Search Results (79)

Accurate and sensitive antibody detection remains critical for advanced COVID-19 diagnostics and monitoring SARS-CoV-2 immunity. This study presents a highly sensitive technique for detecting anti-SARS-CoV-2 antibodies in human serum using an integrated photonic sensing platform. The platform utilizes disposable one-dimensional photonic crystal biochips engineered to sustain Bloch Surface Waves. The biochips are integrated into a custom-made optical set-up, which is capable of dual-mode detection: label-free refractometry and label-based fluorescence. Tests on human serum, including negative controls and positive samples from a recovered COVID-19 patient, confirmed the platform’s effective performance. In fluorescence mode, clear discrimination between positive and negative samples was achieved down to a 1:10⁴ serum dilution, with an optimal operating range centered around 1:10³ dilution. These results demonstrate the potential of the technique as a highly sensitive and versatile platform for antibody detection, with significant relevance for advanced COVID-19 diagnostics. Full article

Integrating artificial intelligence (AI) with wearable sensor technologies can revolutionize the monitoring and management of various chronic diseases and acute conditions. AI-integrated wearables are categorized by their underlying sensing techniques, such as electrochemical, colorimetric, chemical, optical, and pressure/stain. AI algorithms enhance the efficacy of wearable sensors by offering personalized, continuous supervision and predictive analysis, assisting in time recognition, and optimizing therapeutic modalities. This manuscript explores the recent advances and developments in AI-powered wearable sensing technologies and their use in the management of chronic diseases, including COVID-19, Diabetes, and Cancer. AI-based wearables for heart rate and heart rate variability, oxygen saturation, respiratory rate, and temperature sensors are reviewed for their potential in managing COVID-19. For Diabetes management, AI-based wearables, including continuous glucose monitoring sensors, AI-driven insulin pumps, and closed-loop systems, are reviewed. The role of AI-based wearables in biomarker tracking and analysis, thermal imaging, and ultrasound device-based sensing for cancer management is reviewed. Ultimately, this report also highlights the current challenges and future directions for developing and deploying AI-integrated wearable sensors with accuracy, scalability, and integration into clinical practice for these critical health conditions. Full article

COVID-19, caused by SARS-CoV-2, has created unprecedented global health challenges, necessitating rapid and reliable diagnostic strategies. The spike (S) protein, particularly its S1 subunit, plays a critical role in viral entry, making it a prime biomarker for early detection. In this study, we present a disposable, low-cost, and portable electrochemical biosensor employing specifically optimized aptamers (Optimers) for SARS-CoV-2 S1 recognition. The sensing approach is based on aptamer–protein complex formation in solution, followed by immobilization onto pencil graphite electrodes (PGEs). The key parameters, including aptamer concentration, interaction time, redox probe concentration, and immobilization time, were systematically optimized by performing electrochemical measurement in redox probe solution containing ferri/ferrocyanide using differential pulse voltammetry (DPV) technique.Under optimized conditions, the biosensor achieved an ultralow detection limit of 18.80 ag/mL with a wide linear range (10⁻¹–10⁴ fg/mL) in buffer. Importantly, the sensor exhibited excellent selectivity against hemagglutinin antigen and MERS-CoV-S1 protein, while maintaining high performance in artificial saliva with a detection limit of 14.42 ag/mL. Furthermore, its integration with a smartphone-connected portable potentiostat underscores strong potential for point-of-care use. To our knowledge, this is the first voltammetric biosensor utilizing optimized aptamers (Optimers) specific to SARS-CoV-2 S1 on disposable PGEs, providing a robust and field-deployable platform for early COVID-19 diagnostics. Full article

