Locationally activated PRP via an injectable dual-network hydrogel for endometrial regeneration.

Enhancing the effectiveness of platelet-rich plasma (PRP) for endometrial regeneration is challenging, due to its limited mechanical properties and burst release of growth factors. Here, we proposed an injectable interpenetrating dual-network hydrogel that can locationally activate PRP within the uterine cavity, sustained release growth factors and further address the insufficient therapeutic efficacy. Locational activation of PRP is achieved using the dual-network hydrogel. The phenylboronic acid (PBA) modified methacrylated hyaluronic acid (HAMA) dispersion chelates Ca2+ by carboxy groups and polyphenol groups, and in situ crosslinked with PRP-loaded polyvinyl alcohol (PVA) dispersion by dynamic borate ester bonds thus establishing the soft hydrogel. Subsequently, in situ photo-crosslinking technology is employed to enhance the mechanical performance of hydrogels by initiating free radical polymerization of carbon-carbon double bonds to form a dense network. The PRP-hydrogel significantly promoted the endometrial cell proliferation, exhibited strong pro-angiogenic effects, and down-regulated the expression of collagen deposition genes by inhibiting the TGF-ß1-SMAD2/3 pathway in vitro. In vivo experiments using a rat intrauterine adhesion (IUA) model showed that the PRP-hydrogel significantly promoted endometrial regeneration and restored uterine functionality. Furthermore, rats treated with the PRP-hydrogel displayed an increase in the number of embryos, litter size, and birth rate, which was similar to normal rats. Overall, this injectable interpenetrating dual-network hydrogel, capable of locational activation of PRP, suggests a new therapeutic approach for endometrial repair.

Injectable "Homing-Like" Bioactive Short-Fibers for Endometrial Repair and Efficient Live Births.

The prevalence of infertility caused by endometrial defects is steadily increasing, posing a significant challenge to women's reproductive health. In this study, injectable "homing-like" bioactive decellularized extracellular matrix short-fibers (DEFs) of porcine skin origin are innovatively designed for endometrial and fertility restoration. The DEFs can effectively bind to endometrial cells through noncovalent dipole interactions and release bioactive growth factors in situ. In vitro, the DEFs effectively attracted endometrial cells through the "homing-like" effect, enabling cell adhesion, spreading, and proliferation on their surface. Furthermore, the DEFs effectively facilitated the proliferation and angiogenesis of human primary endometrial stromal cells (HESCs) and human umbilical vein endothelial cells (HUVECs), and inhibited fibrosis of pretreated HESCs. In vivo, the DEFs significantly accelerated endometrial restoration, angiogenesis, and receptivity. Notably, the deposition of endometrial collagen decreased from 41.19 ± 2.16% to 14.15 ± 1.70% with DEFs treatment. Most importantly, in endometrium-injured rats, the use of DEFs increased the live birth rate from 30% to an impressive 90%, and the number and development of live births close to normal rats. The injectable "homing-like" bioactive DEFs system can achieve efficient live births and intrauterine injection of DEFs provides a new promising clinical strategy for endometrial factor infertility.

Pathological and pharmacological functions of the metabolites of polyunsaturated fatty acids mediated by cyclooxygenases, lipoxygenases, and cytochrome P450s in cancers.

Oxylipins have garnered increasing attention because they were consistently shown to play pathological and/or pharmacological roles in the development of multiple cancers. Oxylipins are the metabolites of polyunsaturated fatty acids via both enzymatic and nonenzymatic pathways. The enzymes mediating the metabolism of PUFAs include but not limited to lipoxygenases (LOXs), cyclooxygenases (COXs), and cytochrome P450s (CYPs) pathways, as well as the down-stream enzymes. Here, we systematically summarized the pleiotropic effects of oxylipins in different cancers through pathological and pharmacological aspects, with specific reference to the enzyme-mediated oxylipins. We discussed the specific roles of oxylipins on cancer onset, growth, invasion, and metastasis, as well as the expression changes in the associated metabolic enzymes and the associated underlying mechanisms. In addition, we also discussed the clinical application and potential of oxylipins and related metabolic enzymes as the targets for cancer prevention and treatment. We found the specific function of most oxylipins in cancers, especially the underlying mechanisms and clinic applications, deserves and needs further investigation. We believe that research on oxylipins will provide not only more therapeutic targets for various cancers but also dietary guidance for both cancer patients and healthy humans.

Metabolomics study reveals increased deoxycholic acid contributes to deoxynivalenol-mediated intestinal barrier injury.

