Technology For Early Detection Research Articles

Advances in Artificial Intelligence and Blockchain Technologies for Early Detection of Human Diseases

Advances in Artificial Intelligence and Blockchain Technologies for Early Detection of Human Diseases

A Comprehensive Review on Optimizing Machine Learning Models for Early Detection and Forecasting of Monkeypox Outbreaks

This is a significant problem in diagnosing zoonotic opportunistic 'emerging' diseases like Monkeypox, which require not only better diagnostics but also efficient, effective, and affordable diagnostics. This paper considers the possibilities of machine learning (ML), deep learning (DL), and optimization algorithms for diagnosing and predicting Monkeypox. The presently employed strategies can be enhanced because clinical and imaging data can be harnessed to drive these technologies for early detection and subsequent containment activities. Generally, in a review, the authors offer information on how the diagnostic processes using ML and DL result in enhanced accuracy, specificity, and sensitivity of models, thus reducing design reliabilities. Furthermore, outbreak data is subjected to predictive modeling analysis to establish patterns useful in helping risk managers and policymakers prepare to manage future outbreaks. This system poses a new diagnostic model for Monkeypox and other zoonotic diseases by incorporating these complex computational tools into the present healthcare systems. This advancement not only strengthens the diagnostic arsenal of zoonotic diseases but also expands the possibilities for the interception and prevention of such diseases in the future at the world level.

Virtual Reality Technology for Early Detection and Diagnosis of Autism Spectrum Disorder

The presence of Autism Spectrum Disorder (ASD) in a person makes them lag in social interaction. Thus, early detection and diagnosis solution is needed for ASD patients. Hence, an SFP-ANFIS-based VR intervention for the early diagnosis of ASD is proposed. Initially, the EEG signal of an ASD patient is taken and pre-processed, from which features are extracted. Based on the extracted features, the SH-KPA groups the patients with autism and their corresponding emotion. Similarly, from the behavioral images, the features are extracted after landmarking the facial points. Then, the features are classified with SH-KPA. From the SH-KPA results of EEG signal and behavioral images, the similarity is estimated to recognize the ASD child emotion. Then, based on the previous health records of patients, the state of the patient, estimated emotions, and similarity score, the SFP-ANFIS predicts the level of ASD and recommends a possible solution to be conducted with a VR device. Finally, through experiments, the efficacy of the proposed model is proved.

Advancing early detection technologies for Dreissena polymorpha (Pallas, 1771): comparing mussel detection in environmental samples with environmental DNA (eDNA) and detection canines

Advancing early detection technologies for Dreissena polymorpha (Pallas, 1771): comparing mussel detection in environmental samples with environmental DNA (eDNA) and detection canines

Artificial Intelligence in Laboratory Technologies for Early Detection and Prognostication of Sepsis: A Systematic Review

Background: Sepsis: a complex clinical syndrome--represents life-threatening organ dysfunction instigated by an infection's dysregulated host response. Early detection and accurate prognostication of sepsis are crucial; they pave the way for timely intervention, ultimately enhancing patient outcomes. The rise in interest towards Artificial Intelligence (AI) applications within laboratory technologies is directly related to its potential for improving early detection and prognosis forecasting in sepsis cases; this interest comes as AI continues its advancement. Methods: We conducted a systematic review of studies utilizing AI algorithms in laboratory settings for early sepsis detection and prognostication: our methods entailed searching relevant databases for research published until October 2023. Our inclusion criteria spanned original articles; these applied machine learning (ML) and deep learning (DL) techniques to laboratory data--with the aim being sepsis prediction. We assessed the quality of the studies, extracted and synthesized data on AI model performance metrics - including: area under receiver operating characteristic curve (AUROC), sensitivity, specificity, and accuracy. Results: The review encompassed eight studies meeting the inclusion criteria; AI models showcased exceptional predictive capabilities--evidenced by a range of AUROC values from 0.799 to 0.9213, signifying noticeably acceptable performance. However, there was wide variation in sensitivity and specificity among these analyses: an indicator of heterogeneity in model performance. Superior prognostic accuracy and potential for real-time monitoring of patients' early sepsis signs emerged in several models: notably, within the first 12 hours of patient admission - their highest predictive period. The models frequently outperformed traditional scoring systems. Conclusion: Laboratory technology's AI applications significantly promise sepsis' early detection and prognostication. Reviewed studies suggest AI models may surpass traditional methods, offering potential integration into clinical workflows for rapid sepsis identification aid. Nevertheless, we also acknowledged both the variability in model performance and necessity of additional validation across diverse clinical settings. Future research: it must concentrate on two key aspects--the refinement of AI algorithms to enhance sensitivity and precision; furthermore, it should delve into evaluating the clinical impact of tools for sepsis prediction that are assisted by AI.

