Hypocrystalline ceramic aerogels for thermal insulation at extreme conditions.

Thermal insulation under extreme conditions requires materials that can withstand complex thermomechanical stress and retain excellent thermal insulation properties at temperatures exceeding 1,000 degrees Celsius1-3. Ceramic aerogels are attractive thermal insulating materials; however, at very high temperatures, they often show considerably increased thermal conductivity and limited thermomechanical stability that can lead to catastrophic failure4-6. Here we report a multiscale design of hypocrystalline zircon nanofibrous aerogels with a zig-zag architecture that leads to exceptional thermomechanical stability and ultralow thermal conductivity at high temperatures. The aerogels show a near-zero Poisson's ratio (3.3 × 10-4) and a near-zero thermal expansion coefficient (1.2 × 10-7 per degree Celsius), which ensures excellent structural flexibility and thermomechanical properties. They show high thermal stability with ultralow strength degradation (less than 1 per cent) after sharp thermal shocks, and a high working temperature (up to 1,300 degrees Celsius). By deliberately entrapping residue carbon species in the constituent hypocrystalline zircon fibres, we substantially reduce the thermal radiation heat transfer and achieve one of the lowest high-temperature thermal conductivities among ceramic aerogels so far-104 milliwatts per metre per kelvin at 1,000 degrees Celsius. The combined thermomechanical and thermal insulating properties offer an attractive material system for robust thermal insulation under extreme conditions.

Enhancer-MDLF: a novel deep learning framework for identifying cell-specific enhancers.

Enhancers, noncoding DNA fragments, play a pivotal role in gene regulation, facilitating gene transcription. Identifying enhancers is crucial for understanding genomic regulatory mechanisms, pinpointing key elements and investigating networks governing gene expression and disease-related mechanisms. Existing enhancer identification methods exhibit limitations, prompting the development of our novel multi-input deep learning framework, termed Enhancer-MDLF. Experimental results illustrate that Enhancer-MDLF outperforms the previous method, Enhancer-IF, across eight distinct human cell lines and exhibits superior performance on generic enhancer datasets and enhancer-promoter datasets, affirming the robustness of Enhancer-MDLF. Additionally, we introduce transfer learning to provide an effective and potential solution to address the prediction challenges posed by enhancer specificity. Furthermore, we utilize model interpretation to identify transcription factor binding site motifs that may be associated with enhancer regions, with important implications for facilitating the study of enhancer regulatory mechanisms. The source code is openly accessible at https://github.com/HaoWuLab-Bioinformatics/Enhancer-MDLF.

Deacetylation of Septin4 by SIRT2 (Silent Mating Type Information Regulation 2 Homolog-2) Mitigates Damaging of Hypertensive Nephropathy.

BACKGROUND: Hypertension can lead to podocyte damage and subsequent apoptosis, eventually resulting in glomerulosclerosis. Although alleviating podocyte apoptosis has clinical significance for the treatment of hypertensive nephropathy, an effective therapeutic target has not yet been identified. The function of septin4, a proapoptotic protein and an important marker of organ damage, is regulated by post-translational modification. However, the exact role of septin4 in regulating podocyte apoptosis and its connection to hypertensive renal damage remains unclear. METHODS: We investigated the function and mechanism of septin4 in hypertensive nephropathy to discover a theoretical basis for targeted treatment. Mouse models including Rosa 26 (Gt(ROSA)26Sor)-SIRT2 (silent mating type information regulation 2 homolog-2)-Flag-TG (transgenic) (SIRT2-TG) mice SIRT2-knockout, and septin4-K174Q mutant mice, combined with proteomic and acetyl proteomics analysis, followed by multiple molecular biological methodologies, were used to demonstrate mechanisms of SIRT2-mediated deacetylation of septin4-K174 in hypertensive nephropathy. RESULTS: Using transgenic septin4-K174Q mutant mice treated with the antioxidant Tempol, we found that hyperacetylation of the K174 site of septin4 exacerbates Ang II (angiotensin II)- induced hypertensive renal injury resulting from oxidative stress. Proteomics and Western blotting assays indicated that septin4-K174Q activates the cleaved-PARP1 (poly [ADP-ribose] polymerase family, member 1)-cleaved-caspase3 pathway. In septin4-knockdown human renal podocytes, septin4-K174R, which mimics deacetylation at K174, rescues podocyte apoptosis induced by Ang II. Immunoprecipitation and mass spectrometry analyses identified SIRT2 as a deacetylase that interacts with the septin4 GTPase domain and deacetylates septin4-K174. In Sirt2-deficient mice and SIRT2-knockdown renal podocytes, septin4-K174 remains hyperacetylated and exacerbates hypertensive renal injury. By contrast, in Rosa26-Sirt2-Flag (SIRT2-TG) mice and SIRT2-knockdown renal podocytes reexpressing wild-type SIRT2, septin4-K174 is hypoacetylated and mitigates hypertensive renal injury. CONCLUSIONS: Septin4, when activated through acetylation of K174 (K174Q), promotes hypertensive renal injury. Septin4-K174R, which mimics deacetylation by SIRT2, inhibits the cleaved-PARP1-cleaved-caspase3 pathway. Septin4-K174R acts as a renal protective factor, mitigating Ang II-induced hypertensive renal injury. These findings indicate that septin4-K174 is a potential therapeutic target for the treatment of hypertensive renal injury.

