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Reading is an important part of our daily life, and rapid responses to emotional words have received a great deal of research interest. Our study employed rapid serial visual presentation to detect the time course of emotional noun processing using event-related potentials. We performed a dual-task experiment, where subjects were required to judge whether a given number was odd or even, and the category into which each emotional noun fit. In terms of P1, we found that there was no negativity bias for emotional nouns. However, emotional nouns elicited larger amplitudes in the N170 component in the left hemisphere than did neutral nouns. This finding indicated that in later processing stages, emotional words can be discriminated from neutral words. Furthermore, positive, negative, and neutral words were different from each other in the late positive complex, indicating that in the third stage, even different emotions can be discerned. Thus, our results indicate that in a three-stage model the latter two stages are more stable and universal.

Abstract Objective This study was to develop a multi-parametric MRI radiomics model to predict preoperative Ki-67 status. Materials and methods A total of 120 patients with pathologically confirmed breast cancer were retrospectively enrolled and randomly divided into a training set (n = 84) and a validation set (n = 36). Radiomic features were derived from both the intratumoral and peritumoral regions, extending 5 mm from the tumor boundary, using magnetic resonance imaging (MRI). The MRI sequences employed included T2-weighted imaging (T2WI), dynamic contrast-enhanced (DCE) imaging, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. The T-test and the Least Absolute Shrinkage and Selection Operator Cross-Validation (LASSO CV) were conducted for feature selection. Modelintra, modelperi, modelintra+peri were established by eleven supervised machine learning (ML) algorithms to predict the expression status of Ki-67 in breast cancer and were verified by the validation groups. The model’s performance was evaluated by employing metrics such as the area under the curve (AUC), accuracy, sensitivity, and specificity. Results The features of intratumor, peritumor, intratumor + peritumor were extracted 851, 851 and 1702 samples respectively, 14, 23 and 35 features were selected by LASSO. ML algorithms based on modelintra and modelperi consistently yield AUCs that are below 80% in the validation set. Hower, Logistic regression (LR) and linear discriminant analysis (LDA) based on modelintra+peri demonstrated significant advantages over other algorithms, achieving AUCs of 0.92 and 0.98, accuracies of 0.94 and 0.97, sensitivities of 1 and 0.96, and specificities of 0.85 and 1 respectively in the validation set. Conclusion The integrated intra- and peritumoral radiomics model, developed using multiparametric MRI data and machine learning classifiers, exhibits significant predictive power for Ki-67 expression levels. This model could facilitate personalized clinical treatment strategies for individuals diagnosed with breast cancer (BC). Clinical trial number Not applicable.

BackgroundObservational studies have explored the impact of iron homeostasis on infertility; however, establishing definitive causal relationships remains challenging. This study utilized a two-sample Mendelian randomization approach to investigate the potential causal relationship between iron status and infertility.Materials and methodsFour indicators of iron status-serum iron, ferritin, transferrin saturation, and total iron binding capacity, were considered as exposure factors. Infertility was the outcome variable for both men and women. Robust causality was assessed using the primary inverse-variance-weighted method, complemented by three supplementary Mendelian randomization approaches. Sensitivity analyses were performed to enhance the precision and reliability of the results.ResultsNo statistically significant associations were identified between the four indicators of iron status and infertility. These results remained consistent across multiple Mendelian randomization methodologies.ConclusionIn conclusion, there is no evidence of a genetic causal relationship between iron status and infertility. Nevertheless, this does not preclude the possibility of a connection between iron status and infertility at different mechanistic levels.

