Preparation of a nano emodin transfersome and study on its anti-obesity mechanism in adipose tissue of diet-induced obese rats

OBJECTIVE: To describe the preparation of nano emodin transfersome (NET) and investigate its effect on mRNA expression of adipose triglyceride lipase (ATGL) and G0/G1 switch gene 2 (G0S2) in adipose tissue of diet-induced obese rats. METHODS: NET was prepared by film-ultrasonic dispersion method. The effects of emodin components at different ratios on encapsulation efficiency were investigated.The NET envelopment rate was determined by ultraviolet spectrophotometry. The particle size and Zeta potential of NET were evaluated by Zetasizer analyzer. Sixty male SD rats were assigned to groups randomly. After 8-week treatment, body weight, wet weight of visceral fat and the percentage of body fat (PBF) were measured. Fasting blood glucose and serum lipid levels were determined. The adipose tissue section was HE stained, and the cellular diameter and quantity of adipocytes were evaluated by light microscopy. The mRNA expression of ATGL and G0S2 from the peri-renal fat tissue was assayed by RT-PCR. RESULTS: The appropriate formulation was deoxycholic acid sodium salt vs. phospholipids 1:8, cholesterol vs. phospholipids 1:3, vitamin Evs. phospholipids 1:20, and emodin vs. phospholipid 1:6. Zeta potential was −15.11 mV, and the particle size was 292.2 nm. The mean encapsulation efficiency was (69.35 ± 0.25)%. Compared with the obese model group, body weight, wet weight of visceral fat, PBF and mRNA expression of G0S2 from peri-renal fat tissue were decreased significantly after NET treatment (all P < 0.05), while high-density lipoprotein cholesterol (HDL-C), the diameter of adipocytes and mRNA expression of ATGL from peri-renal fat tissue were increased significantly (all P < 0.05). CONCLUSION: The preparation method is simple and reasonable. NET with negative electricity was small and uniform in particle size, with high encapsulation efficiency and stability. NET could reduce body weight and adipocyte size, and this effect was associated with the up-regulation of ATGL, down-regulation of G0S2 expression in the adipose tissue, and improved insulin sensitivity

Pemetrexed plus Platinum as the First-Line Treatment Option for Advanced Non-Small Cell Lung Cancer: A Meta-Analysis of Randomized Controlled Trials

To compare the efficacy and toxicities of pemetrexed plus platinum with other platinum regimens in patients with previously untreated advanced non-small cell lung cancer (NSCLC). Methods: A meta-analysis was performed using trials identified through PubMed, EMBASE, and Cochrane databases. Two investigators independently assessed the quality of the trials and extracted data. The outcomes included overall survival (OS), progression-free survival (PFS), response rate (RR), and different types of toxicity. Hazard ratios (HRs), odds ratios (ORs) and their 95% confidence intervals (CIs) were pooled using RevMan software. Results: Four trials involving 2,518 patients with previously untreated advanced NSCLC met the inclusion criteria. Pemetrexed plus platinum chemotherapy (PPC) improved survival compared with other platinum-based regimens (PBR) in patients with advanced NSCLC (HR = 0.91, 95% CI: 0.83–1.00, p = 0.04), especially in those with non-squamous histology (HR = 0.87, 95% CI: 0.77–0.98, p = 0.02). No statistically significant improvement in either PFS or RR was found in PPC group as compared with PBR group (HR = 1.03, 95% CI: 0.94–1.13, p = 0.57; OR = 1.15, 95% CI: 0.95–1.39, p = 0.15, respectively). Compared with PBR, PPC led to less grade 3–4 neutropenia and leukopenia but more grade 3–4 nausea. However, hematological toxicity analysis revealed significant heterogeneities. Conclusion: Our results suggest that PPC in the first-line setting leads to a significant survival advantage with acceptable toxicities for advanced NSCLC patients, especially those with non-squamous histology, as compared with other PRB. PPC could be considered as the first-line treatment option for advanced NSCLC patients, especially those with non-squamous histology

MicroRNAome of Porcine Pre- and Postnatal Development

The domestic pig is of enormous agricultural significance and valuable models for many human diseases. Information concerning the pig microRNAome (miRNAome) has been long overdue and elucidation of this information will permit an atlas of microRNA (miRNA) regulation functions and networks to be constructed. Here we performed a comprehensive search for porcine miRNAs on ten small RNA sequencing libraries prepared from a mixture of tissues obtained during the entire pig lifetime, from the fetal period through adulthood. The sequencing results were analyzed using mammalian miRNAs, the precursor hairpins (pre-miRNAs) and the first release of the high-coverage porcine genome assembly (Sscrofa9, April 2009) and the available expressed sequence tag (EST) sequences. Our results extend the repertoire of pig miRNAome to 867 pre-miRNAs (623 with genomic coordinates) encoding for 1,004 miRNAs, of which 777 are unique. We preformed real-time quantitative PCR (q-PCR) experiments for selected 30 miRNAs in 47 tissue-specific samples and found agreement between the sequencing and q-PCR data. This broad survey provides detailed information about multiple variants of mature sequences, precursors, chromosomal organization, development-specific expression, and conservation patterns. Our data mining produced a broad view of the pig miRNAome, consisting of miRNAs and isomiRs and a wealth of information of pig miRNA characteristics. These results are prelude to the advancement in pig biology as well the use of pigs as model organism for human biological and biomedical studies

