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1.
DNA Cell Biol ; 2020 Nov 03.
Artigo em Inglês | MEDLINE | ID: mdl-33146560

RESUMO

Long noncoding RNAs (lncRNAs) may serve as potential molecular diagnostic markers to improve the capacity of earlier and more accurate diagnosis of dilated cardiomyopathy (DCM). We integrated five independent transcriptomic datasets (n = 504) from Gene Expression Omnibus for systematic identification of lncRNA-based diagnostic biomarkers in DCM. The multivariate logistic regression model based on the six lncRNAs (AC016722.3, AL589986.2, AC006007.1, AC092687.3, GS1-124K5.4, and AC007126.1) in the ceRNA networks showed high sensitivity and specificity (area under curves >0.8, p < 0.0001) of DCM diagnosis in the training and validation datasets. Functional analysis revealed that the autophagy, protein acetyltransferase, and DNA polymerase activity were associated with high levels of the six-lncRNA signature, while the collagen trimer, extracellular matrix structural constituent, and MHC protein complex were associated with low levels of the signature. Pathway analysis showed that high levels of the six-lncRNA signature were associated with upregulated selective autophagy, interleukin 17 signalings, and extracellular matrix interactions, while were associated with downregulated extracellular matrix organization and collagen formation. The identified six-lncRNA signature, with high performance in molecular diagnosis of DCM, might be applied in future clinical practices combined with traditional markers.

2.
J Recept Signal Transduct Res ; : 1-9, 2020 Nov 04.
Artigo em Inglês | MEDLINE | ID: mdl-33148101

RESUMO

BACKGROUND: Sinonasal squamous cell carcinoma (SNSCC) is a main subtype of sinonasal malignancy with unclear pathogenesis. microRNAs (miRNAs) are involved in SNSCC progression. Nevertheless, the role and mechanism of miR-362-3p in SNSCC development are unclear. METHODS: The SNSCC tissues (n = 23) and normal sinonasal samples (n = 13) were harvested. SNSCC cell line RPMI-2650 cells were transfected using Lipofectamine 3000. miR-362-3p and pituitary tumor-transforming gene 1 (PTTG1) were determined by quantitative reverse transcription polymerase chain reaction and western blot. Cell proliferation was analyzed via Cell Counting Kit-8 and 5-ethynyl-2'-deoxyuridine assays. Cell migration and invasion was assessed using wound healing assay and transwell assay. Epithelial-mesenchymal transition (EMT)-associated protein (E-cadherin, N-cadherin and Vimentin) levels were measured via western blot. The binding relationship was analyzed via bioinformatic analysis and dual-luciferase reporter assay. RESULTS: miR-362-3p abundance was decreased in SNSCC samples. miR-362-3p addition constrained cell proliferation, migration, invasion and EMT, but miR-362-3p knockdown played an opposite effect. PTTG1 was targeted and negatively modulated by miR-362-3p. PTTG1 abundance was elevated in SNSCC samples. PTTG1 overexpression mitigated miR-362-3p-modulated suppression of cell proliferation, migration, invasion and EMT in SNSCC cells. CONCLUSION: miR-362-3p repressed cell proliferation, migration, invasion and EMT in SNSCC via targeting PTTG1.

3.
Artigo em Inglês | MEDLINE | ID: mdl-33166256

RESUMO

OBJECTIVE: The coronavirus disease 2019 (COVID-19) is rapidly spreading inside China and internationally. We aimed to construct a model integrating information from radiomics and deep learning (DL) features to discriminate critical cases from severe cases of COVID-19 using computed tomography (CT) images. METHODS: We retrospectively enrolled 217 patients from three centers in China, including 82 patients with severe disease and 135 with critical disease. Patients were randomly divided into a training cohort (n=174) and a test cohort (n=43). We extracted 102 3-dimensional radiomic features from automatically segmented lung volume and selected the significant features. We also developed a 3-dimensional DL network based on center-cropped slices. Using multivariable logistic regression, we then created a merged model based on significant radiomic features and DL scores. We employed the area under the receiver operating characteristic curve (AUC) to evaluate the model's performance. We then conducted cross validation, stratified analysis, survival analysis, and decision curve analysis to evaluate the robustness of our method. RESULTS: The merged model could distinguish critical patients with AUCs of 0.909 (95% confidence interval [CI]: 0.8590.952) and 0.861 (95% CI: 0.7530.968) in the training and test cohorts, respectively. Stratified analysis indicated that our model was not affected by sex, age, or chronic disease. Moreover, the results of the merged model showed a strong correlation with patient outcomes. SIGNIFICANCE: A model combining radiomic and DL features of the lung could help distinguish critical cases from severe cases of COVID-19.

