Dr. Babasaheb Ambedkar Marathwada University

This paper is aimed at studying the dynamics of community transmission of HIV by constructing a fractal fractional mathematical model whose kernel is a generalized Mittag–Leffler type. First, we collect and analyze statistical data for epidemiological surveillance of HIV/AIDS prevalence in Yemen from 2000 to 2022. Then, we employ the statistical analysis software EViews and apply ARIMA models to predict the number of HIV/AIDS cases from 2023 to 2024. The results of the selected model, free of standard problems, predicted a future increase in HIV/AIDS cases in Yemen. Next, relying on the well-known fixed-point theorem and a set of other associated results, we prove the existence and uniqueness results of the fractional model. Moreover, we use the Adams–Bashforth method to approximate the solutions of this system numerically. Finally, we plot, tabulate, and simulate our results using the Mathematica software and compare them to the results obtained from the statistical model.

Background Cancer is one of the serious health issues and the leading cause of mortality worldwide. Several studies have demonstrated that the overexpression of growth factors and receptors, the triggering of oncogenes, and the deactivation of tumor suppressor genes are the main causes of aggressive and resistant forms of cancer. The epidermal growth factor receptor (EGFR) is a receptor that medications target for cancer treatment. Objective The present study employs computational approaches to explore the anticancer activity of newly identified indole alkaloids from Zanthoxylum nitidum against EGFR kinase. Method Computational techniques, including molecular docking, density functional theory (DFT), and in-silico pharmacokinetic studies, were employed to evaluate the ligand-target interactions. Additionally, drug-likeness was assessed using the Lipinski rule of five. Result and Discussion We evaluated their pharmacokinetics, binding interactions, and stability using molecular docking, drug-likeness prediction, absorption, distribution, metabolism, and excretion (ADMET) profiling, simulations study, and density functional theory (DFT) study. Nitidumalkaloid C showed remarkable binding affinity (-9.7 kcal/mol) to epidermal growth factor receptor tyrosine kinase, while that of standard drugs showed dacomitinib (-9.0 kcal/mol) and osimertinib (-7.9 kcal/mol). The molecular dynamics MD simulation study revealed stable interactions, with nitidumalkaloid C exhibiting the highest stability. These findings indicate indole alkaloids as potentially effective anticancer medicines, with nitidumalkaloid C demanding further modification for pharmaceutical development. This research informs nitidumalkaloid C as a potential indole alkaloid by providing insights into molecular characteristics and binding energies. Conclusión These parameters allow consideration of the most promising candidate, nitidumalkaloid C, for novel anticancer drug development to overcome gene mutations or resistance in EGFR-TK.

Cobalt oxide nanoparticles are increasingly studied for their low cost and high stability. However, their limited photocatalytic performance, primarily due to faster electron–hole recombination, has prompted extensive research over the past decade. To overcome this challenge, we have synthesized Cu-doped Co3O4 nanoparticles using a facile coprecipitation method at room temperature. The X-ray diffraction (XRD) analysis revealed a reduction in the crystalline size of Cobalt Oxide nanoparticles after doping. FESEM showed porous hexagonal nanosheets as the underlined structure of synthesized Cu-doped Co3O4. FTIR spectra confirmed the spinel structure and high purity of the nanosheets. Brunauer–Emmett–Teller (BET) analysis showed a considerable enhancement in the specific surface area following doping, increasing from 4.21 m²/gm for pristine Co3O4 to 80.75 m²/gm for 7.5% Cu-doped Co3O4. UV–Vis spectra of doped nanosheets showed a red shift in the characteristic absorbance indicating a lowering of the optical band gap. The Tauc plot revealed the optical band gap of the 7.5% Cu-doped Co3O4 to be 2.02 eV in contrast to the 2.41 eV of the pristine Co3O4 nanosheets. Furthermore, UV and visible radiation-induced degradation of Crystal Violet dye was conducted in the presence of the synthesized nanosheets to evaluate their efficiency. Remarkably, the 7.5% Cu-doped Co3O4 exhibited degradation of 92.79% and 97.02% under UV and visible radiation respectively in 20 min. These findings emphasize the influence of the Cu-doping structural, optical and photocatalytic properties of Cobalt oxide nanoparticles under UV as well as visible radiation.

