Factors associated with antiretroviral treatment adherence among people living with HIV in Guangdong Province, China: a cross sectional analysis.

BACKGROUND: Understanding factors associated with antiretroviral treatment (ART) adherence is crucial for ART success among people living with HIV (PLHIV) in the "test and treat" era. Multiple psychosocial factors tend to coexist and have a syndemic effect on ART adherence. We aimed to explore factors associated with ART adherence and the syndemic effect of multiple psychosocial factors on ART adherence among PLHIV newly starting ART in Guangdong Province, China. METHODS: Newly diagnosed PLHIV from six cities in Guangdong Province were recruited between May 2018 and June 2019, and then followed up from May 2019 to August 2020. Baseline and follow-up data were collected from a questionnaire and the national HIV surveillance system, the follow-up data of which were analyzed in this study. A Center for Adherence Support Evaluation (CASE) index > 10 points was defined as optimal ART adherence, which was measured via participants' self-reported adherence during follow-up survey. Multivariable logistic regression was used to identify factors associated with ART adherence. Exploratory factor analysis (EFA) and multi-order latent variable structural equation modeling (SEM) were performed to explore the syndemic effect of multiple psychosocial factors on ART adherence. RESULTS: A total of 734 (68.53%) follow-up participants were finally included in this study among the 1071 baseline participants, of whom 91.28% (670/734) had self-reported optimal ART adherence. Unemployment (aOR = 1.75, 95%CI: 1.01-3.02), no medication reminder (aOR = 2.28, 95%CI: 1.09-4.74), low medication self-efficacy (aOR = 2.28, 95%CI: 1.27-4.10), low social cohesion (aOR = 1.82, 95%CI: 1.03-3.19), no social participation (aOR = 5.65, 95%CI: 1.71-18.63), and ART side effects (aOR = 0.46, 95%CI: 0.26-0.81) were barriers to optimal ART adherence. The EFA and second-order latent variable SEM showed a linear relationship (standardized coefficient = 0.43, P < 0.001) between ART adherence and the latent psychosocial (syndemic) factor, which consisted of the three latent factors of medication beliefs and self-efficacy (standardized coefficient = 0.65, P < 0.001), supportive environment (standardized coefficient = 0.50, P < 0.001), and negative emotions (standardized coefficient=-0.38, P < 0.01). The latent factors of medication beliefs and self-efficacy, supportive environment, and negative emotions explained 42.3%, 25.3%, and 14.1% of the variance in the latent psychosocial factor, respectively. CONCLUSIONS: About nine out of ten PLHIV on ART in Guangdong Province self-reported optimal ART adherence. However, more efforts should be made to address barriers to optimal ART adherence.

The discovery of a novel pyrrolo[2,3-b]pyridine as a selective CDK8 inhibitor offers a new approach against psoriasis.

Currently, the drugs used in clinical to treat psoriasis mainly broadly suppress cellular immunity. However, these drugs can only provide temporary and partial symptom relief, they do not cure the condition and may lead to recurrence or even serious toxic side effects. In this study, we describe the discovery of a novel potent CDK8 inhibitor as a treatment for psoriasis. Through structure-based design, compound 46 was identified as the most promising candidate, exhibiting a strong inhibitory effect on CDK8 (IC50 value of 57 nM) along with favourable inhibition against NF-κB. Additionally, it demonstrated a positive effect in an in vitro psoriasis model induced by TNF-α. Furthermore, this compound enhanced the thermal stability of CDK8 and exerted evident effects on the biological function of CDK8, and it had favourable selectivity across the CDK family and tyrosine kinase. This compound showed no obvious inhibitory effect on CYP450 enzyme. Further studies confirmed that compound 46 exhibited therapeutic effect on IMQ-induced psoriasis, alleviated the inflammatory response in mice, and enhanced the expression of Foxp3 and IL-10 in the dorsal skin in vivo. This discovery provides a new strategy for developing selective CDK8 inhibitors with anti-inflammatory activity for the treatment of psoriasis.

Identification of RACK1 as a novel regulator of non-structural protein 4 of chikungunya virus.

