Multi-omics analysis reveals the core microbiome and biomarker for nutrition degradation in alfalfa silage fermentation.

Alfalfa (Medicago sativa L.) is one of the most extensively cultivated forage crops globally, and its nutritional quality critically influences the productivity of dairy cows. Silage fermentation is recognized as a crucial technique for the preservation of fresh forage, ensuring the retention of its vital nutrients. However, the detailed microbial components and their functions in silage fermentation are not fully understood. This study integrated large-scale microbial culturing with high-throughput sequencing to thoroughly examine the microbial community structure in alfalfa silage and explored the potential pathways of nutritional degradation via metagenomic analysis. The findings revealed an enriched microbial diversity in silage, indicated by the identification of amplicon sequence variants. Significantly, the large-scale culturing approach recovered a considerable number of unique microbes undetectable by high-throughput sequencing. Predominant genera, such as Lactiplantibacillus, Leuconostoc, Lentilactobacillus, Weissella, and Liquorilactobacillus, were identified based on their abundance and prevalence. Additionally, genes associated with Enterobacteriaceae were discovered, which might be involved in pathways leading to the production of ammonia-N and butyric acid. Overall, this study offers a comprehensive insight into the microbial ecology of silage fermentation and provides valuable information for leveraging microbial consortia to enhance fermentation quality. IMPORTANCE: Silage fermentation is a microbial-driven anaerobic process that efficiently converts various substrates into nutrients readily absorbable and metabolizable by ruminant animals. This study, integrating culturomics and metagenomics, has successfully identified core microorganisms involved in silage fermentation, including those at low abundance. This discovery is crucial for the targeted cultivation of specific microorganisms to optimize fermentation processes. Furthermore, our research has uncovered signature microorganisms that play pivotal roles in nutrient metabolism, significantly advancing our understanding of the intricate relationships between microbial communities and nutrient degradation during silage fermentation.

Integrated Omnidirectional Design of Non-volatile Solid Additive Enables Binary Organic Solar Cells with Efficiency Exceeding 19.5.

Solid additives have drawn great attention due to their numerous appealing benefits in enhancing the power conversion efficiencies (PCEs) of organic solar cells (OSCs). To date, various strategies have been reported for the selection or design of non-volatile solid additives. However, the lack of a general design/evaluation principles for developing non-volatile solid additives often results in individual solid additives offering only one or two efficiency-boosting attributes. In this work, we propose an integrated omnidirectional strategy for designing non-volatile solid additives. By validating the method on the 4,5,9,10-pyrene diimide (PyDI) system, a novel non-volatile solid additive named PyMC5 was designed. PyMC5 is capable of enhancing device performance by establishing synergistic dual charge transfer channels, forming appropriate interactions with active layer materials, reducing non-radiative voltage loss and optimizing film morphology. Notably, the binary device (PM6:L8-BO) treated by PyMC5 achieved a PCE over 19.5%, ranking among the highest reported to date. In addition, the integration of PyMC5 mitigated the degradation process of the devices under photo- and thermal-stress conditions. This work demonstrates an efficient integrated omnidirectional approach for designing non-volatile solid additives, offering a promising avenue for further advancements in OSC development.

Kelp nanocellulose combined with fucoxanthin achieves lipid-lowering function by reducing oxidative stress with activation of Nrf2/HO-1/NQO1 pathway.