Sustainable development aims to satisfy current demands without risking the capacity of future generations to meet their own. Various policies and measures influence food security and sustainable development, including productivity, sustainability outcomes, price volatility, public-private partnerships, and agriculture. The price volatility of commodities contributes to national food insecurity, as nearly one-third of people rely on agricultural production. Beekeeping is crucial in increasing yield, as bees pollinate flowers, resulting in a one-fourth increase in crop yields. Furthermore, bees provide ecosystem services for sustainable horticulture, agriculture, and food and nutrition security. The quality and quantity of agricultural outputs are enhanced by honeybee pollination services, which raise market value and growers’ earnings. In the United States, pollination generates USD 16 billion annually, with 12 billion due to honeybee accessibility. Culturing stingless honeybees for pollination can be an effective solution as they can withstand severe droughts and low temperatures and require minimal maintenance. In addition to their economic value and contribution to sustainable global food security, these indigenous pollinators support healthy ecosystems needed for clean air, stable soil, clean water, and diverse wildlife.

Introduction Viral infections can cause pneumonia, which is difficult to diagnose using chest X-rays due to its similarities with other respiratory conditions. Current pneumonia diagnosis techniques have limited accuracy. Novelty, of this research is developed a application of deep learning algorithms is essential in enhancing the medical infrastructure used in the diagnosis of chest diseases via the integration of modern technologies into medical devices. Methods This study presents a transfer learning approach, using MobileNetV2, VGG-16, and ResNet50V2 to categorize chest disorders via X-ray images, with the objective of improving the efficiency and accuracy of computer-aided diagnostic systems (CADs). This research project examines the suggested transfer learning methodology using a dataset of 5,863 chest X-ray images classified into two categories: pneumonia and normal. The dataset was restructured to 224 × 224 pixels, and augmentation techniques were used during the training of deep learning models to mitigate overfitting in the proposed system. The classification head was subjected to regularization to improve performance. Many performance criteria are typically used to evaluate the effectiveness of the suggested strategies. The performance of MobileNetV2, given its regularized classification head, exceeds that of the previous models. Results The suggested system identifies images as members of the two categories (pneumonia and normal) with 92% accuracy. The suggested technique exhibits superior accuracy as compared to currently available ones regarding the diagnosis the chest diseases. Discussion This system can help enhance the domain of medical imaging and establish a basis for future progress in deep-learning-based diagnostic systems for pulmonary disorders.

Green synthesis approach has emerging in recent years because of its low cost, less toxic, and environmentally friendly nature. The present study is focused on the preparation of Cr³⁺ doped nickel ferrite nanoparticles by sol-gel auto-combustion route by employing a green synthesis approach utilizing an extract of ginger. X-ray diffraction studies prove the single-phase nature of cubic spinel structure. Crystallite size obtained from Debye Scherrer equation varies between 11 and 16 nm establishing the nanocrystalline nature. The structural parameters like lattice constant, unit cell volume, etc. increases with Cr³⁺ doping. The characteristic absorption bands are seen in FTIR spectra at around 400 cm− 1 and 600 cm− 1. FE-SEM images revealed the spherical structure with grain size of the order of 22 to 27 nm. Raman spectra of spinel ferrite show five Raman active modes. The magnetization measurements show saturation magnetization of the order of 32.23 emu/gm, which goes on decreasing with Cr³⁺ doping. In the photocatalytic application, the dye degradation efficiency of the order of 90% was observed for methylene blue on adding 0.3 gm of nickel ferrite nano-catalyst. The degradation efficiency decreases. Thus, the prepared Cr³⁺ doped nickel ferrite magnetic nano-catalyst shows high catalytic activity under visible light.

Silica-based iron trichloroacetate and trifluoroacetate prompted green synthesis of 4H-chromene-3-carboxamide have been achieved successfully by an economic, sustainable, and environment eco-friendly three-component one-pot reaction of aldehydes, acetoacetanilide, and dimedone. The current synthetic protocol offers several key features, including the ability to enable a wide range of functional groups, rapid reaction, excellent product yields, mild reaction conditions, solvent-free synthesis, and reusability of catalysts. In the current synthetic protocol, both catalysts work fabulously in water and many of the organic solvents; however, the best reaction results were attained at 80 °C temperature via solvent-free medium with excellent reaction atom economy, effective mass yield, and mass efficiency. In addition, the DFT studies of 4H-chromene-3-carboxamides were also performed to showcase the capacity of computational chemistry for elucidating and forecasting the reactivities. As a result, the combination of computational chemistry and experiment straightforwardly became a powerful tool for developing the current synthetic protocol.

