Electron Beam-Supported Fabrication of Biocompatible Silver/iota-Carrageenan for Wound Healing Application.

Silver nanoparticles (AgNPs) are a potent antibacterial agent, especially when used to treat bacteria that are multidrug resistant. However, it is challenging to eliminate the hazardous reducing agents that remain in AgNPs produced by the conventional chemical reduction process. To overcome these challenges, the presented research demonstrates the fabrication of AgNPs using iota-carrageenan (ι-carra) as a carbohydrate polymer using electron beam (EB) irradiation. Well-characterized ι-carra@AgNPs have a face-centered cubic (FCC) structure with spherical morphology and an average size of 26 nm. Herein we explored the approach for fabricating ι-carra@AgNPs that is suitable for scaling up the production of nanoparticles that exhibit excellent water stability. Further, the optimized ι-carra@AgNPs exhibited considerable antibacterial activity of 40% and 30% inhibition when tested with Gram-negative Escherichia coli ATCC 43895 and Gram-positive Staphylococcus aureus (S. aureus) (ATCC 6538), respectively, and low cytotoxicity at 10-50 µg/mL. To establish the potential biomedical application, as proof of the concept, the ι-carra@AgNPs showed significant antibiofilm activity at 20 µg/mL and also showed 95% wound healing abilities at 50 µg/mL compared to the nontreated control groups. Electron beam assisted ι-carra@AgNPs showed significant beneficial effects against specific bacterial strains and may provide a guide for the development of new antibacterial materials for wound dressing for large-scale production for biomedical applications.

Electrochemically microplastic detection using chitosan-magnesium oxide nanosheet.

Microplastics, an invisible threat, are emerging as serious pollutants that continuously affect health by interrupting/contaminating the human cycle, mainly involving food, water, and air. Such serious scenarios raised the demand for developing efficient sensing systems to detect them at an early stage efficiently and selectively. In this direction, the proposed research reports an electrochemical hexamethylenetetramine (HMT) sensing utilizing a sensing platform fabricated using chitosan-magnesium oxide nanosheets (CHIT-MgO NS) nanocomposite. HMT is considered as a hazardous microplastic, which is used as an additive in plastic manufacturers and has been selected as a target analyte. To fabricate sensing electrodes, a facile co-precipitation technique was employed to synthesize MgO NS, which was further mixed with 1% CHIT solution to form a CHIT_MgO NS composite. Such prepared nanocomposite solution was then drop casted to an indium tin oxide (ITO) to fabricate CHIT_MgO NS/ITO sensing electrode to detect HMT electrochemically using cyclic voltammetry (CV) and differential pulse voltammetry (DPV) techniques. To determine the limit of detection (LOD) and sensitivity, DPV was performed. The resulting calibrated curve for HMT, ranging from 0.5 µM to 4.0 µM, exhibited a sensitivity of 12.908 µA (µM)-1 cm-2 with a detection limit of 0.03 µM and a limit of quantitation (LOQ) of 0.10 µM. Further, the CHIT_MgO NS/ITO modified electrode was applied to analyze HMT in various real samples, including river water, drain water, packaged water, and tertiary processed food. The results demonstrated the method's high sensitivity and suggested its potential applications in the field of microplastic surveillance, with a focus on health management.

Exploring the fluorescence properties of tellurium-containing molecules and their advanced applications.

This review article explores the fascinating realm of fluorescence using organochalcogen molecules, with a particular emphasis on tellurium (Te). The discussion encompasses the underlying mechanisms, structural motifs influencing fluorescence, and the applications of these intriguing phenomena. This review not only elucidates the current state of knowledge but also identifies avenues for future research, thereby serving as a valuable resource for researchers and enthusiasts in the field of fluorescence chemistry with a focus on Te-based molecules. By highlighting challenges and prospects, this review sparks a conversation on the transformative potential of Te-containing compounds across different fields, ranging from environmental solutions to healthcare and materials science applications. This review aims to provide a comprehensive understanding of the distinct fluorescence behaviors exhibited by Te-containing compounds, contributing valuable insights to the evolving landscape of chalcogen-based fluorescence research.

Machine Learning-based Deep Analysis of Human Blood using NIR Spectrophotometry Signatures.

