Congenital Rubella Syndrome: A Case Report from Somalia.

Congenital rubella syndrome (CRS) is a serious condition with a high rate of morbidity. It is currently a rare disorder, especially in developed countries, because of universal vaccination campaigns. However, it remains a public health concern in developing countries. Here, we report a case of congenital rubella syndrome in a mother who did not receive any prenatal care or had a known history of vaccination. He is a term male infant, and the infant's positive rubella IgM confirmed the diagnosis. The baby had a bilateral cataract, convulsions, Patent ductus arteriosus (PDA)-related cardiomegaly, and bilateral hearing loss. The only known preventive measure for congenital rubella syndrome is vaccination.

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.

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.

Immunoinformatic-based drug design utilizing hesperetin to target CISD2 activation for liver aging in humans.

The CISD protein family, consisting of CISD1, CISD2, and CISD3, encodes proteins that feature CDGSH iron-sulfur domains crucial for cellular functions and share a common 2Fe-2S domain. CISD2, which is pivotal in cells, regulates intracellular calcium levels, maintains the endoplasmic reticulum and mitochondrial function, and is associated with longevity and overall health, with exercise stimulating CISD2 production. However, CISD2 expression decreases with age, impacting age-related processes. According to in silico docking, HST is a CISD2 activator that affects metabolic dysfunction and age-related illnesses by affecting metabolic pathways. This study investigated the ability of CISD2 and HST to reduce age-related ailments, with a particular emphasis on liver aging. CISD2 deficiency has a major effect on the function of cells, as it undermines the integrity of the ER, mitochondria, and calcium homeostasis. It also increases susceptibility to oxidative stress and metabolic dysregulation, which is linked to Wolfram syndrome and exacerbates age-related illnesses and metabolic disorders. By shielding cells from stress, CISD2 extends the life of cells and maintains liver health as people age. Its protective effecfts on the liver during aging are further enhanced by its control of translation factors such as Nrf2 and IL-6. This work paves the way for future investigations and clinical applications by examining the structural and functional properties of CISD2 and the interaction between CISD2 and HST. This highlights the therapeutic potential of these findings in promoting healthy livers in humans and battling age-related illnesses.

Genotypes, epidemiological variables and fungicides application associated with wheat leaf rust development and grain yield.

The present study was carried out at the Plant Pathology Hafizabad Research Station, the University of Layyah, during the crop seasons 2021-2022 and 2022-2023 to evaluate the response of various wheat genotypes against leaf rust severity (%), environmental conditions favourable for disease development and grain yield. Except for minimum temperature and minimum relative humidity, which had a negative association with disease development, there was a significant correlation between leaf rust severity (%) and all environmental conditions such as maximum temperature, maximum relative humidity, rainfall, and wind speed. All epidemiological variables such as maximum temperature, minimum temperature, minimum relative humidity, rainfall and wind speed significantly affect the disease progression. The disease predictive model accounted for 48-69 % variability in leaf rust severity. The model performance was evaluated using the coefficient of determination (R2 = 0.69) and RMSE, both demonstrated acceptable predictive results for leaf rust severity (%) management. Leaf rust severity (%) increased with an increase in maximum temperature (17.8-30 °C), maximum relative humidity (76.3-85 %), rainfall (2.2-10.85 mm) and wind speed 1.1-2.7 km/h and decreased with the increase of minimum temperature (7.91-16.71 °C) minimum relative humidity (47.15-56.45 %) during both rating seasons 2021-2022 and 2022-2023. The single and two applications of fungicides at the Zadok's scale 3, ZS 4.3, and ZS 5.4 stages led to a significant reduction in grain yield losses caused by leaf rust severity (%) in both the 2021-2022 and 2022-2023 crop seasons. Single and two sprays of prothioconazole, were found to be the first choice among all treatments to reduce the disease severity and increase grain production and maximum gross revenue (513.1-777.8$/ha), as compared to followed by single and two sprays of propiconazole (Progress), tebuconazole + trifloxystrobin, tebuconazole, bixafen + tebuconazole, and propiconazole (Tilt), respectively. These findings recommend the involvement of genotype resistance and weather predictors in wheat leaf rust development, along with fungicide application studies, to improve the predictability of host resistance to disease, future models, and the sustainability of disease control methods.

