Carotenoids modulate antioxidant pathways in In vitro models of Parkinson's disease: A comprehensive scoping review.

Parkinson's disease (PD) is the second most common neurodegenerative disease, and it has affected the living quality of elderly people significantly. PD is characterised by the accumulation of α-Synuclein and progressive loss of dopaminergic neurons at the substantia nigra pars compacta. In the pathogenesis of Parkinson's disease, α-Synuclein, oxidative stress, and electron transport chain (ETC) are the three main factors that contribute to the production of reactive oxygen species (ROS). Currently, there is no commercial disease-modifying agent available for PD; the first-line treatment, Levodopa (l-DOPA), could only relieve the symptoms of PD, with many side effects. Carotenoids, which encompass red, orange, and yellow pigments found in nature and contribute to the colouration of plants, have been associated with various health benefits, including anti-cancer and neuroprotective effects due to their antioxidant properties. This scoping review delves into the impact and underlying mechanisms of carotenoids on cell-based models of neurodegenerative diseases.

Bridging Between Deviation Indices for Non-Tree-Based Phylogenetic Networks.

Phylogenetic networks are a useful model that can represent reticulate evolution and complex biological data. In recent years, mathematical and computational aspects of tree-based networks have been well studied. However, not all phylogenetic networks are tree-based, so it is meaningful to consider how close a given network is to being tree-based; Francis-Steel-Semple (2018) proposed several different indices to measure the degree of deviation of a phylogenetic network from being tree-based. One is the minimum number of leaves that need to be added to convert a given network to tree-based, and another is the number of vertices that are not included in the largest subtree covering its leaf-set. Both values are zero if and only if the network is tree-based. Both deviation indices can be computed efficiently, but the relationship between the above two is unknown, as each has been studied using different approaches. In this study, we derive a tight inequality for the values of the two measures and also give a characterisation of phylogenetic networks such that they coincide. This characterisation yields a new efficient algorithm for the Maximum Covering Subtree Problem based on the maximal zig-zag trail decomposition.

Characterization of Mild Acid Stress Response in an Engineered Acid-Tolerant Escherichia coli Strain.

Engineering acid-tolerant microbial strains is a cost-effective approach to overcoming acid stress during industrial fermentation. We previously constructed an acid-tolerant strain (Escherichia coli SC3124) with enhanced growth robustness and productivity under mildly acidic conditions by fine-tuning the expression of synthetic acid-tolerance module genes consisting of a proton-consuming acid resistance system (gadE), a periplasmic chaperone (hdeB), and ROS scavengers (sodB, katE). However, the precise acid-tolerance mechanism of E. coli SC3124 remained unclear. In this study, the growth of E. coli SC3124 under mild acid stress (pH 6.0) was determined. The final OD600 of E. coli SC3124 at pH 6.0 was 131% and 124% of that of the parent E. coli MG1655 at pH 6.8 and pH 6.0, respectively. Transcriptome analysis revealed the significant upregulation of the genes involved in oxidative phosphorylation, the tricarboxylic acid (TCA) cycle, and lysine-dependent acid-resistance system in E. coli SC3124 at pH 6.0. Subsequently, a weighted gene coexpression network analysis was performed to systematically determine the metabolic perturbations of E. coli SC3124 with mild acid treatment, and we extracted the gene modules highly associated with different acid traits. The results showed two biologically significant coexpression modules, and 263 hub genes were identified. Specifically, the genes involved in ATP-binding cassette (ABC) transporters, oxidative phosphorylation, the TCA cycle, amino acid metabolism, and purine metabolism were highly positively associated with mild acid stress responses. We propose that the overexpression of synthetic acid-tolerance genes leads to metabolic changes that confer mild acid stress resistance in E. coli. Integrated omics platforms provide valuable information for understanding the regulatory mechanisms of mild acid tolerance in E. coli and highlight the important roles of oxidative phosphorylation and ABC transporters in mild acid stress regulation. These findings offer novel insights to better the design of acid-tolerant chasses to synthesize value-added chemicals in a green and sustainable manner.