BACKGROUND: Non-invasive bio-diagnostics are essential for providing patients with safer treatment. In this subject, significant growth is attained for noninvasive anaemia detection in terms of Hb concentration by means of spectroscopic and image analysis. The lower satisfaction rate is found due to inconsistent results in various patient settings. OBJECTIVE: This observational study aims to present an adaptable point-of-care Near-Infrared (NIR) spectrophotometric approach with a constructive Machine Learning (ML) algorithm for monitoring Haemoglobin (Hb) concentration by considering dominating influencing factors into account. METHODS: To accomplish this objective, 121 subjects (19.2-55.4 years) were enrolled in the study, having a wide range of Hb concentrations (8.2-17.4 g/dL) obtained from two standard Laboratory analyzers. To inspect the performance, the unique dimensionality reduction approaches are applied with numerous regression models using 5-fold cross-validation. RESULTS: The optimum accuracy is found using support vector regression (SVR) and mutual information having 3 independent features i.e. Pearson correlation (r)= 0.79, standard deviation (SD)= 1.07 g/dL, bias=-0.13 g/dL and limits of agreement (LoA)=-2.22 to 1.97 g/dL. Additionally, comparability between two standard laboratory analyzers is found as; r=0.97, SD=0.50 g/dL, bias=0.21 g/dL, and LoA= -0.77 to 1.19 g/dL. CONCLUSION: The precision of ±1 g/dL in 5-fold cross-validation ensures the same performance irrespective of different age groups, gender, BMI, smoking level, drinking level, and skin type. The outcomes with the offered NIR sensing system and an exclusive ML algorithm can accelerate its' requirement at remote locative rural areas and critical care units where continuous Hb monitoring is compulsory.

Employing nano-enabled artificial intelligence (AI)-based smart technologies for prediction, screening, and detection of cancer.

Cancer has been classified as a diverse illness with a wide range of subgroups. Its early identification and prognosis, which have become a requirement of cancer research, are essential for clinical treatment. Patients have already benefited greatly from the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms in the field of healthcare. AI simulates and combines data, pre-programmed rules, and knowledge to produce predictions. Data are used to improve efficiency across several pursuits and tasks through the art of ML. DL is a larger family of ML methods based on representational learning and simulated neural networks. Support vector machines, convulsion neural networks, and artificial neural networks, among others, have been widely used in cancer research to construct prediction models that enable precise and effective decision-making. Although using these innovative methods can enhance our comprehension of how cancer progresses, further validation is required before these techniques can be used in routine clinical practice. We cover contemporary methods used in the modelling of cancer development in this article. The presented prediction models are built using a variety of guided ML approaches, as well as numerous input attributes and data collections. Early identification and cost-effective detection of cancer's progression are equally necessary for successful treatment of the disease. Smart material-based detection techniques can give end consumers a portable, affordable instrument to easily detect and monitor their health issues without the need for specialized knowledge. Owing to their cost-effectiveness, excellent sensitivity, multimodal detection capacity, and miniaturization aptitude, two-dimensional (2D) materials have a lot of prospects for clinical examination of various compounds as well as cancer biomarkers. The effectiveness of traditional devices is moving faster towards more useful techniques thanks to developments in 2D material-based biosensors/sensors. The most current developments in the design of 2D material-based biosensors/sensors-the next wave of cancer screening instruments-are also outlined in this article.

Recent advances in stimuli-responsive tailored nanogels for cancer therapy; from bench to personalized treatment.

To improve the quality of health in a personalized manner, better control over pharmacologically relevant cargo formulation, organ-specific targeted delivery, and on-demand release of therapeutic agents is crucial. Significant work has been put into designing and developing revolutionary nanotherapeutics approaches for the effective monitoring and personalized treatment of disease. Nanogel (NG) has attracted significant interest because of its tremendous potential in cancer therapy and its environmental stimuli responsiveness. NG is considered a next-generation delivery technology due to its benefits like as size tunability, high loading, stimuli responsiveness, prolonged drug release via in situ gelling mechanisms, stability, and its potential to provide personalized therapy from the investigation of human genes and the genes in various types of cancers and its association with a selective anticancer drug. Stimuli-responsive NGs can be used as smart nanomedicines to detect and treat cancer and can be tuned as personalized medicine as well. This comprehensive review article's major objectives include the challenges of NGs' clinical translation for cancer treatment as well as its early preclinical successes and prospects.

