FTIR determination of the degree of molar substitution for hydroxypropyl chitosan.

We developed and validated a novel Fourier transform infrared (FTIR) method to determine the degree of molar substitution (MS) for hydroxypropyl chitosan (HPCS) using nuclear magnetic resonance (1H NMR) as a reference, and investigated the factors influencing the MS assay. Through extensive screening of integration methods for candidate bands in the FTIR spectrum of HPCS using 20 HPCS samples with degrees of acetylation (DA) ranging from 0.003 to 0.139, we found that when using band area at 2970 cm-1 as a probe integral, the MS values obtained via the 1H NMR method exhibited linear correlations (R2 > 0.98) with at least 16 integral ratios derived from their FTIR spectra. The optimal reference bands with high reliability are located at 3440 cm-1 and 1415 cm-1, with R2 exceeding 0.99 and a MS range of 0.17-1.92. The band at 2875 cm-1 is less affected by the trace moisture present in HPCS samples than the others. The results of the method validation demonstrated a mean recovery of 98.9 ± 2.8 % and an RSD below 10 %, suggesting a simple, robust, and highly accurate and precise method. This method could be extendable for the determination of the MS of insoluble HPCS derivatives and other hydroxypropylated polysaccharides.

Design and Experimental Study of an Improved Pressure Core Sampler for Marine Gas Hydrates.

A pressure core sampler (PCS) is considered an effective tool to retrieve marine gas hydrate cores from hydrate-bearing sediments. However, according to the sampling application statistics, the success rate of pressure coring changed from 30% to 85% in different drilling wells. Such severe fluctuation will cause huge uncertainty in the practical application of technology and economic benefits. Herein, we present a new PCS designed to improve pressure-retaining reliability. The work principle, design and calculations, and structure composition were described. Through the laboratory tests and drilling experiments, the maximum holding pressure in the pressure chamber was 32.1 MPa, and the pressure loss rates of holding pressure after 2 h changed from 1.96% to 2.46%. The maximum temperature-rising value in the pressure chamber was 0.96 °C under a temperature of 23.5 °C in 2 h. Furthermore, the success rate of the pressure core reached 87.5% and the core recovery was not less than 80%, which were verified by 8 pressure core runs in three different offshore wells. Therefore, we conclude that this new and improved PCS has great application value in gas hydrate exploration that seeks to recover more accurate cores in situ, especially in the silt and sand layers.

Two new species of ant-loving beetles (Coleoptera: Staphylinidae: Pselaphinae) from Jiulong National Wetland Park, China.

Continued collecting efforts at the Jiulong National Wetland Park, Zhejiang, East China revealed two additional species of the ant-loving beetle subfamily Pselaphinae (Coleoptera: Staphylinidae): Trisiniotus jiulong sp. nov. and Arthromelodes lianghongbini sp. nov. Both new species are diagnosed, described, and their important characters are illustrated.

The genus Pseudophanias Raffray (Coleoptera, Staphylinidae, Pselaphinae) from Nanling Priority Area for Biodiversity Conservation, China.

Prior to this study, no species of Pseudophanias Raffray had been reported from Nanling, a vast biodiversity conservation area that spans five provinces in southern China. In this paper, three new species of the genus are described: Pseudophaniasfurcilobussp. nov. (Guizhou, Guangxi), P.leigongsp. nov. (Guizhou), and P.mulunsp. nov. (Guangxi), suggesting that additional study on the diversity of this group in the area is required. These species are characterized, keyed, and compared to similar congeners, supplemented with illustrations of the habitus and other morphological characters.

A new species of Tyrodes Raffray (Coleoptera: Staphylinidae: Pselaphinae) from China.

A new species of the small pselaphine genus Tyrodes Raffray (Pselaphitae: Tyrini), T. tibialis sp. nov., is described and illustrated from Guangxi, southwestern China. This species can be separated from its congeners mainly by the aedeagal characters as well as by the modified sternite 2 (V), and presence of a preapical projection of the metatibiae.

Nomogram to predict unfavorable outcome of endovascular thrombectomy for large ischemic core.

