Model tuning or prompt Tuning? a study of large language models for clinical concept and relation extraction.

OBJECTIVE: To develop soft prompt-based learning architecture for large language models (LLMs), examine prompt-tuning using frozen/unfrozen LLMs, and assess their abilities in transfer learning and few-shot learning. METHODS: We developed a soft prompt-based learning architecture and compared 4 strategies including (1) fine-tuning without prompts; (2) hard-prompting with unfrozen LLMs; (3) soft-prompting with unfrozen LLMs; and (4) soft-prompting with frozen LLMs. We evaluated GatorTron, a clinical LLM with up to 8.9 billion parameters, and compared GatorTron with 4 existing transformer models for clinical concept and relation extraction on 2 benchmark datasets for adverse drug events and social determinants of health (SDoH). We evaluated the few-shot learning ability and generalizability for cross-institution applications. RESULTS AND CONCLUSION: When LLMs are unfrozen, GatorTron-3.9B with soft prompting achieves the best strict F1-scores of 0.9118 and 0.8604 for concept extraction, outperforming the traditional fine-tuning and hard prompt-based models by 0.6 âˆ¼ 3.1 % and 1.2 âˆ¼ 2.9 %, respectively; GatorTron-345 M with soft prompting achieves the best F1-scores of 0.8332 and 0.7488 for end-to-end relation extraction, outperforming other two models by 0.2 âˆ¼ 2 % and 0.6 âˆ¼ 11.7 %, respectively. When LLMs are frozen, small LLMs have a big gap to be competitive with unfrozen models; scaling LLMs up to billions of parameters makes frozen LLMs competitive with unfrozen models. Soft prompting with a frozen GatorTron-8.9B model achieved the best performance for cross-institution evaluation. We demonstrate that (1) machines can learn soft prompts better than hard prompts composed by human, (2) frozen LLMs have good few-shot learning ability and generalizability for cross-institution applications, (3) frozen LLMs reduce computing cost to 2.5 âˆ¼ 6 % of previous methods using unfrozen LLMs, and (4) frozen LLMs require large models (e.g., over several billions of parameters) for good performance.

Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias.

OBJECTIVE: To develop a natural language processing (NLP) package to extract social determinants of health (SDoH) from clinical narratives, examine the bias among race and gender groups, test the generalizability of extracting SDoH for different disease groups, and examine population-level extraction ratio. METHODS: We developed SDoH corpora using clinical notes identified at the University of Florida (UF) Health. We systematically compared 7 transformer-based large language models (LLMs) and developed an open-source package - SODA (i.e., SOcial DeterminAnts) to facilitate SDoH extraction from clinical narratives. We examined the performance and potential bias of SODA for different race and gender groups, tested the generalizability of SODA using two disease domains including cancer and opioid use, and explored strategies for improvement. We applied SODA to extract 19 categories of SDoH from the breast (n = 7,971), lung (n = 11,804), and colorectal cancer (n = 6,240) cohorts to assess patient-level extraction ratio and examine the differences among race and gender groups. RESULTS: We developed an SDoH corpus using 629 clinical notes of cancer patients with annotations of 13,193 SDoH concepts/attributes from 19 categories of SDoH, and another cross-disease validation corpus using 200 notes from opioid use patients with 4,342 SDoH concepts/attributes. We compared 7 transformer models and the GatorTron model achieved the best mean average strict/lenient F1 scores of 0.9122 and 0.9367 for SDoH concept extraction and 0.9584 and 0.9593 for linking attributes to SDoH concepts. There is a small performance gap (∼4%) between Males and Females, but a large performance gap (>16 %) among race groups. The performance dropped when we applied the cancer SDoH model to the opioid cohort; fine-tuning using a smaller opioid SDoH corpus improved the performance. The extraction ratio varied in the three cancer cohorts, in which 10 SDoH could be extracted from over 70 % of cancer patients, but 9 SDoH could be extracted from less than 70 % of cancer patients. Individuals from the White and Black groups have a higher extraction ratio than other minority race groups. CONCLUSIONS: Our SODA package achieved good performance in extracting 19 categories of SDoH from clinical narratives. The SODA package with pre-trained transformer models is available at https://github.com/uf-hobi-informatics-lab/SODA_Docker.

