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1.
J Am Med Dir Assoc ; 2022 Jul 31.
Artigo em Inglês | MEDLINE | ID: mdl-35922015

RESUMO

OBJECTIVE: To critically appraise and quantify the performance studies by employing machine learning (ML) to predict delirium. DESIGN: A systematic review and meta-analysis. SETTING AND PARTICIPANTS: Articles reporting the use of ML to predict delirium in adult patients were included. Studies were excluded if (1) the primary goal was only the identification of various risk factors for delirium; (2) the full-text article was not found; and (3) the article was published in a language other than English/Chinese. METHODS: PubMed, Embase, Cochrane Library database, Web of Science, Grey literature, and other relevant databases for the related publications were searched (from inception to November 30, 2021). The data were extracted using a standard checklist, and the risk of bias was assessed through the prediction model risk of bias assessment tool. Meta-analysis with the area under the receiver operating characteristic curve, sensitivity, and specificity as effect measures, was performed with Metadisc software. Cochran Q and I2 statistics were used to assess the heterogeneity. Meta-regression was performed to determine the potential effect of adjustment for the key covariates. RESULTS: A total of 22 studies were included. Only 4 of 22 studies were quantitatively analyzed. The studies varied widely in reporting about the study participants, features and selection, handling of missing data, sample size calculations, and the intended clinical application of the model. For ML models, the overall pooled area under the receiver operating characteristic curve for predicting delirium was 0.89, sensitivity 0.85 (95% confidence interval 0.84‒0.85), and specificity 0.80 (95% confidence interval 0.81-0.80). CONCLUSIONS AND IMPLICATIONS: We found that the ML model showed excellent performance in predicting delirium. This review highlights the potential shortcomings of the current approaches, including low comparability and reproducibility. Finally, we present the various recommendations on how these challenges can be effectively addressed before deploying these models in prospective analyses.

2.
Gland Surg ; 11(7): 1166-1179, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-35935563

RESUMO

Background: Nomograms can assess the risk of clinicopathological features by quantifying the biological and clinical variables of cancer patients. However, the nomogram based on significant factors that influence the survival of breast cancer in a large population has been rarely explored. This study was to investigate the predictive effectiveness of a nomogram for the survival of patients with breast cancer. Methods: Demographic and clinical data of 275,812 breast cancer patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. All patients aged ≥20 years in this retrospective cohort study were classified as two groups in a random manner, namely the training set (n=193,069) and validation set (n=82,743). The outcomes of our study were the 3- and 5-year survival of breast cancer. The potential predictors of cancer mortality were screened by univariate and multivariable Cox regression analyses. The nomogram was conducted based on the predictors. Harrell's concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curve was utilized to evaluate the performance of the nomogram. Results: The age at diagnosis, race, marital status, tumor size, first malignant primary indicator, American Joint Committee on Cancer (AJCC) T stage, M stage, tumor grade, and number of malignant tumors were independent predictors for the death of patients with breast cancer. The C-indexes of the training set and the validation set were 0.782 and 0.778, respectively. The area under the curve (AUC) values of the nomogram for predicting the 3- and 5-year survival of breast cancer were 0.770 and 0.756, respectively. Furthermore, the C-index values of our nomogram were 0.816, 0.775, 0.773, 0.734, and 0.750 for predicting survival in Asian, White, Hispanic, American Indian, and Black populations, respectively. Conclusions: The nomogram may have predictive performance for predicting the 3- and 5-year survival of breast cancer patients, and future studies need to validate our findings.

