Halechaney5896
Although large datasets are available, to learn a robust dose prediction model from a limited dataset still remains challenging. This work employed cascaded deep-learning models and advanced training strategies with a limited dataset to precisely predict three-dimensional (3D) dose distribution.
A Cascade 3D (C3D) model is developed based on the cascade mechanism and 3D U-Net network units. During model training, data augmentations are used to improve the generalization ability of the prediction model. A knowledge distillation technique is employed to further improve the capability of model learning. The C3D network was evaluated using the OpenKBP challenge dataset and competed with those models proposed by more than 40 teams globally. Additionally, it was compared with five existing cutting-edge dose prediction models. The performance of these prediction models were evaluated by voxel-based mean absolute error (MAE) and clinical-related dosimetric metrics. The code and models are publicly available online
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The MAE of a single C3D model without test-time augmentation is 2.50Gy (3.57% related to prescription dose) for non-zero dose area, which outperforms the other five dose prediction models by about 0.1Gy-1.7Gy. The C3D model won both Dose and DVH streams of AAPM 2020 OpenKBP challenge with dose score of 2.31 and DVH score of 1.55.
The Cascading U-Nets is an ideal solution for 3D dose prediction from a limited dataset. The proper data pre-processing, data augmentation and optimization procedure are more important than architectural modifications of deep learning network.
The Cascading U-Nets is an ideal solution for 3D dose prediction from a limited dataset. The proper data pre-processing, data augmentation and optimization procedure are more important than architectural modifications of deep learning network.The newborn coronaivus disease 2019 (COVID-19) pandemic has become the foremost concern of health system worldwide. Interferon typeI (IFN-I) are among the well-known antiviruses. Hence IFN-α have gained much attention as a treatment for COVID-19 recently. To sum up the efficiency of IFN-α against COVID-19, we searched PubMed, SCOPUS, and EMBASE, from the date of genesis to the 1st of October 2020. Discharge from hospital and virus clearance considered as primary and secondary outcomes, respectively. We compared the aforementioned outcomes of patients treated with standard care protocol and the patients treated with IFN-α in addition to standard care protocol. Out of 356 identified records, 14 studies were subjected for full-text screening. Finally, a systematic review was performed with inclusion of five studies. Majority of the participants were males (ranged from 43.50% to 90.0%). We found that time of viral clearance and polymerase chain reaction negative (days) in most studies were decreased in the INF-α + standard care group. The mean days of virus's clearance in INF-α group and standard group reported 27.3 and 32.43. Likewise, the average days of hospitalization was found also lower in INF-α group (18.55 vs. 24.36). This study provides a stand to conclude that early administration of INF-α may be accounted as a promising treatment of COVID-19.
Inhibition of pancreatic ATP-sensitive K
(K
) channels is the intended effect of oral sulphonylureas to increase insulin release in diabetes. However, pertinent to off-target effects of sulphonylurea medication, sex differences in cardiac K
channel function exist, whereas potential sex differences in vascular K
channel function remain unknown. In the present study, we assessed vascular K
channel function (topical glibenclamide superfused onto fast-twitch oxidative skeletal muscle) supporting blood flow and interstitial O
delivery-utilization matching (
P
O
2
is) during twitch contractions in male, female during pro-oestrus and ovariectomized female (F+OVX) rats. Glibenclamide decreased blood flow (convective O
transport) and interstitial
P
O
2
in male and female, but not F+OVX, rats. Compared to males, females also demonstrated impaired diffusive O
transport and a channel function to compromise muscle Q ̇ m and therefore exercise tolerance. Such an effect, if present, would likely contribute to adverse cardiovascular events in premenopausal females more than males.Over the last few decades, biomedical implants have successfully delivered therapeutic electrical stimulation to reduce the frequency and severity of seizures in people with drug-resistant epilepsy. However, neurostimulation approaches require invasive surgery to implant stimulating electrodes, and surgical, medical, and hardware complications are not uncommon. An endovascular approach provides a potentially safer and less invasive surgical alternative. This article critically evaluates the feasibility of endovascular closed-loop neuromodulation for the treatment of epilepsy. By reviewing literature that reported the impact of direct electrical stimulation to reduce the frequency of epileptic seizures, we identified clinically validated extracranial, cortical, and deep cortical neural targets. We identified veins in close proximity to these targets and evaluated the potential of delivering an endovascular implant to these veins based on their diameter. We then compared the risks and benefits of existing technconventional stimulation targets that are of a diameter large enough for delivery and deployment of an endovascular electrode array, supporting future work to assess clinical efficacy and chronic safety of an endovascular approach to deliver therapeutic neurostimulation.
