Synthesis, Characterization and Bioactivities of Some Novel Oxovanadium(IV) Glycinato Complexes

The novel oxovanadium(IV) complexes, [VIVO(GlyH)(Gly)]+ClO4 - .H2O (1), [VIVO(GlyH)(Gly)]+NO3 - .H2O (2), [VIVO(GlyH)(Gly)]+CH3COO- .H2O (3) were synthesized and characterized by FT-IR, UV-Vis and 1H NMR spectroscopic measurements. The cumulative spectroscopic assessment envisaged that, the complexes adopt a square pyramidal structure, in which the two glycine ligands coordinate to vanadium(IV) center in bidentate fashions conforming a homoleptic structure. The amino nitrogen and a carboxylato oxygen atom coordinate the vanadium(IV) center from both sides making a five members chelate by each side. All the complexes are stable in amorphous state and in aerobic and anaerobic solution. Significantly, all the complexes have the antifungal activities against Aspergillus niger and Penicillium notatum but ineffective against Candida tropicalis. No antibacterial activity was observed for the complexes against tested bacteria and unfortunately, they were found cytotoxic against brine shrimp bioassay

Precision cardiodiet: transforming cardiac care with artificial intelligence-driven dietary recommendations

The subject matter of this research revolves around addressing the escalating global health threat posed by cardiovascular diseases, which have become a leading cause of mortality in recent times. The goal of this study was to develop a comprehensive diet recommendation system tailored explicitly for cardiac patients. The primary task of this study is to assist both medical practitioners and patients in developing effective dietary strategies to counter heart-related ailments. To achieve this goal, this study leverages the capabilities of machine learning (ML) to extract valuable insights from extensive datasets. This approach involves creating a sophisticated diet recommendation framework using diverse ML techniques. These techniques are meticulously applied to analyze data and identify optimal dietary choices for individuals with cardiac concerns. In pursuit of actionable dietary recommendations, classification algorithms are employed instead of clustering. These algorithms categorize foods as "heart-healthy" or "not heart-healthy," aligned with cardiac patients’ specific needs. In addition, this study delves into the intricate dynamics between different food items, exploring interactions such as the effects of combining protein- and carbohydrate-rich diets. This exploration serves as a focal point for in-depth data mining, offering nuanced perspectives on dietary patterns and their impact on heart health. The method used central to the diet recommendation system is the implementation of the Neural Random Forest algorithm, which serves as the cornerstone for generating tailored dietary suggestions. To ensure the system’s robustness and accuracy, a comparative assessment involving other prominent ML algorithms—namely Random Forest, Naïve Bayes, Support Vector Machine, and Decision Tree, was conducted. The results of this analysis underscore the superiority of the proposed -based system, demonstrating higher overall accuracy in delivering precise dietary recommendations compared with its counterparts. In conclusion, this study introduces an advanced diet recommendation system using ML, with the potential to notably reduce cardiac disease risk. By providing evidence-based dietary guidance, the system benefits both healthcare professionals and patients, showcasing the transformative capacity of ML in healthcare. This study underscores the significance of meticulous data analysis in refining dietary decisions for individuals with cardiac conditions

Breast tumor prediction and feature importance score finding using machine learning algorithms

The subject matter of this study is breast tumor prediction and feature importance score finding using machine learning algorithms. The goal of this study was to develop an accurate predictive model for identifying breast tumors and determining the importance of various features in the prediction process.  The tasks undertaken included collecting and preprocessing the Wisconsin Breast Cancer original dataset (WBCD). Dividing the dataset into training and testing sets, training using machine learning algorithms such as Random Forest, Decision Tree (DT), Logistic Regression, Multi-Layer Perceptron, Gradient Boosting Classifier, Gradient Boosting Classifier (GBC), and K-Nearest Neighbors, evaluating the models using performance metrics, and calculating feature importance scores. The methods used involve data collection, preprocessing, model training, and evaluation. The outcomes showed that the Random Forest model is the most reliable predictor with 98.56 % accuracy. A total of 699 instances were found, and 461 instances were reached using data optimization methods. In addition, we ranked the top features from the dataset by feature importance scores to determine how they affect the classification models. Furthermore, it was subjected to a 10-fold cross-validation process for performance analysis and comparison. The conclusions drawn from this study highlight the effectiveness of machine learning algorithms in breast tumor prediction, achieving high accuracy and robust performance metrics. In addition, the analysis of feature importance scores provides valuable insights into the key indicators of breast cancer development. These findings contribute to the field of breast cancer diagnosis and prediction by enhancing early detection and personalized treatment strategies and improving patient outcomes

