Healthcare Technology Research Articles

Technical sandbox for a Global Patient co-Owned Cloud (GPOC)

BackgroundThe use of Cloud-based storage personal health records has increased globally. The GPOC series introduces the concept of a Global Patient co-Owned Cloud (GPOC) of personal health records. Technical sandboxes allow the capability to simulate different scientific concepts before making them production ready. None exist for the medical fields and cloud-based research.MethodsWe constructed and tested the sandbox using open-source infrastructures (Ubuntu, Alpine Linux, and Colaboratory) and demonstrated it on a cloud platform. Data preprocessing utilised standard and in-house libraries. The Mina protocol, implementing zero-knowledge proofs, ensured secure blockchain operations, while the Ethereum smart contract protocol within Hyperledger Besu supported enterprise-grade sandbox development.ResultsHere, we present the GPOC series’ technical sandbox. This is to facilitate future online research and testing of the concept and its security, encryption, movability, research potential, risks and structure. It has several protocols for homomorphic encryption, decentralisation, transfers, and file management.The sandbox is openly available online and tests authorisation, transmission, access control, and integrity live. It invites all committed parties to test and improve the platform. Individual patients, clinics, organisations and regulators are invited to test and develop the concept.The sandbox displays co-ownership of personal health records. Here it is trisected between patients, clinics and clinicians. Patients can actively participate in research and control their health data. The challenges include ensuring that a unified underlying protocol is maintained for cross-border delivery of care based on data management regulations.ConclusionsThe GPOC concept, as demonstrated by the GPOC Sandbox, represents an advancement in healthcare technology. By promoting patient co-ownership and utilising advanced technologies like blockchain and homomorphic encryption, the GPOC initiative enhances individual control over health data and facilitates collaborative medical research globally. The justification for this research lies in its potential to improve evidence-based medicine and AI dissemination. The significance of the GPOC initiative extends to various aspects of healthcare, patient co-ownership of health data, promoting access to resources and healthcare democratisation. The implications include better global health outcomes through continued development and collaboration, ensuring the successful adoption of the GPOC Sandbox and advancing innovation in digital health.

The role of perceived telemedicine quality in enhancing patient satisfaction during the COVID-19 pandemic: The mediation of telemedicine satisfaction

Adopting telemedicine helped countries manage COVID-19, but it comes with several concerns including how to analyze the degree of patient satisfaction. This research explores the use of healthcare technologies to improve patient satisfaction in the Democratic Republic of Congo (DRC) during the COVID-19 pandemic. This study aims to examine the effect of perceived telemedicine quality on patient satisfaction mediated by telemedicine satisfaction. Data was drawn from 384 responses. Data analysis was conducted utilizing PLS-SEM. The results indicate that perceived telemedicine quality has a direct effect on patient satisfaction and telemedicine satisfaction. Telemedicine satisfaction has a direct effect on patient satisfaction. Also, telemedicine satisfaction was found to have a significant mediation effect between perceived telemedicine quality and patient satisfaction. Overall, using healthcare technologies improved patient satisfaction in the DRC. Implementing educational initiatives to improve patients' and healthcare providers' digital literacy is recommended, particularly in underprivileged and rural regions. This will enable individuals to use healthcare technology, understand its benefits, and navigate the platforms. Additionally, continuous education will ensure that healthcare providers stay informed of the most recent developments and are skilled in applying them for patient care.

B-117 Rapid Classification of Cleanliness Grades of Vaginal Discharge through Computational Analysis of SERS Spectra via Variational Auto-Encoder Model And Long Short-Term Memory Networks

