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Abstract Many experiments have supported the contact models, such as the GW and MB models, but the majority of previous validations have been performed under light loads, resulting in a linear relationship between normal force and contact area. However, the real contact area fraction should never equal one; there must be a limit smaller than the apparent area, implying that the real contact area cannot increase linearly indefinitely. In this paper, the real contact area between two polymethylmethacrylate (PMMA) blocks under heavy load is measured using the total reflection method, and the contact area is analyzed using the image processing method. The results show that the real contact area increases with normal load linearly in light loads but non-linearly in heavy loads; the number of contact spots increases with load linearly in light loads but also non-linearly in heavy loads, synchronous with the change in the real contact area. The GW, MB, and Zhao, Maietta, and Chang (ZMC) models were used to predict the experiment results, but none of them predicted the non-linear stage. A revised GW model based on the bulk deformation hypothesis performs better in predicting the non-linear stage. The study’s findings can be applied to PMMA or other similar materials, and they can serve as a useful reference for future research on the contact mechanisms of other materials.

Background: and purpose: The investigation of functional plasticity and remodeling of the brain in patients with retinal detachment (RD) has gained increasing attention and validation. However, the precise alterations in the topological configuration of dynamic functional networks are still not fully understood. This study aimed to investigate the topological structure of dynamic brain functional networks in RD patients. Methods: We recruited 32 patients with RD and 33 healthy controls (HCs) to participate in resting-state fMRI. Employing the sliding time window analysis and K-means clustering method, we sought to identify dynamic functional connectivity (dFC) variability patterns in both groups. The investigation into the topological structure of whole-brain functional networks utilized a graph theoretical approach. Furthermore, we employed machine learning analysis, selecting altered topological properties as classification features to distinguish RD patients from HCs. Results: All participants exhibited four distinct states of dynamic functional connectivity. Compared to the healthy control (HC) group, patients with RD experienced a significant reduction in the number of transitions among these four states. Additionally, the dynamic topological properties of RD patients demonstrated notable changes in both global and node-specific characteristics, with these changes correlating with clinical parameters. The support vector machine (SVM) model used for classification achieved an accuracy of 0.938, an area under the curve (AUC) of 0.988, and both sensitivity and specificity of 0.937. Conclusion: The alterations in the topological properties of the brain in RD patients may indicate the integration function and information exchange efficiency of the whole brain network were reduced. In addition, the topological properties hold considerable promise for distinguishing between RD and HCs.

Abstract Objective The macrophage activation syndrome (MAS) secondary to systemic lupus erythematosus (SLE) is a severe and life-threatening complication. Early diagnosis of MAS is particularly challenging. In this study, machine learning models and diagnostic scoring card were developed to aid in clinical decision-making using clinical characteristics. Methods We retrospectively collected clinical data from 188 patients with either SLE or the MAS secondary to SLE. 13 significant clinical predictor variables were filtered out using the Least Absolute Shrinkage and Selection Operator (LASSO). These variables were subsequently utilized as inputs in five machine learning models. The performance of the models was evaluated using the area under the receiver operating characteristic curve (ROC-AUC), F1 score, and F2 score. To enhance clinical usability, we developed a diagnostic scoring card based on logistic regression (LR) analysis and Chi-Square binning, establishing probability thresholds and stratification for the card. Additionally, this study collected data from four other domestic hospitals for external validation. Results Among all the machine learning models, the LR model demonstrates the highest level of performance in internal validation, achieving a ROC-AUC of 0.998, an F1 score of 0.96, and an F2 score of 0.952. The score card we constructed identifies the probability threshold at a score of 49, achieving a ROC-AUC of 0.994 and an F2 score of 0.936. The score results were categorized into five groups based on diagnostic probability: extremely low (below 5%), low (5–25%), normal (25–75%), high (75–95%), and extremely high (above 95%). During external validation, the performance evaluation revealed that the Support Vector Machine (SVM) model outperformed other models with an AUC value of 0.947, and the scorecard model has an AUC of 0.915. Additionally, we have established an online assessment system for early identification of MAS secondary to SLE. Conclusion Machine learning models can significantly improve the diagnostic accuracy of MAS secondary to SLE, and the diagnostic scorecard model can facilitate personalized probabilistic predictions of disease occurrence in clinical environments.

