Nanoparticle elasticity regulates the formation of cell membrane-coated nanoparticles and their nano-bio interactions.

Cell membrane-coated nanoparticles are emerging as a new type of promising nanomaterials for immune evasion and targeted delivery. An underlying premise is that the unique biological functions of natural cell membranes can be conferred on the inherent physiochemical properties of nanoparticles by coating them with a cell membrane. However, the extent to which the membrane protein properties are preserved on these nanoparticles and the consequent bio-nano interactions are largely unexplored. Here, we synthesized two mesenchymal stem cell (MSC) membrane-coated silica nanoparticles (MCSNs), which have similar sizes but distinctly different stiffness values (MPa and GPa). Unexpectedly, a much lower macrophage uptake, but much higher cancer cell uptake, was found with the soft MCSNs compared with the stiff MCSNs. Intriguingly, we discovered that the soft MCSNs enabled the forming of a more protein-rich membrane coating and that coating had a high content of the MSC chemokine CXCR4 and MSC surface marker CD90. This led to the soft MCSNs enhancing cancer cell uptake mediated by the CD90/integrin receptor-mediated pathway and CXCR4/SDF-1 pathways. These findings provide a major step forward in our fundamental understanding of how the combination of nanoparticle elasticity and membrane coating may be used to facilitate bio-nano interactions and pave the way forward in the development of more effective cancer nanomedicines.

Involvement of CD44 and MAPK14-mediated ferroptosis in hemorrhagic shock.

To elucidate the induction of ferroptotic pathways and the transcriptional modulation of pivotal genes in the context of hemorrhagic shock. The R software was used to analyze the GSE64711 dataset, isolating genes relevant to ferroptosis. Enrichment analyses and protein interaction networks were assembled. Using WGCNA hub genes were identified and intersected with ferroptosis-related genes, highlighting hub genes CD44 and MAPK14. In a rat hemorrhagic shock model, cardiac ROS, Fe2+, MDA, and GSH levels were assessed. Key ferroptotic proteins (SLC7A11/GPX4) in myocardial tissues were examined via western blot. Hub genes, CD44 and MAPK14, expressions were confirmed through immunohistochemistry. Analyzing the GSE64711 dataset revealed 337 differentially expressed genes, including 12 linked to ferroptosis. Enrichment analysis highlighted pathways closely related to ferroptosis. Using Genemania, we found these genes mainly affect ROS metabolism and oxidative stress response. WGCNA identified CD44 and MAPK14 as hub genes. Rat myocardial tissue validation showed significant cardiac damage and elevated ROS and MDA levels, and decreased GSH levels in the hemorrhagic shock model. The ferroptotic pathway SLC7A11/GPX4 was activated, and immunohistochemistry showed a significant increase in the expression levels of CD44 and MAPK14 in the hemorrhagic shock rat model. We demonstrated the presence of tissue ferroptosis in hemorrhagic shock by combining bioinformatics analysis with in vivo experimentation. Specifically, we observed the activation of the SLC7A11/GPX4 ferroptotic pathway. Further, CD44 and MAPK14 were identified as hub genes in hemorrhagic shock.

Effectiveness of a non-physician community health-care provider-led intensive blood pressure intervention versus usual care on cardiovascular disease (CRHCP): an open-label, blinded-endpoint, cluster-randomised trial.