Trends and prospects of 2-D tungsten disulphide (WS2) hybrid nanosystems for environmental and biomedical applications.

Recently, 2D layered transition metal dichalcogenides (TMDCs) with their ultrathin sheet nanostructure and diversified electronic structure have drawn attention for various advanced applications to achieve high-performance parameters. Unique 2D TMDCs mainly comprise transition metal and chalcogen element where chalcogen element layers sandwich the transition metal element layer. In such a case, various properties can be enhanced and controlled depending on the targeted application. Among manipulative 2D TMDCs, tungsten disulphide (WS2) is one of the emerging nano-system due to its fascinating properties in terms of direct band gap, higher mobility, strong photoluminescence, good thermal stability, and strong magnetic field interaction. The advancement in characterization techniques, especially scattering techniques, can help in study of opto-electronic properties of 2D TMDCs along with determination of layer variations and investigation of defect. In this review, the fabrication and applications are well summarized to optimize an appropriate WS2-TMDCs assembly according to focused field of research. Here, the scientific investigations on 2D WS2 are studied in terms of its structure, role of scattering techniques to study its properties, and synthesis routes followed by its potential applications for environmental remediation (e.g., photocatalytic degradation of pollutants, gas sensing, and wastewater treatment) and biomedical domain (e.g., drug delivery, photothermal therapy, biomedical imaging, and biosensing). Further, a special emphasis is given to the significance of 2D WS2 as a substrate for surface-enhanced Raman scattering (SERS). The discussion is further extended to commercial and industrial aspects, keeping in view major research gaps in existing research studies.

Chitosan-Grafted-Poly(N-vinylcaprolactam)-Decorated Fe3O4@SiO2 Core-Shell Nanoformulation as an Efficient Drug Delivery System for Poorly Soluble Drugs.

Hydrocortisone, a commonly used anti-inflammatory drug, has limited aqueous solubility and several side effects. To address this challenge, as a proof-of-concept, this article demonstrates the development of a controlled-release drug delivery system (DDS) for hydrocortisone using chitosan-grafted poly(N-vinylcaprolactam) (CS-g-PNVCL)-coated core-shell Fe3O4@SiO2 nanoformulations (NFs). Reported magnetic nanoparticles (NPs) were synthesized and modified with silica, PNVCL, and CS precursors to enhance the biocompatibility of DDS and drug-loading efficiency. The release rate of hydrocortisone from Fe3O4@SiO2@CS-g-PNVCL NFs was observed to be higher at lower pH values, and the smart polymer coating demonstrated temperature responsiveness, facilitating drug release at higher temperatures. Fe3O4@SiO2@CS-g-PNVCL NFs exhibited a cell viability of around 97.2 to 87.3% (5-100 µg/mL) after 24-48 h, while the hydrocortisone-NFs had a cell viability of around 93.2 to 82.3%. Our findings suggest that CS-g-PNVCL-coated Fe3O4@SiO2 NPs effectively enhance the solubility, loading capacity, and targeted delivery of poorly soluble drugs, thereby improving their therapeutic efficacy and bioavailability.

Pathophysiology to advanced intra-articular drug delivery strategies: Unravelling rheumatoid arthritis.

Rheumatoid arthritis (RA) is one of the most prevalent life-long autoimmune diseases with an unknown genesis. It primarily causes chronic inflammation, pain, and synovial joint-associated cartilage and bone degradation. Unfortunately, limited information is available regarding the etiology and pathogenesis of this chronic joint disorder. In the last few decades, an improved understanding of RA pathophysiology about key immune cells, antibodies, and cytokines has inspired the development of several anti-rheumatic drugs and biopharmaceuticals to act on RA-affected joints. However, life-long frequent systemic high doses of commercially available drugs are currently a limiting factor in the efficient management of RA. To address this issue, various single and double-barrier intra-articular drug delivery systems (IA-DDSs) such as nanocarriers, microparticles, hydrogels, and particles-hybrid hydrogel composite have been developed which can exclusively target the RA-affected joint cavity and release the precisely controlled therapeutic drug concentration for prolonged time whilst avoiding the systemic toxicity. This review provides a comprehensive overview of the pathogenesis of RA and discusses the rational design and development of biomaterials-based novel IA-DDs, ranging from conventional to advanced systems, for improved treatment of RA. Therefore, this review aims to unravel the pathophysiology of rheumatoid arthritis and explore cutting-edge IA-DD strategies exploiting biomaterials. It offers researchers a consolidated and up-to-date resource platform to analyze existing knowledge, identify research gaps, and contribute to the scientific literature.