In this work, we propose a fiber Bragg grating (FBG)-based sensor for curvature measurements. Two gratings are inscribed through the protective coating in a specialty optical fiber using focused femtosecond laser pulses and point-by-point direct writing technology. One grating is inscribed on the central core adjacent to an air channel, while the other is inscribed on the eccentric core. The bending characteristics of the two-core fiber strongly depend on the bending direction due to the asymmetry of the fiber cores. A bending sensitivity of 58 pm/m−1 is achieved by the FBG in the eccentric fiber core over the curvature range of 0–50 m−1. Temperature and humidity cross-sensitivity could be significantly reduced by analyzing the differences in peak shifts between the two gratings. The sensor features a large sensing range and good robustness due to the presence of its protective buffer coating, which makes it a good candidate for curvature sensing in engineering fields.

Summary: Glucocorticoid-induced osteoporosis (GIOP) is a severe and common complication of long-term usage of glucocorticoids (GCs) and lacks of efficient therapy. Here, we investigated the mechanism of anti-inflammation effect and osteoclastogenesis side effect of GCs and immunoglobulin G (IgG) treatment against GIOP. GCs inhibited SLE IgG-induced inflammation, while IgG inhibited GCs-induced osteoclastogenesis. FcγRI and glucocorticoid receptor (GR) were found directly interacted with each other. GCs and IgG could reduce the expression of FcγRI on macrophages. The deficiency of FcγRI affected osteoclastogenesis by GCs and systemic lupus erythematosus (SLE) IgG-induced inflammation. Also, IgG efficiently reduced GIOP in mice. These data showed that GCs could induce osteoporosis and inhibit IgG-induced inflammation through FcγRI while IgG efficiently suppressed osteoporosis induced by GCs through FcγRI. Hence, our findings may help in developing a feasible therapeutic strategy against osteoporosis, such as GIOP.

Abstract Background Coronavirus disease 2019 (COVID-19) is an infectious disease characterized by cough, fever, and fatigue and 20% of cases will develop into severe conditions resulting from acute lung injury with the manifestation of the acute respiratory distress syndrome (ARDS) that accounts for more than 50% of mortality. Currently, it has been reported that some comorbidities are linked with an increased rate of severity and mortality among COVID-19 patients. To assess the role of comorbidity in COVID-19 progression, we performed a systematic review with a meta-analysis on the relationship of COVID-19 severity with 8 different underlying diseases. Methods PubMed, Web of Science, and CNKI were searched for articles investigating the prevalence of comorbidities in severe and non-severe COVID-19 patients. A total of 41 studies comprising 12,526 patients were included. Results Prevalence of some commodities was lower than that in general population such as hypertension (19% vs 23.2%), diabetes (9% vs 10.9%), chronic kidney disease (CKD) (2% vs 9.5%), chronic liver diseases (CLD) (3% vs 24.8%) and chronic obstructive pulmonary disease (COPD) (3% vs 8.6%), while some others including cancer (1% vs 0.6%), cardiovascular disease (6% vs 1.8%) and cerebrovascular disease (2% vs 0.9%) exhibited greater percentage in COVID-19. Cerebrovascular disease (OR = 3.70, 95%CI 2.51–5.45) was found to be the strongest risk factor in disease exacerbation, followed by CKD (OR = 3.60, 95%CI 2.18–5.94), COPD (OR = 3.14, 95% CI 2.35–4.19), cardiovascular disease (OR = 2.76, 95% CI 2.18–3.49), malignancy (OR = 2.63, 95% CI 1.75–3.95), diabetes (OR = 2.49, 95% CI 2.10–2.96) and hypertension (OR = 2.13, 95% CI 1.81–2.51). We found no correlation between CLD and increased disease severity (OR = 1.32, 95% CI 0.96–1.82). Conclusion The impact of all eight underlying diseases on COVID-19 deterioration seemed to be higher in patients outside Hubei. Based on different comorbidities, COVID-19 patients tend to be at risk of developing poor outcomes to a varying degree. Thus, tailored infection prevention and monitoring and treatment strategies targeting these high-risk subgroups might improve prognosis during the COVID-19 pandemic.

