Prevalence and management of diabetic neuropathy in secondary care in Qatar

Aims Diabetic neuropathy (DN) is a “Cinderella” complication, particularly in the Middle East. A high prevalence of undiagnosed DN and those at risk of diabetic foot ulceration (DFU) is a major concern. We have determined the prevalence of DN and its risk factors, DFU and those at risk of (DFU) in patients with T2DM in secondary care in Qatar. Materials and methods Adults with T2DM were randomly selected from the two National Diabetes Centers in Qatar. DN was defined by the presence of neuropathic symptoms and a vibration perception threshold (VPT) ≥ 15 V. Participants with a VPT≥25 V were categorized as high risk for DFU. Painful DN was defined by a DN4 score ≥ 4. Logistic regression analysis was used to identify predictors of DN. Results In 1082 adults with T2DM (age 54 ± 11 years, duration of diabetes 10.0 ± 7.7 years, 60.6% males) the prevalence of DN was 23.0% (95% CI: 20.5%‐25.5%), of whom 33.7% (95% CI: 27.9%‐39.6%) were at high risk of DFU and 6.3% had DFU. 82.0% of the patients with DN were previously undiagnosed. The prevalence of DN increased with age and duration of diabetes and was associated with poor glycemic control (HbA1c ≥ 9%) AOR = 2.1 (95%CI: 1.3‐3.2), hyperlipidemia AOR = 2.7 (95%CI: 1.5‐5.0) and hypertension AOR = 2.0 (95%CI: 1.2‐3.4). Conclusions Despite, DN affecting 23% of adults with T2DM, 82% had not been previously diagnosed with 1/3 at high risk for DFU. This argues for annual screening and identification of patients with DN. Furthermore, we identify hyperglycemia, hyperlipidemia and hypertension as predictors of DN

Prevalence and risk factors for painful diabetic neuropathy in secondary health care in Qatar.

AIMS/INTRODUCTION:Painful diabetic peripheral neuropathy (PDPN) has a significant impact on the patient's quality of life. The prevalence of PDPN in the Middle East and North Africa (MENA) region has been reported to be almost double that of populations in the UK. We sought to determine the prevalence of PDPN and its associated factors in T2DM patients attending secondary care in Qatar. MATERIALS AND METHODS:This is a cross-sectional study of 1095 participants with T2DM attending Qatar's two national diabetes centers. PDPN and impaired vibration perception on the pulp of the large toes were assessed using the DN4 questionnaire with a cut-off ≥4 and the Neurothesiometer with a cut-off ≥15V, respectively. RESULTS:The prevalence of PDPN was 34.5% (95% CI: 31.7%-37.3%), but 80% of these patients had not previously been diagnosed or treated for this condition. Arabs had a higher prevalence of PDPN compared to South Asians (P<0.05). PDPN was associated with impaired vibration perception AOR=4.42 (95%CI: 2.92-6.70), smoking AOR=2.43 (95%CI: 1.43-4.15), obesity AOR=1.74 (95%CI: 1.13-2.66), being female AOR=1.65 (95%CI: 1.03-2.64) and duration of diabetes AOR=1.08 (95%CI: 1.05-1.11). Age, poor glycemic control, hypertension, physical activity and proteinuria showed no association with PDPN. CONCLUSIONS:PDPN occurs in 1/3 of T2DM patients attending secondary care in Qatar, but the majority have not been diagnosed. Arabs are at higher risk for PDPN. Impaired vibration perception, obesity and smoking are associated with PDPN in Qatar. This article is protected by copyright. All rights reserved

Air quality and urban sustainable development: the application of machine learning tools

[EN] Air quality has an efect on a population¿s quality of life. As a dimension of sustainable urban development, governments have been concerned about this indicator. This is refected in the references consulted that have demonstrated progress in forecasting pollution events to issue early warnings using conventional tools which, as a result of the new era of big data, are becoming obsolete. There are a limited number of studies with applications of machine learning tools to characterize and forecast behavior of the environmental, social and economic dimensions of sustainable development as they pertain to air quality. This article presents an analysis of studies that developed machine learning models to forecast sustainable development and air quality. Additionally, this paper sets out to present research that studied the relationship between air quality and urban sustainable development to identify the reliability and possible applications in diferent urban contexts of these machine learning tools. To that end, a systematic review was carried out, revealing that machine learning tools have been primarily used for clustering and classifying variables and indicators according to the problem analyzed, while tools such as artifcial neural networks and support vector machines are the most widely used to predict diferent types of events. The nonlinear nature and synergy of the dimensions of sustainable development are of great interest for the application of machine learning tools.Molina-Gómez, NI.; Díaz-Arévalo, JL.; López Jiménez, PA. (2021). Air quality and urban sustainable development: the application of machine learning tools. 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The phocein homologue SmMOB3 is essential for vegetative cell fusion and sexual development in the filamentous ascomycete Sordaria macrospora