4.
Org Lett ; 2020 Nov 11.
Artigo em Inglês | MEDLINE | ID: mdl-33174421

RESUMO

This paper reports a highly site-selective alkylation of heteroarene N-oxides using hypervalent iodine(III) carboxylates to serve as an alkylating agent in the presence of a cheap copper catalyst under visible light conditions. This mild method proceeds at room temperature in an air atmosphere and can withstand various heteroarene N-oxides as well as various primary, secondary, and tertiary alkyl carboxylic acids. It also provides a practical method for enabling the rapid conversion of commercially available raw materials into medically relevant "drug-like" molecules.

5.
Front Immunol ; 11: 588682, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33163004

RESUMO

Glucocorticoid-induced TNFR-related protein (GITR) is a member of the TNFR superfamily which is expressed in various cells, including T cells, natural killer cells and some myeloid cells. GITR is activated by its ligand, GITRL, mainly expressed on antigen presenting cells and endothelial cells. It has been acknowledged that the engagement of GITR can modulate both innate and adaptive immune responses. Accumulated evidence suggests GITR/GITRL interaction is involved in the pathogenesis of tumor, inflammation and autoimmune diseases. In this review, we describe the effects of GITR/GITRL activation on effector T cells, regulatory T cells (Tregs) and myeloid cells; summarize its role and the underlying mechanisms in modulating autoimmune diseases.

6.
Chem Commun (Camb) ; 2020 Nov 13.
Artigo em Inglês | MEDLINE | ID: mdl-33185645

RESUMO

A simple and practical method to access N-substituted 2-pyridones via a formal [3+3] annulation of enaminones with acrylates based on RhIII-catalyzed C-H functionalization was developed. Control and deuterated experiments led to a plausible mechanism involving C-H bond cross-coupling and aminolysis cyclization. This strategy provides a short synthesis of structural motifs of N-substituted 2-pyridones.

7.
Cancer Lett ; 2020 Nov 04.
Artigo em Inglês | MEDLINE | ID: mdl-33157157

RESUMO

Tumor angiogenesis is a major characteristic of renal cell carcinoma (RCC). Herein, we report a novel mechanism of how lncRNA and androgen receptor (AR) drive the Hedgehog pathway to promote tumor angiogenesis in RCC. We found that the high expression of lncRNA HOTAIR in RCC is associated with poor prognosis. Moreover, HOTAIR and AR form a feedback loop to promote the expression of each other. Interestingly, we also found that in RCC, HOTAIR is associated with the Hedgehog pathway, especially GLI2, via bioinformatics analysis. Furthermore, HOTAIR promotes GLI2 expression in the presence of AR. Mechanistically, HOTAIR interacts with AR and they cooperatively bind to GLI2 promoter and increase its transcription activity. We further confirmed how HOTAIR-AR axis regulates GLI2 expression by analyzing its function in RCC cells and found that HOTAIR and AR synergistically enhanced the expression of GLI2 downstream genes, such as VEGFA, PDGFA, and cancer stem cell transcription factors, and promoted tumor angiogenesis and cancer stemness in RCC cells both in vitro and in tumor xenografts. Overall, these findings suggest that HOTAIR and GLI2 could be novel therapeutic targets against RCC.

