[18F]FDG PET integrated with structural MRI for accurate brain age prediction.

PURPOSE: Brain aging is a complex and heterogeneous process characterized by both structural and functional decline. This study aimed to establish a novel deep learning (DL) method for predicting brain age by utilizing structural and metabolic imaging data. METHODS: The dataset comprised participants from both the Universal Medical Imaging Diagnostic Center (UMIDC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). The former recruited 395 normal control (NC) subjects, while the latter included 438 NC subjects, 51 mild cognitive impairment (MCI) subjects, and 56 Alzheimer's disease (AD) subjects. We developed a novel dual-pathway, 3D simple fully convolutional network (Dual-SFCNeXt) to estimate brain age using [18F]fluorodeoxyglucose positron emission tomography ([18F]FDG PET) and structural magnetic resonance imaging (sMRI) images of NC subjects as input. Several prevailing DL models were trained and tested using either MRI or PET data for comparison. Model accuracies were evaluated using mean absolute error (MAE) and Pearson's correlation coefficient (r). Brain age gap (BAG), deviations of brain age from chronologic age, was correlated with cognitive assessments in MCI and AD subjects. RESULTS: Both PET- and MRI-based models achieved high prediction accuracy. The leading model was the SFCNeXt (the single-pathway version) for PET (MAE = 2.92, r = 0.96) and MRI (MAE = 3.23, r = 0.95) on all samples. By integrating both PET and MRI images, the Dual-SFCNeXt demonstrated significantly improved accuracy (MAE = 2.37, r = 0.97) compared to all single-modality models. Significantly higher BAG was observed in both the AD (P < 0.0001) and MCI (P < 0.0001) groups compared to the NC group. BAG correlated significantly with Mini-Mental State Examination (MMSE) scores (r=-0.390 for AD, r=-0.436 for MCI) and the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) scores (r = 0.333 for AD, r = 0.372 for MCI). CONCLUSION: The integration of [18F]FDG PET with structural MRI enhances the accuracy of brain age prediction, potentially introducing a new avenue for related multimodal brain age prediction studies.

Quantitative analysis of anthocyanins and flavonoids in purple and green leaves of Eleutherococcus senticosus by UPLC-MS/MS techniques.

Ultra performance liquid chromatography-high resolution mass spectrometry (UPLC-MS/MS) were used to target quantitative determination anthocyanins and flavonoids in the fresh leaves (purple and green) of Eleutherococcus senticosus. The results showed that the content of total anthocyanins was 99.68 µg/g (Fresh Weight, FW) in purple leaves and 29.12 µg/g in green leaves. Cyanidin-3-O-galactoside and delphinidin were the main anthocyanins compound in purple and green leaves, and the content of the both declined sharply in green leaves. The content of cyanidin-3-O-galactoside reached 616.23 ng/100 mg in purple leaves and was only fifth in green leaves. The total flavonoids content was 4.90 mg/g in purple leaves and 2.23 mg/g in green leaves. Quercetin-3-ß-D-glucoside (236.96 ng/mg) and kaempferol-3-O-glucoside (145.27 ng/mg) were the main flavonoids compound in purple leaves. Besides the two main flavonoids, large quantities of rutin (269.11 ng/mg) was detected in green leaves of E. senticosus.

Interferometric-scale full-field vibration measurement by a combination of digital image correlation and laser vibrometer.

Digital Image Correlation (DIC) is a crucial noncontact full-field optical measurement method used in various fields. However, in practical applications, DIC is affected by systematic and random noises, leading to experimental resolutions lower than theoretical ones. In this study, we proposed a laser Doppler vibrometer guided DIC to perform vibration measurements. 3D-DIC obtains a sequence of out of displacement field initially. A three-dimensional frequency domain collaborative filtering (3D-FDCF) method that utilizes Laser Doppler vibrometer (LDV) single-point data to assist in processing of the displacement field sequence pixel-wise is adopted. The 3D-FDCF method combines lowpass filtering in the spatial frequency domain with LDV-guided bandpass filtering in the temporal frequency domain. The effectiveness of the 3D-FDCF method is demonstrated through a comparison among DIC data, the filtered DIC data, and continuously scanning LDV data. The experiment results demonstrate the 3D-FDCF method's capability in measuring vibration amplitudes of several hundred nanometers with the size of a test sample of about 100 mm × 100 mm, supporting the statement of interferometric scale full-field vibration measurement by DIC with the guidance of the LDV data.