Abstract Background Classification of cleanliness grades of vaginal discharge plays a pivotal role in clinical laboratories, serving as indicators for the presence of various vaginal infections such as bacterial vaginosis and candidal vulvovaginitis, etc. The conventional method for assessing vaginal discharge cleanliness involves manual microscopy, a technique susceptible to variations based on the examiner's expertise and subjective judgment, hindering the attainment of swift and accurate diagnoses. Methods This study introduces a novel approach by integrating Surface-Enhanced Raman Spectroscopy (SERS) with a deep learning algorithm for the rapid classification of vaginal discharge cleanliness grades. The proposed classification method combines the Variational Auto-Encoder (VAE) and Long Short-Term Memory (LSTM) to process SERS spectra. The quality of the training data is assessed using the signal-to-noise ratio (SNR) as a standard indicator. Results The application of the VAE-LSTM algorithm significantly enhances classification performance, providing swift and accurate results while reducing the required time. Comparative analyses with various classical machine learning algorithms and evaluation metrics reveal the superior predictive capability and time efficiency of the VAE-LSTM algorithm, demonstrating 100% accuracy and a fitting time of 2 seconds. Blind testing of SERS spectra collected from unknown patients further validates the reliability of the proposed method. Conclusions This study presents a promising and practical approach for the rapid classification of vaginal discharge cleanliness grades. The integration of the SERS technique with the VAE-LSTM algorithm not only ensures accuracy and efficiency in clinical diagnosis but also lays the foundation for advancements in non-invasive diagnostic technologies in gynecological healthcare.

B-335 Analytical Performance of the Novel EQA Material for Fecal Immunochemical Test for Hemoglobin

Abstract Background EQA (external quality assurance) program for FIT (fecal immunochemical test for hemoglobin) is performed in many countries, and its demand increases as FIT is widely used in organized colorectal cancer screening programs. The materials used in FIT EQA vary from buffer-based liquid control, and lyophilized stool, to material that mimics stool, artificial stool. We could not find ideal EQA material with hemoglobin uniformly contained, good stability of hemoglobin, and ease in use such as ready-to-use form. The merit to use material that simulates actual sample, stool, is not only to be compliant with ISO15189 but also to suit various methods and sampling devices in FIT. The development of artificial stool material has been awaited by laboratories using FIT to evaluate errors and precisions in specimen collection.We assessed the analytical performance of the new artificial stool developed in HECTEF (Health Care Technology Foundation) to evaluate this artificial stool material was suitable as an EQA material. We examined the following three points: 1) Sample collection, 2) Uniformity, and 3) Stability of hemoglobin. Methods 1) Sample collection: The material was collected with the sampling probes of OC-Auto Sampling Bottle 3 (Eiken Chemical) and the collection amount was calculated by comparing the weight before and after sampling the artificial stool. 2) Uniformity: The uniformity of hemoglobin was evaluated during production before and after bottling based on “Uniformity of dosage units” section of The Japanese Pharmacopoeia 18th edition, 2021. The material was sampled from 10 different points and hemoglobin concentrations were measured using OC-Sensor DIANA. The uniformity of hemoglobin between the vials was evaluated by calculating the CV from the average of hemoglobin concentrations from samples of three points from three vials. 3) Stability of hemoglobin: The materials stored under different temperature conditions were compared with the material stored frozen at -20 °C. Storage conditions were at refrigeration for 20 months, at 30 °C for 1 month, and freeze-thawing for five times. Evaporation from the material was evaluated by weighing the samples before and after storage at 30 °C for 1 month. Results 1) Sample collection: The material was collected with sampling probes and the collection amounts were 106% (compared to theoretical amount).2) Uniformity: Uniformity of hemoglobin during production was within the specification of “Uniformity of dosage units” section of the pharmacopoeias, acceptance value<15.0. Uniformity of hemoglobin among three vials was within ±10% CV.3) Stability of hemoglobin: Stabilities of the material were all within ±10% from the control material (frozen) after storage at all temperature conditions. Evaporation from the material in a vial was below 1% at 30 °C storage for 1 month. Conclusions This artificial stool is suitable for EQA material because it is confirmed that it shows favorable analytical results in sample collection, uniformity, and stability of hemoglobin. The evaluation of this material in the EQA program to show its suitability was performed by Labquality. The result is reported in a poster at ADLM 2024.

