The global prevalence of hepatitis D virus infection:systematic review and metaanalysis

Background and Aims There are uncertainties about the epidemic patterns of hepatitis delta virus (HDV) infection and its contribution to the burden of liver disease. We estimated the global prevalence of HDV infection and explored its contribution to the development of cirrhosis and hepatocellular carcinoma (HCC) among hepatitis B surface antigen (HBsAg)-positive people. Methods We searched Pubmed, EMBASE and Scopus for studies reporting on total or IgG anti-HDV among HBsAg-positive people. Anti-HDV prevalence was estimated using a binomial mixed model, weighting for study quality and population size. The population attributable fraction (PAF) of HDV to cirrhosis and HCC among HBsAg-positive people was estimated using random-effects models. Results We included 282 studies, comprising 376 population samples from 95 countries, which together tested 120,293 HBsAg-positive people for anti-HDV. The estimated anti-HDV prevalence was 4.5% (95% CI 3.6, 5.7) among all HBsAg-positive people and 16.4% (14.6, 18.6) among those attending hepatology clinics. Worldwide, 0.16% (0.11, 0.25) of the general population, totalling 12.0 (8.7, 18.7) million people, were estimated to be anti-HDV positive. Prevalence among HBsAg-positive people was highest in Mongolia, the Republic of Moldova and countries in Western and Middle Africa, and was higher in injecting drug users, haemodialysis recipients, men who have sex with men, commercial sex workers, and those with hepatitis C virus or HIV. Among HBsAg-positive people, preliminary PAF estimates of HDV were 18% (10, 26) for cirrhosis and 20% (8, 33) for HCC. Conclusions An estimated 12 million people worldwide have experienced HDV infection, with higher prevalence in certain geographic areas and populations. HDV is a significant contributor to HBV-associated liver disease. More quality data are needed to improve the precisions of burden estimates

Palliative care and catastrophic costs in Malawi after a diagnosis of advanced cancer: a prospective cohort study

Background Inclusive universal health coverage requires access to quality health care without financial barriers. Receipt of palliative care after advanced cancer diagnosis might reduce household poverty, but evidence from low-income and middle-income settings is sparse. Methods In this prospective study, the primary objective was to investigate total household costs of cancer-related health care after a diagnosis of advanced cancer, with and without the receipt of palliative care. Households comprising patients and their unpaid family caregiver were recruited into a cohort study at Queen Elizabeth Central Hospital in Malawi, between Jan 16 and July 31, 2019. Costs of cancer-related health-care use (including palliative care) and health-related quality-of-life were recorded over 6 months. Regression analysis explored associations between receipt of palliative care and total household costs on health care as a proportion of household income. Catastrophic costs, defined as 20% or more of total household income, sale of assets and loans taken out (dissaving), and their association with palliative care were computed. Findings We recruited 150 households. At 6 months, data from 89 (59%) of 150 households were available, comprising 89 patients (median age 50 years, 79% female) and 64 caregivers (median age 40 years, 73% female). Patients in 55 (37%) of the 150 households died and six (4%) were lost to follow-up. 19 (21%) of 89 households received palliative care. Catastrophic costs were experienced by nine (47%) of 19 households who received palliative care versus 48 (69%) of 70 households who did not (relative risk 0·69, 95% CI 0·42 to 1·14, p=0·109). Palliative care was associated with substantially reduced dissaving (median US$11, IQR 0 to 30 vs$34, 14 to 75; p=0·005). The mean difference in total household costs on cancer-related health care with receipt of palliative care was −36% (95% CI −94 to 594; p=0·707). Interpretation Vulnerable households in low-income countries are subject to catastrophic health-related costs following a diagnosis of advanced cancer. Palliative care might result in reduced dissaving in these households. Further consideration of the economic benefits of palliative care is justified. Funding Wellcome Trust; National Institute for Health Research; and EMMS International

Accuracy of computer-aided chest X-ray in community-based tuberculosis screening: Lessons from the 2016 Kenya National Tuberculosis Prevalence Survey

