Machine learning-driven SERS analysis platform for rapid and accurate detection of precancerous lesions of gastric cancer.

A novel approach is proposed leveraging surface-enhanced Raman spectroscopy (SERS) combined with machine learning (ML) techniques, principal component analysis (PCA)-centroid displacement-based nearest neighbor (CDNN). This label-free approach can identify slight abnormalities between SERS spectra of gastric lesions at different stages, offering a promising avenue for detection and prevention of precancerous lesion of gastric cancer (PLGC). The agaric-shaped nanoarray substrate was prepared using gas-liquid interface self-assembly and reactive ion etching (RIE) technology to measure SERS spectra of serum from mice model with gastric lesions at different stages, and then a SERS spectral recognition model was trained and constructed using the PCA-CDNN algorithm. The results showed that the agaric-shaped nanoarray substrate has good uniformity, stability, cleanliness, and SERS enhancement effect. The trained PCA-CDNN model not only found the most important features of PLGC, but also achieved satisfactory classification results with accuracy, area under curve (AUC), sensitivity, and specificity up to 100%. This demonstrated the enormous potential of this analysis platform in the diagnosis of PLGC.

Associations between the TyG index and the ɑ-Klotho protein in middle-aged and older population relevant to diabetes mellitus in NHANES 2007-2016.

BACKGROUND: The anti-aging protein Klotho has diverse functions in antioxidative stress and energy metabolism through several pathways. While it has been reported that α-Klotho is downregulated in patients with insulin resistance (IR), the association between Klotho and IR is complex and controversial. The triglyceride-glucose (TyG) index has provided a practical method for assessing IR. With this in mind, our study aimed to investigate the relationship between the TyG index and soluble α-Klotho protein levels in US populations, both with and without diabetes mellitus. METHODS: This cross-sectional study analyzed data from middle-aged and older participants in the National Health and Nutrition Examination Survey (NHANES) conducted between 2007 and 2016. The participants were divided into two groups based on their diabetes mellitus status: those with diabetes and those without diabetes. To evaluate the relationship between the TyG index and the concentration of the α-Klotho protein in each group, a series of survey-weighted multivariable linear regression models were employed. Furthermore, to examine the association between these two variables, multivariable-adjusted restricted cubic spline curves and subgroup analysis were generated. RESULTS: The study involved 6,439 adults aged 40 years or older, with a mean age of 57.8 ± 10.9 years. Among them, 1577 (24.5%) had diabetes mellitus. A subgroup analysis indicated that the presence of diabetes significantly affected the relationship between the TyG index and the α-Klotho level. After considering all covariables, regression analysis of the participants without diabetes revealed that the α-Klotho concentration decreased by 32.35 pg/ml (95% CI: -50.07, -14.64) with each one unit increase in TyG (p < 0.001). The decline in α-Klotho levels with elevated TyG was more pronounced in the female population. In patients with diabetes mellitus, a non-linear association between the TyG index and α-Klotho was observed. There was no significant correlation observed between the two when TyG index were below 9.7. However, there was an increase in klotho levels of 106.44 pg/ml for each unit increase in TyG index above 9.7 (95% CI: 28.13, 184.74) (p = 0.008). CONCLUSION: Our findings suggested that the presence of diabetes may influence the relationship between the TyG index and soluble α-Klotho. Furthermore, there seem to be sex differences in individuals without diabetes. Further studies are necessary to validate these findings.

Silicon Hybridization for the Preparation of Room-Temperature Curing and High-Temperature-Resistant Epoxy Resin.

Specialized epoxy resin, capable of achieving room-temperature profound curing and sustaining prolonged exposure to high-temperature environments, stands as a pivotal material in modern high-end manufacturing sectors including aerospace, marine equipment fabrication, machinery production, and the electronics industry. Herein, a silicon-hybridized epoxy resin, amenable to room-temperature curing and designed for high-temperature applications, was synthesized using a sol-gel methodology with silicate esters and silane coupling agents serving as silicon sources. Resin characterization indicates a uniform distribution of silicon elements in the obtained silicon hybrid epoxy resin. In comparison to the non-hybridized epoxy resin, notable improvements are observed in room-temperature curing performance, heat resistance, and mechanical strength.

