Micropillars in Biomechanics: Role in Guiding Mesenchymal Stem Cells Differentiation and Bone Regeneration

Abstract Efficient bone repair relies on biomaterials that exhibit superior biocompatibility and enhanced osteogenic capabilities. In recent, micropatterning technology has emerged as a robust strategy for directing stem cell differentiation, offering a notable shift from traditional chemical induction or genetic modification methods by providing environmental cues to regulate cellular behavior. Among the various types of microstructures, micropillars have garnered significant attention due to their adjustable properties and high‐throughput experimental capabilities. These structures play a pivotal role in governing nuclear deformation and deciphering cellular responses to biomechanical cues. In this comprehensive review, the influence of micropillars on mesenchymal stem cells (MSCs), drawing upon two decades of research findings is critically assessed. The study comprehensively evaluates how micropillar substrates impact crucial cellular processes such as deformation, migration, adhesion, and ultimately, MSC fate determination toward an osteogenic lineage. By synthesizing and analyzing past studies, the potential regulatory mechanisms through which micropillars modulate MSC behavior are aimed to be elucidated. Furthermore, the utility of micropillars as a cutting‐edge biomechanical detection tool and platform for investigating cellular behaviors is explored. By projecting future applications, the review highlights the growing significance of micropillars in the dynamic field of biomechanics, underscoring their transformative potential in tissue engineering and regenerative medicine

Comparative Secretome Analysis of Magnaporthe oryzae Identified Proteins Involved in Virulence and Cell Wall Integrity

Plant fungal pathogens secrete numerous proteins into the apoplast at the plant–fungus contact sites to facilitate colonization. However, only a few secretory proteins were functionally characterized in Magnaporthe oryzae, the fungal pathogen causing rice blast disease worldwide. Asparagine-linked glycosylation 3 (Alg3) is an α-1,3-mannosyltransferase functioning in the N-glycan synthesis of N-glycosylated secretory proteins. Fungal pathogenicity and cell wall integrity are impaired in Δalg3 mutants, but the secreted proteins affected in Δalg3 mutants are largely unknown. In this study, we compared the secretomes of the wild-type strain and the Δalg3 mutant and identified 51 proteins that require Alg3 for proper secretion. These proteins were predicted to be involved in metabolic processes, interspecies interactions, cell wall organization, and response to chemicals. Nine proteins were selected for further validation. We found that these proteins were localized at the apoplastic region surrounding the fungal infection hyphae. Moreover, the N-glycosylation of these proteins was significantly changed in the Δalg3 mutant, leading to the decreased protein secretion and abnormal protein localization. Furthermore, we tested the biological functions of two genes, INV1 (encoding invertase 1, a secreted invertase) and AMCase (encoding acid mammalian chinitase, a secreted chitinase). The fungal virulence was significantly reduced, and the cell wall integrity was altered in the Δinv1 and Δamcase mutant strains. Moreover, the N-glycosylation was essential for the function and secretion of AMCase. Taken together, our study provides new insight into the role of N-glycosylated secretory proteins in fungal virulence and cell wall integrity

A single strain of Bacteroides fragilis protects gut integrity and reduces GVHD

Graft-versus-host disease (GVHD) is a pathological process caused by an exaggerated donor lymphocyte response to host antigens after allogeneic hematopoietic cell transplantation (allo-HCT). Donor T cells undergo extensive clonal expansion and differentiation, which culminate in damage to recipient target organs. Damage to the gastrointestinal tract is a main contributor to morbidity and mortality. The loss of diversity among intestinal bacteria caused by pretransplant conditioning regimens leads to an outgrowth of opportunistic pathogens and exacerbated GVHD after allo-HCT. Using murine models of allo-HCT, we found that an increase of Bacteroides in the intestinal microbiota of the recipients was associated with reduced GVHD in mice given fecal microbial transplantation. Administration of Bacteroides fragilis through oral gavage increased gut microbiota diversity and beneficial commensal bacteria and significantly ameliorated acute and chronic GVHD development. Preservation of gut integrity following B. fragilis exposure was likely attributed to increased short chain fatty acids, IL-22, and regulatory T cells, which in turn improved gut tight junction integrity and reduced inflammatory cytokine production of pathogenic T cells. The current study provides a proof of concept that a single strain of commensal bacteria can be a safe and effective means to protect gut integrity and ameliorate GVHD after allo-HCT

KoLA: Carefully Benchmarking World Knowledge of Large Language Models

The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations. Rather than merely exploring the breadth of LLM abilities, we believe meticulous and thoughtful designs are essential to thorough, unbiased, and applicable evaluations. Given the importance of world knowledge to LLMs, we construct a Knowledge-oriented LLM Assessment benchmark (KoLA), in which we carefully design three crucial factors: (1) For ability modeling, we mimic human cognition to form a four-level taxonomy of knowledge-related abilities, covering $19$ tasks. (2) For data, to ensure fair comparisons, we use both Wikipedia, a corpus prevalently pre-trained by LLMs, along with continuously collected emerging corpora, aiming to evaluate the capacity to handle unseen data and evolving knowledge. (3) For evaluation criteria, we adopt a contrastive system, including overall standard scores for better numerical comparability across tasks and models and a unique self-contrast metric for automatically evaluating knowledge hallucination. We evaluate $21$ open-source and commercial LLMs and obtain some intriguing findings. The KoLA dataset and open-participation leaderboard are publicly released at https://kola.xlore.cn and will be continuously updated to provide references for developing LLMs and knowledge-related systems