MyD88 in osteoclast- and osteoblast-lineages differentially controls bone remodeling in homeostasis and malaria.

Chronic bone loss is an under-recognized complication of malaria, the underlying mechanism of which remains incompletely understood. We have previously shown that persistent accumulation of Plasmodium products in the bone marrow leads to chronic inflammation in osteoblast (OB) and osteoclast (OC) precursors causing bone loss through MyD88, an adaptor molecule for diverse inflammatory signals. However, the specific contribution of MyD88 signaling in OB or OC precursors in malaria-induced bone loss remains elusive. To assess the direct cell-intrinsic role of MyD88 signaling in adult bone metabolism under physiological and infection conditions, we used the Lox-Cre system to specifically deplete MyD88 in the OB or OC lineages. Mice lacking MyD88 primarily in the maturing OBs showed a comparable decrease in trabecular bone density by microcomputed tomography (µCT) to that of controls after PyNL infection. In contrast, mice lacking MyD88 in OC precursors showed significantly less trabecular bone loss than controls, suggesting that malaria-mediated inflammatory mediators are primarily controlled by MyD88 in the OC lineage. Surprisingly, however, depletion of MyD88 in OB, but not in OC precursors, resulted in reduced bone mass with decreased bone formation rates in the trabecular areas of femurs under physiological conditions. Notably, IGF-1, a key molecule for OB differentiation, was significantly lower locally and systemically when MyD88 was depleted in OBs. Thus, our data demonstrate an indispensable intrinsic role for MyD88 signaling in OB differentiation and bone formation, while MyD88 signaling in OC lineages plays a partial role in controlling malaria-induced inflammatory mediators and following bone pathology. These findings may lead to the identification of novel targets for specific intervention of bone pathologies, particularly in malaria-endemic regions.

[Non-destructive detection research for hollow heart of potato based on semi-transmission hyperspectral imaging and SVM].

The quality of potato is directly related to their edible value and industrial value. Hollow heart of potato, as a physiological disease occurred inside the tuber, is difficult to be detected. This paper put forward a non-destructive detection method by using semi-transmission hyperspectral imaging with support vector machine (SVM) to detect hollow heart of potato. Compared to reflection and transmission hyperspectral image, semi-transmission hyperspectral image can get clearer image which contains the internal quality information of agricultural products. In this study, 224 potato samples (149 normal samples and 75 hollow samples) were selected as the research object, and semi-transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images (390-1 040 nn) of the potato samples, and then the average spectrum of region of interest were extracted for spectral characteristics analysis. Normalize was used to preprocess the original spectrum, and prediction model were developed based on SVM using all wave bands, the accurate recognition rate of test set is only 87. 5%. In order to simplify the model competitive.adaptive reweighed sampling algorithm (CARS) and successive projection algorithm (SPA) were utilized to select important variables from the all 520 spectral variables and 8 variables were selected (454, 601, 639, 664, 748, 827, 874 and 936 nm). 94. 64% of the accurate recognition rate of test set was obtained by using the 8 variables to develop SVM model. Parameter optimization algorithms, including artificial fish swarm algorithm (AFSA), genetic algorithm (GA) and grid search algorithm, were used to optimize the SVM model parameters: penalty parameter c and kernel parameter g. After comparative analysis, AFSA, a new bionic optimization algorithm based on the foraging behavior of fish swarm, was proved to get the optimal model parameter (c=10. 659 1, g=0. 349 7), and the recognition accuracy of 10% were obtained for the AFSA-SVM model. The results indicate that combining the semi-transmission hyperspectral imaging technology with CARS-SPA and AFSA-SVM can accurately detect hollow heart of potato, and also provide technical support for rapid non-destructive detecting of hollow heart of potato.

[Multi-Target Recognition of Internal and External Defects of Potato by Semi-Transmission Hyperspectral Imaging and Manifold Learning Algorithm].