Forest Fire Smoke Detection Using Back-Propagation Neural Network Based on MODIS Data

Satellite remote sensing provides global observations of the Earth’s surface and provides useful information for monitoring smoke plumes emitted from forest fires. The aim of this study is to automatically separate smoke plumes from the background by analyzing the MODIS data. An identification algorithm was improved based on the spectral analysis among the smoke, cloud and underlying surface. In order to get satisfactory results, a multi-threshold method is used for extracting training sample sets to train back-propagation neural network (BPNN) classification for merging the smoke detection algorithm. The MODIS data from three forest fires were used to develop the algorithm and get parameter values. These fires occurred in (i) China on 16 October 2004, (ii) Northeast Asia on 29 April 2009 and (iii) Russia on 29 July 2010 in different seasons. Then, the data from four other fires were used to validate the algorithm. Results indicated that the algorithm captured both thick smoke and thin dispersed smoke over land, as well as the mixed pixels of smoke over the ocean. These results could provide valuable information concerning forest fire location, fire spreading and so on

Forest Fire Smoke Detection Using Back-Propagation Neural Network Based on MODIS Data

Satellite remote sensing provides global observations of the Earth’s surface and provides useful information for monitoring smoke plumes emitted from forest fires. The aim of this study is to automatically separate smoke plumes from the background by analyzing the MODIS data. An identification algorithm was improved based on the spectral analysis among the smoke, cloud and underlying surface. In order to get satisfactory results, a multi-threshold method is used for extracting training sample sets to train back-propagation neural network (BPNN) classification for merging the smoke detection algorithm. The MODIS data from three forest fires were used to develop the algorithm and get parameter values. These fires occurred in (i) China on 16 October 2004, (ii) Northeast Asia on 29 April 2009 and (iii) Russia on 29 July 2010 in different seasons. Then, the data from four other fires were used to validate the algorithm. Results indicated that the algorithm captured both thick smoke and thin dispersed smoke over land, as well as the mixed pixels of smoke over the ocean. These results could provide valuable information concerning forest fire location, fire spreading and so on

Correlates of Cancer-Related Fatigue among Colorectal Cancer Patients Undergoing Postoperative Adjuvant Therapy Based on the Theory of Unpleasant Symptoms

Background: Cancer-related fatigue (CRF) is a common and burdensome symptom in cancer patients that is influenced by multiple factors. Identifying factors associated with CRF may help in developing tailored interventions for fatigue management. This study aimed to examine the correlates of CRF among colorectal cancer patients undergoing postoperative adjuvant therapy based on the theory of unpleasant symptoms. Methods: A cross-sectional study was implemented, and finally, a sample of 363 participants from one tertiary general hospital and one tertiary cancer hospital was purposively recruited. Data were collected using the general information questionnaire, cancer fatigue scale, the distress disclosure index, Herth hope index, and perceived social support scale. Univariate analysis and multiple linear regression analysis were performed to determine the correlates of CRF. Results: The mean score of CRF among colorectal cancer patients was 21.61 (SD = 6.16, 95% CI 20.98&ndash;22.25), and the fatigue degree rating was &ldquo;moderate&rdquo;. The multiple linear regression model revealed that 49.1% of the variance in CRF was explained by hope, sleep disorder, internal family support, self-disclosure, pain, and time since operation. Conclusions: Our study identified several significant, modifiable factors (self-disclosure, hope, internal family support, pain, and sleep disorder) associated with CRF. Understanding these correlates and developing targeted psychosocial interventions may be associated with the improvement of CRF in patients with colorectal cancer

Integration of Multiple Spectral Indices and a Neural Network for Burned Area Mapping Based on MODIS Data

