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1.
Talanta ; 235: 122771, 2021 Dec 01.
Artigo em Inglês | MEDLINE | ID: mdl-34517629

RESUMO

Intracellular cysteine and glutathione was deemed as the most important reductants in the cell and played significant roles in the cellular homeostasis and redox adjustment. Here we developed a NIR fluorescent probe (HI) to detect and report the intracellular cysteine and glutathione, and monitor the development of the drug resistance of tumor. HI with both excited wavelength and emitting wavelength located within near infrared area showed no fluorescence in the normal physiological environment. However, when HI responded to cysteine and glutathione, strong NIR fluorescence could be turned on, which was linear dependent to the cysteine concentrations and the limited of detection was 0.18 µM. The response between HI and cysteine/glutathione demonstrated high specificity and no other amino acids showed influence or competition. The HPLC identification of the recognition results confirmed the response of acryloyloxy on the HI and active sulfhydryl on the cysteine/glutathione. DFT calculation of the HOMO and LUMO energy before and after response revealed the intramolecular charge transfer mechanism that induced the generation of the fluorescence. When HI was incubated with PATU-8988 and PATU-8988/Fu cell, the intracellular cysteine and glutathione could be clearly imaged and monitored by the enhanced fluorescence. Meanwhile, when HI was applied to the tumor-bearing mice, the drug resistance of tumor could be monitored and reported.


Assuntos
Cisteína , Corantes Fluorescentes , Animais , Resistência a Medicamentos , Corantes Fluorescentes/farmacologia , Glutationa , Camundongos , Espectrometria de Fluorescência
2.
Nat Commun ; 12(1): 5288, 2021 09 06.
Artigo em Inglês | MEDLINE | ID: mdl-34489441

RESUMO

Despite recent advances of data acquisition and algorithms development, machine learning (ML) faces tremendous challenges to being adopted in practical catalyst design, largely due to its limited generalizability and poor explainability. Herein, we develop a theory-infused neural network (TinNet) approach that integrates deep learning algorithms with the well-established d-band theory of chemisorption for reactivity prediction of transition-metal surfaces. With simple adsorbates (e.g., *OH, *O, and *N) at active site ensembles as representative descriptor species, we demonstrate that the TinNet is on par with purely data-driven ML methods in prediction performance while being inherently interpretable. Incorporation of scientific knowledge of physical interactions into learning from data sheds further light on the nature of chemical bonding and opens up new avenues for ML discovery of novel motifs with desired catalytic properties.

3.
Analyst ; 146(17): 5307-5315, 2021 Sep 07.
Artigo em Inglês | MEDLINE | ID: mdl-34351328

RESUMO

Analyzing intracellular signalling protein activities in living cells promises a better understanding of the signalling cascade and related biological processes. We have previously developed cyclic peptide-based probes for analyzing intracellular AKT signalling activities, but these peptide probes were not cell-permeable. Implementing fusogenic liposomes as delivery vehicles could circumvent the problem when analyzing adherent cells, but it remained challenging to study suspension cells using similar approaches. Here, we present a method for delivering these imaging probes into suspension cells using digitonin, which could transiently perforate the cell membrane. Using U87, THP-1, and Jurkat cells as model systems representing suspended adherent cells, myeloid cells, and lymphoid cells, we demonstrated that low concentrations of digitonin enabled a sufficient amount of probes to enter the cytosol without affecting cell viability. We further combined this delivery method with a microwell single-cell chip and interrogated the AKT signalling dynamics in THP-1 and Jurkat cells, followed by immunofluorescence-based quantitation of AKT expression levels. We resolved the cellular heterogeneity in AKT signalling activities and showed that the kinetic patterns of AKT signalling and the AKT expression levels were related in THP-1 cells, but decoupled in Jurkat cells. We expect that our approach can be adapted to study other suspension cells.