Comparison Accuracy of CNN and VGG16 in Forest Fire Identification: A Case Study

The current research aims to assess the precision of forest fire detection using CNN and VGG16 models, specifically in the context of fire identification. While both models have demonstrated significant promise in visual pattern recognition, a comprehensive analysis regarding their specific benefits in forest fire identification is still needed. The rationale behind this research stems from the significance of promptly identifying forest fires as a preemptive measure to mitigate their detrimental effects on the environment and society. The employed approach involves the application of transfer learning techniques on a diverse and extensive dataset encompassing different forest fire scenarios. The dataset was used to train both CNN and VGG16 models. The test results indicated that the CNN model achieved a forest fire detection accuracy of 96%, while VGG16 achieved 98% accuracy. The primary objective of this research is to enhance comprehension regarding the merits and demerits of each model in the context of forest fire identification scenarios. While VGG16 exhibits marginally superior performance in identifying forest fires, this discrepancy offers valuable insight into the practical applicability of these two models for fire detection in real-world scenarios. These findings establish a solid basis for the advancement of more dependable and efficient early detection technology in the prevention and management of forest fires in the future. This can be accomplished by capitalizing on the unique capabilities of each model to optimize their performance in practical scenarios.

Engineering of novel hemagglutinin biosensors for rapid detection and drug screening of Influenza A H7N9 virus

New pathogenic influenza virus strains are constantly emerging, posing a serious risk to both human health and economic growth. To effectively control the spread of this virus, there is an urgent need for early, rapid, sensitive, simple, and cost-effective detection technologies, as well as new and effective antiviral drugs. In this study, we have successfully achieved a significant milestone by successfully fusing the H7N9 influenza virus hemagglutinin (HA) protein with the nano-luciferase component, resulting in the development of a novel set of biosensors. This remarkable achievement marks the first instance of utilizing this biosensor technology for influenza antibody detection. Our biosensor technology also has the potential to facilitate the development of antiviral drugs targeting specific epitopes of the HA protein, providing a promising avenue for the treatment of H7N9 influenza virus infections. Furthermore, our biosensors have broad applications beyond H7N9 influenza virus detection, as they can be expanded for the detection of other pathogens and drug screening applications in the future. By providing a novel and effective solution to the detection and treatment of influenza viruses, our biosensors have the potential to revolutionize the field of infectious disease control.

Contemporary Fire Safety Engineering in Timber Structures: Challenges and Solutions

As environmental conservation and sustainability gain prominence globally, modern timber structures are receiving increased focus. Nonetheless, the combustible nature of timber raises significant fire safety concerns. This review explores the recent advancements in fire safety engineering for timber structures, emphasizing both contemporary high-rise buildings and historical timber constructions. It covers topics like inherently safer design principles, fire risk prediction, and evacuation methodologies. The review emphasizes the criticality of selecting suitable materials, structural design, firefighting systems, and advanced sensor technologies for early fire detection. Additionally, we analyze and compares various evacuation strategies, offering insights into the challenges and future directions for fire safety in modern timber structures.

Selective miR-21 detection technology based on photocrosslinkable artificial nucleic acid-modified magnetic particles and hybridization chain reaction

Recently, microRNA (miRNA) detection in blood has attracted attention as a new early detection technology for cancer. The extraction of target miRNA is a necessary preliminary step for detection; however, currently, most extraction methods extract all RNA from the blood, which limits the detection selectivity. Therefore, a method for the selective extraction and detection of target miRNA from blood is very important. In this study, we utilized photocrosslinkable artificial nucleic acids and the hybridization chain reaction (HCR) in an attempt to improve upon the current standard method RT-qPCR, which is hampered by problems with primer design and enzymatic amplification. By introducing photocrosslinkable artificial nucleic acids to oligonucleotide probes modified with magnetic particles with a sequence complementary to that of the target miRNA and irradiating them with light, covalent bonds were formed between the target miRNA and the oligonucleotide probes. These tight covalent bonds enabled the capture of miRNA in blood, and intensive washing ensured that only the target miRNA were extracted. After extraction, two types of DNA (H1 and H2) modified with fluorescent dyes were added and the fluorescence signals were amplified by the HCR in the presence of the target miRNA bound to the photocrosslinkable artificial nucleic acids, allowing for isothermal and enzyme-free miRNA detection. The novel method is suitable for selective miRNA detection in real blood samples. Because the reaction proceeds isothermally and no specialized equipment is used for washing, this detection technology is simple and selective and suitable for application to point-of-care technology using microfluidic devices.