8.
Biomolecules ; 10(11)2020 Nov 01.
Artigo em Inglês | MEDLINE | ID: mdl-33139642

RESUMO

The primary cilium, an antenna-like structure on most eukaryotic cells, functions in transducing extracellular signals into intracellular responses via the receptors and ion channels distributed along it membrane. Dysfunction of this organelle causes an array of human diseases, known as ciliopathies, that often feature obesity and diabetes; this indicates the primary cilia's active role in energy metabolism, which it controls mainly through hypothalamic neurons, preadipocytes, and pancreatic ß-cells. The nutrient sensor, O-GlcNAc, is widely involved in the regulation of energy homeostasis. Not only does O-GlcNAc regulate ciliary length, but it also modifies many components of cilia-mediated metabolic signaling pathways. Therefore, it is likely that O-GlcNAcylation (OGN) plays an important role in regulating energy homeostasis in primary cilia. Abnormal OGN, as seen in cases of obesity and diabetes, may play an important role in primary cilia dysfunction mediated by these pathologies.

9.
Artigo em Inglês | MEDLINE | ID: mdl-33033904

RESUMO

BACKGROUND: Cryoballoon (CB) has been widely utilized in the treatment of drug-refractory atrial fibrillation (AF), but the balance point between efficacy and safety has been unclear. The protocol based on the time-to-isolation (TTI) was expected to provide patients with individualized ablation strategies. METHODS: All studies up to June 2020 comparing the CB of TTI-based protocol (TTIP) and conventional protocol (ConP) in PubMed, Embase, and Cochrane Library databases were searched. The pooled OR or SMD with 95% CIs for each outcome were calculated with inverse-variance random effect model. The Egger method was used to evaluate the publication bias and the subgroup analysis was conducted according to the type of atrial fibrillation. RESULTS: Six studies enrolling a total of 1770 patients with drug-refractory AF were included. The pool real-time recording of pulmonary veins potential was 71% (95% CI: 61 ~ 81%, I2 = 97.9%) and a similar incidence of freedom from ATs after 1 year (OR: 1.12; 95% CI: 0.86 ~ 1.46, I2 = 0.0%, P = 0.481) was observed between two protocols. No difference was observed in complications (OR: 0.67; 95% CI: 0.43 ~ 1.04, I2 = 0.0%, P = 0.717) and phrenic nerve palsy (OR: 0.70; 95% CI: 0.37 ~ 1.35, I2 = 0.0%, P = 0.807). TTIP could significantly decrease the CB freezes per patient (SMD: - 2.44; 95% CI: - 4.46 to approximately - 0.41; I2 = 99.5%, P = 0.00) and shorten the cryotherapy application time (SMD: - 3.04; 95% CI: - 4.18 to approximately - 1.89; I2 = 97.4%, P = 0.00), procedure time (SMD: - 1.51; 95% CI: - 2.08 to approximately - 0.94; I2 = 95.4%, P = 0.00), and fluorescence time (SMD: - 0.70; 95% CI: - 1.25 to approximately - 0.15; I2 = 95.7%, P = 0.00). CONCLUSION: TTIP is safe and effective and it opens a new chapter in the field of individualized protocol of CB for patients with AF.

10.
ACS Nano ; 2020 Oct 28.
Artigo em Inglês | MEDLINE | ID: mdl-33111520

RESUMO

Vessel embolization is recommended as the first line treatment for unresectable hepatocellular carcinoma (HCC). However, owing to the imprecise vessel embolization and heterogeneous response performance among patients, its survival benefits are often compromised. Herein, we reported an innovative strategy to extensively embolize the tumor by triggering the coagulation cascade, and predict the embolization effect with vessel density assessment. We synthesized manganese dioxide (MnO2)/verteporfin (BPD) nanocomposites, in which BPD bound to the tumor vessel endothelial cells (TVECs) and MnO2 nanosheets served as the carrier. MnO2 was reduced to Mn2+ ions and self-assembled with BPD to produce nanoBPD, resulting in enhanced TVECs apoptosis and coagulation cascade compared to that with free BPD. Furthermore, multimodal imaging was used to visualize tumor vessel density, which can be used as a predictor to identify the patients who would benefit from embolization. Our findings describe a promising strategy for both tumor eradication and effect prediction to improve survival benefits in unresectable HCC patients.