Members of the striatin family and their highly conserved interacting protein phocein/Mob3 are key components in the regulation of cell differentiation in multicellular eukaryotes. The striatin homologue PRO11 of the filamentous ascomycete Sordaria macrospora has a crucial role in fruiting body development. Here, we functionally characterized the phocein/Mob3 orthologue SmMOB3 of S. macrospora. We isolated the gene and showed that both, pro11 and Smmob3 are expressed during early and late developmental stages. Deletion of Smmob3 resulted in a sexually sterile strain, similar to the previously characterized pro11 mutant. Fusion assays revealed that ∆Smmob3 was unable to undergo self-fusion and fusion with the pro11 strain. The essential function of the SmMOB3 N-terminus containing the conserved mob domain was demonstrated by complementation analysis of the sterile S. macrospora ∆Smmob3 strain. Downregulation of either pro11 in ∆Smmob3, or Smmob3 in pro11 mutants by means of RNA interference (RNAi) resulted in synthetic sexual defects, demonstrating for the first time the importance of a putative PRO11/SmMOB3 complex in fruiting body development

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P &lt; 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

Characteristics of airborne nanoparticles during summertime in Kuwait.

Airborne nanoparticles have prompted a strong research interest in the scientific community due to their adverse effects on human health and the environment. However, there is a notable lack of studies focusing on extreme summertime conditions, where ambient temperatures can reach ~48 °C, relative humidity falls to its minimum values, and dust events are frequently encountered. The overall aims of this research are to understand the behaviour and sources of airborne nanoparticles in hot and arid environmental conditions, develop a statistical prediction model for nanoparticles that uses routinely-monitored air pollutants, and investigate the mitigation measures (i.e., vegetation barriers) used to limit the penetration of on-road nanoparticles to the surrounding vicinity. Size-resolved measurements of particle number distribution (PNDs) and concentrations (PNCs) were carried out continuously for one month at a roadside location in the State of Kuwait using a fast-response differential mobility spectrometer (DMS500) to assess the influence of summertime meteorological conditions on nanoparticles. Further data of trace pollutants (NOx, O3, CO, SO2 and PM10) and meteorological variables (wind speed, wind direction, temperature, relative humidity, and solar radiation), were obtained from the Kuwait Environment Public Authority (KEPA). The collected data was analysed to assess the behaviour of nanoparticles during summertime and to understand any unusual behaviour of PNDs and PNCs during (i) the afternoon, when temperature reaches it maximum and relative humidity to its minimum, and (ii) during the occurrence of Arabian dust events. The collected PNDs data were used to apportion the major sources and their contribution to total PNCs using a positive matrix factorisation (PMF) model. Further, a preliminary attempt to predict nanoparticles in three size ranges (nucleation mode: 5–30 nm, Aitken mode: 30–100 nm, and accumulation mode: 100–300 nm) using artificial neural network (ANN), was made. For the prediction purpose, seven scenarios were considered using different combinations of the routinely-measured meteorological and trace pollutant data as covariates. In addition, intermittent monitoring of PNDs and the associated PNCs were performed using DMS50 at a kerbside location in the United Kingdom (UK) to investigate the effect of vegetation barriers on traffic-generated nanoparticles, as well as pedestrian exposure. PND data was collected at four sampling locations pseudo-simultaneously using a multi-probe switching system. These locations encompassed the vegetation barrier and allowed us to make novel comparisons. Despite high traffic volumes during noon hours, there was a substantial decrease in PNCs with a corresponding increase in geometric mean diameters (GMDs) due to high ambient temperature (∼48 °C) and wind speed (∼15 m s–1). The high wind speed has a dispersive effect (i.e., dilution), and saltation causes the suspension of particles and enhances the coagulation process. Based on the PMF modelling, traffic emissions were found to be a major contributor (73%) to the total apportioned PNCs, whereas Arabian dust transport was found to be the lowest contributor (3%). ANN succeeded in capturing the general trend between observed and predicted PNCs with R2 up to 0.79. The deviations between the observed and predicted PNCs were not substantial, as evidenced by the fact that predicted PNCs were within a factor of two of the observed PNCs. Vegetation barriers were found to reduce not only PNCs by ~37%, but also the associated particle respiratory deposited doses in the human respiratory tract (RDD) by ~36%. The implication of vegetation barrier results are of high importance in the reduction of PNCs and the associated RDD. Besides policy makers and environmental authorities, the findings of this work are important for the modelling community to treat major nanoparticle sources in dispersion modelling and health impact assessments in the region

