Groundwater systems under siege: The silent invasion of microplastics and cock-tails worldwide.

Microplastics (MPs) contamination is one of the significant escalating environmental concerns worldwide, and this stems from the increasing production and unlawful disposal of plastic materials. Regretfully, the synthesis of plastic materials is expected to triple in the upcoming years. Nevertheless, MPs pollution in marine, aquatic, and terrestrial settings has received much attention, unlike in groundwater systems. This study exhaustively reviewed varying degrees of recent publications in various search engines and provided a detailed state of current knowledge and research progress vis-à-vis MPs and cock-tail pollution in groundwater systems. Evidently, groundwater sources are severely contaminated as a result of growing anthropogenic activities and vertical movement of MPs and cock-tails from the atmospheric, terrestrial, and aquatic environments, however, fewer researchers have fixated their attention on estimating the occurrence of MPs in groundwater resources, while sufficient information regarding their sources, sampling methods, abundance, transport pathways, fate, modeling techniques, appropriate and adequate data, sorption properties, separation from other environmental media, toxicity, and remedial measures are extensively lacking. In addition, MPs may combine with other toxic emerging contaminants to improve migration and toxicity; however, no research has been conducted to fully understand cock-tail migration mechanisms and impacts in groundwater systems. Over time, groundwater may be regarded as the primary sink for MPs, if effective actions are neglected. Overall, this study detected a lack of concern and innumerable voids in this field; hence, vital and nascent research gaps were identified for immediate, advanced, and interdisciplinary research investigations.

Frailty before and during austerity: A time series analysis of the English Longitudinal Study of Ageing 2002-2018.

BACKGROUND: Frailty is characterised by a reduced resilience to adversity. In this analysis we examined changes in frailty in people aged 50+ before and during a period of austere public spending in England. METHODS: Data from the English Longitudinal Study of Ageing 2002-2018 were analysed. Associations between austerity and frailty were examined using (1) Multilevel interrupted times series analysis (ITSA); and (2) Accelerated longitudinal modelling comparing frailty trajectories in people of the same age in 2002 and 2012. RESULTS: The analysis included 16,410 people (mean age 67 years, 55% women), with mean frailty index score of 0.16. Mean scores in women (0.16) where higher than in men (mean 0.14), and higher in the poorest tertile (mean 0.20) than the richest (mean 0.12). In the ITSA, frailty index scores increased more quickly during austerity than before, with the additional increase in frailty 2012-2018 being similar in magnitude to the difference in mean frailty score between people aged 65-69 and 70-74 years. Steeper increases in frailty after 2012 were experienced across the wealth-spectrum and in both sexes but were greater in the very oldest (80+). In the accelerated longitudinal analysis, frailty was lower in 2012 than 2002, but increased more rapidly in the 2012 cohort compared to the 2002 cohort; markedly so in people aged 80+. CONCLUSION: The period of austerity politics was associated with steeper increases in frailty with age compared to the pre-austerity period, consistent with previously observed increases in mortality.

Derivation and validation of the CFracture competing risk fracture prediction tool compared with QFracture in older people and people with comorbidity: a population cohort study.

