Liquid-Liquid Phase Separation-Mediated Photocatalytic Subcellular Hybrid System for Highly Efficient Hydrogen Production.

Plant chloroplasts have a highly compartmentalized interior, essential for executing photocatalytic functions. However, the construction of a photocatalytic reaction compartment similar to chloroplasts in inorganic-biological hybrid systems (IBS) has not been reported. Drawing inspiration from the compartmentalized chloroplast and the phenomenon of liquid-liquid phase separation, herein, a new strategy is first developed for constructing a photocatalytic subcellular hybrid system through liquid-liquid phase separation technology in living cells. Photosensitizers and in vivo expressed hydrogenases are designed to coassemble within the cell to create subcellular compartments for synergetic photocatalysis. This compartmentalization facilitates efficient electron transfer and light energy utilization, resulting in highly effective H2 production. The subcellular compartments hybrid system (HM/IBSCS) exhibits a nearly 87-fold increase in H2 production compared to the bare bacteria/hybrid system. Furthermore, the intracellular compartments of the photocatalytic reactor enhance the system's stability obviously, with the bacteria maintaining approximately 81% of their H2 production activity even after undergoing five cycles of photocatalytic hydrogen production. The research brings forward visionary prospects for the field of semi-artificial photosynthesis, offering new possibilities for advancements in areas such as renewable energy, biomanufacturing, and genetic engineering.

Association between high-flow nasal cannula use and mortality in patients with sepsis-induced acute lung injury: a retrospective propensity score-matched cohort study.

BACKGROUND: High-flow nasal cannula (HFNC) has emerged as a promising noninvasive method for delivering oxygen to critically ill patients, particularly those with sepsis and acute lung injury. However, uncertainties persist regarding its therapeutic benefits in this specific patient population. METHODS: This retrospective study utilized a propensity score-matched cohort from the Medical Information Mart in Intensive Care-IV (MIMIC-IV) database to explore the correlation between HFNC utilization and mortality in patients with sepsis-induced acute lung injury. The primary outcome was 28-day all-cause mortality. RESULTS: In the propensity score-matched cohort, the 28-day all-cause mortality rate was 18.63% (95 out of 510) in the HFNC use group, compared to 31.18% (159 out of 510) in the non-HFNC group. The use of HFNC was associated with a lower 28-day all-cause mortality rate (hazard ratio [HR] = 0.53; 95% confidence interval [CI] = 0.41-0.69; P < 0.001). HFNC use was also associated with lower ICU mortality (odds ratio [OR] = 0.52; 95% CI = 0.38-0.71; P < 0.001) and lower in-hospital mortality (OR = 0.51; 95% CI = 0.38-0.68; P < 0.001). Additionally, HFNC use was found to be associated with a statistically significant increase in both the ICU and overall hospitalization length. CONCLUSIONS: These findings indicate that HFNC may be beneficial for reducing mortality rates among sepsis-induced acute lung injury patients; however, it is also associated with longer hospital stays.

Shared genetic architecture and causal relationship between liver and heart disease.

This study investigates the relationship and genetic mechanisms of liver and heart diseases, focusing on the liver-heart axis (LHA) as a fundamental biological basis. Through genome-wide association study analysis, we explore shared genes and pathways related to LHA. Shared genetic factors are found in 8 out of 20 pairs, indicating genetic correlations. The analysis reveals 53 loci with pleiotropic effects, including 8 loci exhibiting shared causality across multiple traits. Based on SNP-p level tissue-specific multi-marker analysis of genomic annotation (MAGMA) analysis demonstrates significant enrichment of pleiotropy in liver and heart diseases within different cardiovascular tissues and female reproductive appendages. Gene-specific MAGMA analysis identifies 343 pleiotropic genes associated with various traits; these genes show tissue-specific enrichment primarily in the liver, cardiovascular system, and other tissues. Shared risk loci between immune cells and both liver and cardiovascular diseases are also discovered. Mendelian randomization analyses provide support for causal relationships among the investigated trait pairs.

