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Regularity for elliptic equations with oscillatory coefficients was concerned. Problem domains were periodic and consisted of a connected region with normal permeability and a disconnected matrix block subset with high permeability. Coefficients of the elliptic equations depending on the permeability of the domains were highly oscillatory. Let $ {\epsilon}\in(0, 1) $ be the periodic size of domain, $ {\epsilon}\mu\in(0, 1) $ the size ratio of a matrix block to the whole domain, and $ \omega^2\in(1, \infty) $ the permeability ratio of the disconnected matrix block subset to the connected sub-region of the domains. This work presented Lipschitz estimate uniformly in $ {\epsilon}, \mu, \omega $ for the Green's functions and the solutions of the elliptic equations.

Background/Objectives: Spinal conditions, such as fractures and herniated intervertebral discs (HIVDs), are often challenging to diagnose due to overlapping clinical symptoms and the difficulty in assessing their functional impact. Accurate differentiation between these conditions is crucial for effective treatment, particularly in the context of preoperative anesthesia evaluation, where understanding the underlying condition can influence anesthesia planning and pain management. Methods and Materials: This study presents a Support Vector Machine (SVM) model designed to distinguish between spinal fractures and HIVDs using key clinical predictors, including age, gender, preoperative Visual Analog Scale (VAS) pain scores, and the number of spinal fractures. A retrospective analysis was conducted on a dataset of 199 patients diagnosed with these conditions. The SVM model, using a radial basis function (RBF) kernel, classified the conditions based on the selected predictors. Model performance was evaluated using precision, recall, accuracy, and the Kappa index, with Leave-One-Out (LOO) cross-validation applied to ensure robust results. Results: The SVM model achieved a precision of 92.1% for fracture cases and 91.2% for HIVDs, with recall rates of 98.1% for fractures and 70.5% for HIVDs. The overall accuracy was 92%, and the Kappa index was 0.76, indicating substantial agreement. The analysis revealed that age and VAS pain scores were the most critical predictors for accurately diagnosing these conditions. Conclusions: These results highlight the potential of the SVM model with an RBF kernel to reliably differentiate between spinal fractures and HIVDs using routine clinical data. Future work could enhance model performance by incorporating additional clinical parameters relevant to preoperative anesthesia evaluation.

Hypocatabolism of the amyloid beta-protein (Abeta) by insulin-degrading enzyme (IDE) is implicated in the pathogenesis of Alzheimer disease (AD), making pharmacological activation of IDE an attractive therapeutic strategy. However, it has not been established whether the proteolytic activity of IDE can be enhanced by drug-like compounds.Based on the finding that ATP and other nucleotide polyphosphates modulate IDE activity at physiological concentrations, we conducted parallel high-throughput screening campaigns in the absence or presence of ATP and identified two compounds--designated Ia1 and Ia2--that significantly stimulate IDE proteolytic activity. Both compounds were found to interfere with the crosslinking of a photoaffinity ATP analogue to IDE, suggesting that they interact with a bona fide ATP-binding domain within IDE. Unexpectedly, we observed highly synergistic activation effects when the activity of Ia1 or Ia2 was tested in the presence of ATP, a finding that has implications for the mechanisms underlying ATP-mediated activation of IDE. Notably, Ia1 and Ia2 activated the degradation of Abeta by approximately 700% and approximately 400%, respectively, albeit only when Abeta was presented in a mixture also containing shorter substrates.This study describes the first examples of synthetic small-molecule activators of IDE, showing that pharmacological activation of this important protease with drug-like compounds is achievable. These novel activators help to establish the putative ATP-binding domain as a key modulator of IDE proteolytic activity and offer new insights into the modulatory action of ATP. Several larger lessons abstracted from this screen will help inform the design of future screening campaigns and facilitate the eventual development of IDE activators with therapeutic utility.