A novel de novo synonymous variant in GREB1L impacts the mRNA splicing associated with aplasia of the urogenital system.

GREB1-like retinoic acid receptor coactivator (GREB1L) gene is associated with autosomal dominant renal hypodysplasia/aplasia 3 (RHDA3) and deafness, autosomal dominant 80 (DFNA80). Among the GREB1L variants reported, most of them are missense or frameshift, while no pathogenic synonymous variants have been recorded. Classical theory paid little attention to synonymous variants and classified it as nonpathogenic; however, recent studies suggest that the variants might be equally important. Here, we report a 7-year-old girl with new symptoms of clitoromegaly, uterovaginal, and ovarian agenesis as well as right kidney missing. A novel de novo GREB1L synonymous variant (NM_001142966: c.4731C>T, p.G1577=) was identified via whole exome sequencing. The variant was predicted to be disease-causing through in silico analysis and was classified as likely pathogenic. Minigene splicing assays confirmed a 6 bp deletion in mutant cDNA comparing with the wild type, leading to two amino acids lost in GREB1L protein. Secondary and tertiary structure modeling showed alterations in protein structure. Our finding reveals a novel GREB1L variant with a new phenotype of urogenital system and is the first to report a pathogenic synonymous variant in GREB1L which affects mRNA splicing, suggesting synonymous variants cannot be ignored in prenatal diagnosis and genetic counseling.

High adsorption capacities for dyes by a pH-responsive sodium alginate/sodium lignosulfonate/carboxylated chitosan/polyethyleneimine adsorbent.

Dyes are indispensable for the rapid development of society, but untreated dye wastewater can threaten human health. In this study, an adsorbent (SA/SL/CCS/PEI@MNPs) was synthesized by one-pot method using magnetic nanoparticles (MNPs), sodium alginate (SA), sodium lignosulfonate (SL), carboxylated chitosan (CCS) and polyethyleneimine (PEI). The adsorbent was mesoporous micrometer-sized particles with pore size of 34.92 nm, which was favorable for dynamic column experiments. SA/SL/CCS/PEI@MNPs possessed pH-responsive performance. Under acidic condition, the maximum adsorption capacities for anionic dyes (tartrazine, reactive black-5, indigo carmine) reached >550 mg/g. Under alkaline condition, those for cationic dyes (methylene blue, methyl violet, neutral red) exceeded 1900 mg/g. The function of the various modifiers was investigated. The results indicated that the incorporation of SL, CCS and PEI was able to provide plenty of sulfonate, carboxylate and amino/imine reactive groups so that adsorption capacities of dyes were improved. The adsorption mechanism was explored by FTIR and XPS. At the same time, the adsorption mechanism was more deeply analyzed using molecular dynamics simulations and radial distribution function. It was demonstrated that the dyes adsorption on the SA/SL/CCS/PEI@MNPs was mainly due to electrostatic attraction and π-π interaction. In addition, the adsorbent had good reusability, and the removal still reached over 90 % after five cycles. In conclusion, the adsorbent displayed a broad prospect for the adsorption of organic dyes.

A novel viscoelastic microfluidic platform for nanoparticle/small extracellular vesicle separation through viscosity gradient-induced migration.

Small extracellular vesicles (sEVs) are extracellular vesicles with diameters ranging from 30 to 150 nm, harboring proteins and nucleic acids that reflect their source cells and act as vital mediators of intercellular communication. The comprehensive analysis of sEVs is hindered by the complex composition of biofluids that contain various extracellular vesicles. Conventional separation methods, such as ultracentrifugation and immunoaffinity capture, face routine challenges in operation complexity, cost, and compromised recovery rates. Microfluidic technologies, particularly viscoelastic microfluidics, offer a promising alternative for sEV separation due to its field-free nature, fast and simple operation procedure, and minimal sample consumption. In this context, we here introduce an innovative viscoelastic approach designed to exploit the viscosity gradient-induced force with size-dependent characteristics, thereby enabling the efficient separation of nano-sized particles and sEVs from larger impurities. We first seek to illustrate the underlying mechanism of the viscosity gradient-induced force, followed by experimental validation with fluorescent nanoparticles demonstrating separation results consistent with qualitative analysis. We believe that this work is the first to report such viscosity gradient-induced phenomenon in the microfluidic context. The presented approach achieves ∼80% for both target purity and recovery rate. We further demonstrate effective sEV separation using our device to showcase its efficacy in the real biological context, highlighting its potential as a versatile, label-free platform for sEV analysis in both fundamental biological research and clinical applications.

