Development and validation of a prediction model for hypoproteinemia after traumatic spinal cord injury: A multicenter retrospective clinical study.

A multicenter retrospective analysis of conventionally collected data. To identify the potential causes of hypoproteinemia after traumatic spinal cord injury (TSCI) and provide a diagnostic model for predicting an individual likelihood of developing hypoproteinemia. Hypoproteinemia is a complication of spinal cord injury (SCI), an independent risk factor for respiratory failure in elderly patients with SCI, and a predictor of outcomes in patients with cervical SCI. Few nomogram-based studies have used clinical indicators to predict the likelihood of hypoproteinemia following TSCI. This multicenter retrospective clinical analysis included patients with TSCI admitted to the First Affiliated Hospital of Guangxi Medical University, Wuzhou GongRen Hospital, and Dahua Yao Autonomous County People Hospital between 2016 and 2020. The data of patients from the First Affiliated Hospital of Guangxi Medical University were used as the training set, and those from the other 2 hospitals were used as the validation set. All patient histories, diagnostic procedures, and imaging findings were recorded. To predict whether patients with TSCI may develop hypoproteinemia, a least absolute shrinkage and selection operator regression analysis was conducted to create a nomogram. The model was validated by analyzing the consequences using decision curve analysis, calibration curves, the C-index, and receiver operating characteristic curves. After excluding patients with missing data, 534 patients were included in this study. Male/female sex, age ≥ 60 years, cervical SCI, pneumonia, pleural effusion, urinary tract infection (UTI), hyponatremia, fever, hypotension, and tracheostomy were identified as independent risk factors of hypoalbuminemia. A simple and easy-to-replicate clinical prediction nomogram was constructed using these factors. The area under the curve was 0.728 in the training set and 0.881 in the validation set. The predictive power of the nomogram was satisfactory. Hypoalbuminemia after TSCI may be predicted using the risk factors of male/female sex, age ≥ 60 years, cervical SCI, pneumonia, pleural effusion, UTI, hyponatremia, fever, hypotension, and tracheostomy.

Development and validation of a risk nomogram to estimate risk of hyponatremia after spinal cord injury: A retrospective single-center study.

OBJECTIVE: This study aimed to establish a nomogram-based assessment for predicting the risk of hyponatremia after spinal cord injury (SCI). DESIGN: The study is a retrospective single-center study. PARTICIPANTS: SCI patients hospitalized in the First Affiliated Hospital of Guangxi Medical University. SETTING: The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China. METHODS: We performed a retrospective clinical study to collect SCI patients hospitalized in the First Affiliated Hospital of Guangxi Medical University from 2016 to 2020. Based on their clinical scores, the SCI patients were grouped as either hyponatremic or non-hyponatremic, SCI patients in 2016-2019 were identified as the training set, and patients in 2020 were identified as the test set. A nomogram was generated, the calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to validate the model. RESULTS: A total of 895 SCI patients were retrieved. After excluding patients with incomplete data, 883 patients were finally included in this study and used to construct the nomograms. The indicators used in the nomogram included sex, completeness of SCI, pneumonia, urinary tract infection, fever, constipation, white blood cell (WBC), albumin and serum Ca2+. These indices were determined by the least absolute shrinkage and selection operator (LASSO) regression analysis. The C-index of the model was 0.81, the area under the curve (AUC) of the training set was 0.82(Cl:0.79-0.85), and the validation set was 0.79(Cl:0.73-0.85). CONCLUSIONS: Nomogram has good predictive ability, sex, completeness of SCI, pneumonia, urinary tract infection, fever, constipation, WBC, albumin and serum Ca2+ were predictors of hyponatremia after SCI.

Estimation of surface ozone concentration over Jiangsu province using a high-performance deep learning model.

Recently, the global background concentration of ozone (O3) has demonstrated a rising trend. Among various methods, groun-based monitoring of O3 concentrations is highly reliable for research analysis. To obtain information on the spatial characteristics of O3 concentrations, it is necessary that the ground monitoring sites be constructed in sufficient density. In recent years, many researchers have used machine learning models to estimate surface O3 concentrations, which cannot fully provide the spatial and temporal information contained in a sample dataset. To solve this problem, the current study utilized a deep learning model called the Residual connection Convolutional Long Short-Term Memory network (R-ConvLSTM) to estimate daily maximum 8-hr average (MDA8) O3 over Jiangsu province, China during 2020. In this research, the R-ConvLSTM model not only provides the spatiotemporal information of MDA8 O3, but also involves residual connection to avoid the problem of gradient explosion and gradient disappearance with the deepening of network layers. We utilized the TROPOMI total O3 column retrieved from Sentinel-5 Precursor, ERA5 reanalysis meteorological data, and other supplementary data to build a pre-trained dataset. The R-ConvLSTM model achieved an overall sample-base cross-validation (CV) R2 of 0.955 with root mean square error (RMSE) of 9.372 µg/m3. Model estimation also showed a city-based CV R2 of 0.896 with RMSE of 14.029 µg/m3, the highest MDA8 O3 in spring being 122.60 ± 31.60 µg/m3 and the lowest in winter being 69.93 ± 18.48 µg/m3.

