A comprehensive survey on exploring and analyzing COVID-19 mobile apps: Meta and exploratory analysis.

During the current COVID-19 pandemic, many digital solutions around the world have been proposed to cope with the deadly virus but the role of mobile-based applications is dominant one. In Pakistan, during the current COVID-19 pandemic, an array of mobile health applications (apps) and platforms have been launched to grapple with the impacts of the COVID-19 situation. In this survey, our major focus is to explore and analyze the starring role of mobile apps based on the features and functionalities to tackle the COVID-19 disease, particularly in Pakistan. In this study, over fifty (50) mobile apps have been scrapped from the well-known three different sources i.e. Google Play Store, iOS Play Store, and web source. We developed our own data set after searching through the different play stores. We have designed two criteria such that the first criteria are known as eligibility criteria, while the second one is known as assessment criteria. The features and functions of each mobile app are pinpointed and discussed against the parameters of the assessment criteria. The major parameters of assessment criteria are: (i) Home monitoring; (ii) COVID-19 awareness; (iii) contact tracing; (iv) telemedicine; (v) health education; (vi) COVID-19 surveillance; (vii) self-assessment; (viii) security; and (ix) accessibility. This study conducted exploratory analysis and quantitative meta-data analysis by adopting PRISMA guidelines. This survey article is not only discussing the function and features of each COVID-19-centered app in Pakistan, but it also sheds light on the limitations of every mobile app as well. The results of this survey might be helpful for the mobile developers to review the current app products and enhance the existing mobile platforms targeted towards the COVID-19 pandemic. This is the first attempt of its kind to present a state-of-the-art survey of the COVID-19-centered mobile health apps in Pakistan.

A comprehensive study on unraveling the advances of immersive technologies (VR/AR/MR/XR) in the healthcare sector during the COVID-19: Challenges and solutions.

The current COVID-19 pandemic has affected almost every aspect of life but its impact on the healthcare landscape is conspicuously adverse. However, digital technologies played a significant contribution in coping with the challenges spawned by this pandemic. In this list of applied digital technologies, the role of immersive technologies in battling COVID-19 is notice-worthy. Immersive technologies consisting of virtual reality (VR), augmented reality (AR), mixed reality (MR), extended reality (XR), metaverse, gamification, etc. have shown enormous market growth within the healthcare system, particularly with the emergence of pandemics. These technologies supplemented interactivity, immersive experience, 3D modeling, touching sensory elements, simulation, and feedback mechanisms to tackle the COVID-19 disease in healthcare systems. Keeping in view the applicability and significance of immersive technological advancement, the major aim of this study is to identify and highlight the role of immersive technologies concerning handling COVID-19 in the healthcare setup. The contribution of immersive technologies in the healthcare domain for the different purposes such as medical education, medical training, proctoring, online surgeries, stress management, social distancing, physical fitness, drug manufacturing and designing, and cognitive rehabilitation is highlighted. A comprehensive and in-depth analysis of the collected studies has been performed to understand the current research work and future research directions. A state-of-the-artwork is presented to identify and discuss the various issues involving the adoption of immersive technologies in the healthcare area. Furthermore, the solutions to these emerging challenges and issues have been provided based on an extensive literature study. The results of this study show that immersive technologies have the considerable potential to provide massive support to stakeholders in the healthcare system during current COVID-19 situation and future pandemics.

Editing microbes to mitigate enteric methane emissions in livestock.

Livestock production significantly contributes to greenhouse gas (GHG) emissions particularly methane (CH4) emissions thereby influencing climate change. To address this issue further, it is crucial to establish strategies that simultaneously increase ruminant productivity while minimizing GHG emissions, particularly from cattle, sheep, and goats. Recent advancements have revealed the potential for modulating the rumen microbial ecosystem through genetic selection to reduce methane (CH4) production, and by microbial genome editing including CRISPR/Cas9, TALENs (Transcription Activator-Like Effector Nucleases), ZFNs (Zinc Finger Nucleases), RNA interference (RNAi), Pime editing, Base editing and double-stranded break-free (DSB-free). These technologies enable precise genetic modifications, offering opportunities to enhance traits that reduce environmental impact and optimize metabolic pathways. Additionally, various nutrition-related measures have shown promise in mitigating methane emissions to varying extents. This review aims to present a future-oriented viewpoint on reducing methane emissions from ruminants by leveraging CRISPR/Cas9 technology to engineer the microbial consortia within the rumen. The ultimate objective is to develop sustainable livestock production methods that effectively decrease methane emissions, while maintaining animal health and productivity.