This study innovatively utilized kelp-derived nanocellulose and sodium caseinate (SC) to prepare fucoxanthin (Fx)-loaded nanoparticles, exploring their efficacy in reducing oxidative stress and inhibiting lipid accumulation. 2, 2, 6, 6-Tetramethylpiperidine-1-oxyl (TEMPO)-mediated oxidation produced well-dispersed, kelp-derived nanocellulose. When these celluloses were mixed with SC at varying mass ratios, the composite nanoparticles showed excellent stability. Specifically, at a TEMPO-oxidized kelp nanocellulose (TKNC) to SC mass ratio of 1:3, the encapsulation efficiency for Fx reached 82.2 %, with a retention of 56.12 % after 14 days of storage. In vitro, the nanoparticles demonstrated good biocompatibility and were efficiently absorbed by cells, significantly enhancing Fx bioavailability. This enhanced delivery efficiency alleviates oxidative stress by activating the Nrf2/HO-1/NQO1 signaling pathways and effectively inhibits lipid droplet formation induced by excessive free fatty acids (FFAs). Moreover, distribution studies in mice revealed effective accumulation of nanoparticles in the intestines and liver, indicating their potential for targeted drug delivery. These findings provide strong experimental support for the use of TKNC and SC as biocompatible materials in nanoparticles for drug delivery and treatment applications.

The effectiveness of an exergame intervention for college students with subthreshold depression: protocol for a mixed methods study.

Background: Subthreshold depression (StD) is a condition that significantly influences the mental health and quality of life of college students and increases the risk of developing major depressive disorder (MDD). Exercise therapy has been found to be effective, but may not be enjoyable for everyone. exergames, as a form of exercise therapy, address the limitations of traditional exercise by incorporating gaming elements to make physical activity more entertaining and interactive. Currently, the Nintendo Switch is one of the most widely used exergame devices. Aims: To explore the effectiveness of a Nintendo Switch-based exergame intervention on college students with StD compared to a control group, and to analyze their perceptions of the program. Methods: This study will employ an explanatory sequential design, starting with a quantitative evaluation using a randomized controlled trial (RCT), followed by a supplementary qualitative study. College students identified as having StD will be randomly allocated in a 1:1 ratio into the exergame intervention group (EIG) or the control group (CG). College students in the EIG will participate in a Nintendo Switch-based exergame program for 8 weeks, with 2-3 sessions per week, lasting 50-60 min each. Participant outcomes in both conditions will be assessed at pre-intervention (T0, week 0), post-intervention (T1, week 8), 1 month after the intervention (T2, week 12), and 2 months after the intervention (T3, week 16), and a generalized linear mixed model will be used for analysis. In the qualitative part of this study, interviews will be conducted with college students with StD from the EIG at T1 to explore their experiences of receiving the intervention, and content analysis will be applied to the data collected. Discussion: Nintendo provides a user-friendly platform for college students with StD to engage in electronic gaming. Limited research has explored the mental health outcomes of interventions using this type of technology in young people with StD. If the exergame program proves to be effective, it could offer a convenient and feasible intervention for further enhancing the psychological well-being of college students. Clinical trial registration: This study was registered in the Chinese Clinical Trial Registry (number: ChiCTR2300068970) on 2nd March 2023.

A foundation model for clinician-centered drug repurposing.

Drug repurposing-identifying new therapeutic uses for approved drugs-is often a serendipitous and opportunistic endeavour to expand the use of drugs for new diseases. The clinical utility of drug-repurposing artificial intelligence (AI) models remains limited because these models focus narrowly on diseases for which some drugs already exist. Here we introduce TxGNN, a graph foundation model for zero-shot drug repurposing, identifying therapeutic candidates even for diseases with limited treatment options or no existing drugs. Trained on a medical knowledge graph, TxGNN uses a graph neural network and metric learning module to rank drugs as potential indications and contraindications for 17,080 diseases. When benchmarked against 8 methods, TxGNN improves prediction accuracy for indications by 49.2% and contraindications by 35.1% under stringent zero-shot evaluation. To facilitate model interpretation, TxGNN's Explainer module offers transparent insights into multi-hop medical knowledge paths that form TxGNN's predictive rationales. Human evaluation of TxGNN's Explainer showed that TxGNN's predictions and explanations perform encouragingly on multiple axes of performance beyond accuracy. Many of TxGNN's new predictions align well with off-label prescriptions that clinicians previously made in a large healthcare system. TxGNN's drug-repurposing predictions are accurate, consistent with off-label drug use, and can be investigated by human experts through multi-hop interpretable rationales.