Assuntos
Fenômenos Biológicos , Proteínas Proto-Oncogênicas c-akt , Digitonina , Humanos , Proteínas Proto-Oncogênicas c-akt/metabolismo , Transdução de Sinais , Análise de Célula Única
4.
Diabetologia ; 64(11): 2415-2424, 2021 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-34390365

RESUMO

AIMS/HYPOTHESIS: Menstrual cycle dysfunction has been associated with many endocrine-related diseases, but evidence linking menstrual cycle dysfunction with gestational diabetes mellitus (GDM) is scant. The current study investigated the association of pre-pregnancy menstrual cycle regularity and length during adolescence, early adulthood and mid-adulthood with the subsequent risk of GDM. METHODS: Between 1993 and 2009, we followed 10,906 premenopausal women participating in the Nurses' Health Study II who reported menstrual cycle characteristics during adolescence (age 14-17 years), early adulthood (age 18-22 years) and mid-adulthood (age 29-46 years). Incident GDM was ascertained from a self-reported questionnaire regarding physician diagnosis. Log-binomial models with generalised estimating equations were used to estimate the RRs and 95% CI for the associations between menstrual cycle characteristics and GDM. RESULTS: We documented 578 incident cases of GDM among 14,418 pregnancies over a 16 year follow-up. After adjusting for potential confounders, women reporting always having irregular menstrual cycles during mid-adulthood had a 65% (95% CI 21, 125%) higher risk of GDM than women reporting very regular cycles. GDM risk was also greater among women reporting that their cycles were usually ≥32 days during mid-adulthood, compared with women reporting cycles between 26 and 31 days (RR 1.42 [95% CI 1.15, 1.75]). The risk of GDM was greater for women whose cycles changed from regular early in their reproductive years to irregular or from <32 days to ≥32 days during mid-adulthood, compared with women whose cycles remained <32 days or regular, respectively. CONCLUSIONS/INTERPRETATION: Women whose cycles were long or irregular during mid-adulthood, but not in adolescence or young adulthood, were at higher risk of GDM.

5.
Environ Int ; 156: 106744, 2021 11.
Artigo em Inglês | MEDLINE | ID: mdl-34256297

RESUMO

BACKGROUND: Conventionally grown fruits and vegetables (FVs) are the main source of general population exposure to pesticide residues. OBJECTIVE: To evaluate the relation of intake of high- and low-pesticide-residue FVs with cancer risk. METHODS: We followed 150,830 women (Nurses' Health Study, 1998-2016, and Nurses' Health Study II, 1999-2017) and 29,486 men (Health Professionals Follow-up Study, 1998-2016) without a history of cancer. We ascertained FV intake via validated food frequency questionnaires and categorized FVs as having high or low pesticide residue levels based on USDA surveillance data. We used Cox proportional hazards models to estimate hazard ratios (HR) and 95% confidence intervals (CI) of total and site-specific cancer related to quintiles of high- and low-pesticide-residue FV intake. RESULTS: We documented 23,678 incident cancer cases during 2,862,118 person-years of follow-up. In the pooled multivariable analysis, neither high- nor low-pesticide-residue FV intake was associated with cancer. The HRs (95% CI) per 1 serving/day increase in intake were 0.99 (0.97-1.01) for high- and 1.01 (0.99-1.02) for low-pesticide-residue FVs. Additionally, we found no association between high-pesticide-residue FV intake and risk of specific sites, including malignancies previously linked to occupational pesticide exposure ([HR, 95% CI comparing extreme quintiles of intake] lung [1.17 (0.95-1.43)], non-Hodgkin lymphoma [0.89 (0.72-1.09)], prostate [1.31 (0.88-1.93)]) or inversely related to intake of organic foods (breasts [1.03 (0.94-1.31)]). CONCLUSIONS: These findings suggest that overall exposure to pesticides through FV intake is not related to cancer risk, although they do not rule out associations with specific chemicals or sub-types of specific cancers.