The emergence or mutation of aggressive viruses represents a latent threat to human health that could lead to new pandemics, so it is important to constantly monitor and analyze the behavior of the diseases they can cause. In this sense, the purpose of this work was to generate models to predict the behavior of recoveries and deaths from COVID-19 in Mexico in the post-pandemic era, applying machine learning techniques to data related to this disease, published by the Mexican government. Models based on artificial neural networks, logistic regression, and classification algorithms were generated and validated, yielding high rates of correct classification, accuracy, and recall, so that they could be used to make predictions about future cases of patients infected with the SARS-CoV-2 virus. Full article

Background: Child Development Accounts (CDAs) were introduced in the 1990s as a long-term asset-building policy aimed at supporting families in accumulating assets to achieve life goals for their children, including higher education, homeownership, and long-term economic security. Beyond their financial benefits, CDAs have been theorized to strengthen family relationships and improve children’s well-being by fostering a future-oriented mindset and increasing parental involvement in educational activities. Objective: This study investigates the impact of CDAs on parent–child educational engagement and children’s sense of hope for the future, contributing to the growing body of research on the multidimensional benefits of asset-based policies for children’s development. Methods: Data were drawn from the third wave of the SEED for Oklahoma Kids (SEED OK) study, a rigorous, longitudinal, randomized policy experiment in the United States. The analytic sample comprised 1425 families. Dependent variables were parent–child educational engagement and children’s hope. The independent variable was participation in the SEED OK CDA policy experiment. Baseline sociodemographic variables related to children, mothers, and households were controlled for in the analysis. Multivariate linear regressions and path analysis techniques were employed to assess direct and indirect effects. Results: Participation in CDAs was found to improve parent–child educational interactions and enhance children’s hope significantly in the pre-COVID-19 sample. The study’s rigorous design and consistent implementation allowed for establishing causal relationships and long-term developmental benefits. Conclusions: CDAs offer not only financial advantages but also contribute meaningfully to strengthening family dynamics and promoting positive psychosocial outcomes for children, supporting their inclusion in comprehensive social policy frameworks. Full article

The COVID-19 pandemic and recent viral outbreaks have highlighted the need for viral diagnostics that balance accuracy with accessibility. While traditional laboratory methods remain essential, point-of-care solutions are critical for decentralized testing at the population level. However, a gap persists between academic proof-of-concept studies and clinically viable tools, with novel technologies remaining inaccessible to clinics due to cost, complexity, training, and logistical constraints. Recent advances in surface functionalization, assay simplification, multiplexing, and performance in complex media have improved the feasibility of both optical and non-optical sensing techniques. These innovations, coupled with scalable manufacturing methods such as 3D printing and streamlined hardware production, pave the way for practical deployment in real-world settings. Additionally, software-assisted data interpretation, through simplified readouts, smartphone integration, and machine learning, enables the broader use of diagnostics once limited to experts. This review explores improvements in viral diagnostic approaches, including colorimetric, optical, and electrochemical assays, showcasing their potential for democratization efforts targeting the clinic. We also examine trends such as open-source hardware, modular assay design, and standardized reporting, which collectively reduce barriers to clinical adoption and the public dissemination of information. By analyzing these interdisciplinary advances, we demonstrate how emerging technologies can mature into accessible, low-cost diagnostic tools for widespread testing. Full article

In this work, we employ two modeling approaches—a mean-field model and a network model—for the purpose of modeling respiratory infection outbreaks in Russia. The presented approaches and their software implementation combine heterogeneity and structural simplicity and, in this sense, they close the gap between the compartmental SEIR models and complex detailed solutions based on agent-based approaches—the two most common modeling techniques for influenza and COVID-19 dynamics. The mathematical description of the approaches is presented, with SEIR compartmental model serving as a baseline for comparison. The experiments demonstrate the similarity of the modeling output of the presented approaches, which allows their interchangeable usage in replicating real outbreak dynamics in Russian cities. The ability of the discussed approaches to mimic data from Russian epidemic surveillance is shown by fitting a mean-field model to data from an influenza outbreak in Saint Petersburg in 2014–2015. The comparison of model complexity and their performance is made using synthetic scenarios. Following the results of numerical experiments, the comparative advantages and drawbacks of the approaches in the application to respiratory infection outbreaks are discussed. The presented modeling techniques, in addition to classical SEIR models and agent-based models as a part of epidemic surveillance, allow one to select the best modeling option for any particular task in outbreak surveillance and control, based on the computational resources at hand, data availability, and data quality. Full article

