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.

Designing evaluation framework for the empirical assessment of COVID-19 mobile apps in Pakistan.

The significant proliferation in the mobile health applications (Apps) amidst Coronaviruses disease 2019 (COVID-19) resulted in decision making problems for healthcare professionals, decision makers and mobile users in Pakistan. This decision making process is also hampered by mobile app trade-offs, multiple features support, evolving healthcare needs and varying vendors. In this regard, evaluation model for mobile apps is presented which completes in three different phases. In first phase, features-based criteria is designed by leveraging Delphi method, and twenty (20) mobile apps are selected from app stores. In second stage, empirical evaluation is performed by using hybrid multi criteria decision approaches like CRiteria Importance Through Inter-criteria Correlation (CRITIC) method has been used for assigning weights to criteria features; and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method has been used for assessment of mobile app alternatives. In last step, decision making is performed to select the best mobile app for COVID-19 situations. The results suggest that proposed model can be adopted as a guideline by mobile app subscribers, patients and healthcare professionals.

Association of AFF3 Gene Polymorphism rs10865035 with Rheumatoid Arthritis: A Population-Based Case-Control Study on a Pakistani Cohort.

Rheumatoid arthritis (RA) is one of the complex diseases with the involvement of the genetic as well as environmental factors in its onset and severity. Different genome-wide association and candidate gene studies have shown the role of several genetic variants in multiple loci/genes with ethnical and geographical variations. This study was designed to detect the association of a single-nucleotide polymorphism (SNP) rs10865035 in the AFF3 gene with the genetic background of rheumatoid arthritis (RA) in the Pakistani cohort. A total of 703 individuals, including 409 RA patients and 294 healthy controls, were genotyped using TaqMan assay and Tri primer ARMS-PCR (amplification-refractory mutation system-polymerase chain reaction) methods. The association of rs10865035 with the RA was statistically determined using different models. Interestingly, besides the homozygous recessive model (G/G vs. A/G + A/A) (OR = 1.693(1.06-2.648); P = 0.025), all other models, which included the codominant (χ 2 = 5.169; P = 0.075), homozygous dominant (A/A vs. G/G + A/G) (OR = 0.867 (0.636-1.187); P = 0.41), heterozygous (A/G vs. A/A + GG) (OR = 0.491 (0.667-1.215); P = 0.49), and additive model (OR = 0.826 (0.665-1.027); P = 0.08) showed insignificant distribution of the genotypes among the cases and controls. These findings suggest that the AFF3 gene (rs10865035) has no significant role in the onset of RA in the Pakistani population.

Alkaline lipase production by novel meso-tolerant psychrophilic Exiguobacterium sp. strain (AMBL-20) isolated from glacier of northeastern Pakistan.

Lipase is an important commercial enzyme with unique and versatile biotechnological applications. This study was conducted to biosynthesize and characterizes alkaliphilic lipase by Exiguobacterium sp. strain AMBL-20T isolated from the glacial water samples of the northeastern (Gilgit-Baltistan) region of Pakistan. The isolated bacterium was identified as Exiguobaterium sp. strain AMBL-20T on the basis of morphological, biochemical, and phylogenetic analysis of 16S rRNA sequences with GenBank accession number MW229267. The bacterial strain was further screened for its lipolytic activity, biosynthesis, and characterization by different parameters with the aim of maximizing lipase activity. Results showed that 2% Olive oil, 0.2% peptone at 25 °C, pH 8, and 24 h of incubation time found optimal for maximum lipase production. The lipase enzyme was partially purified by ammonium sulphate precipitation and its activity was standardized at pH 8 under 30 °C temperature. The enzyme showed functional stability over a range of temperature and pH. Hence, extracellular alkaliphilic lipase from Exiguobacterium sp. is a potential candidate with extraordinary industrial applications, particularly in bio-detergent formulations.

Phenotypic detection of extended-spectrum beta-lactamase in multidrug-resistant acinetobacter baumannii isolated in Fauji Foundation Hospital Rawalpindi.

OBJECTIVE: To evaluate the phenotypic detection of extended-spectrum betalactamase in multidrug-resistant acinetobacter baumannii. METHODS: The cross-sectional study was conducted at the Department of Microbiology, Fauji Foundation Hospital, Rawalpindi, Pakistan, from August 2018 to April 2019, after the ethical approval from the Institutional Review Committee. Consecutive Non- probability sampling technique was used, and comprised clinical specimens, including pus, blood, sputum, urine, tracheal tubes and canula double lumen, which were processed using standard protocols. Colonies of acinetobacter baumannii were identified by gram staining and Analytical Profile Index-20E kit. Combination disc method was used for the identification of extended-spectrum beta-lactamse. Clinical and Laboratory Standards Institute guidelines were used for antimicrobial susceptibility. Data was analysed using SPSS 22 and Sample size was calculated by using earlier study with 5 % margin of error and 95 % confidence level. RESULTS: Of the 78 isolates, 58(74.4%) related to females and 20(25.6%) to males. There was no extended-spectrum beta-lactamse producer. Imipenem, meropenem, cefotaxime, ampicillin and ceftazidime showed 100% resistance, while colistin and polymyxin B were sensitive to all strains. The incidence rate was high in samples isolated from tracheal tubes 47(60.3%), followed by pus 21(26.9%). Age was not found to be a significant factor (p>0.05). CONCLUSIONS: Acinetobacter baumannii showed a high resistance to multiple drugs and was not confined to any specific age group. Colistin and polymyxin B were found to be better choices.