Pt(IV) Complexes in the Search for Novel Platinum Prodrugs with Promising Activity.

The kinetically inert, six coordinated, octahedral Pt(IV) complexes are termed dual-, triple-, or multi-action prodrugs based on the nature of the axially substituted ligands. These ligands are either inert or biologically active, where the nature of these axial ligands provides additional stability, synergistic biological activity or cell-targeting ability. There are many literature reports from each of these classes, mentioning the varied nature of these axial ligands. The ligands comprise drug molecules such as chlorambucil, doxorubicin, valproic acid, ethacrynic acid, biologically active chalcone, coumarin, combretastatin, non-steroidal anti-inflammatory drugs (NSAIDs) and many more, potentiating the anti-proliferative profile or reducing the side effects associated with cisplatin therapy. The targeting and non-targeting nature of these moieties exert additive or synergistic effects on the anti-cancer activity of Pt(II) moieties. Herein, we discuss the effects of these axially oriented ligands and the changes in the non-leaving am(m)ine groups and in the leaving groups on the biological activity. In this review, we have presented the latest developments in the field of Pt(IV) complexes that display promising activity with a reduced resistance profile. We have discussed the structure activity relationship (SAR) and the effects of the ligands on the biological activity of Pt(IV) complexes with cisplatin, oxaliplatin, carboplatin and the Pt core other than approved drugs. This literature work will help researchers to get an idea about Pt(IV) complexes that have been classified based on the aspects of their biological activity.

The identification of novel stroke-related sphingolipid biomarkers using UPLC-MS/MS.

BACKGROUND: Stroke is a prominent contributor to global mortality and morbidity, thus necessitating the establishment of dependable diagnostic indicators. The objective of this study was to ascertain metabolites linked to sphingolipid metabolism and assess their viability as diagnostic markers for stroke. METHODS: Two cohorts, consisting of 56 S patients and 56 healthy volunteers, were incorporated into this investigation. Metabolite data was obtained through the utilization of Ultra Performance Liquid Chromatography and Tandem Mass Spectrometry (UPLC-MS/MS). The mass spectrometry data underwent targeted analysis and quantitative evaluation utilizing the multiple reaction monitoring mode of triple quadrupole mass spectrometry. Various data analysis techniques, including Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA), least absolute shrinkage and selection operator (LASSO) regression, Support Vector Machine (SVM), logistic regression, and Receiver Operating Characteristic (ROC) curves were employed. RESULTS: A comprehensive analysis detected a total of 129 metabolites related to sphingolipid metabolism, encompassing ceramides, 1-phosphoceramides, phytoceramides, glycosphingolipids, sphingomyelins, and sphingomyelins. The implementation of OPLS-DA analysis revealed significant disparities between individuals with stroke and controls, as it successfully identified 31 metabolites that exhibited significant differential expression between the two groups. Furthermore, functional enrichment analysis indicated the participation of these metabolites in diverse biological processes. Six metabolic markers, namely CerP(d18:1/20:3), CerP(d18:1/18:1), CerP(d18:1/18:0), CerP(d18:1/16:0), SM(d18:1/26:1), and Cer(d18:0/20:0), were successfully validated as potential diagnostic markers for stroke. The utilization of ROC analysis further confirmed their diagnostic potential, while a logistic regression model incorporating these markers demonstrated robust efficacy in distinguishing stroke patients from healthy controls. CONCLUSION: these identified metabolic markers exhibit clinical significance and hold promise as valuable tools for the diagnosis of stroke.

Rapid Formation of Intramolecular Disulfide Bridges using Light: An Efficient Method to Control the Conformation and Function of Bioactive Peptides.

