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Abstract 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.

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.

Inducing T lymphocyte (T-cell) activation and proliferation with specificity against a pathogen is crucial in vaccine formulation. Assessing vaccine candidates’ ability to induce T-cell proliferation helps optimize formulation for its safety, immunogenicity, and efficacy. Our in-house vaccine candidates use microparticles (MPs) and nanoparticles (NPs) to enhance antigen stability and target delivery to antigen-presenting cells (APCs), providing improved immunogenicity. Typically, vaccine formulations are screened for safety and immunostimulatory effects using in vitro methods, but extensive animal testing is often required to assess immunogenic responses. We identified the need for a rapid, intermediate screening process to select promising candidates before advancing to expensive and time-consuming in vivo evaluations. In this study, an in vitro overlay assay system was demonstrated as an effective high-throughput preclinical testing method to evaluate the immunogenic properties of early-stage vaccine formulations. The overlay assay’s effectiveness in testing particulate vaccine candidates for immunogenic responses has been evaluated by optimizing the carboxyfluorescein succinimidyl ester (CFSE) T-cell proliferation assay. DCs were overlaid with T-cells, allowing vaccine-stimulated DCs to present antigens to CFSE-stained T-cells. T-cell proliferation was quantified using flow cytometry on days 0, 1, 2, 4, and 6 upon successful antigen presentation. The assay was tested with nanoparticulate vaccine formulations targeting Neisseria gonorrhoeae (CDC F62, FA19, FA1090), measles, H1N1 flu prototype, canine coronavirus, and Zika, with adjuvants including Alhydrogel® (Alum) and AddaVax™. The assay revealed robust T-cell proliferation in the vaccine treatment groups, with variations between bacterial and viral vaccine candidates. A dose-dependent study indicated immune stimulation varied with antigen dose. These findings highlight the assay’s potential to differentiate and quantify effective antigen presentation, providing valuable insights for developing and optimizing vaccine formulations.

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.

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.

Introduction Tobacco smoking is a preventable cause of disease and death worldwide. Shisha has become a popular method of smoking tobacco. In Qatar, the prevalence of smoking in 2019 was 25.2%, of which 20.9% was smoking shisha. Shisha smoking is thought to have a harmful effect on the cardiovascular system. The main objective of this study was to understand the relationship between shisha smoking and cardiovascular disease risks. Methods All data were obtained from the Qatar Biobank (QBB). The study population consisted of 1045 individuals, which included cases defined as participants who had a history of angina, heart attack and/or stroke and their matched healthy controls for age and gender. The measurement of both the exposure and the outcome was done through the survey provided by QBB. A conditional logistic regression model was used to assess the association between smoking and cardiovascular disease (CVD), and adjusted odds ratio (AOR) and 95% confidence intervals (CI) were calculated after adjusting for covariates. Results After adjusting for hypertension diagnosis, diabetes diagnosis, dyslipidemia diagnosis, abdominal obesity, and sedentary lifestyle, exclusive shisha smokers had 1.65 times higher odds of reporting cardiovascular disease diagnoses compared to non-smokers (95% CI: 0.71–1.91). Dual shisha and cigarette smokers also had 1.47 times higher odds of reporting cardiovascular disease diagnoses compared to non-smokers (95% CI: 0.88–2.45). CVD cases had a younger median age of initial shisha smoking compared to controls (20 years vs 25 years, p=0.003). Conclusions Shisha smoking was associated with an increased risk of developing cardiovascular disease. However, this association did not reach the level of statistical significance within this study. A finding to consider that showed strong evidence is the younger age of initial shisha smoking in cases. Further studies are needed to demonstrate the true relationship between shisha smoking and cardiovascular disease.

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.

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.

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.

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.

Abstract Aim The aim of this study is to investigate the prevalence of asymptomatic hyperuricemia in Qatar and to examine its association with changes in markers of dyslipidemia, prediabetes and subclinical inflammation. Methods A cross-sectional study of young adult participants aged 18 - 40 years old devoid of comorbidities collected between 2012 and 2017. Exposure was defined as uric acid level, and outcomes were defined as levels of different blood markers. De-identified data were collected from Qatar Biobank. T-tests, correlation tests and multiple linear regression were all used to investigate the effects of hyperuricemia on blood markers. Statistical analyses were conducted using STATA 16. Results The prevalence of asymptomatic hyperuricemia is 21.2% among young adults in Qatar. Differences between hyperuricemic and normouricemic groups were observed using multiple linear regression analysis and found to be statistically and clinically significant after adjusting for age, gender, BMI, smoking and exercise. Significant associations were found between uric acid level and HDL-c p = 0.019 (correlation coefficient -0.07 (95% CI [-0.14, -0.01]); c-peptide p = 0.018 (correlation coefficient 0.38 (95% CI [0.06, 0.69]) and monocyte to HDL ratio (MHR) p = 0.026 (correlation coefficient 0.47 (95% CI [0.06, 0.89]). Conclusions Asymptomatic hyperuricemia is prevalent among young adults and associated with markers of prediabetes, dyslipidemia, and subclinical inflammation.