OBJECTIVE: The prognosis for patients presenting with a large ischemic core (LIC) following endovascular thrombectomy is relatively poor. This study aimed to construct and validate a nomogram for predicting 3-month unfavorable outcome in patients with anterior circulation occlusion-related LIC who underwent endovascular thrombectomy. METHODS: A retrospective training cohort and a prospective validation cohort of patients with a large ischemic core were studied. The diffusion weighted imaging related radiomic features and pre-thrombectomy clinical features were collected. After the selection of relevant features, a nomogram predicting modified Rankin Scale score of 3-6 as an unfavorable outcome was established. The discriminatory value of the nomogram was evaluated with a receiver operating characteristic curve. RESULTS: A total of 140 patients (mean age 66.3 ± 13.4 years, 35% female) were included in this study, consisting of a training cohort (n = 95) and a validation cohort (n = 45). The percentage of patients with an mRS scores of 0-2 was 30%, 0-3 was 40.7%, and 32.9% were dead. Age, National Institute of Health Stroke Scale (NIHSS) score, and two radiomic features, Maximum2DDiameterColumn and Maximum2DDiameterSlice, were identified as factors associated with unfavorable outcome in the nomogram. The nomogram demonstrated an area under the curve of 0.892 (95% confidence interval [CI], 0.812-0.947) in the training dataset and 0.872 (95% CI, 0.739-0.953) in the validation dataset. INTERPRETATION: This nomogram, which includes age, NIHSS score, Maximum2DDiameterColumn, and Maximum2DDiameterSlice, may predict the risk of unfavorable outcome in patients with LIC caused by anterior circulation occlusion.

CAN: Context-assisted full Attention Network for brain tissue segmentation.

Brain tissue segmentation is of great value in diagnosing brain disorders. Three-dimensional (3D) and two-dimensional (2D) segmentation methods for brain Magnetic Resonance Imaging (MRI) suffer from high time complexity and low segmentation accuracy, respectively. To address these two issues, we propose a Context-assisted full Attention Network (CAN) for brain MRI segmentation by integrating 2D and 3D data of MRI. Different from the fully symmetric structure U-Net, the CAN takes the current 2D slice, its 3D contextual skull slices and 3D contextual brain slices as the input, which are further encoded by the DenseNet and decoded by our constructed full attention network. We have validated the effectiveness of the CAN on our collected dataset PWML and two public datasets dHCP2017 and MALC2012. Our code is available at https://github.com/nwuAI/CAN.

Attention-modulated multi-branch convolutional neural networks for neonatal brain tissue segmentation.

Accurate measurement of brain structures is essential for the evaluation of neonatal brain growth and development. The conventional methods use manual segmentation to measure brain tissues, which is very time-consuming and inefficient. Recent deep learning achieves excellent performance in computer vision, but it is still unsatisfactory for segmenting magnetic resonance images of neonatal brains because they are immature with unique attributes. In this paper, we propose a novel attention-modulated multi-branch convolutional neural network for neonatal brain tissue segmentation. The proposed network is built on the encoder-decoder framework by introducing both multi-scale convolutions in the encoding path and multi-branch attention modules in the decoding path. Multi-scale convolutions with different kernels are used to extract rich semantic features across large receptive fields in the encoding path. Multi-branch attention modules are used to capture abundant contextual information in the decoding path for segmenting brain tissues by fusing both local features and their corresponding global dependencies. Spatial attention connections between the encoding and decoding paths are designed to increase feature propagation for both avoiding information loss during downsampling and accelerating model training convergence. The proposed network was implemented in comparison with baseline methods on three neonatal brain datasets. Our network achieves the average Dice similarity coefficients/the average Hausdorff distances of 0.9116/8.1289, 0.9367/9.8212 and 0.8931/8.1612 on the customized dCBP2021 dataset, 0.8786/11.7863, 0.8965/13.4296 and 0.8539/10.462 on the public NBAtlas dataset, as well as 0.9253/7.7968, 0.9448/9.5472 and 0.9132/7.5877 on the public dHCP2017 dataset in partitioning the brain into gray matter, white matter and cerebrospinal fluid, respectively. The experimental results show that the proposed method achieves competitive state-of-the-art performance in neonatal brain tissue segmentation. The code and pre-trained models are available at https://github.com/zhangyongqin/AMCNN.