Proton-Conductive Polyoxometalate Architectures Constructed from Lanthanide-Incorporated Polyoxoniobate Cages.

Two new extended polyoxometalate (POM) architectures based on lanthanide-incorporated polyoxoniobate (Ln-incorporated PONb) cages, namely, H4[CuII(en)2]4{K4(H2O)2[CuII(en)2]5[CuII5(trz)2(en)4(OH)2][Dy2CuII2(en)2(CO3)3(H2O)2(OH)3][Dy(H2O)4][DyNb23O68(H2O)4]2}·60H2O (1, en = ethylenediamine) and H20[CuII(en)2]4{[CuII(en)2]4[Dy2(C2O4)(H2O)4]2[(Nb32(OH)4(H2O)3O89]2}·54H2O (2), have been successfully synthesized and structurally characterized, demonstrating a feasible strategy to develop functional POM materials. In addition, the proton conductivity and magnetic behaviors of both 1 and 2 were studied.

New Deep Learning Model to Estimate Ozone Concentrations Found Worrying Exposure Level over Eastern China.

Ozone (O3), whose concentrations have been increasing in eastern China recently, plays a key role in human health, biodiversity, and climate change. Accurate information about the spatiotemporal distribution of O3 is crucial for human exposure studies. We developed a deep learning model based on a long short-term memory (LSTM) network to estimate the daily maximum 8 h average (MDA8) O3 across eastern China in 2020. The proposed model combines LSTM with an attentional mechanism and residual connection structure. The model employed total O3 column product from the Tropospheric Monitoring Instrument, meteorological data, and other covariates as inputs. Then, the estimates from our model were compared with real observations of the China air quality monitoring network. The results indicated that our model performed better than other traditional models, such as the random forest model and deep neural network. The sample-based cross-validation R2 and RMSE of our model were 0.94 and 10.64 µg m−3, respectively. Based on the O3 distribution over eastern China derived from the model, we found that people in this region suffered from excessive O3 exposure. Approximately 81% of the population in eastern China was exposed to MDA8 O3 > 100 µg m−3 for more than 150 days in 2020.

Two high-nuclearity isopolyoxoniobates containing {Nb54 O151}-based helical nanotubes for the decomposition of chemical warfare agent simulants.

Two inorganic-organic hybrid isopolyoxoniobates (1 and 2) based on new high-nuclearity {Nb54O151} clusters have been synthesized under hydrothermal conditions. In particular, the combination of the unique {Nb54O151} clusters with copper-amine complexes has led to rare helical nanotubes, which are further linked by alkali metal cations or copper-amine complexes into two 2D similar bamboo-raft-like layered networks (1 and 2), respectively. Compound 1 exhibits effective base-catalytic decomposition of chemical warfare agent simulants dimethyl methylphosphonate (DMMP) and diethyl cyanophosphonate (DECP).

An Improved Method for Road Extraction from High-Resolution Remote-Sensing Images that Enhances Boundary Information.

At present, deep-learning methods have been widely used in road extraction from remote-sensing images and have effectively improved the accuracy of road extraction. However, these methods are still affected by the loss of spatial features and the lack of global context information. To solve these problems, we propose a new network for road extraction, the coord-dense-global (CDG) model, built on three parts: a coordconv module by putting coordinate information into feature maps aimed at reducing the loss of spatial information and strengthening road boundaries, an improved dense convolutional network (DenseNet) that could make full use of multiple features through own dense blocks, and a global attention module designed to highlight high-level information and improve category classification by using pooling operation to introduce global information. When tested on a complex road dataset from Massachusetts, USA, CDG achieved clearly superior performance to contemporary networks such as DeepLabV3+, U-net, and D-LinkNet. For example, its mean IoU (intersection of the prediction and ground truth regions over their union) and mean F1 score (evaluation metric for the harmonic mean of the precision and recall metrics) were 61.90% and 76.10%, respectively, which were 1.19% and 0.95% higher than the results of D-LinkNet (the winner of a road-extraction contest). In addition, CDG was also superior to the other three models in solving the problem of tree occlusion. Finally, in universality research with the Gaofen-2 satellite dataset, the CDG model also performed well at extracting the road network in the test maps of Hefei and Tianjin, China.