NeuroHealth guardian: A novel hybrid approach for precision brain stroke prediction and healthcare analytics.

Stroke is a severe illness, that requires early stroke detection and intervention, as this would help prevent the worsening of the condition. The research is done to solve stroke prediction problem, which may be divided into a number of sub-problems such as an individual's predisposition to develop stroke. To attain this objective, a multiturn dataset consisting of various health features, such as age, gender, hypertension, and glucose levels, takes a central role. A multiple approach was put forward concentrating on integrating the machine learning techniques, such as Logistic Regression, Naive Bayes, K-Nearest Neighbors, and Support Vector Machine (SV), together to develop an ensemble machine called Neuro-Health Guardian. The hypothesis "Neuro-Health Guardian Model" integrates these algorithms into one, purported to make stroke prediction more accurate. The topic dives into each instance of preparation of data for analysis, data visualization techniques, selection of the right model, training, testing, ensembling, evaluation, and prediction. The models are validated with error rate accounted from their accuracy, precision, recall, F1 score, and finally confusion matrices for a look. The study's result is showing that the ensemble model that combines the multiple algorithms has the edge over them and this is evidently by the fact that it can predict stroke rises. Additionally, accuracy, precision, recall, and F1 scores are measured in all models and the comparison is done to provide a clear comparison of the models' performance. In short, the article presented the formation of the ongoing stroke prediction that revealed the ensemble model as a good anticipation. Precise stroke predisposition forecasting can assist in early intervention thereby preventing stroke-related deaths, and limiting disability burden by stroke. The conclusions that have come out of this study offer a great action item for the development of predictive models related to stroke prevention and treatment.

Effects of Active Chronic Cigarette-Smoke Exposure on Circulating Fibrocytes.

PURPOSE: This study aimed to evaluate the hypothesis that active smoking impacts upon mediators and abundance of circulating fibrocyte cells in smoking-related disease characterised by fibrosis. METHODS: Flow cytometry and enzyme-linked immunosorbent assays were used to investigate blood from five patient groups: healthy never-smokers, healthy current smokers, stable chronic obstructive pulmonary disease (COPD) active smokers, idiopathic pulmonary fibrosis (IPF) never-smokers, and IPF active smokers. RESULTS: A significant inverse dose-response relationship was observed in healthy smokers among cumulative smoking burden (pack-years) and fibrocyte abundance (p = 0.006, r = -0.86). Among serum profibrotic fibrocyte chemokines measured, CCL18 rose significantly alongside fibrocyte numbers in all five subject groups, while having an inverse dose-response relationship with pack-year burden in healthy smokers (p = 0.003, r = -0.89). In IPF, CCL2 rose in direct proportion to fibrocyte abundance irrespective of smoking status but had lower serum levels in those currently smoking (p = < 0.001). For the study population, CXCL12 was decreased in pooled current smokers versus never-smokers (p = 0.03). CONCLUSION: The suppressive effect of current, as distinct from former, chronic smoking on circulating fibrocyte abundance in healthy smokers, and modulation of regulatory chemokine levels by active smoking may have implications for future studies of fibrocytes in smoking-related lung diseases as a potential confounding variable.

Robot-based solution for helping Alzheimer patients.

