Vital Information Research Articles

A novel approach to enhancing biomedical signal recognition via hybrid high-order information bottleneck driven spiking neural networks

A novel approach to enhancing biomedical signal recognition via hybrid high-order information bottleneck driven spiking neural networks

Secure and Lightweight Cipher for Resource-constrained IoT Healthcare Applications using Snake Key Generation

The integration of the Internet of Things (IoT) in healthcare has transformed the monitoring of physiological parameters, significantly enhancing the capabilities of medical diagnostics and patient care. This study explores the use of advanced cryptographic techniques to secure IoT healthcare systems, particularly through block chain (BC) technology. Moreover, this study evaluates the effectiveness of two cryptographic key generators—bee swarm key generator and snake key generator—in enhancing data security within a BC-based framework for remote patient monitoring. Our study conducts a series of NIST randomness tests to compare the performance of these key generators in terms of randomness, resilience to cryptographic attacks and computational efficiency. Results show that the snake key generator outperforms the bee swarm key generator, demonstrating higher randomness in key generation and better secure coverage of the orientation space. This finding is crucial for securing important healthcare data, especially given the complexity of IoT devices, which transmit vital patient health information. Furthermore, this study examines the double-layer encryption architecture used to secure data in at rest and during transmission from a sensor’s gathering location to long-term storage. This approach is essential to preserve the privacy and integrity of patient data, which is critical for maintaining trust in IoT healthcare systems. This study aims to provide a comprehensive and comparative analysis to support the continued evolution of secure, efficient and scalable cryptographic solutions within IoT healthcare, demonstrating the importance of selecting the most effective key generation technique to address the unique security challenges of this new field.

Evaluating shoot-tip regrowth of 25 Rubus L. species and hybrids after 15 to 20 years of cryopreserved storage

Evaluating shoot-tip regrowth of 25 Rubus L. species and hybrids after 15 to 20 years of cryopreserved storage

CRCPD E-43 Committee for Interagency Environmental Data Sharing and Communications Endorsed RadResponder Threshold Sets.

Situational awareness is critical in radiological emergency response as it enables responders to understand and interpret real-time data to make informed decisions during high-stress situations. Effective situational awareness involves collecting accurate data on radiation levels, contamination zones, and potential exposure risks, and visualizing this data in a comprehensible format. Tools like RadResponder play a key role in this process by allowing users to assign severity levels to data records and view them on an event map, thus facilitating rapid and informed decision-making. The CRCPD E-43 committee has developed enhanced threshold sets for RadResponder to improve situational awareness during radiological incidents. These threshold sets, based on science and informed by nationally recognized reference documents or regulations, are designed to help responders quickly assess radiation levels and make informed decisions regarding public safety and emergency worker doses. By providing clear, easily identifiable values and a standardized approach to data visualization, these threshold sets enhance the effectiveness of radiological emergency response efforts. The committee also recommends upgrades to the RadResponder system for better visibility and management of threshold sets, ensuring responders can efficiently access vital information during emergencies.

Au nanoparticles anchored carbonized ZIF-8 for enabling real-time and noninvasive glucose monitoring in sweat.

Au nanoparticles anchored carbonized ZIF-8 for enabling real-time and noninvasive glucose monitoring in sweat.

Emergency Nurse Arterial Line Education Using QR Code Reader.

Emergency Nurse Arterial Line Education Using QR Code Reader.

Smart assistive stick for visually impaired people

The "Smart Assistive Stick for Visually Impaired People" uses sophisticated real-time object identification and audio feedback to improve mobility and safety for those with visual impairments. The YOLO (You Only Look Once) object detection method is used by the system to quickly identify and recognize nearby obstacles. The stick's incorporation of an aural feedback mechanism enhances navigation in a variety of contexts by providing the user with vital environmental information. This study examines the state of assistive technology, identifies gaps in current solutions, and presents our system's innovative method of fusing real-time aural help with computer vision. Along with suggested fixes to improve system reliability, major issues like accuracy under various lighting circumstances and real-time processing constraints are discussed. Additionally, this evaluation assesses how well the system performs in real-world situations and investigates its possibilities for further development, such as better item detection models, sensor integration, and wider applications in smart environments. With this project, we hope to make a contribution to the expanding field of assistive technologies by giving people with visual impairments a safer navigation aid that is easier to use and more effective.

Meteorology, weather and war in South East Asia: Malaya c.1940-1960.