Transcriptomic and proteomic elucidation of Z chromosome dosage compensation in Helicoverpa armigera.

Transcriptomic data have been used to study sex chromosome dosage compensation (SCDC) in approximately 10 Lepidoptera ZW species, yielding a consensus compensation pattern of Z ≈ ZZ < AA . $$ \approx \mathrm{ZZ}<\mathrm{AA}. $$ It remains unclear whether this compensation pattern holds when examining more Lepidoptera ZW species and/or using proteomic data to analyse SCDC. Here we combined transcriptomic and proteomic data as well as transcriptional level of six individual Z genes to reveal the SCDC pattern in Helicoverpa armigera, a polyphagous lepidopteran pest of economic importance. Transcriptomic analysis showed that the Z chromosome expression of H. armigera was balanced between male and female but substantially reduced relative to autosome expression, exhibiting an SCDC pattern of Z ≈ ZZ < AA $$ \approx \mathrm{ZZ}<\mathrm{AA} $$ . When using H. amigera midgut proteomic data, the SCDC pattern of this species changed from Z ≈ ZZ < AA $$ \approx \mathrm{ZZ}<\mathrm{AA} $$ at transcriptomic level to Z = ZZ = AA at the proteomic level. RT-qPCR analysis of transcript abundance of six Z genes found that compensation for each Z gene could vary from no compensation to overcompensation, depending on the individual genes and tissues tested. These results demonstrate for the first time the existence of a translational compensation mechanism, which is operating in addition to a translational mechanism, such as has been reported in other lepidopteran species. And the transcriptional compensation mechanism functions to accomplish Z chromosome dosage balance between the sexes (M = F on the Z chromosome), whereas the translation compensation mechanism operates to achieve dosage compensation between Z chromosome and autosome (Z = AA).

Non-contrast CT radiomics-clinical machine learning model for futile recanalization after endovascular treatment in anterior circulation acute ischemic stroke.

OBJECTIVE: To establish a machine learning model based on radiomics and clinical features derived from non-contrast CT to predict futile recanalization (FR) in patients with anterior circulation acute ischemic stroke (AIS) undergoing endovascular treatment. METHODS: A retrospective analysis was conducted on 174 patients who underwent endovascular treatment for acute anterior circulation ischemic stroke between January 2020 and December 2023. FR was defined as successful recanalization but poor prognosis at 90 days (modified Rankin Scale, mRS 4-6). Radiomic features were extracted from non-contrast CT and selected using the least absolute shrinkage and selection operator (LASSO) regression method. Logistic regression (LR) model was used to build models based on radiomic and clinical features. A radiomics-clinical nomogram model was developed, and the predictive performance of the models was evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity. RESULTS: A total of 174 patients were included. 2016 radiomic features were extracted from non-contrast CT, and 9 features were selected to build the radiomics model. Univariate and stepwise multivariate analyses identified admission NIHSS score, hemorrhagic transformation, NLR, and admission blood glucose as independent factors for building the clinical model. The AUC of the radiomics-clinical nomogram model in the training and testing cohorts were 0.860 (95%CI 0.801-0.919) and 0.775 (95%CI 0.605-0.945), respectively. CONCLUSION: The radiomics-clinical nomogram model based on non-contrast CT demonstrated satisfactory performance in predicting futile recanalization in patients with anterior circulation acute ischemic stroke.

Establishing a 4D-CT lung function related volumetric dose model to reduce radiation pneumonia.

