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Lifetime of condition as well as results within older COVID-19 individuals treated with HFNC: a retrospective investigation.
These differentially expressed lncRNAs and mRNAs were assigned to 275 significant GO terms and 179 significant KEGG enriched pathways. qRT-PCR confirmed that the expression of five selected lncRNAs (ENST00000521815, ENST00000603052, ENST00000609220, NR_026543, and ENST00000545920) were consistent with the microarray data. ROC analysis showed that four lncRNAs (ENST00000521815, ENST00000603052, ENST00000609220, and NR_026543) had larger area under the ROC curve (AUC) values compared to serum carcinoembryonic antigens, thereby distinguishing NC from pMMR CC. In conclusion, several lncRNAs play various roles in the adenoma-carcinoma sequence and may serve as potential biomarkers for the early diagnosis of pMMR CC.The target of this study was to evaluate the efficacy, histopathological, oxidative stress, and molecular effects of quercetin (QRC) in mice with oral mucositis induced by 5-fluorouracil (5-FU). Thirty-six albino male mice with oral mucositis induced by 5-FU as a chemotherapeutic agent were used in this study. The animals were randomly divided into 6 groups control group, mucositis (MUC) group, pretreatment group, posttreatment group, and two last groups including nanoemulsion form of QRC with a dose of 5 mg/kg in both pretreatment and posttreatment. In the present evaluation, fewer oral lesions were observed in the QRC posttreatment groups compared to the pretreatment and nanoemulsion receiving groups. In the SOD assay, the most significant difference was observed in the posttreatment nanogroup (41.073 ± 1.24) and pretreatment nanogroup (43.453 ± 2.60) in comparison to the 5-FU group (30.897 ± 1.93). The results of CAT assay also showed a significant difference in nano-posttreatment (124.60 ± 10.85), posttreatment (135.4 ± 9.82), and nano-pretreatment groups (128.80 ± 7.20) compared to the 5-FU group (55.07 ± 8.91). The expression of inflammatory genes such as Hif-1α and NfκB in this group was lower than in the other groups, although this difference was not significant. It seems that the use of QRC can improve the treatment process of oral mucositis induced by 5-FU.Background. Radiation-induced toxicity represents a crucial concern in oncological treatments of patients affected by head and neck neoplasms, due to its impact on survivors' quality of life. Published reports suggested the potential of radiomics combined with machine learning methods in the prediction and assessment of radiation-induced toxicities, supporting a tailored radiation treatment management. In this paper, we present an update of the current knowledge concerning these modern approaches.
A systematic review according to PICO-PRISMA methodology was conducted in MEDLINE/PubMed and EMBASE databases until June 2019. Studies assessing the use of radiomics combined with machine learning in predicting radiation-induced toxicity in head and neck cancer patients were specifically included. Four authors (two independently and two in concordance) assessed the methodological quality of the included studies using the Radiomic Quality Score (RQS). The overall score for each analyzed study was obtained by the sum of the single RQS items; the average and standard deviation values of the authors' RQS were calculated and reported.

Eight included papers, presenting data on parotid glands, cochlea, masticatory muscles, and white brain matter, were specifically analyzed in this review. Only one study had an average RQS was ≤ 30% (50%), while 3 studies obtained a RQS almost ≤ 25%. Potential variability in the interpretations of specific RQS items could have influenced the inter-rater agreement in specific cases.

Published radiomic studies provide encouraging but still limited and preliminary data that require further validation to improve the decision-making processes in preventing and managing radiation-induced toxicities.
Published radiomic studies provide encouraging but still limited and preliminary data that require further validation to improve the decision-making processes in preventing and managing radiation-induced toxicities.
The study is aimed at evaluating knowledge, attitude, and barriers to telemedicine among the general population in Egypt.

A questionnaire-based cross-sectional design was carried out among the general Egyptian population. A convenience sampling method was used to approach the eligible participants from University Teaching Hospitals of eight governorates from May to July 2020.