Gap acceptance behaviour and crash risks of mobile phone distracted young drivers at roundabouts: A random parameters survival model.

Navigating through complex road geometries, such as roundabouts, poses significant challenges and safety risks for drivers. These challenges may be exacerbated when drivers are distracted by mobile phone conversations. The interplay of road geometry, driving state, and driver characteristics in creating compound risks remains an underexplored area in existing literature. Proper understanding of such compound crash risk is not only crucial to improve road geometric design but also to educate young drivers, who are particularly risk-takers and to devise strict penalties for mobile phone usage whilst driving. To fill this gap, this study examines crash risks associated with gap acceptance manoeuvres at roundabouts in the simulated environment of the CARRS-Q driving simulators, where 32 licenced young drivers were exposed to a gap acceptance scenario in three phone conditions: baseline (no phone conversation), handheld, and hands-free. A parametric random parameters survival modelling approach is adopted to understand safety margins-characterised by gap times-during gap acceptance scenarios at roundabouts, concurrently uncover driver-level heterogeneity with mobile phone distraction and capture repeated measures of experiment design. The model specification includes the handheld phone condition as a random parameter and hands-free phone condition, acceleration noise, gap size, crash history, and gender as non-random parameters. Results suggest that the majority of handheld distracted drivers have smaller safety margins, reflecting the negative consequences of engaging in handheld phone conversations. Interestingly, a group of drivers in the same handheld phone condition have been found to exhibit cautious/safer behaviour, as evidenced by longer gap times, reflecting their risk compensation behaviour. Female distracted drivers are also found to exhibit safer gap acceptance behaviour compared to distracted male drivers. The findings of this study shed light on the compound risk of mobile phone distraction and gap acceptance at roundabouts, requiring policymakers and authorities to devise strict penalties and laws for distracted driving.

Compound Learning-Based Model Predictive Control Approach for Ducted-Fan Aerial Vehicles.

Designing an efficient learning-based model predictive control (MPC) framework for ducted-fan unmanned aerial vehicles (DFUAVs) is a difficult task due to several factors involving uncertain dynamics, coupled motion, and unorthodox aerodynamic configuration. Existing control techniques are either developed from largely known physics-informed models or are made for specific goals. In this regard, this article proposes a compound learning-based MPC approach for DFUAVs to construct a suitable framework that exhibits efficient dynamics learning capability with adequate disturbance rejection characteristics. At the start, a nominal model from a largely unknown DFUAV model is achieved offline through sparse identification. Afterward, a reinforcement learning (RL) mechanism is deployed online to learn a policy to facilitate the initial guesses for the control input sequence. Thereafter, an MPC-driven optimization problem is developed, where the obtained nominal (learned) system is updated by the real system, yielding improved computational efficiency for the overall control framework. Under appropriate assumptions, stability and recursive feasibility are compactly ensured. Finally, a comparative study is conducted to illustrate the efficacy of the designed scheme.

Diagnostic Dilemma: IgG4-Related Sclerosing Mesenteritis Mimicking an Abdominal Malignancy Enveloping the Superior Mesenteric Artery.

Sclerosing mesenteritis, a rare fibroinflammatory disease affecting the mesentery, presents a diagnostic challenge due to its varied clinical manifestations and unknown etiology. We present a case of a 50-year-old female presenting with epigastric pain and weight loss, initially suspected of abdominal malignancy. Imaging revealed a mesenteric mass, and histopathological examination confirmed dense lymphoplasmacytic infiltrate with storiform fibrosis, along with elevated serum IgG4 levels, indicative of IgG4-related sclerosing mesenteritis. Treatment with thalidomide and prednisolone resulted in significant mass regression and symptom improvement. Our case highlights the importance of considering sclerosing mesenteritis in the differential diagnosis of abdominal masses and suggests a potential therapeutic approach for this rare condition. Further research is warranted to elucidate its pathogenesis and optimize management strategies.