Chlorophyll plays a critical role in assessing the photosynthetic capacity and health of grasslands. However, existing studies on the hyperspectral inversion of chlorophyll have mainly focused on field crops, leading to limited accuracy when applied to natural grasslands due to their complex canopy structures and species diversity. This study aims to address this challenge by extrapolating the measured leaf chlorophyll to the canopy level using the green vegetation coverage approach. Additionally, fractional-order derivative (FOD) methods are employed to enhance the sensitivity of hyperspectral data to chlorophyll. Several FOD spectral indices are developed to minimize interference from factors such as bare soil and hay, resulting in improved chlorophyll estimation accuracy. The study utilizes partial least squares regression (PLSR) and support vector machine regression (SVR) to construct inversion models based on full-band FOD, two-band FOD spectral indices, and their combination. Through comparative analysis, the optimal model for estimating grassland chlorophyll content is determined, yielding an R2 value of 0.808, RMSE value of 1.720, and RPD value of 2.347.

Available vaccine-based immunity may at high risk of being evaded due to substantial mutations in the variant Omicron. The main protease (Mpro) of SARS-CoV-2 and human neuropilin-1 (NRP1), two less mutable proteins, have been reported to be crucial for SARS-CoV-2 replication and entry into host cells, respectively. Their dual blockade may avoid vaccine failure caused by continuous mutations of the SARS-CoV-2 genome and exert synergistic antiviral efficacy. Herein, four cyclic peptides non-covalently targeting both Mpro and NRP1 were identified using virtual screening. Among them, MN-2 showed highly potent affinity to Mpro (Kd = 18.2 ± 1.9 nM) and NRP1 (Kd = 12.3 ± 1.2 nM), which was about 3,478-fold and 74-fold stronger than that of the positive inhibitors Peptide-21 and EG3287. Furthermore, MN-2 exhibited significant inhibitory activity against Mpro and remarkable anti-infective activity against the pseudotyped variant Omicron BA.2.75 without obvious cytotoxicity. These data demonstrated that MN-2, a novel non-covalent cyclic peptide, is a promising agent against Omicron BA.2.75.

Interaction between the dietary fiber and the gut microbes can regulate host bile acid metabolism. This study sought to explore the effects of guar gum combined with pregelatinized waxy maize starch (GCW) in a gestation diet on reproductive performance, gut microbiota composition, and bile acid homeostasis of sows. A total of 61 large white sows were randomly grouped into the control (n = 33) and 2% GCW (n = 28) groups during gestation. GCW diet increased birth-weight of piglets, and decreased the percentage of intrauterine growth restriction (IUGR) piglets. In addition, dietary GCW reduced gut microbial diversity and modulated gut microbial composition in sows on day 109 of gestation. The relative abundance of bile salt hydrolase (BSH) gene-encoding bacteria, Lactobacillus and Bacteroides decreased after GCW administration, whereas no significant difference was observed in the fecal level of total glycine-conjugated and taurine-conjugated bile acids between the two groups. Dietary GCW increased the relative abundance of Ruminococcaceae (one of few taxa comprising 7α-dehydroxylating bacteria), which was associated with elevated fecal deoxycholic acid (DCA) in the GCW group. GCW administration lowered the concentrations of plasma total bile acid (TBA) and 7α-hydroxy-4-cholesten-3-one (C4) (reflecting lower hepatic bile acid synthesis) at day 90 and day 109 of gestation compared with the control diet. Furthermore, the levels of plasma glycoursodeoxycholic acid (GUDCA), tauroursodeoxycholic acid (TUDCA) and glycohyocholic acid (GHCA) were lower in the GCW group compared with the control group. Spearman correlation analysis showed alterations in the composition of the gut microbiota by GCW treatment was associated with improved bile acid homeostasis and reproductive performance of sows. In conclusion, GCW-induced improves bile acid homeostasis during gestation which may enhance reproductive performance of sows.