BACKGROUND: Effectiveness of a non-physician community health-care provider-led intensive blood pressure intervention on cardiovascular disease has not been established. We aimed to test the effectiveness of such an intervention compared with usual care on risk of cardiovascular disease and all-cause death among individuals with hypertension. METHODS: In this open-label, blinded-endpoint, cluster-randomised trial, we recruited individuals aged at least 40 years with an untreated systolic blood pressure of at least 140 mm Hg or a diastolic blood pressure of at least 90 mm Hg (≥130 mm Hg and ≥80 mm Hg for those at high risk for cardiovascular disease or if currently taking antihypertensive medication). We randomly assigned (1:1) 326 villages to a non-physician community health-care provider-led intervention or usual care, stratified by provinces, counties, and townships. In the intervention group, trained non-physician community health-care providers initiated and titrated antihypertensive medications according to a simple stepped-care protocol to achieve a systolic blood pressure goal of less than 130 mm Hg and diastolic blood pressure goal of less than 80 mm Hg with supervision from primary care physicians. They also delivered discounted or free antihypertensive medications and health coaching for patients. The primary effectiveness outcome was a composite outcome of myocardial infarction, stroke, heart failure requiring hospitalisation, and cardiovascular disease death during the 36-month follow-up in the study participants. Safety was assessed every 6 months. This trial is registered with ClinicalTrials.gov, NCT03527719. FINDINGS: Between May 8 and Nov 28, 2018, we enrolled 163 villages per group with 33 995 participants. Over 36 months, the net group difference in systolic blood pressure reduction was -23·1 mm Hg (95% CI -24·4 to -21·9; p<0·0001) and in diastolic blood pressure reduction, it was -9·9 mm Hg (-10·6 to -9·3; p<0·0001). Fewer patients in the intervention group than the usual care group had a primary outcome (1·62% vs 2·40% per year; hazard ratio [HR] 0·67, 95% CI 0·61-0·73; p<0·0001). Secondary outcomes were also reduced in the intervention group: myocardial infarction (HR 0·77, 95% CI 0·60-0·98; p=0·037), stroke (0·66, 0·60-0·73; p<0·0001), heart failure (0·58, 0·42-0·81; p=0·0016), cardiovascular disease death (0·70, 0·58-0·83; p<0·0001), and all-cause death (0·85, 0·76-0·95; p=0·0037). The risk reduction of the primary outcome was consistent across subgroups of age, sex, education, antihypertensive medication use, and baseline cardiovascular disease risk. Hypotension was higher in the intervention than in the usual care group (1·75% vs 0·89%; p<0·0001). INTERPRETATION: The non-physician community health-care provider-led intensive blood pressure intervention is effective in reducing cardiovascular disease and death. FUNDING: The Ministry of Science and Technology of China and the Science and Technology Program of Liaoning Province, China.

Leveraging Unidentified Metabolic Features for Key Pathway Discovery: Chemical Classification-driven Network Analysis in Untargeted Metabolomics.

Untargeted metabolomics using liquid chromatography-electrospray ionization-high-resolution tandem mass spectrometry (UPLC-ESI-MS/MS) provides comprehensive insights into the dynamic changes of metabolites in biological systems. However, numerous unidentified metabolic features limit its utilization. In this study, a novel approach, the Chemical Classification-driven Molecular Network (CCMN), was proposed to unveil key metabolic pathways by leveraging hidden information within unidentified metabolic features. The method was demonstrated by using the herbivore-induced metabolic response in corn silk as a case study. Untargeted metabolomics analysis using UPLC-MS/MS was performed on wild corn silk and two genetically modified lines (pre- and postinsect treatment). Global annotation initially identified 256 (ESI-) and 327 (ESI+) metabolites. MS/MS-based classifications predicted 1939 (ESI-) and 1985 (ESI+) metabolic features into the chemical classes. CCMNs were then constructed using metabolic features shared classes, which facilitated the structure- or class annotation for completely unknown metabolic features. Next, 844/713 significantly decreased and 1593/1378 increased metabolites in ESI-/ESI+ modes were defined in response to insect herbivory, respectively. Method validation on a spiked maize sample demonstrated an overall class prediction accuracy rate of 95.7%. Potential key pathways were prescreened by a hypergeometric test using both structure- and class-annotated differential metabolites. Subsequently, CCMN was used to deeply amend and uncover the pathway metabolites deeply. Finally, 8 key pathways were defined, including phenylpropanoid (C6-C3), flavonoid, octadecanoid, diterpenoid, lignan, steroid, amino acid/small peptide, and monoterpenoid. This study highlights the effectiveness of leveraging unidentified metabolic features. CCMN-based key pathway analysis reduced the bias in conventional pathway enrichment analysis. It provides valuable insights into complex biological processes.

