Information needs preferences of Chinese colorectal cancer patients receiving chemotherapy: A discrete choice experiment.

Objective: The study aims to investigate the information needs and preferences of colorectal cancer (CRC) patients undergoing chemotherapy using a discrete choice experiment (DCE) to optimize and improve the information support strategy for these patients. Methods: Between May and July 2023, 165 patients with CRC who were receiving chemotherapy at a single hospital in China completed the questionnaire. The survey instruments included a general information questionnaire, a DCE questionnaire, and the Brief Health Literacy Screening Scale. A conditional logit model was used with Stata 16.0 software to analyze patients' preferences. Results: A total of 159 valid questionnaires were collected, and the questionnaire response rate was 96.4%. All 7 included attributes had an impact on patients' information needs preference (P < 0.05). Among them, information providers, knowledge content, and social support had high relative importance, which were 12.16%, 7.57% and 2.25%, respectively. Patients showed a preference for attending doctors (ß = 1.9439, P < 0.05) and primary nurses (ß = 1.7985, P < 0.05). Providing knowledge related to disease basis, treatment, and health promotion also had a significant impact (ß = 1.6224, P < 0.05). Conclusions: Healthcare professionals should be the primary information source for patients and improve the accessibility of information by establishing professional information platforms or identifying reliable channels. It is recommended to provide continuous information on treatment and health promotion to CRC patients at various stages of chemotherapy. Attention should be paid to identifying and providing measures to alleviate the economic and psychological burden and to meet the social support needs of patients.

Predicting and screening high-performance polyimide membranes using negative correlation based deep ensemble methods.

Polyimide polymer membranes have become critical materials in gas separation and storage applications due to their high selectivity and excellent permeability. However, with over 107 known types of polyimides, relying solely on experimental research means potential high-performance candidates are likely to be overlooked. This study employs a deep learning method optimized by negative correlation ensemble techniques to predict the gas permeability and selectivity of polyimide structures, enabling rapid and efficient material screening. We propose a deep neural network model based on negative correlation deep ensemble methods (DNN-NCL), using Morgan molecular fingerprints as input. The DNN-NCL model achieves an R2 value of approximately 0.95 on the test set, which is a 4% improvement over recent model performance, and effectively mitigates overfitting with a maximum discrepancy of less than 0.03 between the training and test sets. High-throughput screening of over 8 million hypothetical polymers identified hundreds of promising candidates for gas separation membranes, with 14 structures exceeding the Robeson upper bound for CO2/N2 separation. Visualization of high-throughput predictions shows that although the Robeson upper bound was never explicitly used as a model constraint, the majority of predictions are compressed below this limit, demonstrating the deep learning model's ability to reflect real-world physical conditions. Reverse analysis of model predictions using SHAP analysis achieved interpretability of the deep learning model's predictions and identified three key functional groups deemed important by the deep neural network for gas permeability: carbonyl, thiophene, and ester groups. This established a bridge between the structure and properties of polyimide materials. Additionally, we confirmed that two polyimide structures predicted by the model to have excellent CO2/N2 selectivity, namely 6-methylpyrimidin-5-amine and 1,4,5,6-tetrahydropyrimidin-2-amine, have been experimentally validated in previous studies. This research demonstrates the feasibility of using deep learning methods to explore the vast chemical space of polyimides, providing a powerful tool for discovering high-performance gas separation membranes.

Inversion Method for Transformer Winding Hot Spot Temperature Based on Gated Recurrent Unit and Self-Attention and Temperature Lag.