Abstract Early detection of prostate cancer (PCa) and benign prostatic hyperplasia (BPH) is crucial for maintaining the health and well-being of aging male populations. This study aims to evaluate the performance of transfer learning with convolutional neural networks (CNNs) for efficient classification of PCa and BPH in transrectal ultrasound (TRUS) images. A retrospective experimental design was employed in this study, with 1380 TRUS images for PCa and 1530 for BPH. Seven state-of-the-art deep learning (DL) methods were employed as classifiers with transfer learning applied to popular CNN architectures. Performance indices, including sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), Kappa value, and Hindex (Youden’s index), were used to assess the feasibility and efficacy of the CNN methods. The CNN methods with transfer learning demonstrated a high classification performance for TRUS images, with all accuracy, specificity, sensitivity, PPV, NPV, Kappa, and Hindex values surpassing 0.9400. The optimal accuracy, sensitivity, and specificity reached 0.9987, 0.9980, and 0.9980, respectively, as evaluated using twofold cross-validation. The investigated CNN methods with transfer learning showcased their efficiency and ability for the classification of PCa and BPH in TRUS images. Notably, the EfficientNetV2 with transfer learning displayed a high degree of effectiveness in distinguishing between PCa and BPH, making it a promising tool for future diagnostic applications.

The central amygdala (CeA) is crucial in integrating sensory and associative information to mediate adaptive responses to emotional stimuli. Recent advances in genetic techniques like optogenetics and chemogenetics have deepened our understanding of distinct neuronal populations within the CeA, particularly those involved in fear learning and memory consolidation. However, challenges remain due to overlapping genetic markers complicating neuron identification. Furthermore, a comprehensive understanding of molecularly defined cell types and their projection patterns, which are essential for elucidating functional roles, is still developing. Recent advancements in transcriptomics are starting to bridge these gaps, offering new insights into the functional dynamics of CeA neurons. In this review, we provide an overview of the expanding genetic markers for amygdala research, encompassing recent developments and current trends. We also discuss how novel transcriptomic approaches are redefining cell types in the CeA and setting the stage for comprehensive functional studies.

Noninvasive tracking devices are widely used to monitor real-time posture. Yet significant potential exists to enhance postural control quantification through walking videos. This study advances computational science by integrating OpenPose with a Support Vector Machine (SVM) to perform highly accurate and robust postural analysis, marking a substantial improvement over traditional methods which often rely on invasive sensors. Utilizing OpenPose-based deep learning, we generated Dynamic Joint Nodes Plots (DJNP) and iso-block postural identity images for 35 young adults in controlled walking experiments. Through Temporal and Spatial Regression (TSR) models, key features were extracted for SVM classification, enabling the distinction between various walking behaviors. This approach resulted in an overall accuracy of 0.990 and a Kappa index of 0.985. Cutting points for the ratio of top angles (TAR) and the ratio of bottom angles (BAR) effectively differentiated between left and right skews with AUC values of 0.772 and 0.775, respectively. These results demonstrate the efficacy of integrating OpenPose with SVM, providing more precise, real-time analysis without invasive sensors. Future work will focus on expanding this method to a broader demographic, including individuals with gait abnormalities, to validate its effectiveness across diverse clinical conditions. Furthermore, we plan to explore the integration of alternative machine learning models, such as deep neural networks, enhancing the system’s robustness and adaptability for complex dynamic environments. This research opens new avenues for clinical applications, particularly in rehabilitation and sports science, promising to revolutionize noninvasive postural analysis.

Various efforts have been made to mimic the regulatory properties of biological channels that exist in cell membranes of living organisms. Such ion channels are able to tune electric signals in response to changes in surrounding environments (e.g., pH or ions). Here, we report the design and fabrication of a novel pH-switchable ionic diode membrane (IDM), TFP-EB COF@PET, composed of a thin layer of sub-3 nm covalent organic framework (COF) channels and a polyethylene terephthalate (PET) membrane having conical nanochannels with tip size of 20–40 nm. The designed IDM is found to exhibit ion current rectification (ICR) over a wide range of electrolyte concentrations, attributable to its structural and charge asymmetries. Interestingly, the TFP-EB COF@PET can exhibit pH-switchable ion transport behaviors even though the surface charge natures of TFP-EB COF and PET channels remain unchanged. The mechanisms for the pH-switchable ICR properties are proposed to explain the inversion phenomenon. This work highlights the pH-switchable ionic transport properties of the sub-3 nm-scale COF-based IDM, as well as its potential for long-term operation under high salinity conditions.