Assuntos
Neoplasias , Resíduos de Praguicidas , Praguicidas , Dieta , Seguimentos , Frutas/química , Humanos , Neoplasias/epidemiologia , Resíduos de Praguicidas/análise , Modelos de Riscos Proporcionais , Fatores de Risco , Verduras
6.
Med Phys ; 48(9): 5017-5028, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34260756

RESUMO

PURPOSE: Borrmann classification in advanced gastric cancer (AGC) is necessarily associated with personalized surgical strategy and prognosis. But few radiomics research studies have focused on specific Borrmann classification, and there is yet no consensus regarding what machine learning methods should be the most effective. METHODS: A combined size of 889 AGC patients was retrospectively enrolled from two centers. Radiomic features were extracted from tumors manually delineated on preoperative computed tomography images. Two classification experiments (Borrmann I/II/III vs. IV and Borrmann II vs. III) were conducted. In each task, we combined three common feature selection methods and five typical machine learning classifiers to construct 15 basic classification models, and then fed the 15 predictions to a designed multilayer perceptron (MLP) network. RESULTS: In internal and external validation cohorts, the proposed ensemble MLP yielded good performance with area under curves of 0.767 and 0.702 for Borrmann I/II/III vs. IV, as well as 0.768 and 0.731 for Borrmann II vs. III. Considering the imbalanced distribution of four Borrmann types (I, 2.9%; II, 12.8%; III, 69.5%; IV, 14.7%), the ensemble MLP surpassed the overfitting barrier and attained fine specificity (0.667 and 0.750 for Borrmann I/II/III vs. IV; 0.714 and 0.620 for Borrmann II vs. III) and sensitivity (0.795 and 0.610 for Borrmann I/II/III vs. IV; 0.652 and 0.703 for Borrmann II vs. III). Also, survival analysis showed that patients could be significantly risk stratified by MLP predicted types in both experiments (p < 0.0001, log-rank test). CONCLUSIONS: This study proposed an MLP-based ensemble learning architecture, which could identify Borrmann type IV automatically and improve the differentiation of Borrmann type II from III. The study provided a new view for specific Borrmann classification in clinical practice.


Assuntos
Neoplasias Gástricas , Humanos , Aprendizado de Máquina , Redes Neurais de Computação , Estudos Retrospectivos , Neoplasias Gástricas/diagnóstico por imagem , Tomografia Computadorizada por Raios X
7.
J Am Chem Soc ; 143(29): 11191-11198, 2021 Jul 28.
Artigo em Inglês | MEDLINE | ID: mdl-34266234

RESUMO

We present a chemical approach to profile fatty acid uptake in single cells. We use azide-modified analogues to probe the fatty acid influx and surface-immobilized dendrimers with dibenzocyclooctyne (DBCO) groups for detection. A competition between the fatty acid probes and BHQ2-azide quencher molecules generates fluorescence signals in a concentration-dependent manner. By integrating this method onto a microfluidics-based multiplex protein analysis platform, we resolved the relationships between fatty acid influx, oncogenic signaling activities, and cell proliferation in single glioblastoma cells. We found that p70S6K and 4EBP1 differentially correlated with fatty acid uptake. We validated that cotargeting p70S6K and fatty acid metabolism synergistically inhibited cell proliferation. Our work provided the first example of studying fatty acid metabolism in the context of protein signaling at single-cell resolution and generated new insights into cancer biology.

8.
Eur J Prev Cardiol ; 2021 Jun 17.
Artigo em Inglês | MEDLINE | ID: mdl-34136913

RESUMO

AIMS : Prevention of cardiovascular outcomes is a goal of the management of patients with type 2 diabetes mellitus patients as important as lowering blood glucose levels. Among the various glucose-lowering agents, the effects of sodium-glucose cotransporter-2 inhibitors (SGLT-2Is) and dipeptidyl peptidase-4 inhibitors (DPP-4Is) on cardiovascular outcomes have become the focus of recent researches. METHODS AND RESULTS : A systematic search was performed through several online database. All studies that compared the effects of SGLT-2Is and DPP-4Is on cardiovascular outcomes and cardiometabolic risk factors were reviewed. A total of 30 studies were included. Compared with DPP-4Is, SGLT-2Is treatment reduced the risk of stroke [risk ratio (RR) = 0.80; 95% confidence interval (CI), 0.76-0.84], myocardial infarction (RR = 0.85; 95% CI, 0.81-0.89), heart failure (RR = 0.58; 95% CI, 0.54-0.62), cardiovascular mortality (RR = 0.55; 95% CI, 0.51-0.60), and all-cause mortality (RR = 0.60; 95% CI, 0.57-0.63). In addition, SGLT-2Is presented favourable effects on hemoglobinA1c, fasting plasma glucose, systolic blood pressure, and diastolic blood pressure. The differences in blood lipids were also compared. CONCLUSION: Sodium-glucose cotransporter-2 inhibitors are superior to DPP-4Is in terms of cardiovascular outcomes. Sodium-glucose cotransporter-2 inhibitors bring more benefits with respect to the cardiometabolic risk factors.