Chikungunya virus (CHIKV) is a neglected arthropod-borne and anthropogenic alphavirus. Over the past two decades, the CHIKV distribution has undergone significant changes worldwide, from the original tropics and subtropics regions to temperate regions, which has attracted global attention. However, the interactions between CHIKV and its host remain insufficiently understood, which dampens the need for the development of an anti-CHIKV strategy. In this study, on the basis of the optimal overexpression of non-structural protein 4 (nsP4), we explore host interactions of CHIKV nsP4 using mass spectrometry-based protein-protein interaction approaches. The results reveal that some cellular proteins that interact with nsP4 are enriched in the ubiquitin-proteasome pathway. Specifically, the scaffold protein receptor for activated C kinase 1 (RACK1) is identified as a novel host interactor and regulator of CHIKV nsP4. The inhibition of the interaction between RACK1 and nsP4 by harringtonolide results in the reduction of nsP4, which is caused by the promotion of degradation but not the inhibition of nsP4 translation. Furthermore, the decrease in nsP4 triggered by the RACK1 inhibitor can be reversed by the proteasome inhibitor MG132, suggesting that RACK1 can protect nsP4 from degradation through the ubiquitin-proteasome pathway. This study reveals a novel mechanism by which the host factor RACK1 regulates CHIKV nsP4, which could be a potential target for developing drugs against CHIKV.

Prediction of cytochrome P450-mediated bioactivation using machine learning models and in vitro validation.

Cytochrome P450 (P450)-mediated bioactivation, which can lead to the hepatotoxicity through the formation of reactive metabolites (RMs), has been regarded as the major problem of drug failures. Herein, we purposed to establish machine learning models to predict the bioactivation of P450. On the basis of the literature-derived bioactivation dataset, models for Benzene ring, Nitrogen heterocycle and Sulfur heterocycle were developed with machine learning methods, i.e., Random Forest, Random Subspace, SVM and Naïve Bayes. The models were assessed by metrics like "Precision", "Recall", "F-Measure", "AUC" (Area Under the Curve), etc. Random Forest algorithms illustrated the best predictability, with nice AUC values of 0.949, 0.973 and 0.958 for the test sets of Benzene ring, Nitrogen heterocycle and Sulfur heterocycle models, respectively. 2D descriptors like topological indices, 2D autocorrelations and Burden eigenvalues, etc. contributed most to the models. Furthermore, the models were applied to predict the occurrence of bioactivation of an external verification set. Drugs like selpercatinib, glafenine, encorafenib, etc. were predicted to undergo bioactivation into toxic RMs. In vitro, IC50 shift experiment was performed to assess the potential of bioactivation to validate the prediction. Encorafenib and tirbanibulin were observed of bioactivation potential with shifts of 3-6 folds or so. Overall, this study provided a reliable and robust strategy to predict the P450-mediated bioactivation, which will be helpful to the assessment of adverse drug reactions (ADRs) in clinic and the design of new candidates with lower toxicities.

Identification and Analysis of Human-Exoskeleton Coupling Parameters in Lower Extremities.

This paper proposed linear and non-linear models for predicting human-exoskeleton coupling forces to enhance the studies of human-exoskeleton coupling dynamics. Then the parameters of these models were identified with a newly designed platform and the help of ten adult male and ten adult female volunteers (Age: 23.65 ±4.03 years, Height: 165.60 ±8.32 mm, Weight: 62.35 ±14.09 kg). Comparing the coupling force error predicted by the models with experimental measurements, one obtained a more accurate and robust prediction of the coupling forces with the non-linear model. Moreover, statistical analysis of the experimental data was performed to reveal the correlation between the coupling parameters and coupling positions and looseness. Finally, backpropagation (BP) neural network and Gaussian Process Regression (GPR) were used to predict the human-exoskeleton coupling parameters. The significance of each input parameter to the human-exoskeleton coupling parameters was assessed by analyzing the sensitivity of GPR performance to its inputs. The novelty and contribution are the establishment of the non-linear coupling model, the design of the coupling experimental platform and a regression model which provides a possibility to obtain human-exoskeleton without experimental measurement and identification. Based on this work, one can optimize control algorithm and design comfortable human-exoskeleton interaction.

Development of a risk estimation model for condomless sex among college students in Zhuhai, China: a cross-sectional study.