This study investigated if bilingual language coactivation would be observed in the context of the Deese-Roediger-McDermott paradigm. Portuguese-English bilinguals listened to 12 lists of semantically associated words that related to either a cognate or a noncognate critical lure. Participants were instructed to recall as many words as they could and to type them on the computer keyboard. Then, they performed a recognition test with words from the study phase of the recall test, as well as critical lures and unstudied words. Statistical analyses showed a small but statistically significant difference in the recall of studied words between conditions. No differences were observed in the recognition. We suggest that participants relied more on verbatim than gist trace, which may have reduced language coactivation effects.

Abstract Background Angiogenesis assessment is important for personalized therapeutic intervention in patients with non-small-cell lung cancer (NSCLC). This study investigated whether radiologic parameters obtained by dynamic contrast-enhanced (DCE)-integrated magnetic resonance-positron emission tomography (MR-PET) could be used to quantitatively assess tumor angiogenesis in NSCLC. Methods This prospective cohort study included 75 patients with NSCLC who underwent DCE-integrated MR-PET at diagnosis. The following parameters were analyzed: metabolic tumor volume (MTV), maximum standardized uptake value (SUVmax), reverse reflux rate constant (k ep), volume transfer constant (K trans), blood plasma volume fraction (v p ), extracellular extravascular volume fraction (v e ), apparent diffusion coefficient (ADC), and initial area under the time-to-signal intensity curve at 60 s post enhancement (iAUC60). Serum biomarkers of tumor angiogenesis, including vascular endothelial growth factor-A (VEGF-A), angiogenin, and angiopoietin-1, were measured by enzyme-linked immunosorbent assays simultaneously. Results Serum VEGF-A (p = 0.002), angiogenin (p = 0.023), and Ang-1 (p 30 cm3 (p = 0.046), K trans > 200 10− 3/min (p = 0.069), and k ep > 900 10− 3/min (p = 0.048), may have benefited from angiogenesis inhibitor therapy, which thus led to significantly longer overall survival. Conclusions The present findings suggest that DCE-integrated MR-PET provides a reliable, non-invasive, quantitative assessment of tumor angiogenesis; can guide the use of angiogenesis inhibitors toward longer survival; and will play an important role in the personalized treatment of NSCLC.

BackgroundPeriodontitis can lead to systemic inflammation and oxidative stress, contributing to the development of various diseases. Periodontitis could also be associated with several ocular diseases.MethodsWe conducted a retrospective population-based cohort study using the National Health Insurance Research Database of Taiwan to evaluate the risk of cataract in people with and without periodontitis. We established a periodontitis cohort and a non-periodontitis cohort, which included 359,254 individuals between 2000 and 2012. Age, gender, and enrolled year were matched. All participants were monitored until the end of 2013. Cox proportional hazard models were applied to estimate hazard ratios (HRs) and confidence intervals (CIs).ResultsPatients with periodontitis had a significantly higher risk to develop cataract than those without periodontitis [10.7 vs. 7.91 per 1,000 person-years, crude HR = 1.35 (95% CI = 1.32–1.39), and adjusted HR = 1.33 (95% CI = 1.30–1.36)]. The significant levels remained the same after stratifying by age, gender, presence of comorbidity, and use of corticosteroid. In addition, we found that diabetes mellitus and hyperlipidemia had a synergistic effect in the interaction of periodontitis and cataract development.ConclusionPatients with periodontitis have a higher risk of cataract development than those without periodontitis. Such patients may request frequent ocular health check-up. Further studies should be performed to confirm the association and to understand the mechanisms.

