Thiamine: An indispensable regulator of paediatric neuro-cardiovascular health and diseases.

The sustainable developmental goals emphasize good health, reduction in preventable neonatal and under-five mortalities, and attaining zero hunger. However, South Asian countries report a higher incidence of neonatal and under-five mortalities when compared to the Western world, many of which are attributed to maternal and perinatal micronutrient deficiencies. Isolated nutrient deficiency in the absence of calorie deficit poses a diagnostic challenge since such deficiencies present with acute multisystemic and enigmatic manifestations. Thiamine (vitamin B1) is a micronutrient of prime importance which exerts indispensable roles in energy metabolism. Deficiency of thiamine can lead to catastrophic consequences. This review provides insight into the biochemical actions of thiamine in energy metabolism, the compromised aerobic metabolism resulting from thiamine deficiency, and the crucial role of thiamine in the proper functioning of the nervous, cardiovascular, and immune systems. The review also explores the acute life-threatening consequences of thiamine deficiencies in neonates and infants and the speculative role of thiamine in other pathologies like encephalopathy, sepsis, and autism spectrum disorders. However, routine assessment of thiamine in pregnant women and neonates is yet to be implemented, due to the lack of affordable and automated diagnostic techniques, and the cost-intensive nature of mass spectrometry-based quantification. CONCLUSION: Physicians are recommended to have a low threshold for suspecting thiamine deficiency especially in vulnerable populations. Laboratory diagnosis of thiamine deficiency needs to be implemented as a standard of care, especially in endemic regions. Further, public health policies on food fortification, mandatory supplementation, and surveillance are imperative to eliminate thiamine deficiency-induced health hazards. WHAT IS KNOWN: • South Asian countries report a higher incidence of neonatal and under-five mortalities, many of which are attributed to maternal and perinatal micronutrient deficiencies. • Preventable causes of neonatal/ infantile deaths include birth factors (low birth weight, birth asphyxia), infectious diseases (pneumonia, diarrhoea, tetanus, tuberculosis, measles, diphtheria, malaria, acute infections), deficiency diseases and genetic diseases (vitamin & mineral deficiencies, IEMs, congenital heart disease, unexplained PPHN, SIDS etc). WHAT IS NEW: • Acute thiamine deficiency presenting as multisystemic syndromes, has unfortunately been a long standing unresolved public health concern. However, accessible surveillance and diagnostic strategies remain elusive in most clinical settings. • Despite decades of reports and emerging guidelines, diagnosis of thiamine deficiency is often missed and policy mandates at national level are yet to be implemented even in endemic countries. • This review provides a comprehensive summary of the biochemical role of thiamine, its key functions and effects on major organ systems, the diagnostic gap, the enigmatic presentation of acute thiamine deficiency, the plausible role of thiamine in other pathologies and the preventive measures at individual and community level.

DrunkChain: Blockchain-Based IoT System for Preventing Drunk Driving-Related Traffic Accidents.

Traffic accidents present significant risks to human life, leading to a high number of fatalities and injuries. According to the World Health Organization's 2022 worldwide status report on road safety, there were 27,582 deaths linked to traffic-related events, including 4448 fatalities at the collision scenes. Drunk driving is one of the leading causes contributing to the rising count of deadly accidents. Current methods to assess driver alcohol consumption are vulnerable to network risks, such as data corruption, identity theft, and man-in-the-middle attacks. In addition, these systems are subject to security restrictions that have been largely overlooked in earlier research focused on driver information. This study intends to develop a platform that combines the Internet of Things (IoT) with blockchain technology in order to address these concerns and improve the security of user data. In this work, we present a device- and blockchain-based dashboard solution for a centralized police monitoring account. The equipment is responsible for determining the driver's impairment level by monitoring the driver's blood alcohol concentration (BAC) and the stability of the vehicle. At predetermined times, integrated blockchain transactions are executed, transmitting data straight to the central police account. This eliminates the need for a central server, ensuring the immutability of data and the existence of blockchain transactions that are independent of any central authority. Our system delivers scalability, compatibility, and faster execution times by adopting this approach. Through comparative research, we have identified a significant increase in the need for security measures in relevant scenarios, highlighting the importance of our suggested model.

