Sotos Syndrome: Deep Neuroimaging Phenotyping Reveals a High Prevalence of Malformations of Cortical Development.

BACKGROUND AND PURPOSE: Sotos syndrome is a rare autosomal dominant condition caused by pathogenic mutations in the NSD1 gene that presents with craniofacial dysmorphism, overgrowth, seizures, and neurodevelopmental delay. Macrocephaly, ventriculomegaly, and corpus callosal dysmorphism are typical neuroimaging features that have been described in the medical literature. The purpose of this study was to expand on the neuroimaging phenotype by detailed analysis of a large cohort of patients with genetically proved Sotos syndrome. MATERIALS AND METHODS: This multicenter, multinational, retrospective observational cohort study systematically analyzed the clinical characteristics and neuroimaging features of 77 individuals with genetically diagnosed Sotos syndrome, via central consensus review with 3 pediatric neuroradiologists. RESULTS: In addition to previously described features, malformations of cortical development were identified in most patients (95.0%), typically dysgyria (92.2%) and polymicrogyria (22.1%), varying in location and distribution. Incomplete rotation of the hippocampus was observed in 50.6% of patients and was associated with other imaging findings, in particular with dysgyria (100% versus 84.2%, P = .012). CONCLUSIONS: Our findings show a link between the genetic-biochemical basis and the neuroimaging features and aid in better understanding the underlying clinical manifestations and possible treatment options. These findings have yet to be described to this extent and correspond with recent studies that show that NSD1 participates in brain development and has interactions with other known relevant genetic pathways.

Combining the Strengths of the Explainable Boosting Machine and Metabolomics Approaches for Biomarker Discovery in Acute Myocardial Infarction.

Acute Myocardial Infarction (AMI), a common disease that can have serious consequences, occurs when myocardial blood flow stops due to occlusion of the coronary artery. Early and accurate prediction of AMI is critical for rapid prognosis and improved patient outcomes. Metabolomics, the study of small molecules within biological systems, is an effective tool used to discover biomarkers associated with many diseases. This study intended to construct a predictive model for AMI utilizing metabolomics data and an explainable machine learning approach called Explainable Boosting Machines (EBM). The EBM model was trained on a dataset of 102 prognostic metabolites gathered from 99 individuals, including 34 healthy controls and 65 AMI patients. After a comprehensive data preprocessing, 21 metabolites were determined as the candidate predictors to predict AMI. The EBM model displayed satisfactory performance in predicting AMI, with various classification performance metrics. The model's predictions were based on the combined effects of individual metabolites and their interactions. In this context, the results obtained in two different EBM modeling, including both only individual metabolite features and their interaction effects, were discussed. The most important predictors included creatinine, nicotinamide, and isocitrate. These metabolites are involved in different biological activities, such as energy metabolism, DNA repair, and cellular signaling. The results demonstrate the potential of the combination of metabolomics and the EBM model in constructing reliable and interpretable prediction outputs for AMI. The discussed metabolite biomarkers may assist in early diagnosis, risk assessment, and personalized treatment methods for AMI patients. This study successfully developed a pipeline incorporating extensive data preprocessing and the EBM model to identify potential metabolite biomarkers for predicting AMI. The EBM model, with its ability to incorporate interaction terms, demonstrated satisfactory classification performance and revealed significant metabolite interactions that could be valuable in assessing AMI risk. However, the results obtained from this study should be validated with studies to be carried out in larger and well-defined samples.

Pilot-Study to Explore Metabolic Signature of Type 2 Diabetes: A Pipeline of Tree-Based Machine Learning and Bioinformatics Techniques for Biomarkers Discovery.

