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.

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.

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.

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.

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.

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.

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.

Identification of potential natural products derived from fungus growing termite, inhibiting Pseudomonas aeruginosa quorum sensing protein LasR using molecular docking and molecular dynamics simulation approach.

Pseudomonas aeruginosa, the most common opportunistic pathogen, is becoming antibiotic-resistant worldwide. The fate of P. aeruginosa, a multidrug-resistant strain, can be determined by multidrug efflux pumps, enzyme synthesis, outer membrane protein depletion, and target alterations. Microbial niches have long used quorum sensing (QS) to synchronize virulence gene expression. Computational methods can aid in the development of novel P. aeruginosa drug-resistant treatments. The tripartite symbiosis in termites that grow fungus may help special microbes find new antimicrobial drugs. To find anti-quorum sensing natural products that could be used as alternative therapies, a library of 376 fungal-growing termite-associated natural products (NPs) was screened for their physicochemical properties, pharmacokinetics, and drug-likeness. Using GOLD, the top 74 NPs were docked to the QS transcriptional regulator LasR protein. The five lead NPs with the highest gold score and drug-like properties were chosen for a 200-ns molecular dynamics simulation to test the competitive activity of different compounds against negative catechin. Fridamycin and Daidzein had stable conformations, with mean RMSDs of 2.48 and 3.67 Å, respectively, which were similar to Catechin's 3.22 Å. Fridamycin and Daidzein had absolute binding energies of -71.186 and -52.013 kcal/mol, respectively, which were higher than the control's -42.75 kcal/mol. All the compounds within the active site of the LasR protein were kept intact by Trp54, Arg55, Asp67, and Ser123. These findings indicate that termite gut and fungus-associated NPs, specifically Fridamycin and Daidzein, are potent QS antagonists that can be used to treat P. aeruginosa's multidrug resistance.Communicated by Ramaswamy H. Sarma.

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.

The miRNA variants MIR196A2 (rs11614913) and MIR423 (rs6505162) contribute to an increase in the risk of myocardial infarction.

INTRODUCTION: MicroRNAs (miRNAs) are small, single-stranded RNA molecules that negatively regulate gene expression and play a key role in the pathogenesis of human diseases. Recent studies have suggested that miRNAs contribute to cardiovascular diseases (CVDs). However, the association between single-nucleotide polymorphisms (SNPs) in miRNAs and myocardial infarction (MI) remains in infancy. AIM: The current study was designed to find out the association of SNPs in MIR196A2 and MIR423 (rs11614913 and rs6505162, respectively). METHODS: Using Tetra-Primer Amplification Refractory Mutation System-Polymerase Chain Reaction (T-ARMS PCR) in 400 cases (MI patients) and 336 healthy controls. Using different inheritance models (co-dominant, homozygous dominant, homozygous recessive, and additive models), the association of these SNPs was genotyped with MI risk. RESULTS: For variant rs11614913, significant distribution of the genotypes among the cases and controls was determined by co-dominant [χ2 = 29.19, 2; p value < 0.0001], dominant (C/C vs. C/T + T/T) [OR = 0.45 (0.34 to 0.61); p < 0.0001], recessive (T/T vs. C/T + C/C) [OR = 1.009 (0.63 to 1.63); p-value p value > 0.999], and additive models [OR = 0.65 (0.52 to 0.80); p value = 0.0001]. Similarly, a significant association of rs6505162 was determined by co-dominant [χ2 = 24.29, 2; p value < 0.0001], dominant (C/C vs. A/C+ A/A) [OR = 0.44 (0.32 to 0.61); p value < 0.0001], recessive (A/A vs. A/C + C/C) [OR = 1.29 (0.85 to 1.98); p value = 0.28], and additive models [OR = 0.65 (0.52 to 0.81); p value = 0.0001]. CONCLUSION: Therefore, the current study showed that both variants rs11614913 and rs6505162 are significantly associated with MI in the Pakistani population.

Multidrug-Resistant Salmonella Endocarditis of a Native Valve: A Rare Case Presentation.

