The interplay between vitamin D status, subclinical inflammation, and prediabetes.

Vitamin D's role extends beyond classical calcium and phosphate homeostasis to encompass a pivotal influence on immune modulation and metabolic health. The mechanisms by which vitamin D exerts these effects involve its conversion to hormonally active calcitriol, which binds intracellular vitamin D receptors, initiating various downstream cascades. In this review, we tease out the evidence showing the relationship between vitamin D deficiency and prediabetes within the context of subclinical inflammation, with a special focus on the novel monocyte-to-HDL ratio (MHR), a novel inflammatory marker reflecting subclinical inflammation. This was based on a thorough literature review using reputable databases covering the period from 1980 to 2024. In light of this, we discuss calcitriol's anti-inflammatory effects and consequently link vitamin D deficiency to both overt and subclinical inflammation. Additionally, the utility of several biomarkers, notably MHR, in investigating this association is also discussed. We further reviewed the role of vitamin D deficiency in precipitating prediabetes and type 2 diabetes mellitus (T2DM) via insulin resistance, decreased insulin synthesis and secretion, and subclinical inflammation. Taken together, this mini review highlights that vitamin D deficiency is significantly associated with subclinical inflammation, playing a critical role in the development of prediabetes and the progression to T2DM. Addressing vitamin D deficiency through appropriate interventions may serve as a preventative measure against the development of prediabetes and T2DM.

Recent Advances in Bacterial Detection Using Surface-Enhanced Raman Scattering.

Rapid identification of microorganisms with a high sensitivity and selectivity is of great interest in many fields, primarily in clinical diagnosis, environmental monitoring, and the food industry. For over the past decades, a surface-enhanced Raman scattering (SERS)-based detection platform has been extensively used for bacterial detection, and the effort has been extended to clinical, environmental, and food samples. In contrast to other approaches, such as enzyme-linked immunosorbent assays and polymerase chain reaction, SERS exhibits outstanding advantages of rapid detection, being culture-free, low cost, high sensitivity, and lack of water interference. This review aims to cover the development of SERS-based methods for bacterial detection with an emphasis on the source of the signal, techniques used to improve the limit of detection and specificity, and the application of SERS in high-throughput settings and complex samples. The challenges and advancements with the implementation of artificial intelligence (AI) are also discussed.

Breastfeeding Duration Reduces the Risk of Childhood Leukemia and Modifies the Risk of Developing Functional Gastrointestinal Disorders.

Objective: The aim of this study was to test the hypothesis that the duration of breastfeeding in infancy reduces the risk of childhood leukemia or lymphoma, and modifies the risk of developing functional gastrointestinal disorders (FGIDs). Subjects and Methods: This case-control study involved the recruitment of children with lymphoid malignancy and functional gastrointestinal symptoms with healthy children as controls. Focused questionnaires were used to collect data on breastfeeding history and other key risk factors. Univariate and multivariate analyses were undertaken. Results: Of the 334 children with lymphoid malignancy, 65% were male. The control group included 334 age- and sex-matched participants. Most (n = 189; 56.6%) of the children with leukemia were <10 years of age. Differences between cases and controls included the duration of breastfeeding (p < 0.0001), mean birthweight (p < 0.001), maternal age (p < 0.001), paternal age (p < 0.001), birth order (p < 0.001), mean number of children (p < 0.001), BMI percentile (p = 0.042), and maternal smoking (p = 0.012). Breastfeeding duration of up to 6 months' duration, when compared with feeding of longer than 6 months, was associated with increased odds ratios (OR) for acute lymphoblastic leukemia (OR = 3.43, 95% confidence interval [CI] 2.37-4.98; p < 0.001), Hodgkin's lymphoma (OR = 1.58, 95% CI: 0.88-2.84, p = 0.120), Non-Hodgkin's lymphoma (OR = 2.14, 95% CI: 1.25-3.65, p = 0.005), and overall (OR = 1.95, 95% CI: 1.40-2.71, p < 0.001). Cases also differed from controls with regard to FGIDs, such as stomach ache (p < 0.001), dyspepsia (p < 0.001), early satiety (p = 0.017), bowel satisfaction (p < 0.001), bloating (p < 0.001), nausea (p = 0.005), vomiting (p = 0.039), constipation (p = 0.003), diarrhea (p = 0.010), gastrointestinal canal congestion (p =0.039), muscle aches pains (p = 0.008), fecal incontinence (p = 0.021), and indigestion (p = 0.003). A multivariate stepwise regression analysis revealed that maternal smoking (p < 0.001), formula feeding (p < 0.001), duration of breastfeeding (p < 0.001), birth order (p = 0.002), mother's age (p = 0.004) and the child's birthweight (p = 0.009) were predictors for leukemia. Further analysis showed that dyspepsia (p < 0.001), gastrointestinal tract canal congestion (p < 0.001), constipation (p = 0.009), diarrhea (p = 0.013), bowel satisfaction (p = 0.021), bloating (p = 0.022), duration of breastfeeding (p < 0.001), and stomach ache (p = 0.025) were significant predictors for developing FGID symptoms after adjusting for age, gender, and other confounding variables. Conclusion: This study confirmed that breastfeeding has some effect on reducing possible risk of childhood lymphoma and leukemia and FGID symptoms compared with healthy control children.