The hot spot temperature of transformer windings is an important indicator for measuring insulation performance, and its accurate inversion is crucial to ensure the timely and accurate fault prediction of transformers. However, existing studies mostly directly input obtained experimental or operational data into networks to construct data-driven models, without considering the lag between temperatures, which may lead to the insufficient accuracy of the inversion model. In this paper, a method for inverting the hot spot temperature of transformer windings based on the SA-GRU model is proposed. Firstly, temperature rise experiments are designed to collect the temperatures of the entire side and top of the transformer tank, top oil temperature, ambient temperature, the cooling inlet and outlet temperatures, and winding hot spot temperature. Secondly, experimental data are integrated, considering the lag of the data, to obtain candidate input feature parameters. Then, a feature selection algorithm based on mutual information (MI) is used to analyze the correlation of the data and construct the optimal feature subset to ensure the maximum information gain. Finally, Self-Attention (SA) is applied to optimize the Gate Recurrent Unit (GRU) network, establishing the GRU-SA model to perceive the potential patterns between output feature parameters and input feature parameters, achieving the precise inversion of the hot spot temperature of the transformer windings. The experimental results show that considering the lag of the data can more accurately invert the hot spot temperature of the windings. The inversion method proposed in this paper can reduce redundant input features, lower the complexity of the model, accurately invert the changing trend of the hot spot temperature, and achieve higher inversion accuracy than other classical models, thereby obtaining better inversion results.

Development and validation of a predictive model for postpartum endometritis.

OBJECTIVE: The aim was to develop a predictive tool for anticipating postpartum endometritis occurrences and to devise strategies for prevention and control. METHODS: Employing a retrospective approach, the baseline data of 200 women diagnosed with postpartum endometritis in a tertiary maternity hospital in Zhejiang Province, spanning from February 2020 to September 2022, was examined. Simultaneously, the baseline data of 1,000 women without endometritis during the same period were explored with a 1:5 ratio. Subsequently, 1,200 women were randomly allocated into a training group dataset and a test group dataset, adhering to a 7:3 split. The selection of risk factors for postpartum endometritis involved employing random forests, lasso regression, and traditional univariate and multifactor logistic regression on the training group dataset. A nomogram was then constructed based on these factors. The model's performance was assessed using the area under the curve (AUC), calculated through plotting the receiver operating characteristic (ROC) curve. Additionally, the Brier score was employed to evaluate the model with a calibration curve. To gauge the utility of the nomogram, a clinical impact curve (CIC) analysis was conducted. This comprehensive approach not only involved identifying risk factors but also included a visual representation (nomogram) and thorough evaluation metrics, ensuring a robust tool for predicting, preventing, and controlling postpartum endometritis. RESULTS: In the multivariate analysis, six factors were identified as being associated with the occurrence of maternal endometritis in the postpartum period. These factors include the number of negative finger tests (OR: 1.159; 95%CI: 1.091-1.233; P < 0.05), postpartum hemorrhage (1.003; 1.002-1.005; P < 0.05), pre-eclampsia (9.769; 4.64-21.155; P < 0.05), maternity methods (2.083; 1.187-3.7; P < 0.001), prenatal reproductive tract culture (2.219; 1.411-3.47; P < 0.05), and uterine exploration (0.441; 0.233-0.803; P < 0.001).A nomogram was then constructed based on these factors, and its predictive performance was assessed using the area under the curve (AUC). The results in both the training group data (AUC: 0.803) and the test group data (AUC: 0.788) demonstrated a good predictive value. The clinical impact curve (CIC) further highlighted the clinical utility of the nomogram. CONCLUSION: The development of an individualized nomogram for postpartum endometritis infection holds promise for doctors in screening high-risk women, enabling early intervention and ultimately reducing the rate of postpartum endometritis infection. This comprehensive approach, integrating key risk factors and predictive tools, enhances the potential for timely and targeted medical intervention.

Application of Cyanobacteria as Chassis Cells in Synthetic Biology.

Synthetic biology is an exciting new area of research that combines science and engineering to design and build new biological functions and systems. Predictably, with the development of synthetic biology, more efficient and economical photosynthetic microalgae chassis will be successfully constructed, making it possible to break through laboratory research into large-scale industrial applications. The synthesis of a range of biochemicals has been demonstrated in cyanobacteria; however, low product titers are the biggest barrier to the commercialization of cyanobacterial biotechnology. This review summarizes the applied improvement strategies from the perspectives of cyanobacteria chassis cells and synthetic biology. The harvest advantages of cyanobacterial products and the latest progress in improving production strategies are discussed according to the product status. As cyanobacteria synthetic biology is still in its infancy, apart from the achievements made, the difficulties and challenges in the application and development of cyanobacteria genetic tool kits in biochemical synthesis, environmental monitoring, and remediation were assessed.

