Beyond stiffness: Multiscale viscoelastic features as biomechanical markers for assessing cell types and states.

Cell mechanics are pivotal in regulating cellular activities, diseases progression, and cancer development. However, the understanding of how cellular viscoelastic properties vary in physiological and pathological stimuli remains scarce. Here, we develop a hybrid self-similar hierarchical theory-microrheology approach to accurately and efficiently characterize cellular viscoelasticity. Focusing on two key cell types associated with livers fibrosis-the capillarized liver sinusoidal endothelial cells and activated hepatic stellate cells-we uncover a universal two-stage power-law rheology characterized by two distinct exponents, αshort and αlong. The mechanical profiles derived from both exponents exhibit significant potential for discriminating among diverse cells. This finding suggests a potential common dynamic creep characteristic across biological systems, extending our earlier observations in soft tissues. Using a tailored hierarchical model for cellular mechanical structures, we discern significant variations in the viscoelastic properties and their distribution profiles across different cell types and states from the cytoplasm (elastic stiffness E1 and viscosity η), to a single cytoskeleton fiber (elastic stiffness E2), and then to the cell level (transverse expansion stiffness E3). Importantly, we construct a logistic-regression-based machine-learning model using the dynamic parameters that outperforms conventional cell-stiffness-based classifiers in assessing cell states, achieving an area under the curve of 97% vs. 78%. Our findings not only advance a robust framework for monitoring intricate cell dynamics but also highlight the crucial role of cellular viscoelasticity in discerning cell states across a spectrum of liver diseases and prognosis, offering new avenues for developing diagnostic and therapeutic strategies based on cellular viscoelasticity.

Mechanical guidance to self-organization and pattern formation of stem cells.

The self-organization of stem cells (SCs) constitutes the fundamental basis of the development of biological organs and structures. SC-driven patterns are essential for tissue engineering, yet unguided SCs tend to form chaotic patterns, impeding progress in biomedical engineering. Here, we show that simple geometric constraints can be used as an effective mechanical modulation approach that promotes the development of controlled self-organization and pattern formation of SCs. Using the applied SC guidance with geometric constraints, we experimentally uncover a remarkable deviation in cell aggregate orientation from a random direction to a specific orientation. Subsequently, we propose a dynamic mechanical framework, including cells, the extracellular matrix (ECM), and the culture environment, to characterize the specific orientation deflection of guided cell aggregates relative to initial geometric constraints, which agrees well with experimental observation. Based on this framework, we further devise various theoretical strategies to realize complex biological patterns, such as radial and concentric structures. Our study highlights the key role of mechanical factors and geometric constraints in governing SCs' self-organization. These findings yield critical insights into the regulation of SC-driven pattern formation and hold great promise for advancements in tissue engineering and bioactive material design for regenerative application.

Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis.

BACKGROUND: Machine learning is a potentially effective method for predicting the response to platinum-based treatment for ovarian cancer. However, the predictive performance of various machine learning methods and variables is still a matter of controversy and debate. OBJECTIVE: This study aims to systematically review relevant literature on the predictive value of machine learning for platinum-based chemotherapy responses in patients with ovarian cancer. METHODS: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically searched the PubMed, Embase, Web of Science, and Cochrane databases for relevant studies on predictive models for platinum-based therapies for the treatment of ovarian cancer published before April 26, 2023. The Prediction Model Risk of Bias Assessment tool was used to evaluate the risk of bias in the included articles. Concordance index (C-index), sensitivity, and specificity were used to evaluate the performance of the prediction models to investigate the predictive value of machine learning for platinum chemotherapy responses in patients with ovarian cancer. RESULTS: A total of 1749 articles were examined, and 19 of them involving 39 models were eligible for this study. The most commonly used modeling methods were logistic regression (16/39, 41%), Extreme Gradient Boosting (4/39, 10%), and support vector machine (4/39, 10%). The training cohort reported C-index in 39 predictive models, with a pooled value of 0.806; the validation cohort reported C-index in 12 predictive models, with a pooled value of 0.831. Support vector machine performed well in both the training and validation cohorts, with a C-index of 0.942 and 0.879, respectively. The pooled sensitivity was 0.890, and the pooled specificity was 0.790 in the training cohort. CONCLUSIONS: Machine learning can effectively predict how patients with ovarian cancer respond to platinum-based chemotherapy and may provide a reference for the development or updating of subsequent scoring systems.