Distributed Denial of Service Attack Detection in Network Traffic Using Deep Learning Algorithm.

Internet security is a major concern these days due to the increasing demand for information technology (IT)-based platforms and cloud computing. With its expansion, the Internet has been facing various types of attacks. Viruses, denial of service (DoS) attacks, distributed DoS (DDoS) attacks, code injection attacks, and spoofing are the most common types of attacks in the modern era. Due to the expansion of IT, the volume and severity of network attacks have been increasing lately. DoS and DDoS are the most frequently reported network traffic attacks. Traditional solutions such as intrusion detection systems and firewalls cannot detect complex DDoS and DoS attacks. With the integration of artificial intelligence-based machine learning and deep learning methods, several novel approaches have been presented for DoS and DDoS detection. In particular, deep learning models have played a crucial role in detecting DDoS attacks due to their exceptional performance. This study adopts deep learning models including recurrent neural network (RNN), long short-term memory (LSTM), and gradient recurrent unit (GRU) to detect DDoS attacks on the most recent dataset, CICDDoS2019, and a comparative analysis is conducted with the CICIDS2017 dataset. The comparative analysis contributes to the development of a competent and accurate method for detecting DDoS attacks with reduced execution time and complexity. The experimental results demonstrate that models perform equally well on the CICDDoS2019 dataset with an accuracy score of 0.99, but there is a difference in execution time, with GRU showing less execution time than those of RNN and LSTM.

Resilience Optimization of Post-Quantum Cryptography Key Encapsulation Algorithms.

Recent developments in quantum computing have shed light on the shortcomings of the conventional public cryptosystem. Even while Shor's algorithm cannot yet be implemented on quantum computers, it indicates that asymmetric key encryption will not be practicable or secure in the near future. The National Institute of Standards and Technology (NIST) has started looking for a post-quantum encryption algorithm that is resistant to the development of future quantum computers as a response to this security concern. The current focus is on standardizing asymmetric cryptography that should be impenetrable by a quantum computer. This has become increasingly important in recent years. Currently, the process of standardizing asymmetric cryptography is coming very close to being finished. This study evaluated the performance of two post-quantum cryptography (PQC) algorithms, both of which were selected as NIST fourth-round finalists. The research assessed the key generation, encapsulation, and decapsulation operations, providing insights into their efficiency and suitability for real-world applications. Further research and standardization efforts are required to enable secure and efficient post-quantum encryption. When selecting appropriate post-quantum encryption algorithms for specific applications, factors such as security levels, performance requirements, key sizes, and platform compatibility should be taken into account. This paper provides helpful insight for post-quantum cryptography researchers and practitioners, assisting in the decision-making process for selecting appropriate algorithms to protect confidential data in the age of quantum computing.

Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction.

Internet of Things (IoT) has made significant strides in energy management systems recently. Due to the continually increasing cost of energy, supply-demand disparities, and rising carbon footprints, the need for smart homes for monitoring, managing, and conserving energy has increased. In IoT-based systems, device data are delivered to the network edge before being stored in the fog or cloud for further transactions. This raises worries about the data's security, privacy, and veracity. It is vital to monitor who accesses and updates this information to protect IoT end-users linked to IoT devices. Smart meters are installed in smart homes and are susceptible to numerous cyber attacks. Access to IoT devices and related data must be secured to prevent misuse and protect IoT users' privacy. The purpose of this research was to design a blockchain-based edge computing method for securing the smart home system, in conjunction with machine learning techniques, in order to construct a secure smart home system with energy usage prediction and user profiling. The research proposes a blockchain-based smart home system that can continuously monitor IoT-enabled smart home appliances such as smart microwaves, dishwashers, furnaces, and refrigerators, among others. An approach based on machine learning was utilized to train the auto-regressive integrated moving average (ARIMA) model for energy usage prediction, which is provided in the user's wallet, to estimate energy consumption and maintain user profiles. The model was tested using the moving average statistical model, the ARIMA model, and the deep-learning-based long short-term memory (LSTM) model on a dataset of smart-home-based energy usage under changing weather conditions. The findings of the analysis reveal that the LSTM model accurately forecasts the energy usage of smart homes.

