Early Disease Prevention Research Articles

Exposure to Air Pollutants and Myocardial Infarction Incidence: A UK Biobank Study Exploring Gene-Environment Interaction.

Unraveling gene-environment interaction can provide a novel insight into early disease prevention. Nevertheless, current understanding of the interplay between genetic predisposition and air pollution in relation to myocardial infarction (MI) risk remains limited. Furthermore, the potential long-term influence of air pollutants on MI incidence risk warrants more conclusive evidence in a community population. We investigated interactions between genetic predisposition and exposure to air pollutants on MI incidence. This study incorporated a sample of 456,354 UK Biobank participants and annual mean air pollution (, , , and ) from the UK Department for Environment, Food and Rural Affairs (2006-2021). The Cox proportional hazards model was employed to explore MI incidence after chronic air pollutants exposure. By quantifying genetic risk through the calculation of polygenic risk score (PRS), this study further examined the interactions between genetic risk and exposure to air pollutants in the development of MI on both additive and multiplicative scales. Among 456,354 participants, 9,114 incident MI events were observed during a median follow-up of 12.08 y. Chronic exposure to air pollutants was linked with an increased risk of MI occurrence. Specifically, the hazard ratios (per interquartile range) were 1.12 (95% CI: 1.10, 1.13) for , 1.20 (95% CI: 1.19, 1.22) for , 1.13 (95% CI: 1.12, 1.15) for , and 1.12 (95% CI: 1.11, 1.13) for . In terms of the joint effects, participants with high PRS and high level of air pollution exposure exhibited the greatest risk of MI among all study participants ( to 324%). Remarkably, both multiplicative and additive interactions were detected in the ambient air pollutants exposure and genetic risk on the incidence of MI. There were interactions between exposure to ambient air pollutants and genetic susceptibility on the risk of MI onset. Moreover, the joint effects of these two exposures were greater than the effect of each factor alone. https://doi.org/10.1289/EHP14291.

Machine learning models for personalised healthcare on marketable generative-AI with ethical implications

Personalised healthcare, underpinned by a deep understanding of individual patient variability, demands innovative solutions. Machine Learning (ML) models offer a promising avenue to achieve this by facilitating the development of enhanced Digital Twins (DTs). This research proposes a novel framework for creating DTs tailored to individual patients, considering not only physical attributes but also the intricate interplay of social, and biological factors. By capturing this comprehensive patient profile, ML-powered DTs have the potential to revolutionise healthcare by enabling predictive, preventive, and personalised care strategies, ultimately leading to improved patient outcomes and the development of a marketable, trust-worthy product. Current healthcare solutions lack personalisation, often failing to consider individual differences in disease presentation and response to treatment. Traditional DTs in healthcare often adopt a disease-centric or organ-specific approach, thereby restricting their capacity to deliver comprehensive, personalised care. To address this limitation, we propose a holistic Artificial Intelligence (AI) framework centered on ML models. Initially focusing on diabetes, our research aims to enhance diagnosis, treatment, and predictive capabilities through personalised insights, thereby optimising patient outcomes and care management. The benefits identified with our ML model are early disease prevention and risk stratification, optimised treatment planning and therapy selection, enhanced patient-physician communication and shared decision-making, reduced healthcare costs and improved resource allocation. Our models are designed to optimise patient care while prioritising safety and societal benefit. To ensure this, we have conducted a thorough assessment of potential ethical implications. Key challenges identified include data privacy and security, algorithmic bias, diagnostic accuracy, data interoperability and standardisation, integration with existing healthcare systems, ethical management of sensitive patient data, refinement of ML methodologies, addressing legal and ethical AI challenges, and suggesting robust ethical guidelines. A comprehensive evaluation of accuracy, reliability, and associated risks will be conducted prior to full-scale integration into the healthcare ecosystem to establish a robust ethical framework for our research models.