Alzheimer's is a progressive and debilitating neurological disorder characterized by cognitive decline, memory loss, and impaired daily functioning. It is an irreversible brain disease that destroys memory, thinking, and the ability to carry out daily activities. It poses significant challenges for patients and healthcare providers. Modern societies are trying to enhance the quality of people's lives, including Alzheimer's patients. In this study, we explored the potential of social robots to provide emotional support, improve cognitive function, and facilitate communication among Alzheimer's patients. This was achieved by initiating conversations on various topics such as family, relationships, and daily activities. This paper contributes to the literature by introducing a novel and well-organized framework for building an Alzheimer's care robot. Further, this study enriches the literature by introducing the Alzheimer Care Companion Robot (ACCR), designed to identify Alzheimer's patients. The ACCR initiates conversations in the native Arab-Kuwaiti dialect, displaying relevant memories through images and videos on its screen to assist in memory recall based on the individuals' life experiences. The proposed ACCR consists of 271 conversations belonging to three main categories: active, proactive, and graphical user interface (GUI) dialogs comprising 112 dialogs, 109 dialogs, and 50 dialogs for active, proactive, and GUI, respectively. The experimental result illustrated the success of the proposed solution.

Phytogenic-Mediated Zinc Oxide Nanoparticles Using the Seed Extract of Citrullus lanatus and Its Integrated Potency against Multidrug Resistant Bacteria.

In the current research study, zinc oxide nanoparticles (ZnO-NPs) were synthesized via a green synthesis technique using the seed extract of Citrullus lanatus. The study further intended to evaluate the potential synergistic effects of ZnO-NPs with antibiotics against multidrug resistant (MDR) bacteria. It was observed that C. lanatus seed extracts obtained by n-hexane and methanolic solvents revealed the presence of constituents, such as tannins, flavonoids, and terpenoids. Furthermore, the extract of n-hexane displayed the strongest antibacterial activity against Yersinia species (17 ± 1.2 mm) and Escherichia coli (17 ± 2.6 mm), while the methanolic extract showed the maximum antibacterial activity against E. coli (17 ± 0.8 mm). Additionally, the ZnO-NP synthesis was confirmed by ultraviolet-visible analysis with a characteristic absorption peak at 280 nm. The Fourier transform infrared spectroscopy analysis suggested the absorption peaks in the 500-3800 cm-1 range, which corresponds to various groups of tertiary alcohol, aldehyde, amine, ester, aromatic compounds, thiol, amine salt, and primary amine. The scanning electron microscopy spectra of ZnO-NPs demonstrated the presence of zero-dimensional spherical particles with well-dispersed character. Moreover, encapsulation with ZnO-NPs improved the antimicrobial activity of antibiotics against the panel of MDR bacteria, and the increases in the effectiveness of particular antibiotics against MDR bacteria were significant (P = 0.0005). In essence, the synthesized ZnO-NPs have the potential as drug carriers with powerful bactericidal properties that work against MDR bacterial strains. These outcomes are an indication of such significance in pharmaceutical science, giving possibilities for further research and development in this field.

Predictors and outcomes of withholding and withdrawal of life-sustaining treatments in intensive care units in Singapore: a multicentre observational study.

BACKGROUND: Clinical practice guidelines on limitation of life-sustaining treatments (LST) in the intensive care unit (ICU), in the form of withholding or withdrawal of LST, state that there is no ethical difference between the two. Such statements are not uniformly accepted worldwide, and there are few studies on LST limitation in Asia. This study aimed to evaluate the predictors and outcomes of withholding and withdrawal of LST in Singapore, focusing on the similarities and differences between the two approaches. METHODS: This was a multicentre observational study of patients admitted to 21 adult ICUs across 9 public hospitals in Singapore over an average of three months per year from 2014 to 2019. The primary outcome measures were withholding and withdrawal of LST (cardiopulmonary resuscitation, invasive mechanical ventilation, and vasopressors/inotropes). The secondary outcome measure was hospital mortality. Multivariable generalised mixed model analysis was used to identify independent predictors for withdrawal and withholding of LST and if LST limitation predicts hospital mortality. RESULTS: There were 8907 patients and 9723 admissions. Of the former, 80.8% had no limitation of LST, 13.0% had LST withheld, and 6.2% had LST withdrawn. Common independent predictors for withholding and withdrawal were increasing age, absence of chronic kidney dialysis, greater dependence in activities of daily living, cardiopulmonary resuscitation before ICU admission, higher Acute Physiology and Chronic Health Evaluation (APACHE) II score, and higher level of care in the first 24 h of ICU admission. Additional predictors for withholding included being of Chinese race, the religions of Hinduism and Islam, malignancy, and chronic liver failure. The additional predictor for withdrawal was lower hospital paying class (with greater government subsidy for hospital bills). Hospital mortality in patients without LST limitation, with LST withholding, and with LST withdrawal was 10.6%, 82.1%, and 91.8%, respectively (p < 0.001). Withholding (odds ratio 13.822, 95% confidence interval 9.987-19.132) and withdrawal (odds ratio 38.319, 95% confidence interval 24.351-60.298) were both found to be independent predictors of hospital mortality on multivariable analysis. CONCLUSIONS: Differences in the independent predictors of withholding and withdrawal of LST exist. Even after accounting for baseline characteristics, both withholding and withdrawal of LST independently predict hospital mortality. Later mortality in patients who had LST withdrawn compared to withholding suggests that the decision to withdraw may be at the point when medical futility is recognised.