An in silico scheme for optimizing the enzymatic acquisition of natural biologically active peptides based on machine learning and virtual digestion.

BACKGROUND: As a potential natural active substance, natural biologically active peptides (NBAPs) are recently attracting increasing attention. The traditional proteolysis methods of obtaining effective NBAPs are considerably vexing, especially since multiple proteases can be used, which blocks the exploration of available NBAPs. Although the development of virtual digesting brings some degree of convenience, the activity of the obtained peptides remains unclear, which would still not allow efficient access to the NBAPs. It is necessary to develop an efficient and accurate strategy for acquiring NBAPs. RESULTS: A new in silico scheme named SSA-LSTM-VD, which combines a sparrow search algorithm-long short-term memory (SSA-LSTM) deep learning and virtually digested, was presented to optimize the proteolysis acquisition of NBAPs. Therein, SSA-LSTM reached the highest Efficiency value reached 98.00 % compared to traditional machine learning algorithms, and basic LSTM algorithm. SSA-LSTM was trained to predict the activity of peptides in the proteins virtually digested results, obtain the percentage of target active peptide, and select the appropriate protease for the actual experiment. As an application, SSA-LSTM was employed to predict the percentage of neuroprotective peptides in the virtual digested result of walnut protein, and trypsin was ultimately found to possess the highest value (85.29 %). The walnut protein was digested by trypsin (WPTrH) and the peptide sequence obtained was analyzed closely matches the theoretical neuroprotective peptide. More importantly, the neuroprotective effects of WPTrH had been demonstrated in nerve damage mouse models. SIGNIFICANCE: The proposed SSA-LSTM-VD in this paper makes the acquisition of NBAPs efficient and accurate. The approach combines deep learning and virtually digested skillfully. Utilizing the SSA-LSTM-VD based strategy holds promise for discovering and developing peptides with neuroprotective properties or other desired biological activities.

Genetically predicted causality between gut microbiota, blood metabolites, and intracerebral hemorrhage: a bidirectional Mendelian randomization study.

Background: Recent research linked changes in the gut microbiota and serum metabolite concentrations to intracerebral hemorrhage (ICH). However, the potential causal relationship remained unclear. Therefore, the current study aims to estimate the effects of genetically predicted causality between gut microbiota, serum metabolites, and ICH. Methods: Summary data from genome-wide association studies (GWAS) of gut microbiota, serum metabolites, and ICH were obtained separately. Gut microbiota GWAS (N = 18,340) were acquired from the MiBioGen study, serum metabolites GWAS (N = 7,824) from the TwinsUK and KORA studies, and GWAS summary-level data for ICH from the FinnGen R9 (ICH, 3,749 cases; 339,914 controls). A two-sample Mendelian randomization (MR) study was conducted to explore the causal effects between gut microbiota, serum metabolites, and ICH. The random-effects inverse variance-weighted (IVW) MR analyses were performed as the primary results, together with a series of sensitivity analyses to assess the robustness of the results. Besides, a reverse MR was conducted to evaluate the possibility of reverse causation. To validate the relevant findings, we further selected data from the UK Biobank for analysis. Results: MR analysis results revealed a nominal association (p < 0.05) between 17 gut microbial taxa, 31 serum metabolites, and ICH. Among gut microbiota, the higher level of genus Eubacterium xylanophilum (odds ratio (OR): 1.327, 95% confidence interval (CI):1.154-1.526; Bonferroni-corrected p = 7.28 × 10-5) retained a strong causal relationship with a higher risk of ICH after the Bonferroni corrected test. Concurrently, the genus Senegalimassilia (OR: 0.843, 95% CI: 0.778-0.915; Bonferroni-corrected p = 4.10 × 10-5) was associated with lower ICH risk. Moreover, after Bonferroni correction, only two serum metabolites remained out of the initial 31 serum metabolites. One of the serum metabolites, Isovalerate (OR: 7.130, 95% CI: 2.648-19.199; Bonferroni-corrected p = 1.01 × 10-4) showed a very strong causal relationship with a higher risk of ICH, whereas the other metabolite was unidentified and excluded from further analysis. Various sensitivity analyses yielded similar results, with no heterogeneity or directional pleiotropy observed. Conclusion: This two-sample MR study revealed the significant influence of gut microbiota and serum metabolites on the risk of ICH. The specific bacterial taxa and metabolites engaged in ICH development were identified. Further research is required in the future to delve deeper into the mechanisms behind these findings.