B-324 Pilot Study Result of EQA for Fecal Immunochemical Test for Hemoglobin Using a Novel Ready-to-Use Sample Material

Abstract Background The primary screening method for colorectal cancer is fecal immunochemical test (FIT) detecting human hemoglobin (Hb) in feces. There are several tests on the market both for automated analyzers and point-of-care testing (POCT). To ensure high quality results, the tests should be monitored with appropriate quality assessment procedures including participation in external quality assessment (EQA) programs. By participating in an EQA program using a stool-like material, clinical laboratories and POCT sites can fulfil the ISO 15189 requirements of monitoring the complete laboratory process from pre- to postanalytical phases in addition to the quantitative analytical phase of FIT. Here we present the first results of an international EQA pilot study for quantitative FIT using a novel ready-to-use stool-like material, performed by EQA provider Labquality. Methods 40 participants from 8 countries using 9 different FIT reagents were recruited for the pilot study performed in 2023. A set of artificial stool samples containing human hemoglobin uniformly was shipped to the participants at ambient temperature. The sample material is developed by HECTEF (Health Care Technology Foundation, Japan). The participants were instructed to collect the sample with their test specific sample collection kit and perform analysis according to the manufacturer’s instructions as a regular patient sample. Quantitative results (µg Hb/g feces) and qualitative interpretations (positive/negative) were reported in Labquality’s electronic platform LabScala. The quantitative target value was a method group consensus mean. A questionnaire regarding the usability of the sample material was sent to all participants. Results Sample S001 contained no Hb and all participants (40/40) reported a result corresponding to the lower detection limit of their test in use or a low numeric result most likely explained by unspecific background. All reported a negative interpretation. Sample S002 was a high positive sample for Hb. The measured value exceeded the upper detection limit of 4/9 tests (Exdia iFOB, Precision Biosensor; Fecal Occult Blood (iFOB) Neo, Boditech; FOB Test, VedaLab; NADAL FOB Quant test, Nal von Minden). The mean values and CV% were 116 µg/g and 8% for iFOBT, Aidian; 192 ug/g and 4% for Standard iFOB FIA, SD Biosensor; 144 µg/g and 45% for FOB assay kit FOB Gold, Sentinel Diagnostics (different clinical chemistry analyzers used); 91 µg/g and 26% for OC-Sensor FIT, Eiken Chemical. All participants responded a positive qualitative clinical interpretation. The majority of the responders on the feedback questionnaire (26/35) found the samples to be easy to use including comments of the sample being user-friendly and ready-to-use, resembling a patient sample and easy to apply with the test specific sample collection kit. Conclusions The fecal-like sample used in this Labquality’s EQA pilot study evaluates the complete analytical process including the preanalytical, analytical and postanalytical phases. An EQA scheme using this sample material is more compliant to ISO 15189 requirements compared to schemes where liquid or lyophilized samples are used. The new sample material used on this pilot study was positively received by most of the participants and it was compatible with all tests included in the study.

AI-enabled ACP on inpatient oncology: Feasibility, acceptability and impact on workflow.

403 Background: Advance care planning (ACP) can help patients understand and share their long-term health goals and values, and in turn facilitate the delivery of medical care consistent with those goals and values. However, in a survey of Californians about attitudes towards death and dying, while almost 80% of respondents indicated that they would like to talk to their doctor about end-of-life care, only 7% had ever had a doctor bring up the subject. To address this problem, the Stanford inpatient oncology service implemented an AI-enabled workflow to facilitate timely identification of patients for ACP conversations before discharge. While the workflow implementation has thus far been successful, less is known of the human factors from patients and providers that influence the feasibility and acceptability of this workflow. Methods: To gather perspectives on the AI-enabled tool, we used qualitative research methods to conduct semi-structured interviews with 15 patients and 15 providers. Patients were over the age of 18 and had been recently admitted to the Stanford Inpatient Oncology Service. Providers were over the age of 18 and worked within the Stanford Inpatient Oncology Service in one of four roles: attending oncologist, advanced practice provider, clinical dietician, occupational therapist. Interviews were conducted virtually via Zoom, recorded and transcribed for subsequent thematic analysis. Once transcribed, interviews were qualitatively coded using an inductive approach. Results: Patients and providers affirm the positive potential for AI implementation in healthcare settings, but emphasize the necessity of maintaining human-centered models of care which prioritize strong interpersonal relationships. Providers observe relatively minimal impacts of the AI-enabled tool on existing advance care planning workflows/practices, but express optimism surrounding the implementation of AI tools in inpatient oncology more generally. Patients exhibit similar levels of optimism and a general openness toward AI applications in healthcare settings, but highlight the need for transparency and informed consent throughout the processes of AI development and implementation. Conclusions: This study provides novel insights on the impacts of AI-enabled workflows in inpatient oncology care, as well as patient and provider perspectives concerning the implementation of AI technology in healthcare more broadly. As AI is increasingly developed with clinical care applications in mind, these insights provide valuable foundations for successful implementation.