The epigenetic mechanism of metabolic risk in bipolar disorder.

Bipolar disorder (BD) is a complex and severe mental illness that causes significant suffering to patients. In addition to the burden of depressive and manic symptoms, patients with BD are at an increased risk for metabolic syndrome (MetS). MetS includes factors associated with an increased risk of atherosclerotic cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM), which may increase the mortality rate of patients with BD. Several studies have suggested a link between BD and MetS, which may be explained at an epigenetic level. We have focused on epigenetic mechanisms to review the causes of metabolic risk in BD.

A foundation model for clinician-centered drug repurposing.

Drug repurposing - identifying new therapeutic uses for approved drugs - is often serendipitous and opportunistic, expanding the use of drugs for new diseases. The clinical utility of drug repurposing AI models remains limited because the models focus narrowly on diseases for which some drugs already exist. Here, we introduce TXGNN, a graph foundation model for zero-shot drug repurposing, identifying therapeutic candidates even for diseases with limited treatment options or no existing drugs. Trained on a medical knowledge graph, TXGNN utilizes a graph neural network and metric-learning module to rank drugs as potential indications and contraindications across 17,080 diseases. When benchmarked against eight methods, TXGNN improves prediction accuracy for indications by 49.2% and contraindications by 35.1% under stringent zero-shot evaluation. To facilitate model interpretation, TXGNN's Explainer module offers transparent insights into multi-hop medical knowledge paths that form TXGNN's predictive rationales. Human evaluation of TXGNN's Explainer showed that TXGNN's predictions and explanations perform encouragingly on multiple axes of performance beyond accuracy. Many of TxGNN's novel predictions align with off-label prescriptions clinicians make in a large healthcare system. TXGNN's drug repurposing predictions are accurate, consistent with off-label drug use, and can be investigated by human experts through multi-hop interpretable rationales.

A nomogram for individualized prediction of mild cognitive impairment in patients with subjective cognitive decline during physical examinations: a cross-sectional study.

Background and objectives: To develop a nomogram for mild cognitive impairment (MCI) in patients with subjective cognitive decline (SCD) undergoing physical examinations in China. Methods: We enrolled 370 patients undergoing physical examinations at the Medical Center of the First Hospital of Jilin University, Jilin Province, China, from October 2022 to March 2023. Of the participants, 256 were placed in the SCD group, and 74 were placed in the MCI group. The population was randomly divided into a training set and a validation set at a 7:3 ratio. A least absolute shrinkage and selection operator (LASSO) regression model was applied to optimize feature selection for the model. Multivariable logistic regression analysis was applied to construct a predictive model. The performance and clinical utility of the nomogram were determined using Harrell's concordance index, calibration curves, and decision curve analysis (DCA). Results: Cognitive reserve (CR), age, and a family history of hypertension were associated with the occurrence of MCI. The predictive nomogram showed satisfactory performance, with a concordance index of 0.755 (95% CI: 0.681-0.830) in internal verification. The Hosmer-Lemeshow test results suggested that the model exhibited good fit (p = 0.824). In addition, DCA demonstrated that the predictive nomogram had a good clinical net benefit. Discussion: We developed a simple nomogram that could help secondary preventive health care workers to identify elderly individuals with SCD at high risk of MCI during physical examinations to enable early intervention.

TDC-2: Multimodal Foundation for Therapeutic Science.