Key considerations when developing and implementing digital technology for early dementia detection

AbstractBackgroundThe World Health Organisation (WHO) promotes using digital technologies to accelerate global attainment of health and well‐being. This has resulted in an increase in research exploring how digital technology can be used to aid early detection of dementia and to support the successful implementation of new technology into healthcare systems, including the English National Health Service (NHS). We conducted a qualitative study to understand the key considerations amongst healthcare professionals (HCP) when developing and implementing digital technology for the early detection of dementia in the NHS.MethodWe recruited primary and secondary care HCPs from various NHS clinical networks. We explored participants’ views on early dementia detection, using digital technology to aid this approach, and any potential implementation challenges. An inductive thematic analysis approach was taken to identify themes using QSR N‐Vivo (Version 1.6.1). Themes were refined by the authors.ResultEighteen semi‐structured interviews were conducted with seven secondary and 11 primary care HCPs. Key themes included considerations for healthcare users and digital technology. Some healthcare professionals felt early detection of dementia would provide healthcare users with the opportunity to future‐plan, for example by “tak[ing] out lasting powers of attorney” (P3 Primary Care Physician (PCP)). Whilst others were concerned “if we’re now looking at screening people who don’t have symptoms, […] the waiting list for memory clinics [referring to secondary care] would dramatically increase” (P6 PCP) which could negativity affect healthcare users. The use of digital technology to aid early detection raised concerns surrounding health equity, to ensure that we are “not excluding people who already are in digital poverty” (P5 PCP). Many HCPs also felt it was important to consider how “dementias have different features” (P14 PCP), so they “would want a tool that tests different modalities” (P6 PCP) to help differentiate between diagnoses.ConclusionThese results will inform future research in developing and implementing digital technology for early dementia detection. Work is needed to explore how best to support patients receiving information about their early detection of dementia, and the HCPs involved in this process. Research is needed to develop digital inclusion strategies to promote health equity.

Key considerations when developing and implementing digital technology for early detection of dementia causing diseases: a qualitative study

Abstract Introduction There has been substantial growth in research exploring use of digital health technologies (DHT) to improve global population health. Harnessing the potential of DHT for the early detection of neurodegenerative diseases is one such area.1 According to the National Institute for Health and Care Excellence, DHTs need to be credible for UK Healthcare Professionals (HCPs); HCPs should be involved in the development and implementation process of DHTs to ensure that their needs are met and to promote the successful adoption into healthcare systems.2 Aim To explore HCPs’ perspectives on key considerations of developing and implementing DHT for the early detection of dementia causing diseases, such as Alzheimer’s, within the National Health Service (NHS). Methods Ethical approval was granted from Newcastle University Ethics Committee. HCPs with patient-facing roles within primary or secondary care NHS settings were recruited through various online networks alongside a snowball sampling approach. Participants took part in a semi-structured audio-recorded interview exploring their experience of current diagnostic practices, opinions on the early detection of dementia causing diseases and the use of DHT to aid this approach, and the challenges of implementing such interventions into healthcare. The framework approach was used to thematically analyse the verbatim transcripts to identify core concepts and themes emerging within the interviews. A list of core concepts and themes was applied systematically to the whole data set using QSR N-Vivo (Version 1.6.1). The research team then used the ‘constant comparison’ technique to move backwards and forwards between these data and evolve explanations until a fit was made. Results Eighteen interviews were conducted with 11 primary and seven secondary care HCPs. Our analysis highlighted three core concepts, centring around considerations for healthcare service users, HCPs, and the DHT when developing and implementing DHT for the early detection of dementia causing diseases. HCPs recognised the potential benefits of providing an early detection of dementia causing diseases to healthcare services users, but only if a suitable intervention was available to allow individuals to potentially reduce their risk. Additionally, HCPs were concerned about the impact on health inequity, such as accessibility of this type of service to individuals who have disabilities or could be possibly digitally excluded. Many HCPs were also concerned about the impact on the healthcare system, with fears that it could overburden the NHS if sufficient resources and support were not provided for its implementation. In regard to the DHT, HCPs were positive about the possibility of a DHT to remotely capture real time data and felt it would be important to capture different modalities to help differentiate between dementia causing diseases. Discussion/Conclusion This study highlights the need to design a DHT for the early detection of dementia causing diseases that is accessible to users, to promote health equity. This study also highlights potential barriers for researchers when implementing a DHT into the NHS, although it cannot be assumed that these are the only barriers that may be encountered; developers will need to consider this so that a future DHT meet HCPs’ needs.