Separable amygdala activation patterns in the evaluations of robots.

Given the increasing presence of robots in everyday environments and the significant challenge posed by social interactions with robots, it is crucial to gain a deeper understanding into the social evaluations of robots. One potentially effective approach to comprehend the fundamental processes underlying controlled and automatic evaluations of robots is to probe brain response to different perception levels of robot-related stimuli. Here, we investigate controlled and automatic evaluations of robots based on brain responses during viewing of suprathreshold (duration: 200 ms) and subthreshold (duration: 17 ms) humanoid robot stimuli. Our behavioral analysis revealed that despite participants' self-reported positive attitudes, they held negative implicit attitudes toward humanoid robots. Neuroimaging analysis indicated that subthreshold presentation of humanoid robot stimuli elicited significant activation in the left amygdala, which was associated with negative implicit attitudes. Conversely, no significant left amygdala activation was observed during suprathreshold presentation. Following successful attenuation of negative attitudes, the left amygdala response to subthreshold presentation of humanoid robot stimuli decreased, and this decrease correlated positively with the reduction in negative attitudes. These findings provide evidence for separable patterns of amygdala activation between controlled and automatic processing of robots, suggesting that controlled evaluations may influence automatic evaluations of robots.

Pushing the Limits of Capacitively Coupled Contactless Conductivity Detection for Capillary Electrophoresis.

Capillary electrophoresis with capacitively coupled contactless conductivity detection (CE-C4D) has proven to be an efficient technique for the separation and detection of charged inorganic, organic, and biochemical analytes. It offers several advantages, including cost-effectiveness, nanoliter injection volume, short analysis time, good separation efficiency, suitability for miniaturization, and portability. However, the routine determination of common inorganic cations (NH4+, K+, Na+, Ca2+, Mg2+, and Li+) and inorganic anions (F-, Cl-, Br-, NO2-, NO3-, PO43-, and SO42-) in water quality monitoring typically exhibits limits of detection of about 0.3-1 µM without preconcentration. This sensitivity often proves insufficient for the applications of CE-C4D in trace analysis situations. Here, we explore methods to push the detection limits of CE-C4D through a comprehensive consideration of signal and noise sources. In particular, we (i) studied the model of C4D and its guiding roles in C4D and CE-C4D, (ii) optimized the bandwidth and noise performance of the current-to-voltage (I-V) converter, and (iii) reduced the noise level due to the strong background signal of the background electrolyte by adaptive differential detection. We characterized the system with Li+; the 3-fold signal-to-noise (S/N) detection limit for Li+ was determined at 20 nM, with a linear range spanning from 60 nM to 1.6 mM. Moreover, the optimized CE-C4D method was applied to the analysis of common mixed inorganic cations (K+, Na+, Ca2+, Mg2+, and Li+), anions (F-, Cl-, Br-, NO2-, NO3-, PO43-, and SO42-), toxic halides (BrO3-) and heavy metal ions (Pb2+, Cd2+, Cr3+, Co2+, Ni2+, Zn2+, and Cu2+) at trace concentrations of 200 nM. All electropherograms showed good S/N ratios, thus proving its applicability and accuracy. Our results have shown that the developed CE-C4D method is feasible for trace ion analysis in water quality control.