Deep learning models for predicting the survival of patients with hepatocellular carcinoma based on a surveillance, epidemiology, and end results (SEER) database analysis.

Hepatocellular carcinoma (HCC) is a common malignancy with poor survival and requires long-term follow-up. Hence, we collected information on patients with Primary Hepatocellular Carcinoma in the United States from the Surveillance, Epidemiology, and EndResults (SEER) database. We used this information to establish a deep learning with a multilayer neural network (the NMTLR model) for predicting the survival rate of patients with Primary Hepatocellular Carcinoma. HCC patients pathologically diagnosed between January 2011 and December 2015 in the SEER (Surveillance, Epidemiology, and End Results) database of the National Cancer Institute of the United States were selected as study subjects. We utilized two deep learning-based algorithms (DeepSurv and Neural Multi-Task Logistic Regression [NMTLR]) and a machine learning-based algorithm (Random Survival Forest [RSF]) for model training. A multivariable Cox Proportional Hazards (CoxPH) model was also constructed for comparison. The dataset was randomly divided into a training set and a test set in a 7:3 ratio. The training dataset underwent hyperparameter tuning through 1000 iterations of random search and fivefold cross-validation. Model performance was assessed using the concordance index (C-index), Brier score, and Integrated Brier Score (IBS). The accuracy of predicting 1-year, 3-year, and 5-year survival rates was evaluated using Receiver Operating Characteristic (ROC) curves, calibration plots, and Area Under the Curve (AUC). The primary outcomes were the 1-year, 3-year, and 5-year overall survival rates. Models were developed using DeepSurv, NMTLR, RSF, and Cox Proportional Hazards regression. Model differentiation was evaluated using the C-index, calibration with concordance plots, and risk stratification capability with the log-rank test. The study included 2197 HCC patients, randomly divided into a training cohort (70%, n = 1537) and a testing cohort (30%, n = 660). Clinical characteristics between the two cohorts showed no significant statistical difference (p > 0.05). The deep learning models outperformed both RSF and CoxPH models, with C-indices of 0.735 (NMTLR) and 0.731 (DeepSurv) in the test dataset. The NMTLR model demonstrated enhanced accuracy and well-calibrated survival estimates, achieving an Area Under the Curve (AUC) of 0.824 for 1-year survival predictions, 0.813 for 3-year, and 0.803 for 5-year survival rates. This model's superior calibration and discriminative ability enhance its utility for clinical prognostication in Primary Hepatocellular Carcinoma. We deployed the NMTLR model as a web application for clinical practice. The NMTLR model have potential advantages over traditional linear models in prognostic assessment and treatment recommendations. This novel analytical approach may provide reliable information on individual survival and treatment recommendations for patients with primary liver cancer.

Encapsulation of zingerone by self-assembling peptides derived from fish viscera: Characterization, interaction and effects on colon epithelial cells.

The purpose of the present work was to encapsulate zingerone (a bioactive compound from ginger) by self-assembling peptides derived from fish viscera. The encapsulation conditions were investigated and the structure of fish peptides-zingerone complex was characterized. The interaction between zingerone and fish peptides was investigated using fluorescence spectroscopy. Further research was performed on the in vitro release of zingerone and fish peptide-zingerone as well as their antiproliferative effects on colon epithelial Caco-2 cells. The results demonstrated that zingerone can be successfully encapsulated by self-assembling peptides derived from fish viscera with high encapsulation efficiency and loading capacity. Furthermore, transmission electron microscope and confocal laser scanning microscope observations revealed the successful encapsulation of zingerone by fish viscera peptides. In addition, in vitro release and antiproliferative activity against Caco-2 cells can be significantly increased by encapsulating zingerone via peptide self-assembly. The current study advances knowledge of encapsulation of bioactive compounds through peptide self-assembly.

Mesenchymal stem cell-derived apoptotic vesicles ameliorate impaired ovarian folliculogenesis in polycystic ovary syndrome and ovarian aging by targeting WNT signaling.