This chapter explores the profound effects of artificial intelligence (AI) on education and e-learning, as well as our prospects going forward with the as-yet-undiscovered uses of AI in educational contexts. This chapter begins with a review of the limitations that traditional educational systems face, followed by a summary of the fundamental ideas behind artificial intelligence (AI), which gives the reader a roadmap for exploring the world of AI in education. This chapter goes into great length about artificial intelligence (AI) in education, covering everything from intelligent tutoring systems and individualised learning platforms to automated assessment and grading. It delves into the adaptive intelligence that AI seems to possess and how it modifies course materials to fit each student’s needs, making learning more personalised and immersive. The versatility of artificial intelligence in various facets of education is further demonstrated by its application in intelligent content creation, gaming, and language instruction.

Aluminum-incorporated nickel ferrite (NiFe2−xAlxO4, x varying between 0.0 and 0.5) nanoparticles were synthesized taking ginger extract as a green fuel. The single-phase formation was proved by the analysis of the X-ray diffraction (XRD) pattern. The average size of the crystals (D), varies between 10 and 15 nm. Fourier transform infrared spectroscopy (FTIR) technique shows two broad bands in the range 370 to 415 cm⁻¹ and 551 to 573 cm⁻¹. Force constant, Debye temperature, and elastic constants determined using FTIR data show a strong dependence on Al³⁺ ions. The (FE-SEM) images reflect the formation of grains with more or less spherical symmetry with an average grain size of 31 nm. The band gap calculated using a Tauc plot varies between 1.44 and 2.19 eV. The magnitude of all the magnetic parameters decreases with increasing aluminum content x and shows superparamagnetic behavior. The composition NiAl0.2Fe1.8O4 (x = 0.2) was found most promising as an antimicrobial activity agent. The antibacterial and antifungal activities for typical samples (x = 0.0, 0.2, 0.4) were tested against gram-positive, gram-negative, and fungi. The results are compared with commercial drugs and are correlated with structural and microstructural parameters.

The Oxygen Evolution Reaction (OER) is considered to be one of the most important processes for energy storage and conversion applications. However, due to its sluggish kinetics it becomes extremely crucial to design and develop cost‐effective, stable and highly efficient electrocatalysts. Herein, we report two lead(II) based coordination polymers (CPs) {[Pb2(TPBN)(TDC)2].3H2O}n (CP1), and {[Pb2(TPBN)(FDC)2].H2O}n (CP2) synthesized via self‐assembly at ambient conditions in good yields (where TPBN= N,N′,N′′′,N′′′′‐tetrakis‐(2‐pyridylmethyl)‐1,4‐diaminobutane, H2TDC=2,5‐2,5‐thiophenedicarboxylic acid, and H2FDC=2,5‐furandicarboxylic acid. Their 3D structures were determined by Single‐crystal X‐ray diffraction (SCXRD) studies. The bulk purity and thermal stability of CP1 and CP2 were studied by powder X‐ray diffraction studies and thermogravimetric analysis, respectively. Both the CPs showed remarkable catalytic activity for the OER. With a current density of 15 mA cm⁻², for both CP1 and CP2, overpotentials of 570 mV and 630 mV with tafel slope values of 112 mV dec⁻¹, and 178 mV dec⁻¹ respectively for CP1 and CP2 in 1.0 M alkaline (KOH) solution was obtained. It is notable that the difference in their catalytic performance – CP1 is better than CP2, can be attributed to the subtle single heteroatom in the dicarboxylate ring (S in thiophene and O in furan, respectively).

Despite advancements in molecular design rules and understanding biochemical processes, the field of drug design and discovery seeks to minimize the number and duration of synthesis‐testing cycles to convert lead compounds into drug candidates. A promising strategy involves gaining insightful understanding of key heteroatoms such as oxygen and nitrogen. This work presents a comprehensive analysis of oxygen atoms in approved drugs, aiming to streamline drug design and discovery efforts. The study examines the frequency, distribution, prevalence, and diversity of oxygen atoms in a dataset of 2049 small molecules approved by the FDA and other agencies. The analysis focuses on various types of oxygen atoms, including sp ³ , sp ² ‐hybridized, ring, and nonring. In general, existence of sp ³ ‐O slightly outperforms sp ² ‐O, which is associated with balancing various factors such as flexibility, solubility, stability, and pharmacokinetics, in addition to activity and selectivity. In approved drugs, majority of oxygen atoms are present within 4 Å from the COM of the molecule. This analysis offers valuable understanding of oxygen distribution, which could be used during the multiparameter optimization process, facilitating the transformation of a hit/lead compound into a potential drug candidate.