Intersectoral collaboration is an instrument that enables better productivity by filling in for possible gaps in knowledge, skills, and competencies in a given department by leveraging them from other departments. In Kenya, there is a paucity of information on intersectoral collaboration in healthcare. This article explores the possibilities of intersectoral collaboration, specifically in maternal healthcare, and what can be done to realize such collaborations to drive universal health coverage (UHC) in Kenya. Free maternity services (FMSs) are among the primary healthcare services that push Kenya towards UHC. In light of the centrality of UHC in driving current health policy, there are still several challenges which must be faced before this goal can be achieved. Moreover, competing priorities in health systems necessitate difficult choices regarding which health actions and investments to fund; these are complex, value-based, and highly political decisions. Therefore, the primary objective of this article is to explore health facility administrators’ views on whether intersectoral collaboration could help with the realization of UHC in Kenya. The study area was Kilifi County, Kenya. The article is based on follow-up qualitative research conducted between March and July 2016 and from January to July 2017, and follow-up interviews conducted during COVID-19 in 2020 and 2021. The data are analyzed through a thematic analysis approach. The findings indicate that through Linda Mama, the expanded free maternity services program is one of the possible pathways to UHC. However, participants noted fair representation of stakeholders, distributed leadership, and local participation, considering bargaining power as a key issue that could enhance the realization of UHC in intersectoral collaboration through Linda Mama. These techniques require a bottom–up strategy to establish accountability, a sense of ownership, and trust, which are essential for UHC. Full article

COVID-19 antibody detection is dependent on highly specialized, time-consuming techniques, such as PCR separation, DNA amplification, and other methods such as spectrophotometric absorption. For these reasons, specialized technical training is necessary because individual diagnostic treatment is difficult. We have attempted to perform rapid sensing with a detection time of only 30 s. Additionally, we used a wearable multi-layer graphene oxide nanocolloid synthetic skin tattoo probe assay for influenza and COVID-19 virus detection with an electrochemical antigen–antibody redox ionic titration circuit. Cyclic voltametric−2 V~2.0 V potential windows were used. The diagnostic detection limit was determined using stripping anodic and cathodic amplifiers, and the working probe was fabricated with a graphene molecule structure with a virus antigen-immobilized amplifier. With redox potential strength obtained within −1.0 V~−1.3 V ionic activity, anodic and cathodic current linearly increased in the phosphate-buffered saline 5 mL electrolyte. The results indicate that instant detection was enabled via individual and wearable tattoo sensors. Full article

The COVID-19 pandemic brought telepresence systems into the spotlight, yet manually controlling remote robots often proves ineffective for handling complex manipulation tasks. To tackle this issue, we present a machine learning-based assistive manipulation approach. This method identifies target objects and computes an inverse kinematic solution for grasping them. The system integrates the generated solution with the user’s arm movements across varying inverse kinematic (IK) fusion levels. Given the importance of maintaining a sense of body ownership over the remote robot, we examine how haptic feedback and assistive functions influence ownership perception and task performance. Our findings indicate that incorporating assistance and haptic feedback significantly enhances the control of the robotic arm in telepresence environments, leading to improved precision and shorter task completion times. This research underscores the advantages of assistive manipulation techniques and haptic feedback in advancing telepresence technology. Full article