Rationale: It has been emergingly recognized that apoptosis generates plenty of heterogeneous apoptotic vesicles (apoVs), which play a pivotal role in the maintenance of organ and tissue homeostasis. However, it is unknown whether apoVs influence postnatal ovarian folliculogenesis. Methods: Apoptotic pathway deficient mice including Fas mutant (Fasmut ) and Fas ligand mutant (FasLmut ) mice were used with apoV replenishment to evaluate the biological function of apoVs during ovarian folliculogenesis. Ovarian function was characterized by morphological analysis, biochemical examination and cellular assays. Mechanistical studies were assessed by combinations of transcriptomic and proteomic analysis as well as molecular assays. CYP17A1-Cre; Axin1fl /fl mice was established to verify the role of WNT signaling during ovarian folliculogenesis. Polycystic ovarian syndrome (PCOS) mice and 15-month-old mice were used with apoV replenishment to further validate the therapeutic effects of apoVs based on WNT signaling regulation. Results: We show that systemic administration of mesenchymal stem cell (MSC)-derived apoptotic vesicles (MSC-apoVs) can ameliorate impaired ovarian folliculogenesis, PCOS phenotype, and reduced birth rate in Fasmut and FasLmut mice. Mechanistically, transcriptome analysis results revealed that MSC-apoVs downregulated a number of aberrant gene expression in Fasmut mice, which were enriched by kyoto encyclopedia of genes and genomes (KEGG) pathway analysis in WNT signaling and sex hormone biosynthesis. Furthermore, we found that apoptotic deficiency resulted in aberrant WNT/ß-catenin activation in theca and mural granulosa cells, leading to responsive action of dickkopf1 (DKK1) in the cumulus cell and oocyte zone, which downregulated WNT/ß-catenin expression in oocytes and, therefore, impaired ovarian folliculogenesis via NPPC/cGMP/PDE3A/cAMP cascade. When WNT/ß-catenin was specially activated in theca cells of CYP17A1-Cre; Axin1fl /fl mice, the same ovarian impairment phenotypes observed in apoptosis-deficient mice were established, confirming that aberrant activation of WNT/ß-catenin in theca cells caused the impairment of ovarian folliculogenesis. We firstly revealed that apoVs delivered WNT membrane receptor inhibitor protein RNF43 to ovarian theca cells to balance follicle homeostasis through vesicle-cell membrane integration. Systemically infused RNF43-apoVs down-regulated aberrantly activated WNT/ß-catenin signaling in theca cells, contributing to ovarian functional maintenance. Since aging mice have down-regulated expression of WNT/ß-catenin in oocytes, we used MSC-apoVs to treat 15-month-old mice and found that MSC-apoVs effectively ameliorated the ovarian function and fertility capacity of these aging mice through rescuing WNT/ß-catenin expression in oocytes. Conclusion: Our studies reveal a previously unknown association between apoVs and ovarian folliculogenesis and suggest an apoV-based therapeutic approach to improve oocyte function and birth rates in PCOS and aging.

Mitophagy in relation to chronic inflammation/ROS in aging.

Various assaults on mitochondria occur during the human aging process, contributing to mitochondrial dysfunction. This mitochondrial dysfunction is intricately connected with aging and diseases associated with it. In vivo, the accumulation of defective mitochondria can precipitate inflammatory and oxidative stress, thereby accelerating aging. Mitophagy, an essential selective autophagy process, plays a crucial role in managing mitochondrial quality control and homeostasis. It is a highly specialized mechanism that systematically removes damaged or impaired mitochondria from cells, ensuring their optimal functioning and survival. By engaging in mitophagy, cells are able to maintain a balanced and stable environment, free from the potentially harmful effects of dysfunctional mitochondria. An ever-growing body of research highlights the significance of mitophagy in both aging and age-related diseases. Nonetheless, the association between mitophagy and inflammation or oxidative stress induced by mitochondrial dysfunction remains ambiguous. We review the fundamental mechanisms of mitophagy in this paper, delve into its relationship with age-related stress, and propose suggestions for future research directions.

Evaluating and Enhancing Large Language Models' Performance in Domain-specific Medicine: Explainable LLM with DocOA.

