC (AICc), and Bayesian Information Criterion (BIC). We observed that SVR exhibits the lowest values of AIC, corrected AIC, and BIC of all the other methods; this indicates the best goodness-of-fit. Eventually, we also compared the result of PCA-MM-SVR with the surface roughness estimated from different empirical and semi-empirical radar backscatter models. The accuracy of the PCA-MM-SVR model is better than the backscatter models. This study provides a robust approach to measure surface roughness at high spatial and temporal resolutions solely from the satellite data.

Mesenchymal stem cells (MSC) can differentiate into several cell types and are desirable candidates for cell therapy and tissue engineering. However, due to poor cell survival, proliferation and differentiation in the patient, the therapy outcomes have not been satisfactory. Although several studies have been done to understand the conditions that promote proliferation, differentiation and migration of MSC in vitro and in vivo, still there is no clear understanding on the effect of non-cellular bio molecules. Of the many factors that influence the cell behavior, the immediate cell microenvironment plays a major role. In this context, we studied the effect of extracellular matrix (ECM) proteins in controlling cell survival, proliferation, migration and directed MSC differentiation. We found that collagen promoted cell proliferation, cell survival under stress and promoted high cell adhesion to the cell culture surface. Increased osteogenic differentiation accompanied by high active RHOA (Ras homology gene family member A) levels was exhibited by MSC cultured on collagen. In conclusion, our study shows that collagen will be a suitable matrix for large scale production of MSC with high survival rate and to obtain high osteogenic differentiation for therapy.

The present study explores the SARS-CoV-2 drugable target inhibition efficacy of phytochemicals from Indian medicinal plants using molecular docking, molecular dynamics (MD) simulation, and MM-PBSA analysis. A total of 130 phytochemicals were screened against SARS-CoV-2 Spike (S)-protein, RNA-dependent RNA polymerase (RdRp), and Main protease (M pro ). Result of molecular docking showed that Isoquercetin potentially binds with the active site/protein binding site of the Spike, RdRP, and Mpro targets with a docking score of -8.22, -6.86, and -9.73 kcal/mole, respectively. Further, MS 3, 7-Hydroxyaloin B, 10-Hydroxyaloin A, showed -9.57, -7.07, -8.57 kcal/mole docking score against Spike, RdRP, and M pro targets respectively. The MD simulation was performed to study the favorable confirmation and energetically stable complex formation ability of Isoquercetin and 10-Hydroxyaloin A phytochemicals in M pro -unbound/ligand bound/standard inhibitor bound system. The parameters such as RMSD, RMSF, Rg, SASA, Hydrogen-bond formation, energy landscape, principal component analysis showed that the lead phytochemicals form stable and energetically stabilized complex with the target protein. Further, MM-PBSA analysis was performed to compare the Gibbs free energy of the M pro -ligand bound and standard inhibitor bound complexes. The analysis revealed that the His-41, Cys145, Met49, and Leu27 amino acid residues were majorly responsible for the lower free energy of the complex. Drug likeness and physiochemical properties of the test compounds showed satisfactory results. Taken together, the study concludes that that the Isoquercetin and 10-Hydroxyaloin A phytochemical possess significant efficacy to bind SARS-Cov-2 M pro active site. The study necessitates further in vitro and in vivo experimental validation of these lead phytochemicals to assess their anti-SARS-CoV-2 potential.

Use of artificial intelligence in medicine in an evolving technology which holds promise for mass screening and perhaps may even help in establishing an accurate diagnosis. The ability of complex computing is to perform pattern recognition by creating complex relationships based on input data and then comparing it with performance standards is a big step. Diabetic retinopathy is an ever-increasing problem. Early screening and timely treatment of the same can reduce the burden of sight threatening retinopathy. Any tool which can aid in quick screening of this disorder and minimize requirement of trained human resource for the same would probably be a boon for patients and ophthalmologists. In this review we discuss the current status of use of artificial intelligence in diabetic retinopathy and few other common retinal disorders.