Informatics applications in the nursing profession

Technology in healthcare has grown exponentially in recent years [1]. Today, information and communication technologies have become widespread in healthcare systems worldwide. Since nurses comprise most of the healthcare workforce, they must possess sufficient skills to work in a technology-mediated environment [2]. The advancements in information technology over recent decades have provided significant opportunities for nurses to stay informed about patient conditions [3]. This has created new roles for nurses and enhanced their decision-making power in delivering nursing care [4]. Consequently, the trend of transformation in nursing informatics is expanding globally. The application of nursing informatics methods is on the rise [5], bringing about considerable changes in healthcare delivery [6]. Nursing informatics combines nursing processes, relevant information, and the management of nursing data along with other information processing systems. The primary goal of this discipline is to support health and well-being globally [7]. Proper utilization of informatics in nursing leads to cost reduction, increased awareness, and innovation and creativity among nurses [8].

India's Potential as a Leader in Cancer Care Progress in the Future: A Synthetic Interdisciplinary Perspective.

This paper comprehensively analyzes India's potential to become a leader in cancer care in the Global South, particularly in precision population cancer medicine (PPCM). Through an interdisciplinary lens, it examines the current landscape of cancer care in India, highlighting its strengths, weaknesses, opportunities, and threats in this domain. This review explores the concept of knowledge translation and its importance in bridging the gap between knowledge generation and implementation in medical sciences and applies this to the Indian healthcare scenario. The review then delves into India's technological prowess, exemplified by its digital health initiatives such as the CoWIN (winning over COVID-19) app and the Ayushman Bharat Digital Mission, which provide a strong foundation for leveraging advanced technologies in healthcare. The authors discuss India's pharmaceutical industry, often referred to as the "pharmacy of the world," emphasizing its crucial role in global drug manufacturing and distribution. It also examines the country's emerging genomic research landscape, including initiatives such as GenomeIndia and the Indian Cancer Genome Atlas Foundation, which are pivotal for advancing personalized medicine. A significant portion of the review is dedicated to analyzing India's clinical trial ecosystem. It traces the evolution of regulatory frameworks governing clinical research in the country and highlights recent reforms that have made India an increasingly attractive destination for global studies, the potential adoption of innovative trial designs and artificial intelligence (AI)-driven analyses. Crucially, the authors confront the formidable obstacles inherent in India's complex healthcare landscape, illuminating the unique challenges that must be overcome. The review acknowledges India's underrepresentation in global clinical trials despite its large population and significant cancer burden. The issue of financial toxicity in cancer care is discussed, underscoring the need for affordable treatment options. The study also points out the nascent state of India's genomic databases, which account for only a small percentage of global genetic data. Despite these challenges, the authors posit that by effectively leveraging its information technology (IT) infrastructure, robust pharmaceutical sector, and large, diverse population, India has the potential to develop unique, country-specific solutions for cancer care. The study suggests that by fostering genomic research, strategically reforming its clinical trial ecosystem, and harnessing its digital capabilities, India could transform its cancer care landscape and emerge as a model for other developing nations in the Global South. In essence, this paper provides a roadmap for India's journey towards becoming a leader in PPCM, offering valuable insights for policymakers, healthcare professionals, and researchers in the field of oncology and precision medicine. Indeed, by using PPCM as a "pilot project," India can learn to use its new strategies to improve non-cancer care disease prevention, early detection, and improved and more cost-effective management. This approach could revolutionize cancer care in India and serve as a model for other developing nations in the Global South. By leveraging the strategies and technologies developed for PPCM, India could significantly enhance its healthcare system, highlighting the importance and urgency of improving cancer care in the region.