BACKGROUND: Evaluating and Enhancing Large Language Models' Performance in Domain-specific Medicine: Explainable LLM with DocOA. OBJECTIVE: This study focused on evaluating and enhancing the clinical capabilities and explainability of LLMs in specific domains, using osteoarthritis (OA) management as a case study. METHODS: A domain specific benchmark framework was developed, which evaluate LLMs across a spectrum from domain-specific knowledge to clinical applications in real-world clinical scenarios. DocOA, a specialized LLM designed for OA management integrating retrieval-augmented generation (RAG) and instructional prompts, was developed. It can identify the clinical evidence upon which its answers are based through RAG, thereby demonstrating the explainability of those answers. The study compared the performance of GPT-3.5, GPT-4, and a specialized assistant, DocOA, using objective and human evaluations. RESULTS: Results showed that general LLMs like GPT-3.5 and GPT-4 were less effective in the specialized domain of OA management, particularly in providing personalized treatment recommendations. However, DocOA showed significant improvements. CONCLUSIONS: This study introduces a novel benchmark framework which assesses the domain-specific abilities of LLMs in multiple aspects, highlights the limitations of generalized LLMs in clinical contexts, and demonstrates the potential of tailored approaches for developing domain-specific medical LLMs.

Preparation, pungency and bioactivity transduction of piperine from black pepper (Piper nigrum L.): A comprehensive review.

Piperine, derived from black pepper (Piper nigrum L.), is responsible for the pungent sensation. The diverse bioactivities of piperine underscores its promising potential as a functional food ingredient. This review presents a comprehensive overview of the research progress in extraction, synthesis, pungency transduction mechanism and bioactivities of piperine. Piperine can be extracted through various methods, such as traditional, modern, and innovative extraction techniques. Its synthesis mainly included both chemical and biosynthetic approaches. It exhibits a diverse range of bioactivities, including anticancer, anticonvulsant, antidepressant, anti-inflammatory, antioxidant, immunomodulatory, anti-obesity, neuroprotective, antidiabetic, hepatoprotective, and cardiovascular protective activities. Piperine can bind to TRPV1 receptor to elicit pungent sensation. Overall, the present review can provide a theoretical reference for advancing the potential application of piperine in the field of food science.

Alzheimer's disease diagnosis from multi-modal data via feature inductive learning and dual multilevel graph neural network.

Multi-modal data can provide complementary information of Alzheimer's disease (AD) and its development from different perspectives. Such information is closely related to the diagnosis, prevention, and treatment of AD, and hence it is necessary and critical to study AD through multi-modal data. Existing learning methods, however, usually ignore the influence of feature heterogeneity and directly fuse features in the last stages. Furthermore, most of these methods only focus on local fusion features or global fusion features, neglecting the complementariness of features at different levels and thus not sufficiently leveraging information embedded in multi-modal data. To overcome these shortcomings, we propose a novel framework for AD diagnosis that fuses gene, imaging, protein, and clinical data. Our framework learns feature representations under the same feature space for different modalities through a feature induction learning (FIL) module, thereby alleviating the impact of feature heterogeneity. Furthermore, in our framework, local and global salient multi-modal feature interaction information at different levels is extracted through a novel dual multilevel graph neural network (DMGNN). We extensively validate the proposed method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and experimental results demonstrate our method consistently outperforms other state-of-the-art multi-modal fusion methods. The code is publicly available on the GitHub website. (https://github.com/xiankantingqianxue/MIA-code.git).

In-situ tool wear condition monitoring during the end milling process based on dynamic mode and abnormal evaluation.

Rapid tool wear conditions during the manufacturing process are crucial for the enhancement of product quality. As an extension of our recent works, in this research, a generic in-situ tool wear condition monitoring during the end milling process based on dynamic mode and abnormal evaluation is proposed. With the engagement of dynamic mode decomposition, the real-time response of the sensing physical quantity during the end milling process can be predicted. Besides, by constructing the graph structure of the time series and calculating the difference between the predicted signal and the real-time signal, the anomaly can be acquired. Meanwhile, the tool wear state during the end milling process can be successfully evaluated. The proposed method is validated in milling tool wear experiments and received positive results (the mean relative error is recorded as 0.0507). The research, therefore, paves a new way to realize the in-situ tool wear condition monitoring.

Prediction of arsenic and fluoride in groundwater of the North China Plain using enhanced stacking ensemble learning.

