Factors associated with baseline smoking self-efficacy among male Qatari residents enrolled in a quit smoking study

Smoking self-efficacy, described as confidence in one’s ability to abstain from smoking in high-risk situations is a key predictor in cessation outcomes; however, there is a dearth of research on factors that influence self-efficacy surrounding smoking behavior. This study examines factors associated with baseline self-efficacy among treatment seeking participants enrolled in a pilot feasibility smoking cessation study. Participants (n = 247) were daily male smokers, residents of Doha in Qatar (18–60 years) who were enrolled in a telephone-based smoking cessation study. Baseline assessments included self-efficacy, home smoking rules, socio-demographic variables, smoking history, and psychosocial characteristics. Factors associated with self-efficacy were assessed using multiple linear regression analysis. Results showed that after controlling for relevant variables, number of cigarettes smoked (β^ = -0.22; 95% CI: -0.37, -0.06), having at least one quit attempt in the past year (β^ = 2.30; 95% CI: 0.27, 4.35), and reporting a complete home smoking ban (β^ = 3.13; 95% CI: 0.56, 5.70) were significantly associated with higher self-efficacy to quit smoking. These results provide data-driven indication of several key variables that can be targeted to increase smoking self-efficacy in this understudied population.

A Smart Colorimetric Platform for Detection of Methanol, Ethanol and Formic Acid

Carbon dioxide (CO2) is a greenhouse gas in the atmosphere and scientists are working on converting it to useful products, thereby reducing its quantity in the atmosphere. For converting CO2, different approaches are used, and among them, electrochemistry is found to be the most common and more efficient technique. Current methods for detecting the products of electrochemical CO2 conversion are time-consuming and complex. To combat this, a simple, cost-effective colorimetric method has been developed to detect methanol, ethanol, and formic acid, which are formed electrochemically from CO2. In the present work, the highly efficient sensitive dyes were successfully established to detect these three compounds under optimized conditions. These dyes demonstrated excellent selectivity and showed no cross-reaction with other products generated in the CO2 conversion system. In the analysis using these three compounds, this strategy shows good specificity and limit of detection (LOD, ~0.03-0.06 ppm). A cost-effective and sensitive Internet of Things (IoT) colorimetric sensor prototype was developed to implement these dyes systems for practical and real-time application. Employing the dyes as sensing elements, the prototype exhibits unique red, green, and blue (RGB) values upon exposure to test solutions with a short response time of 2 s. Detection of these compounds via this new approach has been proven effective by comparing them with nuclear magnetic resonance (NMR). This novel approach can replace heavy-duty instruments such as high-pressure liquid chromatography (HPLC), gas chromatography (G.C.), and NMR due to its extraordinary selectivity and rapidity.

Epigenetic scores for the circulating proteome as tools for disease prediction

Protein biomarkers have been identified across many age-related morbidities. However, characterising epigenetic influences could further inform disease predictions. Here, we leverage epigenome-wide data to study links between the DNA methylation (DNAm) signatures of the circulating proteome and incident diseases. Using data from four cohorts, we trained and tested epigenetic scores (EpiScores) for 953 plasma proteins, identifying 109 scores that explained between 1% and 58% of the variance in protein levels after adjusting for known protein quantitative trait loci (pQTL) genetic effects. By projecting these EpiScores into an independent sample (Generation Scotland; n = 9537) and relating them to incident morbidities over a follow-up of 14 years, we uncovered 137 EpiScore-disease associations. These associations were largely independent of immune cell proportions, common lifestyle and health factors, and biological aging. Notably, we found that our diabetes-associated EpiScores highlighted previous top biomarker associations from proteome-wide assessments of diabetes. These EpiScores for protein levels can therefore be a valuable resource for disease prediction and risk stratification.

Acute intense fatigue does not modify the effect of EVA and TPU custom foot orthoses on running mechanics, running economy and perceived comfort