Self-Sustaining Triboelectric Nanosensors for Real-Time Urine Analysis in Smart Toilets.

Healthcare has undergone a revolutionary shift with the advent of smart technologies, and smart toilets (STs) are among the innovative inventions offering non-invasive continuous health monitoring. The present technical challenges toward this development include limited sensitivity of integrated sensors, poor stability, slow response and the requirement external energy supply alongside manual sample collection. In this article, triboelectric nanosensor array (TENSA) is introduced featuring electrodes crafted from laser-induced 3D graphene with functional polymers like polystyrene, polyimide, and polycaprolactone for real-time urine analysis while generating 50 volts output via urine droplet-based triboelectrification. Though modulating interfacial double-layer capacitance, these sensors exhibit exceptional sensitivity and selectivity in detecting a broad spectrum of urinary biomarkers, including ions, glucose, and urea with a classification precision of 95% and concentration identification accuracy of up to 0.97 (R2), supported by artificial neural networks. Upon exposure to urine samples containing elevated levels of Na+, K+, and NH4 +, a notable decrease (ranging from 32% to 68%) is observed in output voltages. Conversely, urea induces an increase up to 13%. Experimental validation confirms the stability, robustness, reliability, and reproducibility of TENSA, representing a significant advancement in healthcare technology, offering the potential for improved disease management and prevention strategies.

Ultrasensitive and durable borophene-based humidity sensors for advanced human-centric applications

Borophene, a novel two-dimensional (2D) nanomaterials with high surface-to-volume ratio and abundant active sites, show great promise for humidity sensing applications, which are crucial for improving human comfort, public health, and device safety. However, traditional fabrication methods, such as drop-coating, face limitations including uncontrollable sensing layer thickness, poor water-damage resistance, and inherent variability between devices. In this work, we present a high-performance humidity sensor fabricated using borophene glass. The sensor demonstrates an impressive detection range (11–97 % RH), exceptional sensitivity (up to 1.83×105% at 97 % RH), low hysteresis (1.245 %), excellent repeatability, and relatively fast response (28.8 s)/recovery (2.6 s) times. Notably, the sensor exhibits thickness-dependent sensing performance, long-term stability, high selectivity, and water-damage resistance. First-principles calculations show that borophene exhibits an adsorption energy of 0.208 eV for H2O molecules, with the hole doping effect following adsorption driving the sensor’s response to varying relative humidity levels. The sensor’s sustained heightened sensitivity under high humidity conditions is attributed to the Grotthuss mechanism. Additionally, the sensor has been successfully integrated into various human-centric applications, including speech recognition, wireless monitoring of respiratory patterns, and non-contact switches. These advancements pave the way for the integration of borophene humidity sensors into smart devices and non-contact control systems, driving innovation in human–computer interaction and healthcare technologies.

Research on Collaborative Defense Method of Hospital Network Cloud based on End-to-end Edge Computing

This research introduces a groundbreaking collaborative defense mechanism that utilizes end-to-end edge computing to bolster the security of decentralized hospital cloud systems. By integrating intrusion detection systems, firewalls, anomaly detection, and threat intelligence in a unified manner through the efficiency of edge computing, this approach marks a significant advancement in healthcare cybersecurity. Through rigorous testing with a substantial dataset, the system demonstrated exceptional performance metrics, including a remarkable 95% accuracy in threat detection, a low false positive rate, and a swift response time of merely 0.25 seconds. Notably, the system effectively mitigates computational overhead, thereby optimizing resource utilization. Comparative analysis with existing methodologies underscores the superiority of this novel framework, particularly in terms of geolocation accuracy, the minimization of false positives, and expedited reaction capabilities. This study’s collaborative defense strategy, underpinned by end-to-end edge computing, presents a holistic and innovative solution to the escalating cyber threats facing healthcare infrastructures. By redefining the parameters of security in medical settings, it paves the way for a safer and more resilient healthcare information technology ecosystem.

