Antifungal activities of selected Venda medicinal plants against Candida albicans, Candida krusei and Cryptococcus neoformans isolated from South African AIDS patients

Infection with HIV leads to immunosuppression and up to 90% of HIV infected individuals contract fungal infections of which 10 - 20% die as a direct consequence of these infections. In the present study, 76extracts from 30 plants used by Venda traditional healers for the treatment of fungal related ailments, were tested for their antifungal activities against clinical isolates of Candida albicans, Candida krusei andCryptococcus neoformans using the agar diffusion and the microdilution methods. The minimum fungicidal concentrations as well as the time kill curves of the thee most active plants were also determined. Extracts from 25 plants (83.3%) were active against C. albicans, C. krusei or C. neoformans. Thirty two extracts were active against C. neoformans, while 15 were active against C. albicans and 12 were active against C. krusei. Warburgia salutaris, Cassine transvaalensis, Piper capense, Maerua edulis,Pseudolachnostylis maprouneifolia, Berchemia discolor and Lippia javanica were not only inhibitory to fungal growth but also had fungicidal effects against one or all the 3 fungi tested (MIC/MFC between 0.11 and 7.5 mg/ml). Hexane extracts were also active indicating that many of the antifungal components of these plants are non-polar compounds. Time-to- kill experiments indicated an intense time-dependent fungicidal effect against C. albicans, achieving over a 5 h-period a 6 log10-unit decrease in CFU/ml at a concentration of 0.4 mg/ml for W. salutaris. The present study justifies the traditional use of these plants for the treatment of opportunistic infections in the region

Long-term evaluation of air sensor technology under ambient conditions in Denver, Colorado

Air pollution sensors are quickly proliferating for use in a wide variety of applications, with a low price point that supports use in high-density networks, citizen science, and individual consumer use. This emerging technology motivates the assessment under real-world conditions, including varying pollution levels and environmental conditions. A seven-month, systematic field evaluation of low-cost air pollution sensors was performed in Denver, Colorado, over 2015–2016; the location was chosen to evaluate the sensors in a high-altitude, cool, and dry climate. A suite of particulate matter (PM), ozone (O3), and nitrogen dioxide (NO2) sensors were deployed in triplicate and were collocated with federal equivalent method (FEM) monitors at an urban regulatory site. Sensors were evaluated for their data completeness, correlation with reference monitors, and ability to reproduce trends in pollution data, such as daily concentration values and wind-direction patterns. Most sensors showed high data completeness when data loggers were functioning properly. The sensors displayed a range of correlations with reference instruments, from poor to very high (e.g., hourly-average PM Pearson correlations with reference measurements varied from 0.01 to 0.86). Some sensors showed a change in response to laboratory audits/testing from before the sampling campaign to afterwards, such as Aeroqual, where the O3 response slope changed from about 1.2 to 0.6. Some PM sensors measured wind-direction and time-of-day trends similar to those measured by reference monitors, while others did not. This study showed different results for sensor performance than previous studies performed by the U.S. EPA and others, which could be due to different geographic location, meteorology, and aerosol properties. These results imply that continued field testing is necessary to understand emerging air sensing technology.</p

Artificial intelligence for dementia genetics and omics

Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine

Non-emphysematous chronic obstructive pulmonary disease is associated with diabetes mellitus

Abstract Background Chronic obstructive pulmonary disease (COPD) has been classically divided into blue bloaters and pink puffers. The utility of these clinical subtypes is unclear. However, the broader distinction between airway-predominant and emphysema-predominant COPD may be clinically relevant. The objective was to define clinical features of emphysema-predominant and non-emphysematous COPD patients. Methods Current and former smokers from the Genetic Epidemiology of COPD Study (COPDGene) had chest computed tomography (CT) scans with quantitative image analysis. Emphysema-predominant COPD was defined by low attenuation area at -950 Hounsfield Units (LAA-950) ≥10%. Non-emphysematous COPD was defined by airflow obstruction with minimal to no emphysema (LAA-950 < 5%). Results Out of 4197 COPD subjects, 1687 were classified as emphysema-predominant and 1817 as non-emphysematous; 693 had LAA-950 between 5–10% and were not categorized. Subjects with emphysema-predominant COPD were older (65.6 vs 60.6 years, p < 0.0001) with more severe COPD based on airflow obstruction (FEV1 44.5 vs 68.4%, p < 0.0001), greater exercise limitation (6-minute walk distance 1138 vs 1331 ft, p < 0.0001) and reduced quality of life (St. George’s Respiratory Questionnaire score 43 vs 31, p < 0.0001). Self-reported diabetes was more frequent in non-emphysematous COPD (OR 2.13, p < 0.001), which was also confirmed using a strict definition of diabetes based on medication use. The association between diabetes and non-emphysematous COPD was replicated in the ECLIPSE study. Conclusions Non-emphysematous COPD, defined by airflow obstruction with a paucity of emphysema on chest CT scan, is associated with an increased risk of diabetes. COPD patients without emphysema may warrant closer monitoring for diabetes, hypertension, and hyperlipidemia and vice versa. Trial registration Clinicaltrials.gov identifiers: COPDGene NCT00608764 , ECLIPSE NCT00292552 .http://deepblue.lib.umich.edu/bitstream/2027.42/109496/1/12890_2014_Article_599.pd