Abstract Background The survival of OSCC patient needs to be further improved. miR-211 is oncogenic in OSCC and its upregulation is associated with tumor progression and poor patient survival. K14-EGFP-miR-211 transgenic mice also exhibit augmented potential for OSCC induction. Methods Four murine OSCC cell lines, designated MOC-L1 to MOC-L4, are established from tongue tumors induced by 4-nitroquinoline 1-oxide using the K14-EGFP-miR-211 transgenic mouse model. The genetic disruption, in vitro oncogenicity, and the eligibilities of tumorigenesis and metastasis of the cell lines are analyzed. Results All cell lines show green fluorescence and express a range of epithelial markers. The MOC-L1, MOC-L2 and MOC-L3 cells carry missense mutations in the DNA binding domain of the p53 gene. MOC-L1 exhibits a high level of epithelial-mesenchymal transition and has the aggressive characteristics associated with this. MOC-L1 and MOC-L2 are clonogenic in vitro as well as being tumorigenic when implanted into the dermis or tongue of syngeneic recipients. Nonetheless, only MOC-L1 exhibits immense potential for local regional and distal metastasis. Since the expression of miR-196b in MOC-L1 xenografts is drastically decreased on cisplatin treatment, it would seem that targeting of miR-196b might facilitate tumor abrogation. Conclusions As cell lines established in this study originated from the C57BL/6 mouse, the strain most suitable for transgenic engineering, exploring the interplay of these OSCC cells with other genetically modified cells in immune-competent mice would provide important insights into OSCC pathogenesis.

Cancer is one of the leading causes of death worldwide. There are only limited treatment strategies that can be applied to treat cancer, including surgical resection, chemotherapy, and radiotherapy, but these have only limited effectiveness. Developing a new drug for cancer therapy is protracted, costly, and inefficient. Recently, drug repurposing has become a rising research field to provide new meaning for an old drug. By searching a drug repurposing database ReDO_DB, a brief list of anesthetic/sedative drugs, such as haloperidol, ketamine, lidocaine, midazolam, propofol, and valproic acid, are shown to possess anti-cancer properties. Therefore, in the current review, we will provide a general overview of the anti-cancer mechanisms of these anesthetic/sedative drugs and explore the potential underlying signaling pathways and clinical application of these drugs applied individually or in combination with other anti-cancer agents.

Government subsidies are an important policy tool that can help firms develop technological learning, and this technological learning effect plays a key role in firms’ research and development (R&D) efficiency. Thus, this study develops a two-stage approach to illustrate the effect of subsidy policies and technological learning on R&D efficiency in the information technology (IT) industry. The technological learning effect in 128 firms in the IT industry from 2008 to 2015 was measured using the learning experience curve. Subsequently, government R&D subsidy intensity was considered as a categorical variable, and this estimated result was treated as an intangible input into a data envelopment analysis (DEA) structure to evaluate R&D efficiency in 2015. This study makes three major contributions. First, the developed approach incorporates the effect of subsidy policies and technological learning into the DEA structure. Second, the empirical results demonstrate the appropriateness of incorporating subsidy policies and technological learning into evaluations of R&D efficiency. Finally, our results identify the key sources of inefficiency as a shortfall in the number of patents and a lack of technological learning. Based on these key findings, some improved strategies were recommended to decision makers. First published online 19 November 2019