Enhancing Metabolome Annotation by Electron Impact Excitation of Ions from Organics-Molecular Networking.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is widely used in untargeted metabolomics, but large-scale and high-accuracy metabolite annotation remains a challenge due to the complex nature of biological samples. Recently introduced electron impact excitation of ions from organics (EIEIO) fragmentation can generate information-rich fragment ions. However, effective utilization of EIEIO tandem mass spectrometry (MS/MS) is hindered by the lack of reference spectral databases. Molecular networking (MN) shows great promise in large-scale metabolome annotation, but enhancing the correlation between spectral and structural similarity is essential to fully exploring the benefits of MN annotation. In this study, a novel approach was proposed to enhance metabolite annotation in untargeted metabolomics using EIEIO and MN. MS/MS spectra were acquired in EIEIO and collision-induced dissociation (CID) modes for over 400 reference metabolites. The study revealed a stronger correlation between the EIEIO spectra and metabolite structure. Moreover, the EIEIO spectral network outperformed the CID spectral network in capturing structural analogues. The annotation performance of the structural similarity network for untargeted LC-MS/MS was evaluated. For the spiked NIST SRM 1950 human plasma, the annotation coverage and accuracy were 72.94 and 74.19%, respectively. A total of 2337 metabolite features were successfully annotated in NIST SRM 1950 human plasma, which was twice that of LC-CID MS/MS. Finally, the developed method was applied to investigate prostate cancer. A total of 87 significantly differential metabolites were annotated. This study combining EIEIO and MN makes a valuable contribution to improving metabolome annotation.

Super-Flexible Water-Proof Actuators.

Humidity-responsive materials hold broad application prospects in sensing, energy production, and other fields. Particularly, humidity-sensitive, flexibility, and water resistance are pivotal factors in the development of optimized humidity-responsive materials. In this study, hydrophobic linear polyurethane and hydrophilic 4-vinylphenylboronic acid (4-VPBA) form a semi-intercross cross-linking network. This copolymer of polyurethane exhibits excellent humidity-sensitive, mechanical properties, and water resistance. Its maximum tensile strength and maximum elongation can reach 40.56 MPa and 543.47%, respectively. After being immersed in water at various temperatures for 15 days, it exhibited a swelling ratio of only 3.28% in water at 5 °C and 9.58% in water at 70 °C. While the presence of 4-VPBA network imparts humidity-sensitive, reversible, and multidirectional bending abilities, under the stimulus of water vapor, it can bend 43° within 1.4 s. The demonstrated material surpasses current bidirectional humidity actuators in actuating ability. Based on these characteristics, automatically opening waterproof umbrellas and windows, as well as bionic-arms, crawling robots, and self-propelled boats, are successfully developed.

Performance evaluation of computational methods for splice-disrupting variants and improving the performance using the machine learning-based framework.

A critical challenge in genetic diagnostics is the assessment of genetic variants associated with diseases, specifically variants that fall out with canonical splice sites, by altering alternative splicing. Several computational methods have been developed to prioritize variants effect on splicing; however, performance evaluation of these methods is hampered by the lack of large-scale benchmark datasets. In this study, we employed a splicing-region-specific strategy to evaluate the performance of prediction methods based on eight independent datasets. Under most conditions, we found that dbscSNV-ADA performed better in the exonic region, S-CAP performed better in the core donor and acceptor regions, S-CAP and SpliceAI performed better in the extended acceptor region and MMSplice performed better in identifying variants that caused exon skipping. However, it should be noted that the performances of prediction methods varied widely under different datasets and splicing regions, and none of these methods showed the best overall performance with all datasets. To address this, we developed a new method, machine learning-based classification of splice sites variants (MLCsplice), to predict variants effect on splicing based on individual methods. We demonstrated that MLCsplice achieved stable and superior prediction performance compared with any individual method. To facilitate the identification of the splicing effect of variants, we provided precomputed MLCsplice scores for all possible splice sites variants across human protein-coding genes (http://39.105.51.3:8090/MLCsplice/). We believe that the performance of different individual methods under eight benchmark datasets will provide tentative guidance for appropriate method selection to prioritize candidate splice-disrupting variants, thereby increasing the genetic diagnostic yield.

Application of a circular-shaped pulsed field ablation catheter with magnetic sensors for pulmonary vein isolation: a multi-centre clinical study report.