Heterogeneous treatment effects of metformin on risk of dementia in patients with type 2 diabetes: A longitudinal observational study.

INTRODUCTION: Little is known about the heterogeneous treatment effects of metformin on dementia risk in people with type 2 diabetes (T2D). METHODS: Participants (≥ 50 years) with T2D and normal cognition at baseline were identified from the National Alzheimer's Coordinating Center database (2005-2021). We applied a doubly robust learning approach to estimate risk differences (RD) with a 95% confidence interval (CI) for dementia risk between metformin use and no use in the overall population and subgroups identified through a decision tree model. RESULTS: Among 1393 participants, 104 developed dementia over a 4-year median follow-up. Metformin was significantly associated with a lower risk of dementia in the overall population (RD, -3.2%; 95% CI, -6.2% to -0.2%). We identified four subgroups with varied risks for dementia, defined by neuropsychiatric disorders, non-steroidal anti-inflammatory drugs, and antidepressant use. DISCUSSION: Metformin use was significantly associated with a lower risk of dementia in individuals with T2D, with significant variability among subgroups.

Rational Photodeposition of Cobalt Phosphate on Flower-like ZnIn2S4 for Efficient Photocatalytic Hydrogen Evolution.

The high electrons and holes recombination rate of ZnIn2S4 significantly limits its photocatalytic performance. Herein, a simple in situ photodeposition strategy is adopted to introduce the cocatalyst cobalt phosphate (Co-Pi) on ZnIn2S4, aiming at facilitating the separation of electron-hole by promoting the transfer of photogenerated holes of ZnIn2S4. The study reveals that the composite catalyst has superior photocatalytic performance than blank ZnIn2S4. In particular, ZnIn2S4 loaded with 5% Co-Pi (ZnIn2S4/5%Co-Pi) has the best photocatalytic activity, and the H2 production rate reaches 3593 µmol·g-1·h-1, approximately double that of ZnIn2S4 alone. Subsequent characterization data demonstrate that the introduction of the cocatalyst Co-Pi facilitates the transfer of ZnIn2S4 holes, thus improving the efficiency of photogenerated carrier separation. This investigation focuses on the rational utilization of high-content and rich cocatalysts on earth to design low-cost and efficient composite catalysts to achieve sustainable photocatalytic hydrogen evolution.

Machine learning algorithms for predicting direct-acting antiviral treatment failure in chronic hepatitis C: An HCV-TARGET analysis.

BACKGROUND AND AIMS: We aimed to develop and validate machine learning algorithms to predict direct-acting antiviral (DAA) treatment failure among patients with HCV infection. APPROACH AND RESULTS: We used HCV-TARGET registry data to identify HCV-infected adults receiving all-oral DAA treatment and having virologic outcome. Potential pretreatment predictors (n = 179) included sociodemographic, clinical characteristics, and virologic data. We applied multivariable logistic regression as well as elastic net, random forest, gradient boosting machine (GBM), and feedforward neural network machine learning algorithms to predict DAA treatment failure. Training (n = 4894) and validation (n = 1631) patient samples had similar sociodemographic and clinical characteristics (mean age, 57 years; 60% male; 66% White; 36% with cirrhosis). Of 6525 HCV-infected adults, 95.3% achieved sustained virologic response, whereas 4.7% experienced DAA treatment failure. In the validation sample, machine learning approaches performed similarly in predicting DAA treatment failure (C statistic [95% CI]: GBM, 0.69 [0.64-0.74]; random forest, 0.68 [0.63-0.73]; feedforward neural network, 0.66 [0.60-0.71]; elastic net, 0.64 [0.59-0.70]), and all four outperformed multivariable logistic regression (0.51 [0.46-0.57]). Using the Youden index to identify the balanced risk score threshold, GBM had 66.2% sensitivity and 65.1% specificity, and 12 individuals were needed to evaluate to identify 1 DAA treatment failure. Over 55% of patients with treatment failure were classified by the GBM in the top three risk decile subgroups (positive predictive value: 6%-14%). The top 10 GBM-identified predictors included albumin, liver enzymes (aspartate aminotransferase, alkaline phosphatase), total bilirubin levels, sex, HCV viral loads, sodium level, HCC, platelet levels, and tobacco use. CONCLUSIONS: Machine learning algorithms performed effectively for risk prediction and stratification of DAA treatment failure.