11.
Med Phys ; 2020 Oct 29.
Artigo em Inglês | MEDLINE | ID: mdl-33119899

RESUMO

PURPOSE: To develop and validate a deep learning (DL)-based radiomics model to predict the response to chemotherapy in colorectal liver metastases (CRLM). METHODS: In this retrospective study, we enrolled 192 patients diagnosed with CRLM who received first-line chemotherapy and were followed by response assessment. Tumor response was identified according to the Response Evaluation Criteria in Solid Tumors (RECIST). Contrast-enhanced multidetector computed tomography (MDCT) images were fed as inputs of the ResNet10-based DL radiomics model, and the possibility of response was predicted as the output. The final combined DL radiomics model was constructed by integrating the response related clinical factors and the developed DL radiomics signature. A time-independent validation cohort (n=48) was extracted from the 192 patients to evaluate the DL model with area under the receiver operating characteristic curve (AUC), specificity, and sensitivity. Meanwhile, a traditional radiomics model was constructed using least absolute shrinkage and selection operator (lasso) as comparisons with the DL-based model. RESULTS: According to RECIST criteria, 131 patients were identified as responders with complete response, partial response and stable disease, while 61 patients were non-responders with progression disease. The selected predictive clinical factor turned out to be the carcinoembryonic antigen (CEA) level with AUC of 0.489 (95% confidence interval [CI], 0.380-0.599) and 0.558 (95% CI, 0.374-0.741) in the training and validation cohorts, respectively. The DL-based model provided better performance than the traditional classifier-based radiomics model with significantly higher AUC (training: 0.903 [95% CI, 0.851-0.955] vs.0.745 [95% CI, 0.659-0.831]; validation: 0.820 [95% CI, 0.681-0.959] vs. 0.598 [95% CI, 0.422-0.774]). The combination of DL-based model with the CEA level provided slightly increased performance with AUC of 0.935 [95% CI, 0.897-0.973] in the training cohort and 0.830 [95% CI, 0.688-0.973] in the validation cohort. CONCLUSIONS: The developed DL-based radiomics model could improve the efficiency to predict the response to chemotherapy in CRLM, which may assist in subsequent personalized treatment decision-making in CRLM management.

12.
Eur J Radiol ; 132: 109326, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-33049651

RESUMO

PURPOSE: To establish and validate a combined clinical-radiomics model for preoperative prediction of synchronous peritoneal metastasis (PM) in patients with colorectal cancer (CRC). METHOD: We enrolled 779 patients (585 in the training set: 553 with nonmetastasis (NM) and 32 with PM; 194 in the validation set: 184 with NM and 10 with PM) with clinicopathologically confirmed CRC. The significant clinical risk factors were used to build the clinical model; the least absolute shrinkage and selection operator (LASSO) algorithm was adopted to construct a radiomics signature, which included imaging features of the primary lesion and the largest peripheral lymph node, and stepwise logistic regression was applied to select the significant variables to develop the clinical-radiomics model. We used the Akaike information criterion (AIC) and receiver operating characteristic analysis to compare the goodness of fit and the prediction performance of the three models respectively. An independent validation cohort, containing 139 consecutive patients from February to September 2018, was used to evaluate the performance of the optimal model. RESULTS: Among the three models, the clinical-radiomics model (AUC = 0.855; AIC = 1043.2) was identified as the optimal model, with the maximum AUC value and the minimum AIC value (the clinical-only model: AUC = 0.771, AIC = 1277.7; the radiomics-only model: AUC = 0.764, AIC = 1280.5). The clinical-radiomics model also showed good discrimination in both the validation cohort (AUC = 0.793) and the independent validation cohort (AUC = 0.781). CONCLUSIONS: The present study proposes a clinical-radiomics model created with the CT-based radiomics signature and key clinical features that can potentially be applied in the individual preoperative prediction of synchronous PM for CRC patients.