Digital marketing in Bangladesh : A comprehensive analysis of challenge and prospects

Digital marketing is a modern concept. Digital marketing is an alternate name for business or Trade. Traditionally we know business means seller and buyer will meet face to face at a place and buyer will buy some things and pay money hand to hand to seller. Digital marketing is an online-based business or web-based business. Here everything is computerized. Generally, buyers and sellers are introduced online and have no chance to have face-to-face meetings. Everything is managed online. The buyer will place an order online/ in the Internet, the seller will supply the product/service accordingly to the buyer's demand to the instructed place. So, in the modern sense market is not a specific place, the whole world is a market. This is the new dimension of e-business or e-commerce. Electronic commerce or digital marketing involves the buying and selling of products or services over the internet. Simply, digital marketing means conducting business through an online platform or web-based platform. Very recently Bangladesh enters into the digital marketing. But still it’s not spread widely. Bangladesh is trying to go fast but here are some problems and challenge beside that here are some opportunities and prospect to expand the digital marketing also. In these studies, I have found the barrier and opportunities in digital marketing through a set of questions and answers with analytical analysis through quantitative methods in Bangladesh prospect. I have also pointed out the problems and challenges and I gave suggestions and recommendations on how possible to overcome them. Now in Bangladesh how companies are operating their digital marketing, I also reflected on the companies and their activities in this study

Fabrication of platinum nitrogen-doped graphene nanocomposite modified electrode for the electrochemical detection of acetaminophen

An electrochemical sensor for the determination of acetaminophen (AC) was fabricated on glassy carbon electrode (GCE) modified with platinum nitrogen-doped graphene (Pt/NGr) nanocomposite. Electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV) and square-wave voltammetry (SWV) were performed to investigate the electrochemical behaviour of AC. From the electrochemical results, the synergy between platinum nanoparticles and nitrogen-doped graphene improved the interfacial electron transfer process, thus exhibited a higher catalytic performance towards the electrochemical oxidation of AC. A linear range between 0.05–90 μmol L−1 for the determination of AC was achieved with a limit of detection of 0.008 μmol L−1. The prepared sensor demonstrated acceptable reproducibility, stability and good selectivity in the presence of interferences such as ascorbic acid, p-aminophenol and dopamine. In addition, this method showed satisfactory results in commercial tablets

A Review of Electrochemical Reduction of Sodium Metaborate

The recycling of sodium borohydride poses a huge challenge to the drive towards a hydrogen economy. Currently, mechano-chemical, thermo-chemical and electrochemical are the only reported methods of recycling sodium metaborate into sodium borohydride. Much attention has been devoted to the mechano-chemical and thermo-chemical methods of reduction, but little focus has been devoted to electrochemical methods. This review describes the electrochemical behaviour of borohydride (BH4−) and metaborate (BO2−) anions in alkaline solutions. The BH4− is stabilized in highly concentrated alkaline solutions, while the electro-oxidation of BH4− is dependent on the type of electrode material. The attempts to electro-reduce the BO2− into BH4− is reviewed and the challenges, suggestions and future outlook of electro-reduction for the recycling of BO2− into BH4− is highlighted

Hybrid nanocellulose/f-MWCNTs nanocomposite for the electrochemical sensing of diclofenac sodium in pharmaceutical drugs and biological fluids

A nanocomposite from a green nanomaterial (nanocellulose) and f-MWCNTs was modified onto glassy carbon (GC) electrode for the detection of diclofenac sodium (DCF), a non-steroidal anti-inflammatory drug (NSAID) and widely used electroactive painkiller. The presence of OH groups in the nanocellulose provides more binding sites for different analytes. This ensures an axial modulus rearrangement and the incorporation of f-MWCNTs which provides larger surface area, high mechanical strength and improved electrical conductivity. On the other hand, the synergy between both compounds enhances the electrochemical detection of DCF in human blood and urine. Under optimum conditions, the modified electrode exhibited a remarkable improvement in the anodic peak current (41.6 μA) for 50 μM DCF at 0.677 V peak potential. The newly fabricated electrode showed two linear dynamic ranges from 0.05 to 1.00 μM and 2–250 μM DCF with low detection limit of 0.012 μM. In addition, differential pulse voltammetry (DPV) and cyclic voltammetry (CV) showed good sensitivity and selectivity for the determination of DCF. With this technique, the modified electrode was very effective and suitable for DCF determination from commercial tablets, ampoules (pharmaceutical preparations) and also from clinical preparations (human blood serum and urine sample) with good recoveries. © 2019 Elsevier Lt