Regulation of the Oxidase Mimetic Activity of Ceria Nanoparticles by Buffer Composition.

Ceria nanoparticles (CNPs) are important typical nanozymes with multiple enzyme mimetic activities, which could facilitate the oxidation of organic dyes in acidic conditions, because of the oxidase mimetic activity. Usually, the regulation of oxidase mimetic activity is focused on the adjustment of the structure, morphology, composition, surface, and other factors of nanozymes. However, the influence of the surrounding environment is not considered, which is very important during the reaction process. In this work, the oxidase mimetic activity of CNPs in buffer solutions including citric acid, acetic acid and glycine buffer solutions was investigated, with the results that carboxyl group in buffer solution could adsorb the CNPs on the surface to promote the oxidase mimetic activity. Due to the chelation with the cerium ion, the enhancement is more significant by molecules with polycarboxylic groups, and the enhancement is more efficient by carboxyl molecules in buffer solution, compared with the modification of the carboxyl groups on the surface, because of easier operation and smaller steric hindrance. From the viewpoint of increasing the oxidase mimetic activity of CNPs, the work is expected to provide references for the selection of the reaction systems to optimize the oxidase mimetic activity in bio-detection applications.

Contextualized medication information extraction using Transformer-based deep learning architectures.

OBJECTIVE: To develop a natural language processing (NLP) system to extract medications and contextual information that help understand drug changes. This project is part of the 2022 n2c2 challenge. MATERIALS AND METHODS: We developed NLP systems for medication mention extraction, event classification (indicating medication changes discussed or not), and context classification to classify medication changes context into 5 orthogonal dimensions related to drug changes. We explored 6 state-of-the-art pretrained transformer models for the three subtasks, including GatorTron, a large language model pretrained using > 90 billion words of text (including > 80 billion words from > 290 million clinical notes identified at the University of Florida Health). We evaluated our NLP systems using annotated data and evaluation scripts provided by the 2022 n2c2 organizers. RESULTS: Our GatorTron models achieved the best F1-scores of 0.9828 for medication extraction (ranked 3rd), 0.9379 for event classification (ranked 2nd), and the best micro-average accuracy of 0.9126 for context classification. GatorTron outperformed existing transformer models pretrained using smaller general English text and clinical text corpora, indicating the advantage of large language models. CONCLUSION: This study demonstrated the advantage of using large transformer models for contextual medication information extraction from clinical narratives.

Association between SCN5A R225Q variant and dilated cardiomyopathy: potential role of intracellular pH and WNT/ß-catenin pathway.