A comparative analysis of generative artificial intelligence responses from leading chatbots to questions about endometriosis

IntroductionThe use of generative artificial intelligence (AI) has begun to permeate most industries, including medicine, and patients will inevitably start using these large language model (LLM) chatbots as a modality for education. As healthcare information technology evolves, it is imperative to evaluate chatbots and the accuracy of the information they provide to patients and to determine if there is variability between them. ObjectiveThis study aimed to evaluate the accuracy and comprehensiveness of three chatbots in addressing questions related to endometriosis and determine the level of variability between them. Study DesignThree LLMs, including Chat GPT-4 (Open AI), Claude (Anthropic), and Bard (Google) were asked to generate answers to 10 commonly asked questions about endometriosis. The responses were qualitatively compared to current guidelines and expert opinion on endometriosis and rated on a scale by nine gynecologists. The grading scale included the following: (1) Completely incorrect, (2) mostly incorrect and some correct, (3) mostly correct and some incorrect, (4) correct but inadequate, (5) correct and comprehensive. Final scores were averaged between the nine reviewers. Kendall's W and the related chi-square test were used to evaluate the reviewers’ strength of agreement in ranking the LLMs’ responses for each item. ResultsAverage scores for the 10 answers amongst Bard, Chat GPT, and Claude were 3.69, 4.24, and 3.7, respectively. Two questions showed significant disagreement between the nine reviewers. There were no questions the models could answer comprehensively or correctly across the reviewers. The model most associated with comprehensive and correct responses was ChatGPT. Chatbots showed an improved ability to accurately answer questions about symptoms and pathophysiology over treatment and risk of recurrence. ConclusionThe analysis of LLMs revealed that, on average, they mainly provided correct but inadequate responses to commonly asked patient questions about endometriosis. While chatbot responses can serve as valuable supplements to information provided by licensed medical professionals, it is crucial to maintain a thorough ongoing evaluation process for outputs to provide the most comprehensive and accurate information to patients. Further research into this technology and its role in patient education and treatment is crucial as generative AI becomes more embedded in the medical field.

A Preliminary Sentiment Analysis on Digital Barriers among Senior Citizens During Covid-19 Pandemic

Nowadays, digital technology has been an important element in assisting the community in their daily routine. During the COVID-19 era, The Special Committee for Ensuring Access to COVID-19 Vaccine Supply (JKJAV) has been initiated to assist the health and well-being of Malaysian citizens in recovering from the COVID-19 pandemic. MySejahtera, a healthcare application technology, has been used by Malaysian citizens for seeking vaccination information as well as updating their health status. While there are benefits to using the application, there is still a challenge in using the technology among senior citizens. This study aims to investigate digital barriers to using a healthcare application, known as MySejahtera, among senior citizens in Kelantan. This study also reveals lessons learned regarding the use of technology among senior citizens during the Covid-19 era. Convergent parallel mixed method research design is initiated to produce a sentiment analysis for identifying digital barriers issues and challenges among senior citizens in Kelantan. 5 respondents aged from 60 to 80 years old were interviewed to obtain their sentiments on using digital technology for vaccination information and Covid-19 news. This study highlights issues namely awareness of knowledge, misconception about digital technology, information quality and technology usage among the senior citizens in Kelantan. This study summarizes that there is a need to reduce digital barriers among senior citizens to ensure digital inclusion for their benefit in health and well-being.

Artificial intelligence (AI) applications in healthcare and considerations for nursing education

