Predicting dementia from primary care records: a systematic review and meta-analysis

Introduction Possible dementia is usually identified in primary care by general practitioners (GPs) who refer to specialists for diagnosis. Only two-thirds of dementia cases are currently recorded in primary care, so increasing the proportion of cases diagnosed is a strategic priority for the UK and internationally. Clinical entities in the primary care record may indicate risk of developing dementia, and could be combined in a predictive model to help find patients who are missing a diagnosis. We conducted a meta-analysis to identify clinical entities with potential for use in such a predictive model for dementia in primary care. Methods and Findings We conducted a systematic search in PubMed, Web of Science and primary care database bibliographies. We included cohort or case-control studies which used routinely collected primary care data, to measure the association between any clinical entity and dementia. Meta-analyses were performed to pool odds ratios. A sensitivity analysis assessed the impact of non-independence of cases between studies. From a sift of 3836 papers, 20 studies, all European, were eligible for inclusion, comprising >1 million patients. 75 clinical entities were assessed as risk factors for all cause dementia, Alzheimer’s (AD) and Vascular dementia (VaD). Data included were unexpectedly heterogeneous, and assumptions were made about definitions of clinical entities and timing as these were not all well described. Meta-analysis showed that neuropsychiatric symptoms including depression, anxiety, and seizures, cognitive symptoms, and history of stroke, were positively associated with dementia. Cardiovascular risk factors such as hypertension, heart disease, dyslipidaemia and diabetes were positively associated with VaD and negatively with AD. Sensitivity analyses showed similar results. Conclusions These findings are of potential value in guiding feature selection for a risk prediction tool for dementia in primary care. Limitations include findings being UK-focussed. Further predictive entities ascertainable from primary care data, such as changes in consulting patterns, were absent from the literature and should be explored in future studies

A reconfigurable real-time compressive-sampling camera for biological applications

Many applications in biology, such as long-term functional imaging of neural and cardiac systems, require continuous high-speed imaging. This is typically not possible, however, using commercially available systems. The frame rate and the recording time of high-speed cameras are limited by the digitization rate and the capacity of on-camera memory. Further restrictions are often imposed by the limited bandwidth of the data link to the host computer. Even if the system bandwidth is not a limiting factor, continuous high-speed acquisition results in very large volumes of data that are difficult to handle, particularly when real-time analysis is required. In response to this issue many cameras allow a predetermined, rectangular region of interest (ROI) to be sampled, however this approach lacks flexibility and is blind to the image region outside of the ROI. We have addressed this problem by building a camera system using a randomly-addressable CMOS sensor. The camera has a low bandwidth, but is able to capture continuous high-speed images of an arbitrarily defined ROI, using most of the available bandwidth, while simultaneously acquiring low-speed, full frame images using the remaining bandwidth. In addition, the camera is able to use the full-frame information to recalculate the positions of targets and update the high-speed ROIs without interrupting acquisition. In this way the camera is capable of imaging moving targets at high-speed while simultaneously imaging the whole frame at a lower speed. We have used this camera system to monitor the heartbeat and blood cell flow of a water flea (Daphnia) at frame rates in excess of 1500 fps

Earlier age of dementia onset and shorter survival times in dementia patients with diabetes

Diabetes is a risk factor for dementia, but relatively little is known about the epidemiology of the association. A retrospective population study using Western Australian hospital inpatient, mental health outpatient, and death records was used to compare the age at index dementia record (proxy for onset age) and survival outcomes in dementia patients with and without preexisting diabetes (n = 25,006; diabetes, 17.3%). Inpatient records from 1970 determined diabetes history in this study population with incident dementia in years 1990–2005. Dementia onset and death occurred an average 2.2 years and 2.6 years earlier, respectively, in diabetic compared with nondiabetic patients. Age-specific mortality rates were increased in patients with diabetes. In an adjusted proportional hazard model, the death rate was increased with long-duration diabetes, particularly with early age onset dementia. In dementia diagnosed before age 65 years, those with a ≥15-year history of diabetes died almost twice as fast as those without diabetes (hazard ratio = 1.9, 95% confidence interval: 1.3, 2.9). These results suggest that, in patients with diabetes, dementia onset occurs on average 2 years early and survival outcomes are generally poorer. The effect of diabetes on onset, survival, and mortality is greatest when diabetes develops before middle age and after 15 years’ diabetes duration. The impact of diabetes on dementia becomes progressively attenuated in older age groups

Subclinical giant cell arteritis in new onset polymyalgia rheumatica:A systematic review and meta-analysis of individual patient data

Objectives: To determine the prevalence and predictors of subclinical giant cell arteritis (GCA) in patients with newly diagnosed polymyalgia rheumatica (PMR). Methods: PubMed, Embase, and Web of Science Core Collection were systematically searched (date of last search July 14, 2021) for any published information on any consecutively recruited cohort reporting the prevalence of GCA in steroid-naïve patients with PMR without cranial or ischemic symptoms. We combined prevalences across populations in a random-effect meta-analysis. Potential predictors of subclinical GCA were identified by mixed-effect logistic regression using individual patient data (IPD) from cohorts screened with PET/(CT). Results: We included 13 cohorts with 566 patients from studies published between 1965 to 2020. Subclinical GCA was diagnosed by temporal artery biopsy in three studies, ultrasound in three studies, and PET/(CT) in seven studies. The pooled prevalence of subclinical GCA across all studies was 23% (95% CI 14%-36%, I2=84%) for any screening method and 29% in the studies using PET/(CT) (95% CI 13%-53%, I2=85%) (n=266 patients). For seven cohorts we obtained IPD for 243 patients screened with PET/(CT). Inflammatory back pain (OR 2.73, 1.32-5.64), absence of lower limb pain (OR 2.35, 1.05-5.26), female sex (OR 2.31, 1.17-4.58), temperature >37° (OR 1.83, 0.90-3.71), weight loss (OR 1.83, 0.96-3.51), thrombocyte count (OR 1.51, 1.05-2.18), and haemoglobin level (OR 0.80, 0.64-1.00) were most strongly associated with subclinical GCA in the univariable analysis but not C-reactive protein (OR 1.00, 1.00-1.01) or erythrocyte sedimentation rate (OR 1.01, 1.00-1.02). A prediction model calculated from these variables had an area under the curve of 0.66 (95% CI 0.55-0.75). Conclusion: More than a quarter of patients with PMR may have subclinical GCA. The prediction model from the most extensive IPD set has only modest diagnostic accuracy. Hence, a paradigm shift in the assessment of PMR patients in favour of implementing imaging studies should be discussed