In patient-specific quality assurance (QA) for static beam intensity-modulated radiation therapy (IMRT), machine-learning-based dose analysis methods have been developed to identify the cause of an error as an alternative to gamma analysis. Although these new methods have revealed that the cause of the error can be identified by analyzing the dose distribution obtained from the two-dimensional detector, they have not been extended to the analysis of volumetric-modulated arc therapy (VMAT) QA. In this study, we propose a deep learning approach to detect various types of errors in patient-specific VMAT QA.
A total of 161 beams from 104 prostate VMAT plans were analyzed. All beams were measured using a cylindrical detector (Delta4; ScandiDos, Uppsala, Sweden), and predicted dose distributions in a cylindrical phantom were calculated using a treatment planning system (TPS). In addition to the error-free plan, we simulated 12 types of errors two types of multileaf collimator positional errors (systematic or rar VMAT QA.
A multi-task CNN model for detecting errors in patient-specific VMAT QA using a cylindrical measuring device was constructed, and its performance was evaluated. Our results demonstrate that our model was effective in identifying the error type in the dose map for VMAT QA.Covert mortality nodavirus (CMNV), a novel aquatic pathogen, causes viral covert mortality disease (VCMD) in shrimps and also known to infect farmed marine fish. To date, there has no report regarding the ability of this virus to infect freshwater fish. In this study, we screened and discovered CMNV-positive freshwater zebrafish individuals by reverse transcription-nested PCR (RT-nPCR). The sequence of CMNV amplicons from zebrafish was found to share 99% identity with RNA-dependent RNA polymerase (RdRp) gene of the original CMNV isolate. Histopathological examination of the CMNV-positive zebrafish samples revealed extensive vacuolation and karyopyknosis lesions in the retina of the eye and the midbrain mesencephalon. CMNV-like virus particles were visualized in these tissues under transmission electron microscope. Different degrees of pathological damages were also found in muscle, gills, thymus and ovarian tissues. Strong positive signals of CMNV probe were observed in these infected tissues by in situ hybridization. Overall, all results indicated that zebrafish, an acknowledged model organism, could be infected naturally by CMNV. Thus, it is needed to pay close attention to the possible interference of CMNV whether in assessment of toxic substances, or in studying the developmental characterization and the nerval function, when zebrafish was used as model animal.The use of psychedelics, such as psilocybin, has emerged in recent literature as a novel therapeutic treatment for various psychiatric disorders, including substance use, depression, and anxiety. While international and domestic trials are currently underway, there is data demonstrating both the relative safety and potential efficacy of psilocybin. CX-5461 Psychiatric mental health nurse practitioners are essential mental health providers that may be at the forefront of delivering these new treatment modalities. Therefore, they must be aware of the psychopharmacological and psychotherapeutic tenets of psilocybin to be prepared to treat patients.
Owing to histologic complexities of brain tumors, its diagnosis requires the use of multimodalities to obtain valuable structural information so that brain tumor subregions can be properly delineated. In current clinical workflow, physicians typically perform slice-by-slice delineation of brain tumor subregions, which is a time-consuming process and also more susceptible to intra- and inter-rater variabilities possibly leading to misclassification. To deal with this issue, this study aims to develop an automatic segmentation of brain tumor in MR images using deep learning.
In this study, we develop a context deep-supervised U-Net to segment brain tumor subregions. A context block which aggregates multiscale contextual information for dense segmentation was proposed. This approach enlarges the effective receptive field of convolutional neural networks, which, in turn, improves the segmentation accuracy of brain tumor subregions. We performed the fivefold cross-validation on the Brain Tumor Segmentation Chah positive correlation between the tumor volumes generated by proposed method and manual contour.
Overall qualitative and quantitative results of this work demonstrate the potential of translating proposed technique into clinical practice for segmenting brain tumor subregions, and further facilitate brain tumor radiotherapy workflow.
Overall qualitative and quantitative results of this work demonstrate the potential of translating proposed technique into clinical practice for segmenting brain tumor subregions, and further facilitate brain tumor radiotherapy workflow.Timing and causes of hospital mortality in adult patients undergoing veno-arterial extracorporeal membrane oxygenation (V-A ECMO) have been poorly described. Aim of the current review was to investigate the timing and causes of death of adult patients supported with V-A ECMO and subsequently define the "V-A ECMO gap," which represents the patients who are successfully weaned of ECMO but eventually die during hospital stay. A systematic search was performed using electronic MEDLINE and EMBASE databases through PubMed. Studies reporting on adult V-A ECMO patients from January 1993 to December 2020 were screened. The studies included in this review were studies that reported more than 10 adult, human patients, and no mechanical circulatory support other than V-A ECMO. Information extracted from each study included mainly mortality and causes of death on ECMO and after weaning. Complications and discharge rates were also extracted. Sixty studies with 9181 patients were included for analysis in this systematic review.