High-dimensional Polyoxoniobates Constructed from Lanthanide-incorporated High-nuclear {[Ln(H2 O)4 ]3 [Nb24 O69 (H2 O)3 ]2 } Secondary Building Units.

In this work, two rare high-dimensional polyoxoniobates with formulas of H9 [Cu(en)(H2 O)2 ][Cu(en)2 ]8 [Dy(H2 O)4 ]3 [Nb24 -O69 (H2 O)3 ]2 ⋅ 36H2 O (1) and H9 K[Cu(en)2 (H2 O)]5 [Cu(en)2 ]4 -[Eu(H2 O)4 ]3 [Nb24 O69 (H2 O)3 ]2 ⋅ 2en ⋅ 45H2 O (2) have been obtained under hydrothermal conditions. These extended materials are constructed from lanthanide-incorporated triangular-prism-like polyoxoniobate secondary building units (SBUs) {[Ln(H2 O)4 ]3 [Nb24 O69 (H2 O)3 ]2 } (Ln=Dy, Eu). 1 and 2 represent the first examples of high-dimensional polyoxometalate materials based on such lanthanide-incorporated triangular-prism-like polyoxoniobate SBUs. Furthermore, the proton conduction property and the luminescent emission of these materials were evaluated.

A Series of Cube-Shaped Polyoxoniobates Encapsulating Octahedral Cu12X m O n Clusters With Hydrolytic Decomposition for Chemical Warfare Agents.

This study reported a series of cube-shaped polyoxoniobates, {MCu12O8)(Cu12XmOn)(Nb7(OH)O21)8} [M = Nb(1, 2), Ln3+(3), X = I(1, m = 3, n = 3; 2, m = 5, n = 1), Br(3, m = 5, n = 1)]. As the first octahedral Cu12XmOn cluster incorporated polyoxoniobate, the cube-shaped three-shell structure of {MCu12O8)(Cu12XmOn)(Nb7(OH)O21)8} polyanion contains a {MCu12O8} body-centered cuboctahedron, a {Cu12XmOn} octahedron and a {Cu12(Nb7(OH)O21)8} cube. Compounds 1, 2, 3 show effective catalytic activities for the hydrolytic decomposition of chemical warefare agent simulants.

Land Use Classification of the Deep Convolutional Neural Network Method Reducing the Loss of Spatial Features.

Land use classification is a fundamental task of information extraction from remote sensing imagery. Semantic segmentation based on deep convolutional neural networks (DCNNs) has shown outstanding performance in this task. However, these methods are still affected by the loss of spatial features. In this study, we proposed a new network, called the dense-coordconv network (DCCN), to reduce the loss of spatial features and strengthen object boundaries. In this network, the coordconv module is introduced into the improved DenseNet architecture to improve spatial information by putting coordinate information into feature maps. The proposed DCCN achieved an obvious performance in terms of the public ISPRS (International Society for Photogrammetry and Remote Sensing) 2D semantic labeling benchmark dataset. Compared with the results of other deep convolutional neural networks (U-net, SegNet, Deeplab-V3), the results of the DCCN method improved a lot and the OA (overall accuracy) and mean F1 score reached 89.48% and 86.89%, respectively. This indicates that the DCCN method can effectively reduce the loss of spatial features and improve the accuracy of semantic segmentation in high resolution remote sensing imagery.

A Chromium-Substituted Polyoxoniobate with High Ionic Conductivity.

A rare and novel Cr-substituted polyoxoniobate (PONb), [Cr2.5Nb27.5O66(OH)20(H2O)2]7-, has been synthesized by a combination of hydrothermal and conventional solution methods. The PONb shows an unknown tetrameric structure, which is the largest Cr-containing PONb to date. Interestingly, every two PONb tetramers can be joined together by alkali-metal cations to form a discrete cubelike ionic cluster. The obtained PONb not only enriches the very limited members of the Cr-substituted PONb family but also exhibits high ionic conductivity.

A coastline generalization method that considers buffer consistency.

Coastlines are the boundary between the ocean and land and are an important line feature in spatial data. Coastlines must be adapted via map generalization when they are stored in multi-scale spatial databases. This article presents a new method of coastline generalization that considers the buffer consistency from the original coastline to the generalized coastlines. This method uses the geographical distance field to identify the feature points and bends that influence the buffer consistency from the original coastline to the generalized coastline, and the process is also used to simplify the bends and maintain the concave and convex characteristics in the generalization. This method is compared to the bend-simplification method, and the results indicate that the proposed method can preserve the shape characteristics of coastlines. Moreover, the seaward buffer boundaries from the original coastline to the generalized coastline are consistent when the distance is greater than the tolerance of the coastline generalization.

