Microbiota-microglia crosstalk between Blautia producta and neuroinflammation of Parkinson's disease: A bench-to-bedside translational approach.

Parkinson's disease (PD) is intricately linked to abnormal gut microbiota, yet the specific microbiota influencing clinical outcomes remain poorly understood. Our study identified a deficiency in the microbiota genus Blautia and a reduction in fecal short-chain fatty acid (SCFA) butyrate level in PD patients compared to healthy controls. The abundance of Blautia correlated with the clinical severity of PD. Supplementation with butyrate-producing bacterium B. producta demonstrated neuroprotective effects, attenuating neuroinflammation and dopaminergic neuronal death in mice, consequently ameliorating motor dysfunction. A pivotal inflammatory signaling pathway, the RAS-related pathway, modulated by butyrate, emerged as a key mechanism inhibiting microglial activation in PD. The change of RAS-NF-κB pathway in PD patients was observed. Furthermore, B. producta-derived butyrate demonstrated the inhibition of microglial activation in PD through regulation of the RAS-NF-κB pathway. These findings elucidate the causal relationship between specific gut microbiota and PD, presenting a novel microbiota-based treatment perspective for PD.

Malnutrition and cerebral intraparenchymal damage in patients with thrombosis of dural sinuses and/or cerebral veins.

BACKGROUNDS: Thrombosis of dural sinuses and/or cerebral veins (CVT) is an uncommon form of cerebrovascular disease. Malnutrition is common in patients with cerebrovascular disease, and early assessment of malnutrition and individualized nutritional treatment have been reported to improve functional outcomes of these patients. As for CVT patients, little is known about whether these patients would suffer from malnutrition. Also, the correlation between malnutrition and cerebral intraparenchymal damage (CID) in CVT patients was rarely studied. METHODS: Patients with CVT were retrospectively included in this observational study. Multivariate logistic regressions were used to investigate the effects of nutritional indexes on the risk of CID. Subsequently, we used the independent risk factors to construct the nomogram model, and the consistency index (C-index), calibration curve and decision curve analysis (DCA) to assess the reliability and applicability of the model. RESULTS: A total of 165 patients were included in the final analysis. Approximately 72.7% of CVT patients were regarded as malnourished by our malnutrition screening tools, and malnutrition is associated with an increased risk of CID. Prognostic Nutritional Index (PNI) (OR = 0.873; CI: 0.791, 0.963, p = 0.007) remained as an independent predictor for CID after adjustment for other risk factors. The nomogram model showed that PNI and gender have a great contribution to prediction. Besides, the nomogram model was consistent with the actual observations of CID risk (C-index = 0.65) and was of clinical significance. CONCLUSIONS: We reported that malnutrition, as indicated by PNI, was associated with a higher incidence of CID in CVT patients. Also, we have constructed a nomogram for predicting the risk of CID in these patients.

A Time-Frequency Domain Mixed Attention-Based Approach for Classifying Wood-Boring Insect Feeding Vibration Signals Using a Deep Learning Model.

Wood borers, such as the emerald ash borer and holcocerus insularis staudinger, pose a significant threat to forest ecosystems, causing damage to trees and impacting biodiversity. This paper proposes a neural network for detecting and classifying wood borers based on their feeding vibration signals. We utilize piezoelectric ceramic sensors to collect drilling vibration signals and introduce a novel convolutional neural network (CNN) architecture named Residual Mixed Domain Attention Module Network (RMAMNet).The RMAMNet employs both channel-domain attention and time-domain attention mechanisms to enhance the network's capability to learn meaningful features. The proposed system outperforms established networks, such as ResNet and VGG, achieving a recognition accuracy of 95.34% and an F1 score of 0.95. Our findings demonstrate that RMAMNet significantly improves the accuracy of wood borer classification, indicating its potential for effective pest monitoring and classification tasks. This study provides a new perspective and technical support for the automatic detection, classification, and early warning of wood-boring pests in forestry.

Semantic-aware Message Broadcasting for Efficient Unsupervised Domain Adaptation.

Vision transformer has demonstrated great potential in abundant vision tasks. However, it also inevitably suffers from poor generalization capability when the distribution shift occurs in testing (i.e., out-of-distribution data). To mitigate this issue, we propose a novel method, Semantic-aware Message Broadcasting (SAMB), which enables more informative and flexible feature alignment for unsupervised domain adaptation (UDA). Particularly, we study the attention module in the vision transformer and notice that the alignment space using one global class token lacks enough flexibility, where it interacts information with all image tokens in the same manner but ignores the rich semantics of different regions. In this paper, we aim to improve the richness of the alignment features by enabling semantic-aware adaptive message broadcasting. Particularly, we introduce a group of learned group tokens as nodes to aggregate the global information from all image tokens, but encourage different group tokens to adaptively focus on the message broadcasting to different semantic regions. In this way, our message broadcasting encourages the group tokens to learn more informative and diverse information for effective domain alignment. Moreover, we systematically study the effects of adversarial-based feature alignment (ADA) and pseudo-label based self-training (PST) on UDA. We find that one simple two-stage training strategy with the cooperation of ADA and PST can further improve the adaptation capability of the vision transformer. Extensive experiments on DomainNet, OfficeHome, and VisDA-2017 demonstrate the effectiveness of our methods for UDA.

The association between ferritin levels and all-cause mortality in stroke patients.