Study on the permeability characteristics of different geotextiles under weft and warp stretching.

Geotextiles are excellent anti-filtration materials commonly used in the field of water conservancy engineering; however, the factors affecting the water permeability performance of geotextiles under stressed states during operation have not been fully identified. To investigate the effect of unidirectional stretching on the water permeability of geotextiles, a unidirectional rheological head infiltration test was conducted on the geotextiles using a self-developed test apparatus. In addition, the water permeability of geotextiles with different thicknesses and tensile states was calculated using a set of water permeability calculation methods based on the nonlaminar flow state of geotextiles. The results showed that the water permeability of the W120 geotextile samples initially decreased and then increased under warp stretching and gradually increased under weft stretching. However, the water permeability of the W200 geotextile samples initially decreased and then increased under both warp and weft stretching. Therefore, the thickness of the geotextile affected its permeability properties.

Long-term stability and immunogenicity of lipid nanoparticle COVID-19 mRNA vaccine is affected by particle size.

Messenger ribonucleic acid (mRNA) technology has been rapidly applied for the development of the COVID-19 vaccine. However, naked mRNA itself is inherently unstable. Lipid nanoparticles (LNPs) protect mRNAs from extracellular ribonucleases and facilitate mRNA trafficking. For mRNA vaccines, antigen-presenting cells utilize LNPs through uptake to elicit antigen-specific immunity. There are reports on the impact of various physical characteristics of LNPs, particularly those with sizes less than 200 nm, especially 50 to 150 nm, on the overall stability and protective efficacy of mRNA vaccines. To address this, a single change in the size of LNPs using the same mRNA stock solution was assessed for the physicochemical characterization of the resulting mRNA-LNPs vaccine, along with the evaluation of their protective efficacy. Particles of smaller sizes generally disperse more effectively in solutions, with minimized occurrence of particle precipitation and aggregation. Here, we demonstrate that the vaccine containing 80-100 nm mRNA-LNPs showed the best stability and protection at 4°C and -20°C. Furthermore, we can conclude that freezing the vaccine at -20°C is more appropriate for maintaining stability over the long term. This effort is poised to provide a scientific basis for improving the quality of ongoing mRNA vaccine endeavors and providing information on the development of novel products.

Ingenious fluorescent probes for biogenic amine and their applications in bioimaging and food spoilage detection.

Food safety issues have received much attention. Biogenic amines are considered important markers of food spoilage. Accurate detection of biogenic amines is important for food quality monitoring. Herein, we developed two coumarin-difluoroboron ß-diketonate hybrid probes, 1 and 2, for detection of amines. Both probes possess large conjugated structures and donor-acceptor-donor configuration, exhibiting solvatochromic effects due to intramolecular charge transfer mechanism. Upon reaction with amines, the boron atom in difluoroboron unit can interact with lone pair electrons of nitrogen atom, thus resulting in significant changes in absorption and fluorescence properties. These probes were successfully utilized to image amine in live cells and liver tissues. Moreover, by photographing probe-loaded food extract supernatant, we establish the relationship between color parameters and food storage time, which can easily indicate food spoilage process. This work and its findings hold promise for providing potential strategies for real-time and convenient detection of food freshness.

Study on load transfer mechanisms and impact on adjacent railway subgrade within loess regions.

Clarifying load transfer mechanism and the influence of the widening of a newly-built railway is the premise for the construction of adjacent project in loess region. This paper uses monitoring datas obtained from three sections in different stages to analyze the distribution laws of load exert on piles and soil between piles, investigates the variation laws of filling height on the earth pressure at different excavation steps and elucidates the influence of the filling height of newly built subgrade on the existing subgrade. In addition, a fitting formula y = a(1-e-bx) + cx is proposed to describe the relationship between the ratio of the additional displacement to the filling height, which is applicable for similar projects.

Magnetic quaternary ammonium polymer bearing porous agarose for selective extraction of Aristolochic acids in the plasma.