Disulfide bonds are widely found in natural peptides and play a pivotal role in stabilizing their secondary structures, which are highly associated with their biological functions. Herein, we introduce a light-mediated strategy to effectively control the formation of disulfides. Our strategy is based on 2-nitroveratryl (oNv), a widely used photolabile motif, which serves both as a photocaging group and an oxidant (after photolysis). We demonstrated that irradiation of oNv-caged thiols with UV light could release free thiols that are rapidly oxidized by locally released byproduct nitrosoarene, leading to a "break-to-bond" fashion. This strategy is highlighted by the in situ restoration of the antimicrobial peptide tachyplesin I (TPI) from its external disulfide-caged analogue TPI-1. TPI-1 exhibits a distorted structure and a diminished function. However, upon irradiation, the ß-hairpin structure and membrane activity of TPI were largely restored via rapid intramolecular disulfide formation. Our study proposes a powerful method to regulate the conformation and function of peptides in a spatiotemporal manner, which has significant potential for the design of disulfide-centered light-responsive systems.

Breaking the barriers of data scarcity in drug-target affinity prediction.

Accurate prediction of drug-target affinity (DTA) is of vital importance in early-stage drug discovery, facilitating the identification of drugs that can effectively interact with specific targets and regulate their activities. While wet experiments remain the most reliable method, they are time-consuming and resource-intensive, resulting in limited data availability that poses challenges for deep learning approaches. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. To overcome this challenge, we present the Semi-Supervised Multi-task training (SSM) framework for DTA prediction, which incorporates three simple yet highly effective strategies: (1) A multi-task training approach that combines DTA prediction with masked language modeling using paired drug-target data. (2) A semi-supervised training method that leverages large-scale unpaired molecules and proteins to enhance drug and target representations. This approach differs from previous methods that only employed molecules or proteins in pre-training. (3) The integration of a lightweight cross-attention module to improve the interaction between drugs and targets, further enhancing prediction accuracy. Through extensive experiments on benchmark datasets such as BindingDB, DAVIS and KIBA, we demonstrate the superior performance of our framework. Additionally, we conduct case studies on specific drug-target binding activities, virtual screening experiments, drug feature visualizations and real-world applications, all of which showcase the significant potential of our work. In conclusion, our proposed SSM-DTA framework addresses the data limitation challenge in DTA prediction and yields promising results, paving the way for more efficient and accurate drug discovery processes.

Glucose-Triggered Release of trans-N-p-Coumaroyltyramine from Zeolitic Imidazolate Framework-8 (ZIF8) Modified with Dioscorea opposita Thunb Polysaccharide.

A Dioscorea opposita Thunb polysaccharide (DOP)-modified ZIF8 material was developed in this study, which can be used as a "smart" glucose-responsive carrier to control the slow release of drugs. The 3-aminophenylboronic acid (APBA) functionalized carboxylated long-chain polymer poly(ethylene glycol) (PEG) segments, which were first modified on the surface of ZIF8 nanoparticles with a hydrogen bond and then chemically cross-linked with DOP through a borate ester bond, leading to the drugs loaded on ZIF8 being "closed" in PBS but being "open" via taking off the DOP coating in high concentrations of glucose; thus, leakage can be prevented in the drug loaded and a glucose-triggered release can effectively result. Moreover, the materials showed good biocompatibility and the released trans-N-p-coumaroyltyramine (NCT) could work synergistically with the DOP to improve insulin resistance and promote glucose consumption in insulin-resistant HepG2 cells.

Relationship between the triglyceride-glucose index and coronary artery calcification in asymptomatic, non-diabetic patients undergoing maintenance hemodialysis.

OBJECTIVE: Coronary artery calcification (CAC) is positively and independently associated with cardiovascular disease (CVD) in patients undergoing maintenance hemodialysis (MHD). Insulin resistance is independently associated with CAC and is an important risk factor for CVD. The triglyceride-glucose (TyG) index is a reliable biomarker of insulin resistance. This cross-sectional, observational study aimed to investigate the relationship between the TyG index and CAC in asymptomatic non-diabetic patients undergoing MHD. METHODS: The quantitative coronary artery calcification score (CACS) was calculated and expressed using the Agatston score. The TyG index was calculated as ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. Multiple Poisson regression analysis, Spearman correlation analysis, and receiver operating characteristic (ROC) curves were used to investigate the relationship between the TyG index and CAC. RESULTS: The 151 patients were divided into three groups according to the tertiles of the TyG index. With an increase in the TyG index, the CACS significantly increased (Spearman's rho = 0.414, p < 0.001). Poisson regression analysis indicated that the TyG index was independently related to the presence of CAC (prevalence ratio, 1.281 [95% confidence interval, 1.121-1.465], p < 0.001). Furthermore, ROC curve analysis showed that the TyG index was of value in predicting the CAC in asymptomatic non-diabetic patients undergoing MHD, with an area under the curve of 0.667 (p = 0.010). CONCLUSION: The TyG index is independently related to the presence of CAC in asymptomatic, non-diabetic patients undergoing MHD.