13.
Artigo em Inglês | MEDLINE | ID: mdl-33108303

RESUMO

Since its outbreak in December 2019, the persistent coronavirus disease (COVID-19) became a global health emergency. It is imperative to develop a prognostic tool to identify high-risk patients and assist in the formulation of treatment plans. We retrospectively collected 366 severe or critical COVID-19 patients from four centers, including 70 patients who died within 14 days (labeled as high-risk patients) since their initial CT scan and 296 who survived more than 14 days or were cured (labeled as low-risk patients). We developed a 3D densely connected convolutional neural network (termed De-COVID19-Net) to predict the probability of COVID-19 patients belonging to the high-risk or low-risk group, combining CT and clinical information. The area under the curve (AUC) and other evaluation techniques were used to assess our model. The De-COVID19-Net yielded an AUC of 0.952 (95% confidence interval, 0.928-0.977) on the training set and 0.943 (0.904-0.981) on the test set. The stratified analyses indicated that our model's performance is independent of age, sex, and with/without chronic diseases. The Kaplan-Meier analysis revealed that our model could significantly categorize patients into high-risk and low-risk groups (p < 0.001). In conclusion, De-COVID19-Net can non-invasively predict whether a patient will die shortly based on the patient's initial CT scan with an impressive performance, which indicated that it could be used as a potential prognosis tool to alert high-risk patients and intervene in advance.

14.
Med Image Anal ; 67: 101854, 2020 Oct 08.
Artigo em Inglês | MEDLINE | ID: mdl-33091742

RESUMO

Pathology Artificial Intelligence Platform (PAIP) is a free research platform in support of pathological artificial intelligence (AI). The main goal of the platform is to construct a high-quality pathology learning data set that will allow greater accessibility. The PAIP Liver Cancer Segmentation Challenge, organized in conjunction with the Medical Image Computing and Computer Assisted Intervention Society (MICCAI 2019), is the first image analysis challenge to apply PAIP datasets. The goal of the challenge was to evaluate new and existing algorithms for automated detection of liver cancer in whole-slide images (WSIs). Additionally, the PAIP of this year attempted to address potential future problems of AI applicability in clinical settings. In the challenge, participants were asked to use analytical data and statistical metrics to evaluate the performance of automated algorithms in two different tasks. The participants were given the two different tasks: Task 1 involved investigating Liver Cancer Segmentation and Task 2 involved investigating Viable Tumor Burden Estimation. There was a strong correlation between high performance of teams on both tasks, in which teams that performed well on Task 1 also performed well on Task 2. After evaluation, we summarized the top 11 team's algorithms. We then gave pathological implications on the easily predicted images for cancer segmentation and the challenging images for viable tumor burden estimation. Out of the 231 participants of the PAIP challenge datasets, a total of 64 were submitted from 28 team participants. The submitted algorithms predicted the automatic segmentation on the liver cancer with WSIs to an accuracy of a score estimation of 0.78. The PAIP challenge was created in an effort to combat the lack of research that has been done to address Liver cancer using digital pathology. It remains unclear of how the applicability of AI algorithms created during the challenge can affect clinical diagnoses. However, the results of this dataset and evaluation metric provided has the potential to aid the development and benchmarking of cancer diagnosis and segmentation.

15.
Med Image Anal ; 67: 101873, 2020 Oct 18.
Artigo em Inglês | MEDLINE | ID: mdl-33129143

RESUMO

Automatic semantic segmentation in 2D echocardiography is vital in clinical practice for assessing various cardiac functions and improving the diagnosis of cardiac diseases. However, two distinct problems have persisted in automatic segmentation in 2D echocardiography, namely the lack of an effective feature enhancement approach for contextual feature capture and lack of label coherence in category prediction for individual pixels. Therefore, in this study, we propose a deep learning model, called deep pyramid local attention neural network (PLANet), to improve the segmentation performance of automatic methods in 2D echocardiography. Specifically, we propose a pyramid local attention module to enhance features by capturing supporting information within compact and sparse neighboring contexts. We also propose a label coherence learning mechanism to promote prediction consistency for pixels and their neighbors by guiding the learning with explicit supervision signals. The proposed PLANet was extensively evaluated on the dataset of cardiac acquisitions for multi-structure ultrasound segmentation (CAMUS) and sub-EchoNet-Dynamic, which are two large-scale and public 2D echocardiography datasets. The experimental results show that PLANet performs better than traditional and deep learning-based segmentation methods on geometrical and clinical metrics. Moreover, PLANet can complete the segmentation of heart structures in 2D echocardiography in real time, indicating a potential to assist cardiologists accurately and efficiently.

