Sphingosine-1-phosphate promotes the differentiation of human umbilical cord mesenchymal stem cells into cardiomyocytes under the designated culturing conditions

<p>Abstract</p> <p>Background</p> <p>It is of growing interest to develop novel approaches to initiate differentiation of mesenchymal stem cells (MSCs) into cardiomyocytes. The purpose of this investigation was to determine if Sphingosine-1-phosphate (S1P), a native circulating bioactive lipid metabolite, plays a role in differentiation of human umbilical cord mesenchymal stem cells (HUMSCs) into cardiomyocytes. We also developed an engineered cell sheet from these HUMSCs derived cardiomyocytes by using a temperature-responsive polymer, poly(N-isopropylacrylamide) (PIPAAm) cell sheet technology.</p> <p>Methods</p> <p>Cardiomyogenic differentiation of HUMSCs was performed by culturing these cells with either designated cardiomyocytes conditioned medium (CMCM) alone, or with 1 μM S1P; or DMEM with 10% FBS + 1 μM S1P. Cardiomyogenic differentiation was determined by immunocytochemical analysis of expression of cardiomyocyte markers and patch clamping recording of the action potential.</p> <p>Results</p> <p>A cardiomyocyte-like morphology and the expression of α-actinin and myosin heavy chain (MHC) proteins can be observed in both CMCM culturing or CMCM+S1P culturing groups after 5 days' culturing, however, only the cells in CMCM+S1P culture condition present cardiomyocyte-like action potential and voltage gated currents. A new approach was used to form PIPAAm based temperature-responsive culture surfaces and this successfully produced cell sheets from HUMSCs derived cardiomyocytes.</p> <p>Conclusions</p> <p>This study for the first time demonstrates that S1P potentiates differentiation of HUMSCs towards functional cardiomyocytes under the designated culture conditions. Our engineered cell sheets may provide a potential for clinically applicable myocardial tissues should promote cardiac tissue engineering research.</p

WizardLM: Empowering Large Language Models to Follow Complex Instructions

Training large language models (LLM) with open-domain instruction following data brings colossal success. However, manually creating such instruction data is very time-consuming and labor-intensive. Moreover, humans may struggle to produce high-complexity instructions. In this paper, we show an avenue for creating large amounts of instruction data with varying levels of complexity using LLM instead of humans. Starting with an initial set of instructions, we use our proposed Evol-Instruct to rewrite them step by step into more complex instructions. Then, we mix all generated instruction data to fine-tune LLaMA. We call the resulting model WizardLM. Human evaluations on a complexity-balanced test bed show that instructions from Evol-Instruct are superior to human-created ones. By analyzing the human evaluation results of the high complexity part, we demonstrate that outputs from our WizardLM model are preferred to outputs from OpenAI ChatGPT. Even though WizardLM still lags behind ChatGPT in some aspects, our findings suggest that fine-tuning with AI-evolved instructions is a promising direction for enhancing large language models. Our codes and generated data are public at https://github.com/nlpxucan/WizardLMComment: large language model, instruction fine-tun

Synergistic Interplay between Search and Large Language Models for Information Retrieval

Information retrieval (IR) plays a crucial role in locating relevant resources from vast amounts of data, and its applications have evolved from traditional knowledge bases to modern retrieval models (RMs). The emergence of large language models (LLMs) has further revolutionized the IR field by enabling users to interact with search systems in natural languages. In this paper, we explore the advantages and disadvantages of LLMs and RMs, highlighting their respective strengths in understanding user-issued queries and retrieving up-to-date information. To leverage the benefits of both paradigms while circumventing their limitations, we propose InteR, a novel framework that facilitates information refinement through synergy between RMs and LLMs. InteR allows RMs to expand knowledge in queries using LLM-generated knowledge collections and enables LLMs to enhance prompt formulation using retrieved documents. This iterative refinement process augments the inputs of RMs and LLMs, leading to more accurate retrieval. Experiments on large-scale retrieval benchmarks involving web search and low-resource retrieval tasks demonstrate that InteR achieves overall superior zero-shot retrieval performance compared to state-of-the-art methods, even those using relevance judgment. Source code is available at https://github.com/Cyril-JZ/InteRComment: Pre-print. Work in progres

Precise Particle Tracking Against a Complicated Background: Polynomial Fitting with Gaussian Weight