Aristolochic acids (AAs) naturally occurring in the herbal genus Aristolochia are associated with a high risk of kidney failure, multiple tumors and cancers. However, approaches with high selectivity and rapidity for measuring AAs in biological samples are still inadequate. Inspired by the mechanism of AAs-induced nephrotoxicity, we designed a hybrid magnetic polymer-porous agarose (denoted as MNs@SiO2M@DNV-A), mimicking the effect of basic and aromatic residues of organic anion transporter 1 (OAT1) for efficient enriching aristolochic acid I (AA I) and aristolochic acid II (AA II) in the plasma. The monomers of vinylbenzyl trimethylammonium chloride (VBTAC), N-vinyl-2-pyrrolidinone (NVP) and divinylbenzene (DVB) were employed to construct the polymer layer, which provided a selective adsorption for AAs by multiple interactions. The porous agarose shell contributed to remove interfering proteins in the plasma samples. A magnetic solid-phase extraction (MSPE) based on the proposed composite enhanced the selectivity toward AA I and AA II in the plasma samples. In combination of HPLC analysis, the proposed method was proved to be applicable to fast and specific quantification of AAs in blood samples, which was characterized by a good linearity, high sensitivity, acceptable recovery, excellent repeatability and satisfactory reusability.

Evaluation and Comparison of Academic Impact and Disruptive Innovation Level of Medical Journals: Bibliometric Analysis and Disruptive Evaluation.

BACKGROUND: As an important platform for researchers to present their academic findings, medical journals have a close relationship between their evaluation orientation and the value orientation of their published research results. However, the differences between the academic impact and level of disruptive innovation of medical journals have not been examined by any study yet. OBJECTIVE: This study aims to compare the relationships and differences between the academic impact, disruptive innovation levels, and peer review results of medical journals and published research papers. We also analyzed the similarities and differences in the impact evaluations, disruptive innovations, and peer reviews for different types of medical research papers and the underlying reasons. METHODS: The general and internal medicine Science Citation Index Expanded (SCIE) journals in 2018 were chosen as the study object to explore the differences in the academic impact and level of disruptive innovation of medical journals based on the OpenCitations Index of PubMed open PMID-to-PMID citations (POCI) and H1Connect databases, respectively, and we compared them with the results of peer review. RESULTS: First, the correlation coefficients of the Journal Disruption Index (JDI) with the Journal Cumulative Citation for 5 years (JCC5), Journal Impact Factor (JIF), and Journal Citation Indicator (JCI) were 0.677, 0.585, and 0.621, respectively. The correlation coefficient of the absolute disruption index (Dz) with the Cumulative Citation for 5 years (CC5) was 0.635. However, the average difference in the disruptive innovation and academic influence rankings of journals reached 20 places (about 17.5%). The average difference in the disruptive innovation and influence rankings of research papers reached about 2700 places (about 17.7%). The differences reflect the essential difference between the two evaluation systems. Second, the top 7 journals selected based on JDI, JCC5, JIF, and JCI were the same, and all of them were H-journals. Although 8 (8/15, 53%), 96 (96/150, 64%), and 880 (880/1500, 58.67%) of the top 0.1%, top 1%, and top 10% papers selected based on Dz and CC5, respectively, were the same. Third, research papers with the "changes clinical practice" tag showed only moderate innovation (4.96) and impact (241.67) levels but had high levels of peer-reviewed recognition (6.00) and attention (2.83). CONCLUSIONS: The results of the study show that research evaluation based on innovative indicators is detached from the traditional impact evaluation system. The 3 evaluation systems (impact evaluation, disruptive innovation evaluation, and peer review) only have high consistency for authoritative journals and top papers. Neither a single impact indicator nor an innovative indicator can directly reflect the impact of medical research for clinical practice. How to establish an integrated, comprehensive, scientific, and reasonable journal evaluation system to improve the existing evaluation system of medical journals still needs further research.

Association of leukemic molecular profile with efficacy of inotuzumab ozogamicin in adults with relapsed/refractory ALL.

