Decline In Early Stages Research Articles

The neurophysiological mechanisms of medial prefrontal-perirhinal cortex circuit mediating temporal order memory decline in early stage of AD rats

The temporal component of episodic memory has been recognized as a sensitive behavioral marker in early stage of Alzheimer's disease (AD) patients. However, parallel studies in AD animals are currently lacking, and the underlying neural circuit mechanisms remain poorly understood. Using a novel AppNL-G-F knock-in (APP-KI) rat model, the developmental changes of temporal order memory (TOM) and the relationship with medial prefrontal cortex and perirhinal cortex (mPFC-PRH) circuit were determined through in vivo electrophysiology and microimaging technique. We observed a deficit in TOM performance during the object temporal order memory task (OTOMT) in APP-KI rats at 6 month old, which was not evident at 3 or 4 months of age. Alongside behavioral changes, we identified a gradually extensive and aggravated regional activation and functional alterations in the mPFC and PRH during the performance of OTOMT, which occurred prior to the onset of TOM deficits. Moreover, coherence analysis showed that the functional connectivity between the mPFC and PRH could predict the extent of future behavioral performance. Further analysis revealed that the aberrant mPFC-PRH interaction mainly attributed to the progressive deterioration of synaptic transmission, information flow and network coordination from mPFC to PRH, suggesting the mPFC dysfunction maybe the key area of origin underlying the early changes of TOM. These findings identify a pivotal role of the mPFC-PRH circuit in mediating the TOM deficits in the early stage of AD, which holds promising clinical translational value and offers potential early biological markers for predicting AD memory progression.

Feasibility and usefulness of cognitive monitoring using a new home-based cognitive test in mild cognitive impairment: a prospective single arm study

BackgroundThe risk of dementia is increased in subjects with mild cognitive impairment (MCI). Despite the plethora of in-person cognitive tests, those that can be administered over the phone are lacking. We hypothesized that a home-based cognitive test (HCT) using phone calls would be feasible and useful in non-demented elderly. We aimed to assess feasibility and validity of a new HCT as an optional cognitive monitoring tool without visiting hospitals.MethodsOur study was conducted in a prospective design during 24 weeks. We developed a new HCT consisting of 20 questions (score range 0–30). Participants with MCI (n = 38) were consecutively enrolled and underwent regular HCTs during 24 weeks. Associations between HCT scores and in-person cognitive scores and Alzheimer’s disease (AD) biomarkers were evaluated. In addition, HCT scores in MCI participants were cross-sectionally compared with age-matched cognitively normal (n = 30) and mild AD dementia (n = 17) participants for discriminative ability of the HCT.ResultsHCT had good intra-class reliability (test-retest Cronbach’s alpha 0.839). HCT scores were correlated with the Mini-Mental State Examination (MMSE), verbal memory delayed recall, and Stroop test scores but not associated with AD biomarkers. HCT scores significantly differed among cognitively normal, MCI, and mild dementia participants, indicating its discriminative ability. Finally, 32 MCI participants completed follow-up evaluations, and 8 progressed to dementia. Baseline HCT scores in dementia progressors were lower than those in non-progressors (p = 0.001).ConclusionThe feasibility and usefulness of the HCT were demonstrated in elderly subjects with MCI. HCT could be an alternative option to monitor cognitive decline in early stages without dementia.

A comparison of machine learning based‐composite cognitive test scores to track cognitive decline in early stages of Alzheimer’s disease dementia: ADOPIC study