We determined whether fatigue modifies the effect of custom foot orthoses manufactured from ethyl-vinyl acetate (EVA) and expanded thermoplastic polyurethane (TPU) materials, both compared to standardized footwear (CON), on running mechanics, running economy, and perceived comfort. Eighteen well-trained, males ran on an instrumented treadmill for 6 min at the speed corresponding to their first ventilatory threshold (13.8 ± 1.1 km/h) in three footwear conditions (CON, EVA, and TPU). Immediately after completion of a repeated-sprints exercise (8 × 5 s treadmill sprints, rest = 25 s), these run tests were replicated. Running mechanics, running economy and perceived comfort were determined. Two-way repeated measures ANOVA [condition (CON, EVA, and TPU) × fatigue (fresh and fatigued)] were conducted. Flight time shortened (P = 0.026), peak braking (P = 0.016) and push-off (P = 0.032) forces decreased and vertical stiffness increased (P = 0.014) from before to after the repeated-sprint exercise, independent of footwear condition. There was a global fatigue-induced deterioration in running economy (- 1.6 ± 0.4%; P < 0.001). There was no significant condition × fatigue [except mean loading rate (P = 0.046)] for the large majority of biomechanical, cardio-respiratory [except minute ventilation (P = 0.020) and breathing frequency (P = 0.019)] and perceived comfort variables. Acute intense fatigue does not modify the effect of custom foot orthoses with different resilience characteristics on running mechanics, running economy and perceived comfort.

OUP accepted manuscript

Although genome profiling provides important genetic and phenotypic details for applying precision medicine to diabetes, it is imperative to integrate in vitro human cell models, accurately recapitulating the genetic alterations associated with diabetes. The absence of the appropriate preclinical human models and the unavailability of genetically relevant cells substantially limit the progress in developing personalized treatment for diabetes. Human pluripotent stem cells (hPSCs) provide a scalable source for generating diabetes-relevant cells carrying the genetic signatures of the patients. Remarkably, allogenic hPSC-derived pancreatic progenitors and β cells are being used in clinical trials with promising preliminary results. Autologous hiPSC therapy options exist for those with monogenic and type 2 diabetes; however, encapsulation or immunosuppression must be accompanied with in the case of type 1 diabetes. Furthermore, genome-wide association studies-identified candidate variants can be introduced in hPSCs for deciphering the associated molecular defects. The hPSC-based disease models serve as excellent resources for drug development facilitating personalized treatment. Indeed, hPSC-based diabetes models have successfully provided valuable knowledge by modeling different types of diabetes, which are discussed in this review. Herein, we also evaluate their strengths and shortcomings in dissecting the underlying pathogenic molecular mechanisms and discuss strategies for improving hPSC-based disease modeling investigations.

Defining the landscape of metabolic dysregulations in cancer metastasis

Metastasis is the primary cause of cancer related deaths due to the limited number of efficient druggable targets. Signatures of dysregulated cancer metabolism could serve as a roadmap for the determination of new treatment strategies. However, the metabolic signatures of metastatic cells remain vastly elusive. Our aim was to determine metabolic dysregulations associated with high metastatic potential in breast cancer cell lines. We have selected 5 triple negative breast cancer (TNBC) cell lines including three with high metastatic potential (HMP) (MDA-MB-231, MDA-MB-436, MDA-MB-468) and two with low metastatic potential (LMP) (BT549, HCC1143). The normal epithelial breast cell line (hTERT-HME1) was also investigated. The untargeted metabolic profiling of cells and growth media was conducted and total of 479 metabolites were quantified. First we characterized metabolic features differentiating TNBC cell lines from normal cells as well as identified cell line specific metabolic fingerprints. Next, we determined 92 metabolites in cells and 22 in growth medium that display significant differences between LMP and HMP. The HMP cell lines had elevated level of molecules involved in glycolysis, TCA cycle and lipid metabolism. We identified metabolic advantages of cell lines with HMP beyond enhanced glycolysis by pinpointing the role of branched chain amino acids (BCAA) catabolism as well as molecules supporting coagulation and platelet activation as important contributors to the metastatic cascade. The landscape of metabolic dysregulations, characterized in our study, could serve as a roadmap for the identification of treatment strategies targeting cancer cells with enhanced metastatic potential.

Formulation of Lipid-Based Tableted Spray-Congealed Microparticles for Sustained Release of Vildagliptin: In Vitro and In Vivo Studies