Chronic exposure to elevated geogenic arsenic (As) and fluoride (F-) concentrations in groundwater poses a significant global health risk. In regions around the world where regular groundwater quality assessments are limited, the presence of harmful levels of As and F- in shallow groundwater extracted from specific wells remains uncertain. This study utilized an enhanced stacking ensemble learning model to predict the distributions of As and F- in shallow groundwater based on 4,393 available datasets of observed concentrations and forty relevant environmental factors. The enhanced model was obtained by fusing well-suited Extreme Gradient Boosting, Random Forest, and Support Vector Machine as the base learners and a structurally simple Linear Discriminant Analysis as the meta-learner. The model precisely captured the patchy distributions of groundwater As and F- with an AUC value of 0.836 and 0.853, respectively. The findings revealed that 9.0% of the study area was characterized by a high As risk in shallow groundwater, while 21.2% was at high F- risk identified as having a high risk of fluoride contamination. About 0.2% of the study area shows elevated levels of both of them. The affected populations are estimated at approximately 7.61 million, 34.1 million, and 0.2 million, respectively. Furthermore, sedimentary environment exerted the greatest influence on distribution of groundwater As, with human activities and climate following closely behind at 29.5%, 28.1%, and 21.9%, respectively. Likewise, sedimentary environment was the primary factor affecting groundwater F- distribution, followed by hydrogeology and soil physicochemical properties, contributing 27.8%, 24.0%, and 23.3%, respectively. This study contributed to the identification of health risks associated with shallow groundwater As and F-, and provided insights into evaluating health risks in regions with limited samples.

Phlorizin from Lithocarpus litseifolius [Hance] Chun ameliorates FFA-induced insulin resistance by regulating AMPK/PI3K/AKT signaling pathway.

BACKGROUND: Insulin resistance (IR) is the central pathophysiological feature in the pathogenesis of metabolic syndrome, obesity, type 2 diabetes mellitus (T2DM), hypertension, and dyslipidemia. As the main active ingredient in Lithocarpus litseifolius [Hance] Chun, previous studies have shown that phlorizin (PHZ) can reduce insulin resistance in the liver. However, the effect of phlorizin on attenuating hepatic insulin resistance has not been fully investigated, and whether this effect is related to AMPK remains unclear. PURPOSE: The present study aimed to further investigate the effect of phlorizin on attenuating insulin resistance and the potential action mechanism. METHODS: Free fatty acids (FFA) were used to induce insulin resistance in HepG2 cells. The effects of phlorizin and FFA on cell viability were detected by MTT analysis. Glucose consumption, glycogen synthesis, intracellular malondialdehyde (MDA), superoxide dismutase (SOD), total cholesterol (TC), and triglyceride (TG) contents were quantified after phlorizin treatment. Glucose uptake and reactive oxygen species (ROS) levels in HepG2 cells were assayed by flow cytometry. Potential targets and signaling pathways for attenuating insulin resistance by phlorizin were predicted by network pharmacological analysis. Moreover, the expression levels of proteins related to the AMPK/PI3K/AKT signaling pathway were detected by western blot. RESULTS: Insulin resistance was successfully induced in HepG2 cells by co-treatment of 1 mM sodium oleate (OA) and 0.5 mM sodium palmitate (PA) for 24 h. Treatment with phlorizin promoted glucose consumption, glucose uptake, and glycogen synthesis and inhibited gluconeogenesis in IR-HepG2 cells. In addition, phlorizin inhibited oxidative stress and lipid accumulation in IR-HepG2 cells. Network pharmacological analysis showed that AKT1 was the active target of phlorizin, and the PI3K/AKT signaling pathway may be the potential action mechanism of phlorizin. Furthermore, western blot results showed that phlorizin ameliorated FFA-induced insulin resistance by activating the AMPK/PI3K/AKT signaling pathway. CONCLUSION: Phlorizin inhibited oxidative stress and lipid accumulation in IR-HepG2 cells and ameliorated hepatic insulin resistance by activating the AMPK/PI3K/AKT signaling pathway. Our study proved that phlorizin played a role in alleviating hepatic insulin resistance by activating AMPK, which provided experimental evidence for the use of phlorizin as a potential drug to improve insulin resistance.

Cathepsin C from extracellular histone-induced M1 alveolar macrophages promotes NETosis during lung ischemia-reperfusion injury.