Cross-domain heterogeneous residual network for single image super-resolution.

Single image super-resolution is an ill-posed problem, whose purpose is to acquire a high-resolution image from its degraded observation. Existing deep learning-based methods are compromised on their performance and speed due to the heavy design (i.e., huge model size) of networks. In this paper, we propose a novel high-performance cross-domain heterogeneous residual network for super-resolved image reconstruction. Our network models heterogeneous residuals between different feature layers by hierarchical residual learning. In outer residual learning, dual-domain enhancement modules extract the frequency-domain information to reinforce the space-domain features of network mapping. In middle residual learning, wide-activated residual-in-residual dense blocks are constructed by concatenating the outputs from previous blocks as the inputs into all subsequent blocks for better parameter efficacy. In inner residual learning, wide-activated residual attention blocks are introduced to capture direction- and location-aware feature maps. The proposed method was evaluated on four benchmark datasets, indicating that it can construct the high-quality super-resolved images and achieve the state-of-the-art performance. Code and pre-trained models are available at https://github.com/zhangyongqin/HRN.

Research updates on determinants of parental COVID-19 vaccine hesitancy for juvenile / 中国学校卫生

Abstract@#Vaccination is the most effective prevenative measure against infectious diseases. As guardians of minor children, parental attitudes have a decisive influence on children s vaccination. Therefore, it is very important to understand parental attitudes and preference towards the vaccination of minor children in COVID-19 for the successful implementation of vaccination. In this study, the current situation and associated factors of parental COVID-19 vaccine hesitancy for children. It was found that parental vaccine hesitation was mainly influenced by factors related to parents, children, vaccination and information process. Parents should be guided to obtain information through authoritative channels and treat vaccination correctly. At the same time, medical staff should play an important role in health education, submit correct immunisation information, reduce vaccine hesitancy, and promote herd immunity.

Simultaneous determination of molar degree of substitution and its distribution fraction, degree of acetylation in hydroxypropyl chitosan by 1H NMR spectroscopy.

Under the assistance of 13C NMR and 1H-13C HSQC, we develop a novel 1H NMR assay for the substitution sites and degrees in hydroxypropyl chitosan (HPCS) by optimizing sample preparation and measurement method. We find that the chemical shift of HOD peak increases linearly with the increase of DCl concentration but declines with the rise of measurement temperature. According to the regression line, the HOD peak could be moved to a desired position of non-interference with other peaks by changing DCl concentration. Other DCl-responsive peaks are found and elucidated. Accordingly, the substitution fraction (NH2-substitution and OH-substitution) and the degree of acetylation are well discriminated and determined. The total molar degree of substitution (MS) obtained is basically consistent with those of elemental analysis and the existing NMR methods. This structural analysis is extendable to other amino-containing saccharides. The 1H NMR method could be used widely in acid-soluble polysaccharides and their derivatives.

Hydrogen-substituted graphdiyne/graphene as an sp/sp2 hybridized carbon interlayer for lithium-sulfur batteries.

To overcome the shuttle effect in lithium-sulfur (Li-S) batteries, an sp/sp2 hybridized all-carbon interlayer by coating graphene (Gra) and hydrogen-substituted graphdiyne (HsGDY) with a specific surface area as high as 2184 m2 g-1 on a cathode is designed and prepared. The two-dimensional network and rich pore structure of HsGDY can enable the fast physical adsorption of lithium polysulfides (LiPSs). In situ Raman spectroscopy and ex situ X-ray photoelectron spectroscopy (XPS) combined with density functional theory (DFT) computations confirm that the acetylenic bonds in HsGDY can trap the Li+ of LiPSs owing to the strong adsorption of Li+ by acetylenic active sites. The strong physical adsorption and chemical anchoring of LiPSs by the HsGDY materials promote the conversion reaction of LiPSs to further mitigate the shuttling problem. As a result, Li-S batteries integrated with the all-carbon interlayers exhibit excellent cycling stability during long-term cycling with an attenuation rate of 0.089% per cycle at 1 C over 500 cycles.