Therapeutics Data Commons (tdcommons.ai) is an open science initiative with unified datasets, AI models, and benchmarks to support research across therapeutic modalities and drug discovery and development stages. The Commons 2.0 (TDC-2) is a comprehensive overhaul of Therapeutic Data Commons to catalyze research in multimodal models for drug discovery by unifying single-cell biology of diseases, biochemistry of molecules, and effects of drugs through multimodal datasets, AI-powered API endpoints, new multimodal tasks and model frameworks, and comprehensive benchmarks. TDC-2 introduces over 1,000 multimodal datasets spanning approximately 85 million cells, pre-calculated embeddings from 5 state-of-the-art single-cell models, and a biomedical knowledge graph. TDC-2 drastically expands the coverage of ML tasks across therapeutic pipelines and 10+ new modalities, spanning but not limited to single-cell gene expression data, clinical trial data, peptide sequence data, peptidomimetics protein-peptide interaction data regarding newly discovered ligands derived from AS-MS spectroscopy, novel 3D structural data for proteins, and cell-type-specific protein-protein interaction networks at single-cell resolution. TDC-2 introduces multimodal data access under an API-first design using the model-view-controller paradigm. TDC-2 introduces 7 novel ML tasks with fine-grained biological contexts: contextualized drug-target identification, single-cell chemical/genetic perturbation response prediction, protein-peptide binding affinity prediction task, and clinical trial outcome prediction task, which introduce antigen-processing-pathway-specific, cell-type-specific, peptide-specific, and patient-specific biological contexts. TDC-2 also releases benchmarks evaluating 15+ state-of-the-art models across 5+ new learning tasks evaluating models on diverse biological contexts and sampling approaches. Among these, TDC-2 provides the first benchmark for context-specific learning. TDC-2, to our knowledge, is also the first to introduce a protein-peptide binding interaction benchmark.

Advances in preparation and engineering of plant-derived extracellular vesicles for nutrition intervention.

Plant-derived extracellular vesicles (PLEVs), as a type of naturally occurring lipid bilayer membrane structure, represent an emerging delivery vehicle with immense potential due to their ability to encapsulate hydrophobic and hydrophilic compounds, shield them from external environmental stresses, control release, exhibit biocompatibility, and demonstrate biodegradability. This comprehensive review analyzes engineering preparation strategies for natural vesicles, focusing on PLEVs and their purification and surface engineering. Furthermore, it encompasses the latest advancements in utilizing PLEVs to transport active components, serving as a nanotherapeutic system. The prospects and potential development of PLEVs are also discussed. It is anticipated that this work will not only address existing knowledge gaps concerning PLEVs but also provide valuable guidance for researchers in the fields of food science and biomedical studies, stimulating novel breakthroughs in plant-based therapeutic options.

Microfluidic Strategies for Encapsulation, Protection, and Controlled Delivery of Probiotics.

Probiotics are indispensable for maintaining the structure of gut microbiota and promoting human health, yet their survivability is frequently compromised by environmental stressors such as temperature fluctuations, pH variations, and mechanical agitation. In response to these challenges, microfluidic technology emerges as a promising avenue. This comprehensive review delves into the utilization of microfluidic technology for the encapsulation and delivery of probiotics within the gastrointestinal tract, with a focus on mitigating obstacles associated with probiotic viability. Initially, it elucidates the design and application of microfluidic devices, providing a precise platform for probiotic encapsulation. Moreover, it scrutinizes the utilization of carriers fabricated through microfluidic devices, including emulsions, microspheres, gels, and nanofibers, with the intent of bolstering probiotic stability. Subsequently, the review assesses the efficacy of encapsulation methodologies through in vitro gastrointestinal simulations and in vivo experimentation, underscoring the potential of microfluidic technology in amplifying probiotic delivery efficiency and health outcomes. In sum, microfluidic technology represents a pioneering approach to probiotic stabilization, offering avenues to cater to consumer preferences for a diverse array of functional food options.

From Pixels to Principles: A Decade of Progress and Landscape in Trustworthy Computer Vision.