Vascular Health Promotion Project and Vascular Medicine in China-CCVM2004-2023.

Cardiovascular disease(CVD) has become a major disease burden affecting people's health in China. Blood vessels are very important for human health and are the "sentinel" for the development of many cardiovascular and cerebrovascular diseases. The key to effectively preventing fatal, disabling heart, brain and peripheral vascular events lies in controlling traditional and non-traditional risk factors for vascular health from the source, and early assessment and intervention of early vascular lesions. Since 2004, China government promoted the early detection technology of vascular lesions and vascular medicine, and proposed the Beijing Vascular Health Stratification (BVHS) to provide suggestions for the examination, evaluation and management of risk factors, and to provide new ideas for lifelong maintenance of vascular health. This review mainly introduces the establishment and development of the clinical discipline of "vascular medicine" in the past 20 years in China, introduces the indicators for detecting vascular function and structure and the predictive value of vascular events, and carries out intelligent and digital management of vascular health throughout the life cycle of individualized prevention, treatment and rehabilitation for people with different parts or degrees of lesions, effectively reducing the occurrence and development of cardiovascular and cerebrovascular diseases, and the prospect of new technology in maintaining vascular health.

Laser induced fluorescence technology for early detection of diabetes via optical fiber microfluidic sensor

This research explores the use of direct write laser technology to reduce manufacturing costs and time by using Polymethyl methacrylate (PMMA) sheet. The process has a maximum channel width of 208 µm for zigzag channels and 205.66 µm for straight channels, limiting droplet miniaturization. Microfluidics technology offers potential for creating graphene, glucose, and rhodamine B biosensor devices for various applications. Combining these materials offers benefits like miniaturization, reduced response time and chemical consumption, and improved device reproducibility and sensitivity. The study also highlights advancements in chip materials, device assembly, and material preparation for graphene-based microfluidic sensors. The findings show that optical fiber microfluidic biosensor devices have higher spectral resolution than traditional fluorescence spectrometers, enabling precise identification of mixture components and fluorescence enhancement analyses.

A recombinase polymerase amplification and Pyrococcus furiosus Argonaute combined method for ultra-sensitive detection of white spot syndrome virus in shrimp.

White spot disease (WSD) in shrimp is an acute infectious disease caused by white spot syndrome virus (WSSV). WSD has seriously threatened the security of shrimp farming, causing huge economic losses worldwide. As there is currently no effective treatment for WSD, developing early detection technologies for WSSV is of great significance for the prevention. In this study, we have established a detection method for WSSV using a combination of recombinase polymerase amplification (RPA) and Pyrococcus furiosus Argonaute (PfAgo). We have achieved a detection sensitivity of single copy per reaction, which is more sensitive than the previously reported detection methods. Additionally, we have demonstrated high specificity. The entire detection process can be completed within 75 min without the need for precise thermal cyclers, making it suitable for on-site testing. The fluorescence signal generated by the reaction can be quantified using a portable fluorescence detector or observed with the naked eye under a blue light background. This study provides an ultrasensitive on-site detection method for WSSV in shrimp aquaculture and expands the application of PfAgo in the field of aquatic disease diagnosis.

Key Considerations When Developing and Implementing Digital Technology for Early Detection of Dementia-Causing Diseases Among Health Care Professionals: Qualitative Study.

The World Health Organization (WHO) promotes using digital technologies to accelerate global attainment of health and well-being. This has led to a growth in research exploring the use of digital technology to aid early detection and preventative interventions for dementia-causing diseases such as Alzheimer disease. The opinions and perspectives of health care professionals must be incorporated into the development and implementation of technology to promote its successful adoption in clinical practice. This study aimed to explore health care professionals' perspectives on the key considerations of developing and implementing digital technologies for the early detection of dementia-causing diseases in the National Health Service (NHS). Health care professionals with patient-facing roles in primary or secondary care settings in the NHS were recruited through various web-based NHS clinical networks. Participants were interviewed to explore their experiences of the current dementia diagnostic practices, views on early detection and use of digital technology to aid these practices, and the challenges of implementing such interventions in health care. An inductive thematic analysis approach was applied to identify central concepts and themes in the interviews, allowing the data to determine our themes. A list of central concepts and themes was applied systematically to the whole data set using NVivo (version 1.6.1; QSR International). Using the constant comparison technique, the researchers moved backward and forward between these data and evolving explanations until a fit was made. Eighteen semistructured interviews were conducted, with 11 primary and 7 secondary care health care professionals. We identified 3 main categories of considerations relevant to health care service users, health care professionals, and the digital health technology itself. Health care professionals recognized the potential of using digital technology to collect real-time data and the possible benefits of detecting dementia-causing diseases earlier if an effective intervention were available. However, some were concerned about postdetection management, questioning the point of an early detection of dementia-causing diseases if an effective intervention cannot be provided and feared this would only lead to increased anxiety in patients. Health care professionals also expressed mixed opinions on who should be screened for early detection. Some suggested it should be available to everyone to mitigate the chance of excluding those who are not in touch with their health care or are digitally excluded. Others were concerned about the resources that would be required to make the technology available to everyone. This study highlights the need to design digital health technology in a way that is accessible to all and does not add burden to health care professionals. Further work is needed to ensure inclusive strategies are used in digital research to promote health equity.

