Chaneycombs2521
One of the current challenges faced by health centers is to reduce the number of patients who do not attend their appointments. The existence of these patients causes the underutilization of the center's services, which reduces their income and extends patient's access time. In order to reduce these negative effects, several appointment scheduling systems have been developed. With the recent availability of electronic health records, patient scheduling systems that incorporate the patient's no-show prediction are being developed. However, the benefits of including a personalized individual variable time slot for each patient in those probabilistic systems have not been yet analyzed. In this article, we propose a scheduling system based on patients' no-show probabilities with variable time slots and a dynamic priority allocation scheme. The system is based on the solution of a mixed-integer programming model that aims at maximizing the expected profits of the clinic, accounting for first and follow-up visits. We validate our findings by performing an extensive simulation study based on real data and specific scheduling requirements provided by a Spanish hospital. The results suggest potential benefits with the implementation of the proposed allocation system with variable slot times. In particular, the proposed model increases the annual cumulated profit in more than 50% while decreasing the waiting list and waiting times by 30% and 50%, respectively, with respect to the actual appointment scheduling system.
We collected and sorted out the white light endoscopic images of some patients undergoing colonoscopy. The convolutional neural network model is used to detect whether the image contains lesions CRC, colorectal adenoma (CRA), and colorectal polyps. The accuracy, sensitivity, and specificity rates are used as indicators to evaluate the model. Then, the instance segmentation model is used to locate and classify the lesions on the images containing lesions, and mAP (mean average precision), AP
, and AP
are used to evaluate the performance of an instance segmentation model.
In the process of detecting whether the image contains lesions, we compared ResNet50 with the other four models, that is, AlexNet, VGG19, ResNet18, and GoogLeNet. The result is that ResNet50 performs better than several other models. It scored an accuracy of 93.0%, a sensitivity of 94.3%, and a specificity of 90.6%. In the process of localization and classification of the lesion in images containing lesions by Mask R-CNN, its mAP, AP
, and AP
were 0.676, 0.903, and 0.833, respectively.
We developed and compared five models for the detection of lesions in white light endoscopic images. ResNet50 showed the optimal performance, and Mask R-CNN model could be used to locate and classify lesions in images containing lesions.
We developed and compared five models for the detection of lesions in white light endoscopic images. ResNet50 showed the optimal performance, and Mask R-CNN model could be used to locate and classify lesions in images containing lesions.
The purpose of this study was to investigate the relationship between miR-152-3p and the KLF4/IFITM3 axis, thereby revealing the mechanism underlying colon cancer occurrence and development, consequently providing a promising target for colon cancer treatment.
Bioinformatics methods were implemented to analyze the differential expression of miRNAs and mRNAs in colon cancer, confirm the target miRNA, and predict the downstream targeted mRNAs. qRT-PCR and Western blot were performed to detect the expression of miR-152-3p, KLF4, and IFITM3. CCK-8 and colony formation assays were conducted for the assessment of cell proliferation, and flow cytometry was carried out for the detection of cell apoptosis. Finally, dual-luciferase reporter gene assay was employed to verify the targeting relationship between miR-152-3p and KLF4.
miR-152-3p was highly expressed in colon cancer cells, whereas KLF4 was poorly expressed. Dual-luciferase assay verified that miR-152-3p targeted to bind to KLF4 and suppressed its expression. Moreover, silencing miR-152-3p or overexpressing KLF4 was found to downregulate IFITM3, thereby inhibiting cell proliferation and potentiating cell apoptosis. In rescue experiments, we found that miR-152-3p deficiency decreased the expression of IFITM3 and weakened cancer cell proliferation, and such effects were restored when miR-152-3p and KLF4 were silenced simultaneously.
In sum, we discovered that miR-152-3p can affect the pathogenesis of colon cancer via the KLF4/IFITM3 axis.