A deep learning approach to optimize remaining useful life prediction for Li-ion batteries.

Accurately predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is vital for improving battery performance and safety in applications such as consumer electronics and electric vehicles. While the prediction of RUL for these batteries is a well-established field, the current research refines RUL prediction methodologies by leveraging deep learning techniques, advancing prediction accuracy. This study proposes AccuCell Prodigy, a deep learning model that integrates auto-encoders and long short-term memory (LSTM) layers to enhance RUL prediction accuracy and efficiency. The model's name reflects its precision ("AccuCell") and predictive strength ("Prodigy"). The proposed methodology involves preparing a dataset of battery operational features, split using an 80-20 ratio for training and testing. Leveraging 22 variations of current (critical parameter) across three Li-ion cells, AccuCell Prodigy significantly reduces prediction errors, achieving a mean square error of 0.1305%, mean absolute error of 2.484%, and root mean square error of 3.613%, with a high R-squared value of 0.9849. These results highlight its robustness and potential for advancing battery health management.

Interventional Strategies to Mitigate Maternal Stress and Enhance Coping Skills During Neonatal Admission Into Intensive Care Units in Low- and Middle-Income Countries: A Scoping Review.

BACKGROUND: Mothers of high-risk neonates experience tremendous stress during neonatal intensive care unit (NICU) admission. This stress has a negative impact on mothers' participation in neonatal care activities, psychological health and coping skills in the NICU. OBJECTIVE: To determine the impact of interventional strategies to reduce maternal stress and enhance coping skills during neonatal admission to the NICU. DESIGN: A scoping review was carried out following the methodological framework outlined by Arksey and O'Malley. METHODS: This scoping review was conducted as per the Joanna Briggs Institute guidelines, including a quality appraisal checklist for randomised and nonrandomised controlled trials. Patterns, advances, gaps, evidence for practice and research recommendations from the review (PAGER framework) were used to report the results. The following international databases were used to search for primary articles: Medline via PubMed, EBSCOhost via CINAHL, Scopus, Web of Science and the ProQuest Medical Library. Original studies published in English between January 2011 and January 2023 from low- and middle-income countries (LMICs) that assessed maternal stress and coping skills during neonatal NICU admission were included in the review. RESULTS: The review included 15 articles from LMICs, of which 60% were from middle-income, 25% were from lower-middle-income and 15% were from low-income countries. Interventional strategies were described under five categories. Maternal stress decreased significantly across all three subscales of the PSPS: 'sight and sound', 'baby looks and behavior' and 'parental relationship with baby and role alteration' during neonatal NICU admission. Interventional strategies involving family-centred care and emotional and psychological supportive care have been reported to have a consistently positive impact on alleviating maternal stress and enhancing coping skills in the NICU. CONCLUSION: Healthcare professionals, especially nurses, are pivotal in promptly recognising maternal stress and NICU stressors. The participation of mothers in neonatal care, such as through family-centred care and emotional support interventions, significantly reduces maternal stress and enhances coping skills.

Gram-Negative Colonization and Bacterial Translocation Drive Neonatal Sepsis in the Indian Setting.