16.
Nat Commun ; 11(1): 5228, 2020 10 16.
Artigo em Inglês | MEDLINE | ID: mdl-33067442

RESUMO

Two major treatment strategies employed in non-small cell lung cancer, NSCLC, are tyrosine kinase inhibitors, TKIs, and immune checkpoint inhibitors, ICIs. The choice of strategy is based on heterogeneous biomarkers that can dynamically change during therapy. Thus, there is a compelling need to identify comprehensive biomarkers that can be used longitudinally to help guide therapy choice. Herein, we report a 18F-FDG-PET/CT-based deep learning model, which demonstrates high accuracy in EGFR mutation status prediction across patient cohorts from different institutions. A deep learning score (EGFR-DLS) was significantly and positively associated with longer progression free survival (PFS) in patients treated with EGFR-TKIs, while EGFR-DLS is significantly and negatively associated with higher durable clinical benefit, reduced hyperprogression, and longer PFS among patients treated with ICIs. Thus, the EGFR-DLS provides a non-invasive method for precise quantification of EGFR mutation status in NSCLC patients, which is promising to identify NSCLC patients sensitive to EGFR-TKI or ICI-treatments.


Assuntos
Carcinoma Pulmonar de Células não Pequenas/diagnóstico por imagem , Aprendizado Profundo , Neoplasias Pulmonares/diagnóstico por imagem , Inibidores de Proteínas Quinases/administração & dosagem , Idoso , Carcinoma Pulmonar de Células não Pequenas/tratamento farmacológico , Carcinoma Pulmonar de Células não Pequenas/genética , Carcinoma Pulmonar de Células não Pequenas/mortalidade , Receptores ErbB/genética , Receptores ErbB/metabolismo , Feminino , Humanos , Neoplasias Pulmonares/tratamento farmacológico , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/mortalidade , Masculino , Pessoa de Meia-Idade , Mutação , Tomografia Computadorizada com Tomografia por Emissão de Pósitrons , Intervalo Livre de Progressão
17.
J Immunother Cancer ; 8(2)2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-33115941

RESUMO

BACKGROUND: The survival benefits of combining chemotherapy (at the maximum tolerated dose, MTD) with concurrent immunotherapy, collectively referred to as chemoimmunotherapy, for the treatment of squamous cell lung carcinoma (SQCLC) have been confirmed in recent clinical trials. Nevertheless, optimization of chemoimmunotherapy in order to enhance the efficacy of immune checkpoint inhibitors (ICIs) in SQCLC remains to be explored. METHODS: Cell lines, syngeneic immunocompetent mouse models, and patients' peripheral blood mononuclear cells were used in order to comprehensively explore how to enhance ectopic lymphoid-like structures (ELSs) and upregulate the therapeutic targets of anti-programmed death 1 (PD-1)/anti-PD-1 ligand (PD-L1) monoclonal antibodies (mAbs), thus rendering SQCLC more sensitive to ICIs. In addition, molecular mechanisms underlying optimization were characterized. RESULTS: Low-dose chemotherapy contributed to an enhanced antigen exposure via the phosphatidylinositol 3-kinase/Akt/transcription factor nuclear factor kappa B signaling pathway. Improved antigen uptake and presentation by activated dendritic cells (DCs) was observed, thus invoking specific T cell responses leading to systemic immune responses and immunological memory. In turn, enhanced antitumor ELSs and PD-1/PD-L1 expression was observed in vivo. Moreover, upfront metronomic (low-dose and frequent administration) chemotherapy extended the time window of the immunostimulatory effect and effectively synergized with anti-PD-1/PD-L1 mAbs. A possible mechanism underlying this synergy is the increase of activated type I macrophages, DCs, and cytotoxic CD8+ T cells, as well as the maintenance of intestinal gut microbiota diversity and composition. In contrast, when combining routine MTD chemotherapy with ICIs, the effects appeared to be additive rather than synergistic. CONCLUSIONS: We first attempted to optimize chemoimmunotherapy for SQCLC by investigating different combinatorial modes. Compared with the MTD chemotherapy used in current clinical practice, upfront metronomic chemotherapy performed better with subsequent anti-PD-1/PD-L1 mAb treatment. This combination approach is worth investigating in other types of tumors, followed by translation into the clinic in the future.