Association between Serum Vitamin D Status and Circadian Syndrome: A Cross-Sectional Study.

BACKGROUND: Circadian Syndrome (CircS) encompasses cardiometabolic risk factors and comorbidities, indicating an elevated susceptibility to cardiovascular disease and type 2 diabetes. METHODS: This cross-sectional study aimed to investigate the association between vitamin D levels and each of the following: CircS, metabolic syndrome (MetS), and the individual components of CircS. Data from 14,907 adults who participated in the National Health and Nutrition Examination Survey (NHANES) between 2007 and 2018 were utilized. CircS was defined based on MetS components, alongside depression, short sleep, and non-alcoholic fatty liver disease (NAFLD). RESULTS: Our results indicated that low vitamin D levels exhibited meaningful associations with CircS, with vitamin D deficiency and inadequacy demonstrating 2.21-fold (95% CI 1.78-2.74, p < 0.001) and 1.33-fold (95% CI 1.14-1.54, p < 0.001) increases in CircS odds, respectively. The association between vitamin D deficiency and CircS was stronger than that with MetS. Additionally, a dose-response gradient in odds of CircS components, particularly with short sleep duration, was noted as serum vitamin D levels decreased. CONCLUSIONS: our findings highlight a significant association between low serum vitamin D levels and CircS and its components, particularly with short sleep. This suggests a potentially pivotal role of vitamin D in the pathogenesis of Circadian syndrome.

Precision classification and quantitative analysis of bacteria biomarkers via surface-enhanced Raman spectroscopy and machine learning.

The SERS spectra of six bacterial biomarkers, 2,3-DHBA, 2,5-DHBA, Pyocyanin, lipoteichoic acid (LTA), Enterobactin, and ß-carotene, of various concentrations, were obtained from silver nanorod array substrates, and the spectral peaks and the corresponding vibrational modes were identified to classify different spectra. The spectral variations in three different concentration regions due to various reasons have imposed a challenge to use classic calibration curve methods to quantify the concentration of biomarkers. Depending on baseline removal strategy, i.e., local or global baseline removal, the calibration curve differed significantly. With the aid of convolutional neural network (CNN), a two-step process was established to classify and quantify biomarker solutions based on SERS spectra: using a specific CNN model, a remarkable differentiation and classification accuracy of 99.99 % for all six biomarkers regardless of the concentration can be achieved. After classification, six regression CNN models were established to predict the concentration of biomarkers, with coefficient of determination R2 > 0.97 and mean absolute error (MAE) < 0.27. The feature of important calculations indicates the high classification and quantification accuracies were due to the intrinsic spectral features in SERS spectra. This study showcases the synergistic potential of SERS and advanced machine learning algorithms and holds significant promise for bacterial infection diagnostics.