AbstractBackgroundClinical trials of early Alzheimer’s disease (AD) dementia use cognition as a primary outcome and therefore cognitive measures that accurately reflect disease progression and represent multiple cognitive domains are required. Current cognitive endpoints are computed by averaging standardized change from baseline scores (i.e., preclinical Alzheimer cognitive composite (PACC)). Here we compare the PACC to composite scores for which the combination of multiple performance scores has been optimized using machine learning (ML) algorithms in a large, harmonised data set, namely ADOPIC.MethodsA dataset harmonised across various cognitive scores (Table 1) was used to construct composite scores using ML‐based algorithms: a manifold learning dimension reduction technique (UMAP), principal component analysis (PCA) and Latent variable analysis (LVA). Data from ADNI (n = 1470), AIBL (n = 1105) and OASIS participants (n = 412, Table 2) with ≥3 assessments ≤5 years before clinical progression/last visit were included. Participants were classified clinically as; stable cognitively unimpaired (CU), CU progressing to mild cognitive impairment (MCI) or dementia, stable MCI, MCI progressing to dementia, or AD dementia. For UMAP, all stable participants (including dementia) were used in 4‐fold cross‐validation training sets. However, for testing UMAP and unsupervised models (PCA and LVA) all clinical groups (except for dementia) were used. The validity of ML‐based composites was challenged by modelling mean (SD) change over time in the non‐demented clinical groups, using linear mixed model (LMM) analysis. Signal‐to‐noise ratios (SNRs) were calculated (mean change/SD change) for each composite. The mean change was measured in progressives relative to stable participants of each group.ResultsEach ML‐based cognitive composite showed sensitivity to cognitive decline in the progressor groups (Figure 1 A). For the MCI progressors, PCA and UMAP composites had significantly higher SNRs than PACC (P<0.01; Figure 1B), however, LVA performance was not significantly better than PACC. For the CU progressor group, SNRs for PCA and LVA did not show significant differences with PACC and UMAP performed significantly worse than PACC (P<0.01).ConclusionML‐based cognitive composite score computed using PCA provides a practical solution to track cognitive decline with improved performance in tracking cognitive decline in MCI progressors compared to PACC, while being comparable in CUs.

Neuroinflammation, amyloid, NFT markers and initial cognitive status predict cognitive decline in MCI patients

AbstractBackgroundNeuroinflammation has been established as a key component of Alzheimer’s pathology. Numerous cross‐sectional studies have found associations between neuroinflammatory markers and neuronal damage in AD, suggesting neuroinflammation as a potential drive force of disease progression. However, there have been debate on whether neuroinflammation has a causal effect on disease progression. Here we questioned whether neuroinflammation is predictive of future cognitive decline in early stage of the disease.Method21 MCI patients were recruited and had baseline T1‐MRI and 11C‐PBR28 scans. Patients were followed up for at least one year (mean follow‐up period 3.1±1.3 years) and had mini‐mental state examinations (MMSE) at each visit. 19 of them had baseline 18F‐Flutemetamol PET, 11 had 18F‐AV1451 scans. 18F‐AV1451 and 11C‐PBR28 SUVR in the medial temporal lobe were measured, while PCA analyses were performed to evaluate uptakes in the neocortex for all 3 tracers. Principle components of temporal lobe volumetric measurements were computed as the neurodegeneration marker. A linear growth model was fitted for each subjects using all available time points, giving the intercept and slope of individual cognition trajectory. Age, intercept, 11C‐PBR28, and T1 MRI markers were then put into linear models to predict slope, and a backward model selection procedure was conducted. To further validate that neuroinflammation is not just a mediator of amyloid and tau pathologies, we further included amyloid and tau markers in the sub‐cohorts and inspected the final model.ResultThe average baseline MMSE was 28±1.9 in the study cohort and the average annual change of MMSE was ‐0.46. We found 11C‐PBR28 SUVR in neocortex and MMSE intercept, but not temporal cortical thickness, were predictive of the slope of MMSE decline. In subgroups where 18F‐Flutemetamol and 18F‐AV1451 SUVRs were included respectively, we found neocortical amyloid and tau load were also predictive of MMSE slope, while 11C‐PBR28 SUVR remained its significance in the models.ConclusionOur findings suggest that neuroinflammation is an independent factor that drives disease progression and cognitive decline in early stage of AD. Molecular imaging biomarkers may better predict patients’ outcome as they detect underlying biological processes.