BACKGROUND: This study aims to identify unique metabolomics biomarkers associated with Type 2 Diabetes (T2D) and develop an accurate diagnostics model using tree-based machine learning (ML) algorithms integrated with bioinformatics techniques. METHODS: Univariate and multivariate analyses such as fold change, a receiver operating characteristic curve (ROC), and Partial Least-Squares Discriminant Analysis (PLS-DA) were used to identify biomarker metabolites that showed significant concentration in T2D patients. Three tree-based algorithms [eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Adaptive Boosting (AdaBoost)] that demonstrated robustness in high-dimensional data analysis were used to create a diagnostic model for T2D. RESULTS: As a result of the biomarker discovery process validated with three different approaches, Pyruvate, D-Rhamnose, AMP, pipecolate, Tetradecenoic acid, Tetradecanoic acid, Dodecanediothioic acid, Prostaglandin E3/D3 (isobars), ADP and Hexadecenoic acid were determined as potential biomarkers for T2D. Our results showed that the XGBoost model [accuracy = 0.831, F1-score = 0.845, sensitivity = 0.882, specificity = 0.774, positive predictive value (PPV) = 0.811, negative-PV (NPV) = 0.857 and Area under the ROC curve (AUC) = 0.887] had the slight highest performance measures. CONCLUSIONS: ML integrated with bioinformatics techniques offers accurate and positive T2D candidate biomarker discovery. The XGBoost model can successfully distinguish T2D based on metabolites.

Vanillylacetone attenuates cadmium chloride-induced hippocampal damage and memory loss through up-regulation of nuclear factor erythroid 2-related factor 2 gene and protein expression.

JOURNAL/nrgr/04.03/01300535-202412000-00030/figure1/v/2024-04-08T165401Z/r/image-tiff Memory loss and dementia are major public health concerns with a substantial economic burden. Oxidative stress has been shown to play a crucial role in the pathophysiology of hippocampal damage-induced memory impairment. To investigate whether the antioxidant and anti-inflammatory compound vanillylacetone (zingerone) can protect against hippocampal damage and memory loss induced by cadmium chloride (CdCl2) administration in rats, we explored the potential involvement of the nuclear factor erythroid 2-related factor 2 (Nrf2) signaling pathway, which is known to modulate oxidative stress and inflammation. Sixty healthy male Wistar rats were divided into five groups: vehicle-treated (control), vanillylacetone, CdCl2, vanillylacetone + CdCl2, vanillylacetone + CdCl2 + brusatol (a selective pharmacological Nrf2 inhibitor) groups. Vanillylacetone effectively attenuated CdCl2-induced damage in the dental gyrus of the hippocampus and improved the memory function assessed by the Morris Water Maze test. Additionally, vanillylacetone markedly decreased the hippocampal tissue levels of inflammatory biomarkers (interleukin-6, tumor necrosis factor-α, intracellular cell adhesive molecules) and apoptosis biomarkers (Bax and cleaved caspase-3). The control and CdCl2-treated groups treated with vanillylacetone showed reduced generation of reactive oxygen species, decreased malondialdehyde levels, and increased superoxide dismutase and glutathione activities, along with significant elevation of nuclear Nrf2 mRNA and protein expression in hippocampal tissue. All the protective effects of vanillylacetone were substantially blocked by the co-administration of brusatol (a selective Nrf2 inhibitor). Vanillylacetone mitigated hippocampal damage and memory loss induced by CdCl2, at least in part, by activating the nuclear transcription factor Nrf2. Additionally, vanillylacetone exerted its potent antioxidant and anti-inflammatory actions.

Investigating the impact of smoking habits through photoplethysmography analysis.

Smoking is widely recognized as a significant risk factor in the progression of arterial stiffness and cardiovascular diseases. Valuable information related to cardiac arrhythmias and heart function can be obtained by analyzing biosignals such as the electrocardiogram (ECG) and the photoplethysmogram (PPG). The PPG signal is a non-invasive optical technique that can be used to evaluate the changes in blood volume, and thus it can be linked to the health of the vascular system.Objective. In this study, the impact of three smoking habits-cigarettes, shisha, and electronic cigarettes (e-cigarettes)-on the features of the PPG signal were investigated.Approach. The PPG signals are measured for 45 healthy smokers before, during, and after the smoking session and then processed to extract the morphological features. Quantitative statistical techniques were used to analyze the PPG features and provide the most significant features of the three smoking habits. The impact of smoking is observed through significant changes in the features of the PPG signal, indicating blood volume instability.Main results. The results revealed that the three smoking habits influence the characteristics of the PPG signal significantly, which presentseven after 15 min of smoking. Among them, shisha has the greatest impact on PPG features, particularly on heart rate, systolic time, augmentation index, and peak pulse interval change. In contrast, e-cigarettes have the least effect on PPG features. Interestingly, smoking electronic cigarettes, which many participants use as a substitute for traditional cigarettes when attempting to quit smoking, has nearly a comparable effect to regular smoking.Significance. The findings suggest that individuals who smoke shisha are more likely to develop cardiovascular diseases at an earlier age compared to those who have other smoking habits. Understanding the variations in the PPG signal caused by smoking can aid in the early detection of cardiovascular disorders and provide insight into cardiac conditions. This ultimately contributes to the prevention of the development of cardiovascular diseases and the development of a health screening system.

