Macrophage M1 polarization mediated via the IL-6/STAT3 pathway contributes to apical periodontitis induced by Porphyromonas gingivalis

Objective: To investigate the involvement of IL-6/STAT3 signaling pathway activation in macrophage polarization and bone destruction related to apical periodontitis (AP) stimulated by Porphyromonas gingivalis. Methodology: Macrophage polarization, IL-6/STAT3 expression, and the presence of P. gingivalis were detected in human AP tissues via RT-qPCR, western blotting, and immunohistochemistry staining. Murine bone marrow derived macrophages were isolated and cultured with P. gingivalis W83 in vitro, and levels of macrophage IL-6 expression, STAT3 phosphorylation, and macrophage polarization with or without the selective STAT3 phosphorylation inhibitor Stattic (5 μM) were detected via ELISA, western blotting, RT-qPCR, and flow cytometry, respectively. P. gingivalis-induced murine AP models were constructed, and bone destruction and macrophage polarization in the apical region were evaluated. Transwell co-culture systems were used to investigate the effects of macrophages infected with P. gingivalis on osteogenesis and osteoclastogenesis. Results: P. gingivalis was detected in human AP tissues that highly expressed IL-6/STAT3, and the M1 subtype of macrophages was more abundant in these tissues. P. gingivalis infection induced IL-6 expression, STAT3 phosphorylation, and M1 polarization of macrophages, while 5 μM of Stattic partially abolished these activation effects. Systemic STAT3 blockade via oral administration of Stattic at a dose of 25 mg kg-1 alleviated murine periapical bone resorption and apical infiltration of M1 macrophages induced by P. gingivalis infection in vivo. Furthermore, macrophages infected with P. gingivalis promoted bone destruction via secretion of IL-6, TNF-α, and RANKL, which hinder pre-osteoblast expression of Runx2 and accelerate pre-osteoclast expression of NFAT2. Conclusions:The activation of IL-6/STAT3 signaling pathway is involved in mediating macrophages M1 polarization in the P. gingivalis induced apical inflammatory context and may also be intimately involved in the bone loss caused by P. gingivalis infection, directing the M1 macrophage infiltration during the progression of AP.&nbsp

A transcript profiling approach reveals the zinc finger transcription factor ZNF191 is a pleiotropic factor

BACKGROUND: The human zinc finger protein 191 (ZNF191) is a member of the SCAN domain family of Krüppel-like zinc finger transcription factors. ZNF191 shows 94% identity to its mouse homologue zinc finger protein 191(Zfp191), which is the most highly conserved among the human-mouse SCAN family member orthologues pairs. Zfp191 is widely expressed during early embryogenesis and in adult organs. Moreover, Zfp191(-/- )embryos have been shown to be severely retarded in development and die approximately at embryonic day E7.5. ZNF191 can specifically interact with the widespread TCAT motif which constitutes the HUMTH01 microsatellite in the tyrosine hydroxylase (TH) gene. Allelic variations of HUMTH01 have been stated to have a quantitative silencing effect on TH gene expression and to correlate with quantitative and qualitative changes in the binding by ZNF191. In addition, ZNF191 displays a suppressive effect on the transcription; however, little downstream targets have identified. RESULTS: We searched for ZNF191 target genes by using a transient overexpression and knockdown strategy in the human embryo kidney (HEK293) cells. Microarray analyses identified 6094 genes modulated by overexpression of ZNF191 and 3332 genes regulated by knockdown of ZNF191, using a threshold of 1.2-fold. Several interested candidate genes, validated by real time RT-PCR, were correlated well with the array data. Interestingly, 1456 genes were identified in both transient overexpression and transient knockdown strategies. The GenMAPP and MappFinder software packages were further used for pathway analysis of these significantly altered genes. Several gene pathways were found to be involved in processes of the regulation of kinase activity, transcription, angiogenesis, brain development and response to DNA damage. CONCLUSION: Our analysis reveals for the first time that ZNF191 is a pleiotropic factor that has a role in hematopoiesis, brain development and cancers

Optimal methodology for detecting land cover change in a forestry, lakeside environment using NAIP imagery