BACKGROUND: The SCN5A variant is a common cause of familial dilated cardiomyopathy (DCM). We previously reported a SCN5A variant (c.674G>A), located in the high-risk S4 segment of domain I (DI-S4) region in patients with idiopathic DCM and R225Q knockin (p.R225Q) mice carrying the c.674G>A variant exhibited prolonged baseline PR intervals without DCM phenotypes. In this study, we explored the association and mechanism between R225Q variant and DCM phenotype. METHODS: Prevalence of DI-S4 variant was compared between patients with idiopathic DCM and the control participants. R225Q knockin and wild-type (WT) mice were subjected to doxorubicin (DOX), D-galactose (D-gal) or D-gal combined with DOX. RESULTS: Clinical data suggested that the prevalence of DI-S4 variant was higher in DCM group than in the control group (4/90 (4.4%) vs 3/1339 (0.2%), p<0.001). Cardiomyocytes from R225Q knockin mice treated with D-gal and DOX exhibited more significant hypertrophic phenotype and weaker contraction/dilation function and an increased level of apoptosis as compared with WT mice. Mechanistically, we found that R225Q variant could increase intracellular pH and further induce the activation of the WNT/ß-catenin pathway as well as the overexpression of pro-hypertrophic and pro-apoptotic targets. WNT-C59 inhibitor improved cardiac function in the R225Q knockin mice treated with D-gal and DOX. CONCLUSION: Our results suggest that R225Q variant is associated with increased susceptibility to DCM. Ageing could enhance this process via activating WNT/ß-catenin signaling in response to increased intracellular pH. Antagonising the WNT/ß-catenin pathway might be a potential therapeutic strategy for mitigating R225Q variant-related DCM pathogenesis.

Glycyrrhizae Radix et Rhizoma: The popular occurrence of herbal medicine applied in classical prescriptions.

Glycyrrhizae Radix et Rhizoma is a well-known herbal medicine with a wide range of pharmacological functions that has been used throughout Chinese history. This review presents a comprehensive introduction to this herb and its classical prescriptions. The article discusses the resources and distribution of species, methods of authentication and determination chemical composition, quality control of the original plants and herbal medicines, dosages use, common classical prescriptions, indications, and relevant mechanisms of the active content. Pharmacokinetic parameters, toxicity tests, clinical trials, and patent applications are discussed. The review will provide a good starting point for the research and development of classical prescriptions to develop herbal medicines for clinical use.

Early prediction of Alzheimer's disease and related dementias using real-world electronic health records.

INTRODUCTION: This study aims to explore machine learning (ML) methods for early prediction of Alzheimer's disease (AD) and related dementias (ADRD) using the real-world electronic health records (EHRs). METHODS: A total of 23,835 ADRD and 1,038,643 control patients were identified from the OneFlorida+ Research Consortium. Two ML methods were used to develop the prediction models. Both knowledge-driven and data-driven approaches were explored. Four computable phenotyping algorithms were tested. RESULTS: The gradient boosting tree (GBT) models trained with the data-driven approach achieved the best area under the curve (AUC) scores of 0.939, 0.906, 0.884, and 0.854 for early prediction of ADRD 0, 1, 3, or 5 years before diagnosis, respectively. A number of important clinical and sociodemographic factors were identified. DISCUSSION: We tested various settings and showed the predictive ability of using ML approaches for early prediction of ADRD with EHRs. The models can help identify high-risk individuals for early informed preventive or prognostic clinical decisions.

STAT3-mediated upregulation of the AIM2 DNA sensor links innate immunity with cell migration to promote epithelial tumourigenesis.

