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في كتابه \"تحديث الاقتصاد الصيني\" يتناول تشياو جنغ عملية الإصلاح الاقتصادي في الصين خلال ثلاثة عقود تم فيها وصف التطور السريع لاقتصاد الصين ب\"المعجزة الاقتصادية\"، ولكنه وصف غير دقيق -بتعبير المؤلف- وبمعنى أكثر دقة، يعتبر التطور السريع لاقتصاد الصين مجرد عملية انتعاش اقتصادي. إنها عملية مواكبة لاقتصادات الأسواق في المناطق المتقدمة.

Geoscientists mainly identify subsurface geologic features using exploration-derived seismic data. Classification or segmentation of 2D/3D seismic images commonly relies on conventional deep learning methods for image recognition. However, complex reflections of seismic waves tend to form high-dimensional and multi-scale signals, making traditional convolutional neural networks (CNNs) computationally costly. Here we propose a highly efficient and resource-saving CNN architecture (SeismicPatchNet) with topological modules and multi-scale-feature fusion units for classifying seismic data, which was discovered by an automated data-driven search strategy. The storage volume of the architecture parameters (0.73 M) is only ~2.7 MB, ~0.5% of the well-known VGG-16 architecture. SeismicPatchNet predicts nearly 18 times faster than ResNet-50 and shows an overwhelming advantage in identifying Bottom Simulating Reflection (BSR), an indicator of marine gas-hydrate resources. Saliency mapping demonstrated that our architecture captured key features well. These results suggest the prospect of end-to-end interpretation of multiple seismic datasets at extremely low computational cost. The authors present an automated design approach to propose a new neural network architecture for seismic data analysis. The new architecture classifies multiple seismic reflection datasets at extremely low computational cost compared with conventional architectures for image classification.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Background Inflammatory reactions induced by spinal cord injury (SCI) are essential for recovery after SCI. Atractylenolide III (ATL‐III) is a natural monomeric herbal bioactive compound that is mainly derived in Atractylodes macrocephala Koidz and has anti‐inflammatory and neuroprotective effects. Objective Here, we speculated that ATL‐III may ameliorate SCI by modulating microglial/macrophage polarization. In the present research, we focused on investigating the role of ATL‐III on SCI in rats and explored the potential mechanism. Methods The protective and anti‐inflammatory effects of ATL‐III on neuronal cells were examined in a rat SCI model and lipopolysaccharide (LPS)‐stimulated BV2 microglial line. The spinal cord lesion area, myelin integrity, and surviving neurons were assessed by specific staining. Locomotor function was evaluated by the Basso, Beattie, and Bresnahan (BBB) scale, grid walk test, and footprint test. The activation and polarization of microglia/macrophages were assessed by immunohistofluorescence and flow cytometry. The expression of corresponding inflammatory factors from M1/M2 and the activation of relevant signaling pathways were assessed by Western blotting. Results ATL‐III effectively improved histological and functional recovery in SCI rats. Furthermore, ATL‐III promoted the transformation of M1 into M2 and attenuated the activation of microglia/macrophages, further suppressing the expression of corresponding inflammatory mediators. This effect may be partly mediated by inhibition of neuroinflammation through the NF‐κB, JNK MAPK, p38 MAPK, and Akt pathways. Conclusion This study reveals a novel effect of ATL‐III in the regulation of microglial/macrophage polarization and provides initial evidence that ATL‐III has potential therapeutic benefits in SCI rats. ATL‐III has potential therapeutic benefits in promoting histological and functional repair in rats after SCI. This effect may be partly mediated by inhibiting neuroinflammation induced by microglial polarization through the NF‐κB, JNK MAPK, p38 MAPK, and Akt pathways.

