Princemason5799
Network interaction has evolved into a grouping paradigm as civilization has progressed and artificial intelligence technology has advanced. This network group model has quickly extended communication space, improved communication content, and tailored to the demands of netizens. The fast growth of the network community on campus can assist students in meeting a variety of communication needs and serve as a vital platform for their studies and daily lives. It is investigated how to extract opinion material from comment text. A strategy for extracting opinion attitude words and network opinion characteristic words from a single comment text is offered at a finer level. The development of a semiautonomous domain emotion dictionary generating technique improves the accuracy of opinion and attitude word extraction. This paper proposes a window-constrained Latent Dirichlet Allocation (LDA) topic model that improves the accuracy of extracting network opinion feature words and ensures that network opinion feature words and opinion attitude words are synchronized by using the location information of opinion attitude words. The two-stage opinion leader mining approach and the linear threshold model based on user roles are the subjects of model simulation tests in this study. It is demonstrated that the two-stage opinion leader mining method suggested in this study can greatly reduce the running time while properly finding opinion leaders with stronger leadership by comparing the results with existing models. It also shows that the linear threshold model based on user roles proposed in this paper can effectively limit the total number of active users who are activated multiple times during the information diffusion process by distinguishing the effects of different user roles on the information diffusion process.
To investigate clinical benefit and safety of neoadjuvant chemotherapy (NAC) plus bevacizumab combined with total mesorectal excision (TME) in treating patients with BRAF-mutated locally advanced rectal cancer (LARC).
This study included LARC patients with BRAF mutation admitted to the Oncology Department of Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, between June 2013 and December 2018. Patients in the control group received a standard treatment regimen of TME combined with NAC (
= 45), and patients in the observation group received NAC plus bevacizumab combined with TME (
= 55). The short-term clinical efficacy of the two groups after NAC treatment was observed and compared, including differences in the pathological downstaging rate. The incidence of perioperative complications and adverse reactions during neoadjuvant therapy was compared to evaluate the safety of the treatment. Besides, the relapse-free survival (RFS) and overall survival (OS) of patients were analyzed to evaluate the long-term clinical benefit of the treatment.
Compared with the control group, the ypT staging rate (
= 0.014) in the observation group was markedly lower. In addition, patients in the observation group had a prominently lower overall incidence of complications (
< 0.001) during the perioperative period and a remarkably lower incidence of leukopenia (
= 0.037) during neoadjuvant therapy. In terms of long-term clinical benefit, the RFS of patients in the observation group was evidently longer (
= 0.037) than that in the control group.
Compared with TME plus NAC treatment, the short-term and long-term clinical benefits are higher and safety is more favorable of NAC plus bevacizumab combined with TME in treating LARC patients.
Compared with TME plus NAC treatment, the short-term and long-term clinical benefits are higher and safety is more favorable of NAC plus bevacizumab combined with TME in treating LARC patients.We have studied one of the most common distributions, namely, Lindley distribution, which is an important continuous mixed distribution with great ability to represent different systems. We studied this distribution with three parameters because of its high flexibility in modelling life data. The parameters were estimated by five different methods, namely, maximum likelihood estimation, ordinary least squares, weighted least squares, maximum product of spacing, and Cramér-von Mises. Simulation experiments were performed with different sample sizes and different parameter values. The different methods were compared on the generated data by mean square error and mean absolute error. In addition, we compared the methods for real data, which represent COVID-19 data in Iraq/Anbar Province.The artificial intelligence algorithm was used to analyze the characteristics of computed tomography (CT) images before and after interventional treatment of children's lymphangioma. Retrospective analysis was performed, and 30 children with lymphangioma from the hospital were recruited as the study subjects. The ultrasound-guided bleomycin interventional therapy was adopted and applied to CT scanning through convolutional neural network (CNN). The CT imaging-related indicators before and after interventional therapy were detected, and feature analysis was performed. In addition, the CNN algorithm was adopted to segment the image of the tumor was clearer and more accurate. At the same time, the Dice similarity coefficient (DSC) of the CNN algorithm was 0.9, which had a higher degree of agreement. In terms of clinical symptoms, the cured children's lesions disappeared, the skin surface returned to normal color, and the treatment was smooth. In the two cases with effective treatment, the cystic mass at the lesiventional therapy for lymphangioma in children was 96.67%. The CNN algorithm can effectively compare the CT image features before and after interventional treatment for children's lymphangioma. It was suggested that the artificial intelligence algorithm-aided CT imaging examination was helpful to guide physicians in the accurate