4.
Cancers (Basel) ; 14(13)2022 Jun 26.
Artigo em Inglês | MEDLINE | ID: mdl-35804904

RESUMO

BACKGROUND: Prognostication is essential to determine the risk profile of patients with urologic cancers. METHODS: We utilized the SEER national cancer registry database with approximately 2 million patients diagnosed with urologic cancers (penile, testicular, prostate, bladder, ureter, and kidney). The cohort was randomly divided into the development set (90%) and the out-held test set (10%). Modeling algorithms and clinically relevant parameters were utilized for cancer-specific mortality prognosis. The model fitness for the survival estimation was assessed using the differences between the predicted and observed Kaplan-Meier estimates on the out-held test set. The overall concordance index (c-index) score estimated the discriminative accuracy of the survival model on the test set. A simulation study assessed the estimated minimum follow-up duration and time points with the risk stability. RESULTS: We achieved a well-calibrated prognostic model with an overall c-index score of 0.800 (95% CI: 0.795-0.805) on the representative out-held test set. The simulation study revealed that the suggestions for the follow-up duration covered the minimum duration and differed by the tumor dissemination stages and affected organs. Time points with a high likelihood for risk stability were identifiable. CONCLUSIONS: A personalized temporal survival estimation is feasible using artificial intelligence and has potential application in clinical settings, including surveillance management.

5.
Sci Adv ; 8(30): eabn7702, 2022 Jul 29.
Artigo em Inglês | MEDLINE | ID: mdl-35905187

RESUMO

Since the ancestors of modern humans separated from those of Neanderthals, around 100 amino acid substitutions spread to essentially all modern humans. The biological significance of these changes is largely unknown. Here, we examine all six such amino acid substitutions in three proteins known to have key roles in kinetochore function and chromosome segregation and to be highly expressed in the stem cells of the developing neocortex. When we introduce these modern human-specific substitutions in mice, three substitutions in two of these proteins, KIF18a and KNL1, cause metaphase prolongation and fewer chromosome segregation errors in apical progenitors of the developing neocortex. Conversely, the ancestral substitutions cause shorter metaphase length and more chromosome segregation errors in human brain organoids, similar to what we find in chimpanzee organoids. These results imply that the fidelity of chromosome segregation during neocortex development improved in modern humans after their divergence from Neanderthals.


Assuntos
Hominidae , Homem de Neandertal , Animais , Encéfalo , Segregação de Cromossomos/genética , Humanos , Cinesinas , Metáfase , Camundongos , Homem de Neandertal/genética
6.
Int J Biol Sci ; 18(9): 3888-3907, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35813480

RESUMO

Hypoxic microenvironment and circular RNAs (circRNAs) have shown critical implications in breast cancer (BC) progression. However, the specific functions and underlying mechanisms of circRNAs in BC under hypoxia remain largely unknown. We first screened for differentially expressed circRNAs in normoxic and hypoxic MCF-7 cells using circRNA microarray. A novel hypoxia-induced circRNA, circPFKFB4, was identified. Clinical investigation showed that circPFKFB4 was highly expressed in BC tissues and cell lines, and its overexpression was positively correlated with the advanced clinical stage and poor prognosis of BC patients. Functionally, circPFKFB4 promoted the proliferation of BC cells both in vitro and in vivo. Mechanistically, HIF1α bound to hypoxia response elements in the promoter region of the PFKFB4 gene to facilitate the biogenesis of circPFKFB4 under hypoxia. Hypoxia-induced circPFKFB4 directly bound to both DDB1 and DDB2 and promoted the CRL4DDB2 E3 ubiquitin ligase assembly, resulting in p27 ubiquitination and BC progression under hypoxia. Our findings revealed a novel interaction between circPFKFB4 and the CRL4DDB2 E3 ubiquitin ligase, suggesting that circPFKFB4 might serve as a promising biomarker and therapeutic target for BC.


Assuntos
Neoplasias da Mama , RNA Circular , Ubiquitina-Proteína Ligases , Neoplasias da Mama/genética , Neoplasias da Mama/metabolismo , Proteínas de Ligação a DNA/metabolismo , Feminino , Humanos , Hipóxia/genética , Fosfofrutoquinase-2/genética , Fosfofrutoquinase-2/metabolismo , RNA Circular/genética , RNA Circular/metabolismo , Receptores de Interleucina-17 , Microambiente Tumoral , Ubiquitina-Proteína Ligases/genética , Ubiquitina-Proteína Ligases/metabolismo , Ubiquitinação/genética
7.
Nat Biomed Eng ; 2022 Jul 04.
Artigo em Inglês | MEDLINE | ID: mdl-35788685

RESUMO

In the past decade, the application of machine learning (ML) to healthcare has helped drive the automation of physician tasks as well as enhancements in clinical capabilities and access to care. This progress has emphasized that, from model development to model deployment, data play central roles. In this Review, we provide a data-centric view of the innovations and challenges that are defining ML for healthcare. We discuss deep generative models and federated learning as strategies to augment datasets for improved model performance, as well as the use of the more recent transformer models for handling larger datasets and enhancing the modelling of clinical text. We also discuss data-focused problems in the deployment of ML, emphasizing the need to efficiently deliver data to ML models for timely clinical predictions and to account for natural data shifts that can deteriorate model performance.