A novel Imidazo[1,2-a]pyridine derivative modulates active KRASG12D through off-like conformational shifts in switch-I and switch-II regions, mimicking inactive KRASG12D.

KRASG12D are the most prevalent oncogenic mutations and a promising target for solid tumor therapies. However, its inhibition exhibits tremendous challenge due to the necessity of high binding affinity to obviate the need for covalent binders. Here we report the evidence of a novel class of Imidazo[1,2-a]pyridine derivative as potentially significant novel inhibitors of KRASG12D, discovered through extensive ligand-based screening against 2-[(2R)-piperidin-2-yl]-1H-indole, an important scaffold for KRASG12D inhibition via switch-I/II (S-I/II) pocket. The proposed compounds exhibited similar binding affinities and overlapped pose configurations to 2-[(2R)-piperidin-2-yl]-1H-indole, serving as a reliable starting point for drug discovery. Comparative free energy profiles demonstrated that C4 [2-methyl-3-((5-phenyl-1H-1,2,4-triazol-3-yl)methyl)imidazo[1,2-a]pyridine] effectively shifted the protein to a stable low-energy conformation via a prominent transition state. The conformational changes across the transition revealed the conformational shift of switch-I and II to a previously known off-like conformation of inactive KRASG12D with rmsd of 0.91 Å. These conformations were even more prominent than the privileged scaffold 2-[(2R)-piperidin-2-yl]-1H-indole. The representative structure overlay of C4 and another X-ray crystallography solved BI-2852 bound inactive KRASG12D revealed that Switch-I and II exhibited off-like conformations. The cumulative variance across the first eigenvalue that accounted for 57 % of the collective variance validated this on-to-off transition. In addition, the relative interaction of C4 binding showed consistent patterns with BI-2852. Taken together, our results support the inhibitory activity of [2-methyl-3-((5-phenyl-1H-1,2,4-triazol-3-yl)methyl)imidazo[1,2-a]pyridine] by shifting active KRASG12D to an inactive conformation.

Modeling security evaluation framework for IoHT-driven systems using integrated decision-making methodology.

The intensification of the Internet of Health Things devices created security concerns due to the limitations of these devices and the nature of the healthcare data. While dealing with the security challenges, several authentication schemes, protocols, processes, and standards have been adopted. Consequently, making the right decision regarding the installation of a secure authentication solution or procedure becomes tricky and challenging due to the large number of security protocols, complexity, and lack of understanding. The major objective of this study is to propose an IoHT-based assessment framework for evaluating and prioritizing authentication schemes in the healthcare domain. Initially, in the proposed work, the security issues related to authentication are collected from the literature and consulting experts' groups. In the second step, features of various authentication schemes are collected under the supervision of an Internet of Things security expert using the Delphi approach. The collected features are used to design suitable criteria for assessment and then Graph Theory and Matrix approach applies for the evaluation of authentication alternatives. Finally, the proposed framework is tested and validated to ensure the results are consistent and accurate by using other multi-criteria decision-making methods. The framework produces promising results such as 93%, 94%, and 95% for precision, accuracy, and recall, respectively in comparison to the existing approaches in this area. The proposed framework can be picked as a guideline by healthcare security experts and stakeholders for the evaluation and decision-making related to authentication issues in IoHT systems.

Fine needle aspiration diagnosis of benign oncocytic lesions of the head and neck associated with false positive 18F-fluorodeoxyglucose uptake on positron emission tomography scan.