TriFusion enables accurate prediction of miRNA-disease association by a tri-channel fusion neural network

The identification of miRNA-disease associations is crucial for early disease prevention and treatment. However, it is still a computational challenge to accurately predict such associations due to improper information encoding. Previous methods characterize miRNA-disease associations only from single levels, causing the loss of multi-level association information. In this study, we propose TriFusion, a powerful and interpretable deep learning framework for miRNA–disease association prediction. It develops a tri-channel architecture to encode the association features of miRNAs and diseases from different levels and designs a feature fusion encoder to smoothly fuse these features. After training and testing, TriFusion outperforms other leading methods and offers strong interpretability through its learned representations. Furthermore, TriFusion is applied to three high-risk sexually associated cancers (ovarian, breast, and prostate cancers) and exhibits remarkable ability in the identification of miRNAs associated with the three diseases.

Measuring air metagenomic diversity in an agricultural ecosystem

All species shed DNA during life or in death, providing an opportunity to monitor biodiversity via environmental DNA (eDNA). In recent years, combining eDNA, high-throughput sequencing technologies, bioinformatics, and increasingly complete sequence databases has promised a non-invasive and non-destructive environmental monitoring tool. Modern agricultural systems are often large monocultures and so are highly vulnerable to disease outbreaks. Pest and pathogen monitoring in agricultural ecosystems is key for efficient and early disease prevention, lower pesticide use, and better food security. Although the air is rich in biodiversity, it has the lowest DNA concentration of all environmental media and yet is the route for windborne spread of many damaging crop pathogens. Our work suggests that ecosystems can be monitored efficiently using airborne nucleic acid information. Here, we show that the airborne DNA of microbes can be recovered, shotgun sequenced, and taxonomically classified, including down to the species level. We show that by monitoring a field growing key crops we can identify the presence of agriculturally significant pathogens and quantify their changing abundance over a period of 1.5months, often correlating with weather variables. We add to the evidence that aerial eDNA can be used as a source for biomonitoring in terrestrial ecosystems, specifically highlighting agriculturally relevant species and how pathogen levels correlate with weather conditions. Our ability to detect dynamically changing levels of species and strains highlights the value of airborne eDNA in agriculture, monitoring biodiversity changes, and tracking taxa of interest.

Clinical Decision Support Systems and Where to Apply Them: A Systematic Review

Background: Clinical decision support systems (CDSSs) provide healthcare providers with suggestions by evaluating the situation and organizing the decision-making process to reduce their mental workload so they can make decisions more effortlessly and safely in emergencies and critical-safety situations. This article reviews the applications of CDSSs and the benefits that these systems bring to healthcare settings. Method: Scopus, PubMed/Medline, and Web of Science were selected to search high-quality CDSS-related journal articles published in English from 2000 to 2022. Two reviewers, under the supervision of the third reviewer, screened and analyzed the search results based on the inclusion criteria. Results: The required information was extracted from 78 included articles, and it was found that CDSSs have been exploited for four primary purposes, namely, accurate diagnoses, early prevention of diseases, management of clinical/medical processes, and prescriptions. Conclusion: CDSS has many benefits for each of the four applications, the most important of which is to improve patient safety. Generally, the results showed that DSSs in healthcare can positively affect medical decisions by reducing possible errors and putting forward specific medical suggestions for each patient.

Research progress and applications of epigenetic biomarkers in cancer.

Epigenetic changes are heritable changes in gene expression without changes in the nucleotide sequence of genes. Epigenetic changes play an important role in the development of cancer and in the process of malignancy metastasis. Previous studies have shown that abnormal epigenetic changes can be used as biomarkers for disease status and disease prediction. The reversibility and controllability of epigenetic modification changes also provide new strategies for early disease prevention and treatment. In addition, corresponding drug development has also reached the clinical stage. In this paper, we will discuss the recent progress and application status of tumor epigenetic biomarkers from three perspectives: DNA methylation, non-coding RNA, and histone modification, in order to provide new opportunities for additional tumor research and applications.

The Comet Assay as a Tool in Human Biomonitoring Studies of Environmental and Occupational Exposure to Chemicals-A Systematic Scoping Review.

Biomonitoring of human populations exposed to chemical substances that can act as potential mutagens or carcinogens, may enable the detection of damage and early disease prevention. In recent years, the comet assay has become an important tool for assessing DNA damage, both in environmental and occupational exposure contexts. To evidence the role of the comet assay in human biomonitoring, we have analysed original research studies of environmental or occupational exposure that used the comet assay in their assessments, following the PRISMA-ScR method (preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews). Groups of chemicals were designated according to a broad classification, and the results obtained from over 300 original studies (n = 123 on air pollutants, n = 14 on anaesthetics, n = 18 on antineoplastic drugs, n = 57 on heavy metals, n = 59 on pesticides, and n = 49 on solvents) showed overall higher values of DNA strand breaks in the exposed subjects in comparison with the unexposed. In summary, our systematic scoping review strengthens the relevance of the use of the comet assay in assessing DNA damage in human biomonitoring studies.