8.
Phys Med Biol ; 67(15)2022 Jul 27.
Artigo em Inglês | MEDLINE | ID: mdl-35803256

RESUMO

Small field dosimetry is significantly different from the dosimetry of broad beams due to loss of electron side scatter equilibrium, source occlusion, and effects related to the choice of detector. However, use of small fields is increasing with the increase in indications for intensity-modulated radiation therapy and stereotactic body radiation therapy, and thus the need for accurate dosimetry is ever more important. Here we propose to leverage machine learning (ML) strategies to reduce the uncertainties and increase the accuracy in determining small field output factors (OFs). Linac OFs from a Varian TrueBeam STx were calculated either by the treatment planning system (TPS) or measured with a W1 scintillator detector at various multi-leaf collimator (MLC) positions, jaw positions, and with and without contribution from leaf-end transmission. The fields were defined by the MLCs with the jaws at various positions. Field sizes between 5 and 100 mm were evaluated. Separate ML regression models were generated based on the TPS calculated or the measured datasets. Accurate predictions of small field OFs at different field sizes (FSs) were achieved independent of jaw and MLC position. A mean and maximum % relative error of 0.38 ± 0.39% and 3.62%, respectively, for the best-performing models based on the measured datasets were found. The prediction accuracy was independent of contribution from leaf-end transmission. Several ML models for predicting small field OFs were generated, validated, and tested. Incorporating these models into the dose calculation workflow could greatly increase the accuracy and robustness of dose calculations for any radiotherapy delivery technique that relies heavily on small fields.


Assuntos
Radiometria , Planejamento da Radioterapia Assistida por Computador , Aprendizado de Máquina , Aceleradores de Partículas , Dosagem Radioterapêutica , Planejamento da Radioterapia Assistida por Computador/métodos , Incerteza
9.
Sensors (Basel) ; 22(11)2022 May 27.
Artigo em Inglês | MEDLINE | ID: mdl-35684714

RESUMO

Owing to the limited field of view (FOV) and depth of field (DOF) of a conventional camera, it is quite difficult to employ a single conventional camera to simultaneously measure high-precision displacements at many points on a bridge of dozens or hundreds of meters. Researchers have attempted to obtain a large FOV and wide DOF by a multi-camera system; however, with the growth of the camera number, the cost, complexity and instability of multi-camera systems will increase exponentially. This study proposes a multi-point displacement measurement method for bridges based on a low-cost Scheimpflug camera. The Scheimpflug camera, which meets the Scheimpflug condition, can enlarge the depth of field of the camera without reducing the lens aperture and magnification; thus, when the measurement points are aligned in the depth direction, all points can be clearly observed in a single field of view with a high-power zoom lens. To reduce the impact of camera motions, a motion compensation method applied to the Scheimpflug camera is proposed according to the characteristic that the image plane is not perpendicular to the lens axis in the Scheimpflug camera. Several tests were conducted for performance verification under diverse settings. The results showed that the motion errors in x and y directions were reduced by at least 62% and 92%, respectively, using the proposed method, and the measurements of the camera were highly consistent with LiDAR-based measurements.

10.
Comput Biol Med ; : 105710, 2022 Jun 06.
Artigo em Inglês | MEDLINE | ID: mdl-35715260

RESUMO

Deep learning affords enormous opportunities to augment the armamentarium of biomedical imaging. However, the pure data-driven nature of deep learning models may limit the model generalizability and application scope. Here we establish a geometry-informed deep learning framework for ultra-sparse 3D tomographic image reconstruction. We introduce a novel mechanism for integrating geometric priors of the imaging system. We demonstrate that the seamless inclusion of known priors is essential to enhance the performance of 3D volumetric computed tomography imaging with ultra-sparse sampling. The study opens new avenues for data-driven biomedical imaging and promises to provide substantially improved imaging tools for various clinical imaging and image-guided interventions.