INTRODUCTION: 18F-fluorodeoxyglucose (FDG) uptake on positron emission tomography/computed tomography (PET/CT) has become the mainstay for staging and post-therapy surveillance of cancer as malignant neoplasms generally demonstrate higher FDG uptake that benign entities. However, there are certain benign lesions, most notably oncocytic tumors, that can display very high uptake and fine needle aspiration (FNA) is usually done to confirm malignancy. Therefore, it is important to recognize that benign oncocytic lesions of the head and neck may also present as FDG-avid lesions to avoid a diagnostic pitfall. METHODS: Electronic search of institutional surgical and cytopathology archives was conducted to identify cases of benign oncocytic lesions involving the head and neck region diagnosed by FNA from January 2012 to April 2022. Chart review was used to assess whether lesions were initially discovered via PET scanning. RESULTS: One hundred and twenty-five cases of oncocytic lesions were identified; 12 (9%) PET positive lesions were identified in the head and neck region from patients being evaluated for metastasis or for suspicion of malignancy. Cytopathology of all 12 cases demonstrated benign oncocytic lesions; eight (67%) of these cases were consistent with Warthin tumor, one (8.3%) was a benign oncocytic lesion, and one (8.3%) was consistent wit a parathyroid adenoma. Most (58%) of the PET-positive lesions were in parotid region, two from thyroid gland (17%), one from submandibular gland (8%), one from paratracheal area (8%). The PET scan SUVs ranged from 3.3 to 19.5 g mL-1. CONCLUSIONS: Oncocytic lesions including Warthin tumors can result in false-positive FDG uptake on PET scans. Clinicians and cytopathologists should be aware of PET-positive benign oncocytic head and neck lesions.

Pedestrian crash risk analysis using extreme value models: New insights and evidence.

Facilitating proactive pedestrian safety management, the application of extreme value theory (EVT) models has gained popularity due to its extrapolation capabilities of estimating crashes from their precursors (i.e., conflicts). However, past studies either applied EVT models for crash risk analysis of autonomous vehicle-pedestrian interactions or human-driven vehicle-pedestrian interactions at signalised intersections. However, our understanding of human-driven vehicle-pedestrian interactions remains elusive because of scant evidence of (i) EVT models' application for heterogeneous traffic conditions, (ii) appropriate set of determinants, (iii) which EVT approach to be used, and (iv) which conflict measure is appropriate. Addressing these issues, the objective of this study is to investigate pedestrian crash risk analysis in heterogeneous and disordered traffic conditions, where drivers do not follow lane disciplines. Eleven-hour video recording was collected from a busy pedestrian crossing at a midblock location in India and processed using artificial intelligence techniques. Vehicle-pedestrian interactions are characterised by two conflict measures (i.e., post encroachment time and gap time) and modelled using block maxima and peak over threshold approaches. To handle the non-stationarity of pedestrian conflict extremes, several explanatory variables are included in the models, which are estimated using the maximum likelihood estimation procedure. Modelling results indicate that the EVT models provide reasonable estimates of historical crash records at the study location. From the EVT models, a few key insights related to vehicle-pedestrian interactions are as follows. Firstly, a comparison of EVT models shows that the peak over threshold model outperforms the block maxima model. Secondly, post encroachment time conflict measure is found to be appropriate for modelling vehicle-pedestrian interactions compared to gap time. Thirdly, pedestrian crash risk significantly increases when they interact with two-wheelers in contrast with interactions involving buses where the crash risk decreases. Fourthly, pedestrian crash risk decreases when they cross in groups compared to crossing individually. Finally, pedestrian crash risk is positively related to average vehicle speed, pedestrian speed, and five-minute post encroachment time counts less than 1.5 s. Further, different block sizes are tested for the block maxima model, and the five-minute block size yields the most accurate and precise pedestrian crash estimates. These findings demonstrate the applicability of extreme value analysis for heterogeneous and disordered traffic conditions, thereby facilitating proactive safety management in disordered and undisciplined lane conditions.

Revisiting the hybrid approach of anomaly detection and extreme value theory for estimating pedestrian crashes using traffic conflicts obtained from artificial intelligence-based video analytics.