Community-based screening for tuberculosis (TB) could improve detection but is resource intensive. We set out to evaluate the accuracy of computer-aided TB screening using digital chest X-ray (CXR) to determine if this approach met target product profiles (TPP) for community-based screening. CXR images from participants in the 2016 Kenya National TB Prevalence Survey were evaluated using CAD4TBv6 (Delft Imaging), giving a probabilistic score for pulmonary TB ranging from 0 (low probability) to 99 (high probability). We constructed a Bayesian latent class model to estimate the accuracy of CAD4TBv6 screening compared to bacteriologically-confirmed TB across CAD4TBv6 threshold cut-offs, incorporating data on Clinical Officer CXR interpretation, participant demographics (age, sex, TB symptoms, previous TB history), and sputum results. We compared model-estimated sensitivity and specificity of CAD4TBv6 to optimum and minimum TPPs. Of 63,050 prevalence survey participants, 61,848 (98%) had analysable CXR images, and 8,966 (14.5%) underwent sputum bacteriological testing; 298 had bacteriologically-confirmed pulmonary TB. Median CAD4TBv6 scores for participants with bacteriologically-confirmed TB were significantly higher (72, IQR: 58–82.75) compared to participants with bacteriologically-negative sputum results (49, IQR: 44–57, p<0.0001). CAD4TBv6 met the optimum TPP; with the threshold set to achieve a mean sensitivity of 95% (optimum TPP), specificity was 83.3%, (95% credible interval [CrI]: 83.0%—83.7%, CAD4TBv6 threshold: 55). There was considerable variation in accuracy by participant characteristics, with older individuals and those with previous TB having lowest specificity. CAD4TBv6 met the optimal TPP for TB community screening. To optimise screening accuracy and efficiency of confirmatory sputum testing, we recommend that an adaptive approach to threshold setting is adopted based on participant characteristics

The human brainome: network analysis identifies \u3ci\u3eHSPA2\u3c/i\u3e as a novel Alzheimer’s disease target

Our hypothesis is that changes in gene and protein expression are crucial to the development of late-onset Alzheimer’s disease. Previously we examined how DNA alleles control downstream expression of RNA transcripts and how those relationships are changed in late-onset Alzheimer’s disease. We have now examined how proteins are incorporated into networks in two separate series and evaluated our outputs in two different cell lines. Our pipeline included the following steps: (i) predicting expression quantitative trait loci; (ii) determining differential expression; (iii) analysing networks of transcript and peptide relationships; and (iv) validating effects in two separate cell lines. We performed all our analysis in two separate brain series to validate effects. Our two series included 345 samples in the first set (177 controls, 168 cases; age range 65–105; 58% female; KRONOSII cohort) and 409 samples in the replicate set (153 controls, 141 cases, 115 mild cognitive impairment; age range 66–107; 63% female; RUSH cohort). Our top target is heat shock protein family A member 2 (HSPA2), which was identified as a key driver in our two datasets. HSPA2 was validated in two cell lines, with overexpression driving further elevation of amyloid-B40 and amyloid-B42 levels in APP mutant cells, as well as significant elevation of microtubule associated protein tau and phosphorylated-tau in a modified neuroglioma line. This work further demonstrates that studying changes in gene and protein expression is crucial to understanding late onset disease and further nominates HSPA2 as a specific key regulator of late-onset Alzheimer’s disease processes

Additional file 1 of Inequalities in the impact of COVID-19-associated disruptions on tuberculosis diagnosis by age and sex in 45 high TB burden countries

Additional file 1: Model code and additional tables and Figs. Table S1. Country-specific tuberculosis notifications for 2013-2020 by age and sex. Table S2. Country-specific linear models for expected notifications. Fig. S1. Country-specific linear models for expected notifications. Table S3. Country-specific numbers of missed or delayed diagnoses. Table S4. Country-specific risk-ratios for disruption to tuberculosis notifications due to the pandemic for men compared to women (both aged ≥ 15 years). Table S5. Country-specific risk-ratios for disruption to tuberculosis notifications due to the pandemic for children (aged < 15 years) and the elderly (aged ≥ 65 years) compared to adults (aged 15-64 years)

Annotated CRC GWAS p-values for 3q26.2 and 8q24.21, using data from [14] and [15].