9.
Reprod Biol Endocrinol ; 19(1): 78, 2021 Jun 01.
Artigo em Inglês | MEDLINE | ID: mdl-34059045

RESUMO

BACKGROUND: Traditionally, final follicular maturation is triggered by a single bolus of human chorionic gonadotropin (hCG). This acts as a surrogate to the naturally occurring luteinizing hormone (LH) surge to induce luteinization of the granulosa cells, resumption of meiosis and final oocyte maturation. More recently, a bolus of gonadotropin-releasing hormone (GnRH) agonist in combination with hCG (dual trigger) has been suggested as an alternative regimen to achieve final follicular maturation. METHODS: This study was a systematic review and meta-analysis of randomized trials evaluating the effect of dual trigger versus hCG trigger for follicular maturation on pregnancy outcomes in women undergoing in vitro fertilization (IVF). The primary outcome was the live birth rate (LBR) per started cycle. RESULTS: A total of 1048 participants were included in the analysis, with 519 in the dual trigger group and 529 in the hCG trigger group. Dual trigger treatment was associated with a significantly higher LBR per started cycle compared with the hCG trigger treatment (risk ratio (RR) = 1.37 [1.07, 1.76], I2 = 0%, moderate evidence). There was a trend towards an increase in both ongoing pregnancy rate (RR = 1.34 [0.96, 1.89], I2 = 0%, low evidence) and implantation rate (RR = 1.31 [0.90, 1.91], I2 = 76%, low evidence) with dual trigger treatment compared with hCG trigger treatment. Dual trigger treatment was associated with a significant increase in clinical pregnancy rate (RR = 1.29 [1.10, 1.52], I2 = 13%, low evidence), number of oocytes collected (mean difference (MD) = 1.52 [0.59, 2.46), I2 = 53%, low evidence), number of mature oocytes collected (MD = 1.01 [0.43, 1.58], I2 = 18%, low evidence), number of fertilized oocytes (MD = 0.73 [0.16, 1.30], I2 = 7%, low evidence) and significantly more usable embryos (MD = 0.90 [0.42, 1.38], I2 = 0%, low evidence). CONCLUSION: Dual trigger treatment with GnRH agonist and HCG is associated with an increased live birth rate compared with conventional hCG trigger. TRIAL REGISTRATION: CRD42020204452 .

10.
IEEE Trans Biomed Eng ; PP2021 Jun 01.
Artigo em Inglês | MEDLINE | ID: mdl-34061732

RESUMO

Objective: In a few patients with mild COVID-19, there is a possibility of the infection becoming severe or critical in the future. This work aims to identify high-risk patients who have a high probability of changing from mild to critical COVID-19 (only account for 5% of cases). Methods: Using traditional convolutional neural networks for classification may not be suitable to identify this 5% of high-risk patients from an entire dataset due to the highly imbalanced label distribution. To address this problem, we propose a Mix Contrast (MixCo) model, which matches original features with mixed features for contrastive learning. Three modules are proposed for training MixCo: 1) a cumulative learning strategy for synthesizing the mixed feature; 2) a commutative feature combination module for learning the commutative law of feature concatenation; 3) a united pairwise loss assigning adaptive weights for sample pairs with different class anchors based on their current optimization status. Results: We collect a multi-center computed tomography dataset including 918 confirmed COVID-19 patients from four hospitals and evaluate the proposed method on both the COVID-19 mild-to-critical prediction and COVID-19 diagnosis tasks. For mild-to-critical prediction, the experimental results show a recall of 0.80 and a specificity of 0.815. For diagnosis, the model shows comparable results with deep neural networks using a large dataset. Our method demonstrates improvements when the amount of training data is small or imbalanced. Significance: Identifying mild-to-critical COVID-19 patients is important for early prevention and personalized treatment planning.