11.
Artigo em Inglês | MEDLINE | ID: mdl-35737213

RESUMO

ATP-binding cassette E1 (ABCE1) is mainly related to the regulation of viral infection, cell multiplication, and anti-apoptosis. Previous reports confirmed the central role in the regulation of ABCE1 in liver and breast cancer; however, its potential role in gastric adenocarcinoma remains unclear. In our study, siRNA and plasmid were transfected to construct gastric cancer cell lines with low and overexpression of ABCE1, and Western blot, RT-qPCR, and immunohistochemical staining were used to detect ABCE1 expression levels in gastric cancer tissues and cell lines. The effects of ABCE1 on cell growth, metastasis, invasion, cell cycle, and drug resistance were investigated using CCK-8 test, wound healing assay, and clone formation experiment. Functional experiments indicated that si-ABCE1 decreased the proliferation, metastasis, and invasion of gastric adenocarcinoma. Meanwhile, si-ABCE1 has significantly promoted EMT process and enhanced the sensitivity of paclitaxel and cisplatin. In vivo experiments also confirmed that si-ABCE1 group had significantly smaller tumors, and immunohistochemical staining results showed the tumor growth in si-ABCE1 group was reduced obviously. In summary, we found ABCE1 is considered as a crucial role in the evolution of gastric adenocarcinoma and could be a viable therapeutic target for the disease.

12.
Artigo em Inglês | MEDLINE | ID: mdl-35657845

RESUMO

Image reconstruction is an inverse problem that solves for a computational image based on sampled sensor measurement. Sparsely sampled image reconstruction poses additional challenges due to limited measurements. In this work, we propose a methodology of implicit Neural Representation learning with Prior embedding (NeRP) to reconstruct a computational image from sparsely sampled measurements. The method differs fundamentally from previous deep learning-based image reconstruction approaches in that NeRP exploits the internal information in an image prior and the physics of the sparsely sampled measurements to produce a representation of the unknown subject. No large-scale data is required to train the NeRP except for a prior image and sparsely sampled measurements. In addition, we demonstrate that NeRP is a general methodology that generalizes to different imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI). We also show that NeRP can robustly capture the subtle yet significant image changes required for assessing tumor progression.

13.
Huan Jing Ke Xue ; 43(6): 3262-3268, 2022 Jun 08.
Artigo em Chinês | MEDLINE | ID: mdl-35686796

RESUMO

Human activities (land use) and environmental change (land cover change) affect the concentration of Se and heavy metals in soils. The implementation of the "Return Cropland to Forest (RCF)" ecological project has changed the land use and cover, which has provided an ideal experimental area for studying the effects of land use and cover change on selenium (Se) and heavy metals in the soil. In this study, 91 top soil samples from different land use and land cover types, including dry land, paddy land, natural forest land, and secondary forest land, were collected, and the contents of Se, heavy metals, and soil organic matter (SOM) and pH were analyzed. The results showed that:① the average values of ω(Se) (0.42×10-6), ω(As) (13.0×10-6), and ω(Sb) (1.03×10-6) were higher than the soil background values of western Chongqing. ② The concentrations of Se, Cd, Cr, Ni, Pb, and Zn in soils from secondary forest land were significantly higher than those from dry land soils, suggesting that the Se and heavy metals might have significantly increased in the surface soil after the implementation of the RCF ecological project. ③ The SOM was the key controlling factor for the enrichment and distribution of Se and heavy metals in the top soils. Our results indicated that the land use and land cover change would deeply impact the concentrations of Se and heavy metals in the top soils via influencing the soil properties, especially the SOM.