Automatic prediction of cognitive and functional decline using baseline MRI and cognitive scores

AbstractBackgroundPatients in the early stages of AD dementia experience decline in their cognitive abilities at different rates. Accurately predicting the progression rate would enable the enrichment of patient populations in clinical trials. Using the early AD‐related pattern of atrophy and cognitive scores from a baseline visit, we trained a model to predict cognitive and functional decline in early stages of AD.MethodData included 312 patients with mild Alzheimer’s disease from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and were chosen based on amyloid positivity, the Clinical Dementia Rating (CDR=0.5) and the Mini‐Mental State Exam (MMSE, range 24‐30) scores. SNIPE (Scoring by Nonlocal Image Patch Estimator) was used to measure AD‐related atrophy patterns in the hippocampi (HC) and entorhinal cortex (EC) (Coupé et al. 2019). A balanced random forest was used to train models with feature sets including MR‐based z‐scored features (SNIPE scores for HC and EC), and cognitive test scores (Alzheimer’s Disease Assessment Scale (ADAS‐13), MoCA (Montreal Cognitive Assessment), and MMSE) from baseline data. Classifiers were trained using different combinations of features plus age. The classification performance was evaluated based on the measured sensitivity, specificity, and accuracy to predict future functional and cognitive decline (defined as a 2‐point change in CSD‐SB).ResultTable 1 shows the classification performance of all trained models for two and three year follow up periods. Using hippocampal grading scores in addition to MoCA, ADAS‐13 and MMSE, yields the highest accuracy (76.9%) in predicting cognitive decline at 2 years. Figure 1 shows the ROC curve for two models: One consisting of all cognitive features and the other using both cognitive and MRI features. Comparing results between the classifier using only the baseline cognitive score and the corresponding classifier with the added MRI features showed that for both follow‐up periods, the accuracy of prediction is increased when adding MRI features.ConclusionMicroscopic changes due to AD pathology that occur in the HC and EC can be used as a feature in a predict the cognitive decline and increase the accuracy of prediction, even at the early stages of the AD trajectory.

Measuring synaptic loss in early AD stages: Trajectories of SNAP25 and SYT1 using serial CSF sampling

AbstractBackgroundIt is increasingly recognized that synaptic dysfunction plays an important role in Alzheimer pathology, already in an early phase. In this study we investigated the presynaptic biomarkers SNAP25 and SYT1 in early stages of AD. Using serial CSF samples in a cohort of memory clinic patients with detailed clinical follow‐up, we studied prognostic value, the association with cognitive decline and longitudinal trajectories of these biomarkers.MethodWe included 150 subjects with subjective cognitive decline (SCD; 62±7 years), mild cognitive impairment (MCI; 66±8 years) and AD dementia (64±7 years) from the Amsterdam dementia cohort, based on availability of CSF at two time points (median[IQR] 2.1[1.4‐2.7] years) and clinical follow‐up of at least one year. CSF SNAP25 and SYT1 were analyzed using immunoprecipitation mass spectrometry. To assess differences in SNAP25 and SYT1 levels between diagnostic groups at baseline, change in biomarker levels over time and association with cognitive decline we used linear mixed models. We used Cox regression models to assess predictive value of baseline biomarker level for disease progression. Analyses were adjusted for age and gender, Cox models also for ATN biomarker status.ResultTable 1 shows the results of the linear mixed models. Baseline levels of SNAP25 were higher in AD dementia compared to SCD and MCI, SYT1 levels were similar between groups. Over time SNAP25 increased in SCD subjects (B[SE] 0.24[0.10], p<0.05), SYT1 decreased in AD dementia patients (B[SE] ‐2.6[0.8], p<0.01). In SCD and MCI subjects higher SNAP25 was associated with faster decline in MMSE over time (st.B[SE] ‐0.24[0.12], p<0.01 for SCD; ‐0.39[0.16], p<0.05 for MCI). For SYT1 there was an effect only for SCD subjects (st.B[SE] ‐0.18[0.07], p<0.01). Baseline SNAP25 was a significant predictor for conversion to MCI or AD in non‐demented subjects (HR[95%CI] 4.4 [2.1‐9.1]), even after addition of ATN status (HR[95%CI] 2.8 [1.1‐7.3]; table 2, figure 1).ConclusionSNAP25 levels increased over time in SCD subjects, and both SNAP25 and SYT1 were associated with cognitive decline in early stages of AD. High levels of SNAP25 predicted conversion to AD dementia. Hence, they may have potential to be used for disease monitoring or as amyloid‐independent outcome measure in clinical trials.

