Explore the vast range of titles available.

Background The effect of bevacizumab combined with chemotherapy on the expression of stromal cell-derived factor-1 (SDF-1) and receptor CXCR4 in epithelial ovarian cancer tumor cells and its prognosis are unknown. Our work aimed to investigate the effect of chemotherapy +/− bevacizumab on these markers and the impact of this treatment modality in clinical outcomes. Methods Altogether 68 patients with epithelial ovarian cancer who were treated with chemotherapy in our hospital from June 2018 to June 2019 were selected. It was an open-labeled and controlled clinical trial (ethical approval no. 20180435). The patients were grouped according to their admission order. Patients treated with paclitaxel and carboplatin were included in group A, while patients treated with bevacizumab, paclitaxel, and carboplatin were included in group B. qRT-PCR was used to detect the changes of SDF-1 and CXCR4 before and after chemotherapy. Various clinical indicators of patients in the two groups were recorded to analyze the clinical efficacy, and safety of different treatment modalities and the prognosis of the two groups was analyzed. Results The relative expression of SDF-1 and CXCR4 was positively correlated with epithelial ovarian cancer stages ( P <0.00). Together, SDF-1 and CXCR4 were positively correlated in epithelial ovarian cancer staging ( P <0.001). SDF-1 and CXCR4 in both groups after chemotherapy were significantly decreased ( P <0.001), and the downregulation of SDF-1 and CXCR4 expression in group B was significantly higher than that in group A after chemotherapy ( P <0.001). No significant difference in the metastasis rates of the two groups before chemotherapy was observed ( P >0.05), but the recurrence rate after 1 year was lower in group B than in group A ( P <0.05). Conclusion Adding bevacizumab diminished the expression of related cancer markers SDF-1 and CXCR4 more than chemotherapy alone in patients with epithelial ovarian cancer. Furthermore, better rates of recurrence with no concerns regarding adverse drug reactions or quality of life were seen in bevacizumab plus chemotherapy group.

T-cells are crucial in maintanence of intestinal homeostasis, however, it is still unclear how microbiota metabolites regulate T-effector cells. Here we show gut microbiota-derived short-chain fatty acids (SCFAs) promote microbiota antigen-specific Th1 cell IL-10 production, mediated by G-protein coupled receptors 43 (GPR43). Microbiota antigen-specific Gpr43 −/− CBir1 transgenic (Tg) Th1 cells, specific for microbiota antigen CBir1 flagellin, induce more severe colitis compared with wide type (WT) CBir1 Tg Th1 cells in Rag −/− recipient mice. Treatment with SCFAs limits colitis induction by promoting IL-10 production, and administration of anti-IL-10R antibody promotes colitis development. Mechanistically, SCFAs activate Th1 cell STAT3 and mTOR, and consequently upregulate transcription factor B lymphocyte-induced maturation protein 1 (Blimp-1), which mediates SCFA-induction of IL-10. SCFA-treated Blimp1 −/− Th1 cells produce less IL-10 and induce more severe colitis compared to SCFA-treated WT Th1 cells. Our studies, thus, provide insight into how microbiota metabolites regulate Th1 cell functions to maintain intestinal homeostasis. T cells play a critical role in intestinal homeostasis, with increasing evidence suggesting a role for the microbiome metabolome in modulating this response. Here the authors show short-chain fatty acids promote IL-10 production in Th1 cells.

How the gut microbiota regulates intestinal homeostasis is not completely clear. Gut microbiota metabolite short-chain fatty acids (SCFAs) have been reported to regulate T-cell differentiation. However, the mechanisms underlying SCFA regulation of T-cell differentiation and function remain to be investigated. CBir1, an immunodominant microbiota antigen, transgenic T cells were treated with butyrate under various T-cell polarization conditions to investigate butyrate regulation of T-cell differentiation and the mechanism involved. Transfer of butyrate-treated CBir T cells into Rag1-/- mice was performed to study the in vivo role of such T cells in inducing colitis. Although butyrate promoted Th1 cell development by promoting IFN-γ and T-bet expression, it inhibited Th17 cell development by suppressing IL-17, Rorα, and Rorγt expression. Interestingly, butyrate upregulated IL-10 production in T cells both under Th1 and Th17 cell conditions. Furthermore, butyrate induced T-cell B-lymphocyte-induced maturation protein 1 (Blimp1) expression, and deficiency of Blimp1 in T cells impaired the butyrate upregulation of IL-10 production, indicating that butyrate promotes T-cell IL-10 production at least partially through Blimp1. Rag1-/- mice transferred with butyrate-treated T cells demonstrated less severe colitis, compared with transfer of untreated T cells, and administration of anti-IL-10R antibody exacerbated colitis development in Rag-/- mice that had received butyrate-treated T cells. Mechanistically, the effects of butyrate on the development of Th1 cells was through inhibition of histone deacetylase but was independent of GPR43. These data indicate that butyrate controls the capacity of T cells in the induction of colitis by differentially regulating Th1 and Th17 cell differentiation and promoting IL-10 production, providing insights into butyrate as a potential therapeutic for the treatment of inflammatory bowel disease.