Assuntos
Metais Pesados , Selênio , Poluentes do Solo , China , Produtos Agrícolas , Monitoramento Ambiental , Florestas , Humanos , Metais Pesados/análise , Medição de Risco , Solo/química , Poluentes do Solo/análise
14.
Med Phys ; 2022 Jun 29.
Artigo em Inglês | MEDLINE | ID: mdl-35766221

RESUMO

PURPOSE: To develop a geometry-informed deep learning framework for volumetric MRI with sub-second acquisition time in support of 3D motion tracking, which is highly desirable for improved radiotherapy precision but hindered by the long image acquisition time. METHODS: A 2D-3D deep learning network with an explicitly defined geometry module that embeds geometric priors of the k-space encoding pattern was investigated, where a 2D generation network first augmented the sparsely sampled image dataset by generating new 2D representations of the underlying 3D subject. A geometry module then unfolded the 2D representations to the volumetric space. Finally, a 3D refinement network took the unfolded 3D data and outputted high-resolution volumetric images. Patient-specific models were trained for seven abdominal patients to reconstruct volumetric MRI from both orthogonal cine slices and sparse radial samples. To evaluate the robustness of the proposed method to longitudinal patient anatomy and position changes, we tested the trained model on separate datasets acquired more than one month later and evaluated 3D target motion tracking accuracy using the model-reconstructed images by deforming a reference MRI with gross tumor volume (GTV) contours to a 5-min time series of both ground truth and model-reconstructed volumetric images with a temporal resolution of 340 ms. RESULTS: Across the seven patients evaluated, the median distances between model-predicted and ground truth GTV centroids in the superior-inferior direction were 0.4 ± 0.3 mm and 0.5 ± 0.4 mm for cine and radial acquisitions, respectively. The 95-percentile Hausdorff distances between model-predicted and ground truth GTV contours were 4.7 ± 1.1 mm and 3.2 ± 1.5 mm for cine and radial acquisitions, which are of the same scale as cross-plane image resolution. CONCLUSION: Incorporating geometric priors into deep learning model enables volumetric imaging with high spatial and temporal resolution, which is particularly valuable for 3D motion tracking and has the potential of greatly improving MRI-guided radiotherapy precision.

15.
J Appl Clin Med Phys ; 23(8): e13638, 2022 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-35644039

RESUMO

PURPOSE: The RefleXion X1 is a novel radiotherapy machine designed for image-guided radiotherapy (IGRT) and biology-guided radiotherapy (BgRT). Its treatment planning system (TPS) generates IMRT and SBRT plans for a 6MV-FFF beam delivered axially via 50 firing positions with the couch advancing every 2.1 mm. The purpose of this work is to report the TPS commissioning results for the first clinical installation of RefleXion™ X1. METHODS: CT images of multiple phantoms were imported into the RefleXion TPS to evaluate the accuracy of data transfer, anatomical modeling, plan evaluation, and dose calculation. Comparisons were made between the X1, Eclipse™, and MIM™. Dosimetric parameters for open static fields were evaluated in water and heterogeneous slab phantoms. Representative clinical IMRT and SBRT cases were planned and verified with ion chamber, film, and ArcCHECK@ measurements. The agreement between TPS and measurements for various clinical plans was evaluated using Gamma analysis with a criterion of 3%/2 mm for ArcCHECK@ and film. End-to-end (E2E) testing was performed using anthropomorphic head and lung phantoms. RESULTS: The average difference between the TPS-reported and known HU values was -1.4 ± 6.0 HU. For static fields, the agreements between the TPS-calculated and measured PDD10 , crossline profiles, and inline profiles (FWHM) were within 1.5%, 1.3%, and 0.5 mm, respectively. Measured output factors agreed with the TPS within 1.3%. Measured and calculated dose for static fields in heterogeneous phantoms agreed within 2.5%. The ArcCHECK@ mean absolute Gamma passing rate was 96.4% ± 3.4% for TG 119 and TG 244 plans and 97.8% ± 3.6% for the 21 clinical plans. E2E film analysis showed 0.8 mm total targeting error for isocentric and 1.1 mm for off-axis treatments. CONCLUSIONS: The TPS commissioning results of the RefleXion X1 TPS were within the tolerances specified by AAPM TG 53, MPPG 5.a, TG 119, and TG 148. A subset of the commissioning tests has been identified as baseline data for an ongoing QA program.