Passive microfluidic devices for cell separation.

The separation of specific cell populations is instrumental in gaining insights into cellular processes, elucidating disease mechanisms, and advancing applications in tissue engineering, regenerative medicine, diagnostics, and cell therapies. Microfluidic methods for cell separation have propelled the field forward, benefitting from miniaturization, advanced fabrication technologies, a profound understanding of fluid dynamics governing particle separation mechanisms, and a surge in interdisciplinary investigations focused on diverse applications. Cell separation methodologies can be categorized according to their underlying separation mechanisms. Passive microfluidic separation systems rely on channel structures and fluidic rheology, obviating the necessity for external force fields to facilitate label-free cell separation. These passive approaches offer a compelling combination of cost-effectiveness and scalability when compared to active methods that depend on external fields to manipulate cells. This review delves into the extensive utilization of passive microfluidic techniques for cell separation, encompassing various strategies such as filtration, sedimentation, adhesion-based techniques, pinched flow fractionation (PFF), deterministic lateral displacement (DLD), inertial microfluidics, hydrophoresis, viscoelastic microfluidics, and hybrid microfluidics. Besides, the review provides an in-depth discussion concerning cell types, separation markers, and the commercialization of these technologies. Subsequently, it outlines the current challenges faced in the field and presents a forward-looking perspective on potential future developments. This work hopes to aid in facilitating the dissemination of knowledge in cell separation, guiding future research, and informing practical applications across diverse scientific disciplines.

Structural basis for substrate and antibiotic recognition by Helicobacter pylori isoleucyl-tRNA synthetase.

Helicobacter pylori infection is a global health concern, affecting over half of the world's population. Acquiring structural information on pharmacological targets is crucial to facilitate inhibitor design. Here, we have determined the crystal structures of H. pylori isoleucyl-tRNA synthetase (HpIleRS) in apo form as well as in complex with various substrates (Ile, Ile-AMP, Val, and Val-AMP) or an inhibitor (mupirocin). Our results provide valuable insights into substrate specificity, recognition, and the mechanism by which HpIleRS is inhibited by an antibiotic. Moreover, we identified Asp641 as a prospective regulatory site and conducted biochemical analyses to investigate its regulatory mechanism. The detailed structural information acquired from this research holds promise for the development of highly selective and effective inhibitors against H. pylori infection.

Raman spectroscopy combined with partial least squares (PLS) based on hybrid spectral preprocessing and backward interval PLS (biPLS) for quantitative analysis of four PAHs in oil sludge.