Sestrin2 is a highly conserved stress-inducible protein, acting as a crucial part in regulating homeostasis in response to various stress conditions in the cell. However, the role of Sestrin2 in regulating cell apoptosis related to endoplasmic reticulum (ER) has not been fully investigated. Our study presented here aims to reveal the effect of Sestrin2 in tunicamycin (TM)-induced cell apoptosis related to ER stress and its underlying molecular mechanisms. The results demonstrated that Sestrin2 expression was significantly upregulated correlated with ER stress responses in TM treated HepG2 cells. Sestrin2 overexpression obviously alleviated ER stress with the determination of ER stress-related proteins expression. In addition, Sestrin2 overexpression inhibited cell apoptosis with the examination of apoptosis-related proteins and TUNEL assay. However, Sestrin2 knockdown further promoted the ER stress-mediated cell apoptosis. The further mechanistic study revealed that Sestrin2 overexpression inhibited TM-induced mTOR pathway activation. Taken together, our current study indicated that Sestrin2 overexpression ameliorates ER stress-induced apoptosis via inhibiting mTOR pathway in HepG2 cells.

Prostate cancer is the most common cancer and one of the leading causes of cancer mortality in males. Androgen-deprivation therapy (ADT) is an effective strategy to inhibit tumour growth at early stages. However, 10~50% of cases are estimated to progress to metastatic castration-resistant prostate cancer (mCRPC) which currently lacks effective treatments. Clinically, salvage treatment measures, such as endocrine therapy and chemotherapy, are mostly used for advanced prostate cancer, but their clinical outcomes are not ideal. When the existing clinical therapeutic methods can no longer inhibit the development of advanced prostate cancer, human adenovirus (HAdV)-based gene therapy and viral therapy present promising effects. Pre-clinical studies have shown its powerful oncolytic effect, and clinical studies are ongoing to further verify its effect and safety in prostate cancer treatment. Targeting the prostate by HAdV alone or in combination with radiotherapy and chemotherapy sheds light on patients with castration-resistant and advanced prostate cancer. This review summarizes the advantages of oncolytic virus-mediated cancer therapy, strategies of HAdV modification, and existing preclinical and clinical investigations of HAdV-mediated gene therapy to further evaluate the potential of oncolytic adenovirus in prostate cancer treatment.

This paper proposes a nonconvex model (called LogTVSCAD) for deblurring images with impulsive noises, using the log-function penalty as the regularizer and adopting the smoothly clipped absolute deviation (SCAD) function as the data-fitting term. The proposed nonconvex model can effectively overcome the poor performance of the classical TVL1 model for high-level impulsive noise. A difference of convex functions algorithm (DCA) is proposed to solve the nonconvex model. For the model subproblem, we consider the alternating direction method of multipliers (ADMM) algorithm to solve it. The global convergence is discussed based on Kurdyka–Lojasiewicz. Experimental results show the advantages of the proposed nonconvex model over existing models.