Improved pediatric ICU mortality prediction for respiratory diseases: machine learning and data subdivision insights.

The growing concern of pediatric mortality demands heightened preparedness in clinical settings, especially within intensive care units (ICUs). As respiratory-related admissions account for a substantial portion of pediatric illnesses, there is a pressing need to predict ICU mortality in these cases. This study based on data from 1188 patients, addresses this imperative using machine learning techniques and investigating different class balancing methods for pediatric ICU mortality prediction. This study employs the publicly accessible "Paediatric Intensive Care database" to train, validate, and test a machine learning model for predicting pediatric patient mortality. Features were ranked using three machine learning feature selection techniques, namely Random Forest, Extra Trees, and XGBoost, resulting in the selection of 16 critical features from a total of 105 features. Ten machine learning models and ensemble techniques are used to make accurate mortality predictions. To tackle the inherent class imbalance in the dataset, we applied a unique data partitioning technique to enhance the model's alignment with the data distribution. The CatBoost machine learning model achieved an area under the curve (AUC) of 72.22%, while the stacking ensemble model yielded an AUC of 60.59% for mortality prediction. The proposed subdivision technique, on the other hand, provides a significant improvement in performance metrics, with an AUC of 85.2% and an accuracy of 89.32%. These findings emphasize the potential of machine learning in enhancing pediatric mortality prediction and inform strategies for improved ICU readiness.

A Path towards Timely VAP Diagnosis: Proof-of-Concept Study on Pyocyanin Sensing with Cu-Mg Doped Graphene Oxide.

In response to the urgent requirement for rapid, precise, and cost-effective detection in intensive care units (ICUs) for ventilated patients, as well as the need to overcome the limitations of traditional detection methods, researchers have turned their attention towards advancing novel technologies. Among these, biosensors have emerged as a reliable platform for achieving accurate and early diagnoses. In this study, we explore the possibility of using Pyocyanin analysis for early detection of pathogens in ventilator-associated pneumonia (VAP) and lower respiratory tract infections in ventilated patients. To achieve this, we developed an electrochemical sensor utilizing a graphene oxide-copper oxide-doped MgO (GO - Cu - Mgo) (GCM) catalyst for Pyocyanin detection. Pyocyanin is a virulence factor in the phenazine group that is produced by Pseudomonas aeruginosa strains, leading to infections such as pneumonia, urinary tract infections, and cystic fibrosis. We additionally investigated the use of DNA aptamers for detecting Pyocyanin as a biomarker of Pseudomonas aeruginosa, a common causative agent of VAP. The results of this study indicated that electrochemical detection of Pyocyanin using a GCM catalyst shows promising potential for various applications, including clinical diagnostics and drug discovery.

Impact of phthalate metabolites on vitamin D levels and subclinical inflammation: national health and nutrition examination survey, 2013-2018.

This study explores the association between phthalates and total vitamin D levels and the link between phthalates exposure and subclinical inflammation using monocyte percentage to high-density lipoprotein cholesterol ratio (MHR), utilizing three National Health and Nutrition Examination Survey (NHANES) survey cycles 2013-2018. This study is cross-sectional, utilizing one-time urine samples from randomly selected NHANES participants to assess phthalate metabolites. An inverse association between vitamin D and all Di(2-ethylhexyl) phthalate (DEHP) metabolites was found. The molar sum of DEHP metabolites was inversely associated with vitamin D (ß -2.329; 95% CI -3.937,-0.720). An inverse association was observed between monocarboxynonyl phthalate and vitamin D (ß -0.0278; 95% CI -0.0527,-0.00298). A similar relationship was found between monocarboxyoctyl phthalate and vitamin D (ß -0.0160; 95% CI -0.0242,-0.00775). There was no association between phthalate metabolites and MHR. Stratified analysis showed that the association between phthalate metabolites and MHR may vary according to vitamin D status.

Vitamin D status affects proteomic profile of HDL-associated proteins and inflammatory mediators in dyslipidemia.