We present a new particle tracking software algorithm designed to accurately track the motion of low-contrast particles against a background with large variations in light levels. The method is based on a polynomial fit of the intensity around each feature point, weighted by a Gaussian function of the distance from the centre, and is especially suitable for tracking endogeneous particles in the cell, imaged with bright field, phase contrast or fluorescence optical microscopy. Furthermore, the method can simultaneously track particles of all different sizes, and allows significant freedom in their shape. The algorithm is evaluated using the quantitative measures of accuracy and precision of previous authors, using simulated images at variable signal-to-noise ratios. To these we add a new test of the error due to a non-uniform background. Finally the tracking of particles in real cell images is demonstrated. The method is made freely available for non-commencial use as a software package with a graphical user-inferface, which can be run within the Matlab programming environment

MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic Segmentation

Ovarian cancer is one of the most harmful gynecological diseases. Detecting ovarian tumors in early stage with computer-aided techniques can efficiently decrease the mortality rate. With the improvement of medical treatment standard, ultrasound images are widely applied in clinical treatment. However, recent notable methods mainly focus on single-modality ultrasound ovarian tumor segmentation or recognition, which means there still lacks researches on exploring the representation capability of multi-modality ultrasound ovarian tumor images. To solve this problem, we propose a Multi-Modality Ovarian Tumor Ultrasound (MMOTU) image dataset containing 1469 2d ultrasound images and 170 contrast enhanced ultrasonography (CEUS) images with pixel-wise and global-wise annotations. Based on MMOTU, we mainly focus on unsupervised cross-domain semantic segmentation task. To solve the domain shift problem, we propose a feature alignment based architecture named Dual-Scheme Domain-Selected Network (DS2Net). Specifically, we first design source-encoder and target-encoder to extract two-style features of source and target images. Then, we propose Domain-Distinct Selected Module (DDSM) and Domain-Universal Selected Module (DUSM) to extract the distinct and universal features in two styles (source-style or target-style). Finally, we fuse these two kinds of features and feed them into the source-decoder and target-decoder to generate final predictions. Extensive comparison experiments and analysis on MMOTU image dataset show that DS2Net can boost the segmentation performance for bidirectional cross-domain adaptation of 2d ultrasound images and CEUS images. Our proposed dataset and code are all available at https://github.com/cv516Buaa/MMOTU_DS2Net.Comment: code: https://github.com/cv516Buaa/MMOTU_DS2Net paper:18 pages, 12 figures, 11 tables, 16 formula

A review of Curcumin and its derivatives as anticancer agents

Cancer is the second leading cause of death in the world and one of the major public health problems. Despite the great advances in cancer therapy, the incidence and mortality rates of cancer remain high. Therefore, the quest for more efficient and less toxic cancer treatment strategies is still at the forefront of current research. Curcumin, the active ingredient of the Curcuma longa plant, has received great attention over the past two decades as an antioxidant, anti-inflammatory, and anticancer agent. In this review, a summary of the medicinal chemistry and pharmacology of curcumin and its derivatives in regard to anticancer activity, their main mechanisms of action, and cellular targets has been provided based on the literature data from the experimental and clinical evaluation of curcumin in cancer cell lines, animal models, and human subjects. In addition, the recent advances in the drug delivery systems for curcumin delivery to cancer cells have been highlighted

Co-adsorption of peptide amphiphile V6K and conventional surfactants SDS and C12TAB at the solid/water interface