Future in the past: paternal reprogramming of offspring phenotype and the epigenetic mechanisms.

That certain preconceptual paternal exposures reprogram the developmental phenotypic plasticity in future generation(s) has conceptualized the "paternal programming of offspring health" hypothesis. This transgenerational effect is transmitted primarily through sperm epigenetic mechanisms-DNA methylation, non-coding RNAs (ncRNAs) and associated RNA modifications, and histone modifications-and potentially through non-sperm-specific mechanisms-seminal plasma and circulating factors-that create 'imprinted' memory of ancestral information. The epigenetic landscape in sperm is highly responsive to environmental cues, due to, in part, the soma-to-germline communication mediated by epididymosomes. While human epidemiological studies and experimental animal studies have provided solid evidences in support of transgenerational epigenetic inheritance, how ancestral information is memorized as epigenetic codes for germline transmission is poorly understood. Particular elusive is what the downstream effector pathways that decode those epigenetic codes into persistent phenotypes. In this review, we discuss the paternal reprogramming of offspring phenotype and the possible underlying epigenetic mechanisms. Cracking these epigenetic mechanisms will lead to a better appreciation of "Paternal Origins of Health and Disease" and guide innovation of intervention algorithms to achieve 'healthier' outcomes in future generations. All this will revolutionize our understanding of human disease etiology.

Quantification of toxic heavy metals, trace elements and essential minerals contents in traditional herbal medicines commonly utilized in Khyber Pakhtunkhwa, Pakistan.

Traditional herbal medicines and health supplements have been empirically used to treat various disorders but most of them are not standardized and have not been experimentally validated for safety and efficacy. In the present study, various dosage forms of traditional herbal medicines prescribed for specific diseases were collected from local practitioners at different districts of Khyber Pakhtunkhwa, Pakistan. The collected samples were analyzed for heavy metal, trace elements, and minerals using atomic absorption spectroscopy. All the tested samples contained heavy metals, trace elements and minerals in different concentrations. All the samples were tested positive for the presence of toxic heavy metals such as arsenic (As), cadmium (Cd) and lead (Pb). The trace elements like cobalt (Co), iron (Fe), zinc (Zn) and chromium (Cr) were also detected in acceptable range. Similarly, the samples analyzed were rich in some of the essential minerals such as sodium (Na), magnesium (Mg) and calcium (Ca) which are necessary for the proper functioning of the body. The hazard quotient (HQ) values were measured for toxic heavy metals to determine their safe ranges for human body. The HQ values were above the permissible range for arsenic (As) in all detected samples while for cadmium (Cd) and lead (Pb), the values ware above in 50 % of the analyzed samples. The detection of toxic metals and their HQ values beyond the permissible limits in different dosage forms raised questions about their quality. This study suggests that evaluation of traditional herbal remedies for the metals contents and their standardization are strongly recommended for quality assurance and protection of public health.

A comprehensive review on signaling attributes of serine and serine metabolism in health and disease.