Development of visual-motor integration scale for the Korean old people.

Effective methods to detect old people cognitive decline in early stages are needed in Korea, which is the fastest aging country in the world. This study aimed to develop a brief cognitive function measurement, Visual-Motor Integration Scale for the Korean elderly (VMIS-KE). In this methodological research, 16 preliminary items on visual-motor integration for the old people were chosen after literature review and expert validation. One hundred eighty participants aged 20–79 years were recruited to verify the validity and reliability of this measurement from community sources in Daegu city. The collected data were analyzed by exploratory factor analysis and confirmative factor analysis using SPSS and AMOS 18.0 program. The three factors (figure cognition, memory, and spatial cognition) with 12 items were finally extracted. Goodness of the fit was confirmed through confirmatory factor analysis. The Cronbach α was 0.867. The scores of overall VMIS-KE and every subcategory significantly decreased with age, especially in the 70s. VMIS-KE is the first valid and reliable measurement to assess cognitive decline of the Korean old people through visual motor integration, and can easily and quickly detect high-risk groups of dementia regardless of their educational level.

Serum hemoglobin concentration and risk of renal function decline in early stages of diabetic kidney disease: a nationwide, biopsy-based cohort study.

Prognosticating disease progression in patients with diabetic kidney disease (DKD) is challenging, especially in the early stages of kidney disease. Anemia can occur in the early stages of kidney disease in diabetes. We therefore postulated that serum hemoglobin (Hb) concentration, as a reflection of incipient renal tubulointerstitial impairment, can be used as a marker to predict DKD progression. Drawing on nationally representative data of patients with biopsy-proven DKD, 246 patients who had an estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2 at renal biopsy were identified: age 56 (45-63) years; 62.6% men; Hb 13.3 (12.0-14.5) g/dL; eGFR 76.2 (66.6-88.6) mL/min/1.73 m2; urine albumin-to-creatinine ratio 534 (100-1480) mg/g Crea. Serum Hb concentration was divided into quartiles: ≤12, 12.1-13.3, 13.4-14.5 and ≥14.6 g/dL. The association between serum Hb concentration and the severity of renal pathological lesions was explored. A multivariable Cox regression model was used to estimate the risk of DKD progression (new onset of end-stage kidney disease, 50% reduction of eGFR or doubling of serum creatinine). The incremental prognostic value of DKD progression by adding serum Hb concentration to the known risk factors of DKD was assessed. Serum Hb levels negatively correlated with all renal pathological features, especially with the severity of interstitial fibrosis (ρ = -0.52; P < 0.001). During a median follow-up of 4.1 years, 95 developed DKD progression. Adjusting for known risk factors of DKD progression, the hazard ratio in the first, second and third quartile (the fourth quartile was reference) were 2.74 [95% confidence interval (CI) 1.26-5.97], 2.33 (95% CI 1.07-5.75) and 1.46 (95% CI 0.71-3.64), respectively. Addition of the serum Hb concentration to the known risk factors of DKD progression improved the prognostic value of DKD progression (the global Chi-statistics increased from 55.1 to 60.8; P < 0.001). Serum Hb concentration, which reflects incipient renal fibrosis, can be useful for predicting DKD progression in the early stages of kidney disease.

CCR4 and CCR7 differentially regulate thymocyte subset localization with distinct outcomes for central tolerance