The whole-genome molecular epidemiology of sequential isolates of Acinetobacter baumannii colonizing the rectum of patients in an adult intensive care unit of a tertiary hospital.

IMPORTANCE: Acinetobacter baumannii is a multidrug-resistant nosocomial pathogen that colonizes and infects debilitated patients in the ICU. There is very little information on the genomic characteristics of colonizing strains. This information is important to understand the evolution of lineages of A. baumannii that develop resistance while patients receive antibiotic treatment in the ICU. Our study demonstrated different patterns of colonization of the rectum of ICU patients with different STs of A. baumannii while one ST colonized all patients. Some STs carried more antibiotic resistance genes compared to others. However, there was a correlation between ST and a particular resistance gene profile. Our results further elucidate the dynamics of enteric colonization of this opportunistic pathogen.

Metformin Inhibits ROS/TNF-alpha Axis-Mediated Chronic Kidney Disease Induced by TAA Independent of Leukocyte Infiltration in Association with the Inhibition of Kidney Injury Biomarkers / La Metformina Inhibe la Enfermedad Renal Crónica Mediada por el Eje ROS/TNF-alfa Inducida por TAA Independientemente de la Infiltración de Leucocitos en Asociación con la Inhibición de Biomarcadores de Lesión Renal

SUMMARY: The toxic effects of thioacetamide (TAA) and carbon tetrachloride on the human body are well recognized. In this study, we examined whether TAA intoxication can induce kidney leukocyte infiltration (measured as leukocyte common antigen CD45) associated with the augmentation of the reactive oxygen species (ROS)/tumor necrosis factor-alpha (TNF-α) axis, as well as biomarkers of kidney injury with and without metformin treatment. Rats were either injected with TAA (200 mg/kg; twice a week for 8 weeks) before being sacrificed after 10 weeks (experimental group) or were pre-treated with metformin (200 mg/kg) daily for two weeks prior to TAA injections and continued receiving both agents until the end of the experiment, at week 10 (protective group). Using basic histology staining, immunohistochemistry methods, and blood chemistry analysis, we observed profound kidney tissue injury such as glomerular and tubular damage in the experimental group, which were substantially ameliorated by metformin. Metformin also significantly (p0.05) increase in kidney expression of CD45 positive immunostaining cells. In conclusion, we found that TAA induces kidney injury in association with the augmentation of ROS/TNF-α axis, independent of leukocyte infiltration, which is protected by metformin.

Son bien conocidosos los efectos tóxicos de la tioacetamida (TAA) y el tetracloruro de carbono en el cuerpo humano. En este estudio, examinamos si la intoxicación por TAA puede inducir la infiltración de leucocitos renales (medida como antígeno leucocitario común CD45) asociada con el aumento de las especies reactivas de oxígeno (ROS)/factor de necrosis tumoral-alfa (TNF-α), así como biomarcadores de daño renal con y sin tratamiento con metformina. A las ratas se les inyectó TAA (200 mg/kg; dos veces por semana durante 8 semanas) antes de sacrificarlas a las 10 semanas (grupo experimental) o se les pretrató con metformina (200 mg/kg) diariamente durante dos semanas antes de las inyecciones de TAA y continuaron recibiendo ambos agentes hasta el final del experimento, en la semana 10 (grupo protector). Usando tinción histológica básica, métodos de inmunohistoquímica y análisis químico de la sangre, observamos una lesión profunda del tejido renal, como daño glomerular y tubular en el grupo experimental, que mejoraron sustancialmente con la metformina. La metformina también inhibió significativamente (p0,05) en la expresión renal de células de inmunotinción positivas para CD45. En conclusión, encontramos que el TAA induce la lesión renal en asociación con el aumento del eje ROS/TNF-α, independientemente de la infiltración de leucocitos, que está protegida por metformina.