Plasmalogens and Octanoylcarnitine Serve as Early Warnings for Central Retinal Artery Occlusion

Central retinal artery occlusion (CRAO) is a kind of ophthalmic emergency which may cause loss of functional visual acuity. However, the limited treatment options emphasize the significance of early disease prevention. Metabolomics has the potential to be a powerful tool for early identification of individuals at risk of CRAO. The aim of the study was to identify potential biomarkers for CRAO through a comprehensive analysis. We employed metabolomics analysis to compare venous blood samples from CRAO patients with cataract patients for the venous difference, as well as arterial and venous blood from CRAO patients for the arteriovenous difference. The analysis of metabolites showed that PC(P-18:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z)), PC(P-18:0/20:4(5Z,8Z,11Z,14Z)) and octanoylcarnitine were strongly correlated with CRAO. We also used univariate logistic regression, random forest (RF), and support vector machine (SVM) to screen clinical parameters of patients and found that HDL-C and ApoA1 showed significant predictive efficacy in CRAO patients. We compared the predictive performance of the clinical parameter model with combined model. The prediction efficiency of the combined model was significantly better with area under the receiver operating characteristic curve (AUROC) of 0.815. Decision curve analysis (DCA) also exhibited a notably higher net benefit rate. These results underscored the potency of these three substances as robust predictors of CRAO occurrence.

AI in Healthcare: Revolutionizing Patient Care with Predictive Analytics and Decision Support Systems

This article explores the transformative impact of Artificial Intelligence (AI) in healthcare, with a specific focus on how predictive analytics and decision support systems are revolutionizing patient care. Predictive analytics enable early disease prevention and diagnosis by identifying patterns and risk factors, contributing to improved patient outcomes and cost-effective healthcare. Machine learning facilitates personalized treatment plans, leveraging individual patient data for tailored interventions that enhance efficacy and minimize adverse effects. AI-driven algorithms in medical imaging enhance diagnostic accuracy, providing rapid and precise assessments. Decision support systems, powered by AI, streamline healthcare workflows by offering real-time insights based on patient data and clinical guidelines, facilitating evidence-based decision-making. Remote patient monitoring, facilitated by AI, allows for proactive healthcare interventions by tracking vital signs and identifying potential health issues in real time. The article also discusses challenges and ethical considerations associated with AI integration in healthcare, emphasizing the importance of responsible deployment and regulatory frameworks. The comprehensive exploration underscores how AI is not only transforming patient care but also shaping the future of healthcare delivery.

Ultrasensitive detecting of dopamine in complex components by field effect transistor sensor based on the synergistic enhancement effect and overcoming debye length limitations

Field effect transistor (FET) has attracted high attention in biomolecules detection. However, the sensitivity of FET-based biosensors is often limited by the charge screening effect. In addition, the facile and ultra-sensitive detection for small biomolecules, which possess weak charge or electroneutral, remains to be studied. Here, the self-assembled monolayer AuNPs/three-dimensional (3D) crumpled graphene FET is structured for a biosensor by shrinking flexible polystyrene (PS) films through heat treatment method and realized label-free and ultra-sensitive detection of dopamine (DA) using DA aptamer as probes immobilized on the AuNPs/3D crumpled graphene surface. The nanoscale deformation caused by thermal expansion effect of flexible graphene/PS can effectively reduce charge screening and the synergistic application of crumpled graphene and AuNPs can promote electron transfer and the graphene carrier concentration, leading to conductivity increase and hydrophilicity enhancement of 3D graphene. In addition, the binding of DA aptamer to DA will cause conformational changes of the aptamer molecule, which affects the charge transport properties of the sensor, thus improving its selectivity and stability. The biosensor can also easily distinguish interfering substances for DA detection in complex components (PBS, human urine, and fetal calf serum) with the detection limits as low as 60, 240 and 316 zM (10−19–10−11 M), respectively. DA released from exocytosis induced by K+ stimulation was selectively detected. The results show that the biosensor can be used as an excellent tool for ultrasensitive molecular recognition and detecting the effects of exogenous reagents on living cells, which is promising for clinical diagnosis and early disease prevention.