Recent research has reported many attractive benefits from short peptide amphiphiles. A practical route for them to enter the real world of applications is through formulation with conventional surfactants. This study reports the co-adsorption of the surfactant-like peptide, V6K, with conventional anionic and cationic surfactants at the solid/water interface. The time-dependant adsorption behaviour was examined using spectroscopic ellipsometry whilst adsorbed layer composition and structural distribution of the components were investigated by neutron reflection with the use of hydrogen/deuterium labelling of the surfactant molecules. Both binary (surfactant/peptide mixtures) and sequential (peptide followed by surfactant) adsorption have been studied. It was found that at the hydrophilic SiO2/water interface, the peptide was able to form a stable, flat, defected bilayer structure however both the structure and adsorbed amount were highly dependent on the initial peptide concentration. This consequently affected surfactant adsorption. In the presence of a pre-adsorbed peptide layer anionic sodium dodecyl sulfate (SDS) could readily co-adsorb at the interface; however, cationic dodecyl trimethyl ammonium bromide (C12TAB) could not co-adsorb due to the same charge character. However on a trimethoxy octyl silane (C8) coated hydrophobic surface, V6K formed a monolayer, and subsequent exposure to cationic and anionic surfactants both led to some co-adsorption at the interface. In binary surfactant/peptide mixtures, it was found that adsorption was dependent on the molar ratio of the surfactant and peptide. For SDS mixtures below molar unity and concentrations below CMC for C12TAB, V6K was able to dominate adsorption at the interface. Above molar unity, no adsorption was detected for SDS/V6K mixtures. In contrast, C12TAB gradually replaced the peptide and became dominant at the interface. These results thus elucidate the adsorption behaviour of V6K, which was found to dominate interfacial adsorption but its exact adsorbed amount and distribution were affected by interfacial hydrophobicity and interactions with conventional surfactants

Solution Behavior and Activity of a Halophilic Esterase under High Salt Concentration

Background: Halophiles are extremophiles that thrive in environments with very high concentrations of salt. Although the salt reliance and physiology of these extremophiles have been widely investigated, the molecular working mechanisms of their enzymes under salty conditions have been little explored. Methodology/Principal Findings: A halophilic esterolytic enzyme LipC derived from archeaon Haloarcula marismortui was overexpressed from Escherichia coli BL21. The purified enzyme showed a range of hydrolytic activity towards the substrates of p-nitrophenyl esters with different alkyl chains (n = 2−16), with the highest activity being observed for p-nitrophenyl acetate, consistent with the basic character of an esterase. The optimal esterase activities were found to be at pH 9.5 and [NaCl] = 3.4 M or [KCl] = 3.0 M and at around 45°C. Interestingly, the hydrolysis activity showed a clear reversibility against changes in salt concentration. At the ambient temperature of 22°C, enzyme systems working under the optimal salt concentrations were very stable against time. Increase in temperature increased the activity but reduced its stability. Circular dichroism (CD), dynamic light scattering (DLS) and small angle neutron scattering (SANS) were deployed to determine the physical states of LipC in solution. As the salt concentration increased, DLS revealed substantial increase in aggregate sizes, but CD measurements revealed the maximal retention of the α-helical structure at the salt concentration matching the optimal activity. These observations were supported by SANS analysis that revealed the highest proportion of unimers and dimers around the optimal salt concentration, although the coexistent larger aggregates showed a trend of increasing size with salt concentration, consistent with the DLS data. Conclusions/Significance: The solution α-helical structure and activity relation also matched the highest proportion of enzyme unimers and dimers. Given that all the solutions studied were structurally inhomogeneous, it is important for future work to understand how the LipC's solution aggregation affected its activity

Latherin: A Surfactant Protein of Horse Sweat and Saliva

Horses are unusual in producing protein-rich sweat for thermoregulation, a major component of which is latherin, a highly surface-active, non-glycosylated protein. The amino acid sequence of latherin, determined from cDNA analysis, is highly conserved across four geographically dispersed equid species (horse, zebra, onager, ass), and is similar to a family of proteins only found previously in the oral cavity and associated tissues of mammals. Latherin produces a significant reduction in water surface tension at low concentrations (≤1 mg ml−1), and therefore probably acts as a wetting agent to facilitate evaporative cooling through a waterproofed pelt. Neutron reflection experiments indicate that this detergent-like activity is associated with the formation of a dense protein layer, about 10 Å thick, at the air-water interface. However, biophysical characterization (circular dichroism, differential scanning calorimetry) in solution shows that latherin behaves like a typical globular protein, although with unusual intrinsic fluorescence characteristics, suggesting that significant conformational change or unfolding of the protein is required for assembly of the air-water interfacial layer. RT-PCR screening revealed latherin transcripts in horse skin and salivary gland but in no other tissues. Recombinant latherin produced in bacteria was also found to be the target of IgE antibody from horse-allergic subjects. Equids therefore may have adapted an oral/salivary mucosal protein for two purposes peculiar to their lifestyle, namely their need for rapid and efficient heat dissipation and their specialisation for masticating and processing large quantities of dry food material