Spray-congealing (SPC) technology was utilized to prepare lipid-based microparticles (MP) capable of sustaining the release of Vildagliptin (VG) for use as a once-daily treatment for type 2 diabetes mellitus. VG microparticles were prepared using Compritol® and Gelucire®50/13 as lipid carriers in the presence of various amounts of Carbomer 934 NF. The lipid carriers were heated to 10 °C above their melting points, and VG was dispersed in the lipid melt and sprayed through the heated two-fluid nozzle of the spray congealer to prepare the VG-loaded MP (VGMP). The microparticles produced were then compressed into tablets and characterized for their morphological and physicochemical characteristics, content analysis, in vitro dissolution, and in vivo bioavailability studies in mixed-breed dogs. The VGMP were spherical with a yield of 76% of the total amount. VG was found to be in its semicrystalline form, with a drug content of 11.11% per tablet and a percentage drug recovery reaching 98.8%. The in vitro dissolution studies showed that VG was released from the tableted particles in a sustained-release fashion for up to 24 h compared with the immediate-release marketed tablets from which VG was completely released within 30 min. The in vivo pharmacokinetics studies reported a Cmax, Tmax, T1/2, and MRT of 118 ng/mL, 3.4 h, 5.27 h, and 9.8 h, respectively, for the SPC formulations, showing a significant difference (p < 0.05)) from the pk parameters of the immediate-release marketed drug (147 ng/mL, 1 h, 2.16 h, and 2.8 h, respectively). The area under the peak (AUC) of both the reference and tested formulations was comparable to indicate similar bioavailabilities. The in vitro-in vivo correlation (IVIVC) studies using multiple level C correlations showed a linear correlation between in vivo pharmacokinetics and dissolution parameters. In conclusion, SPC was successfully utilized to prepare a once-daily sustained-release VG oral drug delivery system.

Can the Salivary Microbiome Predict Cardiovascular Diseases? Lessons Learned From the Qatari Population

Background: Many studies have linked dysbiosis of the gut microbiome to the development of cardiovascular diseases (CVD). However, studies assessing the association between the salivary microbiome and CVD risk on a large cohort remain sparse. This study aims to identify whether a predictive salivary microbiome signature is associated with a high risk of developing CVD in the Qatari population. Methods: Saliva samples from 2,974 Qatar Genome Project (QGP) participants were collected from Qatar Biobank (QBB). Based on the CVD score, subjects were classified into low-risk (LR < 10) (n = 2491), moderate-risk (MR = 10-20) (n = 320) and high-risk (HR > 30) (n = 163). To assess the salivary microbiome (SM) composition, 16S-rDNA libraries were sequenced and analyzed using QIIME-pipeline. Machine Learning (ML) strategies were used to identify SM-based predictors of CVD risk. Results: Firmicutes and Bacteroidetes were the predominant phyla among all the subjects included. Linear Discriminant Analysis Effect Size (LEfSe) analysis revealed that Clostridiaceae and Capnocytophaga were the most significantly abundant genera in the LR group, while Lactobacillus and Rothia were significantly abundant in the HR group. ML based prediction models revealed that Desulfobulbus, Prevotella, and Tissierellaceae were the common predictors of increased risk to CVD. Conclusion: This study identified significant differences in the SM composition in HR and LR CVD subjects. This is the first study to apply ML-based prediction modeling using the SM to predict CVD in an Arab population. More studies are required to better understand the mechanisms of how those microbes contribute to CVD.

The Proteomic Signature of Recombinant Growth Hormone in Recreational Athletes

OBJECTIVE

Administration of human growth hormone (hGH) is prohibited in competitive sport and its detection in an athlete's sample triggers an adverse analytical finding. However, the biological processes that are modulated by recombinant hGH are not well characterized and associated blood serum proteins may constitute new biomarkers for hGH misuse.

METHODS

Thirty-five recreational athletes were enrolled in a study to investigate the time- and dose-dependent response of serum protein levels to recombinant hGH administration. Participants were randomly assigned to 4 groups, receiving 1 of 3 different doses of recombinant hGH or a placebo. Bio samples were collected at 22 time points over a period of 13 weeks, starting 4 weeks before treatment, during 3 weeks of treatment, and at 6 weeks' follow-up. A total of 749 serum samples were analyzed for 1305 protein markers using the SOMAscan proteomics platform.

RESULTS

We identified 66 proteins that significantly associated with recombinant hGH administration and dosage, including well known hGH targets, such as IGF1, but also previously unknown hGH-related proteins (eg, protease inhibitors, WFIKKN1, and chemokines, CCL2). Network analysis revealed changes in specific biological pathways, mainly related to the immune system and glucose metabolism.

CONCLUSION

Our analysis suggests that hGH administration affects biological processes more strongly than previously acknowledged. Some of the proteins were dysregulated even after hGH treatment and could potentially be developed into biomarkers for hGH misuse. Moreover, our findings suggest new roles for hGH-associated proteins in the etiology of hGH-related diseases and may indicate new risks that may be associated with hGH misuse.

Epidemiology, genetic landscape and classification of childhood diabetes mellitus in the State of Qatar

AIMS/INTRODUCTION

To study the epidemiology, genetic landscape and causes of childhood diabetes mellitus in the State of Qatar.

MATERIALS AND METHODS

All patients (aged 0-18 years) with diabetes mellitus underwent biochemical, immunological and genetic testing. American Diabetes Association guidelines were used to classify types of diabetes mellitus. The incidence and prevalence of all the different types of diabetes mellitus were calculated.