AIMS: A few studies have reported the effect and safety of pulsed field ablation (PFA) catheters for ablating atrial fibrillation (AF), which were mainly based on basket-shaped or flower-shaped designs. However, the clinical application of a circular-shaped multi-electrode catheter with magnetic sensors is very limited. To study the efficacy and safety of a PFA system in patients with paroxysmal AF using a circular-shaped multi-electrode catheter equipped with magnetic sensors for pulmonary vein isolation (PVI). METHODS AND RESULTS: A novel proprietary bipolar PFA system was used for PVI, which utilized a circular-shaped multi-electrode catheter with magnetic sensors and allowed for three-dimensional model reconstruction, mapping, and ablation in one map. To evaluate the efficacy, efficiency, and safety of this PFA system, a prospective, multi-centre, single-armed, pre-market clinical study was performed. From July 2021 to December 2022, 151 patients with paroxysmal AF were included and underwent PVI. The study examined procedure time, immediate success rate, procedural success rate at 12 months, and relevant complications. In all 151 patients, all the pulmonary veins were acutely isolated using the studied system. Pulsed field ablation delivery was 78.4 ± 41.8 times and 31.3 ± 16.7 ms per patient. Skin-to-skin procedure time was 74.2 ± 29.8 min, and fluoroscopy time was 13.1 ± 7.6 min. The initial 11 (7.2%) cases underwent procedures with deep sedation anaesthesia, and the following cases underwent local anaesthesia. In the initial 11 cases, 4 cases (36.4%) presented transient vagal responses, and the rest were all successfully preventatively treated with atropine injection and rapid fluid infusion. No severe complications were found during or after the procedure. During follow-up, 3 cases experienced atrial flutter, and 11 cases had AF recurrence. The estimated 12-month Kaplan-Meier of freedom from arrhythmia was 88.4%. CONCLUSION: The PFA system, comprised of a circular PFA catheter with magnetic sensors, could rapidly achieve PVI under three-dimensional guidance and demonstrated excellent safety with comparable effects.

Depressive symptoms, parenting attitude, and violent discipline among caregivers of left-behind children in rural China: a cross-sectional study.

BACKGROUND: The situation of mental health and discipline behaviors of left-behind children's caregivers were not optimistic in rural China. Caregivers' depression might increase the risk of using violent discipline. However, the specific ways in which depressive symptoms impact violent discipline have rarely been explored in rural areas. This study aims to assess the prevalence of violent discipline among left-behind children under 6 years of age in rural China and explore the potential mechanisms of how caregivers' depressive symptoms affect violent discipline. METHODS: We enrolled a total of 396 pairs of left-behind children and their caregivers in our study, which was conducted in 5 counties of Hebei, Henan, Jiangxi, Guizhou, and Sichuan provinces in China. The depressive symptoms of caregivers were measured by using Zung Self-rating Depression Scale (ZSDS) and violent discipline was assessed by the Child Discipline Module of Multiple Indicator Cluster Surveys (MICS). A self-designed questionnaire was utilized to measure caregiver's parenting attitude. Based on the cross-sectional data, controlling for potential confounders, structural equation modeling (SEM) was used to assess the direct and indirect effects of the mediation models by applying the weighted least squares with mean and variance adjusted (WLSMV) estimate. RESULTS: The prevalence of violent discipline, psychological aggression, and physical punishment was 72.7%, 59.3%, and 60.4% respectively of left-behind children under 6 years of age. According to the results of SEM, parenting attitude acted as a suppressor, suppressing the association between caregivers' depressive symptoms and physical punishment/psychological aggression/violent discipline. The caregivers' depressive symptoms positively influenced all the outcome variables by affecting parenting attitudes (p = 0.002, p = 0.013, p = 0.002). CONCLUSIONS: The presence of depressive symptoms in caregivers increases the use of violent discipline through negative parenting attitudes. The mental health status of primary caregivers of left-behind children in rural China needed emphasis and improvement.

Family responsive care mediating the home visiting effects on left-behind children's early development in rural China.