In order to study how to use pulmonary functional imaging obtained through 4D-CT fusion for radiotherapy planning, and transform traditional dose volume parameters into functional dose volume parameters, a functional dose volume parameter model that may reduce level 2 and above radiation pneumonia was obtained. 41 pulmonary tumor patients who underwent 4D-CT in our department from 2020 to 2023 were included. MIM Software (MIM 7.0.7; MIM Software Inc., Cleveland, OH, USA) was used to register adjacent phase CT images in the 4D-CT series. The three-dimensional displacement vector of CT pixels was obtained when changing from one respiratory state to another respiratory state, and this three-dimensional vector was quantitatively analyzed. Thus, a color schematic diagram reflecting the degree of changes in lung CT pixels during the breathing process, namely the distribution of ventilation function strength, is obtained. Finally, this diagram is fused with the localization CT image. Select areas with Jacobi > 1.2 as high lung function areas and outline them as fLung. Import the patient's DVH image again, fuse the lung ventilation image with the localization CT image, and obtain the volume of fLung different doses (V60, V55, V50, V45, V40, V35, V30, V25, V20, V15, V10, V5). Analyze the functional dose volume parameters related to the risk of level 2 and above radiation pneumonia using R language and create a predictive model. By using stepwise regression and optimal subset method to screen for independent variables V35, V30, V25, V20, V15, and V10, the prediction formula was obtained as follows: Risk = 0.23656-0.13784 * V35 + 0.37445 * V30-0.38317 * V25 + 0.21341 * V20-0.10209 * V15 + 0.03815 * V10. These six independent variables were analyzed using a column chart, and a calibration curve was drawn using the calibrate function. It was found that the Bias corrected line and the Apparent line were very close to the Ideal line, The consistency between the predicted value and the actual value is very good. By using the ROC function to plot the ROC curve and calculating the area under the curve: 0.8475, 95% CI 0.7237-0.9713, it can also be determined that the accuracy of the model is very high. In addition, we also used Lasso method and random forest method to filter out independent variables with different results, but the calibration curve drawn by the calibration function confirmed poor prediction performance. The function dose volume parameters V35, V30, V25, V20, V15, and V10 obtained through 4D-CT are key factors affecting radiation pneumonia. Establishing a predictive model can provide more accurate lung restriction basis for clinical radiotherapy planning.

Association of systemic immune-inflammation index with insulin resistance and prediabetes: a cross-sectional study.

Background and Objective: Previous research suggested a relationship between the Systemic Immune-Inflammation Index (SII) and multiple adverse health conditions. However, the role of SII in prediabetes and insulin resistance (IR) remains poorly understood. Therefore, this study aims to explore the potential relationship between SII and prediabetes and IR, providing data support for effective diabetes prevention by reducing systemic inflammation. Methods: Linear regression models were used to assess the correlation between continuous SII and risk markers for type 2 diabetes (T2D). Subsequently, multivariate logistic regression models and subgroup analyses were employed to evaluate the association between SII tertiles and prediabetes and IR, controlling for various confounding factors. Finally, restricted cubic spline graphs were used to analyze the nonlinear relationship between SII and IR and prediabetes. Results: After controlling for multiple potential confounders, SII was positively correlated with fasting blood glucose (FBG) (ß: 0.100; 95% CI: 0.040 to 0.160), fasting serum insulin (FSI) (ß: 1.042; 95% CI: 0.200 to 1.885), and homeostasis model assessment of insulin resistance (HOMA-IR) (ß: 0.273; 95% CI: 0.022 to 0.523). Compared to participants with lower SII, those in the highest tertile had increased odds of prediabetes (OR: 1.17; 95% CI: 1.02-1.34; p for trend < 0.05) and IR (OR: 1.35; 95% CI: 1.18 to 1.51; p for trend<0.001). Conclusions: Our study results demonstrate an elevated association between SII levels and both IR and prediabetes, indicating SII as a straightforward and cost-effective method identifying individuals with IR and prediabetes.

Heterologous Expression and Characterization of a pH-Stable Chitinase from Micromonospora aurantiaca with a Potential Application in Chitin Degradation.