<p>The data tracks plotted across the two regions, 3q26.2 and 8q24.21, are <b>a.</b> association trend test p-values from a CRC GWAS as a colour intensity track, <b>b.</b> significant Hi-C interactions (3 kb resolution; determined from Hi-C experiments on 3 CRC cell-lines, LS174T, LoVo and Colo205), <b>c.</b> ChromHMM functional annotation, <b>d.</b> CRC GWAS trend test association p-values as a SNAP plot (with SNP type (imputed/typed), values and recombination rate). The figure includes a title, gene tracks for both scales and a legend.</p

Comparison of visPIG with other genetic data visualisation tools.

<p>The comparison has been restricted to the capabilities of visPIG. Some of the other tools have additional features that are not listed here.</p><p>: w = web application, g = locally run graphical user interface, c = locally run from a terminal command line</p><p>: figure output format</p><p>: ability to add a legend onto the output figure</p><p>: v = vector graphics, r = raster graphics</p><p>: possible for some features (e.g. all graphical parameters, the type and order of tracks displayed, the zoom view), but not fully interactive; e.g. to change the plotted regions, the region file needs to be edited and re-uploaded</p><p>: multiple panels can be juxtaposed, which can be used to either show multiple regions and/or zoom in on a specific sub-region; difficult to get different regions on exactly identical scales; we were unable to indicate the location of the zoomed panel on the full view</p><p>: multi-scale in the sense that one can interactively zoom in on a region, but never more than one scale displayed at a given time.</p><p>Comparison of visPIG with other genetic data visualisation tools.</p

Hi-C interactomes of 3q26.2 and 8q24.21, using data from [15].

<p>This figure shows the same two regions as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107497#pone-0107497-g001" target="_blank">Figure 1</a>, 3q26.2 and 8q24.21. The data tracks plotted are the significant 3 kb Hi-C interactions (determined from 3 CRC cell-lines, LS174T, LoVo, Colo205), displayed as arches this time, as well as the corresponding, un-normalised Hi-C reads from the LS174T cell-line as a heat map. Also shown are some manual annotations to highlight the LD block within which one end of the significant interactions have to lie to be plotted, as well as the top CRC risk associated SNP in the LD block, rs6983267.</p

The human brainome: network analysis identifies \u3ci\u3eHSPA2\u3c/i\u3e as a novel Alzheimer’s disease target

Our hypothesis is that changes in gene and protein expression are crucial to the development of late-onset Alzheimer’s disease. Previously we examined how DNA alleles control downstream expression of RNA transcripts and how those relationships are changed in late-onset Alzheimer’s disease. We have now examined how proteins are incorporated into networks in two separate series and evaluated our outputs in two different cell lines. Our pipeline included the following steps: (i) predicting expression quantitative trait loci; (ii) determining differential expression; (iii) analysing networks of transcript and peptide relationships; and (iv) validating effects in two separate cell lines. We performed all our analysis in two separate brain series to validate effects. Our two series included 345 samples in the first set (177 controls, 168 cases; age range 65–105; 58% female; KRONOSII cohort) and 409 samples in the replicate set (153 controls, 141 cases, 115 mild cognitive impairment; age range 66–107; 63% female; RUSH cohort). Our top target is heat shock protein family A member 2 (HSPA2), which was identified as a key driver in our two datasets. HSPA2 was validated in two cell lines, with overexpression driving further elevation of amyloid-B40 and amyloid-B42 levels in APP mutant cells, as well as significant elevation of microtubule associated protein tau and phosphorylated-tau in a modified neuroglioma line. This work further demonstrates that studying changes in gene and protein expression is crucial to understanding late onset disease and further nominates HSPA2 as a specific key regulator of late-onset Alzheimer’s disease processes