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.

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.

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.

Common carotid intima-media thickness (CIMT) is a common measure of atherosclerosis, often assessed through carotid ultrasound images. However, the use of deep learning methods for medical image analysis, segmentation and CIMT measurement in these images has not been extensively explored. This study aims to evaluate the performance of four recent deep learning models, including a convolutional neural network (CNN), a self-organizing operational neural network (self-ONN), a transformer-based network and a pixel difference convolution-based network, in segmenting the intima-media complex (IMC) using the CUBS dataset, which includes ultrasound images acquired from both sides of the neck of 1088 participants. The results show that the self-ONN model outperforms the conventional CNN-based model, while the pixel difference- and transformer-based models achieve the best segmentation performance.

SARS-CoV-2, the causal agent of COVID-19, is a contagious respiratory virus that frequently mutates, giving rise to variant strains and leading to reduced vaccine efficacy against the variants. Frequent vaccination against the emerging variants may be necessary; thus, an efficient vaccination system is needed. A microneedle (MN) vaccine delivery system is non-invasive, patient-friendly, and can be self-administered. Here, we tested the immune response produced by an adjuvanted inactivated SARS-CoV-2 microparticulate vaccine administered via the transdermal route using a dissolving MN. The inactivated SARS-CoV-2 vaccine antigen and adjuvants (Alhydrogel® and AddaVax™) were encapsulated in poly(lactic-co-glycolic acid) (PLGA) polymer matrices. The resulting MP were approximately 910 nm in size, with a high percentage yield and percent encapsulation efficiency of 90.4%. In vitro, the vaccine MP was non-cytotoxic and increased the immunostimulatory activity measured as nitric oxide release from dendritic cells. The adjuvant MP potentiated the immune response of the vaccine MP in vitro. In vivo, the adjuvanted SARS-CoV-2 MP vaccine induced high levels of IgM, IgG, IgA, IgG1, and IgG2a antibodies and CD4+ and CD8+ T-cell responses in immunized mice. In conclusion, the adjuvanted inactivated SARS-CoV-2 MP vaccine delivered using MN induced a robust immune response in vaccinated mice.

There is limited seroepidemiological evidence on the magnitude and long-term durability of antibody titers of mRNA and non-mRNA vaccines in the Qatari population. This study was conducted to generate evidence on long-term anti-S IgG antibody titers and their dynamics in individuals who have completed a primary COVID-19 vaccination schedule. A total of 300 male participants who received any of the following vaccines BNT162b2/Comirnaty, mRNA-1273, ChAdOx1-S/Covishield, COVID-19 Vaccine Janssen/Johnson, or BBIBP-CorV or Covaxin were enrolled in our study. All sera samples were tested by chemiluminescent microparticle immunoassay (CMIA) for the quantitative determination of IgG antibodies to SARS-CoV-2, receptor-binding domain (RBD) of the S1 subunit of the spike protein of SARS-CoV-2. Antibodies against SARS-CoV-2 nucleocapsid (SARS-CoV-2 N-protein IgG) were also determined. Kaplan–Meier survival curves were used to compare the time from the last dose of the primary vaccination schedule to the time by which anti-S IgG antibody titers fell into the lowest quartile (range of values collected) for the mRNA and non-mRNA vaccines. Participants vaccinated with mRNA vaccines had higher median anti-S IgG antibody titers. Participants vaccinated with the mRNA-1273 vaccine had the highest median anti-S-antibody level of 13,720.9 AU/mL (IQR 6426.5 to 30,185.6 AU/mL) followed by BNT162b2 (median, 7570.9 AU/mL; IQR, 3757.9 to 16,577.4 AU/mL); while the median anti-S antibody titer for non-mRNA vaccinated participants was 3759.7 AU/mL (IQR, 2059.7–5693.5 AU/mL). The median time to reach the lowest quartile was 3.53 months (IQR, 2.2–4.5 months) and 7.63 months (IQR, 6.3–8.4 months) for the non-mRNA vaccine recipients and Pfizer vaccine recipients, respectively. However, more than 50% of the Moderna vaccine recipients did not reach the lowest quartile by the end of the follow-up period. This evidence on anti-S IgG antibody titers should be considered for informing decisions on the durability of the neutralizing activity and thus protection against infection after the full course of primary vaccination in individuals receiving different type (mRNA verus non-mRNA) vaccines and those with natural infection.