In sum, we discovered that miR-152-3p can affect the pathogenesis of colon cancer via the KLF4/IFITM3 axis.Although sequencing a human genome has become affordable, identifying genetic variants from whole-genome sequence data is still a hurdle for researchers without adequate computing equipment or bioinformatics support. GATK is a gold standard method for the identification of genetic variants and has been widely used in genome projects and population genetic studies for many years. This was until the Google Brain team developed a new method, DeepVariant, which utilizes deep neural networks to construct an image classification model to identify genetic variants. However, the superior accuracy of DeepVariant comes at the cost of computational intensity, largely constraining its applications. Accordingly, we present DeepVariant-on-Spark to optimize resource allocation, enable multi-GPU support, and accelerate the processing of the DeepVariant pipeline. To make DeepVariant-on-Spark more accessible to everyone, we have deployed the DeepVariant-on-Spark to the Google Cloud Platform (GCP). Users can deploy DeepVariant-on-Spark on the GCP following our instruction within 20 minutes and start to analyze at least ten whole-genome sequencing datasets using free credits provided by the GCP. DeepVaraint-on-Spark is freely available for small-scale genome analysis using a cloud-based computing framework, which is suitable for pilot testing or preliminary study, while reserving the flexibility and scalability for large-scale sequencing projects.The morbidity and mortality of colorectal cancer (CRC) remained to be very high worldwide. Recently, circRNAs had been revealed to have a crucial role in cancer prognosis and progression. Numerous researches have shown that RNA sequencing technology and in silico method were widely used to identify pathogenic mechanisms and uncover promising targets for diagnosis and therapy. In this study, these methods were analyzed to obtain differentially expressed circRNAs (DECs). We identified upregulated 316 circRNAs and reduced 76 circRNAs in CRC samples, in comparison with those in normal tissues. In addition, a competitive endogenous network of circRNA-miRNA-mRNA was established to predict the mechanisms of circRNAs. Bioinformatics analysis revealed that these circRNAs participated in metabolism regulation and cell cycle progression. Of note, we observed the hub genes and miRNAs in this ceRNA network were associated with the survival time in CRC. We think this study could provide potential prognostic biomarkers and targets for CRC.The Traditional Chinese Medicine (TCM) formula is the main treatment method of TCM. A formula often contains multiple herbs where core herbs play a critical therapeutic effect for treating diseases. It is of great significance to find out the core herbs in formulae for providing evidences and references for the clinical application of Chinese herbs and formulae. In this paper, we propose a core herb discovery model CHDSC based on semantic analysis and community detection to discover the core herbs for treating a certain disease from large-scale literature, which includes three stages corpus construction, herb network establishment, and core herb discovery. In CHDSC, two artificial intelligence modules are used, where the Chinese word embedding algorithm ESSP2VEC is designed to analyse the semantics of herbs in Chinese literature based on the stroke, structure, and pinyin features of Chinese characters, and the label propagation-based algorithm LILPA is adopted to detect herb communities and core herbs in the herbal semantic network constructed from large-scale literature. To validate the proposed model, we choose chronic glomerulonephritis (CGN) as an example, search 1126 articles about how to treat CGN in TCM from the China National Knowledge Infrastructure (CNKI), and apply CHDSC to analyse the collected literature. Experimental results reveal that CHDSC discovers three major herb communities and eighteen core herbs for treating different CGN syndromes with high accuracy. The community size, degree, and closeness centrality distributions of the herb network are analysed to mine the laws of core herbs. As a result, we can observe that core herbs mainly exist in the communities with more than 25 herbs. The degree and closeness centrality of core herb nodes concentrate on the range of [15, 40] and [0.25, 0.45], respectively. Thus, semantic analysis and community detection are helpful for mining effective core herbs for treating a certain disease from large-scale literature.An artificial stent implantation is one of the most effective ways to treat coronary artery diseases. It is vital in vascular medical imaging, such as intravascular optical coherence tomography (IVOCT), to be able to track the position of stents in blood vessels effectively. We trained two models, the "You Only Look Once" version 3 (YOLOv3) and the Region-based Fully Convolutional Network (R-FCN), to detect metal support struts in IVOCT, respectively. After rotating the original images in the training set for data augmentation, and modifying the scale of the conventional anchor box in both two algorithms to fit the size of the target strut, YOLOv3 and R-FCN achieved precision, recall, and AP all above 95% in 0.4 IoU threshold. And R-FCN performs better than YOLOv3 in all relevant indicators.The COVID-19 pandemic has emerged as an unprecedented global healthcare emergency and has devastated the global economy. The SARS-CoV-2 virus replicates in the host cells and is seemingly much more virulent compared to other flu viruses, as well as the SARS-CoV-1. The respiratory complications of the disease include acute respiratory distress syndrome (ARDS), cytokine storm, systemic inflammation, and pulmonary fibrosis. Nanoceria (NC) is a versatile rare earth nanoparticle with remarkable catalase and superoxide dismutase mimetic redox regenerative properties. Interestingly, NC possesses promising anti-inflammatory, antioxidant and anti-fibrotic properties, making it an attractive tool to fight against the SARS-CoV-2 as well as the associated systemic complications. Until now, there is no clinically approved vaccine or drug for the treatment of COVID-19, and the conquest to find a novel therapy for this global havoc is being undertaken at a warlike pace. Herein, based on preclinical evidence, we hypothesize that NC owing to its unique pharmacological properties, might be an attractive preclinical candidate to win the battle over COVID-19. Further, it may be used as a prevention or treatment strategy in combination with other drugs.