It remains unknown whether capturing data from electronic health records (EHRs) using natural language processing (NLP) can improve venous thromboembolism (VTE) detection in different clinical settings. The aim of this study was to validate the NLP algorithm in a clinical decision support system for VTE risk assessment and integrated care (DeVTEcare) to identify VTEs from EHRs. All inpatients aged ≥18 years in the Sixth Medical Center of the Chinese People's Liberation Army General Hospital from January 1 to December 31, 2021, were included as the validation cohort. The sensitivity, specificity, positive and negative likelihood ratios (LR+ and LR-, respectively), area under the receiver operating characteristic curve (AUC), and F1-scores along with their 95% CIs were used to analyze the performance of the NLP tool, with manual review of medical records as the reference standard for detecting deep vein thrombosis (DVT) and pulmonary embolism (PE). The primary end point was the performance of the NLP approach embedded into the EHR for VTE identification. The secondary end points were the performances to identify VTE among different hospital departments with different VTE risks. Subgroup analyses were performed among age, sex, and the study season. Among 30,152 patients (median age 56 [IQR 41-67] years; 14,247/30,152, 47.3% females), the prevalence of VTE, PE, and DVT was 2.1% (626/30,152), 0.6% (177/30,152), and 1.8% (532/30,152), respectively. The sensitivity, specificity, LR+, LR-, AUC, and F1-score of NLP-facilitated VTE detection were 89.9% (95% CI 87.3%-92.2%), 99.8% (95% CI 99.8%-99.9%), 483 (95% CI 370-629), 0.10 (95% CI 0.08-0.13), 0.95 (95% CI 0.94-0.96), and 0.90 (95% CI 0.90-0.91), respectively. Among departments of surgery, internal medicine, and intensive care units, the highest specificity (100% vs 99.7% vs 98.8%, respectively), LR+ (3202 vs 321 vs 77, respectively), and F1-score (0.95 vs 0.89 vs 0.92, respectively) were in the surgery department (all P<.001). Among low, intermediate, and high VTE risks in hospital departments, the low-risk department had the highest AUC (1.00 vs 0.94 vs 0.96, respectively) and F1-score (0.97 vs 0.90 vs 0.90, respectively) as well as the lowest LR- (0.00 vs 0.13 vs 0.08, respectively) (DeLong test for AUC; all P<.001). Subgroup analysis of the age, sex, and season demonstrated consistently good performance of VTE detection with >87% sensitivity and specificity and >89% AUC and F1-score. The NLP algorithm performed better among patients aged ≤65 years than among those aged >65 years (F1-score 0.93 vs 0.89, respectively; P<.001). The NLP algorithm in our DeVTEcare identified VTE well across different clinical settings, especially in patients in surgery units, departments with low-risk VTE, and patients aged ≤65 years. This algorithm can help to inform accurate in-hospital VTE rates and enhance risk-classified VTE integrated care in future research.

We previously demonstrated that cancer‐associated fibroblasts (CAFs) promoted the proliferation of gallbladder cancer (GBC) cells, but the mechanism is not clear. Neuropilin‐1 (NRP‐1) plays an important role in various malignancies as transmembrane glycoprotein. Our goal was to reveal the relationship between CAFs and NRP‐1 and their potential functions in GBC. In this study, we found NRP‐1 was overexpressed in GBC tissue, associated with poor survival and was up‐regulated by CAFs. The cytokine array cluster analysis revealed IL‐8 secreted by CAFs facilitated the up‐regulation of NRP‐1 in tumour cells. NRP‐1 knockdown suppressed tumour growth in vivo. Gene expression microarray analysis showed 581 differentially regulated genes under NRP‐1 knockdown conditions. Ingenuity pathway analysis demonstrated that NRP‐1 knockdown may inhibit tumour progression by affecting cell proliferation. We then confirmed that NRP‐1 knockdown in NOZ and GBC‐SD cells significantly inhibited cell proliferation. Additionally, the IL‐8 mediated MDM2 and CCNA2 expression were affected by NRP‐1 knockdown. Our findings suggested that NRP‐1 was up‐regulated by CAF‐secreted IL‐8, which subsequently promoted GBC cell proliferation, and these molecules may serve as useful prognostic biomarkers and therapeutic targets for GBC.

Hepatic arterial infusion chemotherapy (HAIC) using a combination of oxaliplatin, fluorouracil, and leucovorin (FOLFOX) has shown promise for hepatocellular carcinoma (HCC) patients classified under Barcelona Clinic Liver Cancer (BCLC) stage C. In China, the combined therapy of camrelizumab and apatinib is now an approved first-line approach for inoperable HCC. This study (NCT04191889) evaluated the benefit of combining camrelizumab and apatinib with HAIC-FOLFOX for HCC patients in BCLC stage C. Eligible patients were given a maximum of six cycles of HAIC-FOLFOX, along with camrelizumab and apatinib, until either disease progression or intolerable toxicities emerged. The primary outcome measured was the objective response rate (ORR) based on the Response Evaluation Criteria in Solid Tumors (RECIST) v1.1. Thirty-five patients were enrolled. Based on RECIST v1.1 criteria, the confirmed ORR stood at 77.1% (95% CI: 59.9% to 89.6%), with a disease control rate of 97.1% (95% CI: 85.1% to 99.9%). The median progression-free survival was 10.38 months (95% CI: 7.79 to 12.45). Patient quality of life had a transient deterioration within four cycles of treatment, and generally recovered thereafter. The most frequent grade ≥3 or above treatment-related adverse events included reduced lymphocyte count (37.1%) and diminished neutrophil count (34.3%). The combination of camrelizumab, apatinib, and HAIC demonstrated encouraging results and manageable safety concerns for HCC at BCLC stage C.