Pedestrians represent a group of vulnerable road users who are at a higher risk of sustaining severe injuries than other road users. As such, proactively assessing pedestrian crash risks is of paramount importance. Recently, extreme value theory models have been employed for proactively assessing crash risks from traffic conflicts, whereby the underpinning of these models are two sampling approaches, namely block maxima and peak over threshold. Earlier studies reported poor accuracy and large uncertainty of these models, which has been largely attributed to limited sample size. Another fundamental reason for such poor performance could be the improper selection of traffic conflict extremes due to the lack of an efficient sampling mechanism. To test this hypothesis and demonstrate the effect of sampling technique on extreme value theory models, this study aims to develop hybrid models whereby unconventional sampling techniques were used to select the extreme vehicle-pedestrian conflicts that were then modelled using extreme value distributions to estimate the crash risk. Unconventional sampling techniques refer to unsupervised machine learning-based anomaly detection techniques. In particular, Isolation forest and minimum covariance determinant techniques were used to identify extreme vehicle-pedestrian conflicts characterised by post encroachment time as the traffic conflict measure. Video data was collected for four weekdays (6 am-6 pm) from three four-legged intersections in Brisbane, Australia and processed using artificial intelligence-based video analytics. Results indicate that mean crash estimates of hybrid models were much closer to observed crashes with narrower confidence intervals as compared with traditional extreme value models. The findings of this study demonstrate the suitability of machine learning-based anomaly detection techniques to augment the performance of existing extreme value models for estimating pedestrian crashes from traffic conflicts. These findings are envisaged to further explore the possibility of utilising more advanced machine learning models for traffic conflict techniques.

Anaplastic Large Cell Lymphoma of the Spine: Report of a Rare Case.

This abstract discusses a rare case of anaplastic large cell lymphoma (ALCL) involving the cervical and dorsal spine in a 17-year-old female. ALCL is a distinct subtype of lymphoma characterized by abnormal proliferation of lymphocytes and is divided into ALK-positive and ALK-negative subtypes. Spinal involvement in ALCL is uncommon, particularly in the cervical and dorsal regions. The patient presented with persistent fever, weakness, and delayed onset of severe neck pain. Diagnosis involved imaging, bone marrow biopsy, and lymph node biopsy. Treatment strategies for ALCL typically involve a multimodal approach, including chemotherapy, radiotherapy, and targeted therapy. However, due to the rarity of spinal involvement, treatment decisions are based on extrapolation from other ALCL cases. Prognosis is influenced by disease stage and ALK status, but specific outcomes for spinal involvement remain poorly established. This case emphasizes the need for considering lymphoma in patients with unexplained symptoms and abnormal imaging findings. It highlights the importance of further research to improve the understanding and management of ALCL with spinal involvement.

A bi-level framework for real-time crash risk forecasting using artificial intelligence-based video analytics.

This study proposes a bi-level framework for real-time crash risk forecasting (RTCF) for signalised intersections, leveraging the temporal dependency among crash risks of contiguous time slices. At the first level of RTCF, a non-stationary generalised extreme value (GEV) model is developed to estimate the rear-end crash risk in real time (i.e., at a signal cycle level). Artificial intelligence techniques, like YOLO and DeepSort were used to extract traffic conflicts and time-varying covariates from traffic movement videos at three signalised intersections in Queensland, Australia. The estimated crash frequency from the non-stationary GEV model is compared against the historical crashes for the study locations (serving as ground truth), and the results indicate a close match between the estimated and observed crashes. Notably, the estimated mean crashes lie within the confidence intervals of observed crashes, further demonstrating the accuracy of the extreme value model. At the second level of RTCF, the estimated signal cycle crash risk is fed to a recurrent neural network to predict the crash risk of the subsequent signal cycles. Results reveal that the model can reasonably estimate crash risk for the next 20-25 min. The RTCF framework provides new pathways for proactive safety management at signalised intersections.

Whole exome sequencing identifies variable expressivity of CLN6 variants in Progressive myoclonic epilepsy affected families.

