Reported here are modeling results based on ISCAT (Investigation of Sulfur Chemistry of Antarctic Troposphere) 2000 observations recorded at the South Pole (SP) during the Austral Summer of 2000. The observations included a comprehensive set of photochemical parameters, e.g., NO, O 3 , and CO. It is worthy to note that not only were OH and HO 2 observed, but also HO x precursor species CH 2 O, H 2 O 2 , and HONO were measured. Previous studies have suggested that HONO is the major source of OH/HO x in the Arctic; however, observed HONO levels at SP induced dramatic model overprediction of both HO x and NO x when used to constrain the model calculations. In contrast, model predictions constrained by observed values of CH 2 O and H 2 O 2 are consistent with the observations of OH and HO 2 (i.e., within 20%) for more than half of the data. Significant model overpredictions of OH, however, were seen at the NO levels lower than 50 pptv or higher than 150 pptv. An analysis of HO x budget at the median NO level suggests that snow emissions of H 2 O 2 and CH 2 O are the single most important primary source of SP HO x , contributing 46% to the total source. Major sinks for HO x are found to be dry deposition of HO 2 NO 2 and HNO 3 as well as their reactions with OH. Although ISCAT 2000 led to a major progress in our understanding of SP HO x chemistry, critical aspects of this chemistry are still in need of further investigation.
A facile one-pot synthesis of long-chain highly branched polymers (LCHBPs) was accomplished by a tandem ring-opening metathesis polymerization (ROMP) and acyclic diene metathesis (ADMET) polymerization procedure. A telechelic polymer with two terminal allyloxy groups and many pendent acrylates was first prepared through the first generation Grubbs catalyst-mediated chain transfer ROMP of 7-oxanorborn-5-ene-exo,exo-2,3-dicarboxylic acid bis(2-(acryloyloxy)ethyl) ester in the presence of a symmetrical multifunctional olefin 1,4-diallyloxy-cis-2-butene as chain transfer agent (CTA), and then utilized as an A 2 B 2n -type macromonomer in subsequent ADMET polymerization between allyloxy and acrylate triggered by the most activated second generation Grubbs catalyst, yielding LCHBPs as the reacton time prolonged. The CTA, monomer, macromonomer, and the resulting LCHBPs were characterized by mass spectroscopy, elemental analysis, gel permeation chromatography with multiangle laser light scattering, NMR and matrix-assisted laser desorption ionization time-of-flight mass measurements. The LCHBPs have comparatively high molecular weights and relatively moderate polydispersity indices.
Osteoporosis is a serious social issue nowadays. Both the high morbidity and its common complication osteoporotic fracture load a heavy burden on the whole society. The adipose tissue is the biggest endocrinology organ that has a different function on the bone. The adipocytes are differentiated from the same cell lineage with osteoblast, and they can secrete multiple adipokines with various functions on bone remolding. Recently, several novel adipokines have been identified and investigated thoroughly. In this paper, we would like to highlight the complicated relation between the bone metabolism and the novel adipokines, and it may provide us with a new target for prediction and treatment of osteoporosis.
Precision medicine for Alzheimer's disease (AD) necessitates the development of personalized, reproducible, and neuroscientifically interpretable biomarkers, yet despite remarkable advances, few such biomarkers are available. Also, a comprehensive evaluation of the neurobiological basis and generalizability of the end‐to‐end machine learning system should be given the highest priority. For this reason, a deep learning model (3D attention network, 3DAN) that can simultaneously capture candidate imaging biomarkers with an attention mechanism module and advance the diagnosis of AD based on structural magnetic resonance imaging is proposed. The generalizability and reproducibility are evaluated using cross‐validation on in‐house, multicenter ( n = 716), and public ( n = 1116) databases with an accuracy up to 92%. Significant associations between the classification output and clinical characteristics of AD and mild cognitive impairment (MCI, a middle stage of dementia) groups provide solid neurobiological support for the 3DAN model. The effectiveness of the 3DAN model is further validated by its good performance in predicting the MCI subjects who progress to AD with an accuracy of 72%. Collectively, the findings highlight the potential for structural brain imaging to provide a generalizable, and neuroscientifically interpretable imaging biomarker that can support clinicians in the early diagnosis of AD.
Alzheimer's disease (AD) is associated with disruptions in brain activity and networks. However, there is substantial inconsistency among studies that have investigated functional brain alterations in AD; such contradictions have hindered efforts to elucidate the core disease mechanisms. In this study, we aim to comprehensively characterize AD‐associated functional brain alterations using one of the world's largest resting‐state functional MRI (fMRI) biobank for the disorder. The biobank includes fMRI data from six neuroimaging centers, with a total of 252 AD patients, 221 mild cognitive impairment (MCI) patients and 215 healthy comparison individuals. Meta‐analytic techniques were used to unveil reliable differences in brain function among the three groups. Relative to the healthy comparison group, AD was associated with significantly reduced functional connectivity and local activity in the default‐mode network, basal ganglia and cingulate gyrus, along with increased connectivity or local activity in the prefrontal lobe and hippocampus ( p < .05, Bonferroni corrected). Moreover, these functional alterations were significantly correlated with the degree of cognitive impairment (AD and MCI groups) and amyloid‐β burden. Machine learning models were trained to recognize key fMRI features to predict individual diagnostic status and clinical score. Leave‐one‐site‐out cross‐validation established that diagnostic status (mean area under the receiver operating characteristic curve: 0.85) and clinical score (mean correlation coefficient between predicted and actual Mini‐Mental State Examination scores: 0.56, p < .0001) could be predicted with high accuracy. Collectively, our findings highlight the potential for a reproducible and generalizable functional brain imaging biomarker to aid the early diagnosis of AD and track its progression.
Background: Abnormal immune responses are involved in the development of Parkinson's disease (PD), and also affect peripheral blood lymphocytes. The profile of lymphocyte subsets in peripheral blood and whether it is relevant to the clinical features of PD patients remains controversial.Methods: To explore the role of peripheral blood lymphocytes (NK cells, B cells, CD3 + T cells, CD3 + CD4 + T cells and CD3 + CD8 + T cells) in the development of PD, a case-control study including 127 patients and 148 healthy controls was conducted, and peripheral blood lymphocyte subpopulations of participants were analysed by a FACSCalibur flow cytometer.Results: PD patients had a significantly higher percentage of NK cells and a lower percentage of CD3 + T cells and CD3 + CD4 + T cells than controls [16.4% (12.3%) vs. 12.6% (6.2%), 63.7% (14.2%) vs. 69.0% (6.6%), 33.1% (13.1%) vs. 38.9% (7.6%), P<0.05, respectively]. Through a binary logistic regression model adjusted for gender and age, we found that those who were outside of the reference range of peripheral blood lymphocytes (NK cell, B cell, CD3 + T cell and CD3 + CD4 + T cell) had an increased risk of PD [odds ratio (OR): 2.3, 5.1, 3.1 and 4.1, P<0.05, respectively]. Through a multivariable linear regression model adjusted for gender, age and levodopa equivalent daily dose, we found that deviation from the reference range of CD3 + CD8 + T cells (regression coefficient =3.474, P=0.015), course of disease (regression coefficient =0.411, P=0.004) and the Non-Motor Symptoms Scale (NMSS) scores (regression coefficient =0.553, P=5.92E−11) had a positive association with the Movement Disorders Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS)-III score (adjusted R 2 =0.364, F=13.004).Conclusions: Abnormal peripheral blood lymphocyte subpopulations have clinical relevance for PD.
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