BACKGROUND: UK guidelines recommend the QFracture tool to predict the risk of major osteoporotic fracture and hip fracture, but QFracture calibration is poor, partly because it does not account for competing mortality risk. The aim of this study was to derive and validate a competing risk model to predict major osteoporotic fracture and hip fracture (CFracture) and compare its performance with that of QFracture in UK primary care. METHODS: We used UK linked primary care data from the Clinical Practice Research Datalink GOLD database to identify people aged 30-99 years, split into derivation and validation cohorts. In the derivation cohort, we derived models (CFracture) using the same covariates as QFracture with Fine-Gray competing risk modelling, and included the Charlson Comorbidity Index score as an additional predictor of non-fracture death. In a separate validation cohort, we examined discrimination (using Harrell's C-statistic) and calibration of CFracture compared with QFracture. Reclassification analysis examined differences in the characteristics of patients reclassified as higher risk by CFracture but not by QFracture. FINDINGS: The derivation cohort included 1 831 606 women and 1 789 820 men, and the validation cohort included 915 803 women and 894 910 men. Overall discrimination of CFracture was excellent (C-statistic=0·813 [95% CI 0·810-0·816] for major osteoporotic fracture and 0·914 [0·908-0·919] for hip fracture in women; 0·734 [0·729-0·740] for major osteoporotic fracture and 0·886 [0·877-0·895] for hip fracture in men) and was similar to QFracture. CFracture calibration overall and in people younger than 75 years was generally excellent. CFracture overpredicted major osteoporotic fracture and hip fracture in older people and people with comorbidity, but was better calibrated than QFracture. Patients classified as high-risk by CFracture but not by QFracture had a higher prevalence of current smoking and previous fracture, but lower prevalence of dementia, cancer, cardiovascular disease, renal disease, and diabetes. INTERPRETATION: CFracture has similar discrimination to QFracture but is better calibrated overall and in younger people. Both models performed poorly in adults aged 85 years and older. Competing risk models should be recommended for fracture risk prediction to guide treatment recommendations. FUNDING: National Institute for Health and Care Research, Wellcome Trust, Health Data Research UK.

Effect of competing mortality risks on predictive performance of the QFracture risk prediction tool for major osteoporotic fracture and hip fracture: external validation cohort study in a UK primary care population.

Objective: To externally evaluate the QFracture risk prediction tool for predicting the risk of major osteoporotic fracture and hip fracture. Design: External validation cohort study. Setting: UK primary care population. Linked general practice (Clinical Practice Research Datalink (CPRD) Gold), mortality registration (Office of National Statistics), and hospital inpatient (Hospital Episode Statistics) data, from 1 January 2004 to 31 March 2016. Participants: 2 747 409 women and 2 684 730 men, aged 30-99 years, with up-to-standard linked data that had passed CPRD checks for at least one year. Main outcome measures: Two outcomes were modelled based on the QFracture: major osteoporotic fracture and hip fracture. Major osteoporotic fracture was defined as any hip, distal forearm, proximal humerus, or vertebral crush fracture, from general practice, hospital discharge, and mortality data. The QFracture 10 year predicted risk of major osteoporotic fracture and hip fracture was calculated, and performance evaluated versus observed 10 year risk of fracture in the whole population, and in subgroups based on age and comorbidity. QFracture calibration was examined accounting for, and not accounting for, competing risk of mortality from causes other than the major osteoporotic fracture. Results: 2 747 409 women with 95 598 major osteoporotic fractures and 36 400 hip fractures, and 2 684 730 men with 34 321 major osteoporotic fractures and 13 379 hip fractures were included in the analysis. The incidence of all fractures was higher than in the QFracture internal derivation. Competing risk of mortality was more common than fracture from middle age onwards. QFracture discrimination in the whole population was excellent or good for major osteoporotic fracture and hip fracture (Harrell's C statistic in women 0.813 and 0.918, and 0.738 and 0.888 in men, respectively), but was poor to moderate in age subgroups (eg, Harrell's C statistic in women and men aged 85-99 years was 0.576 and 0.624 for major osteoporotic fractures, and 0.601 and 0.637 for hip fractures, respectively). Without accounting for competing risks, QFracture systematically under-predicted the risk of fracture in all models, and more so for major osteoporotic fracture than for hip fracture, and more so in older people. Accounting for competing risks, QFracture still under-predicted the risk of fracture in the whole population, but over-prediction was considerable in older age groups and in people with high comorbidities at high risk of fracture. Conclusions: The QFracture risk prediction tool systematically under-predicted the risk of fracture (because of incomplete determination of fracture rates) and over-predicted the risk in older people and in those with more comorbidities (because of competing mortality). The use of QFracture in its current form needs to be reviewed, particularly in people at high risk of death from other causes.