Vitamin D deficiency and dyslipidemia have substantial implications for human health globally. Vitamin D is essential for bone metabolism and immune modulation, and its insufficiency is linked to various chronic inflammatory conditions. Dyslipidemia, characterized by low levels of high-density lipoprotein (HDL) and elevated levels of low-density lipoprotein (LDL) and triglycerides, is also prevalent. Previous research has shown a connection between vitamin D deficiency and low HDL, but the precise mechanism by which vitamin D influences HDL production and its anti-inflammatory properties remains unclear. This study aimed to investigate the proteomic profiles of individuals with and without vitamin D deficiency and dyslipidemia, specifically focusing on the effects of vitamin D on HDL production, its anti-inflammatory potential, and the molecular pathways associated with vitamin D deficiency and dyslipidemia, particularly inflammation and cancer pathways. By analyzing the proteomic profiles of 274 participants from the Qatar Biobank database, we identified 1301 proteins. Our findings indicated a decrease in HDL-associated apolipoproteins (ApoM and ApoD) in individuals with both dyslipidemia and vitamin D deficiency. Conversely, participants with these conditions exhibited increased expression of acute-phase proteins (SAA1 and SOD1), which are associated with inflammation. Pathway enrichment analysis revealed heightened inflammatory activity in individuals with vitamin D deficiency and dyslipidemia, with notable enrichments in pathways such as MAPK, JAK-STAT, Ras signaling, cytokine-cytokine receptor interaction, AGE-RAGE, ErbB signaling, and cancer pathways. Overall, cases of vitamin D deficiency showed enrichment in inflammation pathways, while individuals with both vitamin D deficiency and dyslipidemia demonstrated enhanced activation of cancer and inflammation pathways.

Real-World Effectiveness of the Varicella Vaccine among Children and Adolescents in Qatar: A Case-Control Study.

BACKGROUND: Despite the availability of a highly efficacious vaccine, varicella outbreaks are still being reported globally. In this study, we evaluated the real-world effectiveness of varicella vaccination among children between the ages of 1 and 18 years old during the period 2017 to 2019 in Qatar. METHODS: A matched case-control study was conducted that included all reported varicella-infected children who visited the primary healthcare system in Qatar from January 2017 to December 2019. The cases were children under the age of 18 years who were clinically diagnosed with varicella. The controls were of the same age, who visited the Primary Health Care Corporation (PHCC) during 2017-2019 with a skin rash where varicella infection was ruled out. The data on varicella vaccination for each participant were obtained from the electronic database in the PHCC during the study period. RESULTS: We included 862 cases of varicella and 5454 matched controls, with a median age of 8 years (IQR 3-12); 47.4% were female and almost 50% were of Qatari nationality. The year 2019 had the highest varicella infection count with a total of 416 cases. The cases were less likely to be vaccinated against varicella, with approximately a quarter (25.6%) of cases and 36.7% of the controls having either one or two doses of the vaccine (p < 0.001). Compared to not being vaccinated, a single dose vaccination showed a 56% reduction in the odds of varicella infection [OR 0.44, 95% CI: 0.34-0.55; p < 0.000], and a two-dose vaccination showed an 86% reduction in the odds of varicella infection [OR 0.13, 95% CI: 0.06-0.29; p < 0.000]. CONCLUSION: In this multicultural setting, a two-dose varicella vaccination shows reasonable protection against varicella infection.

Antibiotics Prescriptions Pattern among Patients Visiting Primary Health Care Centers (PHCC) before and during COVID-19 Pandemic: A Cross-Sectional Population-Based Study from Qatar.