This article interrogates the positioning of British colonial meteorology in Malaysia and Singapore from the 1940s to 1960. This period spanned a global conflict and an internecine war, effecting profound sociopolitical changes from which neither Malaysia nor Singapore would emerge the same. The meteorological services were essential to Britain's armed conflicts, providing vital weather information to the army, navy and, especially, the air forces, as well as supporting the aviation and shipping industry often in difficult and dangerous circumstances. This article argues that British military policy in South East Asia and the specific concerns of the colonial government in Malaya directly commanded the meteorological agenda on the ground during this period, with a secondary but significant impact on tropical climate and weather research. It thus addresses the interplay of science, colonialism and military interest from the perspective of a region that has featured little in the history of science.

Statistical analysis of road accidents in India

India's Transportation Research Department and the National Central Records Bureau issue annual traffic collision, injury, and fatality statistics. These statistics offer vital information about the nation's traffic patterns and associated problems. However, the nation's involvement in highway fatality research is disproportionately tiny, with only 0.7% of papers focusing on this area. Furthermore, injury numbers are frequently overstated. For every 1,000 road-related deaths, there exists less than one published on injuries from traffic accidents, indicating a substantial research vacuum. This emphasizes how urgently more research on diagnosing and treating injuries sustained in traffic is needed. This program is led by healthcare providers and their respective organizations, who carry out research, promote establishing initiatives to lessen injuries caused by vehicles. Improving the safety and health of the public as well as tackling the serious problem of injuries from crashes in India require this coordinated effort.

Afrikaans first language wh-question acquisition in low socio-economic status Grade 1 learners

Young typically developing children find wh-questions challenging, while researchers typically expect children from low socio-economic status (SES) contexts to struggle more with language tasks, even when wh-questions test fundamental grammatical skills, especially in children aged four and older. This study thus aims to ascertain how well low SES typically developing Afrikaans first language (L1) Grade 1 learners perform on the comprehension and production of single and complex wh-questions. An Afrikaans L1 group (n = 24) was tested twice (aged 6;0 to 7;6 and 6;9 to 8;3) with question-elicitation tasks. The production of single wh-questions is developing later (with 62% mastery), while only the comprehension of conjoined adjunct questions was mastered by the end of Grade 1 (with 98% mastery). All other complex questions remain problematic by the end of Grade 1 (with mastery below 90%). Despite statistically significant improvement (p < 0.05) for most complex wh-questions over the course of the year, the low SES learners fall behind with almost all wh-questions types in Afrikaans in comparison to higher SES learners. These results provide vital information for educators as language development data can be used to inform and direct learning and teaching in classroom situations in different SES contexts.

Shortcomings in reporting country-level participation in multi-centre randomised controlled trials involving Ireland as a collaborating partner: A meta-research study.

Multi-centre randomised controlled trials (RCTs) provide vital information about healthcare interventions. Reporting on country-level participation is important for understanding the context of multi-centre RCTs. This study aimed to examine multi-centre RCT reporting of country-level participation, using Ireland as a case study. This is meta-research study included RCTs identified in a previous study of Irish RCTs. The previous study involved searching six databases (inception-2018) for RCTs with participants recruited in Ireland: PubMed, Embase, Scopus, CINAHL, PsychINFO and the Cochrane Register of Controlled Trials. This current study focuses on multi-centre RCTs conducted on humans in healthcare settings with <80% of participants recruited in Ireland. Outcome variables were trial characteristics and reporting rates for several variables, including: number of Irish centres, number of participants recruited in Ireland, and reporting use of relevant reporting guidelines. Descriptive statistics were used for analysis. Overall, 239 RCTs were included. The most common intervention was a drug (74.9% of RCTs). The most common clinical domain was the cardiovascular system (18.0%). Number of Irish centres was reported in 75.3% of RCTs, and number of participants recruited in Ireland in 27.2%. Among RCTs published after the CONSORT reporting guideline was published, 8.3% reported using a relevant reporting guideline. Our findings show deficits in reporting for multi-centre RCTs, particularly in reporting number of participants in Ireland and reporting use of relevant reporting guidelines. The development of a multi-centre trial extension to existing reporting guidelines may partly address country-level reporting issues.