Characteristics of airborne nanoparticles during summertime in Kuwait.

Airborne nanoparticles have prompted a strong research interest in the scientific community due to their adverse effects on human health and the environment. However, there is a notable lack of studies focusing on extreme summertime conditions, where ambient temperatures can reach ~48 °C, relative humidity falls to its minimum values, and dust events are frequently encountered. The overall aims of this research are to understand the behaviour and sources of airborne nanoparticles in hot and arid environmental conditions, develop a statistical prediction model for nanoparticles that uses routinely-monitored air pollutants, and investigate the mitigation measures (i.e., vegetation barriers) used to limit the penetration of on-road nanoparticles to the surrounding vicinity. Size-resolved measurements of particle number distribution (PNDs) and concentrations (PNCs) were carried out continuously for one month at a roadside location in the State of Kuwait using a fast-response differential mobility spectrometer (DMS500) to assess the influence of summertime meteorological conditions on nanoparticles. Further data of trace pollutants (NOx, O3, CO, SO2 and PM10) and meteorological variables (wind speed, wind direction, temperature, relative humidity, and solar radiation), were obtained from the Kuwait Environment Public Authority (KEPA). The collected data was analysed to assess the behaviour of nanoparticles during summertime and to understand any unusual behaviour of PNDs and PNCs during (i) the afternoon, when temperature reaches it maximum and relative humidity to its minimum, and (ii) during the occurrence of Arabian dust events. The collected PNDs data were used to apportion the major sources and their contribution to total PNCs using a positive matrix factorisation (PMF) model. Further, a preliminary attempt to predict nanoparticles in three size ranges (nucleation mode: 5–30 nm, Aitken mode: 30–100 nm, and accumulation mode: 100–300 nm) using artificial neural network (ANN), was made. For the prediction purpose, seven scenarios were considered using different combinations of the routinely-measured meteorological and trace pollutant data as covariates. In addition, intermittent monitoring of PNDs and the associated PNCs were performed using DMS50 at a kerbside location in the United Kingdom (UK) to investigate the effect of vegetation barriers on traffic-generated nanoparticles, as well as pedestrian exposure. PND data was collected at four sampling locations pseudo-simultaneously using a multi-probe switching system. These locations encompassed the vegetation barrier and allowed us to make novel comparisons. Despite high traffic volumes during noon hours, there was a substantial decrease in PNCs with a corresponding increase in geometric mean diameters (GMDs) due to high ambient temperature (∼48 °C) and wind speed (∼15 m s–1). The high wind speed has a dispersive effect (i.e., dilution), and saltation causes the suspension of particles and enhances the coagulation process. Based on the PMF modelling, traffic emissions were found to be a major contributor (73%) to the total apportioned PNCs, whereas Arabian dust transport was found to be the lowest contributor (3%). ANN succeeded in capturing the general trend between observed and predicted PNCs with R2 up to 0.79. The deviations between the observed and predicted PNCs were not substantial, as evidenced by the fact that predicted PNCs were within a factor of two of the observed PNCs. Vegetation barriers were found to reduce not only PNCs by ~37%, but also the associated particle respiratory deposited doses in the human respiratory tract (RDD) by ~36%. The implication of vegetation barrier results are of high importance in the reduction of PNCs and the associated RDD. Besides policy makers and environmental authorities, the findings of this work are important for the modelling community to treat major nanoparticle sources in dispersion modelling and health impact assessments in the region