The role of seabird guano in maintaining North Atlantic summertime productivity.

Nutrients supplied via seabird guano increase primary production in some coastal ecosystems. A similar process may occur in the open ocean. To investigate this directly, we first measured bulk and leachable nutrient concentrations in guano sampled in the North Atlantic. We found that guano was strongly enriched in phosphorus, which was released as phosphate in solution. Nitrogen release was dominated by reduced forms (ammonium and urea) whilst release of nitrate was relatively low. A range of trace elements, including the micronutrient iron, were released. Using in-situ bioassays, we then showed that supply of fresh guano to ambient seawater increases phytoplankton biomass and photochemical efficiencies. Based on these results, modelled seabird distributions, and known defecation rates, we estimate that on annual scales guano is a minor source of nutrients for the surface North Atlantic. However, on shorter timescales in late spring/summer it could be much more important: Estimates of upper-level depositions of phosphorus by seabirds were three orders of magnitude higher than modelled aerosol deposition and comparable to diffusion from deeper waters.

Vitamin E protects against the modulation of TNF-α-AMPK axis and inhibits pancreas injury in a rat model of L-arginine-induced acute necrotising pancreatitis.

BACKGROUND: Acute pancreatitis (AP) associated with the modulation of TNF-α-AMPK axis in the presence and absence of vitamin E has not been investigated before. MATERIAL AND METHODS: Rats were either injected with L-arginine (2.5 gm/kg) before being sacrificed after 48 h or were pre-treated with vitamin E (60 mg/kg) and continued receiving vitamin E until the end of the experiment. RESULTS: AP was developed as demonstrated by infiltration of inflammatory cells and profound pancreas tissue damage, which were substantially protected by vitamin E. In addition, L-arginine injections significantly (p < .0001) increased the expression of TNF-α mRNA and protein, and decreased phospho-AMPK and IL-10 mRNA and protein that was significantly (p < .0001) protected by vitamin E. Furthermore, vitamin E inhibited L-arginine-induced blood levels of LDH, amylase, and myeloperoxidase. CONCLUSIONS: L-arginine-induced acute pancreatitis modulates TNF-α-AMPK axis, IL-10 and other AP biomarkers, which is protected by vitamin E; thus, may offer therapeutic potential in humans.

Metformin ameliorates ROS-p53-collagen axis of fibrosis and dyslipidemia in type 2 diabetes mellitus-induced left ventricular injury.

BACKGROUND: The link between oxidative stress (ROS), apoptosis (p53) and fibrosis (collagen) in type 2 diabetes mellitus (T2DM)-induced cardiac injury in the presence and absence of the antidiabetic drug, metformin has not been investigated before. MATERIAL AND METHODS: T2DM was induced in rats by a combination of high carbohydrate and fat diets (HCFD) and streptozotocin (50 mg/kg) injection. The protection group started metformin (200 mg/kg) treatment 14 days prior to the induction of diabetes and continued on metformin and HCFD until being sacrificed at week 12. RESULTS: Diabetes significantly induced blood levels of ROS and left ventricular p53 and collagen expression that was inhibited by metformin. Metformin also significantly reduced glycated haemoglobin and dyslipidemia induced by diabetes. In addition, a significant correlation between ROS-p53-collagen axis and glycaemia and hyperlipidaemia was observed. CONCLUSIONS: These findings show that metformin provides substantial protection against diabetic cardiomyopathy-induced ROS-p53 mediated fibrosis and dyslipidemia.

Mechanical and Durability Evaluation of Metakaolin as Cement Replacement Material in Concrete.