ABSTRACT: The phase 3 INO-VATE trial demonstrated higher rates of remission, measurable residual disease negativity, and improved overall survival for patients with relapsed/refractory (R/R) acute lymphoblastic leukemia (ALL) who received inotuzumab ozogamicin (InO) vs standard-of-care chemotherapy (SC). Here, we examined associations between genomic alterations and the efficacy of InO. Of 326 randomized patients, 91 (InO, n = 43; SC, n = 48) had samples evaluable for genomic analysis. The spectrum of gene fusions and other genomic alterations observed was comparable with prior studies of adult ALL. Responses to InO were observed in all leukemic subtypes, genomic alterations, and risk groups. Significantly higher rates of complete remission (CR)/CR with incomplete count recovery were observed with InO vs SC in patients with BCR::ABL1-like ALL (85.7% [6/7] vs 0% [0/5]; P = .0076), with TP53 alterations (100% [5/5] vs 12.5% [1/8]; P = .0047), and in the high-risk BCR::ABL1- (BCR::ABL1-like, low-hypodiploid, KMT2A-rearranged) group (83.3% [10/12] vs 10.5% [2/19]; P < .0001). This retrospective, exploratory analysis of the INO-VATE trial demonstrated potential for benefit with InO for patients with R/R ALL across leukemic subtypes, including BCR::ABL1-like ALL, and for those bearing diverse genomic alterations. Further confirmation of the efficacy of InO in patients with R/R ALL exhibiting the BCR::ABL1-like subtype or harboring TP53 alterations is warranted. This trial was registered at www.ClinicalTrials.gov as #NCT01564784.

Study on chromatin regulation patterns of expression vectors in the PhiC31 integration site.

The PhiC31 integration system allows for targeted and efficient transgene integration and expression by recognizing pseudo attP sites in mammalian cells and integrating the exogenous genes into the open chromatin regions of active chromatin. In order to investigate the regulatory patterns of efficient gene expression in the open chromatin region of PhiC31 integration, this study utilized Ubiquitous Chromatin Opening Element (UCOE) and activating RNA (saRNA) to modulate the chromatin structure in the promoter region of the PhiC31 integration vector. The study analysed the effects of DNA methylation and nucleosome occupancy changes in the integrated promoter on gene expression levels. The results showed that for the OCT4 promoter with moderate CG density, DNA methylation had a smaller impact on expression compared to changes in nucleosome positioning near the transcription start site, which was crucial for enhancing downstream gene expression. On the other hand, for the SOX2 promoter with high CG density, increased methylation in the CpG island upstream of the transcription start site played a key role in affecting high expression, but the positioning and clustering of nucleosomes also had an important influence. In conclusion, analysing the DNA methylation patterns, nucleosome positioning, and quantity distribution of different promoters can determine whether the PhiC31 integration site possesses the potential to further enhance expression or overcome transgene silencing effects by utilizing chromatin regulatory elements.

Full-body pose reconstruction and correction in virtual reality for rehabilitation training.

Existing statistical data indicates that an increasing number of people now require rehabilitation to restore compromised physical mobility. During the rehabilitation process, physical therapists evaluate and guide the movements of patients, aiding them in a more effective recovery of rehabilitation and preventing secondary injuries. However, the immutability of mobility and the expensive price of rehabilitation training hinder some patients from timely access to rehabilitation. Utilizing virtual reality for rehabilitation training might offer a potential alleviation to these issues. However, prevalent pose reconstruction algorithms in rehabilitation primarily rely on images, limiting their applicability to virtual reality. Furthermore, existing pose evaluation and correction methods in the field of rehabilitation focus on providing clinical metrics for doctors, and failed to offer patients efficient movement guidance. In this paper, a virtual reality-based rehabilitation training method is proposed. The sparse motion signals from virtual reality devices, specifically head-mounted displays hand controllers, is used to reconstruct full body poses. Subsequently, the reconstructed poses and the standard poses are fed into a natural language processing model, which contrasts the difference between the two poses and provides effective pose correction guidance in the form of natural language. Quantitative and qualitative results indicate that the proposed method can accurately reconstruct full body poses from sparse motion signals in real-time. By referencing standard poses, the model generates professional motion correction guidance text. This approach facilitates virtual reality-based rehabilitation training, reducing the cost of rehabilitation training and enhancing the efficiency of self-rehabilitation training.