BACKGROUND: The early development of left-behind children requires great concern and improvement. Yet, current interventions for left-behind children are mainly focussed on children older than 3. This study aims to assess the effectiveness of a home visiting programme on family responsive care and early development of rural left-behind children and examine whether family responsive care mediates the effects of intervention on child development. METHODS: A quasi-experimental study design was utilized in this study. A stratified clustered sampling was employed to choose villages in programme towns into intervention group. A control village was matched with every intervention village. All of the left-behind children and their caregivers meeting the inclusion criteria in the chosen villages were enrolled in the survey. The outcomes included child development, caregiver's early stimulation, parent-child communication, and learning materials. Baseline assessments were conducted in 2018, and endline assessments were conducted in 2020. RESULTS: In the endline survey, we enrolled 608 children with 258 in the intervention group and 350 in the control group. Left-behind children in the intervention group were less likely to have development delay compared with the control group (odds ratio [OR] = 0.59, 95% confidence interval [CI]: 0.36, 0.96). Migrant parents of children in the intervention group showed higher proportion of expressing emotional support to their children when communicating (OR = 1.69, 95% CI: 1.05, 2.72). Children who received home visits more than once per 2 months had lower level of suspected development delay than children in the control group (OR = 0.34, 95% CI: 0.18, 0.68). Caregiver's early stimulation and migrant parents' emotional support to left-behind children mediated the intervention dose and left-behind children's development. CONCLUSION: Caregiver's early stimulation mediates the intervention and child's development. The findings suggest a promising future for scaling similar early childhood development interventions for left-behind children in rural settings.

Tailored Fluorosurfactants through Controlled/Living Radical Polymerization for Highly Stable Microfluidic Droplet Generation.

Droplet-based microfluidics represents a disruptive technology in the field of chemistry and biology through the generation and manipulation of sub-microlitre droplets. To avoid droplet coalescence, fluoropolymer-based surfactants are commonly used to reduce the interfacial tension between two immiscible phases to stabilize droplet interfaces. However, the conventional preparation of fluorosurfactants involves multiple steps of conjugation reactions between fluorinated and hydrophilic segments to form multiple-block copolymers. In addition, synthesis of customized surfactants with tailored properties is challenging due to the complex synthesis process. Here, we report a highly efficient synthetic method that utilizes living radical polymerization (LRP) to produce fluorosurfactants with tailored functionalities. Compared to the commercialized surfactant, our surfactants outperform in thermal cycling for polymerase chain reaction (PCR) testing, and exhibit exceptional biocompatibility for cell and yeast culturing in a double-emulsion system. This breakthrough synthetic approach has the potential to revolutionize the field of droplet-based microfluidics by enabling the development of novel designs that generate droplets with superior stability and functionality for a wide range of applications.

Prediction of plant secondary metabolic pathways using deep transfer learning.

BACKGROUND: Plant secondary metabolites are highly valued for their applications in pharmaceuticals, nutrition, flavors, and aesthetics. It is of great importance to elucidate plant secondary metabolic pathways due to their crucial roles in biological processes during plant growth and development. However, understanding plant biosynthesis and degradation pathways remains a challenge due to the lack of sufficient information in current databases. To address this issue, we proposed a transfer learning approach using a pre-trained hybrid deep learning architecture that combines Graph Transformer and convolutional neural network (GTC) to predict plant metabolic pathways. RESULTS: GTC provides comprehensive molecular representation by extracting both structural features from the molecular graph and textual information from the SMILES string. GTC is pre-trained on the KEGG datasets to acquire general features, followed by fine-tuning on plant-derived datasets. Four metrics were chosen for model performance evaluation. The results show that GTC outperforms six other models, including three previously reported machine learning models, on the KEGG dataset. GTC yields an accuracy of 96.75%, precision of 85.14%, recall of 83.03%, and F1_score of 84.06%. Furthermore, an ablation study confirms the indispensability of all the components of the hybrid GTC model. Transfer learning is then employed to leverage the shared knowledge acquired from the KEGG metabolic pathways. As a result, the transferred GTC exhibits outstanding accuracy in predicting plant secondary metabolic pathways with an average accuracy of 98.30% in fivefold cross-validation and 97.82% on the final test. In addition, GTC is employed to classify natural products. It achieves a perfect accuracy score of 100.00% for alkaloids, while the lowest accuracy score of 98.42% for shikimates and phenylpropanoids. CONCLUSIONS: The proposed GTC effectively captures molecular features, and achieves high performance in classifying KEGG metabolic pathways and predicting plant secondary metabolic pathways via transfer learning. Furthermore, GTC demonstrates its generalization ability by accurately classifying natural products. A user-friendly executable program has been developed, which only requires the input of the SMILES string of the query compound in a graphical interface.

A village doctor-led multifaceted intervention for blood pressure control in rural China: an open, cluster randomised trial.