Primary graft dysfunction (PGD) is a severe form of acute lung injury resulting from lung ischemia/reperfusion injury (I/R) in lung transplantation (LTx), associated with elevated post-transplant morbidity and mortality rates. Neutrophils infiltrating during reperfusion are identified as pivotal contributors to lung I/R injury by releasing excessive neutrophil extracellular traps (NETs) via NETosis. While alveolar macrophages (AMs) are involved in regulating neutrophil chemotaxis and infiltration, their role in NETosis during lung I/R remains inadequately elucidated. Extracellular histones constitute the main structure of NETs and can activate AMs. In this study, we confirmed the significant involvement of extracellular histone-induced M1 phenotype of AMs (M1-AMs) in driving NETosis during lung I/R. Using secretome analysis, public protein databases, and transwell co-culture models of AMs and neutrophils, we identified Cathepsin C (CTSC) derived from AMs as a major mediator in NETosis. Further elucidating the molecular mechanisms, we found that CTSC induced NETosis through a pathway dependent on NADPH oxidase-mediated production of reactive oxygen species (ROS). CTSC could significantly activate p38 MAPK, resulting in the phosphorylation of the NADPH oxidase subunit p47phox, thereby facilitating the trafficking of cytoplasmic subunits to the cell membrane and activating NADPH oxidase. Moreover, CTSC up-regulated and activated its substrate membrane proteinase 3 (mPR3), resulting in an increased release of NETosis-related inflammatory factors. Inhibiting CTSC revealed great potential in mitigating NETosis-related injury during lung I/R. These findings suggests that CTSC from AMs may be a crucial factor in mediating NETosis during lung I/R, and targeting CTSC inhition may represent a novel intervention for PGD in LTx.

Vividness of visual imagery is associated with the effect of relaxation response meditation training in elderly people with nonorganic insomnia: A randomized, double-blind, multi-center clinical trial.

BACKGROUND: This study aims to explore the efficacy of Relaxation Response Meditation Training (RRMT) on elderly individuals with different levels of vividness of visual imagery. METHODS: In this randomized controlled, double-blind, multi-center clinical trial, we recruited a total of 136 elderly individuals who were over 60 years with nonorganic sleep disorders to participate in a 4-week RRMT intervention from October 2020 to October 2022. The intervention occurred twice a week, totaling eight times. These individuals were divided into high and low groups based on the vividness of visual imagery, and then randomly assigned to either the control or intervention groups, as follows: low-visualizers intervention group (LI group); low-visualizers control group (LC group); high-visualizers intervention group (HI group); high-visualizers control group (HC group). Their social and psychological parameters were assessed before and after the intervention by the Pittsburgh Sleep Quality Index (PSQI), the Revised Piper's fatigue scale (RPFS), General well-being scale (GWB), and Satisfaction rating. The alpha waves of patients were also collected through electroencephalogram to assess their level of relaxation. RESULTS: Compared to the LI group, the HI group had a greater reduction rate in the PSQI score [25.2 % (18.8 % to 31.7 %), P < 0∙001], shorter sleep latency (P = 0.001), lower frequency of sleep medication (P < 0.001), lower PSQI scores (P < 0.001), and higher GWB scores (P < 0.001). There were significant differences in all indicators in the HI group vs. HC group and in the LI group vs. LC group. In the first five relaxation training sessions, there was no statistically significant difference in the proportion of α waves between the LI group and the LC group; however, from the sixth session onward, we observed a statistically significant difference (t = 2.86, P = 0.019)，while The HI group and HC group showing significant differences in the first relaxation training session (t = 4.464, P < 0.001). There was a statistically significant difference in subjective satisfaction between the intervention group and the control group (x2 = 49.605, P < 0.001). CONCLUSION: In this study, we found that most elderly people benefitted from RRMT regardless of their vividness of visual imagery. However, low-visualizers experienced slower and less effective results, so these patients may benefit more from alternative approaches.

Stem Cell-Derived Nanovesicles Embedded in Dual-Layered Hydrogel for Programmed ROS Regulation and Comprehensive Tissue Regeneration in Burn Wound Healing.

Burn wounds often bring high risks of delayed healing process and even death. Reactive oxygen species (ROS) play a crucial role in burn wound repair. However, the dynamic process in wound healing requires both the generation of ROS to inhibit bacteria and the subsequent reduction of ROS levels to initiate and promote tissue regeneration, which calls for a more intelligent ROS regulation dressing system. Hence, a dual-layered hydrogel (Dual-Gel) tailored to the process of burn wound repair is designed: the inner layer hydrogel (Gel 2) first responds to bacterial hyaluronidase (Hyal) to deliver aggregation-induced emission photosensitizer functionalized adipose-derived stem cell nanovesicles, which generate ROS upon light irradiation to eliminate bacteria; then the outer layer hydrogel (Gel 1) continuously starts a long-lasting consumption of excess ROS at the wound site to accelerate tissue regeneration. Simultaneously, the stem cell nanovesicles trapped in the burns wound also provide nutrients and mobilize neighboring tissues to thoroughly assist in inflammation regulation, cell proliferation, migration, and angiogenesis. In summary, this study develops an intelligent treatment approach on burn wounds by programmatically regulating ROS and facilitating comprehensive wound tissue repair.