Abstract Central tolerance is established as T cells mature in the thymus by the combined effects of clonally deleting autoreactive thymocytes or inducing their diversion to the Treg lineage. To ensure T cell tolerance to diverse self-antigens, thymocytes must efficiently enter the medulla and scan heterogeneous antigen presenting cells, which display the majority of the peptidome in a sparse mosaic. While it is widely accepted that CCR7 contributes to central tolerance by promoting thymocyte medullary localization and rapid migration therein, the contribution of CCR4 to these processes remains unclear. For example, it is not known whether CCR4 is simply redundant to CCR7 in promoting medullary entry or if it plays a distinct role in central tolerance. Here, we report that early post-positive selection thymocytes undergo chemotaxis towards CCR4 ligands, while progressively mature cells migrate towards CCR7 ligands. Synchronized positive selection assays reveal that CCR4 is upregulated within hours of positive selection signaling, while CCR7 upregulation is delayed. 2-photon microscopy shows that CCR4 facilitates entry of early post-positive selection cells into the thymic medulla, while CCR7 promotes medullary accumulation of mature subsets. These findings suggest that CCR4 and CCR7 have non-redundant roles in negative selection. Consistent with this possibility, flow cytometric analysis reveals that CCR4 deficiency results in a selective decline in early-stages of negative selection, while CCR7 deficiency impairs only late-stage negative selection. Our data support a new model in which CCR4 and CCR7 mediate medullary entry of different thymocyte subsets and play distinct roles in enforcing central tolerance.

Protein glycation – biomarkers of metabolic dysfunction and early-stage decline in health in the era of precision medicine

Protein glycation provides a biomarker in widespread clinical use, glycated hemoglobin HbA1c (A1C). It is a biomarker for diagnosis of diabetes and prediabetes and of medium-term glycemic control in patients with established diabetes. A1C is an early-stage glycation adduct of hemoglobin with glucose; a fructosamine derivative. Glucose is an amino group-directed glycating agent, modifying N-terminal and lysine sidechain amino groups. A similar fructosamine derivative of serum albumin, glycated albumin (GA), finds use as a biomarker of glycemic control, particularly where there is interference in use of A1C. Later stage adducts, advanced glycation endproducts (AGEs), are formed by the degradation of fructosamines and by the reaction of reactive dicarbonyl metabolites, such as methylglyoxal. Dicarbonyls are arginine-directed glycating agents forming mainly hydroimidazolone AGEs. Glucosepane and pentosidine, an intense fluorophore, are AGE covalent crosslinks. Cellular proteolysis of glycated proteins forms glycated amino acids, which are released into plasma and excreted in urine. Development of diagnostic algorithms by artificial intelligence machine learning is enhancing the applications of glycation biomarkers. Investigational glycation biomarkers are in development for: (i) healthy aging; (ii) risk prediction of vascular complications of diabetes; (iii) diagnosis of autism; and (iv) diagnosis and classification of early-stage arthritis. Protein glycation biomarkers are influenced by heritability, aging, decline in metabolic, vascular, renal and skeletal health, and other factors. They are applicable to populations of differing ethnicities, bridging the gap between genotype and phenotype. They are thereby likely to find continued and expanding clinical use, including in the current era of developing precision medicine, reporting on multiple pathogenic processes and supporting a precision medicine approach.

Memory-relevant nap sleep physiology in healthy and pathological aging.

Aging is associated with detrimental changes in sleep physiology, a process accelerated in Alzheimer's disease. Fine-tuned temporal interactions of non-rapid eye movement slow oscillations and spindles were shown to be particularly important for memory consolidation, and to deteriorate in healthy older adults. Whether this oscillatory interaction further decline in early stages of Alzheimer's disease such as mild cognitive impairment has not been investigated to date, but may have important therapeutic implications. Here, we assessed differences in sleep architecture and memory-relevant slow oscillation, sleep spindles and their functional coupling during a 90-min nap between healthy young and older adults, and in older patients with mild cognitive impairment. Furthermore, associations of nap-sleep characteristics with sleep-dependent memory performance change were evaluated. We found significant differences between young and older healthy adults, and between young adults and patients with mild cognitive impairment, but not between healthy older adults and patients for several sleep metrics, including slow oscillation-spindle coupling. Moreover, sleep-dependent retention of verbal memories was significantly higher in young healthy adults versus older adults with and without mild cognitive impairment, but no difference between the two older groups was observed. Associations with sleep metrics were only found for pre-nap memory performances. In conclusion, our results indicate changes in nap sleep physiology and sleep-related memory consolidation in older adults with and without mild cognitive impairment. Thus, interventions targeted at improving sleep physiology may help to reduce memory decline in both groups, but our study does not indicate additional benefits for patients with mild cognitive impairment. Effects of Brain Stimulation During Daytime Nap on Memory Consolidation in Younger, Healthy Subjects: https://clinicaltrials.gov/ct2/show/NCT01840865; NCT01840865. Effects of Brain Stimulation During a Daytime Nap on Memory Consolidation in Older Adults; https://clinicaltrials.gov/ct2/show/study/NCT01840839?term=01840839&draw=2&rank=1; NCT01840839. Effects of Brain Stimulation During a Daytime Nap on Memory Consolidation in Patients With Mild Cognitive Impairment; https://clinicaltrials.gov/ct2/show/NCT01782365?term=01782365&draw=2&rank=1; NCT01782365.