Due to the increasing prices of cement and its harmful effect on the environment, the use of cement has become highly unsustainable in concrete. There is a considerable need for promoting the use of cement replacement materials. This study investigates the effect of variable percentages of metakaolin (MK) on the mechanical and durability performance of concrete. Kaolin clay (KC) was used in the current research to prepare the MK by the calcination process; it was ground in a ball mill to its maximum achievable fineness value of 2550 m2/Kg. Four replacement levels of MK, i.e., 5%, 10%, 15%, and 20% by weight of cement, in addition to control samples, at a constant water-to-cement (w/c) ratio of 0.55 were used. For evaluating the mechanical and durability performance, 27 cubes (6 in. × 6 in. × 6 in.) and 6 cylinders (3.875 in. diameter, 2 in. height) were cast for each mix. These samples were tested for compressive strength under standard conditions and in an acidic environment, in addition to being subjected to water permeability, sorptivity, and water absorption tests. Chemical analysis revealed that MK could be used as pozzolana as per the American Society for Testing and Materials (ASTM C 618:2003). The results demonstrated an increased compressive strength of concrete owing to an increased percentage of MK in the mix with aging. In particular, the concrete having 20% MK after curing under standard conditions exhibited 33.43% higher compressive strength at 90 days as compared to similarly aged control concrete. However, with increasing MK, the workability of concrete decreased drastically. After being subjected to an acid attack (immersing concrete cubes in 2% sulfuric acid solution), the samples exhibited a significant decrease in compressive strength at 90 days in comparison to those without acid attack at the same age. The density of acid attack increased with increasing MK with a maximum corresponding to 5% MK concrete. The current findings suggest that the local MK has the potential to produce good-quality concrete in a normal environment.

Knowledge Mapping of the Literature on Fiber-Reinforced Geopolymers: A Scientometric Review.

This study examined the bibliographic data on fiber-reinforced geopolymers (FRGPs) using scientometrics to determine their important features. Manual review articles are inadequate in their capability to connect various segments of literature in an ordered and systematic manner. Scientific mapping, co-citation, and co-occurrence are the difficult aspects of current research. The Scopus database was utilized to find and obtain the data needed to achieve the study's aims. The VOSviewer application was employed to assess the literature records from 751 publications, including citation, bibliographic, keyword, and abstract details. Significant publishing outlets, keywords, prolific researchers in terms of citations and articles published, top-cited documents, and locations actively participating in FRGP investigations were identified during the data review. The possible uses of FRGP were also highlighted. The scientometric analysis revealed that the most frequently used keywords in FRGP research are inorganic polymers, geopolymers, reinforcement, geopolymer, and compressive strength. Additionally, 27 authors have published more than 10 articles on FRGP, and 29 articles have received more than 100 citations up to June 2022. Due to the graphical illustration and quantitative contribution of scholars and countries, this study can support scholars in building joint ventures and communicating innovative ideas and practices.

Predicting the Compressive Strength of Concrete Containing Fly Ash and Rice Husk Ash Using ANN and GEP Models.

Climate change has become trending news due to its serious impacts on Earth. Initiatives are being taken to lessen the impact of climate change and mitigate it. Among the different initiatives, researchers are aiming to find suitable alternatives for cement. This study is a humble effort to effectively utilize industrial- and agricultural-waste-based pozzolanic materials in concrete to make it economical and environmentally friendly. For this purpose, a ternary blend of binders (i.e., cement, fly ash, and rice husk ash) was employed in concrete. Different variables such as the quantity of different binders, fine and coarse aggregates, water, superplasticizer, and the age of the samples were considered to study their influence on the compressive strength of the ternary blended concrete using gene expression programming (GEP) and artificial neural networking (ANN). The performance of these two models was evaluated using R2, RMSE, and a comparison of regression slopes. It was observed that the GEP model with 100 chromosomes, a head size of 10, and five genes resulted in an optimum GEP model, as apparent from its high R2 value of 0.80 and 0.70 in the TR and TS phase, respectively. However, the ANN model performed better than the GEP model, as evident from its higher R2 value of 0.94 and 0.88 in the TR and TS phase, respectively. Similarly, lower values of RMSE and MAE were observed for the ANN model in comparison to the GEP model. The regression slope analysis revealed that the predicted values obtained from the ANN model were in good agreement with the experimental values, as shown by its higher R2 value (0.89) compared with that of the GEP model (R2 = 0.80). Subsequently, parametric analysis of the ANN model revealed that the addition of pozzolanic materials enhanced the compressive strength of the ternary blended concrete samples. Additionally, we observed that the compressive strength of the ternary blended concrete samples increased rapidly within the first 28 days of casting.

Application of Soft-Computing Methods to Evaluate the Compressive Strength of Self-Compacting Concrete.