The coronavirus disease 2019 (COVID-19) after outbreaking in Wuhan increasingly spread throughout the world. Fast, reliable, and easily accessible clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. The objective of the study was to develop and validate an early scoring tool to stratify the risk of death using readily available complete blood count (CBC) biomarkers. A retrospective study was conducted on twenty-three CBC blood biomarkers for predicting disease mortality for 375 COVID-19 patients admitted to Tongji Hospital, China from January 10 to February 18, 2020. Machine learning based key biomarkers among the CBC parameters as the mortality predictors were identified. A multivariate logistic regression-based nomogram and a scoring system was developed to categorize the patients in three risk groups (low, moderate, and high) for predicting the mortality risk among COVID-19 patients. Lymphocyte count, neutrophils count, age, white blood cell count, monocytes (%), platelet count, red blood cell distribution width parameters collected at hospital admission were selected as important biomarkers for death prediction using random forest feature selection technique. A CBC score was devised for calculating the death probability of the patients and was used to categorize the patients into three sub-risk groups: low (5% and 50%), respectively. The area under the curve (AUC) of the model for the development and internal validation cohort were 0.961 and 0.88, respectively. The proposed model was further validated with an external cohort of 103 patients of Dhaka Medical College, Bangladesh, which exhibits in an AUC of 0.963. The proposed CBC parameter-based prognostic model and the associated web-application, can help the medical doctors to improve the management by early prediction of mortality risk of the COVID-19 patients in the low-resource countries.

With the onset of the COVID-19 pandemic, the number of critically sick patients in intensive care units (ICUs) has increased worldwide, putting a burden on ICUs. Early prediction of ICU requirement is crucial for efficient resource management and distribution. Early-prediction scoring systems for critically ill patients using mathematical models are available, but are not generalized for COVID-19 and Non-COVID patients. This study aims to develop a generalized and reliable prognostic model for ICU admission for both COVID-19 and non-COVID-19 patients using best feature combination from the patient data at admission. A retrospective cohort study was conducted on a dataset collected from the pulmonology department of Moscow City State Hospital between 20 April 2020 and 5 June 2020. The dataset contains ten clinical features for 231 patients, of whom 100 patients were transferred to ICU and 131 were stable (non-ICU) patients. There were 156 COVID positive patients and 75 non-COVID patients. Different feature selection techniques were investigated, and a stacking machine learning model was proposed and compared with eight different classification algorithms to detect risk of need for ICU admission for both COVID-19 and non-COVID patients combined and COVID patients alone. C-reactive protein (CRP), chest computed tomography (CT), lung tissue affected (%), age, admission to hospital, and fibrinogen parameters at hospital admission were found to be important features for ICU-requirement risk prediction. The best performance was produced by the stacking approach, with weighted precision, sensitivity, F1-score, specificity, and overall accuracy of 84.45%, 84.48%, 83.64%, 84.47%, and 84.48%, respectively, for both types of patients, and 85.34%, 85.35%, 85.11%, 85.34%, and 85.35%, respectively, for COVID-19 patients only. The proposed work can help doctors to improve management through early prediction of the risk of need for ICU admission of patients during the COVID-19 pandemic, as the model can be used for both types of patients.

Vitamin D deficiency is a global disorder associated with several chronic illnesses including dyslipidemia and metabolic syndrome. The impact of this association with both dyslipidemia and vitamin D deficiency on metabolomics profile is not yet fully understood. This study analyses the metabolomics and lipidomic signatures in relation to vitamin D status and dyslipidemia. Metabolomics data were collected from Qatar Biobank database and categorized into four groups based on vitamin D and dyslipidemia status. Metabolomics multivariate analysis was performed using the orthogonal partial least square discriminate analysis (OPLS-DA) whilst linear models were used to assess the per-metabolite association with each of the four dyslipidemia/vitamin D combination groups. Our results indicate a high prevalence of vitamin D deficiency among the younger age group, while dyslipidemia was more prominent in the older group. A significant alteration of metabolomics profile was observed among the dyslipidemic and vitamin D deficient individuals in comparison with control groups. These modifications reflected changes in some key pathways including ceramides, diacylglycerols, hemosylceramides, lysophospholipids, phosphatidylcholines, phosphatidylethanol amines, and sphingomyelins. Vitamin D deficiency and dyslipidemia have a deep impact on sphingomyelins profile. The modifications were noted at the level of ceramides and are likely to propagate through downstream pathways.