In vitro efficacy of Daptomycin against clinical isolates of Methicillin-resistant Staphylococcus aureus (MRSA).

This descriptive cross-sectional study was performed in the Department of Microbiology, Fauji Foundation Hospital, Rawalpindi, from March 2019 to September 2019 to determine the in vitro efficacy of Daptomycin against clinical isolates of Methicillin-Resistant Staphylococcus aureus (MRSA). Consecutive non-probability sampling technique was used and a total number of 270 patients' Pan Cultures having MRSA growth on Cefoxatin Disc with size less than 22 mm zone size were included in the study. Cultures were inoculated on MacConkey, Chocolate and Blood agar and then incubated for 24 hours at 37 degree Celsius. After incubation, Coagulase test, Catalase test and Gram staining technique were used for further identification. Minimum Inhibitory Concentration (MIC) of the isolates for Daptomycin was obtained by using E strips (Oxoid UK) according to Clinical & Laboratory Standards Institute (CLSI) guidelines. The mean age of the patients was 46.73±12.22 years, and the study included 147 (54.44%) males and 123 (45.56%) females. Regarding the type of specimen, there were 154 (57.04%) pus specimens, 54 (20.00%) blood specimens, 27 (10.00%) fluid specimens, 18 (6.67%) urine specimens, 10 (3.70%) high vaginal swabs (HVS) specimens and 7 (2.59%) sputum specimens. Daptomycin was effective in 264 (97.78%) patients with MIC range from .015 to 1 µg/ml on E strip.

Antimicrobial susceptibility pattern of Methicillin-resistant Staphylococcus Aureus isolates in Fauji Foundation Hospital Rawalpindi.

The current study was conducted in the Department of Microbiology, Fauji Foundation Hospital, Rawalpindi, from July 2018 to January 2019. The main purpose of the study was to evaluate Methicillin-resistant Staphylococcus aureus antimicrobial susceptibility pattern. Clinical samples were collected and cultured according to Clinical and Laboratory Standards Institute (CLSI) guidelines. A total of 90(30%) samples were found to be methicillin-resistant out of 300 samples of Staphylococcus aureus. Major isolates were 42 (46.67%) from pus and 22 (24.44%) from tracheal tubes. The incidence ratio of Methicillin-resistant Staphylococcus aureus was high in the samples isolated from 69 (76.67%) females compared to those of 21 (23.33%) males. Patients were more in the age group of 41 to 60 years. Vancomycin 90 (100%) was sensitive to all strains followed by Chloramphenicol 66 (73.33%) and Doxycycline 52 (57.78%). Imipenem, Meropenem andAugmentin showed resistance to all strains.

Susceptibility pattern of tracheal tube isolates from Intensive Care Unit of Fauji Foundation Hospital Rawalpindi.

OBJECTIVE: To determine the prevalence of resistant pathogens and their antimicrobial susceptibility pattern in an intensive care unit. METHODS: The cross-sectional observational study was conducted at Foundation Hospital, Rawalpindi, Pakistan, from May to September 2016, and comprised tracheal tubes which were collected in sputum culture bottles from patients with clinical findings of ventilator associated pneumonia. The tubes were cultured to locate the resistant pathogens. RESULTS: A total of 113 different strains of bacteria were isolated from 80 patients. The main isolated bacteria was acinetobacter baumannii 45(39.8%) followed by klebsiella pneumonia 14(12.3%) and methicillin-resistant staphylococcus aureus 13(11.5%). Polymyxin B was the most appropriate drug for treating patients infected with acinetobacter baumannii with a sensitivity of 64% while vancomycin and linez oli dhad 100% sensitivity for methicill in - resistant staphylococcusaureus. CONCLUSIONS: Acinetobacter baumannii was the most prevalent strain in tracheal tubes and polymyxin B was the most effective medicine.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The deleterious variants of N-acetylgalactosamine-6-sulfatase (GalN6S) enzyme trigger Morquio a syndrome by disrupting protein foldings.

Lysosomal enzymes degrade cellular macromolecules, while their inactivation causes human hereditary metabolic disorders. Mucopolysaccharidosis IVA (MPS IVA; Moquio A syndrome) is one of the lysosomal storage disorders caused by a defective Galactosamine-6-sulfatase (GalN6S) enzyme. In several populations, disease incidence is elevated due to missense mutations brought on by non-synonymous allelic variation in the GalN6S enzyme. Here, we studied the effect of non-synonymous single nucleotide polymorphism (nsSNPs) on the structural dynamics of the GalN6S enzyme and its binding with N-acetylgalactosamine (GalNAc) using all-atom molecular dynamics simulation and an essential dynamics approach. Consequently, in this study, we have identified three functionally disruptive mutations in domain-I and domain-II, that is, S80L, R90W, and S162F, which presumably contribute to post-translational modifications. The study delineated that both domains work cooperatively, and alteration in domain II (S80L, R90W) leads to conformational changes in the catalytic site in domain-I, while mutation S162F mainly provokes higher residual flexibility of domain II. These results show that these mutations impair the hydrophobic core, implying that Morquio A syndrome is caused by misfolding of the GalN6S enzyme. The results also show the instability of the GalN6S-GalNAc complex upon substitution. Overall, the structural dynamics resulting from point mutations give the molecular rationale for Moquio A syndrome and, more importantly, the Mucopolysaccharidoses (MPS) family of diseases, re-establishing MPS IVA as a protein-folding disease.Communicated by Ramaswamy H. Sarma.