Abstract Poly(ethylene oxide)‐ b ‐poly(4‐vinylpyridine) (PEO‐ b ‐P4VP) block copolymers (BCPs) exhibiting lower disorder‐to‐order transition (LDOT) phase behavior are doped with different salts (LiCl, CuCl 2 , and FeCl 3 ), in which both blocks can competitively associate with the metal ions. It is found that the entropy‐driven LDOT phase behavior of PEO‐ b ‐P4VP can be bi‐directionally adjusted by enthalpic interactions, depending on the complexation selectivity of metal ions toward two blocks and doping ratio ( r ). At low r s, Li + ions preferentially interact with PEO block, leading to a decreased disorder‐to‐order transition temperature ( T DOT ). Cu 2+ ions selectively complex with the P4VP block, and the T DOT first increases with increasing r , followed by a decrease. By contrast, Fe 3+ ions interact strongly with both blocks, resulting in increase of T DOT . At high r s, the complexation selectivity becomes weaker, leading to reduced immiscibility and increased T DOT , as compared with the hybrids with low r s. The effects of metal cation and r on the LDOT phase behavior are qualitatively explained by the change of the Flory–Huggins parameter.
Nucleosome positioning is involved in diverse cellular biological processes by regulating the accessibility of DNA sequences to DNA-binding proteins and plays a vital role. Previous studies have manifested that the intrinsic preference of nucleosomes for DNA sequences may play a dominant role in nucleosome positioning. As a consequence, it is nontrivial to develop computational methods only based on DNA sequence information to accurately identify nucleosome positioning, and thus intend to verify the contribution of DNA sequences responsible for nucleosome positioning. In this work, we propose a new deep learning-based method, named DeepNup, which enables us to improve the prediction of nucleosome positioning only from DNA sequences. Specifically, we first use a hybrid feature encoding scheme that combines One-hot encoding and Trinucleotide composition encoding to encode raw DNA sequences; afterwards, we employ multiscale convolutional neural network modules that consist of two parallel convolution kernels with different sizes and gated recurrent units to effectively learn the local and global correlation feature representations; lastly, we use a fully connected layer and a sigmoid unit serving as a classifier to integrate these learned high-order feature representations and generate the final prediction outcomes. By comparing the experimental evaluation metrics on two benchmark nucleosome positioning datasets, DeepNup achieves a better performance for nucleosome positioning prediction than that of several state-of-the-art methods. These results demonstrate that DeepNup is a powerful deep learning-based tool that enables one to accurately identify potential nucleosome sequences.
The prevalence of asthma has gradually increased worldwide in recent years, which has made asthma a global public health problem. However, due to its complexity and heterogeneity, there are a few academic debates on the pathogenic mechanism of asthma. The study of the pathogenesis of asthma through metabolomics has become a new research direction. We aim to uncover the metabolic pathway of children with asthma.
Background:Rectal cancer is a common malignant tumor of the digestive tract, and its morbidity and mortality are increasing yearly.Treatment is mainly based on surgery and chemoradiotherapy, and targeted therapy and immunotherapy are primarily used for locally advanced stages.In this study, we describe a case of rectal cancer with ulcers that gradually healed after local deep hyperthermia combined with chemotherapy.The pain and the local discomfort symptoms were alleviated, and survival and quality of life were improved.This study provides a new clinical treatment model for similar diseases. Case summary:A 53-year-old male patient was diagnosed with rectal cancer in August 2020, with a giant cancerous ulcer (10*7 cm) in the anus and perineal region; after 11 cycles of capecitabine plus Oxaliplatin (XELOX) chemotherapy, stable disease (S.D.) was achieved in March 2021.Specialist examination revealed ulcers at the perineum (from the 6 o'clock position of the anus to the coccyx), which showed bulging edges, uneven surfaces, erosion, and bleeding.The mass had poor mobility.Digital rectal examination showed an empty rectum, a complicated ulcer could be palpated in a half-circle of the anal canal at the 6:30 position, the upper pole was located 3.0 cm above the dentate line, and no blood stains were observed on finger cots.Considering the side effects of Oxaliplatin, 10 rounds of local deep hyperthermia were started in April 2021, chemotherapy with capecitabine monotherapy was administered for 2 cycles, and the ulcer area gradually increased.The tumor shrank, and the ulcer surface gradually healed after 10 sessions of hyperthermia. Conclusion:Local deep hyperthermia combined with chemotherapy can promote gradual healing of ulceration of rectal cancer and improve survival and quality of life.