The prevalence and determinant of overweight and obesity among residents aged 40-69 years in high-risk regions for upper gastrointestinal cancer in southeast China.

Being overweight or obese is one of the public health concerns worldwide, and its prevalence is gradually increasing. Obesity has been proven to be associated with some cancers, including upper gastrointestinal cancer (UGC). However, studies on the prevalence of obesity among residents of areas at high risk of UGC in China are minimal. The aim of this study is to assess the prevalence of obesity and its influencing factors among people aged 40-69 years (high-risk population) in high-risk areas for UGC in Jiangsu Province, southeast China. This cross-sectional study involved 45,036 subjects aged 40-69 years identified in the Rural Early Diagnosis and Treatment of UGC Project database in Jiangsu Province from 2017 to 2021. Differences in prevalence across gender and age were assessed using the Chi-square test. Using a multinomial logistic regression model, we examined independent risk factors for overweight/obesity and their gender and age differences. The prevalence of overweight, obesity, and overweight/obesity varied based on the standards used: Chinese standard (42.1%, 11.9%, and 54.0%) and WHO standard (34.7%, 4.7%, and 39.4%), respectively. Being overweight was more common in men than women, while obesity was more common in women than men. Age of 50-59 years, married, household size of 7-9, drinking, soy products, pickled food, and hot food intake were positively associated with overweight/obesity. Females, 60-69 years, higher education level, household size of 4-6, annual family income of more than 60,000 CNY, smoking, and fresh fruit intake were negatively associated with overweight/obesity. Stratified analysis showed that the effects of age, education and meat, egg and dairy products on overweight/obesity were different across gender. The impact of fresh fruit and vegetables on overweight/obesity was also heterogeneous between the younger (40-59 years) and older (60-69 years) groups. In conclusion, the prevalence of overweight and obesity is high among adults aged 40-69 years from high-risk areas for UGC of Jiangsu Province, southeast China. Independent influencing factors of being overweight/obese included gender, age, marital status, education, household size, annual family income, smoking, drinking, fresh fruit, soy products, pickled food and hot food intake, and may vary by gender and age. Screening-based interventions should be considered to control obesity levels among screened participants. Besides, heterogeneity of influencing factors across subgroups could be focused on to improve intervention effectiveness.

Masked Contrastive Representation Learning for Reinforcement Learning.

In pixel-based reinforcement learning (RL), the states are raw video frames, which are mapped into hidden representation before feeding to a policy network. To improve sample efficiency of state representation learning, recently, the most prominent work is based on contrastive unsupervised representation. Witnessing that consecutive video frames in a game are highly correlated, to further improve data efficiency, we propose a new algorithm, i.e., masked contrastive representation learning for RL (M-CURL), which takes the correlation among consecutive inputs into consideration. In our architecture, besides a CNN encoder for hidden presentation of input state and a policy network for action selection, we introduce an auxiliary Transformer encoder module to leverage the correlations among video frames. During training, we randomly mask the features of several frames, and use the CNN encoder and Transformer to reconstruct them based on context frames. The CNN encoder and Transformer are jointly trained via contrastive learning where the reconstructed features should be similar to the ground-truth ones while dissimilar to others. During policy evaluation, the CNN encoder and the policy network are used to take actions, and the Transformer module is discarded. Our method achieves consistent improvements over CURL on 14 out of 16 environments from DMControl suite and 23 out of 26 environments from Atari 2600 Games. The code is available at https://github.com/teslacool/m-curl.

Effect of different mixing modes on the abundance of Microcystis in Lake Taihu under green light.