BACKGROUND: The gut microbiota, comprising billions of microorganisms, plays a pivotal role in health and disease. This study aims to investigate the effect of sepsis on gut microbiome of neonates admitted to the Neonatal Intensive Care Unit. METHODS: A prospective cohort study was carried out in the NICU of tertiary care hospital in Karnataka, India, from January 2021 to September 2023. Preterm neonates with birth weight < 1500 g and gestational age < 37 weeks were recruited, excluding those with congenital gastrointestinal anomalies, necrotizing enterocolitis, or blood culture-negative infections. The study population was divided into three groups: healthy neonates (Group A), neonates with drug-sensitive GNB sepsis (Group B), and neonates with pan drug-resistant GNB sepsis (Group C). Stool samples were collected aseptically, snapped in liquid nitrogen, and stored at -80°C for extraction of DNA and microbiome analysis. RESULTS: The gut microbiota of healthy neonates (Group A) was dominated by Proteobacteria (24.04%), Actinobacteria (27.13%), Firmicutes (12.74%), and Bacteroidetes (3%). Predominant genera included Bifidobacterium (55.17%), Enterobacter (12.55%), Enterococcus (50.69%), Streptococcus (7.92%), and Bacteroides (3.58%).Groups B and C, the microbiota exhibited higher Proteobacteria abundance (57.16% and 66.58%, respectively) and reduced diversity of beneficial bacteria. Notably, the presence of sepsis was associated with an increase in pathogenic bacteria and a decrease in beneficial commensal bacteria. CONCLUSION: Neonates with sepsis exhibited significant gut microbiome dysbiosis, characterized by increased Proteobacteria and reduced beneficial bacteria diversity. These findings highlight the potential of microbiome profiling as a diagnostic tool and underscore the importance of gut microbiota modulation in managing neonatal sepsis.

Modeling different strategies towards control of lung cancer: leveraging early detection and anti-cancer cell measures.

The global population has encountered significant challenges throughout history due to infectious diseases. To comprehensively study these dynamics, a novel deterministic mathematical model, TCD IL2 Z, is developed for the early detection and treatment of lung cancer. This model incorporates IL2 cytokine and anti-PD-L1 inhibitors, enhancing the immune system's anticancer response within five epidemiological compartments. The TCD IL2Z model is analyzed qualitatively and quantitatively, emphasizing local stability given the limited data-a critical component of epidemic modeling. The model is systematically validated by examining essential elements such as equilibrium points, the reproduction number (R0), stability, and sensitivity analysis. Next-generation techniques based on R0 that track disease transmission rates across the sub-compartments are fed into the system. At the same time, sensitivity analysis helps model how a particular parameter affects the dynamics of the system. The stability on the global level of such therapy agents retrogrades individuals with immunosuppression or treated with IL2 and anti-PD-L1 inhibitors admiring the Lyapunov functions' applications. NSFD scheme based on the implicit method is used to find the exact value and is compared with Euler's method and RK4, which guarantees accuracy. Thus, the simulations were conducted in the MATLAB environment. These simulations present the general symptomatic and asymptomatic consequences of lung cancer globally when detected in the middle and early stages, and measures of anticancer cells are implemented including boosting the immune system for low immune individuals. In addition, such a result provides knowledge about real-world control dynamics with IL2 and anti-PD-L1 inhibitors. The studies will contribute to the understanding of disease spread patterns and will provide the basis for evidence-based intervention development that will be geared toward actual outcomes.

Comparative Growth Outcomes in Very Low Birth Weight Infants: Evaluating Different Feeding Strategies.