16.
Molecules ; 27(9)2022 May 09.
Artigo em Inglês | MEDLINE | ID: mdl-35566394

RESUMO

Graphene, in spite of exceptional physio-chemical properties, still faces great limitations in its use and industrial scale-up as highly selective membranes (enhanced ratio of proton conductivity to fuel cross-over) in liquid alcohol fuel cells (LAFCs), due to complexity and high cost of prevailing production methods. To resolve these issues, a facile, low-cost and eco-friendly approach of liquid phase exfoliation (bath sonication) of graphite to obtain graphene and spray depositing the prepared graphene flakes, above anode catalyst layer (near the membrane in the membrane electrode assembly (MEA)) as barrier layer at different weight percentages relative to the base membrane Nafion 115 was utilized in this work. The 5 wt.% nano-graphene layer raises 1 M methanol/oxygen fuel cell power density by 38% to 91 mW·cm-2, compared to standard membrane electrode assembly (MEA) performance of 63 mW·cm-2, owing to less methanol crossover with mild decrease in proton conductivity, showing negligible voltage decays over 20 h of operation at 50 mA·cm-2. Overall, this work opens three prominent favorable prospects: exploring the usage of nano-materials prepared by liquid phase exfoliation approach, their effective usage in ion-transport membrane region of MEA and enhancing fuel cell power performance.

17.
Materials (Basel) ; 15(9)2022 Apr 25.
Artigo em Inglês | MEDLINE | ID: mdl-35591446

RESUMO

The online preparation of fibers using molten modified blast furnace slag can not only achieve the high-value-added utilization of the slag but can also make use of the sensible heat of the slag. In this paper, blast furnace slag was modified using iron tailings, and was then used to prepare slag fiber online; the effects of the acidity coefficient on the properties of the molten modified blast furnace slag and modified blast furnace slag fiber were investigated. With an increase in the acidity coefficient from 1.2 to 1.6, the temperature range of the slag melt, with viscosity in the 1-3 Pa·s range, increased from 101.2 °C to 119.9 °C. The melting temperature increased from 1326.2 °C to 1388.7 °C, and the suitable fiber-forming temperature range increased from 70.7 °C to 82.9 °C. With the increasing acidity coefficient, the crystallization temperature of the molten modified slag decreased markedly. When the acidity coefficient was greater than 1.4, the slag system was still in a disordered glassy phase at 1100 °C. The hardening speed gradually reduced with the increasing acidity coefficient when the modified slag was cooled at the critical cooling rate, resulting in a gradual increase in fiber formability. The fibers prepared from the modified slag at different acidity coefficients had smooth surfaces, and were arranged in a crossed manner at the macroscopic level. Their color was white, and small quantities of slag balls were doped inside the fibers. With an increase in the acidity coefficient from 1.2 to 1.6, the average fiber diameter increased from 4.2 µm to 8.2 µm, and their slag ball content increased from 0.73% to 4.49%. Overall, the acidity coefficient of modified blast furnace slag should be less than 1.5 in actual production.

18.
Technol Cancer Res Treat ; 21: 15330338221100231, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35579876

RESUMO

Purpose: The first clinical biology-guided radiation therapy (BgRT) system-RefleXionTM X1-was installed and commissioned for clinical use at our institution. This study aimed at evaluating the treatment plan quality and delivery efficiency for IMRT/SBRT cases without PET guidance. Methods: A total of 42 patient plans across 6 cancer sites (conventionally fractionated lung, head, and neck, anus, prostate, brain, and lung SBRT) planned with the EclipseTM treatment planning system (TPS) and treated with either a TrueBeam® or Trilogy® were selected for this retrospective study. For each Eclipse VMAT plan, 2 corresponding plans were generated on the X1 TPS with 10 mm jaws (X1-10mm) and 20 mm jaws (X1-20mm) using our institutional planning constraints. All clinically relevant metrics in this study, including PTV D95%, PTV D2%, Conformity Index (CI), R50, organs-at-risk (OAR) constraints, and beam-on time were analyzed and compared between 126 VMAT and RefleXion plans using paired t-tests. Results: All but 3 planning metrics were either equivalent or superior for the X1-10mm plans as compared to the Eclipse VMAT plans across all planning sites investigated. The Eclipse VMAT and X1-10mm plans generally achieved superior plan quality and sharper dose fall-off superior/inferior to targets as compared to the X1-20mm plans, however, the X1-20mm plans were still considered acceptable for treatment. On average, the required beam-on time increased by a factor of 1.6 across all sites for X1-10mm compared to X1-20mm plans. Conclusions: Clinically acceptable IMRT/SBRT treatment plans were generated with the X1 TPS for both the 10 mm and 20 mm jaw settings.