Optimizing Pix2Pix GAN with Attention Mechanisms for AI-Driven Polyp Segmentation in IoMT-Enabled Smart Healthcare.

This paper introduces an innovative approach for automated polyp segmentation in colonoscopy images, deploying an enhanced Pix2Pix Generative Adversarial Network (GAN) equipped with an integrated attention mechanism in the discriminator. Addressing prevalent challenges in conventional segmentation methods, such as variable polyp appearances, inconsistent image quality, and limited training data, our model significantly augments the precision and reliability of polyp segmentation. The integration of an attention mechanism enables our model to meticulously focus on the intricate features of polyps, improving segmentation accuracy. A unique training strategy, employing both real and synthetic data, is adopted to ensure the model's robust performance under a variety of conditions. The results, validated through rigorous tests on multiple public colonoscopy datasets, indicate a notable improvement in segmentation performance over existing state-of-the-art methods. Our model's enhanced ability to detect critical details early plays a pivotal role in proactive colorectal cancer detection, a key aspect of smart healthcare systems. This work represents an effective amalgamation of advanced AI techniques and the Internet of Medical Things (IoMT), signifying a noteworthy contribution to the evolution of smart healthcare systems. In conclusion, our attention-enhanced Pix2Pix GAN not only offers efficient and reliable polyp segmentation, but also showcases considerable potential for seamless integration into remote health monitoring systems, underlining the increasing relevance and efficacy of AI in advancing IoMT-enabled healthcare.

Current Status and Emerging Trends in Colorectal Cancer Screening and Diagnostics.

Colorectal cancer (CRC) is a prevalent and potentially fatal disease categorized based on its high incidences and mortality rates, which raised the need for effective diagnostic strategies for the early detection and management of CRC. While there are several conventional cancer diagnostics available, they have certain limitations that hinder their effectiveness. Significant research efforts are currently being dedicated to elucidating novel methodologies that aim at comprehending the intricate molecular mechanism that underlies CRC. Recently, microfluidic diagnostics have emerged as a pivotal solution, offering non-invasive approaches to real-time monitoring of disease progression and treatment response. Microfluidic devices enable the integration of multiple sample preparation steps into a single platform, which speeds up processing and improves sensitivity. Such advancements in diagnostic technologies hold immense promise for revolutionizing the field of CRC diagnosis and enabling efficient detection and monitoring strategies. This article elucidates several of the latest developments in microfluidic technology for CRC diagnostics. In addition to the advancements in microfluidic technology for CRC diagnostics, the integration of artificial intelligence (AI) holds great promise for further enhancing diagnostic capabilities. Advancements in microfluidic systems and AI-driven approaches can revolutionize colorectal cancer diagnostics, offering accurate, efficient, and personalized strategies to improve patient outcomes and transform cancer management.

Efficient trapping and destruction of SARS-CoV-2 using PECO-assisted Molekule air purifiers in the laboratory and real-world settings.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is transmitted human-to-human via aerosols and air-borne droplets. Therefore, capturing and destroying viruses from indoor premises are essential to reduce the probability of human exposure and virus transmission. While the heating, ventilation, and air conditioning (HVAC) systems help in reducing the indoor viral load, a targeted approach is required to effectively remove SARS-CoV-2 from indoor air to address human exposure concerns. The present study demonstrates efficient trapping and destruction of SARS-CoV-2 via nano-enabled filter technology using the UV-A-stimulated photoelectrochemical oxidation (PECO) process. Aerosols containing SARS-CoV-2 were generated by nebulization inside an air-controlled test chamber where an air purifier (Air Mini+) was placed. The study demonstrated the efficient removal of SARS-CoV-2 (99.98 %) from the test chamber in less than two minutes and PECO-assisted destruction (over 99%) on the filtration media in 1 h. Furthermore, in a real-world scenario, the Molekule Air-Pro air purifier removed SARS-CoV-2 (a negative RT-qPCR result post-running the filter device) from the circulating air in a COVID-19 testing facility. Overall, the ability of two FDA-approved class II medical devices, Molekule Air-Mini+ and Air-Pro air purifiers, to remove and destroy SARS-CoV-2 in indoor settings was successfully demonstrated. The study indicates that as the "tripledemic" of COVID-19, influenza, and respiratory syncytial virus (RSV) overwhelm the healthcare facilities in the USA, the use of a portable air filtration device will help contain the spread of the viruses in close door facilities, such as in schools and daycare facilities.