Perspective on new cell-free DNA technologies for early cancer detection.

Perspective on new cell-free DNA technologies for early cancer detection.

121. Utility of a sound-based monitoring technology for early cough detection in a swine fattening unit

121. Utility of a sound-based monitoring technology for early cough detection in a swine fattening unit

Method for enhancing transmission image of breast obtained in visible and near-infrared bands

Multispectral transmission imaging is expected to be a new method for early screening of breast cancer. For the transmission images of the breast, which have a very low signal-to-noise ratio, the frame accumulation technique can be used to substantially improve the signal-to-noise ratio, but it will cause data redundancy. Terraced compression method not only reduces the redundancy of data, but also enhances the contrast of the image, so it is well suited for gray-scale compression of image. However, due to factors such as uneven tissue thickness and unevenly distributed illumination, the images contain trend item, which affect the effectiveness of the terraced compression method. Therefore, this paper obtains and eliminates the trend items contained in the images by polynomial fitting to ensure the effectiveness of the terraced compression method. The results of processing transmission images of breasts in the visible and near-infrared bands obtained from clinical trials show that the method of this paper can effectively remove trend item and enhance the contrast of the transmissive images. This can promote the application of multispectral imaging technology for early detection of breast cancer.

Label-free and specific detection of active Botulinum neurotoxin in 0.5 μL drops with the meta-nano-channel field-effect biosensor

Botulinum neurotoxins (BoNT) are the most poisonous and lethal substances known to humans, and as such there is a great need for fast, easy-to-use, and early BoNT detection technologies which are not available today. A biosensor based on field-effect transistor (FET) is a highly pursued sensing platform due to the inherent low-noise and amplification, low-cost, and the ultimate potential for the sensing of multiple analytes from an ultra-small sample volume. In the current study we employ the novel Meta-Nano-Channel (MNC) FET biosensor for specific and label-free sensing of BoNT. The MNC biosensor is a multi-channel device which provides the means to optimize the coupling between the biological interactions and the readout sensing current, by applying a tight control over both the electrodynamics of the sensing current and the electrostatics of the biological interactions. Importantly, the MNC FET biosensor presents excellent stability, repeatability and robustness in wet conditions. Finally, the multi-channel approach of the MNC biosensor allows for tunable sensing performance in terms of limit-of-detection, sensitivity and dynamic range.

The Effect of a Words-in-Noise Training Method on Speech Perception in Noise of Children with Unilateral Hearing Loss

Background and Aim: Despite more affordable and advanced technologies for early detection of congenital hearing loss, unilateral hearing loss is the prevalent form of hearing loss affecting school-aged children. This study aimed to examine the impact of Words-in-Noise (WIN) training on speech perception of noise in children with unilateral hearing loss. Methods: Thirteen children aged 8 to 12 years with unilateral hearing loss underwent a WIN training program in noise. The participants were tested before and after training on word identification in noise and cortical auditory evoked potentials. Results: A comparison of the mean signal-to-noise ratio 50% between pre- and post-training indicated that signal-to-noise ratio 50% score decreased after training sessions. WIN training reduced the latency in N1 and P2 waves in the Fz electrode and the N1 wave in the Pz electrode and increased the amplitude of the waves in the Fz and Pz electrodes. The observed data suggest that all participants’ performance improved on word identification in noise and some electrophysiological parameters. Cortical auditory evoked potentials components changes did not correlate with the WIN scores. Conclusion: The Persian version of the WIN training improved speech perception ability in the presence of competitive noise in children with unilateral hearing loss. Therefore, this software solution can partially solve speech comprehension problems with noise in these children. Keywords: Unilateral hearing loss; word in noise training; children; listening in noise