Enhancing liver fibrosis diagnosis and treatment assessment: a novel biomechanical markers-based machine learning approach.

Accurate diagnosis and treatment assessment of liver fibrosis face significant challenges, including inherent limitations in current techniques like sampling errors and inter-observer variability. Addressing this, our study introduces a novel machine learning (ML) framework, which integrates light gradient boosting machine and multivariate imputation by chained equations to enhance liver status assessment using biomechanical markers. Building upon our previously established multiscale mechanical characteristics in fibrotic and treated livers, this framework employs Gaussian Bayesian optimization for post-imputation, significantly improving classification performance. Our findings indicate a marked increase in the precision of liver fibrosis diagnosis and provide a novel, quantitative approach for assessing fibrosis treatment. This innovative combination of multiscale biomechanical markers with advanced ML algorithms represents a transformative step in liver disease diagnostics and treatment evaluation, with potential implications for other areas in medical diagnostics.

Scaling-law mechanical marker for liver fibrosis diagnosis and drug screening through machine learning.

Studies of cell and tissue mechanics have shown that significant changes in cell and tissue mechanics during lesions and cancers are observed, which provides new mechanical markers for disease diagnosis based on machine learning. However, due to the lack of effective mechanic markers, only elastic modulus and iconographic features are currently used as markers, which greatly limits the application of cell and tissue mechanics in disease diagnosis. Here, we develop a liver pathological state classifier through a support vector machine method, based on high dimensional viscoelastic mechanical data. Accurate diagnosis and grading of hepatic fibrosis facilitates early detection and treatment and may provide an assessment tool for drug development. To this end, we used the viscoelastic parameters obtained from the analysis of creep responses of liver tissues by a self-similar hierarchical model and built a liver state classifier based on machine learning. Using this classifier, we implemented a fast classification of healthy, diseased, and mesenchymal stem cells (MSCs)-treated fibrotic live tissues, and our results showed that the classification accuracy of healthy and diseased livers can reach 0.99, and the classification accuracy of the three liver tissues mixed also reached 0.82. Finally, we provide screening methods for markers in the context of massive data as well as high-dimensional viscoelastic variables based on feature ablation for drug development and accurate grading of liver fibrosis. We propose a novel classifier that uses the dynamical mechanical variables as input markers, which can identify healthy, diseased, and post-treatment liver tissues.

Maternal Malic Acid May Ameliorate Oxidative Stress and Inflammation in Sows through Modulating Gut Microbiota and Host Metabolic Profiles during Late Pregnancy.

Sows suffer oxidative stress and inflammation induced by metabolic burden during late pregnancy, which negatively regulates reproductive and lactating performances. We previously found that L-malic acid (MA) alleviated oxidative stress and inflammation and improved reproductive performances in sows. However, the mechanism underlying the MA's positive effects remains unexplored. Here, twenty Large White × Landrace sows with similar parity were randomly divided into two groups and fed with a basal diet or a diet supplemented with 2% L-malic acid complex from day 85 of gestation to delivery. The gut microbiome, fecal short-chain fatty acids, and untargeted serum metabolome were determined. Results showed that Firmicutes, Bacteroidota, and Spirochaetota were the top abundant phyla identified in late pregnancy for sows. Maternal MA supplementation modulated the composition but not the richness and diversity of gut microbiota during late pregnancy. Correlation analysis between gut microbiota and antioxidant capacity (or inflammation indicators) revealed that unclassified_f_Ruminococcaceae, unclassified_f_Lachnospiraceae, UCG-002, norank_f_norank_o_RF3, and Lactobacillus might play a role in anti-oxidation, and Lachnospiraceae_XPB1014_group, Lachnospiraceae_NK4A136_group, UCG-002, unclassified_f_Ruminococcaceae, Candidatus_Soleaferrea, norank_f_UCG-010, norank_f_norank_o_RF39, and unclassified_f_Lachnospiraceae might be involved in the anti-inflammatory effect. The improved antioxidant and inflammation status induced by MA might be independent of short chain fatty acid changes. In addition, untargeted metabolomics analysis exhibited different metabolic landscapes of sows in the MA group from in the control group and revealed the contribution of modified amino acid and lipid metabolism to the improved antioxidant capacity and inflammation status. Notably, correlation results of gut microbiota and serum metabolites, as well as serum metabolites and antioxidant capacity (or inflammation indicators), demonstrated that differential metabolism was highly related to the fecal microorganisms and antioxidant or inflammation indicators. Collectively, these data demonstrated that a maternal dietary supply of MA can ameliorate oxidative stress and inflammation in sows through modulating gut microbiota and host metabolic profiles during late pregnancy.