Quantum-Inspired Heuristic Algorithm for Secure Healthcare Prediction using Blockchain Technology.

People's health is adversely affected by environmental changes and poor nutritional habits, emphasizing the importance of health awareness. The healthcare system encounters significant challenges, including data insufficiency, threats, errors, and delays. To address these issues and advance medical care, we propose a secure healthcare prediction method, prioritizing patient privacy and data transmission efficiency. The Quantum-inspired heuristic algorithm combined with Kril Herd Optimization (QKHO) is introduced for healthcare prediction, along with a comparison to the Deep Forward Neural Network (DFNN) optimized using Krill Herd Optimization (KHO) and Quantum-inspired heuristic algorithm combined with Kril Herd Optimization. The proposed QKHO model outperforms conventional models and exhibits higher accuracy, precision, recall, and F1-score. Blockchain technology ensures secure data transmission to the server, surpassing the security level of existing RSA and Diffie-Hellman algorithms.

Neural stem cells for Parkinson's disease management: Challenges, nanobased support, and prospects.

Parkinson's disease (PD), characterized by loss of nigrostriatal dopaminergic neurons, is one of the most predominant neurodegenerative diseases affecting the elderly population worldwide. The concept of stem cell therapy in managing neurodegenerative diseases has evolved over the years and has recently rapidly progressed. Neural stem cells (NSCs) have a few key features, including self-renewal, proliferation, and multipotency, which make them a promising agent targeting neurodegeneration. It is generally agreed that challenges for NSC-based therapy are present at every stage of the transplantation process, including preoperative cell preparation and quality control, perioperative procedures, and postoperative graft preservation, adherence, and overall therapy success. In this review, we provided a comprehensive, careful, and critical discussion of experimental and clinical data alongside the pros and cons of NSC-based therapy in PD. Given the state-of-the-art accomplishments of stem cell therapy, gene therapy, and nanotechnology, we shed light on the perspective of complementing the advantages of each process by developing nano-stem cell therapy, which is currently a research hotspot. Although various obstacles and challenges remain, nano-stem cell therapy holds promise to cure PD, however, continuous improvement and development from the stage of laboratory experiments to the clinical application are necessary.

Analysing role of airborne particulate matter in abetting SARS-CoV-2 outbreak for scheming regional pandemic regulatory modalities.

The mutating SARS-CoV-2 necessitates gauging the role of airborne particulate matter in the COVID-19 outbreak for designing area-specific regulation modalities based on the environmental state-of-affair. To scheme the protocols, the hotspots of air pollutants such as PM2.5, PM10, NH3, NO, NO2, SO2, and and environmental factors including relative humidity (RH), and temperature, along with COVID-19 cases and mortality from January 2020 till December 2020 from 29 different ground monitoring stations spanning Delhi, are mapped. Spearman correlation coefficients show a positive relationship between SARS-COV-2 with particulate matter (PM2.5 with r > 0.36 and PM10 with r > 0.31 and p-value <0·001). Besides, SARS-COV-2 transmission showed a substantial correlation with NH3 (r = 0.41), NO2 (r = 0.36), and NO (r = 0.35) with a p-value <0.001, which is highly indicative of their role in SARS-CoV-2 transmission. These outcomes are associated with the source of PM and its constituent trace elements to understand their overtone with COVID-19. This strongly validates temporal and spatial variation in COVID-19 dependence on air pollutants as well as on environmental factors. Besides, the bottlenecks of missing latent data, monotonous dependence of variables, and the role air pollutants with secondary environmental variables are discussed. The analysis set the foundation for strategizing regional-based modalities considering environmental variables (i.e., pollutant concentration, relative humidity, temperature) as well as urban and transportation planning for efficient control and handling of future public health emergencies.