Serine is a metabolite with ever-expanding metabolic and non-metabolic signaling attributes. By providing one­carbon units for macromolecule biosynthesis and functional modifications, serine and serine metabolism largely impinge on cellular survival and function. Cancer cells frequently have a preference for serine metabolic reprogramming to create a conducive metabolic state for survival and aggressiveness, making intervention of cancer-associated rewiring of serine metabolism a promising therapeutic strategy for cancer treatment. Beyond providing methyl donors for methylation in modulation of innate immunity, serine metabolism generates formyl donors for mitochondrial tRNA formylation which is required for mitochondrial function. Interestingly, fully developed neurons lack the machinery for serine biosynthesis and rely heavily on astrocytic l-serine for production of d-serine to shape synaptic plasticity. Here, we recapitulate recent discoveries that address the medical significance of serine and serine metabolism in malignancies, mitochondrial-associated disorders, and neurodegenerative pathologies. Metabolic control and epigenetic- and posttranslational regulation of serine metabolism are also discussed. Given the metabolic similarities between cancer cells, neurons and germ cells, we further propose the relevance of serine metabolism in testicular homeostasis. Our work provides valuable hints for future investigations that will lead to a deeper understanding of serine and serine metabolism in cellular physiology and pathology.

Retinoic acid signaling in development and differentiation commitment and its regulatory topology.

Retinoic acid (RA), the derivative of vitamin A/retinol, is a signaling molecule with important implications in health and disease. It is a well-known developmental morphogen that functions mainly through the transcriptional activity of nuclear RA receptors (RARs) and, uncommonly, through other nuclear receptors, including peroxisome proliferator-activated receptors. Intracellular RA is under spatiotemporally fine-tuned regulation by synthesis and degradation processes catalyzed by retinaldehyde dehydrogenases and P450 family enzymes, respectively. In addition to dictating the transcription architecture, RA also impinges on cell functioning through non-genomic mechanisms independent of RAR transcriptional activity. Although RA-based differentiation therapy has achieved impressive success in the treatment of hematologic malignancies, RA also has pro-tumor activity. Here, we highlight the relevance of RA signaling in cell-fate determination, neurogenesis, visual function, inflammatory responses and gametogenesis commitment. Genetic and post-translational modifications of RAR are also discussed. A better understanding of RA signaling will foster the development of precision medicine to improve the defects caused by deregulated RA signaling.

Unlocking gut microbiota potential of dairy cows in varied environmental conditions using shotgun metagenomic approach.

Food security and environmental pollution are major concerns for the expanding world population, where farm animals are the largest source of dietary proteins and are responsible for producing anthropogenic gases, including methane, especially by cows. We sampled the fecal microbiomes of cows from varying environmental regions of Pakistan to determine the better-performing microbiomes for higher yields and lower methane emissions by applying the shotgun metagenomic approach. We selected managed dairy farms in the Chakwal, Salt Range, and Patoki regions of Pakistan, and also incorporated animals from local farmers. Milk yield and milk fat, and protein contents were measured and correlated with microbiome diversity and function. The average milk protein content from the Salt Range farms was 2.68%, with an average peak milk yield of 45 litters/head/day, compared to 3.68% in Patoki farms with an average peak milk yield of 18 litters/head/day. Salt-range dairy cows prefer S-adenosyl-L-methionine (SAMe) to S-adenosyl-L-homocysteine (SAH) conversion reactions and are responsible for low milk protein content. It is linked to Bacteroides fragilles which account for 10% of the total Bacteroides, compared to 3% in the Patoki region. The solid Non-Fat in the salt range was 8.29%, whereas that in patoki was 6.34%. Moreover, Lactobacillus plantarum high abundance in Salt Range provided propionate as alternate sink to [H], and overcoming a Methanobrevibacter ruminantium high methane emissions in the Salt Range. Furthermore, our results identified ruminant fecal microbiomes that can be used as fecal microbiota transplants (FMT) to high-methane emitters and low-performing herds to increase farm output and reduce the environmental damage caused by anthropogenic gases emitted by dairy cows.

A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis.

