Displaying all 8 publications

Abstract:
Sort:
  1. Nagarajan T, Vilosamy K, Raju G, Shanmugan S, Walvekar R, Rustagi S, et al.
    Chemosphere, 2024 Apr 11;358:141936.
    PMID: 38614393 DOI: 10.1016/j.chemosphere.2024.141936
    This study presents the adsorption of methylene blue (MB) dye using latex char derived from pyrolysis of latex gloves. The adsorption process was investigated systematically using Response Surface Methodology (RSM) with a Central Composite Design (CCD). The effects of four key variables, namely pH, time, temperature, and adsorbent dosage, were studied using a factorial design enriched with center points and axial points. Experimental data were analyzed using a second-order polynomial regression model to construct a response surface model, which elucidated the relationship between the variables and MB removal efficiency. The study found that the char obtained at 800 °C exhibited the highest adsorption capacity due to its increased carbonization, expanded surface area, and diverse pore structure. Analysis of Variance (ANOVA) confirmed the significance of the quadratic model, with remarkable agreement between predicted and experimental outcomes. Diagnostic plots validated the model's reliability, while 3D contour graphs illustrated the combined effects of variables on MB removal efficiency. Optimization using DoE software identified optimal conditions resulting in a 99% removal efficiency, which closely matched experimental results. Additionally, adsorption isotherms revealed that the Freundlich model best described the adsorption behavior, indicating heterogeneous surface adsorption with multilayer adsorption. This comprehensive study provides valuable insights into the adsorption process of MB dye using latex char, with implications for wastewater treatment and environmental remediation.
  2. Chaudhary V, Taha BA, Lucky, Rustagi S, Khosla A, Papakonstantinou P, et al.
    ACS Sens, 2024 Sep 09.
    PMID: 39248694 DOI: 10.1021/acssensors.4c01524
    Lung cancer remains a global health concern, demanding the development of noninvasive, prompt, selective, and point-of-care diagnostic tools. Correspondingly, breath analysis using nanobiosensors has emerged as a promising noninvasive nose-on-chip technique for the early detection of lung cancer through monitoring diversified biomarkers such as volatile organic compounds/gases in exhaled breath. This comprehensive review summarizes the state-of-the-art breath-based lung cancer diagnosis employing chemiresistive-module nanobiosensors supported by theoretical findings. It unveils the fundamental mechanisms and biological basis of breath biomarker generation associated with lung cancer, technological advancements, and clinical implementation of nanobiosensor-based breath analysis. It explores the merits, challenges, and potential alternate solutions in implementing these nanobiosensors in clinical settings, including standardization, biocompatibility/toxicity analysis, green and sustainable technologies, life-cycle assessment, and scheming regulatory modalities. It highlights nanobiosensors' role in facilitating precise, real-time, and on-site detection of lung cancer through breath analysis, leading to improved patient outcomes, enhanced clinical management, and remote personalized monitoring. Additionally, integrating these biosensors with artificial intelligence, machine learning, Internet-of-things, bioinformatics, and omics technologies is discussed, providing insights into the prospects of intelligent nose-on-chip lung cancer sniffing nanobiosensors. Overall, this review consolidates knowledge on breathomic biosensor-based lung cancer screening, shedding light on its significance and potential applications in advancing state-of-the-art medical diagnostics to reduce the burden on hospitals and save human lives.
  3. Nagarajan T, Binti Mohd Fekeri NH, Raju G, Shanmugan S, Jeppu G, Walvekar R, et al.
    Chemosphere, 2024 Sep 02;364:143242.
    PMID: 39233300 DOI: 10.1016/j.chemosphere.2024.143242
    This study investigates the potential of spent coffee ground biochar (SCGB) as a sustainable and cost-effective adsorbent for the removal of methylene blue (MB), a hazardous dye commonly used in the textile and printing industries. A response surface methodology (RSM) approach with central composite design (CCD) was employed to systematically investigate the effects of key process parameters, including adsorbent dosage, solution pH, contact time and temperature, on MB removal efficiency. The analysis revealed that adsorbent dosage and temperature as critical factors influencing MB removal, with a linear model providing a strong correlation. Optimal conditions for MB removal were determined to be 0.99 g of SCGB, 30 min of contact time, 30 °C temperature, and a solution pH of 7. Under these conditions, MB removal reached 99.99%, with a desirability of 1.000. The experimental results closely matched the predicted values, differing by only 0.02%, thus validating the accuracy of the model. Kinetic studies indicated a rapid adsorption process, well-described by both pseudo-first and pseudo-second order models. Isotherm analysis confirmed the applicability of the Freundlich model, suggesting favorable adsorption with increasing MB concentration. The high adsorption capacity of SCGB is attributed to its carbonaceous and porous structure, highlighting its potential as an effective adsorbent for dye removal in wastewater treatment applications.