BACKGROUND: Condom use at last intercourse is an effective indicator for human immunodeficiency virus (HIV) prevention. To identify at-risk individuals and improve prevention strategies, this study explored factors associated with condomless sex at last intercourse in the last year and developed a risk estimation model to calculate the individual possibility of condomless sex among college students in Zhuhai, China. METHODS: A cross-sectional study was conducted among 1430 college students who had sex in the last year from six universities in Zhuhai. The least absolute shrinkage and selection operator (LASSO) and logistic regression were performed to explore the predictors of condomless sex. The nomogram was constructed to calculate the individual possibility of condomless sex. Discrimination and calibration of the nomogram were evaluated using the area under the receiver-operator characteristic curve (AUROC) and the calibration curve. RESULTS: The proportion of students who had condomless sex at last intercourse was 18.2% (260/1430). Students who had experienced more types of intimate partner violence (aOR, 1.58; 95% CI, 1.31 ~ 1.92) and had anal sex (aOR, 1.75; 95% CI, 1.06 ~ 2.84) were more likely to have condomless sex. Students who had heterosexual intercourse (aOR, 0.37; 95% CI, 0.21 ~ 0.70), used condoms at first sex (aOR, 0.20; 95% CI, 0.14 ~ 0.27), had high attitudes towards condom use (aOR, 0.87; 95% CI, 0.80 ~ 0.95) and self-efficacy for condom use (aOR, 0.84; 95% CI, 0.78 ~ 0.90) were less likely to have condomless sex. The nomogram had high accuracy with an AUROC of 0.83 and good discrimination. CONCLUSIONS: Intimate partner violence, anal sex, condom use at first sex, attitude towards condom use, and self-efficacy for condom use were associated with condomless sex among college students. The nomogram was an effective and convenient tool for calculating the individualized possibility of condomless sex among college students. It could help to identify individuals at risk and help universities and colleges to formulate appropriate individualized interventions and sexual health education programs.

Pyrolytic Modification of Heavy Coal Tar by Multi-Polymer Blending: Preparation of Ordered Carbonaceous Mesophase.

In order to achieve the high-value utilization of heavy tar for the production of enhanced-performance graphite foam carbon, the carbon mesophase was ready from the heavy component of low-temperature coal tar, and the coal tar was modified by styrene-butadiene-styrene (SBS), polyethylene (PE) and ethylene-vinyl-acetate (EVA) copolymers. The order degree of the carbonite mesophase was analyzed using a polarizing microscope test, Fourier transform infrared spectroscopy and X-ray diffraction to screen out the most suitable copolymer type and addition amount. Furthermore, the mechanism of modification by this copolymer was analyzed. The results showed that adding SBS, PE and EVA to coal tar would affect the order of carbonaceous mesophase; however, at an addition rate of 10.0 wt.%, the linear-structure SBS copolymer with a styrene/butadiene ratio (S/B) of 30/70 exhibited the optimal degree of ordering in the carbonaceous mesophase. Its foam carbon prepared by polymer modification is the only one that forms a graphitized structure, with d002 of 0.3430 nm, and the maximum values of Lc and La are 3.54 nm and 2.22 nm, respectively. This is because, under elevated pressure and high-temperature conditions, SBS underwent chain scission, releasing a more significant number of methyl and other free radicals that interacted with the coal tar constituents. As a result, it reduced the affinity density of heavy coal tar molecules, enhanced fluidity, promoted the stacking of condensed aromatic hydrocarbons and increased the content of soluble carbonaceous mesophase, ultimately leading to a more favorable alignment of the carbonaceous mesophase.

Discovery of novel and potent CDK8 inhibitors for the treatment of acute myeloid leukaemia.

It has been reported that CDK8 plays a key role in acute myeloid leukaemia. Here, a total of 40 compounds were rational designed and synthesised based on the previous SAR. Among them, compound 12 (3-(3-(furan-3-yl)-1H-pyrrolo[2,3-b]pyridin-5-yl)benzamide) showed the most potent inhibiting activity against CDK8 with an IC50 value of 39.2 ± 6.3 nM and anti AML cell proliferation activity (molm-13 GC50 = 0.02 ± 0.01 µM, MV4-11 GC50 = 0.03 ± 0.01 µM). Mechanistic studies revealed that this compound 12 could inhibit the phosphorylation of STAT-1 and STAT-5. Importantly, compound 12 showed relative good bioavailability (F = 38.80%) and low toxicity in vivo. This study has great significance for the discovery of more efficient CDK8 inhibitors and the development of drugs for treating AML in the future.