Aim/objectiveTo review the current AI applications in healthcare and explore the implications for nurse educators in innovative integration of this technology in nursing education and training programs. BackgroundThere are a variety of Artificial Intelligence (AI) applications currently supporting patient care in many healthcare settings. A nursing workforce that leverages healthcare technology to enhance efficiency and accuracy of patient health outcomes is necessary. Nurse educators must understand the various uses of AI applications in healthcare to equip themselves to effectively prepare students to use the applications. DesignQualitative synthesis and analysis of existing literature. MethodsGenerative AI (ChatGPT) was used to support the development of a list of the current AI applications in healthcare. Each application was evaluated for relevance and accuracy. A literature review to define and understand the use of each application in clinical practice was completed. The search terms “AI” and “Health Education” were used to review the literature for evidence on educational programs used for training learners. ResultsTen current applications of AI in healthcare were identified and explored. There is little evidence that outlines how to integrate AI education into educational training for nurses. ConclusionA comprehensive multimodal educational approach that uses innovative learning strategies has potential to support the integration of AI concepts into nursing curriculum. The use of simulation and clinical practicum experiences to support experiential learning and to offer opportunities for practical application and training. Considerations for ethical use and appropriate critical evaluation of AI applications are necessary.

Estimating the Effectiveness of some biological agents and Palm biochar in control of Dry bubble disease on the second flush of White mushroom Agaricus bisporus

In a time when technology is present in every aspect of our lives, it is crucial to incorporate advanced solutions to protect sensitive medical data in Social Medical Systems (SMS). This study explores the need to improve security in public healthcare by using advanced technologies to strengthen the weaknesses in the growing field of Social Medical Systems. This study specifically examines the analysis of IoT-23 data using machine learning (ML) and deep learning (DL) methods, as technology and healthcare converge. The research highlights the increasing significance of technology in healthcare, specifically focusing on the revolutionary emergence of Social Medical Systems. As these interlinked networks reshape the provision of public healthcare services, security challenges such as data breaches, cyber threats, and privacy concerns become crucial barriers that require innovative solutions. The study utilizes a wide range of machine learning (ML) and deep learning (DL) techniques to examine IoT-23 data, offering a detailed comprehension of the security environment in Social Medical Systems. The chosen models comprise Support Vector Machines (SVM), Isolation Forest, Random Forest, Convolutional Neural Networks (CNN), and Autoencoder. The results and discussions focus on evaluating metrics such as accuracy, precision, recall, and F1 score. These metrics provide insights into how effective each model is in identifying vulnerabilities.

Orthodontic Materials and Antimicrobial Nanomaterials

In a time when technology is present in every aspect of our lives, it is crucial to incorporate advanced solutions to protect sensitive medical data in Social Medical Systems (SMS). This study explores the need to improve security in public healthcare by using advanced technologies to strengthen the weaknesses in the growing field of Social Medical Systems. This study specifically examines the analysis of IoT-23 data using machine learning (ML) and deep learning (DL) methods, as technology and healthcare converge. The research highlights the increasing significance of technology in healthcare, specifically focusing on the revolutionary emergence of Social Medical Systems. As these interlinked networks reshape the provision of public healthcare services, security challenges such as data breaches, cyber threats, and privacy concerns become crucial barriers that require innovative solutions. The study utilizes a wide range of machine learning (ML) and deep learning (DL) techniques to examine IoT-23 data, offering a detailed comprehension of the security environment in Social Medical Systems. The chosen models comprise Support Vector Machines (SVM), Isolation Forest, Random Forest, Convolutional Neural Networks (CNN), and Autoencoder. The results and discussions focus on evaluating metrics such as accuracy, precision, recall, and F1 score. These metrics provide insights into how effective each model is in identifying vulnerabilities.

Design, development and optimization of blockchain-based secure data management for nanotechnology in healthcare

The integration of nanotechnology in healthcare has brought about revolutionary advancements, particularly in diagnostics and therapeutics. However, managing the vast and sensitive data generated by these applications poses significant challenges, particularly concerning security, privacy, and regulatory compliance. This study explores the design, development, and optimization of a blockchain-based secure data management system tailored for nanotechnology applications in the United States healthcare sector. The proposed system leverages blockchain's decentralized and immutable ledger technology to enhance data security and privacy, ensure regulatory compliance, and improve overall system performance. Our implementation showed substantial improvements in data security, with an 86.67% reduction in data breaches and an 85% decrease in unauthorized access cases. Additionally, compliance with major data privacy standards like HIPAA, GDPR, and CCPA increased by over 20%. Performance metrics demonstrated robust scalability, maintaining high transaction speeds and manageable latency across various load conditions. Cost analysis revealed significant reductions in data storage, security, and compliance costs, highlighting the economic benefits of adopting blockchain technology in healthcare. Moreover, the system exhibited lower energy consumption compared to traditional data management systems, addressing environmental concerns. Correlation and scatter plot analyses provided insights into the scalability and performance dynamics of the blockchain system. These findings underscore the transformative potential of blockchain technology in addressing the data management challenges of nanotechnology in healthcare. The study concludes with recommendations for adopting blockchain to enhance data security, regulatory compliance, and economic efficiency in the U.S. healthcare sector.