Zedoarondiol inhibits human bronchial smooth muscle cell proliferation through the CAV-1/PDGF signalling pathway.

Airway remodelling in lung diseases can be treated by inhibiting excessive smooth muscle cell proliferation. Zedoarondiol (Zed) is a natural compound isolated from the Chinese herb Curcuma longa. The caveolin-1 (CAV-1) is widely expressed in lung cells and plays a key role in platelet-derived growth factor (PDGF) signalling and cell proliferation. This study aims to investigate the effect of Zed on human bronchial smooth muscle cell (HBSMC) proliferation and explore its potential molecular mechanisms. We assessed the effect of Zed on the proliferation of PDGF-stimulated HBSMCs and performed proteomic analysis to identify potential molecular targets and pathways. CAV1 siRNA was used to validate our findings in vitro. In PDGF-stimulated HBSMCs, Zed significantly inhibited excessive proliferation of HBSMCs. Proteomic analysis of zedoarondiol-treated HBSMCs revealed significant enrichment of differentially expressed proteins in cell proliferation-related pathways and biological processes. Zed inhibition of HBSMC proliferation was associated with upregulation of CAV1, regulation of the CAV-1/PDGF pathway and inhibition of MAPK and PI3K/AKT signalling pathway activation. Treatment of HBSMCs with CAV1 siRNA partly reversed the inhibitory effect of Zed on HBSMC proliferation. Thus, this study reveals that zedoarondiol potently inhibits HBSMC proliferation by upregulating CAV-1 expression, highlighting its potential value in airway remodelling and related diseases.

Optimization of hydrolysis conditions of alginate based on high performance liquid chromatography.

Alginate is the most abundant polysaccharide compound in brown algae, which is widely used in various fields. At present, the determination of the content of alginate is mostly carried out using sulfuric acid and trifluoroacetic acid hydrolysis followed by the determination of the content, but the results are not satisfactory, and there are problems such as low hydrolysis degree and low recovery rate. Therefore, in this study, based on the optimization of high performance liquid chromatographic conditions for pre-column derivatization of 1-phenyl-3-methyl-5-pyrazolone (PMP), the hydrolysis effects of sulfuric acid, trifluoroacetic acid (TFA), oxalic acid, and formic acid were compared and the hydrolysis conditions were optimized. The results showed that formic acid was the best hydrolyzing acid. The optimal hydrolysis conditions were 95 % formic acid at 110 °C for 10 h. The hydrolysis effect was stable, with high recovery and low destruction of monosaccharides, which made it possible to introduce formic acid into the subsequent polysaccharide hydrolysis. The pre-column derivatization high performance liquid chromatography method established in this study was accurate and reliable, and the hydrolysis acid with better effect was screened, which provided a theoretical basis for the subsequent determination of alginate content.

High-Entropy Lanthanide-Organic Framework as an Efficient Heterogeneous Catalyst for Cycloaddition of CO2 with Epoxides and Knoevenagel Condensation.

Multimetallic synergistic effects have the potential to improve CO2 cycloesterification and Knoevenagel reaction processes, outperforming monometallic MOFs. The results demonstrate superior performance in these processes. To investigate this, we created and characterized a selection of single-component Ln(III)-MOFs (Ln = Eu, Tb, Gd, Dy, Ho) and high-entropy lanthanide-organic framework (HE-LnMOF) using solvent-thermal conditions. The experiments revealed that HE-LnMOF exhibited heightened catalytic efficiency in CO2 cycloesterification and Knoevenagel reactions compared to single-component Ln(III) MOFs. Moreover, the HE-LnMOF displayed significant stability, maintaining their structural integrity after five cycles while sustaining elevated conversion and selectivity rates. The feasible mechanisms of catalytic reactions were also discussed. HE-LnMOF possess multiple unsaturated metal centers, acting as Lewis acid sites, with oxygen atoms connecting the metal, and hydroxyl groups on the ligand serving as base sites. This study introduces a novel method for synthesizing HE-LnMOF and presents a fresh application of HE-LnMOF for converting CO2.