Traditional Chinese Medicine Extract from Huaier Increases the Expression of Duffy Antigen Receptor for Chemokines and Reduces the Expression of Its Ligands.

AIMS: The aim of the present study is to investigate whether the aqueous extract from Huaier, a traditional Chinese medicine (TCM), can affect the expression of Duffy antigen receptor for chemokines (DARC) and its ligands. Moreover, we compare the status of DARC in primary and metastatic breast cancer tissues from the same patient. METHODS: Immunohistochemistry was used to detect the expression of DARC in primary and metastatic focuses in 30 patients with breast cancer. The effect of Huaier aqueous extract on the expression of DARC and its ligands was investigated by quantitative real-time polymerase chain reaction, Western blotting, and enzyme-linked immunosorbent assay. RESULTS: The expression score of DARC in primary focuses was significantly higher than that in metastatic focuses, while changes of ER, PR, and HER2 receptors were not significantly different between primary and metastatic focuses. Huaier aqueous extract promoted the expression of DARC and reduced the secretion of CC chemokine ligand 2 (CCL-2), CXC chemokine ligand 8 (CXCL-8, IL-8), matrix metalloproteinase 2 (MMP-2), and CXC chemokine ligand 1 (CXCL-1). CONCLUSION: The present study demonstrates that difference in expression level of DARC between primary and metastatic focuses of breast cancer was significant, while differences in expression of ER, PR, and HER2 between primary and metastatic focuses were not significant. DARC may play a negative role in the metastasis of breast cancer. Traditional Chinese medicine extract from Huaier can increase DARC expression and reduce the expression of its ligands such as CCL-2, IL-8, MMP-2, and CXCL-1.

{Nb288 O768 (OH)48 (CO3 )12 }: A Macromolecular Polyoxometalate with Close to 300 Niobium Atoms.

A protein-sized (ca. 4.2×4.2×3.6 nm3 ) non-biologically derived molecule {Nb288 O768 (OH)48 (CO3 )12 } (Nb288 ) containing up to 288 niobium atoms has been obtained, which is by far the largest and the highest nuclearity polyoxoniobate (PONb). Particularly, in terms of metal nuclearity number, Nb288 is the second largest cluster so far reported in classic polyoxometalate chemistry (V, Mo, W, Nb, and Ta). Nb288 can be described as a giant windmill-like cluster aggregate of six nanoscale high-nuclearity PONb units {Nb47 O128 (OH)6 (CO3 )2 } (Nb47 ) joined together by six additional Nb ions. Interestingly, the 47-nuclearity Nb47 units generated in situ can be isolated and bridged by copper complexes to form an inorganic-organic hybrid three-dimensional PONb framework, which exhibits effective catalytic activity for hydrolyzing nerve agent simulant of dimethyl methylphosphonate. The unique Nb47 cluster also provides a new type of topology to very limited family of Nb-O clusters.

A Series of Banana-Shaped 3d-4f Heterometallic Cluster Substituted Polyoxometalates: Syntheses, Crystal Structures, and Magnetic Properties.

Four seven-nuclearity 3d-4f heterometallic cluster {Fe6LnO28} substituted polyoxometalates (HPz)11K4Fe6Ln(µ3-O)2(B-α-GeW9O34)2(GeW6O26)· xH2O (1-Ln, Pz = piperazine, Ln = Tb, Dy, Ho, Er for x = 27, 25, 25, 24, respectively) have been hydrothermally prepared and structurally characterized by single-crystal X-ray diffraction, powder X-ray diffraction, infrared spectrometry, thermogravimetric analyses, elemental analyses, and electrospray ionization mass spectrometry. Single-crystal X-ray diffraction analyses revealed that 1-Ln contain an unprecedented banana-shaped polyanion constructed from an iron-lanthanide heterometallic {Fe6LnO28} cluster, two trilacunary {B-α-GeW9O34} units, and one hexalacunary {GeW6O26} fragment. The magnetic susceptibility surveying proved the presence of antiferromagnetic coupling in 1-Ln.

Record High-Nuclearity Polyoxoniobates: Discrete Nanoclusters {Nb114 }, {Nb81 }, and {Nb52 }, and Extended Frameworks Based on {Cu3 Nb78 } and {Cu4 Nb78 }.