RESULTS

Total number of children with diabetes mellitus was 1,325 (type 1 n = 1,096, ≥1 antibody; type 2 n = 104, type 1B n = 53; maturity onset diabetes of the young n = 20; monogenic autoimmune n = 4; neonatal diabetes mellitus n = 10;, syndromic diabetes mellitus n = 23; and double diabetes mellitus n = 15). The incidence and prevalence of type 1 diabetes were 38.05 and 249.73 per 100,000, respectively, and for type 2 were 2.51 and 23.7 per 100,000, respectively. The incidence of neonatal diabetes mellitus was 34.4 per 1,000,000 live births, and in indigenous Qataris the incidence was 43.6 per 1,000,000 live births. The prevalence of type 1 diabetes and type 2 diabetes in Qatari children was double compared with other nationalities. The prevalence of maturity onset diabetes of the young in Qatar was 4.56 per 100,000.

CONCLUSIONS

This is the first prospective and comprehensive study to document the epidemiology and genetic landscape of childhood diabetes mellitus in this region. Qatar has the fourth highest incidence of type 1 diabetes mellitus, with the incidence and prevalence being higher in Qatari compared with non-Qatari. The prevalence of type 2 diabetes mellitus is also higher in Qatar than in Western countries. The incidence of neonatal diabetes mellitus is the second highest in the world. GCK is the most common form of maturity onset diabetes of the young, and a large number of patients have type 1B diabetes mellitus.

Electrochemical Performance of Na3V2(PO4)2F3 Electrode Material in a Symmetric Cell

A NASICON-based Na3V2(PO4)2F3 (NVPF) cathode material is reported herein as a potential symmetric cell electrode material. The symmetric cell was active from 0 to 3.5 V and showed a capacity of 85 mAh/g at 0.1 C. With cycling, the NVPF symmetric cell showed a very long and stable cycle life, having a capacity retention of 61% after 1000 cycles at 1 C. The diffusion coefficient calculated from cyclic voltammetry (CV) and the galvanostatic intermittent titration technique (GITT) was found to be ~10-9-10-11, suggesting a smooth diffusion of Na+ in the NVPF symmetric cell. The electrochemical impedance spectroscopy (EIS) carried out during cycling showed increases in bulk resistance, solid electrolyte interphase (SEI) resistance, and charge transfer resistance with the number of cycles, explaining the origin of capacity fade in the NVPF symmetric cell. Finally, the postmortem analysis of the symmetric cell after 1000 cycles at a 1 C rate indicated that the intercalation/de-intercalation of sodium into/from the host structure occurred without any major structural destabilization in both the cathode and anode. However, there was slight distortion in the cathode structure observed, which resulted in capacity loss of the symmetric cell. The promising electrochemical performance of NVPF in the symmetric cell makes it attractive for developing long-life and cost-effective batteries.

Insulin resistance limits corneal nerve regeneration in patients with type 2 diabetes undergoing intensive glycemic control

AIMS/INTRODUCTION

This study aimed to investigate whether insulin resistance (IR) in individuals with type 2 diabetes undergoing intensive glycemic control determines the extent of improvement in neuropathy.

MATERIALS AND METHODS

This was an exploratory substudy of an open-label, randomized controlled trial of individuals with poorly controlled type 2 diabetes treated with exenatide and pioglitazone or insulin to achieve a glycated hemoglobin <7.0% (<53 mmol/mol). Baseline IR was defined using homeostasis model assessment of IR, and change in neuropathy was assessed using corneal confocal microscopy.

RESULTS

A total of 38 individuals with type 2 diabetes aged 50.2 ± 8.5 years with (n = 25, 66%) and without (n = 13, 34%) IR were studied. There was a significant decrease in glycated hemoglobin (P < 0.0001), diastolic blood pressure (P < 0.0001), total cholesterol (P < 0.01) and low-density lipoprotein (P = 0.05), and an increase in bodyweight (P < 0.0001) with treatment. Individuals with homeostasis model assessment of IR <1.9 showed a significant increase in corneal nerve fiber density (P ≤ 0.01), length (P ≤ 0.01) and branch density (P ≤ 0.01), whereas individuals with homeostasis model assessment of IR ≥1.9 showed no change. IR was negatively associated with change in corneal nerve fiber density after adjusting for change in bodyweight (P < 0.05).

CONCLUSIONS

Nerve regeneration might be limited in individuals with type 2 diabetes and IR undergoing treatment with pioglitazone plus exenatide or insulin to improve glycemic control.