treatment of children's lymphangioma.As we all know, the dietary nutrition of athletes has a great influence on physical condition and exercise ability. A good diet pattern is the basis of a reasonable diet for athletes. It helps to improve the function and physical state of athletes. This article is aimed at studying the impact of nutritious food on athletes' training and physical health. This article proposes the relevant technology of medical image recognition, which is used to study the relationship between nutritious food and the health of volleyball players and athletes, and proposes methods such as weighing method, meal review method, and measurement method, and the purpose is to exercise nutritional research and provide new ideas and methods. In addition, 200 female volleyball players were randomly selected for comparative analysis. The experimental results in this paper show that the energy intake and energy consumption of the female intervention group maintained a balance after the intervention, and there was a significant change in the negative balance state before the intervention. The energy consumption changed from -158.2 ± 156.2 to -157.2 ± 129.6. The number of athletes whose weight is close to the ideal range has increased from 44.8% to 48.5%.The quantitative accuracy and precision of brain positron emission tomography (PET) studies can be considerably improved using dedicated brain PET scanners with a uniform high resolution and a high sensitivity across the brain volume. One approach to building such a system is to construct the PET scanner using depth-of-interaction (DOI) encoding detectors with finely segmented and thick crystal arrays. In this paper, the performance of a DOI PET detector based on two 16 × 16 arrays of 2 × 2 mm2 SiPMs coupled to both ends of a 44 × 44 array of 0.69 × 0.69 × 30 mm3 polished LYSO crystals was evaluated at different temperatures (-9°C, 0°C, 10°C, and 20°C) for brain PET applications. The pitch size of the LYSO array is 0.75 mm. The flood histograms show that all the crystal elements in the LYSO array can be resolved except some edge crystals, due to the limited light sharing. The average energy resolution, average DOI resolution, and average timing resolution across crystal elements are 21.1 ± 3.0%, 3.47 ± 0.17 mm, and 1.38 ± 0.09 ns, respectively, which were obtained at a bias voltage of 56.5 V and a temperature of 0°C.The objective of this study was to investigate the therapeutic effect of seawater pearl powder (SPP) on ultraviolet (UV) irradiation-induced photoaging in mouse skin. The protein and trace elements in SPP were detected by liquid chromatography-mass spectrometry, atomic fluorescence spectrometry, and inductively coupled plasma-atomic emission spectrometry. The effect of SPP on treating skin damage resulting from UV-induced photoaging was observed by gross physical appearance and histopathological analysis. Oxidative stress and melanin synthesis were analyzed using biochemical method. Western blotting was applied to analyze the phosphorylation and expression levels of matrix metalloproteinase-1 (MMP-1), collagen I, and proteins involved in the mitogen-activated protein kinase (MAPK) signaling pathways (p38, ERK, and JNK). The results show that SPP has a significant therapeutic effect on UV-induced photoaging of skin and improves and restores appearance and tissue structure of mouse skin. The major mechanism may be related to reduction of expression level of MMP-1 and enhancement of collagen I production via inhibition of MAPK signaling pathway after scavenging of excess reactive oxygen species (ROS) in the UV-induced photoaged skin of mice. Meanwhile, it may also be involved in reducing melanin content by inhibiting tyrosinase activity after scavenging excess ROS in the UV-induced photoaged skin of mice. Therefore, SPP could be a good substance to treat photoaging skin. Taking cost-effectiveness and efficacy into consideration, the optimal concentration of SPP for treating photoaging skin could be 100 mg/g.
Cancer-related fatigue (CRF) is an increasingly appreciated complication in cancer patients, which severely impairs their quality of life for a long time.
(AR) is a safe and effective treatment to improve CRF, but the related mechanistic studies are still limited.
To systematically analyze the mechanism of AR against CRF by network pharmacology.
TCMSP was searched to obtain the active compounds and targets of AR. The active compound-target (AC-T) network was established and exhibited by related visualization software. The GeneCards database was searched to acquire CRF targets, and the intersection targets with AR targets were used to make the Venny diagram. The protein-protein interaction (PPI) network of intersection targets was established, and further, the therapeutic core targets were selected by topological parameters. The selected core targets were uploaded to Metascape for GO and KEGG analysis. Finally, AutoDock Vina and PyMOL were employed for molecular docking validation.
16 active compounds of AR were obtained, such as quercetin, kaempferol, 7-O-methylisomucronulatol, formononetin, and isorhamnetin. 57 core targets were screened, such as AKT1, TP53, VEGFA, IL-6, and CASP3. KEGG analysis manifested that the core targets acted on various pathways, including 137 pathways such as TNF, IL-17, and the AGE-RAGE signaling pathway. Molecular docking demonstrated that active compounds docked well with the core targets.
The mechanism of AR in treating CRF involves multiple targets and multiple pathways. The present study laid a theoretical foundation for the subsequent research and clinical application of AR and its extracts against CRF.
The mechanism of AR in treating CRF involves multiple targets and multiple pathways. The present study laid a theoretical foundation for the subsequent research and clinical application of AR and its extracts against CRF.