The synthesis of magnetic nanoparticles (MNPs) via the chemical, biological, and physical routes has been reported on along with advantages and attendant limitations. This study focuses on the sensing and emerging theragnostic applications of this category of nanoparticles (NPs) in clinical sciences by unveiling the unique performance of these NPs in the biological sensing of bacteria and nucleotide sequencing. Also, in terms of medicine and clinical science, this review analyzes the emerging theragnostic applications of NPs in drug delivery, bone tissue engineering, deep brain stimulation, therapeutic hyperthermia, tumor detection, magnetic imaging and cell tracking, lymph node visualization, blood purification, and COVID-19 detection. This review presents succinct surface functionalization and unique surface coating techniques to confer less toxicity and biocompatibility during synthesis, which are often identified as limitations in medical applications. This study also indicates that these surface improvement techniques are useful for refining the selective activity of MNPs during their use as sensors and biomarkers. In addition, this study unveils attendant limitations, especially toxicological impacts on biomolecules, and suggests that future research should pay attention to the mitigation of the biotoxicity of MNPs. Thus, this study presents a proficient approach for the synthesis of high-performance MNPs fit for proficient medicine in the detection of microorganisms, better diagnosis, and treatment in medicine. Full article

Based on spatiotemporal sensing techniques, the cross-regional traveling welcome short messaging service (TW-SMS) has been adopted in China and has become popular, typically being used when travelers pass through or arrive in cities. In this service, governmental institutions in combination with telecom operators send welcome messages with the local characteristics. As a typical location-based service for mobile users, the TW-SMS includes reminders or alerts related to COVID-19 prevention and control. In this paper, we investigate the perceptions and behavior of mobile users regarding this special TW-SMS through mixed-methods research. An online survey was conducted among mobile users who engaged in intercity travel. After analyzing samples of TW-SMS data collected during the COVID-19 pandemic, we found that the respondents exhibited a relatively positive overall attitudes and recognized the necessity and helpfulness of the TW-SMS with its trusted content. For content analysis, we found that more than 70% of the messages transmitted by the TW-SMS were released by official departments (e.g., the COVID-19 Prevention and Control Office). Reminders about traveling registration and nucleic acid testing were assigned the highest importance, as they offer convenience in communicating the most up-to-date prevention and control information to mobile users during intercity travel. Through this study, we provide insights into epidemic prevention and control experiences during public health emergencies in cities. Full article

Environmental pollution indicated by the presence of $P{M}_{2.5}$ particulate matter varies based on prevailing atmospheric conditions described by certain meteorological variables. Consequently, it is important to understand atmospheric behavior in areas such as the Aburrá Valley, which experiences recurrent pollution events twice a year. This study examines the behavior of specific meteorological variables and $P{M}_{2.5}$ particulate matter in the Aburrá Valley. By using statistical analysis tools such as correlation coefficients, principal component analysis (PCA), and multiple linear regression models, the research identifies relationships between $P{M}_{2.5}$ and daily cycles of temperature, rainfall, radiation, and wind speed and direction. Datasets were analyzed considering periods before and after the COVID-19 lockdown (pre-pandemic and pandemic, respectively), and specific pollution events were also analyzed. Furthermore, this work considers the relationships between $P{M}_{2.5}$ and meteorological variables, contrasting the pre-pandemic and pandemic periods. This study characterizes diurnal cycles of meteorological variables and their relationship with $P{M}_{2.5}$. There are consistent patterns among temperature, atmospheric boundary layer (ABL) height, and solar radiation, whereas precipitation and relative humidity show the opposite behavior. $P{M}_{2.5}$ exhibits similar relative frequency functions during both daytime and nighttime, regardless of rainfall. An inverse relationship is noted between $P{M}_{2.5}$ levels and ABL height at different times of the day. Moreover, the PCA results show that the first principal component explains around 60% of the total variance in the hydrometeorological data. The second PC explains 10%, and the rest of the variance is distributed among the other three to eight PCs. In this sense, there is no significant difference between the two PCAs with hydrometeorological data from a pre-pandemic period and a COVID-19 pandemic period. Multiple regression analysis indicates a significant and consistent dependence of $P{M}_{2.5}$ on temperature and solar radiation across both analyzed periods. The application of Generalized Additive Models (GAMs) to our dataset yielded promising results, reflecting the complex relationship between meteorological variables and $P{M}_{2.5}$ concentrations. The metrics obtained from the GAM were as follows: Mean Squared Error (MSE) of 98.04, Root Mean Squared Error (RMSE) of 9.90, R-squared (R²) of 0.24, Akaike Information Criterion (AIC) of 110,051.34, and Bayesian Information Criterion (BIC) of 110,140.63. In comparison, the linear regression model exhibited slightly higher MSE (100.49), RMSE (10.02), and lower R-squared (0.22), with AIC and BIC values of 110,407.45 and 110,460.67, respectively. Although the improvement in performance metrics from GAM over the linear model is not conclusive, they indicate a better fit for the complexity of atmospheric dynamics influencing $P{M}_{2.5}$ levels. These findings underscore the intricate interplay of meteorological factors and particulate matter concentration, reinforcing the necessity for advanced modeling techniques in environmental studies. This work presents new insights that enhance the diagnosis, understanding, and modeling of environmental pollution, thereby supporting informed decision-making and strengthening management efforts. Full article