The interrelationships between Chinese learners' trait emotional intelligence and teachers' emotional support in learners' engagement.

BACKGROUND: One noteworthy concern within the realm of education is the level of engagement demonstrated by students. Among the factor that can have a crucial role in this domain is teacher support, especially emotional support which has an impact on several aspects of learners' education. Furthermore, various studies have investigated the relationship between Emotional Intelligence (EI) and learners' engagement. METHODS: Accordingly, this study investigated the possible role of trait EI and the emotional support of teachers and how these constructs may work to associate learners' engagement. For this objective, a total of 309 Chinese students across different colleges and universities in 5 provinces of Beijing, Shanghai, Jiangsu, Hubei, and Shaanxi were enrolled. They were 126 females and 183 males, with ages ranging from 18 to 30 years old (Mean = 24.6). RESULTS: The results of this research through running Structural Equation Modeling (SEM) demonstrated that teachers' emotional support and trait EI both can associate students' learning engagement. The final measurement model shows that about 73% of changes in learners' engagement can be associated by their trait EI and teachers' emotional support. CONCLUSIONS: This study underscores the importance of emotional support from teachers and the trait of EI in relation to students' engagement in learning. Both factors were shown to play a significant role in associating student engagement. Moreover, this study could potentially have wider impacts on members of academic teams.

Association of elastic power in mechanical ventilation with the severity of acute respiratory distress syndrome: a retrospective study.

BACKGROUND: Mechanical power (MP) is the total energy released into the entire respiratory system per minute which mainly comprises three components: elastic static power, Elastic dynamic power and resistive power. However, the energy to overcome resistance to the gas flow is not the key factor in causing lung injury, but the elastic power (EP) which generates the baseline stretch of the lung fibers and overcomes respiratory system elastance may be closely related to the ARDS severity. Thus, this study aimed to investigate whether EP is superior to other ventilator variables for predicting the severity of lung injury in ARDS patients. METHODS: We retrieved patient data from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The retrieved data involved adults (≥ 18 years) diagnosed with ARDS and subjected to invasive mechanical ventilation for ≥ 48 h. We employed univariate and multivariate logistic regression analyses to investigate the correlation between EP and development of moderate-severe ARDS. Furthermore, we utilized restricted cubic spline models to assess whether there is a linear association between EP and incidence of moderate-severe ARDS. In addition, we employed a stratified linear regression model and likelihood ratio test in subgroups to identify potential modifications and interactions. RESULTS: Moderate-severe ARDS occurred in 73.4% (296/403) of the patients analyzed. EP and MP were significantly associated with moderate-severe ARDS (odds ratio [OR] 1.21, 95% confidence interval [CI] 1.15-1.28, p < 0.001; and OR 1.15, 95%CI 1.11-1.20, p < 0.001; respectively), but EP showed a higher area-under-curve (95%CI 0.72-0.82, p < 0.001) than plateau pressure, driving pressure, and static lung compliance in predicting ARDS severity. The optimal cutoff value for EP was 14.6 J/min with a sensitivity of 75% and specificity of 66%. Quartile analysis revealed that the relationship between EP and ARDS severity remained robust and reliable in subgroup analysis. CONCLUSION: EP is a good ventilator variable associated with ARDS severity and can be used for grading ARDS severity. Close monitoring of EP is advised in patients undergoing mechanical ventilation. Additional experimental trials are needed to investigate whether adjusting ventilator variables according to EP can yield significant improvements in clinical outcomes.

[Development and validation of a mechanical power-oriented nomogram model for predicting the risk of weaning failure in mechanically ventilated patients: an analysis using the data from MIMIC-IV].