Background/Purpose: Bioaggregates such as Portland cement (PC) can be an economical alternative for mineral trioxide aggregate (MTA) with additional benefit of less discoloration. MTA has been known to induce differentiations of several dental cells. MicroRNAs are important regulators of biological processes, including differentiation, physiologic homeostasis, and disease progression. This study is to explore how PC enhances the differentiation of periodontal ligament (PDL) cells in microRNAs level. Methods: PDL cells were cultured in a regular PC- or MTA-conditioned medium or an osteoinduction medium (OIM). Alizarin red staining was used to evaluate the extent of mineralization. Transfection of microRNA mimics induced exogenous miR-31 and miR-146a expression. The expression of microRNAs and differentiation markers was assayed using reverse-transcriptase polymerase chain reaction. Results: PC enhanced the mineralization of PDL cells in a dose-dependent manner in the OIM. Exogenous miR-31 and miR-146a expression upregulated alkaline phosphatase (ALP), bone morphogenic protein (BMP), and dentin matrix protein 1 (DMP1) expression. However, miR-31 and miR-146a modulates cementum protein 1 (CEMP1) expression in different ways. PC also enhanced ALP and BMP but attenuated CEMP1 in the OIM. Although the OIM or PC treatment upregulated miR-21, miR-29b, and miR-146a, only miR-146a was able to be induced by PC in combination with OIM. Conclusion: This study demonstrated that PC enhances the differentiation of PDL cells, especially osteogenic through miR-146a upregulation. In order to control the ankylosis after regenerative endodontics with the usage of bioaggregates, further investigations to explore these differentiation mechanisms in the miRNA level may be needed. Keywords: Portland cement, Bioaggregate, miR-146a, Osteogenic differentiation, Periodontal ligament (PDL)

Abstract B lymphocyte-induced maturation protein-1 (Blimp-1) serves as a master regulator of the development and function of antibody-producing B cells. Given that its function in T lymphocytes has been identified within the past decade, we review recent findings with emphasis on its role in coordinated control of gene expression during the development, differentiation, and function of T cells. Expression of Blimp-1 is mainly confined to activated T cells and is essential for the production of interleukin (IL)-10 by a subset of forkhead box (Fox)p3+ regulatory T cells with an effector phenotype. Blimp-1 is also required to induce cell elimination in the thymus and critically modulates peripheral T cell activation and proliferation. In addition, Blimp-1 promotes T helper (Th) 2 lineage commitment and limits Th1, Th17 and follicular helper T cell differentiation. Furthermore, Blimp-1 coordinates with other transcription factors to regulate expression of IL-2, IL-21 and IL-10 in effector T lymphocytes. In CD8+ T cells, Blimp-1 expression is distinct in heterogeneous populations at the stages of clonal expansion, differentiation, contraction and memory formation when they encounter antigens. Moreover, Blimp-1 plays a fundamental role in coordinating cytokine receptor signaling networks and transcriptional programs to regulate diverse aspects of the formation and function of effector and memory CD8+ T cells and their exhaustion. Blimp-1 also functions as a gatekeeper of T cell activation and suppression to prevent or dampen autoimmune disease, antiviral responses and antitumor immunity. In this review, we discuss the emerging roles of Blimp-1 in the complex regulation of gene networks that regulate the destiny and effector function of T cells and provide a Blimp-1-dominated transcriptional framework for T lymphocyte homeostasis.

Anesthesia assessment is most important during surgery. Anesthesiologists use electrocardiogram (ECG) signals to assess the patient’s condition and give appropriate medications. However, it is not easy to interpret the ECG signals. Even physicians with more than 10 years of clinical experience may still misjudge. Therefore, this study uses convolutional neural networks to classify ECG image types to assist in anesthesia assessment. The research uses Internet of Things (IoT) technology to develop ECG signal measurement prototypes. At the same time, it classifies signal types through deep neural networks, divided into QRS widening, sinus rhythm, ST depression, and ST elevation. Three models, ResNet, AlexNet, and SqueezeNet, are developed with 50% of the training set and test set. Finally, the accuracy and kappa statistics of ResNet, AlexNet, and SqueezeNet in ECG waveform classification were (0.97, 0.96), (0.96, 0.95), and (0.75, 0.67), respectively. This research shows that it is feasible to measure ECG in real time through IoT and then distinguish four types through deep neural network models. In the future, more types of ECG images will be added, which can improve the real-time classification practicality of the deep model.