Net ecosystem productivity (NEP) is an important index for the quantitative evaluation of carbon sources and sinks in terrestrial ecosystems. Based on MOD17A3 and meteorological data, the vegetation NEP was estimated from 2000 to 2021 in the Loess Plateau (LP) and its six ecological subregions of the LP (loess sorghum gully subregions:A1, A2; loess hilly and gully subregions:B1, B2; sandy land and agricultural irrigation subregion:C; and earth-rock mountain and river valley plain subregion:D). Combined with the terrain, remote sensing, and human activity data, Theil-Sen Median trend analysis, correlation analysis, multiple regression residual analysis, and geographic detector were used, respectively, to explore the spatio-temporal characteristics of NEP and its response mechanism to climate, terrain, and human activity. The results showed that:① On the temporal scale, from 2000 to 2021 the annual mean NEP of the LP region (in terms of C) was 104.62 g·(m
Background: Major Depressive Disorder (MDD) is common and disabling, but its neural pathophysiology remains unclear. Functional brain network studies in MDD have largely had limited statistical power and data analysis approaches have varied widely. The REST-meta-MDD Project of resting-state fMRI (R-fMRI) addresses these issues.
Major Depressive Disorder (MDD) is one of the most common and disabling psychiatric disorders, but its neural pathophysiology remains largely unknown. Functional brain network studies in MDD have suffered from limited statistical power and high flexibility in data analyses. Here we launched the REST-meta-MDD Project of resting-state fMRI (R-fMRI) data of 1,300 patients with MDD and 1,128 normal controls (NCs) from 25 research groups in China. The data were preprocessed with a standardized protocol across all sites prior to aggregated group analyses. Our analyses focused on functional connectivity (FC) within the default mode network (DMN) which has been frequently reported to show increased FC in MDD. However, we found decreased instead of increased DMN FC in MDD compared to NCs. Specifically, this FC reduction only presented in recurrent MDD but not in first-episode drug naive MDD. Patients with recurrent MDD even demonstrated decreases of DMN FC when directly compared with the first-episode drug naive MDD. Decreased DMN FC was associated with medication usage in MDD but not illness duration or severity. Finally, exploratory analyses revealed alterations of local intrinsic activity in the MDD samples. The pooled R-fMRI metrics of the REST-meta-MDD Project provide an unprecedented opportunity to investigate the key neural underpinnings of MDD and call for longitudinal brain imaging studies to understand the effects of medications, illness duration and severity.
Growth differentiation factor 15 (GDF-15), a stress-responsive biomarker, is known to be independently associated with mortality and cardiovascular events in different disease settings, but data on the prognostic value of GDF-15 after stroke are limited.Baseline serum GDF-15 was measured in 3066 acute ischemic stroke patients from the China Antihypertensive Trial in Acute Ischemic Stroke (CATIS). The primary outcome was a composite of death and major disability within 3 months. Secondary outcomes included death, major disability, vascular events, and stroke recurrence. The associations between GDF-15 and clinical outcomes after stroke were assessed by multivariate logistic regression or Cox proportional hazards models.At 3 months' follow-up, 676 (22.05%), 86 (2.80%), 81 (2.64%), and 51 (1.66%) patients had experienced major disability, death, vascular events, or stroke recurrence, respectively. After adjusting for age, sex, current smoking, alcohol consumption, and baseline National Institutes of Health Stroke Scale score, the odds ratio/hazard ratio (95% CI) of 1 SD higher of base-10 log-transformed GDF-15 was 1.26 (1.15-1.39) for primary outcome, 1.13 (1.02-1.25) for major disability, 1.79 (1.48-2.16) for death, and 1.26 (1.00-1.58) for vascular events. The addition of GDF-15 to established risk factors improved risk prediction of the composite outcome of death and major disability (c-statistic, net reclassification index, and integrated discrimination improvement, all P < 0.05).High GDF-15 concentrations are independently associated with adverse clinical outcomes of acute ischemic stroke, suggesting that baseline serum GDF-15 could provide additional information to identify ischemic stroke patients at high risk of poor prognosis.
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