Salmonella species is a rare cause of infective endocarditis that commonly involves a prosthetic or a previously damaged heart valve. We present a case of a 25-year-old young man with a one and a half month history of cough, fever, shortness of breath, and hemoptysis. Clinical examination revealed bilateral mid-zone crackles, palpable tip of the spleen, and an early diastolic murmur in the aortic (A2) area. Initial laboratory results indicated anemia with leukocytosis, raised inflammatory markers, and low serum albumin. Blood cultures showed the growth of multidrug-resistant Salmonella typhi. A radiological workup showed multiple aortic valve vegetation. Salmonella endocarditis was diagnosed based on Duke's criteria. The patient was treated with culture-sensitive antibiotics and subsequently showed significant clinical recovery. This case highlights a rare multidrug-resistant Salmonella endocarditis of a native valve. It also emphasizes the difficulties in making a diagnosis and the benefit of using a multidisciplinary strategy to manage challenging clinical manifestations.

Monkeypox Entry and Emergence Preparation in Pakistan.

Monkeypox (Mpox) is a virus that first emerged in Africa in 1970 [...].

A Symphony of Challenges: Multi-Drug Resistant Tuberculosis, Pleurocutaneous Fistula, and Empyema Necessitans in Concert.

This case report details the clinical course of a 37-year-old male with multi-drug-resistant tuberculosis (MDR-TB) who initially presented with respiratory symptoms. Following a month of anti-TB therapy, the patient developed a painful chest swelling, diagnosed as empyema necessitans, with a subsequent spontaneous rupture leading to a pleurocutaneous fistula. Despite recommendations for surgery, the patient opted for active surveillance. The follow-up revealed symptom improvement. This case underscores the unique challenges of managing rare complications of MDR-TB, particularly when patients decline surgical interventions. The observed symptom improvement, despite the absence of surgery, illuminates the intricate decision-making process and alternative management strategies involved in addressing such complications, highlighting the complexities inherent in MDR-TB care.

Vaccinomics-aided next-generation novel multi-epitope-based vaccine engineering against multidrug resistant Shigella Sonnei: Immunoinformatics and chemoinformatics approaches.

Shigella sonnei is a gram-negative bacterium and is the primary cause of shigellosis in advanced countries. An exceptional rise in the prevalence of the disease has been reported in Asia, the Middle East, and Latin America. To date, no preventive vaccine is available against S. sonnei infections. This pathogen has shown resistances towards both first- and second-line antibiotics. Therefore, an effective broad spectrum vaccine development against shigellosis is indispensable. In the present study, vaccinomics-aided immunoinformatics strategies were pursued to identify potential vaccine candidates from the S. sonnei whole proteome data. Pathogen essential proteins that are non-homologous to human and human gut microbiome proteome set, are feasible candidates for this purpose. Three antigenic outer membrane proteins were prioritized to predict lead epitopes based on reverse vaccinology approach. Multi-epitope-based chimeric vaccines was designed using lead B- and T-cell epitopes combined with suitable linker and adjuvant peptide sequences to enhance immune responses against the designed vaccine. The SS-MEVC construct was prioritized based on multiple physicochemical, immunological properties, and immune-receptors docking scores. Immune simulation analysis predicted strong immunogenic response capability of the designed vaccine construct. The Molecular dynamic simulations analysis ensured stable molecular interactions of lead vaccine construct with the host receptors. In silico restriction and cloning analysis predicted feasible cloning capability of the SS-MEVC construct within the E. coli expression system. The proposed vaccine construct is predicted to be more safe, effective and capable of inducing robust immune responses against S. sonnei infections and may be worthy of examination via in vitro/in vivo assays.

IoT platforms assessment methodology for COVID-19 vaccine logistics and transportation: a multi-methods decision making model.

The supply chain management (SCM) of COVID-19 vaccine is the most daunting task for logistics and supply managers due to temperature sensitivity and complex logistics process. Therefore, several technologies have been applied but the complexity of COVID-19 vaccine makes the Internet of Things (IoT) a strong use case due to its multiple features support like excursion notification, data sharing, connectivity management, secure shipping, real-time tracking and monitoring etc. All these features can only feasible through choosing and deploying the right IoT platform. However, selection of right IoT platform is also a major concern due to lack of experience and technical knowledge of supply chain managers and diversified landscape of IoT platforms. Therefore, we introduce a decision making model for evaluation and decision making of IoT platforms that fits for logistics and transportation (L&T) process of COVID-19 vaccine. This study initially identifies the major challenges addressed during the SCM of COVID-19 vaccine and then provides reasonable solution by presenting the assessment model for selection of rational IoT platform. The proposed model applies hybrid Multi Criteria Decision Making (MCDM) approach for evaluation. It also adopts Estimation-Talk-Estimation (ETE) approach for response collection during the survey. As, this is first kind of model so the proposed model is validated and tested by conducting a survey with experts. The results of the proposed decision making model are also verified by Simple Additive Weighting (SAW) technique which indicates higher results accuracy and reliability of the proposed model. Similarly, the proposed model yields the best possible results and it can be judged by the precision, accuracy and recall values i.e. 93%, 93% and 94% respectively. The survey-based testing also suggests that this model can be adopted in practical scenarios to deal with complexities which may arise during the decision making of IoT platform for COVID-19 SCM process.

