Monotherapy with major antihypertensive drug classes and risk of hospital admissions for mood disorders

Major depressive and bipolar disorders predispose to atherosclerosis, and there is accruing data from animal model, epidemiological, and genomic studies that commonly used antihypertensive drugs may have a role in the pathogenesis or course of mood disorders. In this study, we propose to determine whether antihypertensive drugs have an impact on mood disorders through the analysis of patients on monotherapy with different classes of antihypertensive drugs from a large hospital database of 525 046 patients with follow-up for 5 years. There were 144 066 eligible patients fulfilling the inclusion criteria: age 40 to 80 years old at time of antihypertensive prescription and medication exposure >90 days. The burden of comorbidity assessed by Charlson and Elixhauser scores showed an independent linear association with mood disorder diagnosis. The median time to hospital admission with mood disorder was 847 days for the 299 admissions (641 685 person-years of follow-up). Patients on angiotensin-converting enzyme inhibitors or angiotensin receptor blockers had the lowest risk for mood disorder admissions, and compared with this group, those on β-blockers (hazard ratio=2.11; [95% confidence interval, 1.12–3.98]; P=0.02) and calcium antagonists (2.28 [95% confidence interval, 1.13–4.58]; P=0.02) showed higher risk, whereas those on no antihypertensives (1.63 [95% confidence interval, 0.94–2.82]; P=0.08) and thiazide diuretics (1.56 [95% confidence interval, 0.65–3.73]; P=0.32) showed no significant difference. Overall, our exploratory findings suggest possible differential effects of antihypertensive medications on mood that merits further study: calcium antagonists and β-blockers may be associated with increased risk, whereas angiotensin-converting enzyme inhibitors and angiotensin receptor blockers may be associated with a decreased risk of mood disorders

Equity and Equality in learning in Asia-Pacific: What do results from large-scale assessments tell us? Gender in focus policy brief

This policy brief investigates gender disparities in learning outcomes and the factors contributing to these disparities as identified in large-scale assessment data in Southeast Asia. It additionally provides a set of recommendations aimed at enhancing equitable student learning outcomes while advocating for comprehensive support of gender-based interventions within educational quality initiatives, spanning both systemic and school-level support. The findings presented in this brief are drawn from large-scale assessment (LSA) datasets and secondary analysis of these sources. For more detailed information regarding the large-scale assessments in Southeast Asia, please refer to the appendix section

Patient satisfaction with treatment for alcohol use disorders: comparing patients with and without severe mental health symptoms

BACKGROUND: Previous studies suggest patients with co-occurring alcohol use disorders (AUDs) and severe mental health symptoms (SMHS) are less satisfied with standard AUD treatment when compared to patients with an AUD alone. This study compared patient satisfaction with standard AUD treatment among patients with and without SMHS and explored how standard treatment might be improved to better address the needs of these patients. METHODS: Eighty-nine patients receiving treatment for an AUD either at an inpatient hospital, outpatient clinic, inpatient detoxification, or residential/therapeutic community services were surveyed. Patient satisfaction with treatment was assessed using the Treatment Perception Questionnaire (range: 0-40). Patients were stratified according to their score on the Depression Anxiety Stress Scale. Forty patients scored in the extremely severe range of depression (score >14) and/or anxiety (score >10) (indicating SMHS) and 49 patients did not. An inductive content analysis was also conducted on qualitative data relating to areas of service improvement. RESULTS: Patients with SMHS were found to be equally satisfied with treatment (mean =25.10, standard deviation =8.12) as patients with an AUD alone (mean =25.43, standard deviation =6.91). Analysis revealed that being an inpatient in hospital was associated with reduced treatment satisfaction. Patients with SMHS were found to be significantly less satisfied with staffs\u27 understanding of the type of help they wanted in treatment, when compared to patients with AUDs alone. Five areas for service improvement were identified, including staff qualities, informed care, treatment access and continuity, issues relating to inpatient stay, and addressing patients\u27 mental health needs. CONCLUSION: While findings suggest that AUD treatment services adequately meet the needs of patients with SMHS in treatment, patients with SMHS do feel that staff lack understanding of their treatment needs. Findings have important implications as to how current health care practice might be improved according to the patient\u27s perspective of care

Diastolic blood pressure J-curve phenomenon in a tertiary-care hypertension clinic

Concerns exist regarding the potential increased cardiovascular risk from lowering diastolic blood pressure (DBP) in hypertensive patients. We analyzed 30-year follow-up data of 10 355 hypertensive patients attending the Glasgow Blood Pressure Clinic. The association between blood pressure during the first 5 years of treatment and cause-specific hospital admissions or mortality was analyzed using multivariable adjusted Cox proportional hazard models. The primary outcome was a composite of cardiovascular admissions and deaths. DBP showed a U-shaped association (nadir, 92 mm Hg) for the primary cardiovascular outcome hazard and a reverse J-shaped association with all-cause mortality (nadir, 86 mm Hg) and noncardiovascular mortality (nadir, 92 mm Hg). The hazard ratio for the primary cardiovascular outcome after adjustment for systolic blood pressure was 1.38 (95% CI, 1.18–1.62) for DBP <80 compared with DBP of 80 to 89.9 mm Hg (referrant), and the subdistribution hazard ratio after accounting for competing risk was 1.33 (1.17–1.51) compared with DBP ≥80 mm Hg. Cause-specific nonfatal outcome analyses showed a reverse J-shaped relationship for myocardial infarction, ischemic heart disease, and heart failure admissions but a U-shaped relationship for stroke admissions. Age-stratified analyses showed DBP had no independent effect on stroke admissions among the older patient subgroup (≥60 years of age), but the younger subgroup showed a clear U-shaped relationship. Intensive blood pressure reduction may lead to unintended consequences of higher healthcare utilization because of increased cardiovascular morbidity, and this merits future prospective studies. Low on-treatment DBP is associated with increased risk of noncardiovascular mortality, the reasons for which are unclear