Urinary Metabolomic Markers of Protein Glycation, Oxidation, and Nitration in Early-Stage Decline in Metabolic, Vascular, and Renal Health.

Glycation, oxidation, nitration, and crosslinking of proteins are implicated in the pathogenic mechanisms of type 2 diabetes, cardiovascular disease, and chronic kidney disease. Related modified amino acids formed by proteolysis are excreted in urine. We quantified urinary levels of these metabolites and branched-chain amino acids (BCAAs) in healthy subjects and assessed changes in early-stage decline in metabolic, vascular, and renal health and explored their diagnostic utility for a noninvasive health screen. We recruited 200 human subjects with early-stage health decline and healthy controls. Urinary amino acid metabolites were determined by stable isotopic dilution analysis liquid chromatography-tandem mass spectrometry. Machine learning was applied to optimise and validate algorithms to discriminate between study groups for potential diagnostic utility. Urinary analyte changes were as follows: impaired metabolic health—increased Nε-carboxymethyl-lysine, glucosepane, glutamic semialdehyde, and pyrraline; impaired vascular health—increased glucosepane; and impaired renal health—increased BCAAs and decreased Nε-(γ-glutamyl)lysine. Algorithms combining subject age, BMI, and BCAAs discriminated between healthy controls and impaired metabolic, vascular, and renal health study groups with accuracy of 84%, 72%, and 90%, respectively. In 2-step analysis, algorithms combining subject age, BMI, and urinary Nε-fructosyl-lysine and valine discriminated between healthy controls and impaired health (any type), accuracy of 78%, and then between types of health impairment with accuracy of 69%-78% (cf. random selection 33%). From likelihood ratios, this provided small, moderate, and conclusive evidence of early-stage cardiovascular, metabolic, and renal disease with diagnostic odds ratios of 6 – 7, 26 – 28, and 34 – 79, respectively. We conclude that measurement of urinary glycated, oxidized, crosslinked, and branched-chain amino acids provides the basis for a noninvasive health screen for early-stage health decline in metabolic, vascular, and renal health.

Grey matter abnormalities are associated only with severe cognitive decline in early stages of Parkinson's disease

Cognitive impairment is common in Parkinson's disease (PD), yet with large heterogeneity in the range and course of deficits. In a cross-sectional study, 124 PD patients underwent extensive clinical and neuropsychological assessment as well as a 3T MRI scan of the brain. Our aim was to identify differences in grey matter volume and thickness, as well as cortical folding, across different cognitive profiles as defined through a data-driven exploratory cluster analysis of neuropsychological data. The identified cognitive groups ranged from cognitively intact patients to patients with severe deficits in all cognitive domains, whilst showing comparable levels of motor disability and disease duration. Each group was compared to the cognitively intact PD group using voxel- and vertex-based morphometry. Results revealed widespread age-related grey matter abnormalities associated with progressive worsening of cognitive functions in mild PD. When adjusted for age, significant differences were only seen between cognitively intact and severely affected PD patients and these were restricted to the right posterior cingulate and the right precuneus. Reduced cortical thickness was seen in the right inferior temporal gyrus and reduced folding in the right temporal region. As these differences were not associated with age, we assume that they are associated with underlying pathology of the cognitive decline. Given the limited involvement of grey matter differences, and the absence of differences in vascular changes across the groups, we hypothesize a more important role for white matter tract changes in cognitive decline in PD.

Continuous monitoring of blood pressure by analyzing the blood flow sound of arteriovenous fistula in hemodialysis patients.