Human blood metabolites and the risk of colorectal cancer: A Mendelian randomization study

Background: Metabolomics can offer vital information into a cancer’s condition. Despite its potential, research on the metabolites linked to colorectal cancer (CRC) remains limited. From a cell molecular biomechanics perspective, understanding these metabolite associations can offer a deeper understanding of the disease’s underlying mechanisms. We performed Mendelian randomisation (MR) analyses to investigate causal associations between 486 blood metabolites and CRC. Methods: Data on blood metabolites were derived from a Genome-wide association study (GWAS) involving 7824 Europeans. Additionally, summary statistics for CRC were sourced from the FinnGen consortium database. To explore the causal relationship between CRC and blood metabolites, we primarily utilized the inverse variance weighted (IVW) analysis. Supplementary analyses incorporated MR-Egger and weighted median methods to ensure the robustness of our findings. The potential for pleiotropic effects was evaluated using the Cochran’s Q test and the MR-Egger intercept test. Furthermore, colocalization analyses were performed to ascertain whether the observed associations were influenced by specific genetic loci within the genomic region. Results: The results of this study indicated significant associations between eight metabolites: Indolelactate (OR = 2.62, 95% confidence interval (CI): 0.26–1.66, p = 0.007), 1-heptadecanoylglycerophosphocholine (OR = 1.37, 95% CI: 0.10–0.54, p = 0.005), 1-stearoylglycerophosphocholine (OR = 3.47, 95% CI: 0.65–1.84, p = 0.00005) , X-11792 (OR = 0.57, 95% CI: −0.94–−0.17, p = 0.005), X-12038 (OR = 0.44, 95% CI: −1.50–−0. 12, p = 0.021), X-12212 (OR = 1.96, 95% CI: 0.10–1.25, p = 0. 022), X-14056 (OR = 0.50, 95% CI: −1.28–−0.12, p = 0.018) , X-14745 (OR 0.41, 95% CI: −1.48–−0.31, p = 0.003) and CRC. These metabolites might play roles in altering the mechanical properties of cells in the colon. They could potentially affect the cytoskeletal structure, cell membrane fluidity, or the way cells interact with the extracellular matrix. Conclusion: The eight identified blood metabolites with causative influence on CRC provide valuable clues for understanding CRC from a cell molecular biomechanics angle, which can further aid in its screening, prevention, and treatment strategies.

A biodegradable and restorative peripheral neural interface for the interrogation of neuropathic injuries

Monitoring the early-stage healing of severe traumatic nerve injuries is essential to gather physiological and pathological information for timely interventions and optimal clinical outcomes. Traditional diagnostic methods relying on physical examinations, imaging tools, and intraoperative electrophysiological testing present great challenges in continuous and remote monitoring. While implantable peripheral nerve interfaces provide direct access to nerve fibers for precise interrogation and modulation, conventional non-degradable designs pose limited utilization in nerve injury rehabilitation. Here, we introduce a biodegradable and restorative neural interface for wireless real-time tracking and recovery of long-gap nerve injuries. Leveraging machine learning techniques, this electronic platform deciphers nerve recovery status and identifies traumatic neuroma formation at the early phase, enabling timely intervention and significantly improved therapeutic outcomes. The biodegradable nature of the device eliminates the need for retrieval procedures, reducing infection risks and secondary tissue damage. This research sheds light on bioresorbable multifunctional peripheral nerve interfaces for probing neuropathic injuries, offering vital information for early diagnosis and therapeutic intervention.

SMART SURVEILLANCE: ADVANCED DEEP LEARNING-BASED VEHICLE DETECTION AND TRACKING MODEL ON UAV IMAGERY

Unmanned aerial vehicles (UAVs) can monitor traffic in different scenarios like surveillance, control, and security. The object detection method depends on UAVs equipped with vision sensors, which have received significant attention in domains such as intelligent transportation systems (ITSs) and UAVs, which can monitor road traffic across some distance and offer vital data for following intelligent traffic supervision tasks, namely traffic situational awareness, detecting sudden accidents, and calculating traffic flow. Nevertheless, most vehicle targets exhibit specific features and lesser sizes that challenge accurate vehicle recognition in UAV overhead view. Employing innovative computer vision (CV) models, vehicle recognition and tracking in UAV images contains detecting and following vehicles in aerial footage taken by UAVs. This procedure leverages deep learning (DL) approaches for perfectly detecting vehicles and a robust tracking method for monitoring their actions through the frames, offering vital information for traffic management, surveillance, and urban planning. Therefore, this study designs an Advanced DL-based Vehicle Detection and Tracking on UAV Imagery (ADLVDT-UAVI) approach. The drive of the ADLVDT-UAVI technique is to detect and classify distinct vehicles in the UAV images correctly as Brain-Like Computing technique for Traffic Flow Optimization in Smart Cities. In this approach, Gaussian filtering (GF) primarily eliminates the noise. Besides, the ADLVDT-UAVI technique utilizes a squeeze-and-excitation capsule network (SE-CapsNet) for feature vector derivation. Meanwhile, the hyperparameter selection process involves using the Fractals coati optimization algorithm (COA). Finally, the self-attention bi-directional long short-term memory (SA-BiLSTM) approach is utilized to classify detected vehicles. To validate the improved results of the ADLVDT-UAVI approach, a wide range of experiments is performed under VEDAI and ISPRS Postdam datasets. The experimental validation of the ADLVDT-UAVI approach portrayed the superior accuracy outcome of 98.35% and 98.96% compared to recent models.

