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Lignocellulose, which consists of cellulose, hemicellulose, and lignin, has very stable properties. Among them, cellulose makes up 30% to 50% of the content, and hemicellulose makes up 20% to 43%. Cellulose and hemicellulose can be converted into fermentable sugar through saccharification, and then into bioresources through fermentation. Pretreatment methods such as high temperature and high pressure, acid and alkali cooking, enzymatic digestion can effectively decompose the lignocellulose structure, remove lignin, increase the porosity of lignocellulose, specific surface area, etc., increase the efficiency of saccharification, and improve the utilization of lignocellulose. Pretreatment is a key stage in the production process of bioresources. However, the pretreatment process produces by-products known as inhibitors such as acetic acid, furfural, and phenols. These inhibitors tend to inhibit the activity of biological enzymes, impede the saccharification of cellulose and hemicellulose, disrupt the integrity of the cell membrane of the fermenting bacteria, lead to mutation of the fermenting bacteria, and result in a decrease in the yield of the bioresource. This paper reviews recent advances in pretreatment methods, analyzes the reasons for the emergence of inhibitors, and summarizes methods to reduce the effects of inhibitors.

IntroductionBrain diseases, particularly the classification of gliomas and brain metastases and the prediction of HT in strokes, pose significant challenges in healthcare. Existing methods, relying predominantly on clinical data or imaging-based techniques such as radiomics, often fall short in achieving satisfactory classification accuracy. These methods fail to adequately capture the nuanced features crucial for accurate diagnosis, often hindered by noise and the inability to integrate information across various scales.MethodsWe propose a novel approach that mask attention mechanisms with multi-scale feature fusion for Multimodal brain disease classification tasks, termed M3, which aims to extract features highly relevant to the disease. The extracted features are then dimensionally reduced using Principal Component Analysis (PCA), followed by classification with a Support Vector Machine (SVM) to obtain the predictive results.ResultsOur methodology underwent rigorous testing on multi-parametric MRI datasets for both brain tumors and strokes. The results demonstrate a significant improvement in addressing critical clinical challenges, including the classification of gliomas, brain metastases, and the prediction of hemorrhagic stroke transformations. Ablation studies further validate the effectiveness of our attention mechanism and feature fusion modules.DiscussionThese findings underscore the potential of our approach to meet and exceed current clinical diagnostic demands, offering promising prospects for enhancing healthcare outcomes in the diagnosis and treatment of brain diseases.

When food waste (FW) undergoes anaerobic digestion, the hydrolysis rate is rapid, and thus causes system instability. Sophora flavescens residues (SFRs) are rich in complex hydrolysed substances, such as lignocellulosic material. When combined FW and SFRs can effectively improve the stability of digestion systems and increase biogas yields. In this work, batch anaerobic experiments were conducted at different co-substrate ratios to investigate the performance of co-digestion and the synergistic effect of FW and SFRs. The co-digestion of the two substrates exerted synergistic effects on biogas production and the highest synergy was 120.8%. After digestion, the ratio of hydrolysed chemical oxygen demand (COD) to the entire COD (RCOD H ) of the co-digestion group was 1.08 times that of the single FW group, which indicated the co-digestion promoted the hydrolysis of substrates. Moreover, the hydrolysis rate constant ( k h ) of co-digestion group increased by 4.10 times in comparison with that of the single FW group, which indicated the co-digestion increased the hydrolysis rate. In other words, the synergistic effect mainly occurred in the hydrolysis acidification process.

In this study, the feasibility of banana straw (BS) hydrolysate as carbon source and reutilizing the pretreated liquor (PL) of BS in the Rhodosporidium toruloides fermentation was explored for the first time. When BS hydrolysate was used as the carbon source, total biomass concentration, lipid concentration, and lipid content under optimal conditions reached 15.52 g/L, 5.83 g/L, and 37.56% (w/w), respectively, which was similar to the results of pure sugar control. After detoxification, 50% PL can be returned to enzymatic hydrolysis and fermentation, and total biomass concentration, lipid concentration, and lipid content can reach 15.14 g/L, 5.59 g/L, and 36.91% (w/w). Then, ethanol stillage (ES) was used as the nitrogen source. The NaCl and glycerol of ES could promote lipid accumulation, reaching 7.52 g/L under optimized conditions. Finally, microbial lipid production from BS hydrolysate and ES without any additional nutrients was investigated, and the maximum total biomass concentration, lipid concentration, and lipid content were 13.55 g/L, 4.88 g/L, and 36.01% (w/w), respectively. Besides, the main compositions of microbial lipid produced were C16 and C18, and the biodiesel production from the microbial lipid could meet Chinese and US standard through theoretical numerical calculation.

Intravenous thrombolysis is the most commonly used drug therapy for patients with acute ischemic stroke, which is often accompanied by complications of intracerebral hemorrhage transformation (HT). This study proposed to build a reliable model for pretreatment prediction of HT. Specifically, 5400 radiomics features were extracted from 20 regions of interest (ROIs) of multiparametric MRI images of 71 patients. Furthermore, a minimal set of all-relevant features were selected by LASSO from all ROIs and used to build a radiomics model through the random forest (RF). To explore the significance of normal ROIs, we built a model only based on abnormal ROIs. In addition, a model combining clinical factors and radiomics features was further built. Finally, the models were tested on an independent validation cohort. The radiomics model with 14 All-ROIs features achieved pretreatment prediction of HT (AUC = 0.871, accuracy = 0.848), which significantly outperformed the model with only 14 Abnormal-ROIs features (AUC = 0.831, accuracy = 0.818). Besides, combining clinical factors with radiomics features further benefited the prediction performance (AUC = 0.911, accuracy = 0.894). So, we think that the combined model can greatly assist doctors in diagnosis. Furthermore, we find that even if there were no lesions in the normal ROIs, they also provide characteristic information for the prediction of HT.