p>The author has identified an error in the department of the authors in the article titled “Highly Efficient Bimetallic Catalyst for the Synthesis of N-substituted Decahydroacridine-1,8-diones and Xanthene-1,8-diones: Evaluation of their Biological Activity” published in Current Organic Synthesis, 2024, 21(3) [1]. Details of the error and a correction are provided here. ORIGINAL 1. SANDEEP T. ATKORE Department of Chemistry, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, 431004, Maharashtra, India 2. PRANITA V. RAITHAK Department of Clinical Biochemistry, Dr. Baba Saheb Ambedkar Marathwada University, Aurangabad, 431004, Maharashtra, India CORRECTED 1. SANDEEP T. ATKORE Department of Biochemistry, Dr. Baba Saheb Ambedkar Marathwada University, Aurangabad, 431004, Maharashtra, India 2. PRANITA V. RAITHAK Department of Botany, Dr. Baba Saheb Ambedkar Marathwada University, Aurangabad, 431004, Maharashtra, India We regret the error and apologize to readers. The original article can be found online at: https://www.benthamscience.com/article/136681</p

Spatio-temporal aspect is the most useful aspect of crime prevention as it is patterned and predictable. This study is mainly conducted to explore the spatio-temporal hotspots of burglary and to test whether spaces of burglary are constrained by time in Ahmednagar city. The temporal hotspots are identified by their frequency in hours. Spatial hotspots within identified temporal hotspots are visualized through GIS maps prepared using Kernel density estimation with natural break methods. The current results and their probable causation are compared with the theories in crime geography and discussed thoroughly. The burglary hotspots in Ahmednagar city are found to vary by space–time together than individual space or time. Spaces of burglary hotspots in Ahmednagar city are found unstable over time indicating that spatial hotspots of burglary are constrained by temporal hotspots. Consistent with existing literature, probable causation behind spatio-temporal variation and temporal constraint of burglary hotspots is closely associated with the combined effect of the land use pattern of the area, movement of mass population within the area, distribution of targets and offenders in the area, and socio-economic condition of the area. This study proposes tailored measures for each burglary hotspot, considering their unique spatial, temporal, socio-economic, and environmental characteristics, to effectively reduce, control, and prevent burglary incidents in the respective areas.

The main objective of this work is to study the mathematical model that combines stem cell therapy and chemotherapy for cancer cells. We study the model using the fractal fractional derivative with the Mittag-Leffler kernel. In the analytical part, we study the existence of the solution and its uniqueness, which was studied based on the fixed point theory. The equilibrium points were also studied and discussed after stem cell therapy, and the approximate solutions for the given model were obtained using the Adam Bashford method, which depends on interpolation with Lagrange polynomials. Finally, the model was simulated using the Mathematica software, and through the figures, we found that the components of the model approach the equilibrium point, which indicates the stability of the model at the equilibrium point. Also, the result of the numerical simulation and graphic for the concentration of cells over time indicate the effects of the therapies on the decay rate of tumor cells and the growth rate of effector cells to modify the cancer patient’s immune system. It is worth noting that we simulated all the model components with different fractional orders, confirming the effect of stem cell therapy and chemotherapy on the cells and the decay of cancer cells.

Introduction Bones are a fundamental component of human anatomy, enabling movement and support. Bone fractures are prevalent in the human body, and their accurate diagnosis is crucial in medical practice. In response to this challenge, researchers have turned to deep-learning (DL) algorithms. Recent advancements in sophisticated DL methodologies have helped overcome existing issues in fracture detection. Methods Nevertheless, it is essential to develop an automated approach for identifying fractures using the multi-region X-ray dataset from Kaggle, which contains a comprehensive collection of 10,580 radiographic images. This study advocates for the use of DL techniques, including VGG16, ResNet152V2, and DenseNet201, for the detection and diagnosis of bone fractures. Results The experimental findings demonstrate that the proposed approach accurately identifies and classifies various types of fractures. Our system, incorporating DenseNet201 and VGG16, achieved an accuracy rate of 97% during the validation phase. By addressing these challenges, we can further improve DL models for fracture detection. This article tackles the limitations of existing methods for fracture detection and diagnosis and proposes a system that improves accuracy. Conclusion The findings lay the foundation for future improvements to radiographic systems used in bone fracture diagnosis.