Since wildfires have occurred frequently in recent years, accurate burned area mapping is required for wildfire severity assessment and burned land reconstruction. Satellite remote sensing is an effective technology that can provide valuable information for wildfire assessment. However, the common approaches based on using a single satellite image to promptly detect the burned areas have low accuracy and limited applicability. This paper develops a new burned area mapping method that surpasses the detection accuracy of previous methods, while still using a single Moderate Resolution Imaging Spectroradiometer (MODIS) sensor image. The key innovation is integrating optimal spectral indices and a neural network algorithm. We used the traditional empirical formula method, multi-threshold method and visual interpretation method to extract the sample sets of five typical types (burned area, vegetation, cloud, bare soil, and cloud shadow) from the MODIS data of several wildfires in the American states of Nevada, Washington and California in 2016. Afterward, the separability index M was adopted to assess the capacity of seven spectral bands and 13 spectral indices to distinguish the burned area from four unburned land cover types. Based on the separability analysis between the burned area and unburned areas, the spectral indices with an M value higher than 1.0 were employed to generate the training sample sets that were assessed to have an overall accuracy of 98.68% and Kappa coefficient of 97.46%. Finally, we utilized a back-propagation neural network (BPNN) to learn the spectral differences of different types from the training sample sets and obtain the output burned area map. The proposed method was applied to three wildfire cases in the American states of Idaho, Nevada and Oregon in 2017. A comparison of detection results between the new MODIS-based burned area map and the reference burned area map compiled from Landsat-8 Operational Land Imager (OLI) data indicates that the proposed method can effectively exploit the spectral characteristics of various land cover types. Also, this new method can achieve higher accuracy with the reduction of commission error (CE, &gt;10%) and omission error (OE, &gt;6%) compared to the traditional empirical formula method. The new burned area mapping method could help managers and the public perform more effective wildfire assessments and emergency management

Acceptance of COVID-19 vaccines among college students: a study of the attitudes, knowledge, and willingness of students to vaccinate

Universities are considered high risk areas for COVID-19 outbreaks given the crowded environment of campuses with high mobility and limited space. As such, vaccination is considered an essential intervention that could greatly reduce the incidence and spread of this deadly infectious disease. However, the willingness of college students to receive the COVID-19 vaccine varies significantly. Therefore, a study on the acceptance of COVID-19 vaccines in college students that explores the attitudes, knowledge, willingness, and key factors influencing vaccination acceptance is of great significance to improve vaccine coverage and control the pandemic. A cross-sectional survey was conducted on students from three universities in China. Descriptive statistics, independent sample t tests/one-way ANOVA (normal distribution), Mann-Whitney U tests/Kruskal-Wallis H tests (skewness distribution) and multivariate linear regression were performed. As a result, a total of 3,256 students participated in the survey. Students’ willingness to receive the COVID-19 vaccine was high (86%), and they had good knowledge of the vaccine (77.9%). However, they had a low-risk perception of COVID-19 and less positive attitudes toward vaccination (69.8%). The main influencing factors were sex, age, specialty, grades, living environment, spending level, traveling to risk areas, and family members’ vaccination experiences. We believed that to increase vaccination coverage among college students, more attention should be paid for students majoring in Science and Engineering, male students, those in the lower age group, students with low or very high economic levels, living in remote or rural areas, and family members having not received the COVID-19 vaccine

Fisher–Shannon and detrended fluctuation analysis of MODIS normalized difference vegetation index (NDVI) time series of fire-affected and fire-unaffected pixels

MODIS-NDVI data from 2002 to 2014 were analysed to evaluate the effect of fire on vegetation in a test site located in Daxing'anling region (Inner Mongolia and Heilongjiang Province). Fire-affected and fire-unaffected areas were processed using two statistical approaches: detrended fluctuation analysis (DFA) and Fisher–Shannon (FS) method. The DFA allows the detection of scaling behaviour in nonstationary signals, whereas the FS method permits to identify the organization/order structure in complex signals. Our findings show that the results obtained by jointly using the two methods are consistent, enabling the characterization and discrimination between the fire-affected and fire-unaffected areas. In particular, among the investigated indices, the mean value of Fisher information measure (FIM) represents the most significant in discriminating between burned and unburned sites; its mean value for burned sites is about 2.5 that is significantly larger than that obtained for unburned sites (∼1.3). FIM is also characterized by the larger effectiveness in discriminating the two classes of sites on the base of its receiver operating characteristic based performance. The scaling exponents estimated by means of the DFA of the fire-affected pixels are averagely higher than those of the fire-unaffected ones, which, furthermore, are characterized by lower organization and higher disorder degree. Both of the two methods would contribute to identify the impact of fires on vegetation

Croconaine nanoparticles with enhanced tumor accumulation for multimodality cancer theranostics

A novel nanoparticle self-assembled by polyethylene glycol (PEG) modified croconaine dye (CR780) is presented for photoacoustic (PA)/near-infrared (NIR) fluorescence imaging-guided photothermal therapy (PTT). The simple PEGylation made CR780 amphiphilic, and led to their self-assembly into well-defined and uniform nanostructures with size tunable by controlling the assembly conditions. The CR780-PEG5K not only displayed the strength of small molecules (including rapid distribution to different organs, fast renal clearance and minimal accumulation to normal tissues), but also demonstrated the advantages of nanomaterials (including high physiological stability, multimodal theranostic ability, high tumor accumulation and retention). These facilely synthesized molecular nanoprobes showed great clinical translation potential as a versatile theranostic agent. Self-assemblies from polyethylene glycol modified croconaine dye (CR780-PEG5K) are proved to be highly effective for in vivo photoacoustic/near-infrared fluorescent imaging guided photothermal therapy. These facile prepared molecular nanoprobes display much improved tumor accumulation and retention, but are still renal clearable like small molecules, which are promising in cancer theranostic. [Display omitted