OBJECTIVE: The absent in melanoma 2 (AIM2) cytosolic pattern recognition receptor and DNA sensor promotes the pathogenesis of autoimmune and chronic inflammatory diseases via caspase-1-containing inflammasome complexes. However, the role of AIM2 in cancer is ill-defined. DESIGN: The expression of AIM2 and its clinical significance was assessed in human gastric cancer (GC) patient cohorts. Genetic or therapeutic manipulation of AIM2 expression and activity was performed in the genetically engineered gp130 F/F spontaneous GC mouse model, as well as human GC cell line xenografts. The biological role and mechanism of action of AIM2 in gastric tumourigenesis, including its involvement in inflammasome activity and functional interaction with microtubule-associated end-binding protein 1 (EB1), was determined in vitro and in vivo. RESULTS: AIM2 expression is upregulated by interleukin-11 cytokine-mediated activation of the oncogenic latent transcription factor STAT3 in the tumour epithelium of GC mouse models and patients with GC. Genetic and therapeutic targeting of AIM2 in gp130 F/F mice suppressed tumourigenesis. Conversely, AIM2 overexpression augmented the tumour load of human GC cell line xenografts. The protumourigenic function of AIM2 was independent of inflammasome activity and inflammation. Rather, in vivo and in vitro AIM2 physically interacted with EB1 to promote epithelial cell migration and tumourigenesis. Furthermore, upregulated expression of AIM2 and EB1 in the tumour epithelium of patients with GC was independently associated with poor patient survival. CONCLUSION: AIM2 can play a driver role in epithelial carcinogenesis by linking cytokine-STAT3 signalling, innate immunity and epithelial cell migration, independent of inflammasome activation.

Prevalence of Alzheimer's and Related Dementia Diseases and Risk Factors Among Transgender Adults, Florida, 2012‒2020.

Objectives. To estimate the prevalence rates of Alzheimer's disease and related dementias (ADRD) and their risk factors in the transgender population and compare the rates to those in cisgender adults. Methods. We identified 1784 transgender adults in the linked electronic health records and claims data between 2012 and 2020 from the OneFlorida Clinical Research Consortium. We calculated the prevalence of ADRD and ADRD risk factors for the transgender and matched cisgender control adults. Results. The prevalence of ADRD was higher in the transgender adults compared with the cisgender control adults. Overall, the prevalence of ADRD risk factors was significantly higher in the transgender adults than the cisgender controls for 11 out of the 13 risk factors, with the only exceptions being traumatic brain injury and visual impairment. Conclusions. Transgender adults are at significantly higher risk for ADRD than cisgender adults. Our study highlights the urgent need for more research on the unique ADRD risks among the aging transgender and larger sexual- and gender-minority populations. (Am J Public Health. 2022;112(5):754-757. https://doi.org/10.2105/AJPH.2022.306720).

Metformin alleviates nickel-refining fumes-induced aerobic glycolysis via AMPK/GOLPH3 pathway in vitro and in vivo.

Nickel (Ni) compounds is recognized industrial carcinogen, which could increase the risk of lung cancer in Ni refineries workers. However, the underlying carcinogenic mechanism still remains to elucidate. Metformin has shown the anticancer properties through suppressing aerobic glycolysis. In the present study, we evaluated the effect of Ni-refining fumes exposure on aerobic glycolysis and the role of AMPK/GOLPH3, as well as how metformin alleviated nickel-induced aerobic glycolysis in vitro and vivo. Firstly, Beas-2B cells were exposed to different concentrations of Ni-refining fumes and pretreated with metformin (activation of AMPK), compound C (AMPK inhibitor) in vitro. Our findings indicated that Ni fumes expose evoked aerobic glycolysis by AMPK/GOLPH3, while metformin attenuated Ni particles-promoted GOLPH3-mediated aerobic glycolysis by p-AMPK expression increase. Then Mito-TEMPT (a mitochondria-targeted antioxidant) and lipopolysaccharide (LPS, ROS activator) were pretreated to affect ROS production in Beas-2B cells. Ni-induced ROS prevented AMPK activation. Moreover, C57BL/6 mice were exposed to 2 mg/kg Ni by non-exposed endotracheal instillation and metformin (100, 200 and 300 mg/kg) via oral gavage for 4 weeks. The effects of AMPK/GOLPH3 axis on Ni-induced aerobic glycolysis were assessed. The results indicated that metformin decreased the protein levels of GOLPH3, LDHA, HK2, MCT-4 and improved p-AMPK expression. Thus, our findings demonstrated metformin antagonized Ni-refining fumes-caused aerobic glycolysis via AMPK/GOLPH3.