AI in Healthcare: Transforming Patient Care through Predictive Analytics and Decision Support Systems

This article explores the transformative impact of Artificial Intelligence (AI) in healthcare, with a specific focus on how predictive analytics and decision support systems are revolutionizing patient care. Predictive analytics enable early disease prevention and diagnosis by identifying patterns and risk factors, contributing to improved patient outcomes and cost-effective healthcare. Machine learning facilitates personalized treatment plans, leveraging individual patient data for tailored interventions that enhance efficacy and minimize adverse effects. AI-driven algorithms in medical imaging enhance diagnostic accuracy, providing rapid and precise assessments. Decision support systems, powered by AI, streamline healthcare workflows by offering real-time insights based on patient data and clinical guidelines, facilitating evidence-based decision-making. Remote patient monitoring, facilitated by AI, allows for proactive healthcare interventions by tracking vital signs and identifying potential health issues in real time. The article also discusses challenges and ethical considerations associated with AI integration in healthcare, emphasizing the importance of responsible deployment and regulatory frameworks. The comprehensive exploration underscores how AI is not only transforming patient care but also shaping the future of healthcare delivery.

A Novel RAA Combined Test Strip Method Based on Dual Gene Targets for Pathogenic Vibrio vulnificus in Aquatic Products.

Vibrio vulnificus can cause disease in aquatic animals and humans, therefore, rapid and simple field detection of pathogenic V. vulnificus is important for early disease prevention. In this study, a novel recombinase-aided amplification (RAA) combined test strip with double T-lines (RAA-TS-DTL) was developed for the rapid detection of V. vulnificus in aquatic products. Pathogenic V. vulnificus was detected using the virulence vvhA gene and the housekeeping gene gyrB gene as the dual target of the test strip. The RAA-TS-DTL method showed 100% specificity for V. vulnificus, and no cross-reaction was observed with Vibrio spp. or other bacteria (n = 14). Furthermore, sensitive detection of V. vulnificus in oysters was achieved. The LODs of the gyrB and vvhA genes were 6 CFU/mL and 23 CFU/mL, respectively, which was about five times higher than that of the commercial test strip. The method was validated with spiked samples (n = 60) of fish, shrimp and oyster. The consistency between RAA-TS-DTL and the traditional culture method was 97.9%. In addition, the entire process of detection, including preparation of the sample, could be completed within 50 min. Our results indicated that the developed RAA-TS-DTL was a reliable and useful tool for rapid screening or on-site detection of pathogenic V. vulnificus in aquatic products and aquaculture water.

Microfluidic-based plasmonic microneedle biosensor for uric acid ultrasensitive monitoring

Uric acid (UA) is considered a potential risk factor in the causation or progression of kidney disease. Capabilities in personalized health monitoring enabled by capture and quantitative analysis of uric acid in the interstitial fluid could make up for the limitations of approach assessment of blood samples. Herein, we report a microfluidic−based wearable plasmonic microneedle sensor for interstitial fluid (ISF) sampling and minimally invasive uric acid monitoring. Under the force of the negative pressure generated by finger push. The hollow microneedle array could extract and deliver subcutaneous fluids to the sensing chamber by a microfluidic channel for SERS detection. The SERS sensing chip based on three-dimensional (3D) gold nanoarrays was found to have excellent reproducibility with 5.96% RSD. This high−density microarray showed ultrasensitive and label-free detection of uric acid molecules with a detection limit of 0.51 µM and enable fast identification of target molecules by integration with a handheld Raman spectrometer. The sensing platform may help bring unlimited possibilities for efficient medical healthcare and early disease prevention in daily life.