Insulin sensitivity variations in apparently healthy Arab male subjects: correlation with insulin and C peptide

INTRODUCTION

Decreased insulin sensitivity occurs early in type 2 diabetes (T2D). T2D is highly prevalent in the Middle East and North Africa regions. This study assessed the variations in insulin sensitivity in normal apparently healthy subjects and the levels of adiponectin, adipsin and inflammatory markers.

RESEARCH DESIGN AND METHODS

A total of 60 participants (aged 18-45, body mass index <28) with a normal oral glucose tolerance test (OGTT) completed hyperinsulinemic-euglycemic clamp (40 mU/m²/min) and body composition test by dual-energy X-ray absorptiometry scan. Blood samples were assayed for glucose, insulin, C peptide, inflammatory markers, oxidative stress markers, adiponectin and adipsin.

RESULTS

The subjects showed wide variations in the whole-body glucose disposal rate (M value) from 2 to 20 mg/kg/min and were divided into three groups: most responsive (M>12 mg/kg/min, n=17), least responsive (M≤6 mg/kg/min, n=14) and intermediate responsive (M=6.1-12 mg/kg/min, n=29). Insulin and C peptide responses to OGTT were highest among the least insulin sensitive group. Triglycerides, cholesterol, alanine transaminase (ALT) and albumin levels were higher in the least responsive group compared with the other groups. Among the inflammatory markers, C reactive protein (CRP) was highest in the least sensitivity group compared with the other groups; however, there were no differences in the level of soluble receptor for advanced glycation end products and Tumor Necrosis Factor Receptor Superfamily 1B (TNFRS1B). Plasma levels of insulin sensitivity markers, adiponectin and adipsin, and oxidative stress markers, oxidized low-density lipoprotein, total antioxidant capacity and glutathione peroxidase 1, were similar between the groups.

CONCLUSIONS

A wide range in insulin sensitivity and significant differences in triglycerides, cholesterol, ALT and CRP concentrations were observed despite the fact that the study subjects were homogenous in terms of age, gender and ethnic background, and all had normal screening comprehensive chemistry and normal glucose response to OGTT. The striking differences in insulin sensitivity reflect differences in genetic predisposition and/or environmental exposure. The low insulin sensitivity status associated with increased insulin level may represent an early stage of metabolic abnormality.

Incidence, risk factors, and feto-maternal outcomes of inappropriate birth weight for gestational age among singleton live births in Qatar: A population-based study

BACKGROUND

Abnormal fetal growth can be associated with factors during pregnancy and at postpartum.

OBJECTIVE

In this study, we aimed to assess the incidence, risk factors, and feto-maternal outcomes associated with small-for-gestational age (SGA) and large-for-gestational age (LGA) infants.

METHODS

We performed a population-based retrospective study on 14,641 singleton live births registered in the PEARL-Peristat Study between April 2017 and March 2018 in Qatar. We estimated the incidence and examined the risk factors and outcomes using univariate and multivariate analysis.

RESULTS

SGA and LGA incidence rates were 6.0% and 15.6%, respectively. In-hospital mortality among SGA and LGA infants was 2.5% and 0.3%, respectively, while for NICU admission or death in labor room and operation theatre was 28.9% and 14.9% respectively. Preterm babies were more likely to be born SGA (aRR, 2.31; 95% CI, 1.45-3.57) but male infants (aRR, 0.57; 95% CI, 0.4-0.81), those born to parous (aRR 0.66; 95% CI, 0.45-0.93), or overweight (aRR, 0.64; 95% CI, 0.42-0.97) mothers were less likely to be born SGA. On the other hand, males (aRR, 1.82; 95% CI, 1.49-2.19), infants born to parous mothers (aRR 2.16; 95% CI, 1.63-2.82), or to mothers with gestational diabetes mellitus (aRR 1.36; 95% CI, 1.11-1.66), or pre-gestational diabetes mellitus (aRR 2.58; 95% CI, 1.8-3.47) were significantly more likely to be LGA. SGA infants were at high risk of in-hospital mortality (aRR, 226.56; 95% CI, 3.47-318.22), neonatal intensive care unit admission or death in labor room or operation theatre (aRR, 2.14 (1.36-3.22).

CONCLUSION

Monitoring should be coordinated to alleviate the risks of inappropriate fetal growth and the associated adverse consequences.

Characterizing epidemiology of prediabetes, diabetes, and hypertension in Qataris: A cross-sectional study

OBJECTIVES

To characterize the epidemiologic profiles of prediabetes mellitus (preDM), diabetes mellitus (DM), and hypertension (HTN) in Qataris using the nationally representative 2012 Qatar STEPwise Survey.