Mapping land cover change is useful for various environmental and urban planning applications, e.g. land management, forest conservation, ecological assessment, transportation planning, and impervious surface control. As the optimal change detection approaches, algorithms, and parameters often depend on the phenomenon of interest and the remote sensing imagery used, the goal of this study is to find the optimal procedure for detecting urban growth in rural, forestry areas using onemeter, four-band NAIP images. Focusing on different types of impervious covers, the authors test the optimal segmentation parameters for object-based image analysis, and conclude that the random tree classifier, among the six classifiers compared, is most optimal for land use/cover change detection analysis with a satisfying overall accuracy of 87.7%. With continuous free coverage of NAIP images, the optimal change detection procedure concluded in this study is valuable for future analyses of urban growth change detection in rural, forestry environments

A Metabolomics Study of the Effects of Eleutheroside B on Glucose and Lipid Metabolism in a Zebrafish Diabetes Model

(1) Background: Diabetes is a common metabolic disease that seriously endangers human health. In the present study, we investigated the therapeutic effects of the active ingredient Eleutheroside B (EB) from the traditional Chinese medicine Eleutheroside on diabetes mellitus in a zebrafish model. Concomitant hepatic injury was also analysed, along with the study of possible molecular mechanisms using metabolomics technology. This work should provide some theoretical references for future experimental studies. (2) Methods: A zebrafish diabetes model was constructed by soaking in a 1.75% glucose solution and feeding a high-fat diet. The intervention drug groups were metformin (100 μg∙mL−1) and EB (50, 100, and 150 μg∙mL−1) via water-soluble exposure for 30 days. Glucose, TG, TC, LDL-C, and HDL-C were evaluated in different treatment groups. GLUT4 protein expression was also evaluated in each group, and liver injury was observed by HE staining. Metabolomics techniques were used to investigate the mechanism by which EB regulates endogenous markers and metabolic pathways during the development of diabetes. (3) Results: All EB treatment groups in diabetic zebrafish showed significantly reduced body mass index (BMI) and improved blood glucose and lipid profiles. EB was found to upregulate GLUT4 protein expression and ameliorate the liver injury caused by diabetes. Metabolomics studies showed that EB causes changes in the metabolic profile of diabetic zebrafish. These were related to the regulation of purine metabolism, cytochrome P450, caffeine metabolism, arginine and proline metabolism, the mTOR signalling pathway, insulin resistance, and glycerophospholipid metabolism. (4) Conclusions: EB has a hypoglycaemic effect in diabetic zebrafish as well as significantly improving disorders of glycolipid metabolism. The mechanism of action of EB may involve regulation of the mTOR signalling pathway, purine metabolism, caffeine metabolism, and glycerophospholipid metabolism

A fractional integral method inverse distance weight-based for denoising depth images

Denoising algorithms for obtaining the effective data of depth images affected by random noise mainly focus on the processing of gray images. These algorithms are not distinct from traditional image-processing methods, and there is no way to evaluate the effectiveness of denoising after the point cloud transformation of denoised depth images. In this paper, the principle of fractional-order integral denoising is studied in detail and inverse distance weighted interpolation is introduced into a denoising model, which is based on the G–L (Grünwald–Letnikov) fractional-order integral to construct a fractional-order integral with an inverse distance weighted denoising model. The model is used to solve the blurring problem caused by sharp changes at the edge and achieves an excellent denoising effect. By using the optimized fractional-order integral denoising operator to construct a denoising model for depth images, the results of the experiments demonstrate that the fractional-order integral of the best denoising effect achieved by the model is −0.6 ≤ ν ≤ −0.4, and the peak signal-to-noise ratio is improved from +6 to +13 dB. In the same condition, median denoising has a distortion of −30 to −15 dB. The depth image that has been denoised is converted into an image of point clouds, and subjective evaluation indicates that the noise is effectively removed. On the whole, the results demonstrate that the fractional-order integral denoising operator with inverse distance weight shows the high efficiency and the outstanding effect in removing noise from depth images while maintaining the image related to the edge and texture information

Photogrammetric UAV Mapping of Terrain under Dense Coastal Vegetation: An Object-Oriented Classification Ensemble Algorithm for Classification and Terrain Correction

Photogrammetric UAV sees a surge in use for high-resolution mapping, but its use to map terrain under dense vegetation cover remains challenging due to a lack of exposed ground surfaces. This paper presents a novel object-oriented classification ensemble algorithm to leverage height, texture and contextual information of UAV data to improve landscape classification and terrain estimation. Its implementation incorporates multiple heuristics, such as multi-input machine learning-based classification, object-oriented ensemble, and integration of UAV and GPS surveys for terrain correction. Experiments based on a densely vegetated wetland restoration site showed classification improvement from 83.98% to 96.12% in overall accuracy and from 0.7806 to 0.947 in kappa value. Use of standard and existing UAV terrain mapping algorithms and software produced reliable digital terrain model only over exposed bare grounds (mean error = −0.019 m and RMSE = 0.035 m) but severely overestimated the terrain by ~80% of mean vegetation height in vegetated areas. The terrain correction method successfully reduced the mean error from 0.302 m to −0.002 m (RMSE from 0.342 m to 0.177 m) in low vegetation and from 1.305 m to 0.057 m (RMSE from 1.399 m to 0.550 m) in tall vegetation. Overall, this research validated a feasible solution to integrate UAV and RTK GPS for terrain mapping in densely vegetated environments