Polycyclic aromatic hydrocarbons (PAHs) contained in a large amount of oily sludge produced in petroleum and petrochemical production has become one of the main environmental protection concerns in the industry. The accurate determination of PAHs is of great significance in the field of petroleum geochemistry and environmental protection. In this study, Raman spectroscopy combined with partial least squares (PLS) based on different hybrid spectral preprocessing methods and variable selection strategies was proposed for quantitative analysis of phenanthrene, fluoranthrene, fluorene and naphthalene (Phe, Flt, Flu and Nap) in oil sludge. At first, PAHs in oily sludge was extracted by solid-liquid extraction with methanol as extractant, and Raman spectra of 21 oily sludge samples were collected by portable Raman spectrometer. And then, the influence of first derivative (D1st), wavelet transform (WT) and their hybrid spectral preprocessing on the predictive performance of the PLS calibration model was discussed. Thirdly, biPLS (backward interval partial least squares) was used to optimize the input variables before and after the hybrid spectral preprocessing methods, and the influence of biPLS and the hybrid spectral preprocessing sequence on the predictive performance of the PLS calibration model was discussed. Finally, the predictive performance of the PLS calibration model was optimized according to the results of leave-one-out cross-validation (LOOCV) method. The results show that the biPLS-D1st-WT-PLS calibration model established by using biPLS first to select the characteristic variables, followed by hybrid spectral preprocessing of the characteristic variables, has better prediction performance for Flt (determination coefficient of prediction (R2P) = 0.9987, and the mean relative error of prediction (MREP) = 0.0606). For Phe, Flu and Nap, the WT-biPLS-PLS calibration model has a better predictive effect (R2P are 0.9995, 0.9996 and 0.9983, and MREP are 0.0426, 0.0719 and 0.0497, respectively). In general, portable Raman spectroscopy combined with PLS calibration model based on different hybrid spectral preprocessing and variable selection strategies has achieved good prediction results for quantitative analysis of four PAHs in oily sludge. It is a new strategy to firstly select the characteristic variables of the original spectra, and secondly to preprocess the characteristic variables by the hybrid spectral preprocessing, which will provide a new idea for the establishment of quantitative analysis methods for PAHs in oily sludge.

Assessing the causal effect of genetically predicted metabolites and metabolic pathways on stroke.

BACKGROUND: Stroke is a common neurological disorder that disproportionately affects middle-aged and elderly individuals, leading to significant disability and mortality. Recently, human blood metabolites have been discovered to be useful in unraveling the underlying biological mechanisms of neurological disorders. Therefore, we aimed to evaluate the causal relationship between human blood metabolites and susceptibility to stroke. METHODS: Summary data from genome-wide association studies (GWASs) of serum metabolites and stroke and its subtypes were obtained separately. A total of 486 serum metabolites were used as the exposure. Simultaneously, 11 different stroke phenotypes were set as the outcomes, including any stroke (AS), any ischemic stroke (AIS), large artery stroke (LAS), cardioembolic stroke (CES), small vessel stroke (SVS), lacunar stroke (LS), white matter hyperintensities (WMH), intracerebral hemorrhage (ICH), subarachnoid hemorrhage (SAH), transient ischemic attack (TIA), and brain microbleeds (BMB). A two-sample Mendelian randomization (MR) study was conducted to investigate the causal effects of serum metabolites on stroke and its subtypes. The inverse variance-weighted MR analyses were conducted as causal estimates, accompanied by a series of sensitivity analyses to evaluate the robustness of the results. Furthermore, a reverse MR analysis was conducted to assess the potential for reverse causation. Additionally, metabolic pathway analysis was performed using the web-based MetOrigin. RESULTS: After correcting for the false discovery rate (FDR), MR analysis results revealed remarkable causative associations with 25 metabolites. Further sensitivity analyses confirmed that only four causative associations involving three specific metabolites passed all sensitivity tests, namely ADpSGEGDFXAEGGGVR* for AS (OR: 1.599, 95% CI 1.283-1.993, p = 2.92 × 10-5) and AIS (OR: 1.776, 95% CI 1.380-2.285, p = 8.05 × 10-6), 1-linoleoylglycerophosph-oethanolamine* for LAS (OR: 0.198, 95% CI 0.091-0.428, p = 3.92 × 10-5), and gamma-glutamylmethionine* for SAH (OR: 3.251, 95% CI 1.876-5.635, p = 2.66 × 10-5), thereby demonstrating a high degree of stability. Moreover, eight causative associations involving seven other metabolites passed both sensitivity tests and were considered robust. The association result of one metabolite (glutamate for LAS) was considered non-robust. As for the remaining metabolites, we speculate that they may potentially possess underlying causal relationships. Notably, no common metabolites emerged from the reverse MR analysis. Moreover, after FDR correction, metabolic pathway analysis identified 40 significant pathways across 11 stroke phenotypes. CONCLUSIONS: The identified metabolites and their associated metabolic pathways are promising circulating metabolic biomarkers, holding potential for their application in stroke screening and preventive strategies within clinical settings.