The Effect of Acid Hydrolysis Parameters on the Properties of Nanocellulose Extracted from Almond Shells

The effects of acid hydrolysis parameters (acid type, acid concentration, reaction time, and temperature) on the properties of nanocellulose (NC) extracted from almond shells were investigated. The highest percentage yield of NC obtained from each type of acid was 71.8% using 25% w/v sulfuric acid at 50°C for 60 min, followed by 71.1% with 50% w/v perchloric acid at 35°C for 15 min, and finally 67% with 50% w/v methane sulfonic acid at 25°C for 10 min. The hydrolysis treatment using sulfuric and perchloric acids greatly affect the NC particle size and length. The extracted NCs show different thermal degradation profiles due to the difference in the crystallinity index and types of acid used. Hydrolysis with strong acids (sulfuric and perchloric acids) produce NC with lower crystallinity due to the degradation of both amorphous and crystalline regions in the cellulose. Furthermore, BET analysis revealed the NCs from the hydrolysis using sulfuric acid possesses the highest surface area, desorption/adsorption pore diameter, desorption, and adsorption cumulative pore volume compared to other acids

Enhanced amperometric detection of paracetamol by immobilized cobalt ion on functionalized MWCNTs - Chitosan thin film

In the present study, a nanocomposite of f-MWCNTs-chitosan-Co was prepared by the immobilization of Co(II) on f-MWCNTs-chitosan by a self-assembly method and used for the quantitative determination of paracetamol (PR). The composite was characterized by field emission scanning electron microscopy (FESEM) and energy dispersive x-ray analysis (EDX). The electroactivity of cobalt immobilized on f-MWCNTs-chitosan was assessed during the electro-oxidation of paracetamol. The prepared GCE modified f-MWCNTs/CTS-Co showed strong electrocatalytic activity towards the oxidation of PR. The electrochemical performances were investigated by cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and differential pulse voltammetry (DPV). Under favorable experimental conditions, differential pulse voltammetry showed a linear dynamic range between 0.1 and 400 μmol L−1 with a detection limit of 0.01 μmol L−1 for the PR solution. The fabricated sensor exhibited significant selectivity towards PR detection. The fabricated sensor was successfully applied for the determination of PR in commercial tablets and human serum sample

Tri-metallic Co-Ni-Cu based metal organic framework nanostructures for the detection of an anticancer drug nilutamide

This work reports a biosensor based on tri-metallic organic framework (MOF) for the determination of an anticancer therapeutic agent, nilutamide (NLM). The tri-metallic Co-Ni-Cu-MOF and the single metal MOFs Co-MOF, Ni-MOF and Cu-MOF were grown onto nickel foam (NF) substrate by a solvothermal method. The crystal, chemical structure, morphology and surface area of the MOFs were studied by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (FESEM), energy dispersive X-ray (EDX) transmission electron microscopy (TEM) and Brunauer-Emmett-Teller (BET) analysis. The electrochemical sensing of NLM by the Co-Ni-Cu-MOF/NF and the single metal MOFs grown onto NF were studied by cyclic voltammetry (CV), differential pulse voltammetry (DPV) and electrochemical impedance spectroscopy (EIS). The CV of the Co-Ni-Cu-MOF/NF shows a redox couple between -0.4 V and 0.3 V in phosphate buffer solution at pH 7.0. The Co-Ni-Cu-MOF/NF sensor shows a strong electrocatalytic activity towards the redox reaction of NLM with a broad concentration range from 0.5–70 μM and 70–900 μM with high sensitivity of 10.712 μA μM−1 cm-2 and a low limit of detection of 0.48 ± 0.02 nM. These results confirm that the tri-metallic MOF possesses high selectivity and sensitivity for the determination of NLM from human serum specimen and pharmaceutical tablet dosage forms