BACKGROUND: The prevalence of uncontrolled hypertension is high and increasing in low-income and middle-income countries. We tested the effectiveness of a multifaceted intervention for blood pressure control in rural China led by village doctors (community health workers on the front line of primary health care). METHODS: In this open, cluster randomised trial (China Rural Hypertension Control Project), 326 villages that had a regular village doctor and participated in the China New Rural Cooperative Medical Scheme were randomly assigned (1:1) to either village doctor-led multifaceted intervention or enhanced usual care (control), with stratification by provinces, counties, and townships. We recruited individuals aged 40 years or older with an untreated blood pressure of 140/90 mm Hg or higher (≥130/80 mm Hg among those with a history of cardiovascular disease, diabetes, or chronic kidney disease) or a treated blood pressure of 130/80 mm Hg or higher. In the intervention group, trained village doctors initiated and titrated antihypertensive medications according to a standard protocol with supervision from primary care physicians. Village doctors also conducted health coaching on home blood pressure monitoring, lifestyle changes, and medication adherence. The primary outcome (reported here) was the proportion of patients with a blood pressure of less than 130/80 mm Hg at 18 months. The analysis was by intention to treat. This trial is registered with ClinicalTrials.gov, NCT03527719, and is ongoing. FINDINGS: Between May 8 and November 28, 2018, we enrolled 33 995 individuals from 163 intervention and 163 control villages. At 18 months, 8865 (57·0%) of 15 414 patients in the intervention group and 2895 (19·9%) of 14 500 patients in the control group had a blood pressure of less than 130/80 mm Hg, with a group difference of 37·0% (95% CI 34·9 to 39·1%; p<0·0001). Mean systolic blood pressure decreased by -26·3 mm Hg (95% CI -27·1 to -25·4) from baseline to 18 months in the intervention group and by -11·8 mm Hg (-12·6 to -11·0) in the control group, with a group difference of -14·5 mm Hg (95% CI -15·7 to -13·3 mm Hg; p<0·0001). Mean diastolic blood pressure decreased by -14·6 mm Hg (-15·1 to -14·2) from baseline to 18 months in the intervention group and by -7·5 mm Hg (-7·9 to -7·2) in the control group, with a group difference of -7·1 mm Hg (-7·7 to -6·5 mm Hg; p<0·0001). No treatment-related serious adverse events were reported in either group. INTERPRETATION: Compared with enhanced usual care, village doctor-led intervention resulted in statistically significant improvements in blood pressure control among rural residents in China. This feasible, effective, and sustainable implementation strategy could be scaled up in rural China and other low-income and middle-income countries for hypertension control. FUNDING: Ministry of Science and Technology of China.

Deep Characterization of Serum Metabolome Based on the Segment-Optimized Spectral-Stitching Direct-Infusion Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Approach.

Direct-infusion Fourier transform ion cyclotron resonance mass spectrometry (DI-FTICR MS) shows great promise for metabolomic analysis due to ultrahigh mass accuracy and resolution. However, most of the DI-FTICR MS approaches focused on high-throughput metabolomics analysis at the expense of sensitivity and resolution and the potential for metabolome characterization has not been fully explored. Here, we proposed a novel deep characterization approach of serum metabolome using a segment-optimized spectral-stitching DI-FTICR MS method integrated with high-confidence and database-independent formula assignments. With varied acquisition parameters for each segment, a highly efficient acquisition was achieved for the whole mass range with sub-ppm mass accuracy. In a pooled human serum sample, thousands of features were assigned with unambiguous formulas and possible candidates based on highly accurate mass measurements. Furthermore, a reaction network was used to select confidently unique formulas from possible candidates, which was constructed by unambiguous formulas and possible candidates connected by the formula differences resulting from biochemical and MS transformation. Compared with full-range and conventional segment acquisition, 8- and 1.2-fold increases in observed features were achieved, respectively. Assignment accuracy was 93-94% for both a standard mixture containing 190 metabolites and a spiked serum sample with the root mean square mass error of 0.15-0.16 ppm. In total, 3534 unequivocal neutral molecular formulas were assigned in the pooled serum sample, 35% of which are contained in the HMDB. This method offers great enhancement in the deep characterization of serum metabolome by DI-FTICR MS.

A Structure-Guided Molecular Network Strategy for Global Untargeted Metabolomics Data Annotation.