This review article will present a comprehensive examination of the use of modeling, spatial analysis, and geographic information systems (GIS) in the surveillance of viruses in wastewater. With the advent of global health challenges like the COVID-19 pandemic, wastewater surveillance has emerged as a crucial tool for the early detection and management of viral outbreaks. This review will explore the application of various modeling techniques that enable the prediction and understanding of virus concentrations and spread patterns in wastewater systems. It highlights the role of spatial analysis in mapping the geographic distribution of viral loads, providing insights into the dynamics of virus transmission within communities. The integration of GIS in wastewater surveillance will be explored, emphasizing the utility of such systems in visualizing data, enhancing sampling site selection, and ensuring equitable monitoring across diverse populations. The review will also discuss the innovative combination of GIS with remote sensing data and predictive modeling, offering a multi-faceted approach to understand virus spread. Challenges such as data quality, privacy concerns, and the necessity for interdisciplinary collaboration will be addressed. This review concludes by underscoring the transformative potential of these analytical tools in public health, advocating for continued research and innovation to strengthen preparedness and response strategies for future viral threats. This article aims to provide a foundational understanding for researchers and public health officials, fostering advancements in the field of wastewater-based epidemiology. Full article

In the context of the Lubumbashi Charcoal Production Basin (LCPB), the socio-economic repercussions of the COVID-19 pandemic have exacerbated pressures on populations dependent on forest resources for their subsistence. This study employs a comprehensive methodological approach, integrating advanced remote sensing techniques, including image classification, mapping, and detailed landscape analysis, to quantify alterations in forest cover within the LCPB during the pandemic period. Our findings reveal a consistent trend of declining forested area, characterized by processes of attrition and dissection observed throughout various study phases, spanning from May 2019 to November 2023. This reduction in forest cover, notably more pronounced in the vicinity of Lubumbashi city and the northern zone of the LCPB, proved to be less pronounced between November 2019 and September 2020, underscoring the influence of COVID-19 pandemic-induced confinement measures on forest management practices in the region. However, subsequent to this period of restriction, deforestation activity intensified, leading to significant landscape transformations within the LCPB, primarily attributable to expanded human activities, consequently resulting in a notable decrease in the proportion of land occupied by these natural ecosystems. Consequently, the size of the largest forest patch declined substantially, decreasing from 14.62% to 8.20% between May 2019 and November 2023, thereby fostering a heightened density of forest edges over time. Our findings provide a significant contribution to understanding the complex interactions between the COVID-19 pandemic and deforestation phenomena, emphasizing the urgent need to adopt adaptive management strategies and appropriate conservation measures in response to current economic challenges. Full article