Anaerobic digestion (AD) has the advantages of utilizing complex substrates and producing renewable energy and is currently one of the mainstream technologies for food waste (FW) resourcing. However, at high organic loads and low inoculum-to-substrate ratios (ISRs), AD with FW as substrate is prone to acid accumulation, resulting in a drastic decrease in gas production and system collapse. This study investigated the effect of the coupled addition of zero-valent iron (ZVI) and activated carbon (AC) on the AD of FW at three low ISRs of 0.715, 0.625, and 0.5. The results showed that the control group acidified and stopped producing biogas when the ISR decreased to 0.625 and 0.5, but ZVI coupled with AC alleviated the acidification and increased the cumulative biogas yield. Especially at ISR = 0.5, the cumulative biogas yield for the ZVI + AC group was 31.5%, 99.5%, and 11.43 times higher than that of the ZVI, AC, and control groups, respectively. ZVI coupled with AC also increased the degradation of volatile fatty acids (70.5–84.4%) and soluble chemical oxygen demand (50.0–72.9%) while decreasing propionate concentration and improving the stability of the AD system. COD mass balance analyses indicated that the coupled addition of ZVI and AC promoted the conversion of particulate organic matter to soluble organic matter and increased the conversion of carbon sources to methane.

Purpose Multiple gastrointestinal stromal tumors (MGISTs) are relatively rare and may exhibit distinct clinical characteristics and prognosis compared to solitary GISTs (SGISTs). The objective of this study was to investigate the clinical features and prognosis of MGISTs. Materials and methods The Surveillance, Epidemiology, and End Results (SEER) database was used to identify all GIST patients diagnosed between 2010 and 2019. Multiple imputation (MI) was utilized to address missing data, while propensity score matching (PSM) was conducted to mitigate selection bias. The impact of demographic and clinical factors on overall survival (OS) was evaluated using Kaplan–Meier analyses and Cox proportional hazards models. Results A total of 6241 patients were included in the study, with 4546 having SGISTs and 1695 having MGISTs. MGISTs have a higher prevalence in males, Caucasians, and elderly patients. Compared to MGISTs, the OS of SGISTs is significantly better (hazard ratio [HR] 0.49, 95% confidence interval [CI] 0.44–0.55, P  < 0.001). After PSM, 3390 patients (equally distributed between the SGISTs and MGISTs groups) were matched for comparison. The OS of SGISTs is still better than that of MGISTs (HR 0.65, 95% CI 0.56–0.75, P  < 0.001). Age, sex, site, surgery, marital status, mitotic rate, and chemotherapy were independent risk factors for OS in MGISTs patients. The OS of MGISTs patients who underwent surgery was significantly better than those who did not ( P  < 0.001). Similarly, chemotherapy-treated MGISTs patients showed improved OS compared to those who did not receive it ( P  = 0.045). Conclusions MGISTs have unique clinical characteristics and show worse OS compared to SGISTs. Surgical intervention and chemotherapy has the potential to ameliorate the prognosis of patients with MGISTs.

Increasing crop yields and ensuring food security is a major global challenge. In order to increase crop production, chemical fertilizers and pesticides are excessively used. However, the significance of root exudates is understudied. Beneficial interactions between plant and rhizosphere microbiome are critical for plant fitness and health. In this review, we discuss the application and progress of current research methods and technologies in terms of root exudates and rhizosphere microbiome. We summarize how root exudates promote plant access to nitrogen, phosphorus, and iron, and how root exudates strengthen plant immunity to cope with biotic stress by regulating the rhizosphere microbiome, and thereby reducing dependence on fertilizers and pesticides. Optimizing these interactions to increase plant nutrient uptake and resistance to biotic stresses offers one of the few untapped opportunities to confront sustainability issues in food security. To overcome the limitations of current research, combination of multi-omics, imaging technology together with synthetic communities has the potential to uncover the interaction mechanisms and to fill the knowledge gap for their applications in agriculture to achieve sustainable development. Graphical abstract

The rising presence of antibiotics in the environment induces the production of resistant genes and thus threatens animal health because there are actually few alternative antibiotics. Here, we review recent trends on antibiotic research in water. For that, we analyzed countries, institutes, journals and keywords of 5420 articles on antibiotics in water or wastewater published between 2000 and 2017. Findings show that China is the first contributor and that the USA has the highest h-index of 104. The major removal techniques are adsorption, photolysis and photocatalysis, biodegradation, ozonation and electrochemical oxidation. New materials and technologies, such as ionizing beam, are actually studied to improve efficiency and decrease cost. Conversion of wastewater into fuels such as H2 and methane is also a current research topic.

Bioethanol is currently the only alternative to gasoline that can be used immediately without having to make any significant changes in the way fuel is distributed. In addition, the carbon dioxide (CO ) released during the combustion of bioethanol is the same as that used by the plant in the atmosphere for its growth, so it does not participate in the increase of the greenhouse effect. Bioethanol can be obtained by fermentation of plants containing sucrose (beet, sugar cane…) or starch (wheat, corn…). However, large-scale use of bioethanol implies the use of very large agricultural surfaces for maize or sugarcane production. Lignocellulosic biomass (LCB) such as agricultural residues for the production of bioethanol seems to be a solution to this problem due to its high availability and low cost even if its growth still faces technological difficulties. In this review, we present an overview of lignocellulosic biomass, the different methods of pre-treatment of LCB and the various fermentation processes that can be used to produce bioethanol from LCB.