Progressive myoclonic epilepsies (PMEs) are a group of neurodegenerative disorders, predominantly affecting adolescents and, characterized by generalized worsening myoclonus epilepsies, ataxia, cognitive deficits, and dementia. To date, several genes, having implications in diverse phenotypic expressions associated with PMEs, have been identified. Genetic diagnosis is available for most of the adolescence-onset myoclonic epilepsies. This study aimed to elucidate the genetic basis of PMEs in three multiplex Pakistani families exhibiting clinically variable phenotypes. Causative variant(s) in the studied families, and mode of segregation were identified by Whole Exome Sequencing (WES) of the probands, followed by bi-directional Sanger sequencing for final validation. We identified homozygous recessive CLN6 missense variant c.768 C>G (p.Asp256Glu) in Family 1, and c.889 C>A (p.Pro297Thr) variant in Family 2. While in Family 3, we found a homozygous variant (c.316dup) that caused a frameshift mutation, leading to a premature stop codon in the CLN6 protein, resulting in a truncated protein (p.Arg106ProfsTer26). Though CLN6 is previously identified to underlie late infantile and adolescent onset neuronal ceroid lipofuscinosis, this study supports and expands the phenotypic spectrum of CLN6 mutations and signifies diagnositc potential CLN6 variants for PMEs. Diverse pathological effects of variant c .768 C>G were observed in Family 1, with same genotypes, suggesting clinical heterogeneity and/or variable expressivity that might be the implication of pleiotropic effects of the gene in these cases.

Identification of the myxobacterial secondary metabolites Aurachin A and Soraphinol A as promising inhibitors of thymidylate kinase of the Monkeypox virus.

Thymidylate kinase (TMPK) of monkeypox virus (MPXV) has emerged as a promising target for potential therapeutics due to its significant role in pyrimidine metabolism. While smallpox drugs are advised for treating monkeypox, the European Medicine Agency has sanctioned Tecovirimat due to its potent nanomolar activity. Nonetheless, there is a need for monkeypox-specific therapeutic options. In this work, we employed docking-based virtual screening and molecular dynamics (MD) simulations to identify myxobacterial secondary metabolites as promising anti-viral natural compounds capable of inhibiting thymidylate kinase. The computational pharmacokinetics and manual curation of top-scoring compounds identified six lead compounds that were compared in terms of protein-ligand contacts and protein-essential dynamics. The study shows that among the six candidates, Aurachin A and the Soraphinol analogues such as Soraphinol A and Soraphinol C remain very stable compared to other compounds, enabling the active site integrity via a stable dynamics pattern. We also show that other compounds such as Phenoxan, Phenylnannolone C, and 8E-Aurafuron B remain unstable and have a negative impact on the active site integrity and may not be suitable binders for TMPK protein. Analyzing the Aurachin A and Soraphinol A binding, the established hydrogen bonds with Arg93 and the conserved hydrophobic interaction with Tyr101 are consistent with previous experimental interactions. Additionally, a deeper insight into the indole and the aromatic ring interaction through π-π stacking and π-cation interactions, as well as the background of Aurachin A and Soraphinol A as a bioactive compound, has significant implications not only for its potential as a promising drug but also for directing future drug discovery efforts targeting the TMPK protein.

Genetic variants rs2910164, rs4636297 and rs895819 may contribute to the onset of acute myocardial infarction in Pakistani population.

The most serious type of coronary artery disease (CAD), acute myocardial infarction (AMI), is a major global cause of death. The development of AMI is accompanied by several risk factors. AMI may be caused by variations in the microRNA (miRNA) genes, which have a negative impact on miRNA-mediated regulation of gene expression. The target mRNAs are dysregulated because of these genetic changes in the miRNA genes, which interfere with the vital biological processes that result in AMI. Using allele-specific PCR, the aim of the study is to examine the association of the variants (rs2910164, rs4636297, and rs895819) in MIR146A, MIR126, and MIR27A with AMI susceptibility. A difference in genotype distribution among the patients and control for variation rs2910164 was identified by co-dominant [χ2 = 68.34,2; P value<0.0001], dominant (G/G vs G/C + C/C) [OR = 4.167 (2.860-6.049); P value<0.0001], recessive (C/C vs G/C + G/G) [OR = 0.2584 (0.1798-0.3731); P value<0.0001], and additive models [OR = 3.847 (2.985-4.959); P value<0.0001]. Whereas the association of rs4636297 was investigated by co-dominant [χ2 = 6.882,2; P value = 0.0320], dominant (G/G vs G/A + A/A) [OR = 0.6914 (0.4849-0.9948); P value = 0.0489], recessive (A/A vs A/G + G/G) [OR = 2.434 (0.9849-5.616830); P value = 0.0595], and additive models [OR = 0.7716 (0.6000-0.9918); P value = 0.0433]. Similarly, association of rs895819 was determined by co-dominant [χ2 = 5.277, 2; P value = 0.0715], dominant (G/G vs G/A + A/A) [OR = 1.654(0.9819-2.801); P value = 0.06440], recessive (A/A vs A/G + G/G) [OR = 0.7227 (0.5132-1.022); P value = 0.0748], and additive models [OR = 1.3337 (1.041-1.719); P value = 0.0233]. The results of this study found a significant association of rs2910164 and rs4636297 with AMI and are considered as the risk factor for AMI in the Pakistani population. We observed no significant association of the variant MIR27A (rs895819) with AMI incidence.