Light and hydrodynamic force are important physical factors affecting growth of Microcystis. The most recent study found that green light has good effect in inhibiting growth of Microcystis. To understand the effect of mixing modes on Microcystis under the green light, we investigated the effects of continuous mixing and intermittent mixing on the abundance of Microcystis in Taihu Lake under field conditions. The study results found that abundance of Microcystis in control, intermittent mixing group, and continuous mixing group decreased 76.62%, 40.36%, and 95.18% on day 7 compared with that on day 1 in this experiment. At the end of the experiment, abundance percentages of diatoms and green algae to total phytoplankton abundance were 1.57% and 0.48% in control, 2.32% and 0.67% in intermittent mixing group, and 22.47% and 20.27% in continuous mixing group. The results indicated that continuous mixing favored the removal of Microcystis under green light conditions and was helpful for the growth of green algae and diatoms. The results provide a new approach for the control of Microcystis blooms.

R2-DDI: relation-aware feature refinement for drug-drug interaction prediction.

Precisely predicting the drug-drug interaction (DDI) is an important application and host research topic in drug discovery, especially for avoiding the adverse effect when using drug combination treatment for patients. Nowadays, machine learning and deep learning methods have achieved great success in DDI prediction. However, we notice that most of the works ignore the importance of the relation type when building the DDI prediction models. In this work, we propose a novel R$^2$-DDI framework, which introduces a relation-aware feature refinement module for drug representation learning. The relation feature is integrated into drug representation and refined in the framework. With the refinement features, we also incorporate the consistency training method to regularize the multi-branch predictions for better generalization. Through extensive experiments and studies, we demonstrate our R$^2$-DDI approach can significantly improve the DDI prediction performance over multiple real-world datasets and settings, and our method shows better generalization ability with the help of the feature refinement design.

Satisfaction and its determinants of rural upper gastrointestinal cancer screening in China: a preliminary cross-sectional study.

OBJECTIVES: To assess participants' satisfaction with and predictors of upper gastrointestinal cancer screening in rural areas. DESIGN: Cross-sectional study. SETTING: Ten screening centres in seven cities across five provinces in China. PARTICIPANTS: Of the 452 participants screened during the survey period, 438 completed the survey (response rate: 96.90%). PRIMARY OUTCOME MEASURES: Screening satisfaction was the primary outcome and it was assessed using the Patient Satisfaction Questionnaire-18. Ordinal logistic regression analysis was used to analyse the predictors of satisfaction. RESULTS: The percentages of satisfaction with the General Satisfaction, Technical Quality, Interpersonal Manner, Communication, Financial Aspects, Time Spent With Doctor, and Convenience and Accessibility dimensions were 95.89%, 79.68%, 88.36%, 83.56%, 75.11%, 82.19% and 66.44%, respectively. Education (OR 0.25, 95% CI 0.07 to 0.90), health self-assessment (OR 15.29, 95% CI 2.86 to 81.78) and family history of cancer (OR 4.20, 95% CI 1.29 to 13.71) were associated with General Satisfaction. Residence (OR 4.31, 95% CI 1.89 to 9.81) was associated with Technical Quality. Occupation (OR 0.27, 95% CI 0.08 to 0.88), health self-assessment (OR 11.30, 95% CI 3.94 to 32.43), screening purpose (OR 0.18, 95% CI 0.03 to 0.92) and distance from the screening centre (OR 4.59, 95% CI 1.35 to 15.61) were associated with interpersonal manner. Gender (OR 1.85, 95% CI 1.02 to 3.34), residence (OR 3.23, 95% CI 1.23 to 8.53) and endoscopy in the previous year (OR 2.79, 95% CI 1.13 to 6.90) were associated with Communication. Body mass index (BMI; OR 5.06, 95% CI 1.40 to 18.25) and health self-assessment (OR 2.09, 95% CI 1.12 to 3.88) were associated with financial aspects. Gender (OR 1.90, 95% CI 1.07 to 3.38), residence (OR 3.19, 95% CI 1.30 to 7.79), BMI (OR 5.26, 95% CI 1.14 to 24.34) and health self-assessment (OR 2.14, 95% CI 1.06 to 4.34) were associated with time spent with doctor. Gender (OR 1.64, 95% CI 1.04 to 2.60) and residence (OR 3.17, 95% CI 1.46 to 6.88) were associated with convenience and accessibility. CONCLUSION: There was heterogeneity across the dimensions of satisfaction with rural upper gastrointestinal cancer screening. Project manager should prioritise improving the aspects related to the convenience and accessibility dimension. Furthermore, to improve the efficiency of potential interventions, the predictors of the various dimensions should be considered.