Heterogeneous stiffness of the bone marrow microenvironment regulates the fate decision of haematopoietic stem and progenitor cells.

The bone marrow (BM) niches are the complex microenvironments that surround cells, providing various external stimuli to regulate a range of haematopoietic stem cell (HSC) behaviours. Recently, it has been proposed that the fate decision of HSCs is often correlated with significantly altered biophysical signals of BM niches. To thoroughly elucidate the effect of mechanical microenvironments on cell fates, we constructed 2D and 3D cell culture hydrogels using polyacrylamide to replicate the mechanical properties of heterogeneous sub-niches, including the inherent rigidity of marrow adipose tissue (2 kPa), perivascular tissue (8 kPa) and endosteum region (35 kPa) in BM. Our observations suggest that HSCs can respond to the mechanical heterogeneity of the BM microenvironment, exhibiting diversity in cell mechanics, haematopoietic pool maintenance and differentiated lineages. Hydrogels with higher stiffness promote the preservation of long-term repopulating HSCs (LT-HSCs), while those with lower stiffness support multi-potent progenitors (MPPs) viability in vitro. Furthermore, we established a comprehensive transcriptional profile of haematopoietic subpopulations to reflect the multipotency of haematopoietic stem and progenitor cells (HSPCs) that are modulated by niche-like stiffness. Our findings demonstrate that HSPCs exhibit completely distinct downstream differentiated preferences within hydrogel systems of varying stiffness. This highlights the crucial role of tissue-specific mechanical properties in HSC lineage decisions, which may provide innovative solutions to clinical challenges.

Potential Plausible Role of Stem Cell for Treating Depressive Disorder: a Retrospective Review.

Depression poses a significant threat to global physical and mental health, impacting around 3.8% of the population with a rising incidence. Current treatment options primarily involve medication and psychological support, yet their effectiveness remains limited, contributing to high relapse rates. There is an urgent need for innovative and more efficacious treatment modalities. Stem cell therapy, a promising avenue in regenerative medicine for a spectrum of neurodegenerative conditions, has recently garnered attention for its potential application in depression. While much of this work remains preclinical, it has demonstrated considerable promise. Identified mechanisms underlying the antidepressant effects of stem cell therapy encompass the stimulation of neurotrophic factors, immune function modulation, and augmented monoamine levels. Nonetheless, these pathways and other undiscovered mechanisms necessitate further investigation. Depression fundamentally manifests as a neurodegenerative disorder. Given stem cell therapy's success in addressing a range of neurodegenerative pathologies, it opens the door to explore its application in depression treatment. This exploration may include repairing damaged nerves directly or indirectly and inhibiting neurotoxicity. Nevertheless, significant challenges must be overcome before stem cell therapies can be applied clinically. Successful resolution of these issues will ultimately determine the feasibility of incorporating stem cell therapies into the clinical landscape. This narrative review provides insights into the progress of research, potential avenues for exploration, and the prevailing challenges in the implementation of stem cell therapy for treatment of depression.