Robust EEG Based Biomarkers to Detect Alzheimer's Disease.

Biomarkers to detect Alzheimer's disease (AD) would enable patients to gain access to appropriate services and may facilitate the development of new therapies. Given the large numbers of people affected by AD, there is a need for a low-cost, easy to use method to detect AD patients. Potentially, the electroencephalogram (EEG) can play a valuable role in this, but at present no single EEG biomarker is robust enough for use in practice. This study aims to provide a methodological framework for the development of robust EEG biomarkers to detect AD with a clinically acceptable performance by exploiting the combined strengths of key biomarkers. A large number of existing and novel EEG biomarkers associated with slowing of EEG, reduction in EEG complexity and decrease in EEG connectivity were investigated. Support vector machine and linear discriminate analysis methods were used to find the best combination of the EEG biomarkers to detect AD with significant performance. A total of 325,567 EEG biomarkers were investigated, and a panel of six biomarkers was identified and used to create a diagnostic model with high performance (≥85% for sensitivity and 100% for specificity).

Early Detection of Alzheimer's Disease with Blood Plasma Proteins Using Support Vector Machines.

The successful development of amyloid-based biomarkers and tests for Alzheimer's disease (AD) represents an important milestone in AD diagnosis. However, two major limitations remain. Amyloid-based diagnostic biomarkers and tests provide limited information about the disease process and they are unable to identify individuals with the disease before significant amyloid-beta accumulation in the brain develops. The objective in this study is to develop a method to identify potential blood-based non-amyloid biomarkers for early AD detection. The use of blood is attractive because it is accessible and relatively inexpensive. Our method is mainly based on machine learning (ML) techniques (support vector machines in particular) because of their ability to create multivariable models by learning patterns from complex data. Using novel feature selection and evaluation modalities, we identified 5 novel panels of non-amyloid proteins with the potential to serve as biomarkers of early AD. In particular, we found that the combination of A2M, ApoE, BNP, Eot3, RAGE and SGOT may be a key biomarker profile of early disease. Disease detection models based on the identified panels achieved sensitivity (SN) > 80%, specificity (SP) > 70%, and area under receiver operating curve (AUC) of at least 0.80 at prodromal stage (with higher performance at later stages) of the disease. Existing ML models performed poorly in comparison at this stage of the disease, suggesting that the underlying protein panels may not be suitable for early disease detection. Our results demonstrate the feasibility of early detection of AD using non-amyloid based biomarkers.

A Robust Blood-based Signature of Cerebrospinal Fluid Aß42 Status.

Early detection of Alzheimer's disease (AD) is of vital importance in the development of disease-modifying therapies. This necessitates the use of early pathological indicators of the disease such as amyloid abnormality to identify individuals at early disease stages where intervention is likely to be most effective. Recent evidence suggests that cerebrospinal fluid (CSF) amyloid ß1-42 (Aß42) level may indicate AD risk earlier compared to amyloid positron emission tomography (PET). However, the method of collecting CSF is invasive. Blood-based biomarkers indicative of CSF Aß42 status may remedy this limitation as blood collection is minimally invasive and inexpensive. In this study, we show that APOE4 genotype and blood markers comprising EOT3, APOC1, CGA, and Aß42 robustly predict CSF Aß42 with high classification performance (0.84 AUC, 0.82 sensitivity, 0.62 specificity, 0.81 PPV and 0.64 NPV) using machine learning approach. Due to the method employed in the biomarker search, the identified biomarker signature maintained high performance in more than a single machine learning algorithm, indicating potential to generalize well. A minimally invasive and cost-effective solution to detecting amyloid abnormality such as proposed in this study may be used as a first step in a multi-stage diagnostic workup to facilitate enrichment of clinical trials and population-based screening.