Crop growth monitoring and yield estimate information can be obtained via appropriate metrics such as the leaf area index (LAI) and biomass. Such information is crucial for guiding agricultural production, ensuring food security, and maintaining sustainable agricultural development. Traditional methods of field measurement and monitoring typically have low efficiency and can only give limited untimely information. Alternatively, methods based on remote sensing technologies are fast, objective, and nondestructive. Indeed, remote sensing data assimilation and crop growth modeling represent an important trend in crop growth monitoring and yield estimation. In this study, we assimilate the leaf area index retrieved from Sentinel-2 remote sensing data for crop growth model of the simple algorithm for yield estimation (SAFY) in wheat. The SP-UCI optimization algorithm is used for fine-tuning for several SAFY parameters, namely the emergence date (D 0 ), the effective light energy utilization rate (ELUE), and the senescence temperature threshold (STT) which is indicative of biological aging. These three sensitive parameters are set in order to attain the global minimum of an error function between the SAFY model predicted values and the LAI inversion values. This assimilation of remote sensing data into the crop growth model facilitates the LAI, biomass, and yield estimation. The estimation results were validated using data collected from 48 experimental plots during 2014 and 2015. For the 2014 data, the results showed coefficients of determination (R 2 ) of the LAI, biomass and yield of 0.73, 0.83 and 0.49, respectively, with corresponding root-mean-squared error (RMSE) values of 0.72, 1.13 t/ha and 1.14 t/ha, respectively. For the 2015 data, the estimated R 2 values of the LAI, biomass, and yield were 0.700, 0.85, and 0.61, respectively, with respective RMSE values of 0.83, 1.22 t/ha, and 1.39 t/ha, respectively. The estimated values were found to be in good agreement with the measured ones. This shows high applicability of the proposed data assimilation scheme in crop monitoring and yield estimation. As well, this scheme provides a reference for the assimilation of remote sensing data into crop growth models for regional crop monitoring and yield estimation.

The number of wheat ears is an essential indicator for wheat production and yield estimation, but accurately obtaining wheat ears requires expensive manual cost and labor time. Meanwhile, the characteristics of wheat ears provide less information, and the color is consistent with the background, which can be challenging to obtain the number of wheat ears required. In this paper, the performance of Faster regions with convolutional neural networks (Faster R-CNN) and RetinaNet to predict the number of wheat ears for wheat at different growth stages under different conditions is investigated. The results show that using the Global WHEAT dataset for recognition, the RetinaNet method, and the Faster R-CNN method achieve an average accuracy of 0.82 and 0.72, with the RetinaNet method obtaining the highest recognition accuracy. Secondly, using the collected image data for recognition, the R2 of RetinaNet and Faster R-CNN after transfer learning is 0.9722 and 0.8702, respectively, indicating that the recognition accuracy of the RetinaNet method is higher on different data sets. We also tested wheat ears at both the filling and maturity stages; our proposed method has proven to be very robust (the R2 is above 90). This study provides technical support and a reference for automatic wheat ear recognition and yield estimation.

The deep sea has abundant resources and is regarded as the “sixth continent” that humans can use. Although research on the deep sea has made some progress, its exploration is not deep enough. The acquisition and analysis of deep-sea images can provide important technical means for deep-sea exploration and the acquisition of information. However, there is currently no complete underwater image dataset. For this reason, this paper is the first deep-sea visual dataset of the South China Sea, contains over 100 TB videos and 1 million high quality images. This paper also applies the dataset to underwater image enhancement and image superresolution, and the experiment proves the effectiveness of the dataset.