A mechanical HSA biosensor based on multi-field-coupling-mediated magnetic sensitization strategy

The conventional mechanical biosensor based on stress and electrical conversion can be an effective method to detect key human biomarkers for clinical diagnosis and early disease prevention. However, the applications of this type of biosensor are greatly limited due to their unsatisfactory sensitivity. In this work, a magnetic-sensitized (MS) mechanical biosensor based on multi-field coupling was developed for higher sensitivity, giving access to detect human serum albumin (HSA). Via introducing secondary magnetic antibodies labeled with magnetized Fe2O3 nanoparticles to the stress and electrical conversion element of the MS-biosensor, the multi-field coupling was realized based on stress, electricity, and magnetism. Under the action of the magnetic field, the magnetic force of the secondary magnetic antibody and the stress of antigen-antibody binding jointly drove and enhanced the deformation of the MS-biosensor, amplifying the electrical signal, and realizing magnetic sensitization. The HSA was detected by the MS-biosensor at a range of 0–80 μg/mL with a limit of detection (LOD) of 0.14 μg/mL, demonstrating the high performance of the MS-biosensor. Moreover, the MS-biosensor showed high selectivity, specificity, and stability, indicating that the magnetic sensitization strategy of the MS-biosensor was significant for the clinical application of mechanical biosensors.

Immunological response of 35 and 42 days old Asian seabass (Lates calcarifer, Bloch 1790) fry following immersion immunization with Streptococcus iniae heat-killed vaccine.

Early disease prevention by vaccination requires understanding when fry fish develop specific immunity to a given pathogen. In this research, we explored the immune responses of Asian seabass (Lates calcarifer) at the stages of 35- and 42- days post-hatching (dph) to an immersive heat-killed Streptococcus iniae (Si) vaccine to determine whether fish can produce specific antibodies against the pathogen. The vaccinated fish of each stage (V35 and V42) were immersed with the Si vaccine at 107CFU/ml for 3h, whereas the control groups (C35 and C42) were immersed with tryptic soy broth (TSB) in the same manner. Specific antibodies were measured by enzyme-linked immunosorbent assay (ELISA) before and post-immunization (i.e., 0, 7, and 14 days post-immunization, dpi). Expression of innate (TNFα and IL-1β) and adaptive (MHCI, MHCII, CD4, CD8, IgM-like, IgT-like, and IgD-like) immune-related genes were evaluated at the same time points with the addition of 1 dpi. The results showed that a subset of immunized fish from both V35 and V42 fry could elicit specific antibodies (IgM) against Si at 14 dpi. All tested innate and adaptive immune genes upregulated at 7 dpi among fish in V35 group. Interestingly, 42 dph fish appeared to respond to the Si vaccine faster than that of 35 dph, as a significant increase in transcripts was observed in CD4, IL-1β, IgM-like, and IgD-like at 1 dpi; and specific antibody titers of some fish, although not all, were higher than a threshold (p=0.05) since 7 dpi. In conclusion, this study reveals that 35-42 dph Asian seabass fry can elicit specific immunity to Si immersion vaccine, suggesting that early vaccination of 35 dph fry Asian seabass is feasible.

Improving healthcare for children in rural China: commentary on WeChat-based self-assessment with tailored feedback

Improving healthcare for children in rural China: commentary on WeChat-based self-assessment with tailored feedback

A Novel Cloud Enabled Access Control Model for Preserving the Security and Privacy of Medical Big Data

In the context of healthcare, big data refers to a complex compilation of digital medical data collected from many sources that are difficult to manage with normal technology and software due to its size and complexity. These big data are useful in various aspects of healthcare, such as disease diagnosis, early prevention of diseases, and predicting epidemics. Even though medical big data has many advantages and a lot of potential for revolutionizing healthcare, it also has a lot of drawbacks and problems, of which security and privacy are of the utmost concern, owing to the severity of the complications once the medical data is compromised. On the other hand, it is evident that existing security and privacy safeguards in healthcare organizations are insufficient to protect their massive, big data repositories and ubiquitous environment. Thus, motivated by the synthesizing of the current knowledge pertaining to the security and privacy of medical big data, including the countermeasures, in the study, firstly, we provide a comprehensive review of the security and privacy of medical big data, including countermeasures. Secondly, we propose a novel cloud-enabled hybrid access control framework for securing the medical big data in healthcare organizations, and the result of this research indicates that the proposed access control model can withstand most cyber-attacks, and it is also proven that the proposed framework can be utilized as a primary base to build secure and safe medical big data solutions. Thus, we believe this research would be useful for future researchers to comprehend the knowledge on the security and privacy of medical big data and the development of countermeasures.