Establishment of a Nomogram for Predicting Early Death in Viral Myocarditis

Objective. This research aimed to establish a nomogram for predicting early death in viral myocarditis (VMC) patients. Method. A total of 362 consecutive VMC patients in Fujian Medical University Affiliated First Quanzhou Hospital between January 1, 2009, and December 31, 2019, were included. A least absolute shrinkage and selection operator (LASSO) regression model was used to detect the risk factors that most consistently and correctly predicted early death in VMC. The performance of the nomogram was assessed by calibration, discrimination, and clinical utility. Result. 9 factors were screened by LASSO regression analysis for predicting the early death of VMC. Combined with the actual clinical situation, the heart failure (HF) (OR: 2.13, 95% CI: 2.76–5.95), electrocardiogram (ECG) (OR: 6.11, 95% CI: 1.05–8.66), pneumonia (OR: 3.62, 95% CI: 1.43–9.85), brain natriuretic peptide (BNP) (OR: 4.66, 95% CI: 3.07–24.06), and lactate dehydrogenase (LDH) (OR: 1.90, 95% CI: 0.19–9.39) were finally used to construct the nomogram. The nomogram’s C-index was 0.908 in the training cohort and 0.924 in the validation cohort. And the area under the receiver operating characteristic curve of the nomogram was 0.91 in the training cohort and 0.924 in the validating cohort. Decision curve analysis (DCA) also showed that the nomogram was clinically useful. Conclusion. This nomogram achieved an good prediction of the risk of early death in VMC patients

Photogrammetric UAV Mapping of Terrain under Dense Coastal Vegetation: An Object-Oriented Classification Ensemble Algorithm for Classification and Terrain Correction

Photogrammetric UAV sees a surge in use for high-resolution mapping, but its use to map terrain under dense vegetation cover remains challenging due to a lack of exposed ground surfaces. This paper presents a novel object-oriented classification ensemble algorithm to leverage height, texture and contextual information of UAV data to improve landscape classification and terrain estimation. Its implementation incorporates multiple heuristics, such as multi-input machine learning-based classification, object-oriented ensemble, and integration of UAV and GPS surveys for terrain correction. Experiments based on a densely vegetated wetland restoration site showed classification improvement from 83.98% to 96.12% in overall accuracy and from 0.7806 to 0.947 in kappa value. Use of standard and existing UAV terrain mapping algorithms and software produced reliable digital terrain model only over exposed bare grounds (mean error = −0.019 m and RMSE = 0.035 m) but severely overestimated the terrain by ~80% of mean vegetation height in vegetated areas. The terrain correction method successfully reduced the mean error from 0.302 m to −0.002 m (RMSE from 0.342 m to 0.177 m) in low vegetation and from 1.305 m to 0.057 m (RMSE from 1.399 m to 0.550 m) in tall vegetation. Overall, this research validated a feasible solution to integrate UAV and RTK GPS for terrain mapping in densely vegetated environments

Changes in diet, exercise and psychology of the quarantined population during the COVID-19 outbreak in Shanghai.

BackgroundIn March 2022, a severe outbreak of COVID-19 broke out in Shanghai, with the virus spreading rapidly. In the most severe two months, more than 50,000 people were diagnosed with COVID-19. For this reason, Shanghai adopted three-district hierarchical management, requiring corresponding people to stay at home to contain the spread of the virus. Due to the requirements of prevention and control management, the diet, exercise and mental health of the corresponding population are affected to a certain extent.ObjectivesThis article aimed to understand the population in the diet, exercise and psychological changes.MethodsThis study carried out the research by distributing the electronic questionnaire and carried out the statistical analysis.ResultsPeople reduced the intake of vegetables and fruits (P = 0.000ConclusionIn terms of psychological state, people have some depression, anxiety and easy to feel tired after lockdown. This study can also provide reference for policy adjustment and formulation of normalized epidemic management in the future