This research examined machine learning (ML) techniques for predicting the compressive strength (CS) of self-compacting concrete (SCC). Multilayer perceptron (MLP), bagging regressor (BR), and support vector machine (SVM) were utilized for analysis. A total of 169 data points were retrieved from the various published articles. The data set was based on 11 input parameters, such as cement, limestone, fly ash, ground granulated blast-furnace slag, silica fume, rice husk ash, coarse aggregate, fine aggregate, superplasticizers, water, viscosity modifying admixtures, and one output with compressive strength of SCC. In terms of properly predicting the CS of SCC, the BR technique outperformed both the SVM and MLP models, as determined by the research results. In contrast to SVM and MLP, the coefficient of determination (R2) for the BR model was 0.95, whereas for SVM and MLP, the R2 was 0.90 and 0.86, respectively. In addition, a k-fold cross-validation approach was adopted to check the accuracy of the employed models. The statistical measures mean absolute percent error, mean absolute error, and root mean square error ensure the validity of the model. Using sensitivity analysis, the influence of input factors on the intended CS of SCC was also explored. This analysis reveals that the highest contributing parameter towards the CS of SCC was cement with 16.2%, while rice husk ash contributed the least with 4.25% among all the input variables.

Data-Driven Techniques for Evaluating the Mechanical Strength and Raw Material Effects of Steel Fiber-Reinforced Concrete.

Estimating concrete properties using soft computing techniques has been shown to be a time and cost-efficient method in the construction industry. Thus, for the prediction of steel fiber-reinforced concrete (SFRC) strength under compressive and flexural loads, the current research employed advanced and effective soft computing techniques. In the current study, a single machine learning method known as multiple-layer perceptron neural network (MLPNN) and ensembled machine learning models known as MLPNN-adaptive boosting and MLPNN-bagging are used for this purpose. Water; cement; fine aggregate (FA); coarse aggregate (CA); super-plasticizer (SP); silica fume; and steel fiber volume percent (Vf SF), length (mm), and diameter were the factors considered (mm). This study also employed statistical analysis such as determination coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) to assess the performance of the algorithms. It was determined that the MLPNN-AdaBoost method is suitable for forecasting SFRC compressive and flexural strengths. The MLPNN technique's higher R2, i.e., 0.94 and 0.95 for flexural and compressive strength, respectively, and lower error values result in more precision than other methods with lower R2 values. SHAP analysis demonstrated that the volume of cement and steel fibers have the greatest feature values for SFRC's compressive and flexural strengths, respectively.

Multiplex gyrB PCR Assay for Identification of Acinetobacter baumannii Is Validated by Whole Genome Sequence-Based Assays.

OBJECTIVE: A multiplex gyrB PCR assay has been used to diagnose Acinetobacter baumannii. However, this assay has not been validated against the gold standard DNA-DNA hybridization assay, which is a laborious method. DNA-DNA hybridization assay is now replaced by whole genome sequence (WGS)-based methods. Two such methods are a k-mer-based search of sequence reads using the Kraken 2 program and average nucleotide identity (ANI). The objective was to validate the gyrB PCR assay with WGS-based methods. SUBJECTS AND METHODS: We cultured 270 sequential A. baumannii isolates from the rectal swabs of 32 adult patients. The identity of the isolates was determined by gyrB PCR. The sequences of 269 isolates were determined by Illumina sequencing and the taxonomy was inferred by the Kraken 2 program and ANI. RESULTS: All the 269 isolates were confirmed as A. baumannii by Kraken 2 and ANI. CONCLUSION: The gyrB PCR assay is now validated for easy identification of A. baumannii in comparison with gold standard WGS-based assays.

Lipopolysaccharide induces acute lung injury and alveolar haemorrhage in association with the cytokine storm, coagulopathy and AT1R/JAK/STAT augmentation in a rat model that mimics moderate and severe Covid-19 pathology.