Abstract Background Helicobacter pylori (H. pylori) delivers oncoprotein CagA into gastric epithelial cells via the T4SS and drives activation of multiple oncogenic signalling pathways. YAP, a core effector of the Hippo tumour suppressor pathway, is frequently overexpressed in human cancers, suggesting its potential tumor-promoting role. Although CagA is a casual factor in H. pylori induced gastric carcinogenesis, the link between CagA and YAP pathway has not been identified. In this work, we investigated the regulation of oncogenic YAP pathway by H. pylori CagA. Methods Expression of YAP and E-cadherin protein in human gastric biopsies were assessed by immunohistochemistry. H. pylori PMSS1 cagA − isogenic mutant strains were generated. Gastric epithelial cells were co-cultured with H. pylori wild-type cagA + strains or isogenic mutants and were also treated by recombinant CagA expression. Immunofluorescence was performed for YAP localization. Immunoblot and quantitative PCR were performed for examining levels of YAP, downstream effectors and markers of epithelial-mesenchymal transition. Verteporfin and siRNA silencing were used to inhibit YAP activity. Results YAP is significantly upregulated in human gastric carcinogenesis. We generated PMSS1 CagA isogenic mutant strains with chloramphenicol resistance successfully. Our analysis indicated that H. pylori infection induced YAP and downstream effectors in gastric epithelial cells. Importantly, knockout of CagA in 7.13 and PMSS1 strains reduced the expression of YAP by H. pylori infection. Moreover, Inhibition of YAP suppressed H. pylori infection-induced Epithelial-mesenchymal transition (EMT). Conclusion Our results indicated that H. pylori CagA as a pathogenic protein promotes oncogenic YAP pathway, which contributes to EMT and gastric tumorigenesis. This study provided a novel mechanistic insight into why cagA + H. pylori infection is associated with a higher risk for the development of gastric cancer.

Rice panicle temperature (Tp) is a key factor for studying high temperature impacts on spikelet sterility. Comparing with measuring Tp by hand, a Tp simulation model could obtain Tp data readily. The two-layer energy budget model which divides the soil layer and canopy layer was widely used to predict rice canopy temperature (Tc), but panicle existed mostly in the upper layer canopy, and we have proved that Tc was different from the upper layer canopy temperature (Tc1), and the upper layer must be separated from the whole canopy for the purpose of estimating Tp. Thus, we developed the three-layer model, contained upper canopy layer with panicle (50–100 cm), lower rice canopy layer (10–40 cm), and water surface layer (≤10 cm) to estimate Tp with general meteorological and vegetation growth data. There were two steps to estimate Tp. The first step was calculating Tc1 and lower layer canopy temperature (Tc2) by solving heat balance equations with canopy resistances. And the second step was estimating Tp with following parameters: (a) the inclination factors of leaves and panicles (F1, F2, and Fp) which were decided by fitting the calculated transmissivity of downward solar radiation (TDSR) to the measured TDSR, (b) the aerodynamic resistance between the panicle and atmosphere (rap) denoted by wind speed, (c) the panicle resistance for transpiration (rp) denoted by days after heading, and (d) air temperature and humidity at the panicle’s height (Tac1 and eac1) calculated from the resistances of the pathways of sensible and latent heat fluxes in accordance with Ohm’s law. The model simulated fairly well the Tc1, Tc2, and Tp with root mean square errors (RMSEs) of 0.76°C, 0.75°C, and 0.81°C, respectively, where RMSE of measured Tp and predicted Tp by integrated micrometeorology model for panicle and canopy temperature (IM2PACT) including two-layer model was 1.27°C. This model was validated well by two other rice cultivars, and thus, it demonstrated the three-layer model was a new feasible way to estimate Tp.

Emerging evidence suggests that Helicobacter pylori infection is associated with insulin resistance (IR) yet the underlying mechanisms are still obscure. The vital role of gut microbiota in triggering IR has been increasingly reported, however, no study has explored the correlation of gut microbiota and H. pylori-associated IR. Using H. pylori-infected mice model fed different diet structures, we demonstrated that H. pylori infection significantly aggravated high-fat diet (HFD)-induced metabolic disorders at the early stage, the extent of which was close to the effect of long-term HFD. Interestingly, we observed dynamic alterations in gut microbiota that were consistent with the changes in the metabolic phenotype induced by H. pylori and HFD. There may be an interaction among H. pylori, diet and gut microbiota, which dysregulates the host metabolic homeostasis, and treatment of H. pylori may be beneficial to the patients with impaired glucose tolerance in addition to diet control.