METHODS

A secondary data analysis of a cross-sectional survey that included 2,497 Qatari nationals aged 18-64 years. Descriptive and analytical statistical analyses were conducted.

RESULTS

Prevalence of preDM, DM, and HTN in Qataris aged 18-64 years was 11.9% (95% confidence interval [CI] 9.6%-14.7%), 10.4% (95% CI 8.4%-12.9%), and 32.9% (95% CI 30.4%-35.6%), respectively. Age was the common factor associated with the three conditions. Adjusted analyses showed that unhealthy diet (adjusted odds ratio (aOR) = 1.84, 95% CI 1.01-3.36) was significantly associated with preDM; that physical inactivity (aOR = 1.66, 95% CI 1.12-2.46), central obesity (aOR = 2.08, 95% CI 1.02-4.26), and HTN (aOR = 2.18, 95% CI 1.40-3.38) were significantly associated with DM; and that DM (aOR = 2.07, 95% CI 1.34-3.22) was significantly associated with HTN. Population attributable fraction of preDM associated with unhealthy diet was 7.7%; of DM associated with physical inactivity, central obesity, and HTN, respectively, was 14.9%, 39.8%, and 17.5%; and of HTN associated with DM was 3.0%.

CONCLUSIONS

One in five Qataris is living with either preDM or DM, and one in three is living with HTN, conditions that were found to be primarily driven by lifestyle factors. Prevention, control, and management of these conditions should be a national priority to reduce their disease burden and associated disease sequelae.

Implementation of a Vaccination Program Based on Epidemic Geospatial Attributes: COVID-19 Pandemic in Ohio as a Case Study and Proof of Concept

Geospatial vaccine uptake is a critical factor in designing strategies that maximize the population-level impact of a vaccination program. This study uses an innovative spatiotemporal model to assess the impact of vaccination distribution strategies based on disease geospatial attributes and population-level risk assessment. For proof of concept, we adapted a spatially explicit COVID-19 model to investigate a hypothetical geospatial targeting of COVID-19 vaccine rollout in Ohio, United States, at the early phase of COVID-19 pandemic. The population-level deterministic compartmental model, incorporating spatial-geographic components at the county level, was formulated using a set of differential equations stratifying the population according to vaccination status and disease epidemiological characteristics. Three different hypothetical scenarios focusing on geographical subpopulation targeting (areas with high versus low infection intensity) were investigated. Our results suggest that a vaccine program that distributes vaccines equally across the entire state effectively averts infections and hospitalizations (2954 and 165 cases, respectively). However, in a context with equitable vaccine allocation, the number of COVID-19 cases in high infection intensity areas will remain high; the cumulative number of cases remained >30,000 cases. A vaccine program that initially targets high infection intensity areas has the most significant impact in reducing new COVID-19 cases and infection-related hospitalizations (3756 and 213 infections, respectively). Our approach demonstrates the importance of factoring geospatial attributes to the design and implementation of vaccination programs in a context with limited resources during the early stage of the vaccine rollout.

Between Inflammation and Autophagy: The Role of Leptin-Adiponectin Axis in Cardiac Remodeling

Cardiac remodeling is the process by which the heart adapts to stressful stimuli, such as hypertension and ischemia/reperfusion; it ultimately leads to heart failure upon long-term exposure. Autophagy, a cellular catabolic process that was originally considered as a mechanism of cell death in response to detrimental stimuli, is thought to be one of the main mechanisms that controls cardiac remodeling and induces heart failure. Dysregulation of the adipokines leptin and adiponectin, which plays essential roles in lipid and glucose metabolism, and in the pathophysiology of the neuroendocrine and cardiovascular systems, has been shown to affect the autophagic response in the heart and to contribute to accelerate cardiac remodeling. The obesity-associated protein leptin is a pro-inflammatory, tumor-promoting adipocytokine whose elevated levels in obesity are associated with acute cardiovascular events, and obesity-related hypertension. Adiponectin exerts anti-inflammatory and anti-tumor effects, and its reduced levels in obesity correlate with the pathogenesis of obesity-associated cardiovascular diseases. Leptin- and adiponectin-induced changes in autophagic flux have been linked to cardiac remodeling and heart failure. In this review, we describe the different molecular mechanisms of hyperleptinemia- and hypoadiponectinemia-mediated pathogenesis of cardiac remodeling and the involvement of autophagy in this process. A better understanding of the roles of leptin, adiponectin, and autophagy in cardiac functions and remodeling, and the exact signal transduction pathways by which they contribute to cardiac diseases may well lead to discovery of new therapeutic agents for the treatment of cardiovascular remodeling.