Impact of baseline cardiovascular risk on the outcomes of intensive blood pressure intervention: a post hoc analysis of the China rural hypertension control project.

BACKGROUND: The 2018/2023 ESC/ESH Guidelines underlined a gap how baseline cardiovascular disease (CVD) risk predicted blood pressure (BP) lowering benefits. Further, 2017 ACC/AHA Guideline and 2021 WHO Guideline recommended implementation studies about intensive BP control. Now, to bridge these guideline gaps, we conducted a post hoc analysis to validate whether the baseline CVD risk influences the effectiveness of the intensive BP control strategy, which was designed by China Rural Hypertension Control Project (CRHCP). METHODS: This is a post hoc analysis of CRHCP, among which participants were enrolled except those having CVD history, over 80 years old, or missing data. Subjects were stratified into quartiles by baseline estimated CVD risk and then grouped into intervention and usual care group according to original assignment in CRHCP. Participants in the intervention group received an integrated, multi-faceted treatment strategy, executed by trained non-physician community health-care providers, aiming to achieve a BP target of < 130/80 mmHg. Cox proportional-hazards models were used to estimate the hazard ratios of outcomes for intervention in each quartile, while interaction effect between intervention and estimated CVD risk quartiles was additionally assessed. The primary outcome comprised myocardial infarction, stroke, hospitalization for heart failure, or CVD deaths. RESULTS: Significant lower rates of primary outcomes for intervention group compared with usual care for each estimated CVD risk quartile were reported. The hazard ratios (95% confidence interval) in the four quartiles (from Q1 to Q4) were 0.59 (0.40, 0.87), 0.54 (0.40, 0.72), 0.72 (0.57, 0.91) and 0.65 (0.53, 0.80), respectively (all Ps < 0.01). There's no significant difference of hazard ratios by intervention across risk quartiles (P for interaction = 0.370). Only the relative risk of hypotension, not symptomatic hypotension, was elevated in the intervention group among upper three quartiles. CONCLUSIONS: Intensive BP lowering strategy designed by CRHCP group was effective and safe in preventing cardiovascular events independent of baseline CVD risk. TRIAL REGISTRATION: The trial is registered with ClinicalTrials.gov, NCT03527719.

StackTADB: a stacking-based ensemble learning model for predicting the boundaries of topologically associating domains (TADs) accurately in fruit flies.

Chromosome is composed of many distinct chromatin domains, referred to variably as topological domains or topologically associating domains (TADs). The domains are stable across different cell types and highly conserved across species, thus these chromatin domains have been considered as the basic units of chromosome folding and regarded as an important secondary structure in chromosome organization. However, the identification of TAD boundaries is still a great challenge due to the high cost and low resolution of Hi-C data or experiments. In this study, we propose a novel ensemble learning framework, termed as StackTADB, for predicting the boundaries of TADs. StackTADB integrates four base classifiers including Random Forest, Logistic Regression, K-NearestNeighbor and Support Vector Machine. From the analysis of a series of examinations on the data set in the previous study, it is concluded that StackTADB has optimal performance in six metrics, AUC, Accuracy, MCC, Precision, Recall and F1 score, and it is superior to the existing methods. In addition, the comparison of the performance of multiple features shows that Kmers-based features play an essential role in predicting TADs boundaries of fruit flies, and we also apply the SHapley Additive exPlanations (SHAP) framework to interpret the predictions of StackTADB to identify the reason why Kmers-based features are vital. The experimental results show that the subsequences matching the BEAF-32 motif play a crucial role in predicting the boundaries of TADs. The source code is freely available at https://github.com/HaoWuLab-Bioinformatics/StackTADB and the webserver of StackTADB is freely available at http://hwtad.sdu.edu.cn:8002/StackTADB.

Targeting platelet-derived CXCL12 impedes arterial thrombosis.

