Sant Gadge Baba Amravati University, formerly Amravati University, named after Sant Gadge Baba, is a public state university located at Amravati in the Vidarbha region of the state of Maharashtra, India. Today, it is one of the largest University in the country with 382 affiliated colleges and about 3.50 lac students..
We report the synthesis of undoped and Cd doped CuS nanoparticles (NPs) using L-alanine as a surface passivating agent via simple co-precipitation method. Powder samples of CuS NPs and thin films of poly (methyl methacrylate) (PMMA)-CuS NPs, prepared by spin coating on glass substrate, were investigated for their structural, morphological, linear and nonlinear optical (NLO) properties. Powder X-ray diffraction confirmed the formation of covellite CuS with average crystallite size of 5-20 nm. Energy dispersive X-ray spectroscopy (EDS) validated the sample purity, while high-resolution transmission electron microscopy (HR-TEM) revealed uniform morphology of NPs. Linear optical studies using ultraviolet-visible (UV-vis) absorption spectroscopy showed localized surface plasmon resonance (LSPR) in the wavelength range 600-680 nm. A pronounced blue shift in the absorption wavelength and an optical band gap up to 2.28 eV was observed for 1 wt% Cd doped CuS NPs. Thin film samples (undoped and Cd-doped) were further characterized by z-scan technique, which revealed thermally stimulated third-order NLO effects in Cd doped CuS-PMMA nanocomposite thin films, attributed to non-local thermal contributions from surface states, dielectric effects, and photoacoustic interactions.
The growing incidence of fraudulent academic and professional certificates has created a critical need for secure, transparent, and globally interoperable verification systems. Traditional certificate validation approaches are largely centralized, inefficient, and unable to support seamless verification across institutions and borders. While blockchain technology offers a tamper-resistant solution, existing implementations are typically confined to single-chain architectures, leading to scalability limitations, high operational costs, and restricted interoperability. This paper proposes a scalable cross-chain blockchain framework for global certificate authentication and validation, integrating multiple blockchain networks through interoperability protocols to enable efficient cross-platform communication. The framework adopts a hybrid design that combines on-chain storage for verification data with off-chain distributed storage using IPFS to reduce storage overhead and improve performance. Smart contracts are employed to automate certificate issuance, validation, and revocation processes, ensuring transparency and security. Experimental evaluation demonstrates that the proposed system significantly enhances scalability, reduces transaction costs, and improves verification speed while maintaining high levels of trust and data integrity. This work contributes a robust and practical solution for next-generation digital credential management systems
Accurate crop recommendation is essential for sustainable agriculture, yet existing machine learning and deep learning models often struggle with high-dimensional, redundant soil and environmental features, leading to reduced generalization in real-world settings. To address this limitation, this study presents a hybrid Firefly-optimized Autoencoder model that integrates nonlinear representation learning with metaheuristic feature refinement. The Autoencoder compresses NPK nutrient values, temperature, humidity, rainfall, and pH into a compact latent space, while the Firefly Algorithm selects the most discriminative latent dimensions by maximizing feature variance. A deep neural classifier trained on the optimized features achieves highly precise multi-class prediction. The final results tested on a benchmark 22-crop dataset, conducted using Google Colab Pro, shows that the proposed model attains 99.32% accuracy, outperforming advanced methods such as TCN (99.09%), CNN–LSTM federated learning (98.77%), and tuned Random Forest (99.05%). The results highlight the significance of combining latent-space learning with swarm intelligence to deliver a robust, scalable, and high-accuracy crop recommendation system.
Alzheimer's Disease (AD) is characterized by amyloid-β (Aβ) deposits and neurofibrillary tangles containing phosphorylated tau protein. Around 57 million people worldwide were estimated by the World Health Organization to have dementia in 2021, with AD being the most prevalent type. The burden of AD is still rising. By 2036, there will likely be close to 20 million AD cases worldwide, according to projections that show a sharp increase brought on by population aging. Normal neural transmission is disrupted with Aβ plaques and hyperphosphorylated tau tangles, eventually resulting in the death of neurons and cognitive impairment. Currently, no therapies provide a cure; available treatments are symptomatic. The main focus of scientists has been on targeting Aβ and tau proteins. A number of anti-Aβ and anti-tau monoclonal antibody therapies targeting the buildup of Aβ and tau in the brain have been discovered recently. An announcement was made in September 2022 regarding the phase 3 research, suggesting Lecanemab had achieved its main objective of slowing the progression of clinical dementia. The CLARITY AD Phase 3 trial, which was published in the New England Journal of Medicine on January 5, 2023, assessed lecanemab, a monoclonal antibody, in patients with early-stage Alzheimer's disease or Mild Cognitive Impairment (MCI) brought on by Alzheimer's disease who had biomarker evidence of amyloid pathology. Lecanemab showed a statistically significant, although clinically modest, slowing of decline on the Clinical Dementia Rating-Sum of Boxes (CDR-SB) in the 18-month double-blind, placebocontrolled trial. Clinical worsening was reduced by 27% when compared to placebo (mean difference of -0.45; 95% CI: -0.67 to -0.23). Similar modest benefits were observed across quality of life and daily functioning measures, and all primary and secondary cognitive and functional endpoints were met. The change in CDR-SB fell short of the minimal clinically significant difference that is frequently mentioned for this patient population, and the observed clinical benefit is typically characterized as small to moderate. Crucially, lecanemab-treated participants had a higher incidence of amyloidrelated imaging abnormalities (ARIA) such as edema and microhemorrhages (about 21.5%) than the placebo group (about 9%). Although the majority of ARIA events were controllable, there were a few documented instances of severe consequences, especially when anticoagulation was used. On 9th June 2023, an FDA advisory group unanimously decided that Lecanemab demonstrates clinical benefits for therapy of early Alzheimer's disease, marking the opening door for full approval. The results demonstrate lower Aβ and tau, a real therapeutic advantage, going beyond simple alterations in biomarkers, representing a major breakthrough in the treatment of AD. This review provides a summary of mechanisms, results of the clinical trials of anti-tau and anti-Aβ antibody treatments up to February 2024, as well as recommendations for their future developments.
Adverse drug reactions (ADRs) are frequently under-recognized in patients with gastrointestinal disorders. Differences may exist between patient-reported experiences and formal pharmacovigilance records. Therefore, it is of interest to evaluate self-reported ADRs among 265 adults receiving treatment for gastrointestinal conditions and compared findings with regional pharmacovigilance data from the same period. A structured and pretested survey collected demographic, clinical and ADR-related information. Overall, 51.7% reported at least one ADR, most commonly abdominal discomfort (27.2%), nausea (21.9%) and headache (17.4%). Awareness of formal ADR reporting systems was low, with only 38.5% having heard of such systems and 25.7% knowing how to report. Concordance between patient reports and pharmacovigilance data was high for gastrointestinal and central nervous system reactions but lower for severe and dermatological reactions. Female gender, higher education and polypharmacy were significantly associated with increased ADR reporting (p < 0.05). Thus, gaps in awareness of ADR reporting support integration of patient-reported outcomes into pharmacovigilance systems.