Development and validation of an improved 3-methyl-2-benzothiazolinone hydrazone method for quantitative determination of reducing sugar ends in chitooligosaccharides.

An accurate and sensitive analytical method for detecting and quantifying reducing sugar ends (RSE) in chitooligosaccharides (COSs) is the key quality parameter for evaluating their structure-function relationship and potential applications. In this work, we develop and validate a novel colorimetric assay with high accuracy and precision for determining RSE content using 3-methyl-2-benzothiazolinone hydrazone (MBTH). Under optimal conditions, the stoichiometry is verified using mono-, di-, and tri- glucosamine hydrochlorides, and the dilution ratio does not interfere with the RSE content measured at 590 nm. The regression equation of glucosamine reveal a good linear relationship (R2 = 0.9999). The detection limit, quantification limit, mean relative standard deviation (RSD), and recovery are 2.28 µM, 9.11 µM, 1.90%, and 98.0%, respectively. The newly developed method is potentially useful for monitoring COS hydrolysis, number average molecular weight, and chitosanase activity.

Responses of metastatic primary fallopian tube carcinoma to pembrolizumab and nab-paclitaxel: A case report.

RATIONALE: Primary fallopian tube carcinoma (PFTC) is an extremely rare but invasive malignancy with a dismal prognosis. Very few data exist on the salvage treatment for patients with PFTC. Here we report a case showing an impressive response to immunotherapy combined with chemotherapy, which have never been reported before on patients with metastatic PFTC. PATIENT CONCERNS: A 42-year-old woman, who was diagnosed with PFTC in 2010, had been failed of multiple systemic therapies and antiangiogenic therapy because of the disease recurrence and progression. DIAGNOSIS: Metastatic primary fallopian tube carcinoma. INTERVENTIONS: The patient underwent surgery in May 2010 and had multi-line chemotherapies plus an anti-vascular endothelial growth factor (anti-VEGF) monoclonal antibody for about 9 years. Due to treatment failure the patient accepted the immunotherapy with the checkpoint inhibitor, pembrolizumab, combined with nab-paclitaxel from December 2018 to April 2019. OUTCOMES: The patient showed a complete response after 6 cycles treatment. Thus far, the patient is taking pembrolizumab as maintenance and remains in good health. LESSONS: Pembrolizumab combined with chemotherapy for treatment of PFTC may provide a positive antitumor effect in multiple metastatic lesions, but more clinical evidence is needed to confirm the efficacy and safety.

Synthesized 7T MRI from 3T MRI via deep learning in spatial and wavelet domains.

Ultra-high field 7T MRI scanners, while producing images with exceptional anatomical details, are cost prohibitive and hence highly inaccessible. In this paper, we introduce a novel deep learning network that fuses complementary information from spatial and wavelet domains to synthesize 7T T1-weighted images from their 3T counterparts. Our deep learning network leverages wavelet transformation to facilitate effective multi-scale reconstruction, taking into account both low-frequency tissue contrast and high-frequency anatomical details. Our network utilizes a novel wavelet-based affine transformation (WAT) layer, which modulates feature maps from the spatial domain with information from the wavelet domain. Extensive experimental results demonstrate the capability of the proposed method in synthesizing high-quality 7T images with better tissue contrast and greater details, outperforming state-of-the-art methods.

Estimating Tissue Microstructure with Undersampled Diffusion Data via Graph Convolutional Neural Networks.

Advanced diffusion models for tissue microstructure are widely employed to study brain disorders. However, these models usually require diffusion MRI (DMRI) data with densely sampled q-space, which is prohibitive in clinical settings. This problem can be resolved by using deep learning techniques, which learn the mapping between sparsely sampled q-space data and the high-quality diffusion microstructural indices estimated from densely sampled data. However, most existing methods simply view the input DMRI data as a vector without considering data structure in the q-space. In this paper, we propose to overcome this limitation by representing DMRI data using graphs and utilizing graph convolutional neural networks to estimate tissue microstructure. Our method makes full use of the q-space angular neighboring information to improve estimation accuracy. Experimental results based on data from the Baby Connectome Project demonstrate that our method outperforms state-of-the-art methods both qualitatively and quantitatively.