Selection of diazotrophic bacterial communities in biological sand filter mesocosms used for the treatment of phenolic-laden wastewater

Agri effluents such as winery or olive mill waste-waters are characterized by high phenolic concentrations. These compounds are highly toxic and generally refractory to biodegradation. Biological sand filters (BSFs) represent inexpensive, environmentally friendly, and sustainable wastewater treatment systems which rely vastly on microbial catabolic processes. Using denaturing gradient gel electrophoresis and terminal-restriction fragment length polymorphism, this study aimed to assess the impact of increasing concentrations of synthetic phenolic-rich wastewater, ranging from 96 mg L−1 gallic acid and138 mg L−1 vanillin (i.e., a total chemical oxygen demand (COD) of 234 mg L−1) to 2,400mg L−1 gallic acid and 3,442 mg L−1 vanillin (5,842 mg COD L−1), on bacterialcommunities and the specific functional diazotrophic community from BSF mesocosms. This amendment procedure instigated efficient BSF phenolic removal, significant modifications of the bacterial communities, and notably led to the selection of a phenolic-resistant and less diverse diazotrophic community. This suggests that bioavailable N is crucial in the functioning of biological treatment processes involving microbial communities, and thus that functional alterations in the bacterial communities in BSFs ensure provision of sufficient bioavailable nitrogen for the degradation of wastewater with a high C/N ratio.Web of Scienc

On the way to large-scale and high-resolution brain-chip interfacing

Brain-chip-interfaces (BCHIs) are hybrid entities where chips and nerve cells establish a close physical interaction allowing the transfer of information in one or both directions. Typical examples are represented by multi-site-recording chips interfaced to cultured neurons, cultured/acute brain slices, or implanted “in vivo”. This paper provides an overview on recent achievements in our laboratory in the field of BCHIs leading to enhancement of signals transmission from nerve cells to chip or from chip to nerve cells with an emphasis on in vivo interfacing, either in terms of signal-to-noise ratio or of spatiotemporal resolution. Oxide-insulated chips featuring large-scale and high-resolution arrays of stimulation and recording elements are presented as a promising technology for high spatiotemporal resolution interfacing, as recently demonstrated by recordings obtained from hippocampal slices and brain cortex in implanted animals. Finally, we report on an automated tool for processing and analysis of acquired signals by BCHIs

A new method for chlorhexidine (CHX) determination: CHX release after application of differently concentrated CHX-containing preparations on artificial fissures

Aims of the study were (1) to establish a method for quantification of chlorhexidine (CHX) in small volumes and (2) to determine CHX release from differently concentrated CHX-containing preparations, varnishes, and a CHX gel applied on artificial fissures. CHX determination was conducted in a microplate reader using polystyrene wells. The reduced intensity of fluorescence of the microplates was used for CHX quantification. For verification of the technique, intra- and inter-assay coefficients of variation were calculated for graded series of CHX concentrations, and the lower limit of quantification (LLOQ) was determined. Additionally, artificial fissures were prepared in 50 bovine enamel samples, divided into five groups (A–E, n = 10) and stored in distilled water (7 days); A: CHX-varnish EC40; B: CHX-varnish Cervitec; C: CHX-gel Chlorhexamed; D: negative control, no CHX application; and E: CXH-diacetate standard (E1, n = 5) or CHX-digluconate (E2, n = 5) in the solution. The specimens were brushed daily, and CHX in the solution was measured. The method showed intra- and inter-assay coefficients of variation of <10 and <20%, respectively; LLOQ was 0.91–1.22 nmol/well. The cumulative CHX release (mean ± SD) during the 7 days was: EC40 (217.2 ± 41.8 nmol), CHX-gel (31.3 ± 8.5 nmol), Cervitec (18.6 ± 1.7 nmol). Groups A–C revealed a significantly higher CHX release than group D and a continuous CHX-release with the highest increase from day 0 to 7 for EC40 and the lowest for Chlorhexamed. The new method is a reliable tool to quantify CHX in small volumes. Both tested varnishes demonstrate prolonged and higher CHX release from artificial fissures than the CHX-gel tested

Evaluation of the Performance of Information Theory-Based Methods and Cross-Correlation to Estimate the Functional Connectivity in Cortical Networks

Functional connectivity of in vitro neuronal networks was estimated by applying different statistical algorithms on data collected by Micro-Electrode Arrays (MEAs). First we tested these “connectivity methods” on neuronal network models at an increasing level of complexity and evaluated the performance in terms of ROC (Receiver Operating Characteristic) and PPC (Positive Precision Curve), a new defined complementary method specifically developed for functional links identification. Then, the algorithms better estimated the actual connectivity of the network models, were used to extract functional connectivity from cultured cortical networks coupled to MEAs. Among the proposed approaches, Transfer Entropy and Joint-Entropy showed the best results suggesting those methods as good candidates to extract functional links in actual neuronal networks from multi-site recordings