The emergence of drug-resistant gonorrhea infections worldwide combined with the lack of a vaccine is alarming. We prepared a novel microparticulate (MP) vaccine formulation using whole-cell inactivated Neisseria gonorrhoeae as the vaccine antigen, with Alum and AddaVax™ as vaccine adjuvants. The adjuvanted vaccine MP formulation was assessed for in vitro immunostimulatory activity, autophagy, and antigen presentation ability. The data shows that the adjuvanted gonococci vaccine MP enhanced autophagy induction in antigen presenting cells (APCs) compared to gonococci vaccine MP without adjuvants, which is important for enhancing antigen presentation. In addition, the adjuvanted vaccine formulation increased the surface expression of antigen presenting molecules MHCI and MHCII as well as co-stimulatory molecules CD40 and CD86 on the surface of dendritic cells. In addition, the gonococci vaccine microparticles at lower doses did not significantly increase the expression of the death receptor CD95 in APCs, which when elevated leads to suboptimal antigen presentation and reduced immune responses. The adjuvanted whole-cell inactivated gonococci microparticle vaccine formulation enhanced antigen uptake, processing, and antigen presentation.

Problem—Since the outbreak of the COVID-19 pandemic, mass testing has become essential to reduce the spread of the virus. Several recent studies suggest that a significant number of COVID-19 patients display no physical symptoms whatsoever. Therefore, it is unlikely that these patients will undergo COVID-19 testing, which increases their chances of unintentionally spreading the virus. Currently, the primary diagnostic tool to detect COVID-19 is a reverse-transcription polymerase chain reaction (RT-PCR) test from the respiratory specimens of the suspected patient, which is invasive and a resource-dependent technique. It is evident from recent researches that asymptomatic COVID-19 patients cough and breathe in a different way than healthy people. Aim—This paper aims to use a novel machine learning approach to detect COVID-19 (symptomatic and asymptomatic) patients from the convenience of their homes so that they do not overburden the healthcare system and also do not spread the virus unknowingly by continuously monitoring themselves. Method—A Cambridge University research group shared such a dataset of cough and breath sound samples from 582 healthy and 141 COVID-19 patients. Among the COVID-19 patients, 87 were asymptomatic while 54 were symptomatic (had a dry or wet cough). In addition to the available dataset, the proposed work deployed a real-time deep learning-based backend server with a web application to crowdsource cough and breath datasets and also screen for COVID-19 infection from the comfort of the user’s home. The collected dataset includes data from 245 healthy individuals and 78 asymptomatic and 18 symptomatic COVID-19 patients. Users can simply use the application from any web browser without installation and enter their symptoms, record audio clips of their cough and breath sounds, and upload the data anonymously. Two different pipelines for screening were developed based on the symptoms reported by the users: asymptomatic and symptomatic. An innovative and novel stacking CNN model was developed using three base learners from of eight state-of-the-art deep learning CNN algorithms. The stacking CNN model is based on a logistic regression classifier meta-learner that uses the spectrograms generated from the breath and cough sounds of symptomatic and asymptomatic patients as input using the combined (Cambridge and collected) dataset. Results—The stacking model outperformed the other eight CNN networks with the best classification performance for binary classification using cough sound spectrogram images. The accuracy, sensitivity, and specificity for symptomatic and asymptomatic patients were 96.5%, 96.42%, and 95.47% and 98.85%, 97.01%, and 99.6%, respectively. For breath sound spectrogram images, the metrics for binary classification of symptomatic and asymptomatic patients were 91.03%, 88.9%, and 91.5% and 80.01%, 72.04%, and 82.67%, respectively. Conclusion—The web-application QUCoughScope records coughing and breathing sounds, converts them to a spectrogram, and applies the best-performing machine learning model to classify the COVID-19 patients and healthy subjects. The result is then reported back to the test user in the application interface. Therefore, this novel system can be used by patients in their premises as a pre-screening method to aid COVID-19 diagnosis by prioritizing the patients for RT-PCR testing and thereby reducing the risk of spreading of the disease.

Pneumonia is a lung infection that threatens all age groups. In this paper, we use CT scans to investigate the effectiveness of active contour models (ACMs) for segmentation of pneumonia caused by the Coronavirus disease (COVID-19) as one of the successful methods for image segmentation. A comparison has been made between the performances of the state-of-the-art methods performed based on a database of lung CT scan images. This review helps the reader to identify starting points for research in the field of active contour models on COVID-19, which is a high priority for researchers and practitioners. Finally, the experimental results indicate that active contour methods achieve promising results when there are not enough images to use deep learning-based methods as one of the powerful tools for image segmentation.