Dual-domain convolutional neural networks for improving structural information in 3 T MRI.

We propose a novel dual-domain convolutional neural network framework to improve structural information of routine 3 T images. We introduce a parameter-efficient butterfly network that involves two complementary domains: a spatial domain and a frequency domain. The butterfly network allows the interaction of these two domains in learning the complex mapping from 3 T to 7 T images. We verified the efficacy of the dual-domain strategy and butterfly network using 3 T and 7 T image pairs. Experimental results demonstrate that the proposed framework generates synthetic 7 T-like images and achieves performance superior to state-of-the-art methods.

Super-resolution reconstruction of neonatal brain magnetic resonance images via residual structured sparse representation.

Magnetic resonance images of neonates, compared with toddlers, exhibit lower signal-to-noise ratio and spatial resolution. In this paper, we propose a novel method for super-resolution reconstruction of neonate images with the help of toddler images, using residual-structured sparse representation with convex regularization. Specifically, we introduce a two-layer image representation, consisting of a base layer and a detail layer, to cater to signal variation across scanners and sites. The base layer consists of the smoothed version of the image obtained via Gaussian filtering. The detail layer is the difference between the original image and the base layer. High-frequency details in the detail layer are borrowed across subjects for super-resolution reconstruction. Experimental results on T1 and T2 images demonstrate that the proposed algorithm can recover fine anatomical structures, and generally outperform the state-of-the-art methods both qualitatively and quantitatively.

Longitudinally Guided Super-Resolution of Neonatal Brain Magnetic Resonance Images.

Neonatal magnetic resonance (MR) images typically have low spatial resolution and insufficient tissue contrast. Interpolation methods are commonly used to upsample the images for the subsequent analysis. However, the resulting images are often blurry and susceptible to partial volume effects. In this paper, we propose a novel longitudinally guided super-resolution (SR) algorithm for neonatal images. This is motivated by the fact that anatomical structures evolve slowly and smoothly as the brain develops after birth. We propose a strategy involving longitudinal regularization, similar to bilateral filtering, in combination with low-rank and total variation constraints to solve the ill-posed inverse problem associated with image SR. Experimental results on neonatal MR images demonstrate that the proposed algorithm recovers clear structural details and outperforms state-of-the-art methods both qualitatively and quantitatively.

Bacterial-derived Neutrophilic Inflammation Drives Lung Remodeling in a Mouse Model of Chronic Obstructive Pulmonary Disease.

Loss of secretory IgA is common in the small airways of patients with chronic obstructive pulmonary disease and may contribute to disease pathogenesis. Using mice that lack secretory IgA in the airways due to genetic deficiency of polymeric Ig receptor (pIgR-/- mice), we investigated the role of neutrophils in driving the fibrotic small airway wall remodeling and emphysema that develops spontaneously in these mice. By flow cytometry, we found an increase in the percentage of neutrophils among CD45+ cells in the lungs, as well as an increase in total neutrophils, in pIgR-/- mice compared with wild-type controls. This increase in neutrophils in pIgR-/- mice was associated with elastin degradation in the alveolar compartment and around small airways, along with increased collagen deposition in small airway walls. Neutrophil depletion using anti-Ly6G antibodies or treatment with broad-spectrum antibiotics inhibited development of both emphysema and small airway remodeling, suggesting that airway bacteria provide the stimulus for deleterious neutrophilic inflammation in this model. Exogenous bacterial challenge using lysates prepared from pathogenic and nonpathogenic bacteria worsened neutrophilic inflammation and lung remodeling in pIgR-/- mice. This phenotype was abrogated by antiinflammatory therapy with roflumilast. Together, these studies support the concept that disruption of the mucosal immune barrier in small airways contributes to chronic obstructive pulmonary disease progression by allowing bacteria to stimulate chronic neutrophilic inflammation, which, in turn, drives progressive airway wall fibrosis and emphysematous changes in the lung parenchyma.