Association between depressive symptoms and incident cardiovascular diseases

Importance: It is uncertain whether depressive symptoms are independently associated with subsequent risk of cardiovascular diseases (CVD). Objective: To characterize the association between depressive symptoms and CVD incidence across the spectrum of lower mood. Design, setting and participants: A pooled analysis of individual-participant-data from the Emerging Risk Factors Collaboration (ERFC; 162,036 participants; 21 cohorts; baseline surveys, 1960-2008; latest follow-up, March 2020) and UK Biobank (UKB; 401,219 participants; baseline surveys, 2006-2010; latest follow-up, March 2020). Eligible participants had information about self-reported depressive symptoms and no CVD history at baseline. Exposure: Depressive symptoms were recorded using validated instruments. ERFC scores were harmonized across studies to a scale representative of the Centre for Epidemiological Studies Depression scale (CES-D; range 0-60; ≥16 indicates possible depressive disorder). UKB recorded the Patient Health Questionnaire-2 (PHQ-2; range 0-6; ≥3 indicates possible depressive disorder). Main Outcomes and Measures: Primary outcomes were incident fatal/nonfatal coronary heart disease (CHD), stroke and CVD (composite of CHD and stroke). Hazard ratios (HRs) per 1-SD higher log-CES-D or PHQ-2 adjusted for age, sex, smoking and diabetes were reported. Results: Among 162,036 participants from the ERFC, 73% were female, mean (SD) age at baseline was 63 (9) years, and 5,078 CHD and 3,932 stroke events were recorded (median follow-up, 9.5-years). Associations with CHD, stroke and CVD were log-linear. HRs (95%CI) per 1SD higher depression score for CHD, stroke and CVD respectively were 1.07 (1.03-1.11), 1.05 (1.01-1.10), and 1.06 (1.04-1.08). This reflects, 36 versus 29 CHD events, 28 versus 25 stroke events, and 63 versus 54 CVD events per 1000 individuals over 10 years in the highest versus lowest quintile of CES-D (geometric mean CES-D score, 19 versus 1). Among 401,219 participants from the UKB, 55% were female, mean baseline age was 56 (8) years, and 4607 CHD and 3253 stroke events were recorded (median follow-up, 8.1-years). HRs per 1SD higher depression score for CHD, stroke and CVD respectively were 1.11 (1.08-1.14), 1.10 (1.06-1.14) and 1.10 (1.08-1.13). This reflects, 21 versus 14 CHD events, 15 versus 10 stroke events, and 36 versus 25 CVD events per 1000 individuals over 10 years in those with PHQ2 ≥4 versus 0. The magnitude and statistical significance of the HRs were not materially changed after adjustment for additional risk factors. Conclusions and Relevance: In a pooled analysis of 563,255 participants in 22 cohorts, baseline depressive symptoms were associated with CVD incidence, including at symptom levels below the threshold indicative of a depressive disorder. However, the magnitude of associations was modest.Lisa Pennells, Stephen Kaptoge and Sarah Spackman are funded by a British Heart Foundation Programme Grant (RG/18/13/33946). Steven Bell was funded by the National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024). Tom Bolton is funded by the National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024). Angela Wood is supported by a BHF-Turing Cardiovascular Data Science Award and by the EC-Innovative Medicines Initiative (BigData@Heart). John Danesh holds a British Heart Foundation Professorship and a National Institute for Health Research Senior Investigator Award.* *The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care

External validation of risk prediction models for incident colorectal cancer using UK Biobank.

BACKGROUND: This study aimed to compare and externally validate risk scores developed to predict incident colorectal cancer (CRC) that include variables routinely available or easily obtainable via self-completed questionnaire. METHODS: External validation of fourteen risk models from a previous systematic review in 373 112 men and women within the UK Biobank cohort with 5-year follow-up, no prior history of CRC and data for incidence of CRC through linkage to national cancer registries. RESULTS: There were 1719 (0.46%) cases of incident CRC. The performance of the risk models varied substantially. In men, the QCancer10 model and models by Tao, Driver and Ma all had an area under the receiver operating characteristic curve (AUC) between 0.67 and 0.70. Discrimination was lower in women: the QCancer10, Wells, Tao, Guesmi and Ma models were the best performing with AUCs between 0.63 and 0.66. Assessment of calibration was possible for six models in men and women. All would require country-specific recalibration if estimates of absolute risks were to be given to individuals. CONCLUSIONS: Several risk models based on easily obtainable data have relatively good discrimination in a UK population. Modelling studies are now required to estimate the potential health benefits and cost-effectiveness of implementing stratified risk-based CRC screening

Artificial intelligence for dementia genetics and omics

Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high‐dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia‐related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine. Highlights: We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research

Cardiometabolic effects of genetic upregulation of the interleukin 1 receptor antagonist: a Mendelian randomisation analysis