BACKGROUND: The COVID-19 pandemic, caused by the novel coronavirus 2 (SARS-CoV-2), has been associated with an increased risk of secondary bacterial infections. Numerous studies have reported a surge in antibiotic usage during the COVID-19 pandemic. This study aims to examine the impact of the COVID-19 pandemic on the frequency and patterns of antibiotic prescriptions at Primary Health Care Centers (PHCC) in Qatar, comparing the period before and during the pandemic. METHODS: This population-based, cross-sectional study analyzed all antibiotic prescriptions issued in two-month intervals before COVID-19 (November and December 2019) and during the initial wave (June and July 2020) of COVID-19. The study included 27 PHCCs in Qatar. RESULTS: Prior to the COVID-19 outbreak, the PHCCs dispensed a total of 74,909 antibiotic prescriptions in November and December. During the first wave of COVID-19, the number decreased to 29,273 prescriptions in June and July 2020. Antibiotics were most commonly prescribed for adults and least commonly for the elderly, both before and during the COVID-19 period. In the pre-COVID-19 period, Betalactams and macrolides accounted for the majority (73%) of all antibiotic prescriptions across all age groups. However, during the COVID-19 period, Betalactams and other antibiotics such as Nitrofurantoin and Metronidazole (73%) were the most frequently prescribed. CONCLUSION: The rate of antibiotic prescriptions during the first wave of COVID-19 was lower compared to the two months preceding the pandemic at the PHCC in Qatar.

Ultrasound-Based Image Analysis for Predicting Carotid Artery Stenosis Risk: A Comprehensive Review of the Problem, Techniques, Datasets, and Future Directions.

The carotid artery is a major blood vessel that supplies blood to the brain. Plaque buildup in the arteries can lead to cardiovascular diseases such as atherosclerosis, stroke, ruptured arteries, and even death. Both invasive and non-invasive methods are used to detect plaque buildup in the arteries, with ultrasound imaging being the first line of diagnosis. This paper presents a comprehensive review of the existing literature on ultrasound image analysis methods for detecting and characterizing plaque buildup in the carotid artery. The review includes an in-depth analysis of datasets; image segmentation techniques for the carotid artery plaque area, lumen area, and intima-media thickness (IMT); and plaque measurement, characterization, classification, and stenosis grading using deep learning and machine learning. Additionally, the paper provides an overview of the performance of these methods, including challenges in analysis, and future directions for research.

Toward a meta-vaccine future: Promoting vaccine confidence in the metaverse.

The metaverse has a promising role to serve as a global platform and tackle one of the most intractable public health challenges; vaccine hesitancy. Active efforts in this field can enhance vaccine acceptance thus leading to better community health protection. By embracing digital health innovations, the metaverse potentially creates an interactive environment for interdisciplinary collaborations that can foster novel approaches in tackling vaccine hesitancy as well as future pandemics. This paper aims to highlight the unique areas where the metaverse can enhance vaccination confidence, educate about vaccine working principles, and offer collaborative healthcare initiatives in this virtual community.

BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data.

Nowadays, quick, and accurate diagnosis of COVID-19 is a pressing need. This study presents a multimodal system to meet this need. The presented system employs a machine learning module that learns the required knowledge from the datasets collected from 930 COVID-19 patients hospitalized in Italy during the first wave of COVID-19 (March-June 2020). The dataset consists of twenty-five biomarkers from electronic health record and Chest X-ray (CXR) images. It is found that the system can diagnose low- or high-risk patients with an accuracy, sensitivity, and F1-score of 89.03%, 90.44%, and 89.03%, respectively. The system exhibits 6% higher accuracy than the systems that employ either CXR images or biomarker data. In addition, the system can calculate the mortality risk of high-risk patients using multivariate logistic regression-based nomogram scoring technique. Interested physicians can use the presented system to predict the early mortality risks of COVID-19 patients using the web-link: Covid-severity-grading-AI. In this case, a physician needs to input the following information: CXR image file, Lactate Dehydrogenase (LDH), Oxygen Saturation (O2%), White Blood Cells Count, C-reactive protein, and Age. This way, this study contributes to the management of COVID-19 patients by predicting early mortality risk. Supplementary Information: The online version contains supplementary material available at 10.1007/s00521-023-08606-w.

Gonococcal microparticle vaccine in dissolving microneedles induced immunity and enhanced bacterial clearance in infected mice.