Core-genome-mediated promising alternative drug and multi-epitope vaccine targets prioritization against infectious Clostridium difficile.

Prevention of Clostridium difficile infection is challenging worldwide owing to its high morbidity and mortality rates. C. difficile is currently being classified as an urgent threat by the CDC. Devising a new therapeutic strategy become indispensable against C. difficile infection due to its high rates of reinfection and increasing antimicrobial resistance. The current study is based on core proteome data of C. difficile to identify promising vaccine and drug candidates. Immunoinformatics and vaccinomics approaches were employed to construct multi-epitope-based chimeric vaccine constructs from top-ranked T- and B-cell epitopes. The efficacy of the designed vaccine was assessed by immunological analysis, immune receptor binding potential and immune simulation analyses. Additionally, subtractive proteomics and druggability analyses prioritized several promising and alternative drug targets against C. difficile. These include FMN-dependent nitroreductase which was prioritized for pharmacophore-based virtual screening of druggable molecule databases to predict potent inhibitors. A MolPort-001-785-965 druggable molecule was found to exhibit significant binding affinity with the conserved residues of FMN-dependent nitroreductase. The experimental validation of the therapeutic targets prioritized in the current study may worthy to identify new strategies to combat the drug-resistant C. difficile infection.

A Bayesian extreme value theory modelling framework to assess corridor-wide pedestrian safety using autonomous vehicle sensor data.

Pedestrians are a vulnerable road user group, and their crashes are generally spread across the network rather than in a concentrated location. As such, understanding and modelling pedestrian crash risk at a corridor level becomes paramount. Studies on pedestrian crash risks, particularly with the traffic conflict data, are limited to single or multiple but scattered intersections. A lack of proper modelling techniques and the difficulties in capturing pedestrian interaction at the network or corridor level are two main challenges in this regard. With autonomous vehicles trialled on public roads generating massive (and unprecedented) datasets, utilising such rich information for corridor-wide safety analysis is somewhat limited where it appears to be most relevant. This study proposes an extreme value theory modelling framework to estimate corridor-wide pedestrian crash risk using autonomous vehicle sensor/probe data. Two types of models were developed in the Bayesian framework, including the block maxima sampling-based model corresponding to Generalised Extreme Value distribution and the peak over threshold sampling-based model corresponding to Generalised Pareto distribution. The proposed framework was applied to autonomous vehicle data from Argoverse-a Ford Motors subsidiary. This autonomous vehicle fleet of Agro AI (owner of Argoverse dataset) is equipped with two 64 beams synchronised LiDAR sensors, a cluster of seven high-resolution cameras, and a pair of stereo-vison high-resolution cameras to capture surrounding road users' information within a range of 200 meters. A subset of the Argoverse dataset, focussing on an arterial corridor in Miami, USA, was used to extract pedestrian and vehicle trajectories. From these trajectories, vehicle-pedestrian conflicts were identified and measured using post encroachment time. The non-stationarity of extremes was captured by vehicle volume, pedestrian volume, average vehicle speed, and average pedestrian speed in the extreme value model. Both block maxima and peak over threshold sampling-based models were found to provide a reasonable estimate of historical pedestrian crash frequencies. Notably, the block maxima sampling-based model was more accurate than the peak over threshold sampling-based model based on mean crash estimates and confidence intervals. This study demonstrates the potential of using autonomous vehicle sensor data for network-level safety, enabling an efficient identification of pedestrian crash risk zones in a transport network.