A capillary-driven LoC-SERS device integrated with catalytic hairpin assembly amplification technology for NSCLC-related biomarkers detection.

In this study, we apply catalytic hairpin assembly (CHA) as the signal amplification strategy for the quantification of carcinoembryonic antigen (CEA) and cytokeratin fragment antigen 21-1 (CYFRA21-1) with a surface-enhanced Raman scattering (SERS) microfluidic chip (LoC-SERS) as the carrier. Herein, antibody-DNA conjugates are designed to assist the application of CHA amplification in protein detection. In the presence of protein biomarkers, antibody-DNA conjugates can specifically bind to the target proteins, forming the antigen@antibody-DNA conjugates. The terminal free part of the DNA on the conjugates can trigger the CHA events to connect SERS nanotags to capture nanoprobes. Then, micro-magnet can gather the CHA products in a rectangular chamber, resulting in the aggregation of SERS nanotags, which can ultimately generate abundant "hot spots" for SERS signal enhancement. Using this strategy, CEA and CYFRA21-1 can be successfully determined with a limit of detection (LOD) as low as pg mL-1, much lower than recently reported methods. Meanwhile, a non-small cell lung cancer (NSCLC)-xenografted mouse model was established, and SERS was applied to analyze the expression level of CEA and CYFRA21-1 in tumorigenesis and development. The comparison between SERS results and those of the ELISA method demonstrated a high degree of consistency, suggesting that the proposed CHA-assisted LoC-SERS device has satisfying accuracy. Thus, introducing the CHA strategy via the design of antibody-DNA conjugates opens new gates to ultra-sensitive and specific SERS detection of protein biomarkers.

Discovering drug-target interaction knowledge from biomedical literature.

MOTIVATION: The interaction between drugs and targets (DTI) in human body plays a crucial role in biomedical science and applications. As millions of papers come out every year in the biomedical domain, automatically discovering DTI knowledge from biomedical literature, which are usually triplets about drugs, targets and their interaction, becomes an urgent demand in the industry. Existing methods of discovering biological knowledge are mainly extractive approaches that often require detailed annotations (e.g. all mentions of biological entities, relations between every two entity mentions, etc.). However, it is difficult and costly to obtain sufficient annotations due to the requirement of expert knowledge from biomedical domains. RESULTS: To overcome these difficulties, we explore an end-to-end solution for this task by using generative approaches. We regard the DTI triplets as a sequence and use a Transformer-based model to directly generate them without using the detailed annotations of entities and relations. Further, we propose a semi-supervised method, which leverages the aforementioned end-to-end model to filter unlabeled literature and label them. Experimental results show that our method significantly outperforms extractive baselines on DTI discovery. We also create a dataset, KD-DTI, to advance this task and release it to the community. AVAILABILITY AND IMPLEMENTATION: Our code and data are available at https://github.com/bert-nmt/BERT-DTI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

TiO2 nanotube immobilised 5-lipoxygenase-mediated screening and isolation of anti-inflammatory active compounds from the leaves of lonicera japonica thunb.

In this work, a highly effective separation approach mediated by 5-Lipoxygenase (5-LOX) was established for screening and isolation of anti-inflammatory ingredients from leaves of Lonicera japonica Thunb. (LLJT). Using 5-LOX immobilised on TiO2 nanotubes as a microreactor, the targeted screening was exploited by combining with HPLC-MS system. Four compounds confirmed as luteolin, luteoside, lonicerin, and isochlorogenic acid C and a fraction (M1) were screened out to be potent inhibitors of 5-LOX. Their anti-inflammatory activities were further investigated and confirmed by RAW 264.7 cells inflammation model and rat foot swelling model. Furthermore, M1 was prepared by MCI GEL CHP20P column chromatography, and further separated by Pre-HPLC. One new compound confirmed to be 5,7,3',4'-tetrahydroxyflavone-7-O-sambubioside was first isolated from LLJT. The results provide a new method for the effective separation of active components derived from natural products.HighlightsA 5-LOX mediated separation method was established for isolation of anti-inflammatory compounds.An anti-inflammatory ingredient was separated by MCI GEL CHP20P column chromatography.One new compound was first isolated from leaves of Lonicera japonica Thunb.5-LOX was immobilised on TiO2 nanotubes and exploited by combining with HPLC-MS system.The anti-inflammatory activity of screened components was evaluated. [Figure: see text].