There is an alarming rise in the number of gonorrhea cases worldwide. Neisseria gonorrhoeae, the bacteria that causes gonorrhea infection, has gradually developed antimicrobial resistance over the years. To date, there is no licensed vaccine for gonorrhea. This study investigates the in vivo immunogenicity of a whole-cell inactivated gonococci in a microparticle formulation (Gc-MP) along with adjuvant microparticles (Alhydrogel®- Alum MP and AddaVax™ MP) delivered transdermally using dissolving microneedles (MN). The proposed vaccine formulation (Gc-MP + Alum MP + AddaVax™ MP) was assessed for induction of humoral, cellular, and protective immune responses in vivo. Our results show the induction of significant gonococcal-specific serum IgG, IgG1, IgG2a, and vaginal mucosal IgA antibodies in mice immunized with Gc-MP + Alum MP + AddaVax™ MP and Gc-MP when compared to the control groups receiving blank MN or no treatment. The serum bactericidal assay revealed that the antibodies generated in mice after immunization with Gc-MP + Alum MP + AddaVax™ MP were bactericidal towards live Neisseria gonorrhoeae. Gc-MP + Alum MP + AddaVax™ MP and Gc-MP-immunized mice showed enhanced clearance rate of gonococcal bacterial infection post challenge. In contrast, the control groups did not begin to clear the infection until day 10. In addition, the mice which received Gc-MP + Alum MP + AddaVax™ MP showed enhanced expression of cellular immunity markers CD4 and CD8 on the surface of T cells in the spleen and lymph nodes. Taken together, the data shows that microneedle immunization with whole-cell inactivated gonococci MP in mice induced humoral, cellular, and protective immunity against gonococcal infection.

A Deep Learning Framework for the Detection of Abnormality in Cerebral Blood Flow Velocity Using Transcranial Doppler Ultrasound.

Transcranial doppler (TCD) ultrasound is a non-invasive imaging technique that can be used for continuous monitoring of blood flow in the brain through the major cerebral arteries by calculating the cerebral blood flow velocity (CBFV). Since the brain requires a consistent supply of blood to function properly and meet its metabolic demand, a change in CBVF can be an indication of neurological diseases. Depending on the severity of the disease, the symptoms may appear immediately or may appear weeks later. For the early detection of neurological diseases, a classification model is proposed in this study, with the ability to distinguish healthy subjects from critically ill subjects. The TCD ultrasound database used in this study contains signals from the middle cerebral artery (MCA) of 6 healthy subjects and 12 subjects with known neurocritical diseases. The classification model works based on the maximal blood flow velocity waveforms extracted from the TCD ultrasound. Since the signal quality of the recorded TCD ultrasound is highly dependent on the operator's skillset, a noisy and corrupted signal can exist and can add biases to the classifier. Therefore, a deep learning classifier, trained on a curated and clean biomedical signal can reliably detect neurological diseases. For signal classification, this study proposes a Self-organized Operational Neural Network (Self-ONN)-based deep learning model Self-ResAttentioNet18, which achieves classification accuracy of 96.05% with precision, recall, f1 score, and specificity of 96.06%, 96.05%, 96.06%, and 96.09%, respectively. With an area under the ROC curve of 0.99, the model proves its feasibility to confidently classify middle cerebral artery (MCA) waveforms in near real-time.

Cytotoxicity and Antibiofilm Activity of Silver-Polypropylene Nanocomposites.