  4. Bhadola P, Chaudhary V, Markandan K, Talreja RK, Aggarwal S, Nigam K, et al.
    Environ Res, 2023 Nov 01;236(Pt 1):116646.
    PMID: 37481054 DOI: 10.1016/j.envres.2023.116646
    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.
  5. Taha BA, Al Mashhadany Y, Al-Jubouri Q, Rashid ARBA, Luo Y, Chen Z, et al.
    Sci Total Environ, 2023 Jul 01;880:163333.
    PMID: 37028663 DOI: 10.1016/j.scitotenv.2023.163333
    Constantly mutating SARS-CoV-2 is a global concern resulting in COVID-19 infectious waves from time to time in different regions, challenging present-day diagnostics and therapeutics. Early-stage point-of-care diagnostic (POC) biosensors are a crucial vector for the timely management of morbidity and mortalities caused due to COVID-19. The state-of-the-art SARS-CoV-2 biosensors depend upon developing a single platform for its diverse variants/biomarkers, enabling precise detection and monitoring. Nanophotonic-enabled biosensors have emerged as 'one platform' to diagnose COVID-19, addressing the concern of constant viral mutation. This review assesses the evolution of current and future variants of the SARS-CoV-2 and critically summarizes the current state of biosensor approaches for detecting SARS-CoV-2 variants/biomarkers employing nanophotonic-enabled diagnostics. It discusses the integration of modern-age technologies, including artificial intelligence, machine learning and 5G communication with nanophotonic biosensors for intelligent COVID-19 monitoring and management. It also highlights the challenges and potential opportunities for developing intelligent biosensors for diagnosing future SARS-CoV-2 variants. This review will guide future research and development on nano-enabled intelligent photonic-biosensor strategies for early-stage diagnosing of highly infectious diseases to prevent repeated outbreaks and save associated human mortalities.
  6. Kaur T, Devi R, Negi R, Kumar S, Singh S, Rustagi S, et al.
    PMID: 38668814 DOI: 10.1007/s12223-024-01168-x
    In the past few decades, the pressure of higher food production to satisfy the demand of ever rising population has inevitably increased the use synthetic agrochemicals which have deterioration effects. Biostimulants containing beneficial microbes (single inoculants and microbial consortium) were found as an ideal substitute of synthetic chemical fertilizers. In recent years, microbial consortium is known as a better bioinoculant in comparison to single inoculant bioformulation because of multifarious plant growth-promoting advantages. Looking at the advantageous effect of consortium, in present investigation, different bacteria were isolated from rhizospheric soil and plant samples collected from the Himalayan mountains on the green slopes of the Shivaliks, Himachal Pradesh. The isolated bacteria were screened for nitrogen (N) fixation, phosphorus (P) solubilization and potassium (K) solubilization plant growth promoting attributes, and efficient strains were identified through 16S rRNA gene sequencing and BLASTn analysis. The bacteria showing a positive effect in NPK uptake were developed as bacterial consortium for the growth promotion of eggplant crop. A total of 188 rhizospheric and endophytic bacteria were sorted out, among which 13 were exhibiting nitrogenase activity, whereas 43 and 31 were exhibiting P and K solubilization traits, respectively. The selected three efficient and potential bacterial strains were identified using 16S rRNA gene sequencing as Enterobacter ludwigii EU-BEN-22 (N-fixer; 35.68 ± 00.9 nmol C2H4 per mg protein per h), Micrococcus indicus EU-BRP-6 (P-solubilizer; 201 ± 0.004 mg/L), and Pseudomonas gessardii EU-BRK-55 (K-solubilizer; 51.3 ± 1.7 mg/mL), and they were used to develop a bacterial consortium. The bacterial consortium evaluation on eggplant resulted in the improvement of growth (root/shoot length and biomass) and physiological parameters (chlorophyll, carotenoids, total soluble sugar, and phenolic content) of the plants with respect to single culture inoculation, chemical fertilizer, and untreated control. A bacterial consortium having potential to promote plant growth could be used as bioinoculant for horticulture crops growing in hilly regions.
  7. Jain L, Pradhan S, Aggarwal A, Padhi BK, Itumalla R, Khatib MN, et al.
    JMIR Public Health Surveill, 2024 May 24;10:e41567.
    PMID: 38787607 DOI: 10.2196/41567
    BACKGROUND: Undernutrition among children younger than 5 years is a subtle indicator of a country's health and economic status. Despite substantial macroeconomic progress in India, undernutrition remains a significant burden with geographical variations, compounded by poor access to water, sanitation, and hygiene services.