11.
Artigo em Inglês | MEDLINE | ID: mdl-34061740

RESUMO

Mammogram mass detection is crucial for diagnosing and preventing breast cancers in clinical practice. The complementary effect of multi-view mammogram images provides valuable information about the breast anatomical prior structure and is of great significance in digital mammography interpretation. However, unlike radiologists who can utilize reasoning ability to identify masses, how to endow existing models with capability of multi-view reasoning is vital in clinical diagnosis. In this paper, we propose an Anatomy-aware Graph convolutional Network (AGN), which is tailored for mammogram mass detection and endows existing methods with multi-view reasoning ability. The proposed AGN consists of three steps. Firstly, we introduce a Bipartite Graph convolutional Network (BGN) to model intrinsic geometric and semantic relations of ipsilateral views. Secondly, considering that visual asymmetry of bilateral views is widely adopted in clinical practice to assist the diagnosis of breast lesions, we propose an Inception Graph convolutional Network (IGN) to model structural similarities of bilateral views. Finally, based on the constructed graphs, the multi-view information is propagated through nodes methodically, which equips the learned features with multi-view reasoning ability. Experiments on two benchmarks reveal that AGN significantly exceeds the state-of-the-art performance. Visualization results show that AGN provides interpretable visual cues for clinical diagnosis.

12.
IEEE J Biomed Health Inform ; 25(7): 2353-2362, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-33905341

RESUMO

OBJECTIVE: Coronavirus disease 2019 (COVID-19) has caused considerable morbidity and mortality, especially in patients with underlying health conditions. A precise prognostic tool to identify poor outcomes among such cases is desperately needed. METHODS: Total 400 COVID-19 patients with underlying health conditions were retrospectively recruited from 4 centers, including 54 dead cases (labeled as poor outcomes) and 346 patients discharged or hospitalized for at least 7 days since initial CT scan. Patients were allocated to a training set (n = 271), a test set (n = 68), and an external test set (n = 61). We proposed an initial CT-derived hybrid model by combining a 3D-ResNet10 based deep learning model and a quantitative 3D radiomics model to predict the probability of COVID-19 patients reaching poor outcome. The model performance was assessed by area under the receiver operating characteristic curve (AUC), survival analysis, and subgroup analysis. RESULTS: The hybrid model achieved AUCs of 0.876 (95% confidence interval: 0.752-0.999) and 0.864 (0.766-0.962) in test and external test sets, outperforming other models. The survival analysis verified the hybrid model as a significant risk factor for mortality (hazard ratio, 2.049 [1.462-2.871], P < 0.001) that could well stratify patients into high-risk and low-risk of reaching poor outcomes (P < 0.001). CONCLUSION: The hybrid model that combined deep learning and radiomics could accurately identify poor outcomes in COVID-19 patients with underlying health conditions from initial CT scans. The great risk stratification ability could help alert risk of death and allow for timely surveillance plans.


Assuntos
COVID-19 , Aprendizado Profundo , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Tomografia Computadorizada por Raios X/métodos , Idoso , Idoso de 80 Anos ou mais , COVID-19/diagnóstico por imagem , COVID-19/mortalidade , Comorbidade , Feminino , Humanos , Imageamento Tridimensional , Pulmão/diagnóstico por imagem , Masculino , Pessoa de Meia-Idade , Prognóstico , Curva ROC , Estudos Retrospectivos , SARS-CoV-2
13.
Analyst ; 146(11): 3474-3481, 2021 Jun 07.
Artigo em Inglês | MEDLINE | ID: mdl-33913937