AIMS: Deoxynivalenol (DON), namely vomitoxin, is one of the most prevalent fungal toxins in cereal crops worldwide. However, the underlying toxic mechanisms of DON remain largely unknown. MAIN METHODS: DON exposure-caused changes in the murine plasma metabolome and gut microbiome were investigated by an LC-MS/MS-based nontargeted metabolomics approach and sequencing of 16S rRNA in fecal samples, respectively. Cellular models were then used to validate the findings from the metabolomics study. KEY FINDINGS: DON exposure increased intestinal barrier permeability evidenced by its-mediated decrease in colonic Claudin 5 and E-cadherin, as well as increases in colonic Ifn-γ, Cxcl9, Cxcl10, and Cxcr3. Furthermore, DON exposure resulted in a significant increase in murine plasma levels of deoxycholic acid (DCA). Also, DON exposure led to gut microbiota dysbiosis, which was associated with DON exposure-caused increase in plasma DCA. In addition, we found not only DON but also DCA dose-dependently caused a significant increase in the levels of IFN-γ, CXCL9, CXCL10, and/or CXCR3, as well as a significant decrease in the expression levels of Claudin 5 and/or E-cadherin in the human colonic epithelial cells (NCM460). SIGNIFICANCE: DON-mediated increase in DCA contributes to DON-caused intestinal injury. DCA may be a potential therapeutic target for DON enterotoxicity.

Machine Learning Approaches to Classify Self-Reported Rheumatoid Arthritis Health Scores Using Activity Tracker Data: Longitudinal Observational Study.

BACKGROUND: The increasing use of activity trackers in mobile health studies to passively collect physical data has shown promise in lessening participation burden to provide actively contributed patient-reported outcome (PRO) information. OBJECTIVE: The aim of this study was to develop machine learning models to classify and predict PRO scores using Fitbit data from a cohort of patients with rheumatoid arthritis. METHODS: Two different models were built to classify PRO scores: a random forest classifier model that treated each week of observations independently when making weekly predictions of PRO scores, and a hidden Markov model that additionally took correlations between successive weeks into account. Analyses compared model evaluation metrics for (1) a binary task of distinguishing a normal PRO score from a severe PRO score and (2) a multiclass task of classifying a PRO score state for a given week. RESULTS: For both the binary and multiclass tasks, the hidden Markov model significantly (P<.05) outperformed the random forest model for all PRO scores, and the highest area under the curve, Pearson correlation coefficient, and Cohen κ coefficient were 0.750, 0.479, and 0.471, respectively. CONCLUSIONS: While further validation of our results and evaluation in a real-world setting remains, this study demonstrates the ability of physical activity tracker data to classify health status over time in patients with rheumatoid arthritis and enables the possibility of scheduling preventive clinical interventions as needed. If patient outcomes can be monitored in real time, there is potential to improve clinical care for patients with other chronic conditions.

Bidirectional Representation Learning From Transformers Using Multimodal Electronic Health Record Data to Predict Depression.

Advancements in machine learning algorithms have had a beneficial impact on representation learning, classification, and prediction models built using electronic health record (EHR) data. Effort has been put both on increasing models' overall performance as well as improving their interpretability, particularly regarding the decision-making process. In this study, we present a temporal deep learning model to perform bidirectional representation learning on EHR sequences with a transformer architecture to predict future diagnosis of depression. This model is able to aggregate five heterogenous and high-dimensional data sources from the EHR and process them in a temporal manner for chronic disease prediction at various prediction windows. We applied the current trend of pretraining and fine-tuning on EHR data to outperform the current state-of-the-art in chronic disease prediction, and to demonstrate the underlying relation between EHR codes in the sequence. The model generated the highest increases of precision-recall area under the curve (PRAUC) from 0.70 to 0.76 in depression prediction compared to the best baseline model. Furthermore, the self-attention weights in each sequence quantitatively demonstrated the inner relationship between various codes, which improved the model's interpretability. These results demonstrate the model's ability to utilize heterogeneous EHR data to predict depression while achieving high accuracy and interpretability, which may facilitate constructing clinical decision support systems in the future for chronic disease screening and early detection.

HCET: Hierarchical Clinical Embedding With Topic Modeling on Electronic Health Records for Predicting Future Depression.

Recent developments in machine learning algorithms have enabled models to exhibit impressive performance in healthcare tasks using electronic health record (EHR) data. However, the heterogeneous nature and sparsity of EHR data remains challenging. In this work, we present a model that utilizes heterogeneous data and addresses sparsity by representing diagnoses, procedures, and medication codes with temporal Hierarchical Clinical Embeddings combined with Topic modeling (HCET) on clinical notes. HCET aggregates various categories of EHR data and learns inherent structure based on hospital visits for an individual patient. We demonstrate the potential of the approach in the task of predicting depression at various time points prior to a clinical diagnosis. We found that HCET outperformed all baseline methods with a highest improvement of 0.07 in precision-recall area under the curve (PRAUC). Furthermore, applying attention weights across EHR data modalities significantly improved the performance as well as the model's interpretability by revealing the relative weight for each data modality. Our results demonstrate the model's ability to utilize heterogeneous EHR information to predict depression, which may have future implications for screening and early detection.