OBJECTIVE: To develop and validate a mechanical power (MP)-oriented nomogram prediction model of weaning failure in mechanically ventilated patients. METHODS: Patients who underwent invasive mechanical ventilation (IMV) for more than 24 hours and were weaned using a T-tube ventilation strategy were collected from the Medical Information Mart for Intensive Care-IV v1.0 (MIMIC-IV v1.0) database. Demographic information and comorbidities, respiratory mechanics parameters 4 hours before the first spontaneous breathing trial (SBT), laboratory parameters preceding the SBT, vital signs and blood gas analysis during SBT, length of intensive care unit (ICU) stay and IMV duration were collected and all eligible patients were enrolled into the model group. Lasso method was used to screen the risk factors affecting weaning outcomes, which were included in the multivariate Logistic regression analysis. R software was used to construct the nomogram prediction model and build the dynamic web page nomogram. The discrimination and accuracy of the nomogram were assessed by receiver operator characteristic curve (ROC curve) and calibration curves, and the clinical validity was assessed by decision curve analysis (DCA). The data of patients undergoing mechanical ventilation hospitalized in ICU of the First People's Hospital of Lianyungang City and the Second People's Hospital of Lianyungang City from November 2021 to October 2022 were prospectively collected to externally validate the model. RESULTS: A total of 3 695 mechanically ventilated patients were included in the model group, and the weaning failure rate was 38.5% (1 421/3 695). Lasso regression analysis finally screened out six variables, including positive end-expiratory pressure (PEEP), MP, dynamic lung compliance (Cdyn), inspired oxygen concentration (FiO2), length of ICU stay and IMV duration, with coefficients of 0.144, 0.047, -0.032, 0.027, 0.090 and 0.098, respectively. Logistic regression analysis showed that the six variables were all independent risk factors for predicting weaning failure risk [odds ratio (OR) and 95% confidence interval (95%CI) were 1.155 (1.111-1.200), 1.048 (1.031-1.066), 0.968 (0.963-0.974), 1.028 (1.017-1.038), 1.095 (1.076-1.113), and 1.103 (1.070-1.137), all P < 0.01]. The MP-oriented nomogram prediction model of weaning failure in mechanically ventilated patients showed accurate discrimination both in the model group and external validation group, with area under the ROC curve (AUC) and 95%CI of 0.832 (0.819-0.845) and 0.879 (0.833-0.925), respectively. Furthermore, its predictive accuracy was significantly higher than that of individual indicators such as MP, Cdyn, and PEEP. Calibration curves showed good correlation between predicted and observed outcomes. DCA indicated that the nomogram model had high net benefits, and was clinically beneficial. CONCLUSIONS: The MP-oriented nomogram prediction model of weaning failure accurately predicts the risk of weaning failure in mechanical ventilation patients and provides valuable information for clinicians making decisions on weaning.

Evaluation of crowdsourced mortality prediction models as a framework for assessing artificial intelligence in medicine.

OBJECTIVE: Applications of machine learning in healthcare are of high interest and have the potential to improve patient care. Yet, the real-world accuracy of these models in clinical practice and on different patient subpopulations remains unclear. To address these important questions, we hosted a community challenge to evaluate methods that predict healthcare outcomes. We focused on the prediction of all-cause mortality as the community challenge question. MATERIALS AND METHODS: Using a Model-to-Data framework, 345 registered participants, coalescing into 25 independent teams, spread over 3 continents and 10 countries, generated 25 accurate models all trained on a dataset of over 1.1 million patients and evaluated on patients prospectively collected over a 1-year observation of a large health system. RESULTS: The top performing team achieved a final area under the receiver operator curve of 0.947 (95% CI, 0.942-0.951) and an area under the precision-recall curve of 0.487 (95% CI, 0.458-0.499) on a prospectively collected patient cohort. DISCUSSION: Post hoc analysis after the challenge revealed that models differ in accuracy on subpopulations, delineated by race or gender, even when they are trained on the same data. CONCLUSION: This is the largest community challenge focused on the evaluation of state-of-the-art machine learning methods in a healthcare system performed to date, revealing both opportunities and pitfalls of clinical AI.

Elastic power, a novel predictor of the severity and prognosis of ARDS.