Metformin antagonizes nickel-refining fumes-induced cell pyroptosis via Nrf2/GOLPH3 pathway in vitro and in vivo.

Nickel compounds, an international carcinogen in the industrial environment, increased the risk of lung inflammation even lung cancer in Ni refinery workers. Metformin has displayed the intense anti-inflammation and anti-cancer properties through regulating pyroptosis. This study was designed to explore whether Nickel-refining fumes (NiRF) can induce cell pyroptosis and how AMPK/CREB/Nrf2 mediated the protection afforded by metformin against Ni particles-induced lung impairment. Our results represented that Ni fumes exposure evoked pyroptosis via GOLPH3 and induced oxidative stress, while, metformin treatment alleviated Ni particles-mediated above changes. Moreover, nuclear factor erythroid 2-related factor 2 (Nrf2) involved in the protection of metformin, and the deficiency of Nrf2 attenuated the beneficial protection. We also determined that Nrf2 was a downstream molecule of AMPK/CREB pathway. Furthermore, male C57BL/6 mice were administered with Ni at a dose of 2 mg/kg by non-exposed endotracheal instillation and metformin (100, 200 and 300 mg/kg) via oral gavage for 4 weeks. The results indicated that NiRF promoted GOLPH3 and pyroptosis by stimulating NLRP3, caspase-1, N-GSDMD, IL-18 and IL-1ß expression. However, various doses of metformin reduced GOLPH3 and the above protein levels of pyroptosis, also improved AMPK/CREB/Nrf2 expression. In summary, we found that metformin suppressed NiRF-connected GOLPH3-prompted pyroptosis via AMPK/CREB/Nrf2 signaling pathway to confer pulmonary protection.

Stimulating the expression of sphingosine kinase 1 (SphK1) is beneficial to reduce acrylamide-induced nerve cell damage.

Sphingosine kinase 1 (SphK1) is an important signaling molecule for cell proliferation and survival. However, the role of SphK1 in acrylamide (ACR)-induced nerve injury remains unclear. The purpose of this study was to investigate the role and potential mechanism of SphK1 in ACR-induced nerve injury. Liquid chromatography triple quadrupole tandem mass spectrometry (LC-MS/MS) and reverse transcription-quantitative PCR (RT-qPCR) were used to detect sphingosine 1-phosphate (S1P) content in serum and SphK1 content in whole blood from an occupational work group exposed to ACR compared to a non-exposed group. For in vitro experiments, SphK1 in human SH-SY5Y neuroblastoma cells was activated using SphK1-specific activator phorbol 12-myristate 13-acetate (PMA). Our research also utilized cell viability assays, flow cytometry, western blots, RT-qPCR and related protein detection to assess activity of the mitogen activated protein kinase (MAPK) signaling pathway. The results of the population study showed that the contents of SphK1 and S1P in the ACR-exposed occupational contact group were lower than in the non-exposed group. The results of in vitro experiments showed that expression of SphK1 decreased with the increase in ACR concentration. Activating SphK1 improved the survival rate of SH-SY5Y cells and decreased the apoptosis rate. Activating SphK1 in SH-SY5Y cells also regulated MAPK signaling, including enhancing the phosphorylation of extracellular signal-regulated protein kinases (ERK) and inhibiting the phosphorylation of c-Jun N-terminal kinase (JNK) and p38. These results suggest that activating SphK1 can protect against nerve cell damage caused by ACR.

Identify diabetic retinopathy-related clinical concepts and their attributes using transformer-based natural language processing methods.