Primary liver cancer is one of the leading causes for cancer-related death worldwide. Transforming growth factor beta (TGF-β) is a pleiotropic cytokine that signals through membrane receptors and intracellular Smad proteins, which enter the nucleus upon receptor activation and act as transcription factors. TGF-β inhibits liver tumorigenesis in the early stage by inducing cytostasis and apoptosis, but promotes malignant progression in more advanced stages by enhancing cancer cell survival, EMT, migration, invasion and finally metastasis. Understanding the molecular mechanisms underpinning the multi-faceted roles of TGF-β in liver cancer has become a persistent pursuit during the last two decades. Contextual regulation fine-tunes the robustness, duration and plasticity of TGF-β signaling, yielding versatile albeit specific responses. This involves multiple feedback and feed-forward regulatory loops and also the interplay between Smad signaling and non-Smad pathways. This review summarizes the known regulatory mechanisms of TGF-β signaling in liver cancer, and how they channel, skew and even switch the actions of TGF-β during cancer progression.

Angiogenesis plays important roles in development, stress response, wound healing, tumorigenesis and cancer progression, diabetic retinopathy, and age-related macular degeneration. It is a complex event engaging many signaling pathways including vascular endothelial growth factor (VEGF), Notch, transforming growth factor-beta/bone morphogenetic proteins (TGF-β/BMPs), and other cytokines and growth factors. Almost all of them eventually funnel to two crucial molecules, VEGF and hypoxia-inducing factor-1 alpha (HIF-1α) whose expressions could change under both physiological and pathological conditions. Hypoxic conditions stabilize HIF-1α, while it is upregulated by many oncogenic factors under normaxia. HIF-1α is a critical transcription activator for VEGF. Recent studies have shown that intracellular metabolic state participates in regulation of sprouting angiogenesis, which may involve AMP-activated protein kinase (AMPK). Indeed, AMPK has been shown to exert both positive and negative effects on angiogenesis. On the one hand, activation of AMPK mediates stress responses to facilitate autophagy which stabilizes HIF-1α, leading to increased expression of VEGF. On the other hand, AMPK could attenuate angiogenesis induced by tumor-promoting and pro-metastatic factors, such as the phosphoinositide 3-kinase /protein kinase B (Akt)/mammalian target of rapamycin (PI3K/Akt/mTOR), hepatic growth factor (HGF), and TGF-β/BMP signaling pathways. Thus, this review will summarize research progresses on these two opposite effects and discuss the mechanisms behind the discrepant findings.

Recent studies have established SIRT1 as an important regulator of lipid metabolism, although the mechanism of its action at the molecular level has not been revealed. Here, we show that knockdown of SIRT1 with the help of small hairpin RNA decreases basal and isoproterenol-stimulated lipolysis in cultured adipocytes. This effect is attributed, at least in part, to the suppression of the rate-limiting lipolytic enzyme, adipose triglyceride lipase (ATGL), at the level of transcription. Mechanistically, SIRT1 controls acetylation status and functional activity of FoxO1 that directly binds to the ATGL promoter and regulates ATGL gene transcription. We have also found that depletion of SIRT1 decreases AMP-dependent protein kinase (AMPK) activity in adipocytes. To determine the input of AMPK in regulation of lipolysis, we have established a stable adipose cell line that expresses a dominant-negative α1 catalytic subunit of AMPK under the control of the inducible TET-OFF lentiviral expression vector. Reduction of AMPK activity does not have a significant effect on the rates of lipolysis in this cell model. We conclude, therefore, that SIRT1 controls ATGL transcription primarily by deacetylating FoxO1.