Inertial Separation of Particles Assisted by Symmetrical Sheath Flows in a Straight Microchannel.

Over the past two decades, inertial microfluidics, which works at an intermediate range of Reynolds number (∼1 < Re < ∼100), has been widely used for particle separation due to its high-throughput and label-free features. This work proposes a novel method for continuous separation of particles by size using inertial microfluidics, with the assistance of symmetrical sheath flows in a straight microchannel. Here, larger particles (>3 µm) are arranged close to the channel sidewalls, while smaller particles (<2 µm) remain flowing along the channel centerline. This conclusion is supported by experimental data with particles of different sizes ranging from 0.79 to 10.5 µm. Symmetrical Newtonian sheath flows are injected on both sides of particle mixtures into a straight rectangular microchannel with an aspect ratio (AR = height/width) of 2.5. Results show that the separation performance of the developed microfluidic device is affected by three main factors: channel length, total flow rate, and flow rate ratio of sheath to sample. Besides, separation of platelets from whole blood is demonstrated. The developed microfluidic platform owns the advantages of low fabrication cost, simple experiment setup, versatile selections of particle candidates, and stable operations. This systematic study provides a new perspective for particle separation, which is expected to find applications across various fields spanning physics, biology, biomedicine, and industry.

Rapid quantitative analysis of rare earth elements Lu and Y in rare earth ores by laser induced breakdown spectroscopy combined with iPLS-VIP and partial least squares.

Rare earth ores are complex in composition and diverse in mineral composition, requiring high technical requirements for the selection of rare earth ores. It is of great significance to explore the on-site rapid detection and analysis methods of rare earth elements in rare earth ores. Laser induced breakdown spectroscopy (LIBS) is an important tool to detect rare earth ores, which can be used for in situ analyses without complicated sample preparation. In this study, a rapid quantitative analysis method for rare earth elements Lu and Y in rare earth ores was established by LIBS combined with an iPLS-VIP hybrid variable selection strategy and partial least squares (PLS) method. First, the LIBS spectra of 25 samples were studied using laser induced breakdown spectrometry. Second, taking the spectrum processed by wavelet transform (WT) as the input variables, PLS calibration models based on interval partial least squares (iPLS), variable importance projection (VIP) and iPLS-VIP hybrid variable selection were constructed to quantitatively analyze rare earth elements Lu and Y, respectively. The results show that the WT-iPLS-VIP-PLS calibration model has better prediction performance for rare earth elements Lu and Y, and the optimal coefficient of determination (R2) of Lu and Y were 0.9897 and 0.9833, the root mean square error (RMSE) were 0.8150 µg g-1 and 97.1047 µg g-1, and the mean relative error (MRE) were 0.0754 and 0.0766, respectively. It shows that LIBS technology combined with the iPLS-VIP and PLS calibration model provides a new method for in situ quantitative analysis of rare earth elements in rare earth ores.

Microfibrillar-associated protein 5 suppresses adipogenesis by inhibiting essential coactivator of PPARγ.