    OBJECTIVE: This study aimed to explore the spatial trends of child growth failure (CGF) indicators and their association with household sanitation practices in India.

    METHODS: We used data from the Indian Demographic and Health Surveys spanning 1998-2021. District-level CGF indicators (stunting, wasting, and underweight) were cross-referenced with sanitation and sociodemographic characteristics. Global Moran I and Local Indicator of Spatial Association were used to detect spatial clustering of the indicators. Spatial regression models were used to evaluate the significant determinants of CGF indicators.

    RESULTS: Our study showed a decreasing trend in stunting (44.9%-38.4%) and underweight (46.7%-35.7%) but an increasing prevalence of wasting (15.7%-21.0%) over 15 years. The positive values of Moran I between 1998 and 2021 indicate the presence of spatial autocorrelation. Geographic clustering was consistently observed in the states of Madhya Pradesh, Jharkhand, Odisha, Uttar Pradesh, Chhattisgarh, West Bengal, Rajasthan, Bihar, and Gujarat. Improved sanitation facilities, a higher wealth index, and advanced maternal education status showed a significant association in reducing stunting. Relative risk maps identified hotspots of CGF health outcomes, which could be targeted for future interventions.

    CONCLUSIONS: Despite numerous policies and programs, malnutrition remains a concern. Its multifaceted causes demand coordinated and sustained interventions that go above and beyond the usual. Identifying hotspot locations will aid in developing control methods for achieving objectives in target areas.

  8. Rahbeni TA, Satapathy P, Itumalla R, Marzo RR, Mugheed KAL, Khatib MN, et al.
    JMIR Public Health Surveill, 2024 Apr 30;10:e54769.
    PMID: 38687992 DOI: 10.2196/54769
    BACKGROUND: The unprecedented emergence of the COVID-19 pandemic necessitated the development and global distribution of vaccines, making the understanding of global vaccine acceptance and hesitancy crucial to overcoming barriers to vaccination and achieving widespread immunization.

    OBJECTIVE: This umbrella review synthesizes findings from systematic reviews and meta-analyses to provide insights into global perceptions on COVID-19 vaccine acceptance and hesitancy across diverse populations and regions.

    METHODS: We conducted a literature search across major databases to identify systematic reviews and meta-analysis that reported COVID-19 vaccine acceptance and hesitancy. The AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews) criteria were used to assess the methodological quality of included systematic reviews. Meta-analysis was performed using STATA 17 with a random effect model. The data synthesis is presented in a table format and via a narrative.

    RESULTS: Our inclusion criteria were met by 78 meta-analyses published between 2021 and 2023. Our analysis revealed a moderate vaccine acceptance rate of 63% (95% CI 0.60%-0.67%) in the general population, with significant heterogeneity (I2 = 97.59%). Higher acceptance rates were observed among health care workers and individuals with chronic diseases, at 64% (95% CI 0.57%-0.71%) and 69% (95% CI 0.61%-0.76%), respectively. However, lower acceptance was noted among pregnant women, at 48% (95% CI 0.42%-0.53%), and parents consenting for their children, at 61.29% (95% CI 0.56%-0.67%). The pooled vaccine hesitancy rate was 32% (95% CI 0.25%-0.39%) in the general population. The quality assessment revealed 19 high-quality, 38 moderate-quality, 15 low-quality, and 6 critically low-quality meta-analyses.

    CONCLUSIONS: This review revealed the presence of vaccine hesitancy globally, emphasizing the necessity for population-specific, culturally sensitive interventions and clear, credible information dissemination to foster vaccine acceptance. The observed disparities accentuate the need for continuous research to understand evolving vaccine perceptions and to address the unique concerns and needs of diverse populations, thereby aiding in the formulation of effective and inclusive vaccination strategies.

    TRIAL REGISTRATION: PROSPERO CRD42023468363; https://tinyurl.com/2p9kv9cr.

Related Terms
Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links