Healthcare researchers have been working on mortality prediction for COVID-19 patients with differing levels of severity. A rapid and reliable clinical evaluation of disease intensity will assist in the allocation and prioritization of mortality mitigation resources. The novelty of the work proposed in this paper is an early prediction model of high mortality risk for both COVID-19 and non-COVID-19 patients, which provides state-of-the-art performance, in an external validation cohort from a different population. Retrospective research was performed on two separate hospital datasets from two different countries for model development and validation. In the first dataset, COVID-19 and non-COVID-19 patients were admitted to the emergency department in Boston (24 March 2020 to 30 April 2020), and in the second dataset, 375 COVID-19 patients were admitted to Tongji Hospital in China (10 January 2020 to 18 February 2020). The key parameters to predict the risk of mortality for COVID-19 and non-COVID-19 patients were identified and a nomogram-based scoring technique was developed using the top-ranked five parameters. Age, Lymphocyte count, D-dimer, CRP, and Creatinine (ALDCC), information acquired at hospital admission, were identified by the logistic regression model as the primary predictors of hospital death. For the development cohort, and internal and external validation cohorts, the area under the curves (AUCs) were 0.987, 0.999, and 0.992, respectively. All the patients are categorized into three groups using ALDCC score and death probability: Low (probability < 5%), Moderate (5% < probability < 50%), and High (probability > 50%) risk groups. The prognostic model, nomogram, and ALDCC score will be able to assist in the early identification of both COVID-19 and non-COVID-19 patients with high mortality risk, helping physicians to improve patient management.

Bacterial endotoxins, as major components of Gram-negative bacterial outer membrane leaflets and a well-characterized TLR4-MD-2 ligand, are lipopolysaccharides (LPSs) that are constantly shed from bacteria during growth and infection. For the first time, we report that unique surface-enhanced Raman scattering (SERS) spectra of enteric LPSs from E. coli, S. typhimurium, S. minnesota, V. cholerae, Rhizobium species R. CE3, and R. NGR, as well as Neisseria meningitidis endotoxin structures, LPSs, lipid A, and KDO2-lipid A can be obtained. The characteristic peaks of the SERS spectra reveal that most of the tested LPS structures are from lipids and saccharides, i.e., the major components of LPSs, and these spectra can be successfully used to differentiate between endotoxins with principal components analysis. In addition, all the LPS samples here are measured at a concentration of 10 nmole/mL, which corresponds to their relevant pathophysiological concentrations in clinical infections. This study demonstrates that LPSs can be used as biomarkers for the highly sensitive detection of bacteria using SERS-based methods.

The protective effect of meningococcal vaccines targeting disease causing serogroups exemplified by the introduction of MenAfriVac™ in Africa, is well established and documented in large population-based studies. Due to the emergence of other meningococcal disease causing serogroups, novel vaccine formulations are needed. There is a high potential for novel nanotechnology-based meningococcal vaccine formulations that can provide wider vaccine coverage. The proposed meningococcal vaccine formulation contains spherical shaped micro and nanoparticles that are biological mimics of Niesseria meningitidis, therefore present to immune system as invader and elicit robust immune responses. Vaccine nanoparticles encapsulate meningococcal CPS polymers in a biodegradable material that slowly release antigens, therefore enhance antigen presentation by exerting antigen depot effect. The antigenicity of meningococcal vaccine delivered in nanoparticles is significantly higher when compared to vaccine delivered in solution. Preclinical studies are required to assess the immunogenicity of novel vaccine formulations. Therefore, implementing various in-vitro human immune cell-based assays that mimic in-vivo interactions, would provide good insight on optimal antigen dose, effective antigen presentation, facilitate screening of various vaccine and adjuvant combinations and predict in-vivo immunogenicity. This rapid approach is cost-effective and provides data required for the preclinical immunogenicity assessment of novel meningococcal vaccine formulations.

Vitamin D deficiency is highly prevalent in children and adults with cystic fibrosis (CF). Recent studies have found an association between vitamin D status and risk of pulmonary exacerbations in children and adults with CF. The ongoing Vitamin D for enhancing the Immune System in Cystic fibrosis (DISC) study, a multi-center, double-blind, randomized, placebo-controlled trial, will test the hypothesis of whether high dose vitamin D given as a single oral bolus of 250,000 IU to adults with CF during a pulmonary exacerbation followed by a maintenance dose of vitamin D will improve time to next pulmonary exacerbation and re-hospitalization, improve survival and lung function compared to placebo and reduce the rates of pulmonary exacerbation. Subjects will be randomized 1:1 at each clinical site to vitamin D or placebo within 72 h of hospital admission for pulmonary exacerbation. Clinical follow-up visits will occur at 1, 2, 3, and 7 days, and 1, 3, 6 and 12 months after randomization. Blood and sputum will be collected and determination of clinical outcomes will be assessed at each visit. The primary endpoint will be the time to next pulmonary exacerbation requiring antibiotics, re-hospitalization or death. The secondary endpoints will include lung function assessed by forced expiratory volume in 1 s (FEV1), blood markers of inflammatory cytokines, anti-microbial peptide expression by peripheral blood mononuclear cells and circulating concentrations in blood. Other exploratory endpoints will examine the phenotype of neutrophils and monocyte/macrophages in sputum. Nutritional status will be assessed by 3 day food records and food frequency questionnaire.