Predicting COVID-19 cases using bidirectional LSTM on multivariate time series

To assist policymakers in making adequate decisions to stop the spread of the COVID-19 pandemic, accurate forecasting of the disease propagation is of paramount importance. This paper presents a deep learning approach to forecast the cumulative number of COVID-19 cases using bidirectional Long Short-Term Memory (Bi-LSTM) network applied to multivariate time series. Unlike other forecasting techniques, our proposed approach first groups the countries having similar demographic and socioeconomic aspects and health sector indicators using K-means clustering algorithm. The cumulative case data of the clustered countries enriched with data related to the lockdown measures are fed to the bidirectional LSTM to train the forecasting model. We validate the effectiveness of the proposed approach by studying the disease outbreak in Qatar and the proposed model prediction from December 1st until December 31st, 2020. The quantitative evaluation shows that the proposed technique outperforms state-of-art forecasting approaches.

The relationship between smartphone use and dry eye disease: A systematic review with a narrative synthesis

BACKGROUND

Emerging research findings suggest a potential relationship between smartphone use (SPU) and dry eye disease (DED), which has not been systematically reviewed.

OBJECTIVES

The aim of this study was to systematically review and synthesize evidence on the relationship between SPU and DED.

METHODS

A systematic search of Medline, EMBASE, CINAHL, and PsychINFO bibliographic databases from their inception to January 15, 2021. Study screening, full-text assessment, study selection and exclusion, data extraction, and quality assessment was performed independently by at least two review authors.

RESULTS

Four studies were included in the review (3 cross-sectional studies and 1 nonrandomized clinical trial). A narrative synthesis of findings was used due to heterogeneity among study designs and measures of association summarizing the relationship between SPU and DED. All included studies were conducted in South Korea and included school children, college students, or young adults. Three of the 4 included studies showed an association between SPU and DED.

CONCLUSIONS

There is some evidence that SPU is associated with DED. However, this evidence is limited by a small number of studies of satisfactory methodological quality. There is a great need for high-quality studies to further investigate the relationship between SPU and DED and identify mechanisms underlying this potential relationship. This information is important for raising public awareness about the negative effect of SPU on eye health and development of clinical guidelines for this potentially emerging SPU-driven eye condition.

SARS-CoV-2 infection hospitalization, severity, criticality, and fatality rates in Qatar

The SARS-CoV-2 pandemic resulted in considerable morbidity and mortality as well as severe economic and societal disruptions. Despite scientific progress, true infection severity, factoring both diagnosed and undiagnosed infections, remains poorly understood. This study aimed to estimate SARS-CoV-2 age-stratified and overall morbidity and mortality rates based on analysis of extensive epidemiological data for the pervasive epidemic in Qatar, a country where < 9% of the population are ≥ 50 years. We show that SARS-CoV-2 severity and fatality demonstrate a striking age dependence with low values for those aged < 50 years, but rapidly growing rates for those ≥ 50 years. Age dependence was particularly pronounced for infection criticality rate and infection fatality rate. With Qatar's young population, overall SARS-CoV-2 severity and fatality were not high with < 4 infections in every 1000 being severe or critical and < 2 in every 10,000 being fatal. Only 13 infections in every 1000 received any hospitalization in acute-care-unit beds and < 2 in every 1000 were hospitalized in intensive-care-unit beds. However, we show that these rates would have been much higher if Qatar's population had the demographic structure of Europe or the United States. Epidemic expansion in nations with young populations may lead to considerably lower disease burden than currently believed.

Painful diabetic neuropathy is associated with increased nerve regeneration in patients with type 2 diabetes undergoing intensive glycemic control

AIMS/INTRODUCTION

Painful diabetic peripheral neuropathy (pDPN) is associated with small nerve fiber degeneration and regeneration. This study investigated whether the presence of pDPN might influence nerve regeneration in patients with type 2 diabetes undergoing intensive glycemic control.

MATERIALS AND METHODS

This exploratory substudy of an open-label randomized controlled trial undertook the Douleur Neuropathique en 4 questionnaire and assessment of electrochemical skin conductance, vibration perception threshold and corneal nerve morphology using corneal confocal microscopy in participants with and without pDPN treated with exenatide and pioglitazone or basal-bolus insulin at baseline and 1-year follow up, and 18 controls at baseline only.