Assuntos
Radiocirurgia , Radioterapia de Intensidade Modulada , Biologia , Humanos , Masculino , Órgãos em Risco , Dosagem Radioterapêutica , Planejamento da Radioterapia Assistida por Computador , Estudos Retrospectivos
19.
Cancer Med ; 2022 May 24.
Artigo em Inglês | MEDLINE | ID: mdl-35608100

RESUMO

BACKGROUND: Long noncoding RNAs (lncRNAs) are implicated in the oncogenesis and metastasis of multiple human cancers. Nonetheless, the precise molecular mechanisms underlying the oncogenic role of lncRNA in esophageal squamous cell carcinoma (ESCC) remains to be clarified. METHODS: The expression of GK intronic transcript 1 (GK-IT1) was analyzed using ESCC RNA-seq data from The Cancer Genome Atlas database. Quantitative real-time PCR was used to measure the expression of GK-IT1 in ESCC clinical samples and cells. The correlation between GK-IT1 expression and clinicopathological variables was examined using chi-squared tests. Kaplan-Meier survival and Cox regression analyses were employed to generate the survival curve and assess the prognostic value of GK-IT1. Functional experiments were utilized to explore the role of GK-IT1 in promoting cell migration, invasion, proliferation, and suppressing apoptosis and autophagy in ESCC. To understand the mechanism, an RNA pulldown assay, RNA immunoprecipitation, agarose gel electrophoresis, immunofluorescence, and co-immunoprecipitation assays were used. RESULTS: In this study we identified an unreported lncRNA, termed GK-IT1 that was aberrantly overexpressed in ESCC tissues and cells. GK-IT1 was closely associated with advanced clinical stage, and it was an independent prognostic indicator of ESCC. Functional assays verified that GK-IT1 significantly promoted ESCC proliferation, invasion, and migration, and suppressed ESCC apoptosis and autophagy. Furthermore, tumorigenesis experiments in nude mice indicated that GK-IT1 promoted ESCC tumor growth and metastasis. Mechanistically, GK-IT1 competitively bound to mitogen-activated protein kinase 1 (MAPK1) to prevent the interaction between dual specificity phosphatase 6 (DUSP6) and MAPK1, thereby controlling the phosphorylation of MAPK1 and promoting ESCC progression. CONCLUSION: Our study revealed that GK-IT1 competed with DUSP6 to attenuate the interaction between DUSP6 and MAPK1, leading to activation of the ERK/MAPK pathway, thereby promoting progression of ESCC. Our research indicated that GK-IT1 served as a novel potential target for the diagnosis and treatment of ESCC.

20.
Zhongguo Zhong Yao Za Zhi ; 47(7): 1881-1887, 2022 Apr.
Artigo em Chinês | MEDLINE | ID: mdl-35534258

RESUMO

This study employed Box-Behnken design combined with flux attenuation to explore the nanofiltration conditions for separation of alcohol precipitation liquid during the preparation of Reduning Injection and discussed the applicability of nanofiltration in the separation of the liquid with high-concentration ethanol. The effects of nanofiltration molecular weight cut-off(MWCO) and pH on the rejection of chlorogenic acid, 3,5-dicaffeoylquinic acid, and 4,5-dicaffeoylquinic acid were consistent with the principles of pore size sieving and charge effect, respectively. The rejection of the three phenolic acids was reduced by concentration polarization effect caused by trans-membrane pressure(TMP). The swelling of membrane surface decreased the pore size and membrane flux for effective separation. Chlorogenic acid and 4,5-dicaffeoylquinic acid were more sensitive to pH and ethanol concentration than 3,5-dicaffeoylquinic acid. A certain correlation existed between the compound structure and the separation factors of nanofiltration, and the separation rules were associated with the comprehensive effect of charge effect, pore size sieving, concentration polarization, steric hindrance and so on.


Assuntos
Ácido Clorogênico , Medicamentos de Ervas Chinesas , Medicamentos de Ervas Chinesas/química , Etanol , Injeções
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