Lung cancer is a substantial health issue globally, and it is one of the main causes of mortality. Malignant mesothelioma (MM) is a common kind of lung cancer. The majority of patients with MM have no symptoms. In the diagnosis of any disease, etiology is crucial. MM risk factor detection procedures include positron emission tomography, magnetic resonance imaging, biopsies, X-rays, and blood tests, which are all necessary but costly and intrusive. Researchers primarily concentrated on the investigation of MM risk variables in the study. Mesothelioma symptoms were detected with the help of data from mesothelioma patients. The dataset, however, included both healthy and mesothelioma patients. Classification algorithms for MM illness diagnosis were carried out using computationally efficient data mining techniques. The support vector machine outperformed the multilayer perceptron ensembles (MLPE) neural network (NN) technique, yielding promising findings. With 99.87% classification accuracy achieved using 10-fold cross-validation over 5 runs, SVM is the best classification when contrasted to the MLPE NN, which achieves 99.56% classification accuracy. In addition, SPSS analysis is carried out for this study to collect pertinent and experimental data.

Genetic characteristics of complete mtDNA genome sequence of Indonesian local rabbit (Oryctolagus cuniculus).

BACKGROUND: Indonesian local rabbit (Oryctolagus cuniculus) is a local breed in Indonesia. We reveal the mitochondrial genome sequence of the Indonesian local Rabbit for the first time. A better understanding of the mechanisms underlying these beneficial aspects of local breeds over imported ones requires detailed genetic investigations, of which mtDNA genome sequencing is of particular importance. Such an investigation will solve the major issues of misidentification with Javanese hares (Lepus nigricollis) and maternal lineage. In addition, this information will guide better statistics on the Indonesian local rabbit breed population and strategies for its conservation and breeding plans. This study aimed to identify and explore the characteristics of the mtDNA genomes of Indonesian local rabbits. RESULT: This study observed that the length of the mtDNA genome is 17,469 bp, consisting of two ribosomal RNA (12S rRNA, 16S rRNA), 22 transfer RNA genes (trnR, trnG, trnK, trnD, trnS, trnY, trnC, trnN, trnA, trnW, trnM, trnQ, trnl, trnL, trnV, trnF, trnP, trnT, trnE, trnL, trnS, trnH), 13 protein-coding genes (PCGs) (ND4l, ND3, COX3, ATP6, ATP8, COX2, COX1, ND2, ND1, CYTB, ND6, ND5, ND4), a replication origin, and a noncoding control region (D-loop). CONCLUSIONS: mtDNA genome of Indonesian local rabbit was the longest and had the most extended D-loop sequence among the other references of Oryctolagus cuniculus. Other specific differences were also found in the percentage of nucleotides and variation in most of the PCGs when they were aligned with Oryctolagus cuniculus references from GenBank. Indonesian local Rabbits strongly suspected brought from Europe during the colonial period in Indonesia.

Measurement of renal congestion and compliance following intravenous fluid administration using shear wave elastography.

Objective: Ultrasound shear wave elastography (SWE) is a novel technique that may provide non-invasive measurements of renal compliance. We aimed to investigate the relationship between intravenous (IV) fluid administration and change in SWE measurements. We hypothesised that following IV fluid administration in healthy volunteers, global kidney stiffness would increase and that this increase in stiffness could be quantified using SWE. Our second hypothesis was that graduated doses of IV fluids would result in a dose-dependent increase in global kidney stiffness measured by SWE. Design: Randomised prospective study. Setting: Intensive Care Unit. Participants: Healthy volunteers aged 18-40 years. Interventions: Participants were randomised to receive 20 ml/kg, 30 ml/kg, or 40 ml/kg of normal saline. The volume of fluid infused was based on the actual body weight recorded. Main outcome measures: We recorded average SWE stiffness (kPa with standard deviation of the mean), median SWE stiffness (kPa), and the interquartile range. Results: Ninety-eight percent of participants (44/45) demonstrated an increase in global kidney stiffness following administration of IV fluids. The average SWE pre fluid administration was 7.572 kPa ± 2.38 versus 14.9 kPa ± 4.81 post fluid administration (p < 0.001). In subgroup analysis, there were significant changes in global kidney stiffness pre and post fluid administration with each volume (ml/kg) of fluid administered. Average percentage change in global kidney stiffness from baseline was compared between the three groups. There was no significant difference when comparing groups 1 and 2 (197.1% increase ± 49.5 vs 216.1% ± 72.0, p » 0.398), groups 2 and 3 (216.1% increase ± 72.0 vs 197.8% ± 59.9, p » 0.455), or groups 1 and 3 (197.1% increase ± 49.5 vs 197.8% ± 59.9, p » 0.972). Conclusions: Fluid administration results in immediately visible and quantifiable changes in global kidney stiffness across all infused volumes of fluid.