Tuberculosis (TB) is a major global health problem. Patients with TB have a high rate of vitamin D deficiency, both at diagnosis and during the course of treatment with anti-tuberculosis drugs. Although data on the efficacy of vitamin D supplementation on Mycobacterium tuberculosis (Mtb) clearance are uncertain from randomized controlled trials (RCTs), vitamin D enhances the expression of the anti-microbial peptide human cathelicidin (hCAP18) in cultured macrophages in vitro. One possible explanation for the mixed (primarily negative) results of RCTs examining vitamin D treatment in TB infection is that anti-TB drugs given to enrolled subjects may impact actions of vitamin D to enhance cathelicidin in macrophages. To address this hypothesis, human macrophage-like monocytic (THP-1) cells were treated with varying doses of first-line anti-tuberculosis drugs in the presence of the active form of vitamin D, 1,25-dihydroxyvitamin D3 (1,25(OH)2D3). The expression of hCAP18 was determined by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). 1,25(OH)2D3 strongly induced expression of hCAP18 mRNA in THP-1 cells (fold-change from control). The combination of the standard 4-drug TB therapy (isoniazid, rifampicin, pyrazinamide and ethambutol) in the cultured THP-1 cells demonstrated a significant decrease in hCAP18 mRNA at the dosage of 10 µg/mL. In 31 subjects with newly diagnosed drug-sensitive TB randomized to either high-dose vitamin D3 (1.2 million IU over 8 weeks, n = 13) versus placebo (n = 18), there was no change from baseline to week 8 in hCAP18 mRNA levels in peripheral blood mononuclear cells or in plasma concentrations of LL-37, the protein product of hCAP18. These data suggest that first-line anti-TB drugs may alter the vitamin D-dependent increase in hCAP18 and LL-37 human macrophages.

Transdermal vaccination route using biodegradable microneedles is a rapidly progressing field of research and applications. The fear of painful needles is one of the primary reasons most people avoid getting vaccinated. Therefore, developing an alternative pain-free method of vaccination using microneedles has been a significant research area. Microneedles comprise arrays of micron-sized needles that offer a pain-free method of delivering actives across the skin. Apart from being pain-free, microneedles provide various advantages over conventional vaccination routes such as intramuscular and subcutaneous. Microneedle vaccines induce a robust immune response as the needles ranging from 50 to 900 μm in length can efficiently deliver the vaccine to the epidermis and the dermis region, which contains many Langerhans and dendritic cells. The microneedle array looks like band-aid patches and offers the advantages of avoiding cold-chain storage and self-administration flexibility. The slow release of vaccine antigens is an important advantage of using microneedles. The vaccine antigens in the microneedles can be in solution or suspension form, encapsulated in nano or microparticles, and nucleic acid-based. The use of microneedles to deliver particle-based vaccines is gaining importance because of the combined advantages of particulate vaccine and pain-free immunization. The future of microneedle-based vaccines looks promising however, addressing some limitations such as dosing inadequacy, stability and sterility will lead to successful use of microneedles for vaccine delivery. This review illustrates the recent research in the field of microneedle-based vaccination.

Autophagy is a homeostatic process that regulates and recycles intracellular structures and is a host defense mechanism that facilitates bacterial clearance. Uric acid in plasma is a major antioxidant but in certain conditions acts as an inflammatory danger signal. The aim of this study is to investigate the effect of soluble uric acid on autophagy and the inflammatory responses in macrophages during bacterial infection. Herein, we employed murine RAW264.7 macrophages that express uricase enzyme and human THP-1 cells that are uricase-deficient. Three different strains of Staphylococcus aureus and two different strains of Klebsiella pneumoniae were used to infect macrophages in presence and absence of soluble uric acid. We found that soluble uric acid enhanced autophagy flux in infected macrophages. We observed that IL-1β increased during bacterial infection but decreased when macrophages were co-stimulated with bacteria and uric acid. In contrast to IL-1β, soluble uric acid did not affect TNFα release and there were no dramatic differences when macrophages were infected with S. aureus or K. pneumoniae. In conclusion, uric acid enhances autophagy flux during bacterial infection, consequently reducing inflammasome activation in macrophages. Understanding the effect of uric acid on the interplay between autophagy and inflammation will facilitate therapeutic design.