RESUMO

We present here a cyclic peptide ligand, cy(WQETR), that binds to the terbium ion (Tb3+) and enhances Tb3+ luminescence intensity through the antenna effect. This peptide was identified through screening a cyclic peptide library against Tb3+ with an apparent EC50 of 540 µM. The tryptophan residue from the peptide directly interacts with the Tb3+ ion, which provides access to a low-lying triplet excited state of the tryptophan. Direct excitation of this triplet state enables energy transfer to the Tb3+ ion and enhances Tb3+ luminescence intensity by 150 fold. We further showcase the application of this cy(WQETR)-Tb3+ system by demonstrating the detection of tromethamine with a detection limit of 0.5 mM.


Assuntos
Luminescência , Térbio , Transferência de Energia , Ligantes , Peptídeos Cíclicos
14.
Math Biosci Eng ; 18(2): 1370-1405, 2021 01 21.
Artigo em Inglês | MEDLINE | ID: mdl-33757190

RESUMO

Numerical treatment of singular charges is a grand challenge in solving the Poisson-Boltzmann (PB) equation for analyzing electrostatic interactions between the solute biomolecules and the surrounding solvent with ions. For diffuse interface PB models in which solute and solvent are separated by a smooth boundary, no effective algorithm for singular charges has been developed, because the fundamental solution with a space dependent dielectric function is intractable. In this work, a novel regularization formulation is proposed to capture the singularity analytically, which is the first of its kind for diffuse interface PB models. The success lies in a dual decomposition - besides decomposing the potential into Coulomb and reaction field components, the dielectric function is also split into a constant base plus space changing part. Using the constant dielectric base, the Coulomb potential is represented analytically via Green's functions. After removing the singularity, the reaction field potential satisfies a regularized PB equation with a smooth source. To validate the proposed regularization, a Gaussian convolution surface (GCS) is also introduced, which efficiently generates a diffuse interface for three-dimensional realistic biomolecules. The performance of the proposed regularization is examined by considering both analytical and GCS diffuse interfaces, and compared with the trilinear method. Moreover, the proposed GCS-regularization algorithm is validated by calculating electrostatic free energies for a set of proteins and by estimating salt affinities for seven protein complexes. The results are consistent with experimental data and estimates of sharp interface PB models.


Assuntos
Algoritmos , Proteínas , Entropia , Solventes , Eletricidade Estática
15.
Med Image Anal ; 71: 101999, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-33780707

RESUMO

Detecting breast soft-tissue lesions including masses, structural distortions and asymmetries is of great importance due to the high risk leading to breast cancer. Most existing deep learning based approaches detect lesions with only unilateral images. However, multi-view mammogram images provide highly related and complementary information which helps to make the clinical analysis more comprehensive and reliable. In this paper, we propose a multi-view network for breast soft-tissue lesion detection called C2-Net (Compare and Contrast, C2) that fuses information across different views. The proposed model contains the following three modules. The spatial context enhancing (SCE) module compares ipsilateral views and extracts complementary features to model lesion inherent 3D structure. The multi-scale kernel pooling (MKP) module contrasts contralateral views with added misalignment tolerance. Finally, the logic guided fusion (LGF) module fuses multi-view features by enhancing logic modeling capacity. Experimental results on both the public DDSM dataset and the in-house multi-center dataset demonstrate that the proposed method has achieved state-of-the-art performance.


Assuntos
Neoplasias da Mama , Processamento de Imagem Assistida por Computador , Mama/diagnóstico por imagem , Neoplasias da Mama/diagnóstico por imagem , Feminino , Humanos , Mamografia
16.
Nat Commun ; 11(1): 6132, 2020 Nov 30.
Artigo em Inglês | MEDLINE | ID: mdl-33257689

RESUMO

Building upon the d-band reactivity theory in surface chemistry and catalysis, we develop a Bayesian learning approach to probing chemisorption processes at atomically tailored metal sites. With representative species, e.g., *O and *OH, Bayesian models trained with ab initio adsorption properties of transition metals predict site reactivity at a diverse range of intermetallics and near-surface alloys while naturally providing uncertainty quantification from posterior sampling. More importantly, this conceptual framework sheds light on the orbitalwise nature of chemical bonding at adsorption sites with d-states characteristics ranging from bulk-like semi-elliptic bands to free-atom-like discrete energy levels, bridging the complexity of electronic descriptors for the prediction of novel catalytic materials.