Advances in Mesoporous Silica Nanoparticles: Synthesis, Characterization, and Biomedical Uses

Mesoporous silica nanoparticles (MSNs) have drawn significant attention due to their exceptional properties and diverse range of applications, particularly in nanomedicine. The distinctive properties of MSNs, such as their high surface area, tunable pore size, and versatile surface chemistry, make them ideal candidates for various biomedical applications. This review aims to present a detailed understanding of MSNs, from synthesis and characterization to their versatile applications in biomedicine, highlighting their significant potential in advancing healthcare technologies. The synthesis methods for MSNs were comprehensively discussed, emphasizing the influence of parameters like solvent, base, alkoxysilane concentrations, and template surfactants on the size and shape of the nanostructures. Different types of MSNs, including MCM-41, SBA-15, KIT-6, and hollow MSNs, are discussed, along with their synthesis protocols and unique characteristics. The review also covers various spectroscopic techniques, such as XRD, XPS, FTIR, NMR, and fluorescence spectroscopy, which are crucial for characterizing MSNs. Furthermore, the biomedical applications of MSNs are highlighted, demonstrating their potential in drug delivery systems, imaging, and diagnostics. The review concludes with a discussion of the future perspectives and challenges in the field, providing insights into potential developments and the prospects for clinical translation.

Machine Learning Applied to Edge Computing and Wearable Devices for Healthcare: Systematic Mapping of the Literature

The integration of machine learning (ML) with edge computing and wearable devices is rapidly advancing healthcare applications. This study systematically maps the literature in this emerging field, analyzing 171 studies and focusing on 28 key articles after rigorous selection. The research explores the key concepts, techniques, and architectures used in healthcare applications involving ML, edge computing, and wearable devices. The analysis reveals a significant increase in research over the past six years, particularly in the last three years, covering applications such as fall detection, cardiovascular monitoring, and disease prediction. The findings highlight a strong focus on neural network models, especially Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs), and diverse edge computing platforms like Raspberry Pi and smartphones. Despite the diversity in approaches, the field is still nascent, indicating considerable opportunities for future research. The study emphasizes the need for standardized architectures and the further exploration of both hardware and software to enhance the effectiveness of ML-driven healthcare solutions. The authors conclude by identifying potential research directions that could contribute to continued innovation in healthcare technologies.

Inside out: Exploring edible biocatalytic biosensors for health monitoring

Edible biosensors can measure a wide range of physiological and biochemical parameters, including temperature, pH, gases, gastrointestinal biomarkers, enzymes, hormones, glucose, and drug levels, providing real-time data. Edible biocatalytic biosensors represent a new frontier within healthcare technology available for remote medical diagnosis. The main challenges to develop edible biosensors are: i) finding edible materials (i.e. redox mediators, conductive materials, binders and biorecognition elements such as enzymes) complying with Food and Drug Administration (FDA), European Food Safety Authority (EFSA) and European Medicines Agency (EMEA) regulations; ii) developing bioelectronics able to operate in extreme working conditions such as low pH (∼pH 1.5 gastric fluids etc.), body temperature (between 37 °C and 40 °C) and highly viscous bodily fluids that may cause surface biofouling issues. Nowadays, advanced printing techniques can revolutionize the design and manufacturing of edible biocatalytic biosensors.This review outlines recent research on biomaterials suitable for creating edible biocatalytic biosensors, focusing on their electrochemical properties such as electrical conductivity and redox potential. It also examines biomaterials as substrates for printing and discusses various printing methods, highlighting challenges and perspectives for edible biocatalytic biosensors.