Natural Xanthone α-Mangostin Inhibits LPS-Induced Microglial Inflammatory Responses and Memory Impairment by Blocking the TAK1/NF-κB Signaling Pathway.

SCOPE: The effect of α-mangostin (α-M), a polyphenolic xanthone isolated from mangostin, on lipopolysaccharide (LPS)-induced microglial activation and memory impairment is explored. The possible underlying mechanisms are also investigated. METHODS AND RESULTS: Cytokine production and activation of transforming growth factor activated kinase-1 (TAK1) and nuclear factor-κB (NF-κB) are detected by enzyme-linked immunosorbent assay (ELISA) or Western blot. Microglial migration and phagocytosis are evaluated with scratch wound-healing assay and phagocytosis of fluorescent latex beads, respectively. Learning and memory abilities of mice are evaluated with the Morris water maze test. The nanomolar (100-500 nm) α-M suppresses LPS-induced pro-inflammatory cytokine production and inducible nitric oxide synthase (iNOS) expression in microglia. It also inhibits LPS-induced microglial migration and phagocytosis. α-M rescues LPS-caused, microglia-mediated neuronal dendritic damage. Moreover, α-M represses LPS-induced toll-like receptor 4 (TLR4) expression and activation of TAK1 and NF-κB. In a mouse neuroinflammation model, α-M (50 mg kg-1 day-1 ) shows obvious anti-neuroinflammatory, neuroprotective, and memory-improving effects in vivo. CONCLUSION: α-M inhibits microglia-mediated neuroinflammation and prevents neurotoxicity and memory impairment from inflammatory damage. These results indicate that α-M has great potential to be used as a nutritional preventive strategy for neuroinflammation-related neurodegenerative disorders such as Alzheimer's disease.

A Machine Learning Approach to Classifying Self-Reported Health Status in a Cohort of Patients With Heart Disease Using Activity Tracker Data.

Constructing statistical models using personal sensor data could allow for tracking health status over time, thereby enabling the possibility of early intervention. The goal of this study was to use machine learning algorithms to classify patient-reported outcomes (PROs) using activity tracker data in a cohort of patients with stable ischemic heart disease (SIHD). A population of 182 patients with SIHD were monitored over a period of 12 weeks. Each subject received a Fitbit Charge 2 device to record daily activity data, and each subject completed eight Patient-Reported Outcomes Measurement Information Systems short form at the end of each week as a self-assessment of their health status. Two models were built to classify PRO scores using activity tracker data. The first model treated each week independently, whereas the second used a hidden Markov model (HMM) to take advantage of correlations between successive weeks. Retrospective analysis compared the classification accuracy of the two models and the importance of each feature. In the independent model, a random forest classifier achieved a mean area under curve (AUC) of 0.76 for classifying the physical function PRO. The HMM model achieved significantly better AUCs for all PROs (p < 0.05) other than Fatigue and Sleep Disturbance, with a highest mean AUC of 0.79 for the physical function-short form 10a. Our study demonstrates the ability of activity tracker data to classify health status over time. These results suggest that patient outcomes can be monitored in real time using activity trackers.

Structure of polysaccharide from Polygonatum cyrtonema Hua and the antiherpetic activity of its hydrolyzed fragments.

A neutral polysaccharide named PD was isolated from the traditional Chinese medicinal herb, Polygonatum cyrtonema Hua. Five fragments were isolated by Bio-Gel P4 chromatography from hydrolysates of PD. Using assays of cytopathic effect inhibition, neutral red dye uptake and plaque forming inhibition, it was proved that the fragments with degree of polymerization (DP) of 4 and 5 were the shortest ones which retained the activity against herpes simplex virus type 2 (HSV-2) in vero cell culture. The structures of PD and one of its activity-retaining fragments, B3, were determined by permethylation followed with reductive cleavage, mass spectrometry and nuclear magnetic resonance spectrometry. It was shown that PD was a branched fructan with average DP of 28. There was one two-residue side chain composed of (2 --> 6)-linked beta-d-fructofuranosyl (Fruf) residues every three (2 --> 1)-linked beta-d-Fruf residues in the backbone of PD, whereas B3 was a mixture containing 1-kestose and neokestose series of oligosaccharides of DP 3-5 without branches.