17.
Cancer Imaging ; 20(1): 83, 2020 Nov 23.
Artigo em Inglês | MEDLINE | ID: mdl-33228815

RESUMO

BACKGROUND: Preoperative prediction of the Lauren classification in gastric cancer (GC) is very important to the choice of therapy, the evaluation of prognosis, and the improvement of quality of life. However, there is not yet radiomics analysis concerning the prediction of Lauren classification straightly. In this study, a radiomic nomogram was developed to preoperatively differentiate Lauren diffuse type from intestinal type in GC. METHODS: A total of 539 GC patients were enrolled in this study and later randomly allocated to two cohorts at a 7:3 ratio for training and validation. Two sets of radiomic features were derived from tumor regions and peritumor regions on venous phase computed tomography (CT) images, respectively. With the least absolute shrinkage and selection operator logistic regression, a combined radiomic signature was constructed. Also, a tumor-based model and a peripheral ring-based model were built for comparison. Afterwards, a radiomic nomogram integrating the combined radiomic signature and clinical characteristics was developed. All the models were evaluated regarding classification ability and clinical usefulness. RESULTS: The combined radiomic signature achieved an area under receiver operating characteristic curve (AUC) of 0.715 (95% confidence interval [CI], 0.663-0.767) in the training cohort and 0.714 (95% CI, 0.636-0.792) in the validation cohort. The radiomic nomogram incorporating the combined radiomic signature, age, CT T stage, and CT N stage outperformed the other models with a training AUC of 0.745 (95% CI, 0.696-0.795) and a validation AUC of 0.758 (95% CI, 0.685-0.831). The significantly improved sensitivity of radiomic nomogram (0.765 and 0.793) indicated better identification of diffuse type GC patients. Further, calibration curves and decision curves demonstrated its great model fitness and clinical usefulness. CONCLUSIONS: The radiomic nomogram involving the combined radiomic signature and clinical characteristics holds potential in differentiating Lauren diffuse type from intestinal type for reasonable clinical treatment strategy.


Assuntos
Neoplasias Gástricas/diagnóstico por imagem , Idoso , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Estadiamento de Neoplasias , Nomogramas , Neoplasias Gástricas/classificação , Neoplasias Gástricas/patologia , Tomografia Computadorizada por Raios X/métodos
18.
BMC Nephrol ; 21(1): 498, 2020 11 23.
Artigo em Inglês | MEDLINE | ID: mdl-33225908

RESUMO

BACKGROUND: Acute kidney injury (AKI) is a life-threatening complication of rhabdomyolysis (RM). The aim of the present study was to assess patients at high risk for the occurrence of severe AKI defined as stage II or III of KDIGO classification and in-hospital mortality of AKI following RM. METHODS: We performed a retrospective study of patients with creatine kinase levels > 1000 U/L, who were admitted to the West China Hospital of Sichuan University between January 2011 and March 2019. The sociodemographic, clinical and laboratory data of these patients were obtained from an electronic medical records database, and univariate and multivariate regression analyses were subsequently conducted. RESULTS: For the 329 patients included in our study, the incidence of AKI was 61.4% and the proportion of stage I, stage II, stage III were 18.8, 14.9 and 66.3%, respectively. The overall mortality rate was 19.8%; furthermore, patients with AKI tended to have higher mortality rates than those without AKI (24.8% vs. 11.8%; P < 0.01). The clinical conditions most frequently associated with RM were trauma (28.3%), sepsis (14.6%), bee sting (12.8%), thoracic and abdominal surgery (11.2%) and exercise (7.0%). Furthermore, patients with RM resulting from sepsis, bee sting and acute alcoholism were more susceptible to severe AKI. The risk factors for the occurrence of stage II-III AKI among RM patients included hypertension (OR = 2.702), high levels of white blood cell count (OR = 1.054), increased triglycerides (OR = 1.260), low level of high-density lipoprotein cholesterol (OR = 0.318), elevated serum phosphorus (OR = 5.727), 500010,000 U/L (OR = 8.093). Age ≥ 60 years (OR = 2.946), sepsis (OR = 3.206) and elevated prothrombin time (OR = 1.079) were independent risk factors for in-hospital mortality in RM patients with AKI. CONCLUSIONS: AKI is independently associated with mortality in patients with RM, and several risk factors were found to be associated with the occurrence of severe AKI and in-hospital mortality. These findings suggest that, to improve the quality of medical care, the early prevention of AKI should focus on high-risk patients and more effective management.