To promote the bioconversion of marine chitin waste into value-added products, we expressed a novel pH-stable Micromonospora aurantiaca-derived chitinase, MaChi1, in Escherichia coli and subsequently purified, characterized, and evaluated it for its chitin-converting capacity. Our results indicated that MaChi1 is of the glycoside hydrolase (GH) family 18 with a molecular weight of approximately 57 kDa, consisting of a GH18 catalytic domain and a cellulose-binding domain. We recorded its optimal activity at pH 5.0 and 55 °C. It exhibited excellent stability in a wide pH range of 3.0-10.0. Mg2+ (5 mM), and dithiothreitol (10 mM) significantly promoted MaChi1 activity. MaChi1 exhibited broad substrate specificity and hydrolyzed chitin, chitosan, cellulose, soluble starch, and N-acetyl chitooligosaccharides with polymerization degrees ranging from three to six. Moreover, MaChi1 exhibited an endo-type cleavage pattern, and it could efficiently convert colloidal chitin into N-acetyl-D-glucosamine (GlcNAc) and (GlcNAc)2 with yields of 227.2 and 505.9 mg/g chitin, respectively. Its high chitin-degrading capacity and exceptional pH tolerance makes it a promising tool with potential applications in chitin waste treatment and bioactive oligosaccharide production.

Selective adsorption of anionic dyes by a macropore magnetic lignin-chitosan adsorbent.

Dyes pollution is well known for their hazardous impacts on human health and the environment. The removal of dyes from wastewater has become an important issue. In this study, magnetic micrometer-sized particles AL-CTS@MNPs were synthesized from alkaline lignin (AL) and chitosan (CTS) by "one-pot method". The adsorbent presented higher selectivity adsorption effect on anionic dyes than amphoteric and cationic dyes, and even no adsorption effect on cationic methylene blue (MB), which showed that the anionic dyes could be better separated from the other two types of dyes. The adsorption isotherms of the dyes were highly consistent with the Langmuir model, and the maximum adsorption capacity was 329.50 mg/g for methyl orange (MO) and 20.00 mg/g for rhodamine B (RhB). AL-CTS@MNPs showed good adsorption of anionic dyes (MO) in the pH range of 3-9. Meanwhile, the adsorbent AL-CTS@MNPs were also characterized, showing rough surface with specific surface areas of 37.38 m2/g, pore diameter of 95.8 nm and porosity of 17.62 %. The particle sizes were ranged from 800 µm to 1300 µm. The electrostatic attraction and π-π* electron donor-acceptor interactions were the main forces between the adsorbent and anionic dyes. While the electrostatic repulsive force between the adsorbent and the cationic dyes resulted in the non-absorption of MB by AL-CTS@MNPs. Subsequently, the adsorbent maintained a removal rate of >95 % after five adsorption-desorption cycles, demonstrating its excellent stability and recoverability. Ultimately, the prepared AL-CTS@MNPs illuminated good prospect on complex components dyes wastewater treatment.

Versatile filter membrane for effective sampling and real-time quantitative detection of airborne pathogens.

Construction of air filter membranes bearing prominent collecting and transferring capability is highly desirable for detecting airborne pathogens but remains challenging. Here, a hyaluronic acid air filter membrane (HAFM) with tunable heterogeneous micro-nano porous structures is straightforwardly constructed through the ethanol-induced phase separation strategy. Airborne pathogens can be trapped and collected by HAFM with high performance due to the ideal trade-off between removal efficiency and pressure drop. By exempting the sample elution and extraction processes, the HAFM after filtration sampling can not only directly disperse on the agar plate for colony culture but also turn to an aqueous solution for centrifugal enrichment, which significantly reduces the damage and losses of the captured microorganisms. The following combination with ATP bioluminescence endows the HAFM with a real-time quantitative detection function for the captured airborne pathogens. Benefiting from high-efficiency sampling and non-traumatic transfer of airborne pathogens, the real-world bioaerosol concentration can be facilely evaluated by the HAFM-based ATP assay. This work thus not only provides a feasible strategy to fabricate air filter membranes for efficient microbial collection and enrichment but also sheds light on designing advanced protocols for real-time detection of bioaerosols in the field.