Femoral head necrosis is responsible for severe pain and its incidence is increasing. Abnormal adipogenic differentiation and fat cell hypertrophy of bone marrow mesenchymal stem cells increase intramedullary cavity pressure, leading to osteonecrosis. By analyzing gene expression before and after adipogenic differentiation, we found that Microfibril-Associated Protein 5 (MFAP5) is significantly down-regulated in adipogenesis whilst the mechanism of MFAP5 in regulating the differentiation of bone marrow mesenchymal stem cells is unknown. The purpose of this study was to clarify the role of MAFP5 in adipogenesis and therefore provide a theoretical basis for future therapeutic options of osteonecrosis. By knockdown or overexpression of MFAP5 in C3H10 and 3T3-L1 cells, we found that MFAP5 was significantly down-regulated as a key regulator of adipogenic differentiation, and identified the underlying downstream molecular mechanism. MFAP5 directly bound to and inhibited the expression of Staphylococcal Nuclease And Tudor Domain Containing 1, an essential coactivator of PPARγ, exerting an important regulatory role in adipogenesis.

Quantitative analysis of phenanthrene in soil by fluorescence spectroscopy coupled with the CARS-PLS model.

Polycyclic aromatic hydrocarbons (PAHs) are typical organic pollutants in soil and are teratogenic and carcinogenic. Therefore, rapid and accurate analysis of PAHs in soil can provide a theoretical basis and data support for soil contamination risk assessment. In this work, a fluorescence spectroscopy technique combined with partial least squares (PLS) was proposed for rapid quantitative analysis of phenanthrene (PHE) in soil. At first, the fluorescence spectra of 29 soil samples with different concentrations (0.3-10 mg g-1) of PHE were collected by RF-5301 PC fluorescence spectrophotometer. Secondly, the effects of different spectral preprocessing methods were investigated on the prediction performance of the PLS calibration model. And then, the influence of competitive adaptive reweighted sampling (CARS) wavelength points on the prediction performance of PLS calibration model was discussed. Finally, according to the selected wavelength points, a quantitative analytical model for PHE content in soil was constructed using the PLS calibration method. To further explore the predictive performance of the CARS-PLS calibration model, the predictive results were compared with those of the RAW spectrum-partial least squares calibration model (RAW-PLS) and the wavelet transform-standard normal variation (WT-SNV) calibration model. The CARS-PLS calibration model showed the optimal predictive performance and its coefficient of determination of cross-validation (R cv 2) and root mean square error of 10-fold cross-validation (RMSEcv) were 0.9957 and 18.98%, respectively. The coefficient of determination of prediction set (R p 2) and root mean square error of prediction set (RMSEp) were 0.9963 and 16.13%, respectively. Hence, the CARS algorithm based on fluorescence spectrum coupled with PLS can give a rapid and accurate quantitative analysis of the PHE content in soil.

Quantitative Analysis of the Cu Element Enhanced by AgNPs in a Single Microsized Suspended Particle Based on Optical Trapping-LIBS and Machine Learning.

Extremely severe and persistent particulate pollution caused by industrialization and urbanization impacts air quality, regional and global climates, and human health. The unstable and complex spectral signal of laser-induced breakdown spectroscopy (LIBS) with minimal feature information and interference signals considerably influences the accuracy of qualitative and quantitative analysis. In response to overcome this phenomenon, in this work, quantitative analysis of Cu element enhanced by silver nanoparticles (AgNPs) in a single microsized suspended particle was proposed herein using optical trapping-LIBS and machine learning method was proposed. Initially, the optimal AgNPs enhancement conditions were optimized. The LIBS spectra of 15 polluted black carbon samples were collected and various spectral pretreatment methods were compared to optimize the LIBS spectra. Variable selection methods include variable importance measurement (VIM), variable importance projection (VIP), VIM-successive projections algorithm (VIM-SPA), VIM-genetic algorithm (VIM-GA), and VIM-mutual information (VIM-MI). Finally, several hybrid variable selection methods were implemented in random forest (RF) calibration models. In particular, a wavelet transform (WT)-VIM-SPA-RF calibration model has constructed under the WT spectral pretreatment method and the selected and optimized input variables (VIM-SPA). Results elucidate that the WT-VIM-SPA-RF calibration model (R2P = 0.9858, MREP = 0.0396) have the best prediction performance than the WT-RF and Raw-RF models in predicting the Cu level in a single microsized black carbon particle. Compared to the WT-RF and Raw-RF models, MREP values decreased by 37% and 62%, respectively. The values of RSD, RPD, and RER of this calibration model are 2.8%, 8.39%, and 17.79%, respectively. The aforementioned results demonstrate that the WT-VIM-SPA-RF calibration model with accuracy, stability, and robustness is a promising approach for improving the quantitative accuracy of the Cu level in carbon black particles.