Low serum 25-hydroxyvitamin D [25(OH)D] is linked to an altered lipid profile. Monocytes play an important role in inflammation and lipid metabolism. Recently, monocyte percentage to HDL-cholesterol ratio (MHR) has emerged as a novel marker of inflammation. We investigated the association between serum 25(OH)D concentrations and MHR and serum lipids in young healthy adults. Data from the Qatar Biobank were utilized to investigate the relation between serum 25(OH)D and inflammation and serum lipid concentrations in healthy Qatari adults using multivariate regression analysis. Prevalence of serum 25(OH)D concentrations

Neisseria meningitidis is a gram-negative bacterium that causes a severe acute infection, called the meningococcal disease [...]

Epigenetic reprogramming in macrophages is termed trained innate immunity, which regulates immune tolerance and limits tissue damage during infection. Neisseria gonorrhoeae is a strict human pathogen that causes the sexually transmitted infection termed gonorrhea. Here, we report that this pathogen harbors a gene that encodes a histone deacetylase-like enzyme (Gc-HDAC) that shares high 3D-homology to human HDAC1, HDAC2 and HDAC8. A Gc-HDAC null mutant was constructed to determine the biologic significance of this gene. The results showed that WT gonococci reduced the expression of host defense peptides LL-37, HBD-1 and SLPI in macrophages when compared to its Gc-HDAC-deficient isogenic strain. The enrichment of epigenetic marks in histone tails control gene expression and are known to change during bacterial infections. To investigate whether gonococci exert epigenetic modifications on host chromatin, the enrichment of acetylated lysine 9 in histone 3 (H3K9ac) was investigated using the TLR-focused ChIP array system. The data showed that infection with WT gonococci led to higher H3K9ac enrichment at the promoters of pro-inflammatory mediators’ genes, many TLRs, adaptor proteins and transcription factors, suggesting gene activation when compared to infection with the Gc-HDAC-deficient mutant. Taken together, the data suggest that gonococci can exert epigenetic modifications on host cells to modulate certain macrophage defense genes, leading to a maladaptive state of trained immunity.

Protegrins are porcine antimicrobial peptides (AMPs) that belong to the cathelicidin family of host defense peptides. Protegrin-1 (PG-1), the most investigated member of the protegrin family, is an arginine-rich peptide consisting of 18 amino acid residues, its main chain adopting a β-hairpin structure that is linked by two disulfide bridges. We report on the immune modulatory activity of PG-1 and its analogs in neutralizing bacterial endotoxin and capsular polysaccharides, consequently inhibiting inflammatory mediators’ release from macrophages. We demonstrate that the β-hairpin structure motif stabilized with at least one disulfide bridge is a prerequisite for the immune modulatory activity of this type of AMP.

Neisseria gonorrhoeae is a strict human pathogen responsible for more than 100 million new sexually transmitted infections worldwide each year. Due to the global emergence of antibiotic resistance, the Center for Disease control (CDC) recently listed N. gonorrhoeae as an urgent threat to public health. No vaccine is available in spite of the huge disease burden and the possibility of untreatable gonorrhea. The aim of this study is to investigate the immunogenicity of a novel whole-cell-based inactivated gonococcal microparticle vaccine formulation loaded in dissolvable microneedles for transdermal administration. The nanotechnology-based vaccine formulation consists of inactivated whole-cell gonococci strain CDC-F62, spray dried and encapsulated into biodegradable cross-linked albumin matrix with sustained slow antigen release. The dry vaccine nanoparticles were then loaded in a dissolvable microneedle skin patch for transdermal delivery. The efficacy of the whole-cell microparticles vaccine formulation loaded in microneedles was assessed in vitro using dendritic cells and macrophages as well as in vivo mouse model. Antibody titers were measured using an enzyme immunosorbent assay (ELISA) and antigen-specific T lymphocytes were assessed in spleens and lymph nodes. Here we report that whole-cell-based gonococcal microparticle vaccine loaded in dissolvable microneedles for transdermal administration induced significant increase in antigen-specific IgG antibody titers and antigen-specific CD4 and CD8 T lymphocytes in mice compared to gonococcal antigens in solution or empty microneedles. Significant increase in antigen-specific IgG antibody levels was observed at the end of week 2 in groups that received the vaccine compared to the group receiving empty nanoparticles. The advantages of using formalin-fixed whole-cell gonococci that all immunogenic epitopes are covered and preserved from degradation. The spherical shaped micro and nanoparticles are biological mimics of gonococci, therefore present to the immune system as invaders but without the ability to suppress adaptive immunity. In conclusion, the transdermal delivery of microparticles vaccine via a microneedle patch was shown to be an effective system for vaccine delivery. The novel gonorrhea nanovaccine is cheap to produce in a stable dry powder and can be delivered in microneedle skin patch obviating the need for needle use or the cold chain.