RESULTS

Participants with type 2 diabetes, with (n = 13) and without (n = 28) pDPN had comparable corneal nerve fiber measures, electrochemical skin conductance and vibration perception threshold at baseline, and pDPN was not associated with the severity of DPN. There was a significant glycated hemoglobin reduction (P < 0.0001) and weight gain (P < 0.005), irrespective of therapy. Participants with pDPN showed a significant increase in corneal nerve fiber density (P < 0.05), length (P < 0.0001) and branch density (P < 0.005), and a decrease in the Douleur Neuropathique en 4 score (P < 0.01), but no change in electrochemical skin conductance or vibration perception threshold. Participants without pDPN showed a significant increase in corneal nerve branch density (P < 0.01) and no change in any other neuropathy measures. A change in the severity of painful symptoms was not associated with corneal nerve regeneration and medication for pain.

CONCLUSIONS

This study showed that intensive glycemic control is associated with greater corneal nerve regeneration and an improvement in the severity of pain in patients with painful diabetic neuropathy.

Mortality Prediction Utilizing Blood Biomarkers to Predict the Severity of COVID-19 Using Machine Learning Technique

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.

ANFIS-Net for automatic detection of COVID-19

Among the most leading causes of mortality across the globe are infectious diseases which have cost tremendous lives with the latest being coronavirus (COVID-19) that has become the most recent challenging issue. The extreme nature of this infectious virus and its ability to spread without control has made it mandatory to find an efficient auto-diagnosis system to assist the people who work in touch with the patients. As fuzzy logic is considered a powerful technique for modeling vagueness in medical practice, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed in this paper as a key rule for automatic COVID-19 detection from chest X-ray images based on the characteristics derived by texture analysis using gray level co-occurrence matrix (GLCM) technique. Unlike the proposed method, especially deep learning-based approaches, the proposed ANFIS-based method can work on small datasets. The results were promising performance accuracy, and compared with the other state-of-the-art techniques, the proposed method gives the same performance as the deep learning with complex architectures using many backbone.

Tailoring the defects of sub-100 nm multipodal titanium nitride/oxynitride nanotubes for efficient water splitting performance

Deciphering the photocatalytic-defect relationship of photoanodes can pave the way towards the rational design for high-performance solar energy conversion. Herein, we rationally designed uniform and aligned ultrathin sub-100 nm multipodal titanium nitride/oxynitride nanotubes (TiON _x NTs) (x = 2, 4, and 6 h) via the anodic oxidation of Ti-foil in a formamide-based electrolyte followed by annealing under ammonia gas for different durations. XPS, XPS imaging, Auger electron spectra, and positron annihilation spectroscopy disclosed that the high nitridation rate induced the generation of a mixture of Ti-nitride and oxynitride with various vacancy-type defects, including monovacancies, vacancy clusters, and a few voids inside TiO _x NTs. These defects decreased the bandgap energy to 2.4 eV, increased visible-light response, and enhanced the incident photon-to-current collection efficiency (IPCE) and the photocurrent density of TiON _x NTs by nearly 8 times compared with TiO₂NTs, besides a quick carrier diffusion at the nanotube/electrolyte interface. The water-splitting performance of sub-100 nm TiON₆NT multipodal nanotubes was superior to the long compacted TiON _x NTs with different lengths and TiO₂ nanoparticles. Thus, the optimization of the nitridation rate tailors the defect concentration, thereby achieving the highest solar conversion efficiency.

GWAS Identifies a Region Containing the SALL1 Gene in Variation of Pigmentation Intensity Within the Chestnut Coat Color of Horses

Chestnut coat color in horses is determined by a missense mutation within the MC1R gene. However, the intensity of the chestnut color can vary widely within individuals possessing this genotype. Here, we investigated this variation using standardized photographs of 96 horses. Each horse was ranked lightest to darkest within the cohort for phenotype by 3 blinded observers. A genome-wide association study utilizing the relative shade ranking as the phenotype and using 268 487 single-nucleotide polymorphisms (SNPs) genotyped using the Affymetrix Equine 670k array identified a single significantly associated region on chromosome 3 (P = 2.934 × 10-8). Analysis of whole-genome sequences for horses spanning the diverse range of chestnut color identified candidate SNPs within the coding sequence of the only gene in the region: SALL1. The function of SALL1 is largely unknown, though it is predicted to interact with the Hermansky-Pudlak Syndrome type 1 (HPS1) protein, which causes partial albinism in humans. However, with only one study suggesting a circumstantial influence of the SALL1 protein on pigmentation, additional work is needed to confirm this new coat color locus in larger populations and investigate the function of this protein for impacts on equine health.