Discovery of novel 2-(4-(benzyloxy)-5-(hydroxyl) phenyl) benzothiazole derivatives as multifunctional MAO-B inhibitors for the treatment of Parkinson's disease.

To discover novel multifunctional agents for the treatment of Parkinson's disease, a series of 2-(4-(benzyloxy)-5-(hydroxyl) phenyl) benzothiazole derivatives was designed, synthesized and evaluated. The results revealed that representative compound 3h possessed potent and selective MAO-B inhibitory activity (IC50 = 0.062 µM), and its inhibitory mode was competitive and reversible. Additionally, 3h also displayed excellent anti-oxidative effect (ORAC = 2.27 Trolox equivalent), significant metal chelating ability and appropriate BBB permeability. Moreover, 3h exhibited good neuroprotective effect and anti-neuroinflammtory ability. These results indicated that compound 3h was a promising candidate for further development against PD.

Crown Ether-Based Ion Transporters in Bilayer Membranes.

Bilayer membranes that enhance the stability of the cell are essential for cell survival, separating and protecting the interior of the cell from its external environment. Membrane-based channel proteins are crucial for sustaining cellular activities. However, dysfunction of these proteins would induce serial channelopathies, which could be substituted by artificial ion channel analogs. Crown ethers (CEs) are widely studied in the area of artificial ion channels owing to their intrinsic host-guest interaction with different kinds of organic and inorganic ions. Other advantages such as lower price, chemical stability, and easier modification also make CE a research hotspot in the field of synthetic transmembrane nanopores. And numerous CEs-based membrane-active synthetic ion channels were designed and fabricated in the past decades. Herein, the recent progress of CEs-based synthetic ion transporters has been comprehensively summarized in this review, including their design principles, functional mechanisms, controllable properties, and biomedical applications. Furthermore, this review has been concluded by discussing the future opportunities and challenges facing this research field. It is anticipated that this review could offer some inspiration for the future fabrication of novel CEs-derived ion transporters with more advanced structures, properties, and practical applications.

Novel Random Forest Ensemble Modeling Strategy Combined with Quantitative Structure-Property Relationship for Density Prediction of Energetic Materials.

With the further development of the concept of green chemistry, the new generation of energetic materials tends to exhibit detonation properties such as higher insensitivity, higher density, and higher energy. Therefore, the precise molecular design and green and efficient synthesis of energetic materials will be one of the serious challenges. For the purpose of accurate prediction of detonation performance of energetic materials, an ensemble modeling strategy based on the combination of Monte Carlo (MC) and variable importance measurement (VIM) improved random forest (RF) and quantitative structure-property relationship (QSPR) is proposed, which was successfully used for density prediction of energetic materials. First, the structure of 162 energetic compounds was optimized by Gaussian software, and the molecular descriptor data were calculated by CODESSA software based on the optimized molecular structure. Then, the MCVIMRF_Med ensemble model was constructed on the basis of the above molecular descriptor data and the corresponding energetic compound density index. The joint X-Y distance algorithm (SPXY) is used to partition the data set. And then, MC is used to further divide the calibration set data into multiple subsets for the construction of the ensemble model. The subset size and the number of iterations of the MCVIMRF_Med ensemble model were optimized through MC cross validation. The final output strategy of the ensemble model is optimized based on the optimized parameters, and an output optimization method based on median screening is proposed and successfully applied for the prediction performance optimization of the MCVIMRF_Med ensemble model. To further investigate the performance of the MCVIMRF_Med ensemble model, the performance of it was compared with partial least squares, RF, VIMRF, and MCVIMRF calibration models. It shows that the MCVIMRF_Med ensemble model can achieve a better prediction result for the density of energetic materials, with R 2 CV of 0.9596, RMSECV of 0.0437 g/cm3, R 2 P of 0.9768, RMSEP of 0.0578 g/cm3, and relative analysis deviation of prediction set of 3.951. Therefore, the MCVIMRF_Med ensemble modeling strategy combined with QSPR is an effective approach for the density prediction of energetic materials. This work is expected to provide new research ideas and technical support for accurate prediction of detonation performance of energetic materials.