The rapid development of computer vision technologies and applications has brought forth a range of social and ethical challenges. Due to the unique characteristics of visual technology in terms of data modalities and application scenarios, computer vision poses specific ethical issues. However, the majority of existing literature either addresses artificial intelligence as a whole or pays particular attention to natural language processing, leaving a gap in specialized research on ethical issues and systematic solutions in the field of computer vision. This paper utilizes bibliometrics and text-mining techniques to quantitatively analyze papers from prominent academic conferences in computer vision over the past decade. It first reveals the developing trends and specific distribution of attention regarding trustworthy aspects in the computer vision field, as well as the inherent connections between ethical dimensions and different stages of visual model development. A life-cycle framework regarding trustworthy computer vision is then presented by making the relevant trustworthy issues, the operation pipeline of AI models, and viable technical solutions interconnected, providing researchers and policymakers with references and guidance for achieving trustworthy CV. Finally, it discusses particular motivations for conducting trustworthy practices and underscores the consistency and ambivalence among various trustworthy principles and technical attributes.

Effectiveness of integrated care for older adults-based interventions on depressive symptoms: A systematic review and meta-analysis.

AIM: To pool existing studies to assess the overall effectiveness of integrated care for older adults (ICOPE)-based interventions in improving depressive symptoms in older adults. DESIGN: A systematic review and meta-analysis. DATA SOURCES: Ten databases were systematically searched from inception to 15 July 2023 and the search was last updated on 2 September 2023. METHODS: Standardized mean difference (SMD) was calculated using random effects models. RoB 2 and GRADEpro GDT were used to assess the methodological quality and confidence in the cumulative evidence. Funnel plots, egger's test and begg's test were used to analyse publication bias. Sensitivity, subgroup and meta-regression analyses were performed to explore potential sources of heterogeneity. RESULTS: The results of 18 studies showed ICOPE-based interventions had a significant effect on improving depressive symptoms (SMD = -.84; 95% CI, -1.20 to -.3647; p < .001; 18 RCTs, 5010 participants; very low-quality evidence). Subgroup analysis showed the intervention group was characterized by mean age (70-80 years old), intervention duration between 6 to 12 months, gender (female <50%), non-frail older adults, depressed older adults and mixed integration appeared to be more effective. Sensitivity analysis found the results to be robust. CONCLUSION: ICOPE-based interventions may be a potentially effective alternative approach to reduce depressive symptoms in the older adults. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Healthcare professionals are expected to use ICOPE as one of the interventions for depressive symptoms in older adults, and this ICOPE could provide more comprehensive care services for older adults to reduce depressive symptoms. IMPACT: ICOPE-based interventions may be a potentially effective alternative approach to reduce depressive symptoms in the older adults. ICOPE-based interventions had a significant effect on reducing depressive symptoms in the older adults. The intervention group characterized by mean age of older adults, intervention duration, gender ratio, health condition and integration types may influence the effect size. REPORTING METHOD: According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PATIENT OR PUBLIC CONTRIBUTION: No Patient or Public Contribution.

Core-shell nanofibers based on microalgae proteins/alginate complexes for enhancing survivability of probiotics.

In this study, a novel one-step coaxial electrospinning process is employed to fabricate shell-core structure fibers choosing Chlorella pyrenoidosa proteins (CP) as the core material. These nanofibers, serving as the wall material for probiotic encapsulation, aimed to enhance the stability and antioxidant activity of probiotics in food processing, storage, and gastrointestinal environments under sensitive conditions. Morphological analysis was used to explore the beads-on-a-string morphology and core-shell structure of the electrospun fibers. Probiotics were successfully encapsulated within the fibers (7.97 log CFU/g), exhibiting a well-oriented structure along the distributed fibers. Compared to free probiotics and uniaxial fibers loaded with probiotics, encapsulation within microalgae proteins/alginate core-shell structure nanofibers significantly enhanced the probiotic cells' tolerance to simulated gastrointestinal conditions (p < 0.05). Thermal analysis indicated that microalgae proteins/alginate core-shell structure nanofibers displayed superior thermal stability compared to uniaxial fibers. The introduction of CP resulted in a 50 % increase in the antioxidant capacity of probiotics-loaded microalgae proteins/alginate nanofibers compared to uniaxial alginate nanofibers, with minimal loss of viability (0.8 log CFU/g) after 28 days of storage at 4 °C. In summary, this dual-layer carrier holds immense potential in probiotic encapsulation and enhancing their resistance to harsh conditions.