Effects of the Qigong Tuina therapy on patients with Chronic fatigue syndrome: study protocol for a randomized controlled trial

BackgroundChronic fatigue syndrome (CFS) is a chronic disease characterized by fatigue, complex symptoms and long duration. It incurs significant economic costs every year. Qigong Tuina therapy (QTT), a traditional Chinese therapy, is skilled in the treatment of fatigue. The objective of this study is to observe the effectiveness of QTT in treating patients with CFS.MethodsA randomized controlled trial will recruit 128 patients with CFS. The patients will be allocated randomly in a 1:1 ratio to either the QTT group or the cognitive behavioral therapy (CBT) group. The interventions for both groups will be carried out once per week for 8 weeks. Then a 4 weeks follow-up will be conducted for all patients after the intervention. The primary outcome will be the changes in the Multidimensional Fatigue Inventory (MFI-20). Secondary outcomes will be measured by the 36-Item Short Form Health Survey (SF-36), Pittsburgh Sleep Quality Index (PSQI), Hospital Anxiety and Depression Scale (HADS), and functional magnetic resonance imaging (fMRI). The data will be analyzed at the baseline, 4 weeks into the intervention, at the end of the intervention, and 4 weeks after the intervention. The safety of interventions will be assessed after each treatment session.DiscussionThe trial aims to establish whether QTT is not inferior to CBT for CFS patients. This study provides vital information for an alternative and complementary therapy aimed at patients afflicted with CFS.Ethics and disseminationEthical approval was granted by Ethics Committee of Shanghai Municipal Hospital of Traditional Chinese Medicine (2023SHL-KY-100–02). The findings will be disseminated through publications, conferences, and briefs to professional organizations.Trial registrationChiCTR2400081828, Chinese Clinical Trial Registry. Registered on March 13, 2024.

RADEX: a rule-based clinical and radiology data extraction tool demonstrated on thyroid ultrasound reports.

Radiology reports contain valuable information for research and audits, but relevant details are often buried within free-text fields. This makes them challenging and time-consuming to extract for secondary analyses, including training artificial intelligence (AI) models. This study presents a rule-based RAdiology Data EXtraction tool (RADEX) to enable biomedical researchers and healthcare professionals to automate information extraction from clinical documents. RADEX simplifies the translation of domain expertise into regular-expression models, enabling context-dependent searching without specialist expertise in Natural Language Processing. Its utility was demonstrated in the multi-label classification of fourteen clinical features in a large retrospective dataset (n = 16,246) of thyroid ultrasound reports from five hospitals in the United Kingdom (UK). A tuning subset (n = 200) was used to iteratively develop the search strategy, and a holdout test subset (n = 202) was used to evaluate the performance against reference-standard labels. The dataset cardinality was 3.06, and the label density was 0.34. Cohen's Kappa was 0.94 for rater 1 and 0.95 for rater 2. For RADEX, micro-average sensitivity, specificity, and F1-score were 0.97, 0.96, and 0.94, respectively. The processing time was 12.3 milliseconds per report, enabling fast and reliable information extraction. RADEX is a versatile tool for bespoke research and audit applications, where access to labelled data or computing infrastructure is limited, or explainability and reproducibility are priorities. This offers a time-saving and freely available option to accelerate structured data collection, enabling new insights and improved patient care. Question Radiology reports contain vital information that is buried in unstructured free-text fields. Can we extract this information effectively for research and audit applications? Findings A rule-based RAdiology Data Extraction tool (RADEX) is described and used to classify fourteen key findings from thyroid ultrasound reports with sensitivity and specificity > 0.95. Clinical relevance RADEX offers clinicians and researchers a time-saving tool to accelerate structured data collection. This practical approach prioritises transparency, repeatability, and usability, enabling new insights into improved patient care.

Label-Free and Rapid Microfluidic Design Rules for Circulating Tumor Cell Enrichment and Isolation: A Review and Simulation Analysis.

Enriching and isolating circulating tumor cells (CTCs) have attracted significant interest due to their important role in early cancer diagnosis and prognosis, allowing for minimally invasive approaches and providing vital information about metastasis at the cellular level. This review comprehensively summarizes the recent developments in microfluidic devices for CTC enrichment and isolation. The advantages and limitations of several microfluidic devices are discussed, and the design specifications of microfluidic devices for CTC enrichment are highlighted. We also developed a set of methodologies and design rules of label-free microfluidics such as spiral, deterministic lateral displacement (DLD) and dielectrophoresis (DEP) to allow researchers to design and develop microfluidic devices systematically and effectively, promoting rapid research on design, fabrication, and experimentation.