19.
Proc Natl Acad Sci U S A ; 117(49): 31018-31025, 2020 12 08.
Artigo em Inglês | MEDLINE | ID: mdl-33229579

RESUMO

The Chinese "coal-to-gas" and "coal-to-electricity" strategies aim at reducing dispersed coal consumption and related air pollution by promoting the use of clean and low-carbon fuels in northern China. Here, we show that on top of meteorological influences, the effective emission mitigation measures achieved an average decrease of fine particulate matter (PM2.5) concentrations of ∼14% in Beijing and surrounding areas (the "2+26" pilot cities) in winter 2017 compared to the same period of 2016, where the dispersed coal control measures contributed ∼60% of the total PM2.5 reductions. However, the localized air quality improvement was accompanied by a contemporaneous ∼15% upsurge of PM2.5 concentrations over large areas in southern China. We find that the pollution transfer that resulted from a shift in emissions was of a high likelihood caused by a natural gas shortage in the south due to the coal-to-gas transition in the north. The overall shortage of natural gas greatly jeopardized the air quality benefits of the coal-to-gas strategy in winter 2017 and reflects structural challenges and potential threats in China's clean-energy transition.


Assuntos
Poluição do Ar/análise , Carvão Mineral/análise , Gás Natural/análise , Estações do Ano , China , Cidades , Política Ambiental , Calefação , Material Particulado/análise
20.
Exp Ther Med ; 20(5): 115, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-33005241

RESUMO

The aim of the present study was to explore the role of toll-like receptor 4 (TLR4)/myeloid differentiation primary response 88 (MyD88)/nuclear factor (NF)-κB signaling in the contrast-induced injury of renal tubular epithelial cells, and to investigate the potential mechanisms. HK-2 cells cultured in vitro were randomly divided into six groups as follows: i) The blank group; ii) the iohexol group; iii) the NF-κB RNAi group (NF-κB siRNA + iohexol); iv) the TLR4 RNAi group (TLR4 siRNA + iohexol); v) the NF-κB blocker group (PDTC + iohexol); and vi) the TLR4 blocker group (CLI-095 + iohexol). The expression of the TLR4/MyD88/NF-κB signaling pathway proteins was detected by reverse transcription-quantitative (RT-q)PCR and western blot analysis, and the cellular proliferation rate was determined using the Cell Counting Kit-8 assay. The mRNA expression levels of the inflammatory cytokines tumor necrosis factor (TNF)-α, interleukin (IL)-1ß and IL-6 were also detected using RT-qPCR, and apoptosis was assessed by flow cytometry and western blotting to detect apoptosis-associated proteins (caspase-3, caspase-9 and cleaved caspase-9). Compared with the blank group, the apoptotic rates and the expression levels of TLR4, MyD88, NF-κB, caspase-3, cleaved caspase-9, TNF-α, IL-1ß and IL-6 were upregulated in the iohexol group (P<0.001). However, when TLR4 or NF-κB were blocked or silenced, these effects were reversed (P<0.001). Collectively, the results of the present study indicated that TLR4/MyD88/NF-κB signaling is involved in the contrast-induced injury of renal tubular epithelial cells by inducing inflammation and apoptosis.

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