Carbon Dots Embedded Hybrid Microgel with Phenylboronic Acid as Monomer for Fluorescent Glucose Sensing and Glucose-Triggered Insulin Release at Physiological pH.

A multifunctional and biocompatible hybrid microgel (poly(VPBA-AAm)-CD) using N, S-doped carbon dots (CDs) and ethylene glycol dimethacrylate (EGDMA) as cross-linking agents, and 4-vinylbenzene boronic acid (VPBA) and acrylamide (AAm) as monomers, was designed in this work. This microgel can be easily prepared by a simple one-pot radical dispersion polymerization of the reactants using a rationally designed hydrogen-bonded complex method. The hybrid microgels were spherical particles with a smooth surface and an average particle size of 234 ± 8 nm. The poly(VPBA-AAm)-CD microgel displayed the glucose-responsive swelling within a clinically concerned range at a physiological pH and could realize the controllable release of insulin. In addition, the release rate of insulin in the hybrid microgel (poly(VPBA-AAm)-CD) could be triggered by glucose concentrations in the solution, and the increasing glucose concentrations can accelerate the insulin release. Further in vitro cytotoxicity studies showed that the microgel had good biocompatibility and no obvious toxicity to the cells. These indicate that the prepared microgel (poly(VPBA-AAm)-CD) may supply a new pattern for the self-regulating therapy of insulin deficiency in diabetes.

SPRoBERTa: protein embedding learning with local fragment modeling.

Well understanding protein function and structure in computational biology helps in the understanding of human beings. To face the limited proteins that are annotated structurally and functionally, the scientific community embraces the self-supervised pre-training methods from large amounts of unlabeled protein sequences for protein embedding learning. However, the protein is usually represented by individual amino acids with limited vocabulary size (e.g. 20 type proteins), without considering the strong local semantics existing in protein sequences. In this work, we propose a novel pre-training modeling approach SPRoBERTa. We first present an unsupervised protein tokenizer to learn protein representations with local fragment pattern. Then, a novel framework for deep pre-training model is introduced to learn protein embeddings. After pre-training, our method can be easily fine-tuned for different protein tasks, including amino acid-level prediction task (e.g. secondary structure prediction), amino acid pair-level prediction task (e.g. contact prediction) and also protein-level prediction task (remote homology prediction, protein function prediction). Experiments show that our approach achieves significant improvements in all tasks and outperforms the previous methods. We also provide detailed ablation studies and analysis for our protein tokenizer and training framework.

A Dioscorea opposita Thunb Polysaccharide-Based Dual-Responsive Hydrogel for Insulin Controlled Release.

A novel hydrogel (DOP/PEI-PBA) based on the "three-component" reaction of 2-formylphenylboric acid (2-FPBA), the primary amine group of polyethyleneimine (PEI) and the cis-o-dihydroxy groups of Dioscorea opposita Thunb polysaccharide (DOP) was designed in this work. The hydrogel can be easily prepared by simply mixing the three reactants at room temperature. The hydrogel had dual responsiveness to glucose and pH, and can realize the controllable release of insulin. Moreover, the hydrogel combining insulin and DOP can inhibit the reactive oxygen species (ROS) level and malondialdehyde (MDA) content, and promote glucose consumption as well as the level of superoxide dismutase (SOD), in high-glucose-induced injury in HL-7702 cells, which reflects the synergistic effect of insulin and DOP to protect hepatocytes from oxidative stress at the same time. Further in vitro cytotoxicity studies showed that the hydrogel had good biocompatibility and no obvious toxicity to cells. These indicate that the prepared hydrogel (DOP/PEI-PBA) can be expected to be applied in the clinical treatment of insulin deficiency in diabetes.