The prevention and treatment of arterial thrombosis continue to be clinically challenging, and understanding the relevant molecular mechanisms in detail may facilitate the quest to identify novel targets and therapeutic approaches that improve protection from ischemic and bleeding events. The chemokine CXCL12 augments collagen-induced platelet aggregation by activating its receptor CXCR4. Here we show that inhibition of CXCR4 attenuates platelet aggregation induced by collagen or human plaque homogenate under static and arterial flow conditions by antagonizing the action of platelet-secreted CXCL12. We further show that platelet-specific CXCL12 deficiency in mice limits arterial thrombosis by affecting thrombus growth and stability without increasing tail bleeding time. Accordingly, neointimal lesion formation after carotid artery injury was attenuated in these mice. Mechanistically, CXCL12 activated via CXCR4 a signaling cascade involving Bruton's tyrosine kinase (Btk) that led to integrin αIIbß3 activation, platelet aggregation, and granule release. The heterodimeric interaction between CXCL12 and CCL5 can inhibit CXCL12-mediated effects as mimicked by CCL5-derived peptides such as [VREY]4. An improved variant of this peptide, i[VREY]4, binds to CXCL12 in a complex with CXCR4 on the surface of activated platelets, thereby inhibiting Btk activation and preventing platelet CXCL12-dependent arterial thrombosis. In contrast to standard antiplatelet therapies such as aspirin or P2Y12 inhibition, i[VREY]4 reduced CXCL12-induced platelet aggregation and yet did not prolong in vitro bleeding time. We provide evidence that platelet-derived CXCL12 is involved in arterial thrombosis and can be specifically targeted by peptides that harbor potential therapeutic value against atherothrombosis.

iPro-WAEL: a comprehensive and robust framework for identifying promoters in multiple species.

Promoters are consensus DNA sequences located near the transcription start sites and they play an important role in transcription initiation. Due to their importance in biological processes, the identification of promoters is significantly important for characterizing the expression of the genes. Numerous computational methods have been proposed to predict promoters. However, it is difficult for these methods to achieve satisfactory performance in multiple species. In this study, we propose a novel weighted average ensemble learning model, termed iPro-WAEL, for identifying promoters in multiple species, including Human, Mouse, E.coli, Arabidopsis, B.amyloliquefaciens, B.subtilis and R.capsulatus. Extensive benchmarking experiments illustrate that iPro-WAEL has optimal performance and is superior to the current methods in promoter prediction. The experimental results also demonstrate a satisfactory prediction ability of iPro-WAEL on cross-cell lines, promoters annotated by other methods and distinguishing between promoters and enhancers. Moreover, we identify the most important transcription factor binding site (TFBS) motif in promoter regions to facilitate the study of identifying important motifs in the promoter regions. The source code of iPro-WAEL is freely available at https://github.com/HaoWuLab-Bioinformatics/iPro-WAEL.

Enhanced Path Planning and Obstacle Avoidance Based on High-Precision Mapping and Positioning.

High-precision positioning and multi-target detection have been proposed as key technologies for robotic path planning and obstacle avoidance. First, the Cartographer algorithm was used to generate high-quality maps. Then, the iterative nearest point (ICP) and the occupation probability algorithms were combined to scan and match the local point cloud, and the positions and attitudes of the robot were obtained. Furthermore, Sparse Matrix Pose Optimization was carried out to improve the positioning accuracy. The positioning accuracy of the robot in x and y directions was kept within 5 cm, the angle error was controlled within 2°, and the positioning time was reduced by 40%. An improved timing elastic band (TEB) algorithm was proposed to guide the robot to move safely and smoothly. A critical factor was introduced to adjust the distance between the waypoints and the obstacle, generating a safer trajectory, and increasing the constraint of acceleration and end speed; thus, smooth navigation of the robot to the target point was achieved. The experimental results showed that, in the case of multiple obstacles being present, the robot could choose the path with fewer obstacles, and the robot moved smoothly when facing turns and approaching the target point by reducing its overshoot. The proposed mapping, positioning, and improved TEB algorithms were effective for high-precision positioning and efficient multi-target detection.

Transcriptome Analysis of Sesame (Sesamum indicum L.) Reveals the LncRNA and mRNA Regulatory Network Responding to Low Nitrogen Stress.