The development of biocompatible nanomaterials that interface with human skin and tissue is critical for advancing prosthetics and other therapeutic medical needs. In this perspective, the development of nanoparticles with cytotoxicity and antibiofilm properties and biocompatibility characteristics are important. Metallic silver (Ag) exhibits good biocompatibility, but it is often challenging to integrate it into a nanocomposite without compromising its antibiofilm properties for optimal applications. In this study, new polymer nanocomposites (PNCs) with ultra-low filling content (0.0023-0.046 wt%) of Ag nanoplates were manufactured and tested. The cytotoxicity and antibiofilm activity of different composites with polypropylene (PP) matrix were examined. At first, PNCs surface were analyzed by means of AFM (atomic force microscopy) with phase contrast evaluation and FTIR (Fourier-transform infrared spectroscopy) to study the Ag nanoplates distribution. Subsequently, the cytotoxicity and growth properties of biofilms were assessed by MTT assay protocol and detection of nitric oxide radicals. Antibacterial and antibiofilm activities were measured against Gram-positive bacteria (Staphylococcus aureus) and Gram-negative bacteria (K. pneumoniae). The PNCs with silver exhibited antibiofilm activity although they did not inhibit regular planktonic bacterial growth. Moreover, the PNCs were not cytotoxic to mammalian cells and did not induce significant immune response. These features reveal the potential of the PNCs developed in this study for usage in fabrication of prosthetics and other smart structures for biomedical applications.

Association Between Serum Bicarbonate Levels and Prediabetes and Subclinical Inflammation in Young Healthy Adults: A Cross-sectional Study.

Purpose: Low bicarbonate, a hallmark of metabolic acidosis is associated with various diseases. This study investigated associations between bicarbonate levels with prediabetes and subclinical inflammation among healthy young adults in Qatar. Patients and Methods: A cross-sectional study was carried out with 825 participants aged 18-40 years, devoid of any known comorbidities, using data from the Qatar Biobank. For each participant, blood samples were taken for measurements of bicarbonate, prediabetes, and subclinical inflammation biomarkers. Prediabetes was defined using HbA1c between 5.7 and 6.4% and subclinical inflammation was defined using monocyte to high density lipoprotein (HDL) cholesterol ratio (MHR). Associations between bicarbonate levels and the outcomes were analyzed using multivariable linear and logistic regression and then stratified by gender. Results: A total of 825 participants with mean age 29.2 years (5.9) of which 365 (44.2%) were males. After multivariable logistic regression, each unit increase in serum bicarbonate was associated with a 17% decreased risk of prediabetes (OR: 0.83, 95%CI: 0.70-0.99, p=0.034), in males but no association was observed for females. Similarly, after multivariable linear regression, a unit increase in serum bicarbonate was associated with a 0.18 unit decrease in MHR (beta -0.18, 95%CI: -0.29, -0.07, p=0.002), again with no association observed in females. Conclusion: In a healthy young adult population, higher serum bicarbonate levels were inversely associated with both prediabetes and subclinical inflammation in males, but not in females.

ApoM binds endotoxin contributing to neutralization and clearance by High Density Lipoprotein.

Background: HDL possesses anti-inflammatory properties, however, the exact mechanism is not fully understood. Endotoxin is a potent inducers of TLR4 signaling, leading to inflammatory mediators' release. It has been estimated that TLR4 recognizes about 5% of circulating lipopolysaccharide whereas 95% is cleared by plasma lipoproteins, mainly HDL. ApoM is required for HDL biogenesis and 95% of plasma ApoM is found associated with HDL, both are significantly reduced during sepsis. Aim: The aim of this study is to investigate whether ApoM binds endotoxin and contributes to anti-inflammatory activity of HDL. Methods: Isothermal Titration Calorimetry (ITC) was used to determine the binding of ultrapure E. coli LPS to the recombinant ApoM protein. Purified human HDL and recombinant ApoM was used to investigate LPS neutralization using human and murine macrophages and computational simulation was performed. Result: ApoM shows high affinity for E. coli LPS, forming 1:1 complexes with Kd values below 1 µΜ, as revealed by ITC. The binding process is strongly exothermic and enthalpy-driven (ΔrH = -36.5 kJ/mol), implying the formation of an extensive network of interactions between ApoM and LPS in the bound state. Computational simulation also predicted high-affinity binding between ApoM and E. coli LPS and the best scoring models showed E. coli LPS docking near the calyx of ApoM without blocking the pocket. The biological significance of this interaction was further demonstrated in macrophages where purified HDL neutralized an E. coli LPS effect and significantly reduced TNFα release from human THP-1 cells. Conclusion: ApoM binds LPS to facilitate endotoxin neutralization and clearance by HDL.