Development, dissemination and community response towards the first community notice regarding misrepresented illicit anabolic-androgenic steroids in circulation in Australia.

Drug alerts aimed at both people who use drugs and health workers help to prevent acute harms from unpredictable illicit drug markets and by equipping health workers to handle unusual drug events and share vital information with service users. However, there has never been an alert produced for anabolic-androgenic steroids (AAS), an important class of illicit drugs. We report on the development, implementation and community receptivity of the first-ever AAS community drug alert. Based on findings from samples collected during the first month of an AAS checking trial conducted by drug checking service CheQpoint, we identified contamination in two oxandrolone samples, which prompted issuing of the first-ever AAS community notice. Drawing on digital ethnographic techniques, we collected and analysed social media comments on the notice to assess AAS community perceptions and the broader impact of this harm reduction initiative. The Instagram post by CheQpoint reached 1376 users, with 3429 impressions and 87 interactions. Community feedback indicated receptivity to the notice, with several people in the community recognising the prevalence of AAS adulteration. Responses highlighted the need for more thorough testing and indication of sample content, given the perception of a growing number of new people using AAS. This study, the first to describe a community notice for illicit market AAS, reveals a strong demand for harm reduction interventions. We call for the urgent expansion of drug-checking services to provision for AAS and, thus, provide equitable health support to address systemic gaps for this group.

Assessing the inflammation in pediatric MOGAD: Significance of CSF HMGB1 and related biomarkers.

Myelin-oligodendrocyte glycoprotein antibody associated disease (MOGAD) is a common inflammatory disease of the central nervous system (CNS) in children that can lead to demyelination. Evaluation and monitoring of biomarkers associated with its pathogenesis would provide vital information on disease progression and therapeutic assessment. We assessed NLRP3, HMGB1, IL-6, and IL-33 levels in the cerebrospinal fluid (CSF) of pediatric patients with MOGAD at different time points and their association with the risk of disease. We recruited 30 patients with MOGAD (20 in the acute phase and 10 in remission) and 10 control patients with noninflammatory demyelinating disease. The expanded disability status scale (EDSS) was used to assess disease severity. NLRP3, HMGB1, and IL-6 levels in the CSF were significantly higher in patients with MOGAD during the acute phase than in remission (P < 0.05, P < 0.05, P < 0.05) and the control group (P < 0.01, P < 0.0001, P < 0.01). HMGB1 levels were significantly correlated with NLRP3 levels (P < 0.01) during the acute phase. Moreover, we found notable correlation between HMGB1 levels and EDSS (P < 0.05) scores. IL-6 levels were significantly correlated with the total number of attacks (P < 0.05), but not with EDSS scores. These findings suggest that NLRP3, HMGB1, and IL-6 in the CSF may be potential therapeutic targets and are at least partly involved in the pathogenesis of pediatric MOGAD. HMGB1 in the CSF may be a potential biomarker correlating with pediatric MOGAD severity. Further investigations are warranted to validate potential cytokine pathways between that NLRP3, HMGB1, and IL-6 of MOGAD.

Explainable deep learning approach for recognizing “Egyptian Cobra” bite in real-time

Abstract The Egyptian cobra is among the deadliest snake species, capable of causing death within a short span of 15 min. Also, every snake species has its own anti-venom type. So, a quick identifying the Egyptian Cobra bite from other snake species is a challenging and critical task. This research employs Internet of things (IoT) and deep learning methods to precisely recognize bites of Egyptian cobra, in the real-time, by analyzing images of the bite marks. We deploy IoT-enabled wearable devices equipped with sensors capable of detecting snake bites, whereas these sensors measure changes in physiological parameters indicative of a snakebite, such as heart rate, blood pressure, and temperature sensors based on our proposed mathematical algorithm. Also, we present a real case study in which we used our mathematical algorithm to determine based on its sensor readings whether the victim was exposed to a snake bite or not in the real-time. These wearable devices can be worn by individuals working or living in areas prone to snake encounters, such as farmers. When a snake bite occurs, the IoT sensors embedded in the wearable devices will immediately detect the bite and transmit real-time data, including vital information about the bite marks, to a central monitoring system or victim relative. Also, we assembled a dataset consisting of 500 images depicting Egyptian cobra bites and 600 images of bites from various other snake species indigenous to Egypt. To bolster the model’s trustworthiness and facilitate understanding of its decisions, we employed the contemporary method of explainable deep learning. Also, notably, our methodology yielded an accuracy of 90.9%.