The present paper put forward a non-destructive detection method which combines semi-transmission hyperspectral imaging technology with manifold learning dimension reduction algorithm and least squares support vector machine (LSSVM) to recognize internal and external defects in potatoes simultaneously. Three hundred fifteen potatoes were bought in farmers market as research object, and semi-transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images of normal external defects (bud and green rind) and internal defect (hollow heart) potatoes. In order to conform to the actual production, defect part is randomly put right, side and back to the acquisition probe when the hyperspectral images of external defects potatoes are acquired. The average spectrums (390-1,040 nm) were extracted from the region of interests for spectral preprocessing. Then three kinds of manifold learning algorithm were respectively utilized to reduce the dimension of spectrum data, including supervised locally linear embedding (SLLE), locally linear embedding (LLE) and isometric mapping (ISOMAP), the low-dimensional data gotten by manifold learning algorithms is used as model input, Error Correcting Output Code (ECOC) and LSSVM were combined to develop the multi-target classification model. By comparing and analyzing results of the three models, we concluded that SLLE is the optimal manifold learning dimension reduction algorithm, and the SLLE-LSSVM model is determined to get the best recognition rate for recognizing internal and external defects potatoes. For test set data, the single recognition rate of normal, bud, green rind and hollow heart potato reached 96.83%, 86.96%, 86.96% and 95% respectively, and he hybrid recognition rate was 93.02%. The results indicate that combining the semi-transmission hyperspectral imaging technology with SLLE-LSSVM is a feasible qualitative analytical method which can simultaneously recognize the internal and external defects potatoes and also provide technical reference for rapid on-line non-destructive detecting of the internal and external defects potatoes.

Identification of biomarkers for early diagnosis of multiple myeloma by weighted gene co-expression network analysis and their clinical relevance.

BACKGROUND: Multiple myeloma is an incurable hematologic malignancy, its early diagnosis is important. However, the biomarker for early diagnosis is limited; hence more need to be identified. The present study aimed to explore the easily tested new biomarker in multiple myeloma by weighted gene co-expression network analysis (WGCNA). METHODS: Differentially expressed genes (DEGs) were screened using GSE47552. WGCNA was used to screen hub genes. Subsequently. Hub genes of multiple myeloma were obtained by intersection of DEGs and WGCNA. We used the T-test to screen highly expressed genes. Then, the diagnostic value of key genes was evaluated by the receiver operating characteristic (ROC) curve. Finally, expression levels of key genes were tested and proved by RT-PCR. RESULTS: 278 DEGs were screened by Limma package. Three modules were most significantly correlated with multiple myeloma. 238 key genes were screened after the intersection of WGCNA with DEGs. In addition, SNORNA is rarely studied in multiple myeloma, and ROC curve analysis in our prediction model showed that SNORA71A had a good prediction effect (p = 0.07). The expression of SNORA71A was increased in samples of multiple myeloma (P = 0.05). RT-PCR results showed that SNORA71A was upregulated in 51 patient specimens compared to the healthy group (P < 0.05). Linear correlation analysis showed that creatinine was positively correlated with SNORA71A (r = 0.49 P = 0.0002). CONCLUSIONS: This study found that SNORA71A was up-regulated and associated with the clinical stages in multiple myeloma; it suggests that SNORA71A could be used as a novel biomarker for early diagnosis and a potential therapeutic target in multiple myeloma.

Callnudoids A-H: Highly modified labdane diterpenoids with anti-inflammation from the leaves of Callicarpa nudiflora.

Eight undescribed 3,4-seco-norlabdane diterpenoids, callnudoids A-H, as well as two known analogues were isolated from the leaves of Callicarpa nudiflora. The structures were elucidated using spectroscopic methods and were compared with published NMR spectroscopic data. The absolute configurations of callnudoids D and E were defined based on ECD data or single-crystal X-ray diffraction. Callnudoids A-C are the highly modified labdane diterpenoids featuring rearranged 3,4-seco-ring and the formation of an undescribed cyclohexene moiety via C2-C18 cyclization. They only contain 15 carbon atoms on the carbon skeleton. Callnudoid D represents the unusual 3,4-seco-15,16-norlabdane diterpenoid with C13-C17 cyclization, and a putative biosynthesis pathway for callnudoids A, B, D, and E was proposed. All compounds were evaluated for their anti-inflammatory activities by inhibiting the lipopolysaccharide (LPS)-induced nitric oxide (NO) released in RAW264.7 cells; callnudoids A-E and H, and methylcallicarpate obviously inhibited pro-inflammatory cytokines TNF-α and IL-1ß in a dose-dependent manner.

Integrated bioinformatics analysis reveals dynamic candidate genes and signaling pathways involved in the progression and prognosis of diffuse large B-cell lymphoma.