Theoretical Study of Electrocatalytic CO2 Reduction Mechanism on Typical MXenes under Realistic Conditions.

MXenes are a revolutionary class of two-dimensional materials that have been recently demonstrated to exhibit promising capability of electrocatalytic CO2 reduction reaction (CO2RR) in theory and experiment. In electrocatalytic reactions, the active phases, the mechanism, and the performance can be greatly influenced by electrochemical conditions such as applied electrode potential, pH, and electrolyte. Therefore, in this first-principles study, the stable surface structures of three typical MXenes (V2C, Mo2C, and Ti3C2) with variation of electrocatalytic conditions were determined by the Pourbaix phase diagrams. Additionally, the reaction mechanism for CO2RR toward C1 products was investigated based on the thermal dynamically stable phases. The computation revealed that surfaces of all three MXenes are dominated by H* termination throughout the practical CO2RR electrochemical condition ranges. Meanwhile, the bicarbonate ions, which serve as the major electrolyte in CO2RR, show thermal dynamic unfavorability to adsorb on the surfaces. Among the three types of MXenes, V2CH exhibits higher activity in generating CO and HCOOH through the CO2RR, while Mo2CH exhibits higher activity in producing HCHO, CH3OH, and CH4. This comprehensive study provides crucial insights into the mechanism of electrocatalytic CO2RR on MXenes under realistic electrochemical conditions.

Engineering marine phospholipid nanoliposomes via glycerol-infused proliposomes: Mechanisms, strategies, and versatile applications in scalable food-grade nanoliposome production.

This study presents a novel approach using polyol-based proliposome to produce marine phospholipids nanoliposomes. Proliposomes were formulated by blending glycerol with phospholipids across varying mass ratios (2:1 to 1:10) at room temperature. Analysis employing polarized light microscopy, FTIR, and DSC revealed that glycerol disrupted the stacked acyl groups within phospholipids, lowering the phase transition temperature (Tm). Krill oil phospholipids (KOP) proliposomes exhibited superior performance in nanoliposomes formation, with a mean diameter of 125.60 ± 3.97 nm, attributed to the decreased Tm (-7.64 and 7.00 °C) compared to soybean phospholipids, along with a correspondingly higher absolute zeta potential (-39.77 ± 1.18 mV). The resulting KOP proliposomes demonstrated liposomes formation stability over six months and under various environmental stresses (dilution, thermal, ionic strength, pH), coupled with in vitro absorption exceeding 90 %. This investigation elucidates the mechanism behind glycerol-formulated proliposomes and proposes innovative strategies for scalable, solvent-free nanoliposome production with implications for functional foods and pharmaceutical applications.

Optimized OTSU Segmentation Algorithm-Based Temperature Feature Extraction Method for Infrared Images of Electrical Equipment.

Infrared image processing is an effective method for diagnosing faults in electrical equipment, in which target device segmentation and temperature feature extraction are key steps. Target device segmentation separates the device to be diagnosed from the image, while temperature feature extraction analyzes whether the device is overheating and has potential faults. However, the segmentation of infrared images of electrical equipment is slow due to issues such as high computational complexity, and the temperature information extracted lacks accuracy due to the insufficient consideration of the non-linear relationship between the image grayscale and temperature. Therefore, in this study, we propose an optimized maximum between-class variance thresholding method (OTSU) segmentation algorithm based on the Gray Wolf Optimization (GWO) algorithm, which accelerates the segmentation speed by optimizing the threshold determination process using OTSU. The experimental results show that compared to the non-optimized method, the optimized segmentation method increases the threshold calculation time by more than 83.99% while maintaining similar segmentation results. Based on this, to address the issue of insufficient accuracy in temperature feature extraction, we propose a temperature value extraction method for infrared images based on the K-nearest neighbor (KNN) algorithm. The experimental results demonstrate that compared to traditional linear methods, this method achieves a 73.68% improvement in the maximum residual absolute value of the extracted temperature values and a 78.95% improvement in the average residual absolute value.