2-Hydroxy-4-benzyloxylimine Resveratrol Derivatives as Potential Multifunctional Agents for the Treatment of Parkinson's Disease.

A series of 2-hydroxy-4-benzyloxylimine resveratrol derivatives was designed, synthesized and evaluated as multifunctional agents for the treatment of Parkinson's disease. The results revealed that most derivatives possessed good multifunctional activities. Among them, representative compound (E)-5-[(4-fluorobenzyl)oxy]-2-{[(4-hydroxyphenyl)imino]methyl}phenol (7 h) exhibited excellent MAO-B inhibition (IC50 =8.43×10-3  µM) and high antioxidant activity (ORAC=3.45 Trolox equivalent). Additionally, 7 h displayed good metal chelating ability, appropriate blood-brain barrier (BBB) permeability, significant neuroprotective effect, and great anti-neuroinflammatory activity. Furthermore, 7 h can also ameliorate 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced Parkinson's disease symptoms in mice. Therefore, compound 7 h was found to be a promising candidate for further development against PD.

Microfluidic Separation and Enrichment of Escherichia coli by Size Using Viscoelastic Flows.

Here, we achieve the separation and enrichment of Escherichia coli clusters from its singlets in a viscoelastic microfluidic device. E. coli, an important prokaryotic model organism and a widely used microbial factory, can aggregate in clusters, leading to biofilm development that can be detrimental to human health and industrial processes. The ability to obtain high-purity populations of E. coli clusters is of significance for biological, biomedical, and industrial applications. In this study, polystyrene particles of two different sizes, 1 and 4.8 µm, are used to mimic E. coli singlets and clusters, respectively. Experimental results show that particles migrate toward the channel center in a size-dependent manner, due to the combined effects of inertial and elastic forces; 4.8 and 1 µm particles are found to have lateral equilibrium positions closer to the channel centerline and sidewalls, respectively. The size-dependent separation performance of the microdevice is demonstrated to be affected by three main factors: channel length, the ratio of sheath to sample flow rate, and poly(ethylene oxide) (PEO) concentration. Further, the separation of E. coli singlets and clusters is achieved at the outlets, and the separation efficiency is evaluated in terms of purity and enrichment factor.

Discovery of novel 2-hydroxyl-4-benzyloxybenzyl aniline derivatives as potential multifunctional agents for the treatment of Parkinson's disease.

To discover novel multifunctional agents for the treatment of Parkinson's disease, a series of 2-hydroxyl-4-benzyloxybenzyl aniline derivatives was designed, synthesized and evaluated. The biological screening indicated that representative compound 6h possessed excellent MAO-B inhibition (IC50 = 0.014 µM), high antioxidant activity (ORAC = 2.14 Trolox equivalent), good metal chelating ability, appropriate BBB permeability and significant neuroprotective effect. Additionally, 6h exhibited great ability to alleviate the neuroinflammtion by suppressing the activation of NF-κB pathway in vitro. Furthermore, 6h can also ameliorate MPTP induced Parkinson's disease symptoms in mice by improving the dopamine level and repressing oxidative damage. These results indicated that compound 6h was a promising candidate for further development against PD.