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous malignancy with varied outcomes. However, the fundamental mechanisms remain to be fully defined. AIM: We aimed to identify core differentially co-expressed hub genes and perturbed pathways relevant to the pathogenesis and prognosis of DLBCL. METHODS: We retrieved the raw gene expression profile and clinical information of GSE12453 from the Gene Expression Omnibus (GEO) database. We used integrated bioinformatics analysis to identify differentially co-expressed genes. The CIBERSORT analysis was also applied to predict tumor-infiltrating immune cells (TIICs) in the GSE12453 dataset. We performed survival and ssGSEA (single-sample Gene Set Enrichment Analysis) (for TIICs) analyses and validated the hub genes using GEPIA2 and an independent GSE31312 dataset. RESULTS: We identified 46 differentially co-expressed hub genes in the GSE12453 dataset. Gene expression levels and survival analysis found 15 differentially co-expressed core hub genes. The core genes prognostic values and expression levels were further validated in the GEPIA2 database and GSE31312 dataset to be reliable (p < 0.01). The core genes' main KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichments were Ribosome and Coronavirus disease-COVID-19. High expressions of the 15 core hub genes had prognostic value in DLBCL. The core genes showed significant predictive accuracy in distinguishing DLBCL cases from non-tumor controls, with the area under the curve (AUC) ranging from 0.992 to 1.00. Finally, CIBERSORT analysis on GSE12453 revealed immune cells, including activated memory CD4+ T cells and M0, M1, and M2-macrophages as the infiltrates in the DLBCL microenvironment. CONCLUSION: Our study found differentially co-expressed core hub genes and relevant pathways involved in ribosome and COVID-19 disease that may be potential targets for prognosis and novel therapeutic intervention in DLBCL.

Spatio-temporally smoothed coherence factor for ultrasound imaging.

Coherence-factor-like beamforming methods, such as the coherence factor (CF), the phase coherence factor (PCF), or the sign coherence factor (SCF), have been applied to suppress side and/or grating lobes and clutter in ultrasound imaging. These adaptive weighting factors can be implemented effectively with low computational complexity to improve image contrast properties. However, because of low SNR, the resulting images may suffer from deficiencies, including reduced overall image brightness, increased speckle variance, black-region artifacts surrounding hyperechoic objects, and underestimated magnitudes of point targets. To overcome these artifacts, a new spatio-temporal smoothing procedure is introduced to the CF method. It results in a smoothed coherence factor which measures the signal coherence among the beamsums of the divided subarrays over the duration of a transmit pulse. In addition, the procedure is extended to the SCF using the sign bits of the received signals. Simulated and real experimental data sets demonstrate that the proposed methods can improve the robustness of the CF and SCF with reduced speckle variance and significant removal of black-region artifacts, while preserving the ability to suppress clutter. Consequently, image contrast can be enhanced, especially for anechoic cysts.

Ultrasound common carotid artery segmentation based on active shape model.

Carotid atherosclerosis is a major reason of stroke, a leading cause of death and disability. In this paper, a segmentation method based on Active Shape Model (ASM) is developed and evaluated to outline common carotid artery (CCA) for carotid atherosclerosis computer-aided evaluation and diagnosis. The proposed method is used to segment both media-adventitia-boundary (MAB) and lumen-intima-boundary (LIB) on transverse views slices from three-dimensional ultrasound (3D US) images. The data set consists of sixty-eight, 17 × 2 × 2, 3D US volume data acquired from the left and right carotid arteries of seventeen patients (eight treated with 80 mg atorvastatin and nine with placebo), who had carotid stenosis of 60% or more, at baseline and after three months of treatment. Manually outlined boundaries by expert are adopted as the ground truth for evaluation. For the MAB and LIB segmentations, respectively, the algorithm yielded Dice Similarity Coefficient (DSC) of 94.4% ± 3.2% and 92.8% ± 3.3%, mean absolute distances (MAD) of 0.26 ± 0.18 mm and 0.33 ± 0.21 mm, and maximum absolute distances (MAXD) of 0.75 ± 0.46 mm and 0.84 ± 0.39 mm. It took 4.3 ± 0.5 mins to segment single 3D US images, while it took 11.7 ± 1.2 mins for manual segmentation. The method would promote the translation of carotid 3D US to clinical care for the monitoring of the atherosclerotic disease progression and regression.