Large-scale metabolite annotation is a bottleneck in untargeted metabolomics. Here, we present a structure-guided molecular network strategy (SGMNS) for deep annotation of untargeted ultra-performance liquid chromatography-high resolution mass spectrometry (MS) metabolomics data. Different from the current network-based metabolite annotation method, SGMNS is based on a global connectivity molecular network (GCMN), which was constructed by molecular fingerprint similarity of chemical structures in metabolome databases. Neighbor metabolites with similar structures in GCMN are expected to produce similar spectra. Network annotation propagation of SGMNS is performed using known metabolites as seeds. The experimental MS/MS spectra of seeds are assigned to corresponding neighbor metabolites in GCMN as their "pseudo" spectra; the propagation is done by searching predicted retention times, MS1, and "pseudo" spectra against metabolite features in untargeted metabolomics data. Then, the annotated metabolite features were used as new seeds for annotation propagation again. Performance evaluation of SGMNS showed its unique advantages for metabolome annotation. The developed method was applied to annotate six typical biological samples; a total of 701, 1557, 1147, 1095, 1237, and 2041 metabolites were annotated from the cell, feces, plasma (NIST SRM 1950), tissue, urine, and their pooled sample, respectively, and the annotation accuracy was >83% with RSD <2%. The results show that SGMNS fully exploits the chemical space of the existing metabolomes for metabolite deep annotation and overcomes the shortcoming of insufficient reference MS/MS spectra.

Increased circulating phenylacetylglutamine concentration elevates the predictive value of cardiovascular event risk in heart failure patients.

BACKGROUND: Phenylacetylglutamine (PAGln)-a newly discovered microbial metabolite produced by phenylalanine metabolism-is reportedly associated with cardiovascular events via adrenergic receptors. Nonetheless, its association with cardiovascular outcomes in heart failure (HF) patients remains unknown. OBJECTIVES: This study aimed to prospectively investigate the prognostic value of PAGln for HF. METHODS: Plasma PAGln levels were quantified by liquid chromatography-tandem mass spectrometry. We first assessed the association between plasma PAGln levels and the incidence of adverse cardiovascular events in 3152 HF patients (including HF with preserved and reduced ejection fraction) over a median follow-up period of 2 years. The primary endpoint was the composite of cardiovascular death or heart transplantation. We then assessed the prognostic role of PAGln in addition to the classic biomarker N-terminal pro-B-type natriuretic peptide (NT-proBNP). The correlation between PAGln levels and ß-blocker use was also investigated. RESULTS: In total, 520 cardiovascular deaths or heart transplantations occurred in the HF cohort. Elevated PAGln levels were independently associated with a higher risk of the primary endpoint in a dose-response manner, regardless of HF subtype. Concurrent assessment of PAGln and NT-proBNP levels enhanced risk stratification among HF patients. PAGln further showed prognostic value at low NT-proBNP levels. Additionally, the interaction effects between PAGln and ß-blocker use were not significant. CONCLUSIONS: Plasma PAGln levels are an independent predictor of an increased risk of adverse cardiovascular events in HF. Our work could provide joint and complementary prognostic value to NT-proBNP levels in HF patients.

Stimuli-Triggered Multishape, Multimode, and Multistep Deformations Designed by Microfluidic 3D Droplet Printing.

Elastomers generally possess low Young's modulus and high failure strain, which are widely used in soft robots and intelligent actuators. However, elastomers generally lack diverse functionalities, such as stimulated shape morphing, and a general strategy to implement these functionalities into elastomers is still challenging. Here, a microfluidic 3D droplet printing platform is developed to design composite elastomers architected with arrays of functional droplets. Functional droplets with controlled size, composition, position, and pattern are designed and implemented in the composite elastomers, imparting functional performances to the systems. The composited elastomers are sensitive to stimuli, such as solvent, temperature, and light, and are able to demonstrate multishape (bow- and S-shaped), multimode (gradual and sudden), and multistep (one- and two-step) deformations. Based on the unique properties of droplet-embedded composite elastomers, a variety of stimuli-responsive systems are developed, including designable numbers, biomimetic flowers, and soft robots, and a series of functional performances are achieved, presenting a facile platform to impart diverse functionalities into composite elastomers by microfluidic 3D droplet printing.

Improving phytase production in Pichia pastoris fermentations through de-repression and methanol induction optimization.