Nitrogen is one of the important factors restricting the development of sesame planting and industry in China. Cultivating sesame varieties tolerant to low nitrogen is an effective way to solve the problem of crop nitrogen deficiency. To date, the mechanism of low nitrogen tolerance in sesame has not been elucidated at the transcriptional level. In this study, two sesame varieties Zhengzhi HL05 (ZZ, nitrogen efficient) and Burmese prolific (MD, nitrogen inefficient) in low nitrogen were used for RNA-sequencing. A total of 3964 DEGs (differentially expressed genes) and 221 DELs (differentially expressed lncRNAs) were identified in two sesame varieties at 3d and 9d after low nitrogen stress. Among them, 1227 genes related to low nitrogen tolerance are mainly located in amino acid metabolism, starch and sucrose metabolism and secondary metabolism, and participate in the process of transporter activity and antioxidant activity. In addition, a total of 209 pairs of lncRNA-mRNA were detected, including 21 pairs of trans and 188 cis. WGCNA (weighted gene co-expression network analysis) analysis divided the obtained genes into 29 modules; phenotypic association analysis identified three low-nitrogen response modules; through lncRNA-mRNA co-expression network, a number of hub genes and cis/trans-regulatory factors were identified in response to low-nitrogen stress including GS1-2 (glutamine synthetase 1-2), PAL (phenylalanine ammonia-lyase), CHS (chalcone synthase, CHS), CAB21 (chlorophyll a-b binding protein 21) and transcription factors MYB54, MYB88 and NAC75 and so on. As a trans regulator, lncRNA MSTRG.13854.1 affects the expression of some genes related to low nitrogen response by regulating the expression of MYB54, thus responding to low nitrogen stress. Our research is the first to provide a more comprehensive understanding of DEGs involved in the low nitrogen stress of sesame at the transcriptome level. These results may reveal insights into the molecular mechanisms of low nitrogen tolerance in sesame and provide diverse genetic resources involved in low nitrogen tolerance research.

CLNN-loop: a deep learning model to predict CTCF-mediated chromatin loops in the different cell lines and CTCF-binding sites (CBS) pair types.

MOTIVATION: Three-dimensional (3D) genome organization is of vital importance in gene regulation and disease mechanisms. Previous studies have shown that CTCF-mediated chromatin loops are crucial to studying the 3D structure of cells. Although various experimental techniques have been developed to detect chromatin loops, they have been found to be time-consuming and costly. Nowadays, various sequence-based computational methods can capture significant features of 3D genome organization and help predict chromatin loops. However, these methods have low performance and poor generalization ability in predicting chromatin loops. RESULTS: Here, we propose a novel deep learning model, called CLNN-loop, to predict chromatin loops in different cell lines and CTCF-binding sites (CBS) pair types by fusing multiple sequence-based features. The analysis of a series of examinations based on the datasets in the previous study shows that CLNN-loop has satisfactory performance and is superior to the existing methods in terms of predicting chromatin loops. In addition, we apply the SHAP framework to interpret the predictions of different models, and find that CTCF motif and sequence conservation are important signs of chromatin loops in different cell lines and CBS pair types. AVAILABILITY AND IMPLEMENTATION: The source code of CLNN-loop is freely available at https://github.com/HaoWuLab-Bioinformatics/CLNN-loop and the webserver of CLNN-loop is freely available at http://hwclnn.sdu.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Stacking and freestanding borophene for lithium-ion battery application.