Predicting the next sentence (not word) in large language models: What model-brain alignment tells us about discourse comprehension.

Current large language models (LLMs) rely on word prediction as their backbone pretraining task. Although word prediction is an important mechanism underlying language processing, human language comprehension occurs at multiple levels, involving the integration of words and sentences to achieve a full understanding of discourse. This study models language comprehension by using the next sentence prediction (NSP) task to investigate mechanisms of discourse-level comprehension. We show that NSP pretraining enhanced a model's alignment with brain data especially in the right hemisphere and in the multiple demand network, highlighting the contributions of nonclassical language regions to high-level language understanding. Our results also suggest that NSP can enable the model to better capture human comprehension performance and to better encode contextual information. Our study demonstrates that the inclusion of diverse learning objectives in a model leads to more human-like representations, and investigating the neurocognitive plausibility of pretraining tasks in LLMs can shed light on outstanding questions in language neuroscience.

High Performance As-Cast Organic Solar Cells Enabled by a Refined Double-Fibril Network Morphology and Improved Dielectric Constant of Active Layer.

High performance organic solar cells (OSCs) are usually realized by using post-treatment and/or additive, which can induce the formation of metastable morphology, leading to unfavorable device stability. In terms of the industrial production, the development of high efficiency as-cast OSCs is crucially important, but it remains a great challenge to obtain appropriate active layer morphology and high power conversion efficiency (PCE). Here, efficient as-cast OSCs are constructed via introducing a new polymer acceptor PY-TPT with a high dielectric constant into the D18:L8-BO blend to form a double-fibril network morphology. Besides, the incorporation of PY-TPT enables an enhanced dielectric constant and lower exciton binding energy of active layer. Therefore, efficient exciton dissociation and charge transport are realized in D18:L8-BO:PY-TPT-based device, affording a record-high PCE of 18.60% and excellent photostability in absence of post-treatment. Moreover, green solvent-processed devices, thick-film (300 nm) devices, and module (16.60 cm2) are fabricated, which show PCEs of 17.45%, 17.54%, and 13.84%, respectively. This work brings new insight into the construction of efficient as-cast devices, pushing forward the practical application of OSCs.

Hollow UiO-66-NH2 Encapsulated Pd Catalysts for Highly Selective Hydrogenation of Furfural to Furfuryl Alcohol.

The selective hydrogenation of furfural (FFA) to furfuryl alcohol (FA) is regarded as attractive transformation to achieve the sustainable synthesis of value-added chemicals from biomass resources. However, the conventional supported catalysts are significantly restricted by their narrow pore size, ununiform dispersion and easy leaching or aggregation of catalytic sites. Herein, we designed hollow UiO-66-NH2 as the support to encapsulate Pd nanoparticles (Pd@H-UiO-66-NH2) to achieve the highly active and selective conversion of FFA to FA. Benefiting from the void-confinement effect and substrate enrichment of hollow structure, as well as the surface wrinkles, the as-prepared catalyst Pd@H-UiO-66-NH2 exhibited 96.8 % conversion of FFA with satisfactory selectivity reaching up to 92.4 % at 80 °C, 0.5 MPa H2 in isopropanol solvent within 6 h. More importantly, as-prepared Pd@H-UiO-66-NH2 catalyst exhibited excellent long-term stability, as well as good universality toward a series of hydrogenation of unsaturated hydrocarbons.

The effects of visual art therapy on adults with depressive symptoms: A systematic review and meta-analysis.