Pichia pastoris (Komagataella phaffii) is a fast-growing methylotrophic yeast with the ability to assimilate several carbon sources such as methanol, glucose, or glycerol. It has been shown to have outstanding secretion capability with a variety of heterologous proteins. In previous studies, we engineered P. pastoris to co-express Escherichia coli AppA phytase and the HAC1 transcriptional activator using a bidirectional promoter. Phytase production was characterized in shake flasks and did not reflect industrial conditions. In the present study, phytase expression was explored and optimized using instrumented fermenters in continuous and fed-batch modes. First, the production of phytase was investigated under glucose de-repression in continuous culture at three dilution factors, 0.5 d-1 , 1 d-1 , and 1.5 d-1 . The fermenter parameters of these cultures were used to inform a kinetic model in batch and fed-batch modes for growth and phytase production. The kinetic model developed aided to design the glucose-feeding profile of a fed-batch culture. Kinetic model simulations under glucose de-repression and fed-batch conditions identified optimal phytase productivity at the specific growth rate of 0.041 h-1 . Validation of the model simulation with experimental data confirmed the feasibility of the model to predict phytase production in our newly engineered strain. Methanol was used only to induce the expression of phytase at high cell densities. Our results showed that high phytase production required two stages, the first stage used glucose under de-repression conditions to generate biomass while expressing phytase, and stage two used methanol to induce phytase expression. The production of phytase was improved 3.5-fold by methanol induction compared to the expression with glucose alone under de-repression conditions to a final phytase activity of 12.65 MU/L. This final volumetric phytase production represented an approximate 36-fold change compared to the flask fermentations. Finally, the phytase protein produced was assayed to confirm its molecular weight, and pH and temperature profiles. This study highlights the importance of optimizing protein production in P. pastoris when using novel promoters and presents a general approach to performing bioprocess optimization in this important production host.

Stretchable Semi-Interpenetrating Carboxymethyl Guar Gum-Based Composite Hydrogel for Moisture-Proof Wearable Strain Sensor.

Wearable strain sensors of conductive hydrogels have very broad application prospects in electronic skins and human-machine interfaces. However, conductive hydrogels suffer from unstable signal transmission due to environmental humidity and inherent shortcomings of their materials. Herein, we introduce a novel moisture-proof conductive hydrogel with high toughness (2.89 MJ m-3), mechanical strength (1.00 MPa), and high moisture-proof sensing performance by using dopamine-functionalized gold nanoparticles as conductive fillers into carboxymethyl guar gum and acrylamide. Moreover, the hydrogel can realize real-time monitoring of major and subtle human movements with good sensitivity and repeatability. In addition, the hydrogel-assembled strain sensor exhibits stable sensing signals after being left for 1 h, and the relative resistance change rate under different strains (25-300%) shows no obvious noise signal up to 99% relative humidity. Notably, the wearable strain sensing is suitable for wearable sensor devices with high relative humidity.

Encapsulation of enzymes in food industry using spray drying: recent advances and process scale-ups.

Enzymes are widely used in the food industry due to their ability in improving the functional, sensory, and nutritional properties of food products. However, their poor stability under harsh industrial conditions and their compromised shelf-lives during long-term storage limit their applications. This review introduces typical enzymes and their functionality in the food industry and demonstrates spray drying as a promising approach for enzyme encapsulation. Recent studies on encapsulation of enzymes in the food industry using spray drying and the key achievements are summarized. The latest developments including the novel design of spray drying chambers, nozzle atomizers and advanced spray drying techniques are also analyzed and discussed in depth. In addition, the scale-up pathways connecting laboratory scale trials and industrial scale productions are illustrated, as most of the current studies have been limited to lab-scales. Enzyme encapsulation using spray drying is a versatile strategy to improve enzyme stability in an economical and industrial viable way. Various nozzle atomizers and drying chambers have recently been developed to increase process efficiency and product quality. A comprehensive understanding of the complex droplet-to-particle transformations during the drying process would be beneficial for both process optimization and scale-up design.

Encapsulation of enzyme using spray drying is a versatile approach for improving enzyme stability and shelf-life in food industry.This paper gives an overview of recent development and progress in enzyme encapsulation using spray drying.Emerging spray drying techniques and novel design of spray drying chambers and atomizers are summarized.Ex ante process simulations and technoeconomic analysis are also presented providing critical insights for commercial production of encapsulated enzymes.