Lipid nanoparticle-based formulations for high-performance dentistry applications.

In the growing field of dentistry research, there is significant scope for investigating novel and high-performance functional biomaterials for dental care, mainly to combat oral health diseases. Considering the growing economic burden on dental care, there is an urgent need to investigate affordable and biologically acceptable functional antibacterial nanostructures capable of exhibiting desired pharmacological properties. Although a wide range of materials has been investigated for dentistry applications, their acceptability and scaling-up clinical acceptance remain a challenge to cytotoxicity and alterations in cellular function. To address these challenges, nanolipids are emerging as potential materials to develop the next generation of treatment modalities for dental care and oral diseases. However, there is a need to cover the knowledge gap between developing good quality nanolipid formulations, their introduction in dental research, establishing a track from laboratory to clinical application, exploring associated risks, and proposing step-by-step systematic research to obtain FDA approval for recommending nanolipids for next-generation systems for dentistry applications. This study also summarizes the outcomes of the literature carefully and critically to provide a clear view about selecting an appropriate nanolipid system to manage a targeted dental issue. These programmable nanolipids can be designed and developed using optimized chemistry and pharmacology to be used in a controlled manner by manipulating their responsiveness according to the demand of targeted disease management, i.e., a programmable system. The future of this research, keeping clinical adaptability as a focus, is also discussed in this review, along with the possible challenges and possible alternative approaches.

Photoacoustic imaging for investigating tumor hypoxia: a strategic assessment.

Hypoxia causes the expression of signaling molecules which regulate cell division, lead to angiogenesis, and further, in the tumor microenvironment, promote resistance to chemotherapy and radiotherapy, and induce metastasis. Photoacoustic imaging (PAI) takes advantage of unique absorption characteristics of chromophores in tissues and provides the opportunity to construct images with a high degree of spatial and temporal resolution. In this review, we discuss the physiologic characteristics of tumor hypoxia, and current applications of PAI using endogenous (label free imaging) and exogenous (organic and inorganic) contrast agents. Features of various methods in terms of their efficacy for determining physiologic and proteomic phenomena are analyzed. This review demonstrates that PAI has the potential to understand tumor growth and metastasis development through measurement of regulatory molecule concentrations, oxygen gradients, and vascular distribution.

Correction: Lipid nanoparticle-based formulations for high-performance dentistry applications.

Correction for 'Lipid nanoparticle-based formulations for high-performance dentistry applications' by Isha Mutreja et al., J. Mater. Chem. B, 2023, https://doi.org/10.1039/D3TB00431G.

COVID-19 associated thyroid dysfunction and other comorbidities and its management using phytochemical-based therapeutics: a natural way.

The present severe acute respiratory syndrome-2 (SARS-CoV-2) mediated Coronavirus pandemic (COVID-19) and post-COVID-19 complications affect human life drastically. Patients who have been cured of COVID-19 infection are now experiencing post-COVID-19 associated comorbidities, which have increased mortality rates. The SARS-CoV-2 infection distresses the lungs, kidneys, gastrointestinal tract, and various endocrine glands, including the thyroid. The emergence of variants which includes Omicron (B.1.1.529) and its lineages threaten the world severely. Among different therapeutic approaches, phytochemical-based therapeutics are not only cost-effective but also have lesser side effects. Recently a plethora of studies have shown the therapeutic efficacy of various phytochemicals for the treatment of COVID-19. Besides this, various phytochemicals have been found efficacious in treating several inflammatory diseases, including thyroid-related anomalies. The method of the phytochemical formulation is quick and facile and the raw materials for such herbal preparations are approved worldwide for human use against certain disease conditions. Owing to the advantages of phytochemicals, this review primarily discusses the COVID-19-related thyroid dysfunction and the role of key phytochemicals to deal with thyroid anomaly and post-COVID-19 complications. Further, this review shed light on the mechanism via which COVID-19 and its related complication affect organ function of the body, along with the mechanistic insight into the way by which phytochemicals could help to cure post-COVID-19 complications in thyroid patients. Considering the advantages offered by phytochemicals as a safer and cost-effective medication they can be potentially used to combat COVID-19-associated comorbidities.