Depression constitutes a pervasive global mental health concern and stands as a principal determinant of elevated suicide rates worldwide. Recent empirical investigations have showcased the significant potential of visual art therapy (VAT) in ameliorating symptoms among individuals with depression. Nevertheless, specific studies have yielded findings marked by inconclusiveness, underscoring the imperative need for further research to comprehensively establish its efficacy. This study is a systematic review and meta-analysis of extant research, to ascertain the efficacy and effect size of VAT as an intervention for adults with depressive symptoms. A comprehensive search was conducted across 10 databases. The search encompassed articles published from the inception of these databases up until October 18, 2023. Two researchers screened the literature in accordance with inclusion and exclusion criteria and performed a thorough quality assessment. The original data and the data obtained from the literature were extracted for further analysis. The statistical analysis of the data was performed using Stata 17.0 software. fifteen studies were included, encompassing a total of 932 participants. The outcomes of meta-analysis unveiled a statistically significant effect of VAT in diminishing depressive symptoms among adults (SMD = -0.73; 95% CI, -1.07 to -0.39; p < 0.001; 15 randomised controlled trials (RCTs); low-quality evidence). The subgroup analysis indicated that VAT exhibited heightened effectiveness among adults below 65 years of age, with interventions lasting ≤12 weeks demonstrating superior efficacy. Additionally, sensitivity analysis underscored the robustness and reliability of the findings. VAT appears to alleviate depressive symptoms among adults. Existing research indicates that the effectiveness of VAT is influenced by factors, such as intervention population characteristics and intervention duration. However, to comprehensively probe the efficacy of VAT, future studies should strive for larger sample sizes, multicentre collaborations, and long-term follow-ups.

Generation and characterization of monoclonal antibodies against pathologically phosphorylated TDP-43.

Inclusions containing TAR DNA binding protein 43 (TDP-43) are a pathological hallmark of frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). One of the disease-specific features of TDP-43 inclusions is the aberrant phosphorylation of TDP-43 at serines 409/410 (pS409/410). Here, we developed rabbit monoclonal antibodies (mAbs) that specifically detect pS409/410-TDP-43 in multiple model systems and FTD/ALS patient samples. Specifically, we identified three mAbs (26H10, 2E9 and 23A1) from spleen B cell clones that exhibit high specificity and sensitivity to pS409/410-TDP-43 peptides in an ELISA assay. Biochemical analyses revealed that pS409/410 of recombinant TDP-43 and of exogenous 25 kDa TDP-43 C-terminal fragments in cultured HEK293T cells are detected by all three mAbs. Moreover, the mAbs detect pS409/410-positive TDP-43 inclusions in the brains of FTD/ALS patients and mouse models of TDP-43 proteinopathy by immunohistochemistry. Our findings indicate that these mAbs are a valuable resource for investigating TDP-43 pathology both in vitro and in vivo.

Mucoadhesive liposomal delivery system synergizing anti-inflammation and anti-oxidation for enhanced treatment against dry eye disease.

Dry eye disease (DED) is a common and frequent ocular surface disease worldwide, which can cause severe ocular surface discomfort and blurred vision. Inflammation and reactive oxygen species (ROS) play decisive roles in the development of DED. However, existing treatments usually focus on anti-inflammation while ignore the role of ROS in DED. Ever worse, the clinical preparations are easily cleared by nasolacrimal ducts, resulting in poor therapeutic effect. To circumvent these obstacles, here we designed a phenylboronic acid (PBA) modified liposome co-loading immunosuppressant cyclosporin A (CsA) and antioxidant crocin (Cro). The CsA/Cro PBA Lip achieved mucoadhesion through the formation of covalent bonds between PBA and the sialic acid residues on mucin, and consequently improved the retention of drugs on the ocular surface. By inhibiting ROS production and blocking NF-κB inflammatory pathway, CsA/Cro PBA Lip successfully promoted the healing of damaged corneal epithelium, eventually achieving the goal of relieving DED. CsA/Cro PBA Lip is proven a simple yet effective dual-drug delivery system, exhibiting superior antioxidant and anti-inflammatory effects both in vitro and in vivo. This approach holds great potential in the clinical treatment of DED and other related mucosal inflammations.