Progress in the study of Covid-19 disease in rodents has been hampered by the lack of angiotensin-converting enzyme 2 (ACE2; virus entry route to the target cell) affinities for the virus spike proteins across species. Therefore, we sought to determine whether a modified protocol of lipopolysaccharide (LPS)-induced acute respiratory distress syndrome in rats can mimic both cell signalling pathways as well as severe disease phenotypes of Covid-19 disease. Rats were injected via intratracheal (IT) instillation with either 15 mg/kg of LPS (model group) or saline (control group) before being killed after 3 days. A severe acute respiratory syndrome (SARS)-like effect was observed in the model group as demonstrated by the development of a "cytokine storm" (>2.7 fold increase in blood levels of IL-6, IL-17A, GM-CSF, and TNF-α), high blood ferritin, demonstrable coagulopathy, including elevated D-dimer (approximately 10-fold increase), PAI-1, PT, and APTT (p < 0.0001). In addition, LPS increased the expression of lung angiotensin II type I receptor (AT1R)-JAK-STAT axis (>4 fold increase). Chest imaging revealed bilateral small patchy opacities of the lungs. Severe lung injury was noted by the presence of both, alveolar collapse and haemorrhage, desquamation of epithelial cells in the airway lumen, infiltration of inflammatory cells (CD45+ leukocytes), widespread thickening of the interalveolar septa, and ultrastructural alterations similar to Covid-19. Thus, these findings demonstrate that IT injection of 15 mg/kg LPS into rats, induced an AT1R/JAK/STAT-mediated cytokine storm with resultant pneumonia and coagulopathy that was commensurate with moderate and severe Covid-19 disease noted in humans.

The 79°N Glacier cavity modulates subglacial iron export to the NE Greenland Shelf.

Approximately half of the freshwater discharged from the Greenland and Antarctic Ice Sheets enters the ocean subsurface as a result of basal ice melt, or runoff draining via the grounding line of a deep ice shelf or marine-terminating glacier. Around Antarctica and parts of northern Greenland, this freshwater then experiences prolonged residence times in large cavities beneath floating ice tongues. Due to the inaccessibility of these cavities, it is unclear how they moderate the freshwater associated supply of nutrients such as iron (Fe) to the ocean. Here, we show that subglacial dissolved Fe export from Nioghalvfjerdsbrae (the '79°N Glacier') is decoupled from particulate inputs including freshwater Fe supply, likely due to the prolonged ~162-day residence time of Atlantic water beneath Greenland's largest floating ice-tongue. Our findings indicate that the overturning rate and particle-dissolved phase exchanges in ice cavities exert a dominant control on subglacial nutrient supply to shelf regions.

Cancer Management in Saudi Arabia: Recommendations by the Saudi Oncology HeAlth Economics ExpeRt GrouP (SHARP).

Cancer is widely recognized as a major global health problem and is estimated to rank as one of the leading causes of death worldwide. Saudi Arabia has undergone remarkable socioeconomic development in the past 40 years which has contributed to the increase in cancer incidence. The high costs of new oncology medications in combination with uncertainty of long-term effectiveness and safety outcomes highlight the importance of considering value, in terms of clinical outcomes, relative to cost. We convened a group of experts to discuss key factors impacting the current state of cancer management in Saudi Arabia and to agree on a list of recommendations, with a focus on value-based care, considering evidence, patients, and costs.

Expanding the phenotype, genotype and biochemical knowledge of ALG3-CDG.

Congenital disorders of glycosylation (CDGs) are a continuously expanding group of monogenic disorders of glycoprotein and glycolipid biosynthesis that cause multisystem diseases. Individuals with ALG3-CDG frequently exhibit severe neurological involvement (epilepsy, microcephaly, and hypotonia), ocular anomalies, dysmorphic features, skeletal anomalies, and feeding difficulties. We present 10 unreported individuals diagnosed with ALG3-CDG based on molecular and biochemical testing with 11 novel variants in ALG3, bringing the total to 40 reported individuals. In addition to the typical multisystem disease seen in ALG3-CDG, we expand the symptomatology of ALG3-CDG to now include endocrine abnormalities, neural tube defects, mild aortic root dilatation, immunodeficiency, and renal anomalies. N-glycan analyses of these individuals showed combined deficiencies of hybrid glycans and glycan extension beyond Man5 GlcNAc2 consistent with their truncated lipid-linked precursor oligosaccharides. This spectrum of N-glycan changes is unique to ALG3-CDG. These expanded features of ALG3-CDG facilitate diagnosis and suggest that optimal management should include baseline endocrine, renal, cardiac, and immunological evaluation at the time of diagnosis and with ongoing monitoring.