Global dataset on seagrass meadow structure, biomass and production

Seagrass meadows provide valuable socio-ecological ecosystem services, including a key role in climate change mitigation and adaption. Understanding the natural history of seagrass meadows across environmental gradients is crucial to deciphering the role of seagrasses in the global ocean. In this data collation, spatial and temporal patterns in seagrass meadow structure, biomass and production data are presented as a function of biotic and abiotic habitat characteristics. The biological traits compiled include measures of meadow structure (e.g. percent cover and shoot density), biomass (e.g. above-ground biomass) and production (e.g. shoot production). Categorical factors include bioregion, geotype (coastal or estuarine), genera and year of sampling. This dataset contains data extracted from peer-reviewed publications published between 1975 and 2020 based on a Web of Science search and includes 11 data variables across 12 seagrass genera. The dataset excludes data from mesocosm and field experiments, contains 14271 data points extracted from 390 publications and is publicly available on the PANGAEA® data repository (10.1594/PANGAEA.929968; Strydom et al., 2021). The top five most studied genera are Zostera, Thalassia, Cymodocea, Halodule and Halophila (84 % of data), and the least studied genera are Phyllospadix, Amphibolis and Thalassodendron (2.3 % of data). The data hotspot bioregion is the Tropical Indo-Pacific (25 % of data) followed by the Tropical Atlantic (21 %), whereas data for the other four bioregions are evenly spread (ranging between 13 and 15 % of total data within each bioregion). From the data compiled, 57 % related to seagrass biomass and 33 % to seagrass structure, while the least number of data were related to seagrass production (11 % of data). This data collation can inform several research fields beyond seagrass ecology, such as the development of nature-based solutions for climate change mitigation, which include readership interested in blue carbon, engineering, fisheries, global change, conservation and policy

Downgrading Recent Estimates of Land Available for Biofuel Production

Recent estimates of additional land available for bioenergy production range from 320 to 1411 million ha. These estimates were generated from four scenarios regarding the types of land suitable for bioenergy production using coarse-resolution inputs of soil productivity, slope, climate, and land cover. In this paper, these maps of land availability were assessed using high-resolution satellite imagery. Samples from these maps were selected and crowdsourcing of Google Earth images was used to determine the type of land cover and the degree of human impact. Based on this sample, a set of rules was formulated to downward adjust the original estimates for each of the four scenarios that were previously used to generate the maps of land availability for bioenergy production. The adjusted land availability estimates range from 56 to 1035 million ha depending upon the scenario and the ruleset used when the sample is corrected for bias. Large forest areas not intended for biofuel production purposes were present in all scenarios. However, these numbers should not be considered as definitive estimates but should be used to highlight the uncertainty in attempting to quantify land availability for biofuel production when using coarse-resolution inputs with implications for further policy development

Assessing machine learning for diagnostic classification of hypertension types identified by ambulatory blood pressure monitoring

Background: Inaccurate blood pressure classification results in inappropriate treatment. We tested if machine learning (ML), using routine clinical data, can serve as a reliable alternative to Ambulatory Blood Pressure Monitoring (ABPM) in classifying blood pressure status. Methods: This study employed a multi-centre approach involving three derivation cohorts from Glasgow, Gdańsk, and Birmingham, and a fourth independent evaluation cohort. ML models were trained using office BP, ABPM, and clinical, laboratory, and demographic data, collected from patients referred for hypertension assessment. Seven ML algorithms were trained to classify patients into five groups: Normal/Target, Hypertension-Masked, Normal/Target-White-Coat, Hypertension-White-Coat, and Hypertension. The 10-year cardiovascular outcomes and 27-year all-cause mortality risks were calculated for the ML-derived groups using the Cox proportional hazards model. Results: Overall XGBoost showed the highest AUROC of 0.85-0.88 across derivation cohorts, Glasgow (n=923; 43% females; age 50.7±16.3 years), Gdańsk (n=709; 46% females; age 54.4±13 years), and Birmingham (n=1,222; 56% females; age 55.7±14 years). But accuracy (0·57-0·72) and F1 scores (0·57-0·69) were low across the three patient cohorts. The evaluation cohort (n=6213, 51% females; age 51.2±10.8 years) indicated elevated 10-year risks of composite cardiovascular events in the Normal/Target-White-Coat and Hypertension-White-Coat groups, with heightened 27-year all-cause mortality observed in all groups except Hypertension-Masked, compared to the Normal/Target group. Conclusions: Machine learning has limited potential in accurate blood pressure classification when ABPM is unavailable. Larger studies including diverse patient groups and different resource settings are warranted

A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform

A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent

The human body at cellular resolution: the NIH Human Biomolecular Atlas Program

Abstract: Transformative technologies are enabling the construction of three-dimensional maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible three-dimensional molecular and cellular atlas of the human body, in health and under various disease conditions