OBJECTIVES: To assess the growth pattern of preterm, very low birth weight (VLBW) appropriate for gestational age (AGA) infants on three different feeding regimens. METHODS: This prospective open label three-arm parallel randomized controlled trial was conducted at neonatal intensive care unit, Kasturba Hospital, Manipal. One hundred twenty VLBW (weight between 1000-1500 g and gestational age 28-32 wk) preterm AGA infants admitted from April 2021 through September 2022 were included. Three feeding regimens were compared: Expressed breast milk (EBM); EBM supplemented with Human milk fortifier (HMF); EBM supplemented with Preterm formula feed (PTF). Primary outcome measure was assessing the growth parameters such as weight, length, head circumference on three different feeding regimens at birth 2, 3, 4, 5 and 6 wk/discharge. Secondary outcomes included incidence of co-morbidities and cost-effectiveness. RESULTS: Of 112 infants analyzed, Group 2 supplemented with HMF showed superior growth outcomes by 6th wk/discharge of intervention, with mean weight of 2053±251 g, mean length of 44.6±1.9 cm, and mean head circumference of 32.9±1.4 cm. However, infants in Group 3, supplemented with PTF, registered mean weight of 1968±203 g, mean length of 43.6±2.0 cm, and mean head circumference of 32.0±1.6 cm. Infants exclusively on EBM presented with mean weight of 1873±256 g, mean length of 43.0±2.0 cm and mean head circumference of 31.4±1.6 cm. CONCLUSIONS: Addition of 1 g of HMF to 25 ml of EBM in neonates weighing 1000-1500 g showed better weight gain and head circumference at 6 wk/discharge, which was statistically significant. However, no significant differences in these parameters were observed at postnatal or 2, 3, 4, and 5 wk.

Assessment of an Antimicrobial Stewardship Program for Enhancing Clinical Knowledge in Neonatal Care Settings With High Antimicrobial Resistance.

INTRODUCTION: Antimicrobial resistance (AMR) is a global problem, which is particularly challenging in developing countries like India. This study attempts to determine the competencies of health care professionals and to update evidence-based policies to address AMR. METHOD: A survey-based educational interventional study was conducted using a validated structured survey and knowledge questionnaire under 3 domains through an antimicrobial stewardship program. Pooled data were analyzed using SPSS version 16.0. RESULTS: Out of 58 participants, 53 (91%) have observed an increasing trend of multidrug-resistant infections over the last 5 years. There is a significant difference between the overall pretest mean scores (8.12 ± 2.10) and posttest mean scores (12.5 ± 1.49) of clinicians' knowledge with a mean difference of 4.38 ± 0.61, 95% CI of 5.003-3.92, t(57) = 16.62, P < .001). DISCUSSION: The antimicrobial stewardship program was effective in improving the competencies of clinical physicians to improve antimicrobial prescribing and reduce AMR. Moreover, improving the knowledge and competencies among health care professionals will minimize neonatal morbidity and mortality.

Novel model to authenticate role-based medical users for blockchain-based IoMT devices.

The IoT (Internet of Things) has played a promising role in e-healthcare applications during the last decade. Medical sensors record a variety of data and transmit them over the IoT network to facilitate remote patient monitoring. When a patient visits a hospital he may need to connect or disconnect medical devices from the medical healthcare system frequently. Also, multiple entities (e.g., doctors, medical staff, etc.) need access to patient data and require distinct sets of patient data. As a result of the dynamic nature of medical devices, medical users require frequent access to data, which raises complex security concerns. Granting access to a whole set of data creates privacy issues. Also, each of these medical user need to grant access rights to a specific set of medical data, which is quite a tedious task. In order to provide role-based access to medical users, this study proposes a blockchain-based framework for authenticating multiple entities based on the trust domain to reduce the administrative burden. This study is further validated by simulation on the infura blockchain using solidity and Python. The results demonstrate that role-based authorization and multi-entities authentication have been implemented and the owner of medical data can control access rights at any time and grant medical users easy access to a set of data in a healthcare system. The system has minimal latency compared to existing blockchain systems that lack multi-entity authentication and role-based authorization.

An enhanced approach for predicting air pollution using quantum support vector machine.