Association of IL-17F rs2397084 (E126G), rs11465553 (V155I) and rs763780 (H161R) variants with rheumatoid arthritis and their effects on the stability of protein.

Interleukin-17F (IL-17F), considered a pro-inflammatory cytokine, has been shown to contribute to skeletal tissue degradation and hence chronic inflammation in rheumatoid arthritis (RA). In this study we utilized bioinformatics tools to analyze the effect of three exonic SNPs (rs2397084, rs11465553, and rs763780) on the structure and function of the IL-17F gene, and evaluated their association with RA in Pakistani patients. The predicted deleterious and damaging effects of identified genetic variants were assessed through the utilization of multiple bioinformatics tools including PROVEAN, SNP&GO, SIFT, and PolyPhen2. Structural and functional effects of these variants on protein structures were evaluated through the use of additional tools such as I-Mutant, MutPred, and ConSurf. Three-dimensional (3D) models of both the wild-type and mutant proteins were constructed through the utilization of I-TASSER software, with subsequent structural comparisons between the models conducted through the use of the TM-align score. A total of 500 individuals, 250 cases and 250 controls, were genotyped through Tri-ARMS-PCR method and the resultant data was statistically analyzed using various inheritance models. Our bioinformatics analysis showed significant structural differences for wild type and mutant protein (TM-scores and RMSD values were 0.85934 and 2.34 for rs2397084 (E126G), 0.87388 and 2.49 for rs11465553 (V155I), and 0.86572 and 0.86572 for rs763780 (H161R) with decrease stability for the later. Overall, these tools enabled us to predict that these variants are crucial in causing disease phenotypes. We further tested each of these single nucleotide variants for their association with RA. Our analysis revealed a strong positive association between the genetic variant rs763780 and the risk of developing rheumatoid arthritis (RA) at both the genotypic and allelic levels. The genotypic association was statistically significant[χ2 = 111.8; P value <0.0001], as was the allelic level [OR 3.444 (2.539-4.672); P value 0.0008]. These findings suggest that the presence of this genetic variant may increase the susceptibility to RA. Similarly, we observed a significant distribution of the genetic variant rs11465553 at the genotypic level [χ2 = 25.24; P value = 0.0001]. However, this variant did not show a significant association with RA at the allelic level [OR = 1.194 (0.930-1.531); P value = 0.183]. However, the distribution of variant rs2397084 was more or less random across our sample with no significant association either at genotypic and or allelic level. Put together, our association study and in silico prediction of decreasing of IL17-F protein stabilty confirmed that two SNPs, rs11465553 and rs763780 are crucial to the suscetibility of and showed that these RA in Pakistani patients.

Whole-Exome sequencing identifies GYS2 biallelic variants in individuals with suspected epilepsy.

BACKGROUND: Adequate glucose supply is essential for brain function, therefore hypoglycemic states may lead to seizures. Since blood glucose supply for brain is buffered by liver glycogen, an impairment of liver glycogen synthesis by mutations in the liver glycogen synthase gene (GYS2) might result in a substantial neurological involvement. Here, we describe the phenotypes of affected siblings of two families harboring biallelic mutations in GYS2. METHODS: Two suspected families - a multiplex Pakistani family (family A) with three affected siblings and a family of Moroccan origin (family B) with a single affected child who presented with seizures and reduced fasting blood glucose levels were genetically characterized. Whole exome sequencing (WES) was performed on the index patients, followed by Sanger sequencing-based segregation analyses on all available members of both families. RESULTS: The variant prioritization of WES and later Sanger sequencing confirmed three mutations in the GYS2 gene (12p12.1) consistent with an autosomal recessive pattern of inheritance. A homozygous splice acceptor site variant (NM_021957.3, c. 1646 -2A>G) segregated in family A. Two novel compound heterozygous variants (NM_021957.3: c.343G>A; p.Val115Met and NM_021957.3: c.875A>T; p.Glu292Val) were detected in family B, suggesting glycogen storage disorder. A special diet designed to avoid hypoglycemia, in addition to change of the anti-seizure medication led to reduction in seizure frequency. CONCLUSIONS: This study suggests that the seizures in patients initially diagnosed with epilepsy might be directly caused, or influenced by hypoglycemia due to pathogenic variants in the GYS2 gene.