18.
PLoS One ; 15(10): e0240359, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33104724

RESUMO

Considering that the Pc-Crash multibody dynamics software can reproduce the accident process accurately and obtain the collision parameters of pedestrian heads at the moment of head landing, the finite element analysis method can accurately analyze the injury of the pedestrian head when the boundary conditions are known. This paper combines the accident reconstruction method with the finite element analysis method to study the injury mechanism of pedestrian head impact on the ground in vehicle pedestrian collision accidents to provide a theoretical basis for pedestrian protection and the improvement of vehicle shapes. First, a real-life vehicle pedestrian collision is reproduced by Pc-Crash. The simulation results show that the rigid multibody model can accurately simulate the scene of the accident, then the speed and angle of the pedestrian head landing moment can be obtained at the same time. Second, the finite element model of human heads with a detailed facial structure is established and verified. Finally, the collision parameters obtained from the accident reconstruction are used as the boundary conditions to analyze the collision between the pedestrian head and the ground, and the biomechanical parameters, such as intracranial pressure, von Mises stress, shear stress and strain, can be determined. The results show that the stress wave will propagate inside and outside the skull and cause stress concentration in the skull and the brain tissue to varying degrees after the pedestrian head strikes the ground. When the stress exceeds a certain limit, it will cause different degrees of brain tissue injury.

19.
Artigo em Inglês | MEDLINE | ID: mdl-33002542

RESUMO

PURPOSE: The extreme microscopic heterogeneity of tumors makes it difficult to characterize tumor hypoxia. We evaluated how changes in the spatial resolution of oxygen imaging could alter measures of tumor hypoxia and their correlation to radiation therapy response. METHODS AND MATERIALS: Cherenkov-Excited Luminescence Imaging (CELI) in combination with an oxygen probe, Oxyphor PtG4 was used to directly image tumor pO2 distributions with 0.2 mm spatial resolution at the time of radiation delivery. These pO2 images were analyzed with variations of reduced spatial resolution from 0.2 mm to 5 mm, to investigate the influence of how reduced imaging spatial resolution would affect the observed tumor hypoxia. As an in vivo validation test, mice bearing tumor xenografts were imaged for hypoxic fraction and median pO2 to examine the predictive link with tumor response to radiation therapy, while accounting for spatial resolution. RESULTS: In transitioning from voxel sizes of 200 µm to 3mm, the median pO2 values increased by a few mmHg, while the hypoxic fraction decreased by more than 50%. When looking at radiation-responsive tumors, the median pO2 values changed just a few mmHg as a result of treatment, while the hypoxic fractions changed by as much as 50%. This latter change, however, could only be seen when sampling was performed with high spatial resolution. Median pO2 or similar quantities obtained from low resolution measurements are commonly used in clinical practice, however these parameters are much less sensitive to changes in the tumor microenvironment than the tumor hypoxic fraction obtained from high-resolution oxygen images. CONCLUSIONS: This study supports the hypothesis that for adequate measurements of the tumor response to radiation therapy, oxygen imaging with high spatial resolution is required in order to accurately characterize the hypoxic fraction.

20.
Org Lett ; 22(21): 8193-8197, 2020 Nov 06.
Artigo em Inglês | MEDLINE | ID: mdl-33052688

RESUMO

A mild and biocompatible method for the construction of disulfide bridging in peptides using dichloroacetophenone derivatives is developed. This method is highly selective (chemo, diastereo, regio, etc.) and atom economic and works under biocompatible reaction conditions (metal-free, water, pH 7, rt, etc.).

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