Traffic Management in IoT Backbone Networks Using GNN and MAB with SDN Orchestration.

Traffic management is a critical task in software-defined IoT networks (SDN-IoTs) to efficiently manage network resources and ensure Quality of Service (QoS) for end-users. However, traditional traffic management approaches based on queuing theory or static policies may not be effective due to the dynamic and unpredictable nature of network traffic. In this paper, we propose a novel approach that leverages Graph Neural Networks (GNNs) and multi-arm bandit algorithms to dynamically optimize traffic management policies based on real-time network traffic patterns. Specifically, our approach uses a GNN model to learn and predict network traffic patterns and a multi-arm bandit algorithm to optimize traffic management policies based on these predictions. We evaluate the proposed approach on three different datasets, including a simulated corporate network (KDD Cup 1999), a collection of network traffic traces (CAIDA), and a simulated network environment with both normal and malicious traffic (NSL-KDD). The results demonstrate that our approach outperforms other state-of-the-art traffic management methods, achieving higher throughput, lower packet loss, and lower delay, while effectively detecting anomalous traffic patterns. The proposed approach offers a promising solution to traffic management in SDNs, enabling efficient resource management and QoS assurance.

The emerging era of lactate: A rising star in cellular signaling and its regulatory mechanisms.

Cellular metabolites are ancient molecules with pleiotropic implications in health and disease. Beyond their cognate roles, they have signaling functions as the ligands for specific receptors and the precursors for epigenetic or posttranslational modifications. Lactate has long been recognized as a metabolic waste and fatigue product mainly produced from glycolytic metabolism. Recent evidence however suggests lactate is an unique molecule with diverse signaling attributes in orchestration of numerous biological processes, including tumor immunity and neuronal survival. The copious metabolic and non-metabolic functions of lactate mediated by its bidirectional shuttle between cells or intracellular organelles lead to a phenotype called "lactormone." Importantly, the mechanisms of lactate signaling, via acting as a molecular sensor and a regulator of NAD+ metabolism and AMP-activated protein kinase signaling, and via the newly identified lactate-driven lactylation, have been discovered. Further, we include a brief discussion about the autocrine regulation of efferocytosis by lactate in Sertoli cells which favoraerobic glycolysis. By emphasizing a repertoire of the most recent discovered mechanisms of lactate signaling, this review will open tantalizing avenues for future investigations cracking the regulatory topology of lactate signaling covered in the veil of mystery.

Development of a Smart Signalization for Emergency Vehicles.

As the population increases, the number of motorized vehicles on the roads also increases. As the number of vehicles increases, traffic congestion occurs. Traffic lights are used at road junctions, intersections, pedestrian crossings, and other places where traffic needs to be controlled to avoid traffic chaos. Due to traffic lights installed in the city, queues of vehicles are formed on the streets for most of the day, and many problems arise because of this. One of the most important problems is that emergency vehicles, such as ambulances, fire engines, police cars, etc., cannot arrive on time despite traffic priorities. Emergency vehicles such as hospitals and police departments need to reach the scene in a very short time. Time loss is a problem that needs to be addressed, especially for emergency vehicles traveling in traffic. In this study, ambulances, fire brigades, police, etc., respond to emergencies. A solution and a related application have been developed so privileged vehicles can reach their target destination as soon as possible. In this study, a route is determined between the current location of an emergency vehicle and its target location in an emergency. Communication between traffic lights is provided with a mobile application developed specifically for the vehicle driver. In this process, the person controlling the lights can turn on the traffic lights during the passage of vehicles. After the vehicles with priority to pass passed, traffic signaling was normalized via the mobile application. This process was repeated until the vehicle reached its destination.