Delayed intestinal obstruction from an unintentionally retained surgical gauze in a 24-year old woman two years after caesarean section: a case report.

BACKGROUND: One of the most common surgical emergencies, intestinal obstruction is rarely the result of an inadvertently retained foreign object (also known as a gossypiboma), which may not present symptoms for a lifetime. It also carries additional legal burdens, which may account for the rarity of its reports. CASE PRESENTATION: We report a 24-year-old Sudanese female with a history of emergency Caesarean section two years before the admission presented with abdominal distension and absolute constipation, which was diagnosed as intestinal obstruction with a retained gauzed found within the small intestine. Moreover, a review of recent African-reported cases was done to find relatively similar cases. CONCLUSION: Adhering to the standard of care in surgical theaters and integrating new methods of prevention like tagged gauze could help to decrease the rate of such cases in the future.

Immunoinformatic-guided novel mRNA vaccine designing to elicit immunogenic responses against the endemic Monkeypox virus.

Monkeypox virus (MPXV) is an orthopoxvirus, causing zoonotic infections in humans with smallpox-like symptoms. The WHO reported MPXV cases in May 2022 and the outbreak caused significant morbidity threats to immunocompromised individuals and children. Currently, no clinically validated therapies are available against MPXV infections. The present study is based on immunoinformatics approaches to design mRNA-based novel vaccine models against MPXV. Three proteins were prioritized based on high antigenicity, low allergenicity, and toxicity values to predict T- and B-cell epitopes. Lead T- and B-cell epitopes were used to design vaccine constructs, linked with epitope-specific linkers and adjuvant to enhance immune responses. Additional sequences, including Kozak sequence, MITD sequence, tPA sequence, Goblin 5', 3' UTRs, and a poly(A) tail were added to design stable and highly immunogenic mRNA vaccine construct. High-quality structures were predicted by molecular modeling and 3D-structural validation of the vaccine construct. Population coverage and epitope-conservancy speculated broader protection of designed vaccine model against multiple MPXV infectious strains. MPXV-V4 was eventually prioritized based on its physicochemical and immunological parameters and docking scores. Molecular dynamics and immune simulations analyses predicted significant structural stability and binding affinity of the top-ranked vaccine model with immune receptors to elicit cellular and humoral immunogenic responses against the MPXV. The pursuance of experimental and clinical follow-up of these prioritized constructs may lay the groundwork to develop safe and effective vaccine against MPXV.Communicated by Ramaswamy H. Sarma.

A comprehensive protein interaction map and druggability investigation prioritized dengue virus NS1 protein as promising therapeutic candidate.

Dengue Virus (DENV) is a serious threat to human life worldwide and is one of the most dangerous vector-borne diseases, causing thousands of deaths annually. We constructed a comprehensive PPI map of DENV with its host Homo sapiens and performed various bioinformatics analyses. We found 1195 interactions between 858 human and 10 DENV proteins. Pathway enrichment analysis was performed on the two sets of gene products, and the top 5 human proteins with the maximum number of interactions with dengue viral proteins revealed noticeable results. The non-structural protein NS1 in DENV had the maximum number of interactions with the host protein, followed by NS5 and NS3. Among the human proteins, HBA1 and UBE2I were associated with 7 viral proteins, and 3 human proteins (CSNK2A1, RRP12, and HSP90AB1) were found to interact with 6 viral proteins. Pharmacophore-based virtual screening of millions of compounds in the public databases was performed to identify potential DENV-NS1 inhibitors. The lead compounds were selected based on RMSD values, docking scores, and strong binding affinities. The top ten hit compounds were subjected to ADME profiling which identified compounds C2 (MolPort-044-180-163) and C6 (MolPort-001-742-737) as lead inhibitors against DENV-NS1. Molecular dynamics trajectory analysis and intermolecular interactions between NS1 and the ligands displayed the molecular stability of the complexes in the cellular environment. The in-silico approaches used in this study could pave the way for the development of potential specie-specific drugs and help in eliminating deadly viral infections. Therefore, experimental and clinical assays are required to validate the results of this study.