Objectives In order to provide a more comprehensive understanding of the effects of SARS‐CoV‐2 on oral health and possible saliva transmission, we performed RNA‐seq profiles analysis from public databases and also a questionnaire survey on oral‐related symptoms of COVID‐19 patients. Materials and methods To analyse ACE2 expression in salivary glands, bulk RNA‐seq profiles from four public datasets including 31 COVID‐19 patients were recruited. Saliva and oropharyngeal swabs were collected. SARS‐CoV‐2 nucleic acids in saliva were detected by real‐time polymerase chain reaction (RT‐PCR). Additionally, a questionnaire survey on various oral symptoms such as dry mouth and amblygeustia was also carried out on COVID‐19 patients. Results ACE2 expression was present at detectable levels in the salivary glands. In addition, of four cases with positive detection of salivary SARS‐CoV‐2 nucleic acids, three (75%) were critically ill on ventilator support. Furthermore, we observed the two major oral‐related symptoms, dry mouth (46.3%) and amblygeustia (47.2%), were manifested by a relatively high proportion of 108 COVID‐19 patients who accepted the questionnaire survey. Conclusions This study confirms the expression of ACE2 in the salivary glands and demonstrates the possibility of SARS‐CoV‐2 infection of salivary glands. Saliva may be a new source of diagnostic specimens for critically ill patients, since it can be easily collected without any invasive procedures. In addition, dry mouth and amblygeustia can be considered as initial symptoms of COVID‐19 infection. In this study, based on the confirmation of ACE2 receptor expression in salivary glands through database analysis, it was assumed and confirmed that RNA of SARS‐COV‐2 could be detected in saliva samples. At the same time, we also clinically observed that the initial oral‐related symptoms of SARS‐COV‐2 infection may be amblygeustia and dry mouth.

Bergenin is a natural PPARγ agonist that can prevent neutrophil aggregation, and often be used in clinics for treating respiratory diseases. Recent data show that Th17 cells are important for neutrophil aggregation and asthma through secreting IL-17A. In this study, we investigated the effects of bergenin on Th17 differentiation in vitro and subsequent neutrophilic asthma in mice. Naïve T cells isolated from mouse mesenteric lymph nodes were treated with IL-23, TGF-β, and IL-6 to induce Th17 differentiation. We showed that in naïve T cells under Th17-polarizing condition, the addition of bergenin (3, 10, 30 μM) concentration-dependently decreased the percentage of CD4 + IL-17A + T cells and mRNA expression of specific transcription factor RORγt, and function-related factors IL-17A/F, IL-21, and IL-22, but did not affect the cell vitality and apoptosis. Furthermore, bergenin treatment prevented GLS1-dependent glutaminolysis in the progress of Th17 differentiation, slightly affected the levels of SLC1A5, SLC38A1, GLUD1, GOT1, and GPT2. Glutamine deprivation, the addition of glutamate (1 mM), α-ketoglutarate (1 mM), or GLS1 plasmid all significantly attenuated the above-mentioned actions of bergenin. Besides, we demonstrated that bergenin (3, 10, and 30 μM) concentration-dependently activated PPARγ in naïve T cells, whereas PPARγ antagonist GW9662 and siPPARγ abolished bergenin-caused inhibition on glutaminolysis and Th17 differentiation. Furthermore, we revealed that bergenin inhibited glutaminolysis by regulating the level of CDK1, phosphorylation and degradation of Cdh1, and APC/C-Cdh1-mediated ubiquitin-proteasomal degradation of GLS1 after activating PPARγ. We demonstrated a correlation existing among bergenin-affected GLS1-dependent glutaminolysis, PPARγ, “CDK1-APC/C-Cdh1” signaling, and Th17 differentiation. Finally, the therapeutic effect and mechanisms for bergenin-inhibited Th17 responses and neutrophilic asthma were confirmed in a mouse model of neutrophilic asthma by administration of GW9662 or GLS1 overexpression plasmid in vivo. In conclusion, bergenin repressed Th17 differentiation and then alleviated neutrophilic asthma in mice by inhibiting GLS1-dependent glutaminolysis via regulating the “CDK1-APC/C-Cdh1” signaling after activating PPARγ.

The aim of this study was to develop and validate a machine learning-based mortality risk prediction model for patients with severe community-acquired pneumonia (SCAP) in the intensive care unit (ICU). We collected data from two centers as the development and external validation cohorts. Variables were screened using the Recursive Feature Elimination method. Five machine learning algorithms were used to build predictive models. Models were evaluated through nested cross-validation to select the best one. The model was interpreted using Shapley Additive Explanations. We selected the optimal model to generate the web calculator. A total of 23 predictive features were selected. The Light Gradient Boosting Machine (LightGBM) model had an area under the receiver operating characteristic curve (AUC) of 0.842 (95% CI: 0.757–0.927), with an external 5-fold cross-validation average AUC of 0.842 ± 0.038, which was superior to the other models. External validation results also demonstrated good performance by the LightGBM model with an AUC of 0.856 (95% CI: 0.792–0.921). Based on this, we generated a web calculator by combining five high importance predictive factors. The LightGBM model was confirmed to be efficient and stable in predicting the mortality risk of patients with SCAP admitted to the ICU. The web calculator based on the LightGBM model can provide clinicians with a prognostic evaluation tool.