Background To investigate potential cardiovascular and other effects of long-term pharmacological interleukin 1 (IL-1) inhibition, we studied genetic variants that produce inhibition of IL-1, a master regulator of inflammation. Methods We created a genetic score combining the effects of alleles of two common variants (rs6743376 and rs1542176) that are located upstream of IL1RN, the gene encoding the IL-1 receptor antagonist (IL-1Ra; an endogenous inhibitor of both IL-1 alpha and IL-1 beta); both alleles increase soluble IL-1Ra protein concentration. We compared effects on inflammation biomarkers of this genetic score with those of anakinra, the recombinant form of IL-1Ra, which has previously been studied in randomised trials of rheumatoid arthritis and other inflammatory disorders. In primary analyses, we investigated the score in relation to rheumatoid arthritis and four cardiometabolic diseases (type 2 diabetes, coronary heart disease, ischaemic stroke, and abdominal aortic aneurysm; 453 411 total participants). In exploratory analyses, we studied the relation of the score to many disease traits and to 24 other disorders of proposed relevance to IL-1 signalling (746 171 total participants). Findings For each IL1RN minor allele inherited, serum concentrations of IL-1Ra increased by 0.22 SD (95% CI 0.18-0.25; 12.5%; p=9.3 x 10(-33)), concentrations of interleukin 6 decreased by 0.02 SD (-0.04 to -0.01; -1,7%; p=3.5 x 10(-3)), and concentrations of C-reactive protein decreased by 0.03 SD (-0.04 to -0.02; -3.4%; p=7.7 x 10(-14)). We noted the effects of the genetic score on these inflammation biomarkers to be directionally concordant with those of anakinra. The allele count of the genetic score had roughly log-linear, dose-dependent associations with both IL-1Ra concentration and risk of coronary heart disease. For people who carried four IL-1Ra-raising alleles, the odds ratio for coronary heart disease was 1.15 (1.08-1.22; p=1.8 x 10(-6)) compared with people who carried no IL-1Ra-raising alleles; the per-allele odds ratio for coronary heart disease was 1.03 (1.02-1.04; p=3.9 x 10(-10)). Perallele odds ratios were 0.97 (0.95-0.99; p=9.9 x 10(-4)) for rheumatoid arthritis, 0.99 (0.97-1.01; p=0.47) for type 2 diabetes, 1.00 (0.98-1.02; p=0.92) for ischaemic stroke, and 1.08 (1.04-1.12; p=1.8 x 10(-5)) for abdominal aortic aneurysm. In exploratory analyses, we observed per-allele increases in concentrations of proatherogenic lipids, including LDL-cholesterol, but no clear evidence of association for blood pressure, glycaemic traits, or any of the 24 other disorders studied. Modelling suggested that the observed increase in LDL-cholesterol could account for about a third of the association observed between the genetic score and increased coronary risk. Interpretation Human genetic data suggest that long-term dual IL-1 alpha/beta inhibition could increase cardiovascular risk and, conversely, reduce the risk of development of rheumatoid arthritis. The cardiovascular risk might, in part, be mediated through an increase in proatherogenic lipid concentrations. Copyright (C) The Interleukin 1 Genetics Consortium. Open Access article distributed under the terms of CC-BY-NC-ND

Non-emphysematous chronic obstructive pulmonary disease is associated with diabetes mellitus

Background: Chronic obstructive pulmonary disease (COPD) has been classically divided into blue bloaters and pink puffers. The utility of these clinical subtypes is unclear. However, the broader distinction between airway-predominant and emphysema-predominant COPD may be clinically relevant. The objective was to define clinical features of emphysema-predominant and non-emphysematous COPD patients. Methods: Current and former smokers from the Genetic Epidemiology of COPD Study (COPDGene) had chest computed tomography (CT) scans with quantitative image analysis. Emphysema-predominant COPD was defined by low attenuation area at -950 Hounsfield Units (LAA-950) ≥10%. Non-emphysematous COPD was defined by airflow obstruction with minimal to no emphysema (LAA-950 < 5%). Results: Out of 4197 COPD subjects, 1687 were classified as emphysema-predominant and 1817 as non-emphysematous; 693 had LAA-950 between 5-10% and were not categorized. Subjects with emphysema-predominant COPD were older (65.6 vs 60.6 years, p < 0.0001) with more severe COPD based on airflow obstruction (FEV1 44.5 vs 68.4%, p < 0.0001), greater exercise limitation (6-minute walk distance 1138 vs 1331 ft, p < 0.0001) and reduced quality of life (St. George's Respiratory Questionnaire score 43 vs 31, p < 0.0001). Self-reported diabetes was more frequent in non-emphysematous COPD (OR 2.13, p < 0.001), which was also confirmed using a strict definition of diabetes based on medication use. The association between diabetes and non-emphysematous COPD was replicated in the ECLIPSE study. Conclusions: Non-emphysematous COPD, defined by airflow obstruction with a paucity of emphysema on chest CT scan, is associated with an increased risk of diabetes. COPD patients without emphysema may warrant closer monitoring for diabetes, hypertension, and hyperlipidemia and vice versa