Highly Selective Lithium Transport through Crown Ether Pillared Angstrom Channels.

Biological ion channels use the synergistic effects of various strategies to realize highly selective ion sieving. For example, potassium channels use functional groups and angstrom-sized pores to discriminate rival ions and enrich target ions. Inspired by this, we constructed a layered crystal pillared by crown ether that incorporates these strategies to realize high Li+ selectivity. The pillared channels and crown ether have an angstrom-scale size. The crown ether specifically allows the low-barrier transport of Li+ . The channels attract and enrich Li+ ions by up to orders of magnitude. As a result, our material sieves Li+ out of various common ions such as Na+ , K+ , Ca2+ , Mg2+ and Al3+ . Moreover, by spontaneously enriching Li+ ions, it realizes an effective Li+ /Na+ selectivity of 1422 in artificial seawater where the Li+ concentration is merely 25 µM. We expect this work to spark technologies for the extraction of lithium and other dilute metal ions.

Coupling process for preparing biomass-based furfural and levulinic acid from corncob: Extraction, green chemistry and techno-economic assessment.

The purpose of this study is to design and investigate two coupling processes for acid-catalyzed hydrolysis of corncob, achieving the simultaneous preparation of biomass-based furfural and levulinic acid (LA). Meanwhile, high concentration and yield of LA were obtained through a situ feeding strategy of pretreated furfural residue with high solids loading (20%, w/v). In Scenario A, 2-methyltetrahydrofuran was selected as the solvent for the LA extraction process compared with the neutralization process in Scenario B. Techno-economic assessment results show that Scenario A is technically feasible and cost-competitive, with an internal rate of return of 21.92%, a net present value of 121 million US dollars, a carbon efficiency of 72%, an environmental factor of 4.38, and a process mass intensity of 32.19. This study will provide new insights for fully utilizing lignocellulosic biomass to prepare renewable energy resources, comprehensively evaluating the economic feasibility, and promoting green and low-carbon development.

Expression mapping of GREM1 and functional contribution of its secreting cells in the brain using transgenic mouse models.

GREMLIN1 (GREM1) is a secreted protein that antagonizes bone morphogenetic proteins (BMPs). While abnormal GREM1 expression has been reported to cause behavioral defects in postpartum mice, the spatial and cellular distribution of GREM1 in the brain and the influence of the GREM1-secreting cells on brain function and behavior remain unclear. To address this, we designed a genetic cassette incorporating a 3×Flag-TeV-HA-T2A-tdTomato sequence, resulting in the creation of a novel Grem1Tag mouse model, expressing an epitope tag (3×Flag-TeV-HA-T2A) followed by a fluorescent reporter (tdTomato) under the control of the endogenous Grem1 promoter. This design facilitated precise tracking of the cell origin and distribution of GREM1 in the brain using tdTomato and Flag (or HA) markers, respectively. We confirmed that the Grem1Tag mouse exhibited normal motor, cognitive, and social behaviors at postnatal 60 days (P60), compared with C57BL/6J controls. Through immunofluorescence staining, we comprehensively mapped the distribution of GREM1-secreting cells across the central nervous system. Pervasive GREM1 expression was observed in the cerebral cortex (Cx), medulla, pons, and cerebellum, with the highest levels in the Cx region. Notably, within the Cx, GREM1 was predominantly secreted by excitatory neurons, particularly those expressing calcium/calmodulin-dependent protein kinase II alpha (Camk2a), while inhibitory neurons (parvalbumin-positive, PV+) and glial cells (oligodendrocytes, astrocytes, and microglia) showed little or no GREM1 expression. To delineate the functional significance of GREM1-secreting cells, a selective ablation at P42 using a diphtheria toxin A (DTA) system resulted in increased anxiety-like behavior and impaired memory in mice. Altogether, our study harnessing the Grem1Tag mouse model reveals the spatial and cellular localization of GREM1 in the mouse brain, shedding light on the involvement of GREM1-secreting cells in modulating brain function and behavior. Our Grem1Tag mouse serves as a valuable tool for further exploring the precise role of GREM1 in brain development and disease.