Patients with end-stage renal disease undergoing hemodialysis (HD) have an elevated risk of cardiovascular disease-related morbidity and mortality. To prevent from such a life-threatening event, the continuous blood pressure (BP) monitoring system may contribute to detect BP decline in early stages and may help to do appropriate disposal. Our research team has introduced an electronic stethoscope (Asahi Kasei Co, Ltd., Tokyo, Japan), which translates sound intensity of Arteriovenous Fistula (AVF) to BP data using the technique of Fourier transformation that can predict continuous BP non-invasively. This study, we investigated whether electronic stethoscope-guided estimated BP (e-BP) would actually reflect systolic BP measured by sphygmomanometer (s-BP), and whether e-BP could predict fall of BP during HD. Twenty-six patients who underwent HD treatment in our hospital were evaluated prospectively. We obtained sound intensity data from the electronic stethoscope which was equipped with the return line of HD. Then, the data were translated into e-BP data to be compared with s-BP. Correlation of total of 315 data sets obtained from each method was examined. An accuracy of diagnosis of intra-dialytic hypotension (IDH) was evaluated. Total of 315 data sets were obtained. A close correlation was observed between e-BP and s-BP (r=0.887, p<0.0001). Sensitivity and positive predictive value of predicted-BP for detection of IDH was 90 and 81.3%, respectively. Electronic stethoscope-guided BP measurement would be helpful for real-time diagnosis of BP fall in HD patients. Further investigations are needed.

Idiopathic pulmonary fibrosis in the era of antifibrotic therapy: Searching for new opportunities grounded in evidence

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease that up to now has been associated with a poor prognosis. However, the results of the INPULSIS and ASCEND trials and the approval of nintedanib and pirfenidone have marked the beginning of a new era for IPF patients. Questions remain, however. Should these drugs be used earlier? What effect will they have on more severe disease? Will their effects last beyond the trial period? This manuscript is the outcome of a multidisciplinary meeting between pulmonology, radiology, and pathology clinicians on the use of antifibrotic agents in IPF. In our opinion, the existing data show that pirfenidone and nintedanib slow functional decline in early stages of disease. These drugs also appear to result in therapeutic benefits when administered to patients with advanced disease at diagnosis and maintain effective over time. The data also suggest that continuing antifibrotic therapy after disease progression may confer benefits, but more evidence is needed. Early diagnosis and treatment are crucial for reducing functional decline, slowing disease progression, and improving quality of life.

Numerical Studies of Methane Gas Production from Hydrate Decomposition by Depressurization in Porous Media

Abstract As a kind of potential new sources of energy, the dissociation processes of gas hydrates using the depressurization method has been investigated by experimental observations and numerical simulations. In this study, on the basis of summarizing the existing model, a one-dimensional mathematical model containing four phase (water phase, gas phase, hydrate phase, ice phase) and three constituents (water, gas, hydrate) using the finite difference method (FDM) was established for methane hydrates decomposition by depressurization in porous media. This paper focuses on the ice generation and distribution characteristics through changing the parameters of the relevant settings, and analyzes the effect of ice generation on the pressure, temperature, permeability, cumulative gas production and other parameters. The results show that, generation of ice increases gradually in the hydrates decomposition process, and occurred early near the side area because of the large pressure gradient. The absolute permeability and instantaneous gas generation rate at the early stage decline with ice generation, and the local pressure rise.

Loss of developmental pluripotency occurs in two stages during leaf aging in Arabidopsis thaliana