Systemic inflammatory response after cardiovascular surgery is associated with poor prognosis, to which gut barrier impairment is related. To investigate whether perioperative changes of the gut microbiome are associated with systemic and intestinal inflammatory response, we examined changes of the gut microbiome, intestinal homeostasis, and systemic inflammatory response in cardiovascular patients before (Pre) surgery and on the first defecation day [postoperative time 1 (Po1)] or a week [postoperative time 2 (Po2)] postsurgery. Markedly, the enhanced systemic inflammatory response was observed in Po1 and Po2 compared with that in Pre. In line with inflammatory response, impaired gut barrier and elevated gut local inflammation were observed in Po1 and Po2. Microbiome analysis showed a remarkable and steady decline of alpha diversity perioperatively. In addition, microbial composition in the postoperation period was characterized by significant expansion of Enterococcus along with a decrease in anaerobes (Blautia, Faecalibacterium, Bifidobacterium, Roseburia, Gemmiger, [Ruminococcus], and Coprococcus), which were typically health-associated bacteria. Spearman correlation analysis showed microbiome disorder was associated with enhanced systemic inflammatory response and intestinal dysbiosis. These results suggest that microbiome disorder was related to disturbed gut homeostatic and subsequently elevates plasma endotoxin and systemic inflammatory response after cardiovascular surgery. This study not only highlights gut microbiome would be considered in future clinical practice but also proposes a promising perspective of potential diagnostic and therapeutic options for perioperative management of cardiovascular surgery patients.

Plasmonic nanostructures, particularly of noble-metal Au and Ag, have attracted long-lasting research interests because of their intriguing physical and chemical properties. Under light excitation, their conduction electrons can form collective oscillation with the electromagnetic fields at particular wavelength, leading to localized surface plasmon resonance (LSPR). The remarkable characteristic of LSPR is the absorption and scattering of light at the resonant wavelength and greatly enhanced electric fields in localized areas. In response to the chemical and physical changes, these optical properties of plasmonic nanostructures will exhibit drastic color changes and highly sensitive peak shifts, which has been extensively used for biological imaging and disease treatments. In this mini review, we aim to briefly summarize recent progress of preparing responsive plasmonic nanostructures for biodiagnostics, with specific focus on cancer imaging and treatment. We start with typical synthetic approaches to various plasmonic nanostructures and elucidate practical strategies and working mechanism in tuning their LSPR properties. Current achievements in using responsive plasmonic nanostructures for advanced cancer diagnostics will be further discussed. Concise perspectives on existing challenges in developing plasmonic platforms for clinic diagnostics is also provided at the end of this review.

Estimating the carbon (C), nitrogen (N), and phosphorus (P) contents of a large-span grassland transect is essential for evaluating ecosystem functioning and monitoring biogeochemical cycles. However, the field measurements are scattered, such that they cannot indicate the continuous gradient change in the grassland transect. Although remote sensing methods have been applied for the estimation of nutrient elements at the local scale in recent years, few studies have considered the effective estimation of C, N, and P contents over large-span grassland transects with complex environment including a variety of grassland types (i.e., meadow, typical grassland, and desert grassland). In this paper, an information enhancement algorithm (involving spectral enhancement, regional enhancement, and feature enhancement) is used to extract the weak information related to C, N, and P. First, the spectral simulation algorithm is used to enhance the spectral information of Sentinel-2 imagery. Then, the enhanced spectra and meteorological data are fused to express regional characteristics and the fractional differential (FD) algorithm is used to extract sensitive spectral features related to C, N, and P, in order to construct a partial least-squares regression (PLSR) model. Finally, the C, N, and P contents are estimated over a West–East grassland transect in Inner Mongolia, China. The results demonstrate that: (i) the contents of C, N, and P in large-span transects can be effectively estimated through use of the information enhancement method involving spectral enhancement, regional feature enhancement, and information enhancement, for which the estimation accuracies (R2) were 0.88, 0.78, and 0.85, respectively. Compared with the estimation results of raw Sentinel-2 imagery, the RMSE was reduced by 3.42 g/m2, 0.14 g/m2, and 13.73 mg/m2, respectively; and (ii) the continuous change trend and spatial distribution characteristics of C, N, and P contents in the west–east transect of the Inner Mongolia Plateau were obtained, which showed decreasing trends in C, N, and P contents from east to west and the characteristics of meadow > typical grassland > desert grassland. Thus, the information enhancement algorithm can help to improve estimates of C, N, and P contents when considering large-span grassland transects.