The growth of artificial synthesis two-dimensional (2D) materials usually demands for suitable substrate due to their rare bulk allotropies. Borophene, as a typical artificial synthetic material, has been proved its substrate-growth on metal or nonmetals and its high theoretical specific capacity (1720 mAh g-1) for next-genatration electrode material, but structural instability and transfer difficulties have hindered the development of its applications. Here, a structurally stable and freestanding AA-stacked-α'-4H-borophene sheets have been synthesized byin situlithium eutectic salt-assisted synthetic method to realize the application of borophene in lithium-ion battery. The atomic structure of AA-α'-4H-borophene with interlayer VdWs was established by comparing the experimental observation with DFT optimal calculation. Different stacking configurations (AA- and AB-) of borophene was realized by a temperature-structure-photoluminescence intensity relationship, and the AA-stacked borophene exhibits higher specific capacity than AB structure. Based on electrochemical performance, the AA-borophene exhibits excellent rate capability and cycling performance due to its non-collapsible stacking configurations, which dominates great initial coulombic efficiency of 87.3% at 200 mA g-1superior to that of black phosphorus-based and borophene/graphene. Meanwhile, it still maintains the coulombic efficiency of 99.13% after 1000 cycles. It also shows a reversible capacity of 181 mAh g-1at 10 mA g-1between the voltage window of 0.01 and 2 V, which improves the reported capacity (43 mAh g-1) of bulk boron anode by over 430%. This work brings fantastic new view of fabricating stable, stacking and freestanding borophene and provides a significative idea on applications of borophene in energy storage domain.

Comparing the performance of machine learning and conventional models for predicting atherosclerotic cardiovascular disease in a general Chinese population.

BACKGROUND: Accurately predicting the risk of atherosclerotic cardiovascular disease (ASCVD) is crucial for implementing individualized prevention strategies and improving patient outcomes. Our objective is to develop machine learning (ML)-based models for predicting ASCVD risk in a prospective Chinese population and compare their performance with conventional regression models. METHODS: A hybrid dataset consisting of 551 features was used, including 98 demographic, behavioral, and psychological features, 444 Electrocardiograph (ECG) features, and 9 Echocardiography (Echo) features. Seven machine learning (ML)-based models were trained, validated, and tested after selecting the 30 most informative features. We compared the discrimination, calibration, net benefit, and net reclassification improvement (NRI) of the ML models with those of conventional ASCVD risk calculators, such as the Pooled Cohort Equations (PCE) and Prediction for ASCVD Risk in China (China-PAR). RESULTS: The study included 9,609 participants (mean age 53.4 ± 10.4 years, 53.7% female), and during a median follow-up of 4.7 years, 431 (4.5%) participants developed ASCVD. In the testing set, the final ML-based ANN model outperformed PCE, China-PAR, recalibrated PCE, and recalibrated China-PAR in predicting ASCVD. This was demonstrated by the model's higher area under the curve (AUC) of 0.800, compared to 0.777, 0.780, 0.779, and 0.779 for the other models, respectively. Additionally, the model had a lower Hosmer-Lemeshow χ2 of 9.1, compared to 37.3, 67.6, 126.6, and 18.6 for the other models. The net benefit at a threshold of 5% was also higher for the ML-based ANN model at 0.017, compared to 0.016, 0.013, 0.017, and 0.016 for the other models, respectively. Furthermore, the NRI was 0.089 for the ML-based ANN model, while it was 0.355, 0.098, and 0.088 for PCE, China-PAR, and recalibrated PCE, respectively. CONCLUSIONS: Compared to conventional regression ASCVD risk calculators, such as PCE and China-PAR, the ANN prediction model may help optimize identification of individuals at heightened cardiovascular risk by flexibly incorporating a wider range of potential predictors. The findings may help guide clinical decision-making and ultimately contribute to ASCVD prevention and management.

Factors Influencing the Incidence of Pneumonia after Coronary Artery Bypass Grafting.

OBJECTIVE: This study aimed to explore and analyze the factors affecting the incidence of pneumonia after coronary artery bypass grafting (CABG) to provide reference for the prevention of such situation. METHODS: A total of 500 patients who underwent CABG in a hospital were selected. From March 2019 to March 2022, 410 patients without pneumonia and 90 patients with pneumonia were divided into groups A and B. The influencing factors and pathogen composition of postoperative pneumonia were discussed and analyzed. RESULTS: Univariable analysis results showed that age, cardiac function grade, occurrence of smoking, operation time, tracheal intubation time, suspended red-blood-cell transfusion and hospital stay in group B were higher than those in group A. Multivariable logistic analysis results showed that operation time, smoking history, and tracheal intubation time were risk factors for pneumonia after CABG. Among the 90 patients with postoperative pneumonia, 90 had pathogens, 81 had Gram-negative bacteria, 4 had Gram-positive bacteria, and 5 had fungi. CONCLUSIONS: Patients after CABG were more likely to develop pneumonia. Operation time, smoking history, and tracheal intubation time were the risk factors of pneumonia after CABG. Most of these patients had Gram-negative bacteria. Patient intervention based on the influencing factors can effectively prevent the occurrence of postoperative pneumonia.