BACKGROUND: Diabetic retinopathy (DR) is a leading cause of blindness in American adults. If detected, DR can be treated to prevent further damage causing blindness. There is an increasing interest in developing artificial intelligence (AI) technologies to help detect DR using electronic health records. The lesion-related information documented in fundus image reports is a valuable resource that could help diagnoses of DR in clinical decision support systems. However, most studies for AI-based DR diagnoses are mainly based on medical images; there is limited studies to explore the lesion-related information captured in the free text image reports. METHODS: In this study, we examined two state-of-the-art transformer-based natural language processing (NLP) models, including BERT and RoBERTa, compared them with a recurrent neural network implemented using Long short-term memory (LSTM) to extract DR-related concepts from clinical narratives. We identified four different categories of DR-related clinical concepts including lesions, eye parts, laterality, and severity, developed annotation guidelines, annotated a DR-corpus of 536 image reports, and developed transformer-based NLP models for clinical concept extraction and relation extraction. We also examined the relation extraction under two settings including 'gold-standard' setting-where gold-standard concepts were used-and end-to-end setting. RESULTS: For concept extraction, the BERT model pretrained with the MIMIC III dataset achieve the best performance (0.9503 and 0.9645 for strict/lenient evaluation). For relation extraction, BERT model pretrained using general English text achieved the best strict/lenient F1-score of 0.9316. The end-to-end system, BERT_general_e2e, achieved the best strict/lenient F1-score of 0.8578 and 0.8881, respectively. Another end-to-end system based on the RoBERTa architecture, RoBERTa_general_e2e, also achieved the same performance as BERT_general_e2e in strict scores. CONCLUSIONS: This study demonstrated the efficiency of transformer-based NLP models for clinical concept extraction and relation extraction. Our results show that it's necessary to pretrain transformer models using clinical text to optimize the performance for clinical concept extraction. Whereas, for relation extraction, transformers pretrained using general English text perform better.

High-Performance Flexible Piezoelectric Nanogenerator Based on Specific 3D Nano BCZT@Ag Hetero-Structure Design for the Application of Self-Powered Wireless Sensor System.

With the popularity of portable and miniaturized electronic devices in people's live, flexible piezoelectric nanogenerators (PENG) have become a research hotspot for harvesting energy from the living environment to power small-scale electronic equipment and systems because of its stability. For further enhancing output performance of PENG, chemical modification and structural design for piezoelectric fillers are effective ways. Thus, the 3D porous hetero-structure fillers of BCZT@Ag are prepared by freeze-drying method and subsequent chemical seeding reduction. The silicone rubber as matrix is filled into the micro-voids of fillers to prepare specialized composite. The charge transport mechanism and stress transfer efficiency in PENG can be effectively improved through specialized design which is proven by experimental results and multi-physics simulations. The improved PENG exhibit a significantly enhanced output of 38.6 V and 5.85 µA, which is 3.3 and 3.5 times higher than those of PENG without specific design. The prepared PENG can effectively harvest biomechanical energy through walk and joint bending of human body. Moreover, the PENG can be used as a trigger to remotely control wireless collision alarm system, which can acquire rapid response and shows great potential application in Internet of Things.

Shexiang Baoxin Pill Attenuates Ischemic Injury by Promoting Angiogenesis by Activation of Aldehyde Dehydrogenase 2.

ABSTRACT: Promoting angiogenesis is a critical treatment strategy for ischemic cardiovascular diseases. Shexiang Baoxin Pill (SBP), a traditional Chinese medicine, has been reported to be capable of relieving angina and improve heart function by promoting angiogenesis. The aim of this study was to determine the role of mitochondrial aldehyde dehydrogenase 2 (ALDH2) in SBP-induced angiogenesis. Left femoral artery ligation was performed in wild-type mice (WT) and ALDH2 knockout mice, which were administrated with SBP (20 mg/kg/d) or equal volume saline per day by gastric gavage for 2 weeks. Perfusion recovery, angiogenesis in chronic hind limb ischemia, was significantly improved in the WT + SBP group than in the WT group. However, these beneficial effects were absent in ALDH2 knockout mice. In vitro, hypoxia impaired the ability of proliferation, migration and tube formation, sprouting angiogenesis, and promoted apoptosis in cardiovascular microvascular endothelial cells, whereas the hypoxia damage was restored by SBP. The protective effect of SBP was remarkably weakened by ALDH2 knockdown. Furthermore, SBP suppressed hypoxia-induced ALDH2/protein kinase B (AKT)/mammalian target of rapamycin pathways. In conclusion, this study demonstrated that SBP protected lower limb from ischemia injury through the ALDH2-dependent pathway. The protective mechanism of SBP in cardiovascular microvascular endothelial cells was partly mediated through ALDH2/AKT/mammalian target of rapamycin pathways.