Chronic kidney disease affects 40% of adults aged 65 and older. Anemia of CKD is present in 30% of patients with CKD and is associated with increased cardiovascular risk, decreased quality of life, and increased mortality. Hepcidin-25 (hepcidin), the key iron regulating hormone, prevents iron egress from macrophages and thus prevents normal recycling of the iron needed to support erythropoiesis. Hepcidin levels are increased in adults and children with CKD. Vitamin D insufficiency is highly prevalent in CKD and is associated with erythropoietin hyporesponsiveness. Recently, hepcidin levels were found to be inversely correlated with vitamin D status in CKD. The aim of this study was to investigate the role of vitamin D in the regulation of hepcidin expression in vitro and in vivo. This study reports that 1,25-dihydroxyvitamin D3 (1,25(OH)2D3), the hormonally active form of vitamin D, is associated with decreased hepcidin and increased ferroportin expression in lipopolysaccharide (LPS) stimulated THP-1 cells. 1,25(OH)2D3 also resulted in a dose-dependent decrease in pro-hepcidin cytokines, IL-6 and IL-1β, release in vitro. Further, we show that high-dose vitamin D therapy impacts systemic hepcidin levels in subjects with early stage CKD. These data suggest that improvement in vitamin D status is associated with lower systemic concentrations of hepcidin in subjects with CKD. In conclusion, vitamin D regulates the hepcidin-ferroportin axis in macrophages which may facilitate iron egress. Improvement in vitamin D status in patients with CKD may reduce systemic hepcidin levels and may ameliorate anemia of CKD.

Macrophages play a very important role in host defense and in iron homeostasis by engulfing senescent red blood cells and recycling iron. Hepcidin is the master iron regulating hormone that limits dietary iron absorption from the gut and limits iron egress from macrophages. Upon infection macrophages retain iron to limit its bioavailability which limits bacterial growth. Recently, a short chain butyrate dehydrogenase type 2 (BDH2) protein was reported to contain an iron responsive element and to mediate cellular iron trafficking by catalyzing the synthesis of the mammalian siderophore that binds labile iron; therefore, BDH2 plays a crucial role in intracellular iron homeostasis. However, BDH2 expression and regulation in macrophages have not yet been described. Here we show that LPS-induced inflammation combined with ER stress led to massive BDH2 downregulation, increased the expression of ER stress markers, upregulated hepcidin expression, downregulated ferroportin expression, caused iron retention in macrophages, and dysregulated cytokine release from macrophages. We also show that ER stress combined with inflammation synergistically upregulated the expression of the iron carrier protein NGAL and the stress-inducible heme degrading enzyme heme oxygenase-1 (HO-1) leading to iron liberation. This is the first report to show that inflammation and ER stress downregulate the expression of BDH2 in human THP-1 macrophages.

Pseudomonas aeruginosa is the most common pathogen that persists in the cystic fibrosis lungs. Bacteria such as P. aeruginosa secrete siderophores (iron-chelating molecules) and the host limits bacterial growth by producing neutrophil-gelatinase-associated lipocalin (NGAL) that specifically scavenges bacterial siderophores, therefore preventing bacteria from establishing infection. P. aeruginosa produces a major siderophore known as pyoverdine, found to be important for bacterial virulence and biofilm development. We report that pyoverdine did not bind to NGAL, as measured by tryptophan fluorescence quenching, while enterobactin bound to NGAL effectively causing a strong response. The experimental data indicate that pyoverdine evades NGAL recognition. We then employed a molecular modeling approach to simulate the binding of pyoverdine to human NGAL using NGAL’s published crystal structures. The docking of pyoverdine to NGAL predicted nine different docking positions; however, neither apo- nor ferric forms of pyoverdine docked into the ligand-binding site in the calyx of NGAL where siderophores are known to bind. The molecular modeling results offer structural support that pyoverdine does not bind to NGAL, confirming the results obtained in the tryptophan quenching assay. The data suggest that pyoverdine is a stealth siderophore that evades NGAL recognition allowing P. aeruginosa to establish chronic infections in CF lungs.