Differential Regulation of GPVI-Induced Btk and Syk Activation by PKC, PKA and PP2A in Human Platelets.

Bruton's tyrosine kinase (Btk) and spleen tyrosine kinase (Syk) are major signaling proteins in human platelets that are implicated in atherothrombosis and thrombo-inflammation, but the mechanisms controlling their activities are not well understood. Previously, we showed that Syk becomes phosphorylated at S297 in glycoprotein VI (GPVI)-stimulated human platelets, which limits Syk activation. Here, we tested the hypothesis that protein kinases C (PKC) and A (PKA) and protein phosphatase 2A (PP2A) jointly regulate GPVI-induced Btk activation in platelets. The GPVI agonist convulxin caused rapid, transient Btk phosphorylation at S180 (pS180↑), Y223 and Y551, while direct PKC activation strongly increased Btk pS180 and pY551. This increase in Btk pY551 was also Src family kinase (SFK)-dependent, but surprisingly Syk-independent, pointing to an alternative mechanism of Btk phosphorylation and activation. PKC inhibition abolished convulxin-stimulated Btk pS180 and Syk pS297, but markedly increased the tyrosine phosphorylation of Syk, Btk and effector phospholipase Cγ2 (PLCγ2). PKA activation increased convulxin-induced Btk activation at Y551 but strongly suppressed Btk pS180 and Syk pS297. PP2A inhibition by okadaic acid only increased Syk pS297. Both platelet aggregation and PLCγ2 phosphorylation with convulxin stimulation were Btk-dependent, as shown by the selective Btk inhibitor acalabrutinib. Together, these results revealed in GPVI-stimulated platelets a transient Syk, Btk and PLCγ2 phosphorylation at multiple sites, which are differentially regulated by PKC, PKA or PP2A. Our work thereby demonstrated the GPVI-Syk-Btk signalosome as a tightly controlled protein kinase network, in agreement with its role in atherothrombosis.

Efficacy of recombinant human interleukin-11 in preventing and treating oral mucositis after chemotherapy for patients with acute leukemia.

OBJECTIVE: This study aimed to investigate the clinical effects of recombinant human interleukin-11 (rhIL-11) gargle on preventing and treating oral mucositis (OM) after chemotherapy for acute leukemia. METHODS: This single-site, prospective, observer-blinded, nonrandomized controlled trial was conducted on 74 patients with acute leukemia, who were divided into the experimental and control groups. The patients in the experimental group were treated with IL-11 gargle, and those in the control group were treated with sodium bicarbonate gargle. We examined the time and severity of oral mucositis, severity and duration of associated pain, healing time of mucositis, effects of OM on eating, and levels of T-cell subset indicators before and after treatment to evaluate the effects of IL-11 treatment. RESULTS: The proportion of patients with severe OM was significantly lower in the experimental group than in the control group. Mucositis occurred later in the experimental group compared with the control group. The degree and duration of pain, ulcer healing time, and effects on eating were lower in the experimental group compared with the control group. Following treatment, the levels of all T-cell subset indicators improved in each of the two groups. However, the rate of improvement was significantly higher in the experimental group than in the control group. These differences were statistically significant (P < 0.05). CONCLUSIONS: IL-11 gargle reduced the severity of OM after chemotherapy for acute leukemia. Treatment with IL-11 relieved pain, promoted healing, and improved the curative effect of the condition, making it worthy of clinical promotion.

A meta-analysis examined the effect of topical vancomycin application in decreasing sternal wound infections post cardiac surgery.