A urinary metabolomic study from subjects after long-term occupational exposure to low concentration acrylamide using UPLC-QTOF/MS.

Because long-term occupational exposure to low concentrations of acrylamide (ACR) has the potential to cause neurological damage, it is important to identify biomarkers that can be used to evaluate this risk. In the present study, urine metabolomics of the ACR-exposed and non-exposed groups to identify potential metabolites was carried out using ultra high performance liquid chromatography coupled with quadrupole time of flight mass spectrometry. Serum biochemical indexes of the exposed and non-exposed groups were also determined. Principal component analysis showed a differential separation between exposed group and non-exposed group and a total of 7 metabolites were identified in positive and negative ionization modes; Area under curve of anthranilic acid, ß-guanidinopropionic acid and mesobilirubinogen were 0.980, 0.843 and 0.801 respectively and these metabolites showed high sensitivity and specificity. The 13 biochemical indexes were divided into three classes based on physiological functions. Only biomarkers of dysregulated liver function including alanine aminotransferase, aspartic transaminase, total bilirubin, direct bilirubin and triglyceride were significantly higher in the exposed group than in the non-exposed group. This study identifies important related metabolic changes in the bodies of workers after long-term occupational exposure to low concentration ACR and suggests new biomarkers of nervous system injury caused by ACR. The study also provides a sound basis for exploring the biochemical mechanisms and metabolic pathways of nervous system toxicity caused by ACR.

Salvianolic acid a inhibits platelet activation and aggregation in patients with type 2 diabetes mellitus.

BACKGROUND: Platelets in patients with type 2 diabetes mellitus (DM2) are characterized by increased activation and aggregation, which tends to be associated with a high morbidity and mortality due to cardiovascular disease (CVD). Moreover, a large proportion of DM2 patients show an inadequate response to standard antiplatelet treatments, contributing to recurrent cardiovascular events. In our previous study, we indicated that Salvianolic acid A (SAA) presents an antiplatelet effect in healthy volunteers. However, whether it can inhibit "activated platelets" with a pathologic status has not been explored. Therefore, this study was designed to investigate the antiplatelet effect of SAA and its diabetic complication-related difference in DM2. METHODS: Forty patients diagnosed with DM2 from January 2018 to April 2018 were recruited. Fibrinogen-binding (PAC-1) and P-selectin (CD62p) flow cytometry reagents were measured under resting and stimulated conditions by flow cytometry, while agonist-induced platelet aggregation was conducted by light transmission aggregometry. Before all these measurements were conducted, all platelet samples were preincubated with a vehicle or SAA for 10 min. Additionally, the diabetic complication-related difference in the antiplatelet effect of SAA was further studied in enrolled patients. RESULTS: The expressions of PAC-1 and CD62p were elevated in DM2, as well as the maximal platelet aggregation. In addition, SAA decreased the expressions of PAC-1 and CD62p, which were enhanced by ADP and thrombin (all P < 0.01). It also reduced the platelet aggregation induced by ADP (P < 0.001) and thrombin (P < 0.05). Comparing the antiplatelet effect of SAA on DM2, with and without diabetic complications, no statistically significant difference was found (all P > 0.05). CONCLUSIONS: The present study demonstrated that SAA can inhibit platelet activation and aggregation in patients with DM2, and the inhibition did not abate for the existence of diabetic complications.