Bone defects are common orthopedic conditions, and due to the limited regenerative capacity of bone tissue, their repair remains a challenge in orthopedic surgery. Mesenchymal stem cells (MSCs) have demonstrated strong potential for osteogenic differentiation; however, their efficiency in vivo remains restricted, particularly in terms of differentiation and migration. Photobiomodulation (PBM), a non-invasive therapeutic technique, has shown great promise in promoting stem cell differentiation. In this study, we cultured human umbilical cord mesenchymal stem cells (hUCMSCs) in vitro and treated them with 635/808 nm laser light. We measured alkaline phosphatase (ALP) activity, mineralized nodule formation, and the expression of osteogenesis-related genes and factors after 7, 14, and 21 days. The results showed that PBM treatment significantly enhanced hUCMSC proliferation and osteogenic differentiation. The mechanism behind this effect involves PBM activating the upstream Akt signaling pathway, increasing P-Akt expression, and elevating reactive oxygen species (ROS) levels to induce mild oxidative stress. This process enhances ALP activity, mineralized nodule formation, and the expression of osteogenesis-related genes and factors, thus promoting the osteogenic differentiation of hUCMSCs.

Textile printing and dyeing wastewater is usually characterized by high pH, high turbidity, poor bio-degradability, complex composition, and high chrominance, and is discharged in large amounts. It has been regarded as one of the hardest to treat forms of industrial wastewater. Conventional physicochemical technologies can remove these contaminants from water bodies, but at the expense of high energy consumption and high cost. Alternatively, biological processes with limited energy consumption, low cost and high efficiency are considered as promising technologies. Among them, the anaerobic biological processes have been proven to be effective for the treatment of high-concentration textile printing and dyeing wastewater. In this mini-review, recent advances on high-rate anaerobic technologies for such purposes are reviewed. Current limitations of these technologies are summarized, and future research directions are indicated.Graphical abstract

Contemporary classification of hypertrophic cardiomyopathy (HCM) was mainly based on the site of myocardial hypertrophy and left ventricular outflow tract obstruction. A complementary classification based on left ventricular function could provide a powerful tool to identify individuals with high risk of adverse cardiovascular outcomes and guide individualized managements. Multi-dimensional echocardiographic parameters of left ventricular function derived from conventional echocardiography, tissue Doppler imaging, and speckle tracking echocardiography were obtained in 266 HCM patients and 169 healthy controls (HCs). According to these parameters, HCM subtypes were calculated by principal component analysis and unsupervised cluster analysis. Variables of different groups were compared. The prognosis between HCM subtypes were evaluated. There were two HCM subtypes generated, subtype 1 HCMs (n = 123) and subtype 2 HCMs (n = 143). Compared to HCs, left ventricular diastolic and systolic function were significantly declined to varying degrees in both subtype 1 HCMs and subtype 2 HCMs, especially in subtype 1 HCMs (all P value < 0.001). Subtype 1 HCMs characterized as increased LAVI and E/E′, decreased mean E′ and untwist rate, increased global and segmental longitudinal strains, circumferential strains and radial strains, decreased rotation degree, twist degree, and twist rate, in comparison with subtype 2 HCMs (all P value < 0.001). Notably, subtype 1 HCMs were more susceptible to adverse prognosis of atrial fibrillation (HR: 4.34; 95% CI 1.08–17.53; P value: 0.039). Collectively, we stratified HCM patients into two subtypes with different diastolic and systolic performance and risk of atrial fibrillation. This complementary classification might eventually help to target management of HCM patients who would benefit most.

Loess environmental parameters reflect regional environmental evolution and provide basis for regional environmental change studies. Loess samples from different soil layers in Zhengzhou were collected, and their hyperspectral data measured. The characteristics of the original spectra, first-order differential spectra, second-order differential spectra, de-enveloping line spectra, and reciprocal logarithm spectra were analysed and correlated with environmental parameters, including mean particle size, standard deviation, Fe2O3, CaO, MgO, and total organic carbon content, based on partial least squares regression. Inversion models were constructed. After transformation, the correlations between the various spectra and the environmental parameters were enhanced. Among them, the first-order and second-order differential spectra showed the greatest correlation with average particle size (correlation coefficient: 0.74). The correlation coefficients of the order differential spectrum with standard deviation, Fe2O3, and CaO were − 0.69, 0.67, and 0.75, respectively; the accuracy grade of the original spectrum inversion model was D. After spectral transformation, it was mostly improved; the best inversion model accuracy grade for standard deviation was C, that of the other parameters was B. Partial transformation is the optimal spectral transformation to establish an inversion model for these environmental parameters. This provides a new soil parameter inversion and monitoring method using hyperspectral remote sensing technology.