Quantification of resistant starch content in rice after hydrothermal treatments using terahertz spectroscopy.

Since hydrothermal treatments can enhance resistant starch (RS) content in rice and provide health benefits when consumed, a less laborious and non-destructive method to determine RS content is needed. Terahertz (THz) spectroscopy is hypothesized as a suitable method to quantify RS content in rice after hydrothermal treatment with its sensitivity for the intermolecular forces increase in the formation of RS. In this study, we first used the traditional in vitro hydrolysis method to determine the content of RS in rice. Then, the potential of starch absorbance peaks to quantify RS content after three commonly used hydrothermal methods, soaking, mild heat-moisture treatment, and parboiling, was investigated. The second derivative intensities of the peak at 9.0, 10.5, 12.1, and 13.1 THz were confirmed as being correlated with RS content and showed the high accuracy to predict RS content in samples (R2 > 0.96). Our results indicate the RS content of hydrothermally treated rice can be accurately quantified using these peaks.

A rapid and sensitive aptamer-based biosensor for beta-lactoglobulin in milk.

Beta-lactoglobulin (ß-Lg), a prominent milk protein, is a major contributor to milk allergies. The quantitative assessment of ß-Lg is a valuable method for assessing the allergenic potential of dairy products. In this study, a specific aptamer, ß-Lg-01, with an affinity constant (KD) of 28.6 nM for ß-Lg was screened through seven rounds of magnetic bead SELEX (MB-SELEX). A novel bio-layer interferometry (BLI)-based aptasensor was developed, which had a limit of detection (LOD) of 0.3 ng mL-1, a linear range of 1.5 ng mL-1-15 µg mL-1, and a recovery rate of 102-116% among the milk samples. This aptasensor provides a potential tool for the detection and risk assessment of ß-Lg within 10 min.

Robust, Self-Healing, and Multi-Use Poly(Urethane-Urea-Imide) Elastomer as a Durable Adhesive for Thermal Interface Materials.

Currently, research on thermal interface materials (TIMs) is primarily focused on enhancing thermal conductivity. However, strong adhesion and multifunctionality are also important characteristics for TIMs when pursing more stable interface heat conduction. Herein, a novel poly(urethane-urea-imide) (PUUI) elastomer containing abundant dynamic hydrogen bonds network and reversible disulfide linkages is successfully synthesized for application as a TIM matrix. The PUUI can self-adapt to the metal substrate surface at moderate temperatures (80 °C) and demonstrates a high adhesion strength of up to 7.39 MPa on aluminum substrates attributed its noncovalent interactions and strong intrinsic cohesion. Additionally, the PUUI displays efficient self-healing capability, which can restore 94% of its original mechanical properties after self-healing for 6 h at room temperature. Furthermore, PUUI composited with aluminum nitride and liquid metal hybrid fillers demonstrates a high thermal conductivity of 3.87 W m-1 K-1 while maintaining remarkable self-healing capability and adhesion. When used as an adhesive-type TIM, it achieves a low thermal contact resistance of 22.1 mm2 K W-1 at zero pressure, only 16.7% of that of commercial thermal pads. This study is expected to break the current research paradigm of TIMs and offers new insights for the development of advanced, reliable, and sustainable TIMs.

A Novel Microfluidic Strategy for Efficient Exosome Separation via Thermally Oxidized Non-Uniform Deterministic Lateral Displacement (DLD) Arrays and Dielectrophoresis (DEP) Synergy.