PURPOSE: To explore the predictive value of the new comprehensive respiratory mechanics parameters elastic power (EP) and elastic power normalized to the compliance (Cst-EP) in the evaluation of the severity and 28-day prognosis of ARDS patients. METHODS: The MIMIC-III database was used to identify ARDS patients under invasive mechanical ventilation for at least 48 h. Their baseline data and ventilatory variables were collected. EP, elastic energy, driving pressure and mechanical power were calculated according to the corresponding formulas. Their value in assessing the severity of ARDS was evaluated. The correlation between Cst-EP and 28-day prognosis of ARDS patients was analyzed. RESULTS: EP was independently associated with the severity of the ARDS and the odds ratio (OR) was 1.301 [95% CI (1.190-1.423), p < 0.001]. It has higher accuracy for the severity of ARDS, with an optimal cut-off value of 14.6 J/min. The Cst-EP was significantly associated with increased risk of death and the hazard ratio (HR) per 100 J/min × cmH2O/ml × 10-3 was 1.169 [95% CI (1.093-1.250), p < 0.001]. In addition, the 28-day cumulative survival rate of the high Cst-EP group was significantly lower than that of the low Cst-EP group. CONCLUSION: EP can be used to predict the severity of ARDS, and Cst-EP is associated with mortality during controlled mechanical ventilation in ARDS.

Preference-Aware User Access Control Policy in Internet of Things.

There are multiple types of services in the Internet of Things, and existing access control methods do not consider situations wherein the same types of services have multiple access options. In order to ensure the QoS quality of user access and realize the reasonable utilization of Internet of Things network resources, it is necessary to consider the characteristics of different services to design applicable access control strategies. In this paper, a preference-aware user access control strategy in slices is proposed, which can increase the number of users in the system while balancing slice resource utilization. First, we establish the user QoS model and slice QoS index range according to the delay, rate and reliability requirements, and we select users with multiple access options. Secondly, a user preference matrix is established according to the user QoS requirements and the slice QoS index range. Finally, a preference matrix of the slice is built according to the optimization objective, and access control decisions are made for users through the resource utilization state of the slice and the preference matrix. The verification results show that the proposed strategy not only balances slice resource utilization but also increases the number of users who can access the system.

Genetic Architecture and Functional Implications of the CSF-Contacting Nucleus.

We previously identified a unique nucleus, the cerebrospinal fluid (CSF)-contacting nucleus. This study aims to understand its gene architecture and preliminarily suggest its functions. The results showed that there were about 19,666 genes in this nucleus, of which 913 were distinct from the dorsal raphe nucleus (non-CSF contacting). The top 40 highly-expressed genes are mainly related to energy metabolism, protein synthesis, transport, secretion, and hydrolysis. The main neurotransmitter is 5-HT. The receptors of 5-HT and GABA are abundant. The channels for Cl-, Na+, K+, and Ca2+ are routinely expressed. The signaling molecules associated with the CaMK, JAK, and MAPK pathways were identified accurately. In particular, the channels of transient receptor potential associated with nociceptors and the solute carrier superfamily members associated with cell membrane transport were significantly expressed. The relationship between the main genes of the nucleus and life activities is preliminarily verified.

SnRNA-Seq analysis reveals ten hub genes associated with alveolar epithelial cell injury during pulmonary acute respiratory distress syndrome.

Background: Alveolar epithelial cell injury is a key factor in the occurrence and development of pulmonary acute respiratory distress syndrome (ARDSp). Yet the gene expression profile of alveolar epithelial cells of patients with ARDSp remains unclear. Methods: We analyzed single nuclear RNA sequencing (snRNA-Seq) data from autopsy lung tissues of both ARDSp patients and healthy donors. Sequence data for type 2 alveolar epithelizal cells (AT2) were extracted by the Seurat package. Differentially expressed genes (DEGs) in AT2 were identified by the criteria |log2FC| ≥ 0.25 and P < 0.05 with DESeq2. A protein interaction network was constructed using Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software to identify hub genes. We then constructed an ARDSp rat model through induction by lipopolysaccharide (LPS) airway instillation. Left lung RNA was extracted and sequenced via Illumina Hiseq platforms. Analysis of the rat RNA sequencing data was then used to verify hub genes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on the identified hub genes. Results: In AT2, a total of 289 genes were identified as differentially expressed between those from ARDSp patients and healthy donors, and these included 190 upregulated and 99 downregulated genes. Ten hub genes were further identified (RPS27A, ACTG1, CAV1, HSP90AA1, HSPA5, CCND1, ITGA3, B2M, NEDD4L, and SEMA5A). There was a similar expression trend of HSPA5 between rat RNA and snRNA sequencing data. Discussion: ARDSp altered the gene expression profile of AT2. The identified hub genes were enriched in biological processes mainly involved in cell growth and transformation. Relatedly, ferroptosis and autophagy are possibly involved in AT2 injury during ARDSp. These novel insights into ARDSp may aid the discovery of potential targets for the diagnosis and treatment of ARDSp.