A series containing the highest nuclearity polyoxoniobate (PONb) nanoclusters, ranging from dimers to tetramers, has been obtained. They include one 114-nuclear {Li8 ⊂Nb114 O316 }, one 81-nuclear {Li3 K⊂Nb81 O225 }, and one 52-nuclear {H4 Nb52 O150 }. The Nb nuclearity of these PONbs is remarkably larger than those of all known high-nuclearity PONbs (≤32). Furthermore, the introduction of 3d Cu2+ ions can lead to the generation of extended inorganic-organic hybrid frameworks built from novel, high-nuclearity, nanoscale heterometallic PONb building blocks {H3 Cu3 Nb78 O222 } or {H3 Cu4 (en)Nb78 O222 }. These building blocks also contain the largest number of Nb centers of any heterometallic PONbs reported to date. The synthesis of new-type PONbs has long been a challenging subject in PONb chemistry.

[Impacts of high-volume hemofiltration on hemodynamics and vascular endothelial permeability in children with septic shock].

OBJECTIVE: To investigate the effects of high-volume hemofiltration (HVHF) on hemodynamics, vasoactive factors, and vascular endothelial permeability in children with septic shock by a comparative analysis. METHODS: Thirty-six children who were diagnosed with septic shock between January 2013 and September 2014 were randomly divided into control and observation groups (n=18 each). Children in the control group were treated with the standard-volume hemofiltration (SVHF), while children in the observation group were treated with HVHF. The hemodynamic indices and levels of vasoactive factors including 6-keto-prostaglandin F1α (6-keto-PGF1α), thromboxane B2 (TXB2), soluble E-selectin (sE-selectin), and endothelium-derived relaxing factor (EDRF) were determined before and after treatment. In addition, the effects of ultrafiltrate on endothelial cell permeability were assessed. RESULTS: Compared with the control group, the observation group had significantly higher mean arterial pressure, significantly higher blood oxygen saturation, and a significantly lower heart rate after treatment (P<0.05). The levels of TXB2 and sE-selectin were significantly lower in the observation group than in the control group (P<0.05), while the levels of 6-keto-PGF1α and EDRF were significantly higher in the observation group than in the control group (P<0.05). Compared with the control group, the ultrafiltrate significantly attenuated the transepithelial electrical resistance in the observation group (P<0.05). CONCLUSIONS: Compared with SVHF, HVHF is a more effective approach for improving the hemodynamics and levels of vasoactive factors and reducing the vascular endothelial permeability in children with septic shock.

Myc induced miR-144/451 contributes to the acquired imatinib resistance in chronic myelogenous leukemia cell K562.

Imatinib resistance remains the big hurdle for CML therapy. Previous study reveals that c-myc is important for bcr-abl CML cell proliferation, while its role in imatinib resistance is largely unknown. In this study, we first found that c-myc expression is upregulated in imatinib resistant K562R cells, which in turn enhances the expression of miR-144/451. Knockdown of c-myc or restoration of miR-144/451 in the K562R cells sensitizes K562R cells to imatinib therapy. Our study here reveals an regulatory pathway between myc and miR-144/451 and highlights that targeting either myc or miR-144/451 might be valuable for eliminating the imatinib resistant CML cells.

Co-culture of apoptotic breast cancer cells with immature dendritic cells: a novel approach for DC-based vaccination in breast cancer

A dendritic cell (DC)-based vaccine strategy could reduce the risk of recurrence and improve the survival of breast cancer patients. However, while therapy-induced apoptosis of hepatocellular and colorectal carcinoma cells can enhance maturation and antigen presentation of DCs, whether this effect occurs in breast cancer is currently unknown. In the present study, we investigated the effect of doxorubicin (ADM)-induced apoptotic MCF-7 breast cancer cells on the activation of DCs. ADM-induced apoptotic MCF-7 cells could effectively induce immature DC (iDC) maturation. The mean fluorescence intensity (MFI) of DC maturity marker CD83 was 23.3 in the ADM-induced apoptotic MCF-7 cell group compared with 8.5 in the MCF-7 cell group. The MFI of DC co-stimulatory marker CD86 and HLA-DR were also increased after iDCs were treated with ADM-induced apoptotic MCF-7 cells. Furthermore, the proliferating autologous T-lymphocytes increased from 14.2 to 40.3% after incubated with DCs induced by apoptotic MCF-7 cells. The secretion of interferon-γ by these T-lymphocytes was also increased. In addition, cell-cell interaction between apoptotic MCF-7 cells and iDCs, but not soluble factors released by apoptotic MCF-7 cells, was crucial for the maturation of iDCs. These findings constitute a novel in vitro DC-based vaccine strategy for the treatment of breast cancer by ADM-induced apoptotic MCF-7 cells.