Aging in plants and animals can result in the loss of cellular pluripotency and the inability to regenerate multiple organs from differentiated, somatic tissues. Detached cotyledons from Arabidopsis thaliana (L.) Heynh. seedlings can proliferate to form callus as well as root and shoot organs in the presence of the phytohormones auxin and cytokinin or their synthetic analogs. In this study, detached cotyledons from the ecotype Landsberg erecta (Ler-0) demonstrated two separate developmental intervals of pluripotency loss. During a 48- to 60-h interval, occurring approximately 4 to 6 d after germination (DAG), detached cotyledons from Ler-0 could proliferate new cells (callus) but lost shoot regeneration competency, which the ecotype Nossen-0 (No-0) retained. In older cotyledons, the pluripotency loss was more severe, as detached cotyledons failed to proliferate callus. In the first interval, Ler-0 cotyledons lost the ability to respond to treatment with a high concentration of synthetic auxin, a level previously hypothesized to promote dedifferentiation. However, both young and old Ler-0 cotyledons were found to induce WUSCHEL::NLS-GUS, a marker for undifferentiated shoot apical meristem cells. The late-stage interval positively correlated to the timing of slowed growth in cotyledons still attached to intact seedlings. Also, cytokinin overexpression could rescue (or delay) this late-stage callus proliferation failure, but not the early-stage decline in shoot regeneration ability. Combined, the results demonstrate that loss of pluripotency in vitro occurs in distinct temporal stages and involves auxin and/or cytokinin signaling. Furthermore, there is genetic variation for this loss of pluripotency within Arabidopsis.

Can angiotensin-converting enzyme inhibitors impact cognitive decline in early stages of Alzheimer's disease? An overview of research evidence in the elderly patient population.

Alzheimer's disease (AD) is a neurodegenerative disease, in which an accumulation of toxic amyloid beta in the brain precedes the emergence of clinical symptoms. AD spectrum consists of presymptomatic, early symptomatic, and symptomatic phase of dementia. At present, no pharmacotherapy exists to modify or reverse a course of AD, and only symptomatic treatments are available. Many elderly patients, diagnosed with multiple medical conditions (such as cardiovascular diseases, Type 2 diabetes mellitus, and cerebrovascular diseases) are at increased risk of the development of mild cognitive impairment (MCI), AD, and vascular dementia. Studies have revealed reduced rates of cognitive decline, in elderly patients, who were treated with centrally active angiotensin-converting enzyme inhibitors (ACE-Is) (that have an ability to cross the blood–brain barrier). This article reviews recently published literature, focused on possible protective influence of the centrally active ACE-Is, in the elderly population, at risk for cognitive decline.

Blood biomarkers associated with cognitive decline in early stage and drug-naive Parkinson's disease patients

Objectives: Early diagnosis of Parkinson's disease (PD) continues to be a major challenge. We previously identified blood RNA biomarkers in medicated PD patients, thus, expression of these biomarkers in de novo patients remains unknown.

Blood biomarkers associated with cognitive decline in early stage and drug-naive Parkinson's disease patients

Early diagnosis of Parkinson’s disease (PD) continues to be a major challenge in the field. The lack of a robust biomarker to detect early stage PD patients has considerably slowed the progress toward the development of potential therapeutic agents. We have previously evaluated several RNA biomarkers in whole blood from participants enrolled in two independent clinical studies. In these studies, PD patients were medicated, thus, expression of these biomarkers in de novo patients remains unknown. To this end, we tested ten RNA biomarkers in blood samples from 99 untreated PD patients and 101 HC nested in the cross-sectional Parkinson’s Progression Markers Initiative by quantitative real-time PCR. One biomarker out of ten, COPZ1 trended toward significance (nominal p = 0.009) when adjusting for age, sex, and educational level. Further, COPZ1, EFTUD2 and PTBP1 mRNAs correlated with clinical features in PD patients including the Hoehn and Yahr scale, Movement Disorder Society revision of Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) and Montreal Cognitive Assessment (MoCA) score. Levels of EFTUD2 and PTBP1 were significantly higher in cognitively normal PD patients (PD-CN) compared to cognitively impaired PD patients (PD-MCI). Interestingly, blood glucose levels were significantly higher in PD and PD-MCI patients (≥ 100 mg/dL, pre-diabetes) compared to HC. Collectively, we report the association of three RNA biomarkers, COPZ1, EFTUD2 and PTBP1 with clinical features including cognitive decline in early drug-naive PD patients. Further, our results show that drug-naive PD and PD-MCI patients have glucose levels characteristic of pre-diabetes patients, suggesting that impaired glucose metabolism is an early event in PD. Evaluation of these potential biomarkers in a larger longitudinal study is warranted.