A Simple Method for the Fabrication of Silicon Inverted Pyramid Substrates for Surface-Enhanced Raman Spectroscopy.

Silicon inverted pyramids have been shown to exhibit superior SERS properties compared to ortho-pyramids, yet low-cost, simple preparation processes are lacking at present. This study demonstrates a simple method, silver-assisted chemical etching combined with PVP, to construct silicon inverted pyramids with a uniform size distribution. Two types of Si substrates for surface-enhanced Raman spectroscopy (SERS) were prepared via silver nanoparticles deposited on the silicon inverted pyramids by electroless deposition and radiofrequency sputtering, respectively. The experiments were conducted using rhodamine 6G (R6G), methylene blue (MB) and amoxicillin (AMX) molecules to test the SERS properties of the Si substrates with inverted pyramids. The results indicate that the SERS substrates show high sensitivity to detect the above molecules. In particular, the sensitivity and reproducibility of the SERS substrates with a denser silver nanoparticle distribution, prepared by radiofrequency sputtering, are significantly higher than those of the electroless deposited substrates to detect R6G molecules. This study sheds light on a potential low-cost and stable method for preparing silicon inverted pyramids, which is expected to replace the costly commercial Klarite SERS substrates.

HD-SiO2/SiO2 Sol@PDMS Superhydrophobic Coating with Good Durability and Anti-Corrosion for Protection of Al Sheets.

Superhydrophobic coatings with excellent water-repellent properties imply a wide range of application areas. However, improvements are needed in terms of stability and complex processing procedures. In the present study, a superhydrophobic coating on Al sheets was prepared by mixing hexadecyltrimethoxysilane (HDTMS)-modified SiO2 nanoparticles and acid-catalyzed silica sols (HD-SiO2/SiO2 Sol) with polydimethylsiloxane (PDMS) binder. The HD-SiO2 nanoparticles and acid-catalyzed silica sol (SiO2 sol) form a binary graded micro-nanostructure, providing excellent superhydrophobicity (Water Contact Angle = 158.5°, Sliding angle = 0°). Superhydrophobic coatings with excellent water-repellent properties have potential for corrosion prevention. However the commonly used organic resins have poor chemical and mechanical properties. In the present study, the results of outdoor exposure for 30 days, immersion in acid and alkaline solutions for 24 h, grit abrasion, and water impact experiments, respectively, showed that the prepared superhydrophobic coating has good wear resistance. The integrated superhydrophobic coating on the Al sheets exhibited good corrosion inhibition with an efficiency (η) of 98.9%, which is much higher than that of the uncoated sheets. The present study provides a promising approach for producing stable superhydrophobic coatings at a low cost, with the potential to supplant conventional organic resin anti-corrosion coatings.

Enhanced Mechanical and Hydrophobic Antireflective Nanocoatings Fabricated on Polycarbonate Substrates by Combined Treatment of Water and HMDS Vapor.

Polycarbonate (PC) with high transmittance, stable mechanical performance and environmental resistance is crucial for practical applications. In this work, we report a method for the preparation of a robust antireflective (AR) coating by a simple dip-coating process of a mixed ethanol suspension consisting of tetraethoxysilane (TEOS) base-catalyzed silica nanoparticles (SNs) and acid-catalyzed silica sol (ACSS). ACSS greatly improved the adhesion and durability of the coating, and the AR coating exhibited high transmittance and mechanical stability. Water and hexamethyldisilazane (HMDS) vapor treatment were further employed to improve the hydrophobicity of the AR coating. The as-prepared coating exhibited excellent antireflective properties, with an average transmittance of 96.06% in the wavelength range of 400 to 1000 nm, which is 7.55% higher than the bare PC substrate. After sand and water droplet impact tests, the AR coating still maintained enhanced transmittance and hydrophobicity. Our method shows a potential application for the preparation of hydrophobic AR coatings on a PC substrate.