Exosomes, with diameters ranging from 30 to 150 nm, are saucer-shaped extracellular vesicles (EVs) secreted by various type of human cells. They are present in virtually all bodily fluids. Owing to their abundant nucleic acid and protein content, exosomes have emerged as promising biomarkers for noninvasive molecular diagnostics. However, the need for exosome separation purification presents tremendous technical challenges due to their minuscule size. In recent years, microfluidic technology has garnered substantial interest as a promising alternative capable of excellent separation performance, reduced reagent consumption, and lower overall device and operation costs. In this context, we hereby propose a novel microfluidic strategy based on thermally oxidized deterministic lateral displacement (DLD) arrays with tapered shapes to enhance separation performance. We have achieved more than 90% purity in both polystyrene nanoparticle and exosome experiments. The use of thermal oxidation also significantly reduces fabrication complexity by avoiding the use of high-precision lithography. Furthermore, in a simulation model, we attempt to integrate the use of dielectrophoresis (DEP) to overcome the size-based nature of DLD and distinguish particles that are close in size but differ in biochemical compositions (e.g., lipoproteins, exomeres, retroviruses). We believe the proposed strategy heralds a versatile and innovative platform poised to enhance exosome analysis across a spectrum of biochemical applications.

Analysis of the effect of flow channel structures on the centrifugal accelerated motion of particles in a vacuum state.

In this study, the impact of flow channel structures on the acceleration of metal particles in a vacuum environment is explored, with the aim of enhancinge the acceleration quality in the centrifugal impact molding of metal powders. To assess this phenomenon, three evaluation indices are introduced: the average speed of particles thrown V p , the average speed of the particles V all , and the particle velocity distribution Vf (t). Additionally, the effects of six distinct runner structures on the centrifugal acceleration of the particles are analyzed in this research. The findings indicate that the arc-shaped flow channel structure not only ensures a more consistent acceleration process but also results in a higher ejection speed, leading to an improved acceleration effect. The unique contribution of this study is the examination of the relationship between flow channel designs and particle accelerations in a vacuum.

Data-mining framework for in-depth quantitative study of influences on low-wind-velocity area from morphological parameters of cuboid-form buildings.

Wind environment is important in architectural sustainable design, as existing studies show that it can be considerably influenced by building morphologies. This study aimed to develop a data-mining framework to quantitatively evaluate and compare influences on Low-Wind-Velocity Area (LWVA) of common cuboid-form buildings with typical morphological parameters. The data-mining framework was originally developed by integrating multiple computational methods for rapid in-depth iterative analyses, including the generation of building models using parametric modelling, the big data generation based on hybrid model, and the statistical metric analysis method. The hybrid model was created by combining the CFD model and machine learning model. The accuracy and efficiency of the framework were fully demonstrated through the comprehensive validation and analyses of different models. The data of more than fifty thousand building cases with different morphological parameters and relevant wind conditions were generated and analyzed. Influences on LWVA of morphological parameters of cuboid-form building was comprehensively evaluated, including the visualization of multiple parameters, calculation and comparison of several correlation coefficients. It suggested that the reduction of height and width on the windward side would significantly decrease the LWVA and promote the outdoor ventilation. The change of depth would have relatively limited influence on the LWVA. Multivariate regression model-fit and variance analyses were further implemented, and it was found that there was a relatively significant linear correlation between the LWVA and morphological parameters. The equation of multivariate regression model was provided for extra rapid prediction. The study outcome could contribute to efficient evaluation of LWVA and provide useful information for sustainable design.

Regulation of inflammation during wound healing: the function of mesenchymal stem cells and strategies for therapeutic enhancement.

A wound takes a long time to heal and involves several steps. Following tissue injury, inflammation is the primary cause of tissue regeneration and repair processes. As a result, the pathophysiological processes involving skin damage, healing, and remodeling depend critically on the control of inflammation. The fact that it is a feasible target for improving the prognosis of wound healing has lately become clear. Mesenchymal stem cells (MSCs) are an innovative and effective therapeutic option for wound healing due to their immunomodulatory and paracrine properties. By controlling the inflammatory milieu of wounds through immunomodulation, transplanted MSCs have been shown to speed up the healing process. In addition to other immunomodulatory mechanisms, including handling neutrophil activity and modifying macrophage polarization, there may be modifications to the activation of T cells, natural killer (NK) cells, and dendritic cells (DCs). Furthermore, several studies have shown that pretreating MSCs improves their ability to modulate immunity. In this review, we summarize the existing knowledge about how MSCs influence local inflammation in wounds by influencing immunity to facilitate the healing process. We also provide an overview of MSCs optimizing techniques when used to treat wounds.