The adaptive decomposition algorithm is a powerful tool for signal analysis, because it can decompose signals into several narrow-band components, which is advantageous to quantitatively evaluate signal characteristics. In this paper, we present a comparative study of four kinds of adaptive decomposition algorithms, including some algorithms deriving from empirical mode decomposition (EMD), empirical wavelet transform (EWT), variational mode decomposition (VMD) and Vold–Kalman filter order tracking (VKF_OT). Their principles, advantages and disadvantages, and improvements and applications to signal analyses in dynamic analysis of mechanical system and machinery fault diagnosis are showed. Examples are provided to illustrate important influence performance factors and improvements of these algorithms. Finally, we summarize applicable scopes, inapplicable scopes and some further works of these methods in respect of precise filters and rough filters. It is hoped that the paper can provide a valuable reference for application and improvement of these methods in signal processing.

Emerging evidence has shown that cellular energy metabolism is regulated by the AMPK and MLK3-JNK signaling pathways, but the functional link between them remains to be determined. The present study aimed to explore the crosstalk between MLK3 and AMPK. We found that both JNK and AMPK were phosphorylated at their activation sites by TNF-α, Anisomycin, H2O2 and sorbitol. Interestingly, sorbitol stimulated phosphorylation of AMPK at T172 in LKB1-deficient cells. Following the screening of more than 100 kinases, we identified that MLK3 induced phosphorylation of AMPK at T172. Our in vitro analysis further revealed that MLK3-mediated phosphorylation of AMPK at T172 was independent of AMP, but addition of AMP caused a mobility shift of AMPK, an indication of autophosphorylation, suggesting that AMP binding and phosphorylation of T172 leads to maximal activation of AMPK. GST-pull down assays showed a direct interaction between AMPKα1 subunit and MLK3. Altogether, our results indicate that MLK3 serves as a common upstream kinase of AMPK and JNK and functions as a direct upstream kinase for AMPK independent of LKB1.

A simple sequential quadratic programming method is proposed to solve the constrained minimax problem. At each iteration, through introducing an auxiliary variable, the descent direction is given by solving only one quadratic programming. By solving a corresponding quadratic programming, a high-order revised direction is obtained, which can avoid the Maratos effect. Furthermore, under some mild conditions, the global and superlinear convergence of the algorithm is achieved. Finally, some numerical results reported show that the algorithm in this paper is successful.

A new parallel variable distribution algorithm based on interior point SSLE algorithm is proposed for solving inequality constrained optimization problems under the condition that the constraints are block-separable by the technology of sequential system of linear equation. Each iteration of this algorithm only needs to solve three systems of linear equations with the same coefficient matrix to obtain the descent direction. Furthermore, under certain conditions, the global convergence is achieved.

An improved filter-SQP algorithm with active set for constrained finite minimax problems is proposed. Firstly, an active constraint subset is obtained by a pivoting operation procedure. Then, a new quadratic programming (QP) subproblem is constructed based on the active constraint subset. The main search direction dk is obtained by solving this (QP) subproblem which is feasible at per iteration point and need not to consider the penalty function by using the filter technique. Under some suitable conditions, the global convergence of our algorithm is established. Finally, some numerical results are reported to show the effectiveness of the proposed algorithm.

In the research of dynamic MRI imaging, we have utilized the minimax-concave (MC) function to replace the traditional nuclear norm, thereby significantly enhancing the model's fitting accuracy. The Total Variation (TV) duality technique is introduced to refine the model resolution further, better capturing underlying structures in MRI data. The splitting method is also employed to establish a comprehensive solution framework for the model. A high-efficiency algorithm, designed using the proximity operator, is developed to solve sub-problems within this framework, ensuring swift and accurate convergence. Numerical experiments on various dynamic MRI datasets and undersampling templates demonstrate that the new method outperforms traditional approaches in several key metrics, including CPU runtime, iteration count, and reconstruction performance. The new method reduces computational overhead, expedites the image reconstruction process, and significantly improves the quality of reconstructed images. [ABSTRACT FROM AUTHOR]