Prediction and diagnosis of chronic kidney disease development and progression using machine-learning: Protocol for a systematic review and meta-analysis of reporting standards and model performance.

Chronic Kidney disease (CKD) is an important yet under-recognized contributor to morbidity and mortality globally. Machine-learning (ML) based decision support tools have been developed across many aspects of CKD care. Notably, algorithms developed in the prediction and diagnosis of CKD development and progression may help to facilitate early disease prevention, assist with early planning of renal replacement therapy, and offer potential clinical and economic benefits to patients and health systems. Clinical implementation can be affected by the uncertainty surrounding the methodological rigor and performance of ML-based models. This systematic review aims to evaluate the application of prognostic and diagnostic ML tools in CKD development and progression. The protocol has been prepared using the Preferred Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) guidelines. The systematic review protocol for CKD prediction and diagnosis have been registered with the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42022356704, CRD42022372378). A systematic search will be undertaken of PubMed, Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), the Web of Science, and the IEEE Xplore digital library. Studies in which ML has been applied to predict and diagnose CKD development and progression will be included. The primary outcome will be the comparison of the performance of ML-based models with non-ML-based models. Secondary analysis will consist of model use cases, model construct, and model reporting quality. This systematic review will offer valuable insight into the performance and reporting quality of ML-based models in CKD diagnosis and prediction. This will inform clinicians and technical specialists of the current development of ML in CKD care, as well as direct future model development and standardization.

Dual Mode Strain–Temperature Sensor with High Stimuli Discriminability and Resolution for Smart Wearables

AbstractStrain and temperature are important physiological parameters for health monitoring, providing access to the respiration state, movement of joints, and inflammation processes. The challenge for smart wearables is to unambiguously discriminate strain and temperature using a single sensor element assuring a high degree of sensor integration. Here, a dual‐mode sensor with two electrodes and tubular mechanically heterogeneous structure enabling simultaneous sensing of strain and temperature without cross‐talk is reported. The sensor structure consists of a thermocouple coiled around an elastic strain‐to‐magnetic induction conversion unit, revealing a giant magnetoelastic effect, and accommodating a magnetic amorphous wire. The thermocouple provides access to temperature and its coil structure allows to measure impedance changes caused by the applied strain. The dual‐mode sensor also exhibits interference‐free temperature sensing performance with high coefficient of 54.49 µV °C−1, low strain and temperature detection limits of 0.05% and 0.1 °C, respectively. The use of these sensors in smart textiles to monitor continuously breathing, body movement, body temperature, and ambient temperature is demonstrated. The developed multifunctional wearable sensor is needed for applications in early disease prevention, health monitoring, and interactive electronics as well as for smart prosthetics and intelligent soft robotics.

Capacity Building of a Self-Reliant Model Community for Cholangiocarcinoma Prevention by Producing Fruit and Vegetable Juice Products in a High-Risk Area of Thailand.

This participatory action research aims to develop healthy fruit and vegetable juice products (FVPs) to enhance people's income and quality of life (QOL) in a community with the highest prevalence of cholangiocarcinoma (CCA) in Thailand. We randomly sampled study areas from 25 homes in a Thai community to solve poverty and improve QOL. Descriptive and inferential statistics were employed for data analysis. Moreover, qualitative data were analyzed by content analysis. The community management system relies on local resources under religious inspiration. Academics also keep developing FVPs from local raw materials planted by organic farming under "Nong Lak Rak Sukapab." The product's nutritional value was measured by In-House and Nutrition Labeling Methods from National Food Institute Laboratory (IEC/ISO17025). According to the Bureau of Nutrition, a 230 ml bottle of each wheatgrass and corn juice contains nutrients beneficial to health and necessary for the body. In addition, the community has participated in production planning, marketing, distribution, and household accounting. As a result, average monthly household income and QOL increased with statistical significance after the project implementation. Conclusions: These findings demonstrated that improving communities' capacity to be health leaders by producing FVPs to prevent CCA in high-risk areas is critical to early disease prevention and community health care. These issues can also be applied to public policy implications for other regions and diseases in order to emphasize community health care and long-term community development.