A total of 686 participants filled the questionnaire (49.4% were males, mean age 36.7 ± 11.2 years old). Half of the participants stated that they previously used a telemedicine tool, mainly to follow up laboratory results (67.3%). Video or phone calls (39.3%) and mobile applications (23.7%) were the most commonly recognized telemedicine tools by the participants. The included participants exhibited a high level of knowledge and attitude towards telemedicine. On the other hand, 21.9% stated that telemedicine services could jeopardize patient privacy. 32.8% reported that telemedicine service could lead to disclosing medical informatians appear to perceive the benefits of telemedicine positively and are willing to use it. However, some barriers that have been found must be taken into consideration to adopt telemedicine successfully, especially for people who are old, are low educated, and live in remote areas. Future studies should address the utility of telemedicine in improving the quality of healthcare and patient's health outcome and quality of life.Stroke is a leading cause of motor disability worldwide, and robot-assisted therapies have been increasingly applied to facilitate the recovery process. However, the underlying mechanism and induced neuroplasticity change remain partially understood, and few studies have investigated this from a multimodality neuroimaging perspective. The current study adopted BCI-guided robot hand therapy as the training intervention and combined multiple neuroimaging modalities to comprehensively understand the potential association between motor function alteration and various neural correlates. We adopted EEG-informed fMRI technique to understand the functional regions sensitive to training intervention. Additionally, correlation analysis among training effects, nonlinear property change quantified by fractal dimension (FD), and integrity of M1-M1 (M1 primary motor cortex) anatomical connection were performed. EEG-informed fMRI analysis indicated that for iM1 (iM1 ipsilesional M1) regressors, regions with significantly inign of therapies for chronic stroke patients.Recognition of human activities is an essential field in computer vision. The most human activity consists of the interaction between humans and objects. Many successful works have been done on human-object interaction (HOI) recognition and achieved acceptable results in recent years. Still, they are fully supervised and need to train labeled data for all HOIs. Due to the enormous space of human-object interactions, listing and providing the training data for all possible categories is costly and impractical. We propose an approach for scaling human-object interaction recognition in video data through the zero-shot learning technique to solve this problem. Our method recognizes a verb and an object from the video and makes an HOI class. Recognition of the verbs and objects instead of HOIs allows identifying a new combination of verbs and objects. So, a new HOI class can be identified, which is not seen by the recognizer system. We introduce a neural network architecture that can understand and represent the vs. Therefore, the number of classes identifiable by the system is greater than the number of classes used for training.An important challenge facing cloud computing is how to correctly and effectively handle and serve millions of users' requests. Efficient task scheduling in cloud computing can intuitively affect the resource configuration and operating cost of the entire system. However, task and resource scheduling in a cloud computing environment is an NP-hard problem. In this paper, we propose a three-layer scheduling model based on whale-Gaussian cloud. In the second layer of the model, a whale optimization strategy based on the Gaussian cloud model (GCWOAS2) is used for multiobjective task scheduling in a cloud computing which is to minimize the completion time of the task via effectively utilizing the virtual machine resources and to keep the load balancing of each virtual machine, reducing the operating cost of the system. In the GCWOAS2 strategy, an opposition-based learning mechanism is first used to initialize the scheduling strategy to generate the optimal scheduling scheme. Then, an adaptive mobility factor is proposed to dynamically expand the search range. The whale optimization algorithm based on the Gaussian cloud model is proposed to enhance the randomness of search. Finally, a multiobjective task scheduling algorithm based on Gaussian whale-cloud optimization (GCWOA) is presented, so that the entire scheduling strategy can not only expand the search range but also jump out of the local maximum and obtain the global optimal scheduling strategy. Experimental results show that compared with other existing metaheuristic algorithms, our strategy can not only shorten the task completion time but also balance the load of virtual machine resources, and at the same time, it also has a better performance in resource utilization.
There is a dearth of studies conducted to understand the socio-professional profile of the vascular surgery specialty and the population demands of specific regions, which are needed to support creation of care policies and direct infrastructure improvements in healthcare.

The purpose of this study was to describe the socio-professional profile of vascular surgeons in the state of Pará, Brazil, to guide creation of tools for professional improvement.

A cross-sectional, self-report survey was conducted in Pará using a questionnaire comprising 30 questions covering six main topics.

All vascular surgeons actively practicing in the state participated in this study. The total number of specialists was 59, with 71.2% working in the greater Belém area and 16.9% exclusively practicing in the interior of the state. check details The mean age of these professionals was 48 ± 11.1 years, 86.4% of respondents were men, 64.4% of surgeons had completed medical residency, and 96.6% (n=57) of the surgeons would like to improve theirgery, amputations, and provision of hemodialysis access. More than 90% of these surgeons were satisfied professionally and reported that they would choose the specialty again. However, 22% had a pessimistic view of the specialty's future. The vast majority of professionals (96.6%) consider that training or a continuing education program are necessary.Since the coronavirus pandemic set in in Spain in March 2020, a noteworthy increase in the incidence of acute limb ischemia (ALI) has been observed. It has been recently discovered that SARS-CoV 2 may lead to ALI secondary to arterial thrombosis. Elevation of D-dimer (DD) in patients with coronavirus infection (COVID-19) indicates that a hypercoagulable state causes acute arterial thrombosis. A remarkably high DD elevation has been reported to be a poor prognosis factor in COVID-19. The ways in which SARS-CoV 2 results in arterial thrombosis may be multiple. On the other hand, surgical revascularization for ALI is associated with poor outcomes in COVID-19 patients, probably in relation to hypercoagulability. Here, we describe two ALI cases in patients who required urgent surgical treatment for limb salvage and were positive for the novel coronavirus infection (COVID 19).
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