This is the study of CoFe2O4 and CoAl0.2Fe1.8O4 ferrite nanoparticles synthesized by sol–gel method, and their structural and magnetic properties with aluminum doping are studied here. Metal nitrates of high purity were used as chelating and polymerization agents along with citric acid and ethylene glycol for the synthesis. The insertion of Al³⁺ ions into the structure was confirmed by XRD in the present study, where values of lattice parameters decreased and crystallite size decreased. The FT-IR result indicated the same that the Al³⁺ ions sample has the better metal–oxygen bond. The SEM–EDS results indicated that the addition of Al³⁺ resulted in finer particles relative to the undoped sample. Saturation magnetization, Ms, was reduced from 80 emu/g to 70 emu/g; Hc was decreased to 650 Oe to 500 Oe; and the anisotropy constant K1 was decreased to 1.2 × 10⁶ erg/cm³ to 9.5 × 10⁵ erg/cm³ from VSM result confirming the lower magnetic hardness due to the Al³⁺ doping. Based on the study results, it is clear that the dope of the Al³⁺ donor has a dramatic effect on the properties of ferrite nanoparticles.

In this work, an amino-functionalized graphene oxide/polypyrrole (AMGO/PPy) composite-based novel sensing platform was established to monitor lead ions (Pb²⁺) at high sensitivity. AMGO was synthesized through a hydrothermal process and later formed a composite with PPy at varying concentrations. A physicochemical investigation of the synthesized materials was carried out using various characterization tools, while the electrochemical properties were examined by cyclic voltammetry (CV), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS) methods. The AMGO/PPy composite was deposited on a glassy carbon electrode (GCE), which was used for the real-time electrochemical detection of Pb²⁺. The AMGO/PPy sensor exhibited lower limits of detection (LOD) of 0.91 nM. In addition, the developed Pb²⁺ sensor exhibited excellent reproducibility, repeatability, selectivity, sensitivity, and long-term stability for 25 days. The AMGO/PPy composite emerges as a ground-breaking material for the electrochemical detection of Pb²⁺, holding significant potential for environmental monitoring and the protection of human health.

Present communication holds synthesized PANI-GO functionalized with EDTA (chelating ligand) (PANI-GO-EDTA) which possess octahedral geometry to catch up the heavy metal ions at specific pH. The synthesized composites were applied for detection of Pb (II) ions. The detection was carried out under different experimental conditions using differential pulse stripping voltammtry (DPSV). In this study the materials are studied to assess the repeatability, reproducibility and detection of heavy metal ions at another pH which was 4.5 for Pb ion. These results were discussed in detail which reflects that, present work holds superior sensitivity, selectivity, repeatability compare with earlier reported data.

The development of an efficient gas‐sensing materials is crucial for applications in healthcare, industry, environmental monitoring, and agriculture. In this study, Mn‐doped CuO thin films were synthesized and evaluated for their gas‐sensing properties. The fabricated films exhibited a monoclinic CuO structure with (002) and (111) orientations, and their morphology reveals a uniform, spherical‐like crystalline distribution. Mn doping adjusts the optical bandgap from 2.11 eV to 2.03 eV. Compared with pure, 3% Mn‐doped CuO‐based sensor demonstrated a high response value of 82% to 20 ppm formaldehyde at room temperature with swift response and recovery times of 9 and 36 s, respectively. The high sensitivity and stability of these sensors make Mn‐doped CuO films a promising candidate for practical gas detection applications.

Nanocatalysis is the newest invention in the area of synthetic chemistry that has changed the process of chemical transformation. The nanocatalysts have various benefits as compared to traditional catalysts, such as simple and economical methods of synthesis, high surface-to-volume ratio, large number of active sites, excellent selectivity, increased stability, rapid recovery, and recyclability. In recent years, nanomaterials have been extensively employed in the production of heterocyclic moieties. This study intends to emphasize the function of distinct nanocatalysts in the synthesis of various nitrogen-containing heterocyclic compounds. An update on the catalytic efficiency of different nanocatalysts, such as magnetic nanocatalysts, nanomixed metal oxides, core-shell nanocatalysts, nano-supported catalysts, and graphene-based nanocatalysts for the production of heterocycles has been provided in this article.