To assess the impact of topical vancomycin (TV) application in decreasing sternal wound infections (SWIs) post cardiac surgery (CS), we lead a meta-analysis. Twenty-three thousand seven hundred and forty five participants had CS at the outset of the investigations, according to a thorough evaluation of the literature done up to November 2022; 8730 of them used TV, while 15 015 were controls. To assess the effectiveness of TV application in lowering SWIs following CS, odds ratios (OR) with 95% confidence intervals (CIs) were computed with dichotomous technique with a fixed- or random-effect model. The TV had significantly lower SWIs post CS (OR, 0.34; 95% CI, 0.20-0.57; P < .001), and deep SWIs post CS (OR, 0.26; 95% CI, 0.11-0.65; P = .004) compared with control as shown in Figures 2 and 3. Yet, there was no significant difference found amongst TV and control in superficial SWIs post CS (OR, 0.30; 95% CI, 0.07-1.30; P = .011). The TV had significantly lower SWIs, and deep SWIs post CS, and no significant difference was found in superficial SWIs post CS compared with control. The low number of included studies in this meta-analysis for superficial SWIs calls for precaution when analysing the outcomes.

Development and validation of a simple tool composed of items on dyspnea, respiration rates, and C-reactive protein for pneumonia prediction among acute febrile respiratory illness patients in primary care settings.

BACKGROUND: Acute febrile respiratory illness (AFRI) patients are susceptible to pneumonia and suffer from significant morbidity and mortality throughout the world. In primary care settings, the situation is worse. Limited by computerized tomography resources and physician experiences, AFRI patients in primary care settings may not be diagnosed appropriately, which would affect following treatment. In this study, we aimed to develop and validate a simple prediction model to help physicians quickly identify AFRI patients of pneumonia risk in primary care settings. METHODS: A total of 1977 AFRI patients were enrolled at two fever clinics in Shanghai, China, and among them, 727 patients who underwent CT scans were included in the analysis. Acute alveolar or interstitial infiltrates found on CT images were diagnosed with pneumonia. Characteristics and blood parameters were compared between pneumonia and non-pneumonia patients. Then a multivariable model for pneumonia prediction was developed through logistic regression analysis. Its value for pneumonia prediction was prospectively assessed in an external multi-center population, which included 1299 AFRI patients in primary settings from 5 different provinces throughout China. RESULTS: In the model development population, pneumonia patients (n = 227) had a longer duration of fever; higher frequencies of purulent sputum, dyspnea, and thoracic pain; and higher levels of respiration rates and C-reactive protein (CRP) than non-pneumonia patients (n = 500). Logistic regression analysis worked out a model composed of items on dyspnea, respiration rates > 20/min, and CRP > 20 mg/l (DRC) for pneumonia prediction with an area under curve (AUC) of 0.8506. In the external validation population, the predictive accuracy of the DRC model was the highest when choosing at least one positive item (1 score) as a cut-off point with a sensitivity of 87.0% and specificity of 80.5%. DRC scores increased with pneumonia severity and lung lobe involvement and showed good performance for both bacterial and viral pneumonia. For viral pneumonia, dyspnea plus respiration rates > 20/min had good predictive capacity regardless of CRP concentration. CONCLUSIONS: DRC model is a simple tool that predicts pneumonia among AFRI patients, which would help physicians utilize medical resources rationally in primary care settings.

Self-referenced refractive index sensor based on double-dips method with bimetal-dielectric and double-groove grating.

Self-referenced refractive index sensors allow more accurate measurements and reduce the influence of extraneous factors. This work proposed a high-sensitivity, self-referenced surface plasmon resonance sensor with Na grating and Au-ZnS composite grating. When Transverse Magnetic-polarized light is incident into the prism, three surface plasmon resonances are excited at the interface of Na-MgF2 grating and Au-ZnS grating. The first one is treated as the reference angle, the second and third are forward and backward surface plasmon resonance, respectively. Using the angular modulation, the single-dip sensitivities are 329.41 deg/RIU and 788.24 deg/RIU in the range of 1.330-1.347. To further improve the performance of the sensor, the double-dips method is adopted, and the average sensitivity in the range of 1.330-1.347 is 1117.65 deg/RIU, while the maximum reaches 4390 deg/RIU. Due to high sensitivity, a good figure of merit can be obtained even with a larger full width at half maximum of 3.30°. This proposed sensor provides potential application in the research of biomolecular detection and chemical testing.