Purpose: The purpose of study was to describe the association between ferritin and all-cause mortality of cases with stroke. Methods: Clinical data derived from Multiparameter Intelligent Monitoring in Intensive Care were analyzed. The primary endpoint was 30-day mortality. The potential prognostic roles of Ferritin L were analyzed by Cox proportional hazard models. The independent prognostic roles of Ferritin L in the cases were analyzed by smooth curve fitting. Results: Concerning 30-day mortality, the HR (95% CI) for a high Ferritin (≥373) was 1.925 (1.298, 2.854; p = 0.00113), compared to a low ferritin (< 373). After adjusting for multiple confounders, the HR (95% CI) for a high Ferritin (≥373) was 1.782 (1.126, 2.820; p = 0.01367), compared to a low Ferritin (< 373). A non-linear association between Ferritin and 30-day mortality was found. Using recursive algorithm and two-piecewise linear regression model, inflection point (IP) was calculated, which was 2,204. On the left side of the IP, there was a positive relationship between Ferritin and 30-day mortality, and the effect size, 95% CI and p value were 1.0006 (1.0004, 1.0009) p < 0.0001, respectively. On the right of the IP, the effect size, 95% CI and p value were 1.0000 (1.0000, 1.0000) and 0.3107, respectively. Conclusion: Ferritin was associated with increased risk of stroke; it is important to further examine the association if the increased uric acid would increase the outcome of stroke in a longitudinal study. The non-linear relationship between Ferritin and all-cause mortality of stroke was observed. Ferritin was a risk factor for the outcome of stroke when ferritin was <2204.

Active ingredient identification and purification of organosilicon spray adjuvant and its uptake and translocation in rice (Oryza sativa L.).

The assessment of residue, absorption, conduction, and degradation of agricultural organosilicon surfactants in the environment is hindered by the lack of information on active ingredients and corresponding quantitative standards for organosilicon spray adjuvants. The spray adjuvant 'Jiexiaoli,' a primary organosilicon spray agent in China, was identified as hydroxy (polyethylene) propyl-heptamethyl trisiloxane (TSS-H) with 3-15 ethoxy (EO) groups. Purification of TSS-H was achieved through semi-preparative separation using high-performance liquid chromatography (HPLC), resulting in TSS-H purity exceeding 96%. An accurate residual detection method for nine oligomers (4-12 EO) of TSS-H in rice roots, stems, leaves, and culture solution samples was developed using HPLC tandem high-resolution mass spectrometry (HPLC-HRMS). Recoveries for nine oligomers of TSS-H in the four matrices ranged from 80.22% to 104.01%. Foliar application experiments demonstrated that TSS-H did not transfer from the upper to the lower parts of the rice plant. The half-lives of each oligomer (4-12 EO) in leaves were less than 3.21 days. Root application experiments revealed a root concentration factor (RCF) ranging from 0.20 to 0.56, a biological enrichment factor (BCF) ranging from 0.36 to 0.68, a transpiration factor (TSCF) ranging from 0.069 to 0.086, and a transport factor (TF) ranging from 0.08 to 0.43. These results indicated that TSS-H could be absorbed by rice roots and conducted to the above-ground parts of rice plants. This study fills the data gap in the environmental risk and food safety assessment of agricultural silicone spray adjuvants. © 2024 Society of Chemical Industry.

METTL3/IGF2BP1 influences the development of non-small-cell lung cancer by mediating m6A methylation modification of TRPV1.

BACKGROUND: Methyltransferase 3 (METTL3) accelerates N6-methyladenosine (m6A) modifications and affects cancer progression, including non-small-cell lung cancer (NSCLC). In this study, we aimed to explore the regulatory mechanisms of METTL3 underling NSCLC. METHODS: Immunohistochemical assay, quantitative real-time polymerase chain reaction (qRT-PCR) assay, and western blot assay were conducted for gene expression. MTT assay and colony formation assay were performed to explore cell proliferation capacity. Cell apoptosis and THP-1 cell polarization were estimated by flow cytometry analysis. Cell migration and invasion capacities were evaluated by transwell assay. Methylated RNA immunoprecipitation assay, dual-luciferase reporter assay, actinomycin D treatment and RIP assay were performed to analyze the relationships of METTL3, insulin-like growth factor 2 mRNA binding protein 1 (IGF2BP1), and transient receptor potential cation channel subfamily V member 1 (TRPV1). The functions of METTL3 and TRPV1 in vivo were investigated through establishing the murine xenograft model. RESULTS: TRPV1 expression was upregulated in NSCLC and related poor prognosis. TRPV1 silencing inhibited NSCLC cell growth and metastasis, induced NSCLC cell apoptosis, and repressed M2 macrophage polarization. The results showed that METTL3 and IGF2BP1 could regulate TRPV1 expression through m6A methylation modification. Moreover, METTL3 deficiency inhibited NSCLC cell growth, metastasis, and M2 macrophage polarization and facilitated NSCLC cell apoptosis, while TRPV1 overexpression restored the impacts. In addition, METTL3 knockdown restrained tumor growth in vivo via regulating TRPV1 expression. CONCLUSION: METTL3 bound to IGF2BP1 and enhanced IGF2BP1's m6A recognition of TRPV1 mRNA, thereby promoting NSCLC cell growth and metastasis, and inhibiting M2 macrophage polarization.