The essence of quantum machine learning is to optimize problem-solving by executing machine learning algorithms on quantum computers and exploiting potent laws such as superposition and entanglement. Support vector machine (SVM) is widely recognized as one of the most effective classification machine learning techniques currently available. Since, in conventional systems, the SVM kernel technique tends to sluggish down and even fail as datasets become increasingly complex or jumbled. To compare the execution time and accuracy of conventional SVM classification to that of quantum SVM classification, the appropriate quantum features for mapping need to be selected. As the dataset grows complex, the importance of selecting an appropriate feature map that outperforms or performs as well as the classification grows. This paper utilizes conventional SVM to select an optimal feature map and benchmark dataset for predicting air quality. Experimental evidence demonstrates that the precision of quantum SVM surpasses that of classical SVM for air quality assessment. Using quantum labs from IBM's quantum computer cloud, conventional and quantum computing have been compared. When applied to the same dataset, the conventional SVM achieved an accuracy of 91% and 87% respectively, whereas the quantum SVM demonstrated an accuracy of 97% and 94% respectively for air quality prediction. The study introduces the use of quantum Support Vector Machines (SVM) for predicting air quality. It emphasizes the novel method of choosing the best quantum feature maps. Through the utilization of quantum-enhanced feature mapping, our objective is to exceed the constraints of classical SVM and achieve unparalleled levels of precision and effectiveness. We conduct precise experiments utilizing IBM's state-of-the-art quantum computer cloud to compare the performance of conventional and quantum SVM algorithms on a shared dataset.

Probiotic effect in preterm neonates with sepsis - A systematic review protocol.

Background: The microbiota in the intestine is made up of trillions of living bacteria that coexist with the host. Administration of antibiotics during neonatal infection causes depletion of gut flora resulting in gut dysbiosis. Over the last few decades, probiotics have been created and promoted as microbiota management agents to enrich gut flora. Probiotics decrease the overgrowth of pathogenic bacteria in the gut of preterm neonates, reducing the frequency of nosocomial infections in the Neonatal Intensive Care Unit (NICUs). Methods: The systematic review will include randomized control trials (RCTs) of premier neonates with sepsis. Studies will be retrieved from global databases like Cochrane CENTRAL, CINAHL Plus via EBSCO host, MEDLINE via PubMed, EMBASE, SCOPUS, Ovid, Web of Science, ProQuest Medical Library, Microsoft academic, and DOAJ by utilizing database-specific keywords. Screening, data extraction, and critical appraisal of included research will be carried out separately by two review writers. Findings will be reported in accordance with the PRISMS-P 2020 guidelines. Conclusions: The findings of this systematic review will help to translate the evidence-based information needed to encourage the implementation of potential research output in the field of neonatal intensive care, guide best clinical practise, assist policy making and implementation to prevent gut dysbiosis in neonates with sepsis by summarising and communicating the evidence on the topic. PROSPERO registration number: This systematic review protocol has been registered in PROSPERO (Prospective Register of Systematic Reviews) on 10 th March 2022. The registration number is CRD42022315980.

Spammer group detection and diversification of customers' reviews.

Online reviews regarding different products or services have become the main source to determine public opinions. Consequently, manufacturers and sellers are extremely concerned with customer reviews as these have a direct impact on their businesses. Unfortunately, to gain profit or fame, spam reviews are written to promote or demote targeted products or services. This practice is known as review spamming. In recent years, Spam Review Detection problem (SRD) has gained much attention from researchers, but still there is a need to identify review spammers who often work collaboratively to promote or demote targeted products. It can severely harm the review system. This work presents the Spammer Group Detection (SGD) method which identifies suspicious spammer groups based on the similarity of all reviewer's activities considering their review time and review ratings. After removing these identified spammer groups and spam reviews, the resulting non-spam reviews are displayed using diversification technique. For the diversification, this study proposed Diversified Set of Reviews (DSR) method which selects diversified set of top-k reviews having positive, negative, and neutral reviews/feedback covering all possible product features. Experimental evaluations are conducted on Roman Urdu and English real-world review datasets. The results show that the proposed methods outperformed the existing approaches when compared in terms of accuracy.