Modeling an evaluation of the efficacy of the novel neuroanalgesic drug mirogabalin for diabetic peripheral neuropathic pain and postherpetic neuralgia therapy.

Diabetic peripheral neuropathic pain (DPNP) and postherpetic neuralgia (PHN) are challenging and often intractable complex medical conditions, with a substantial impact on the quality of life. Mirogabalin, a novel voltage-gated Ca2+ channel α2δ ligand, was approved for the indication of DPNP and PHN. However, the time course of effects has not yet been clarified.We aimed to establish pharmacodynamic and placebo effect models of mirogabalin and pregabalin in DPNP and PHN, and to quantitatively compare the efficacy characteristics (maximum efficacy, onset time, and other pharmacodynamic parameters) and safety of mirogabalin and pregabalin. Public databases were comprehensively searched for randomized placebo-controlled clinical trials. A model-based meta-analysis (MBMA) was developed to describe the time course of drug efficacy and placebo effects. Adverse events were compared using a fixed-effects meta-analysis. Sixteen studies including 5,147 participants were eligible for this study. The placebo effect was relatively high and gradually increased with time, and it required at least eight weeks to reach a plateau. The pharmacodynamic model revealed that the maximum pure efficacy for mirogabalin and pregabalin was approximately -7.85% and -8.86%, respectively; the efficacy of mirogabalin to relieve DPNP and PHN was not superior to that of pregabalin, and both drugs had similar safety. While the rate constant of the onset rate of pregabalin was approximately thrice as high as that of mirogabalin. In addition, the baseline level of pain was an important factor affecting pregabalin efficacy. These findings are helpful in evaluating the clinical extension value of mirogabalin. They suggest that the high placebo effect and the baseline level of pain should be considered when grouping patients in future research and development of voltage-gated Ca2+ channel neuroanalgesic.

The association between PM2.5 components and blood pressure changes in late pregnancy: A combined analysis of traditional and machine learning models.

BACKGROUND: PM2.5 is a harmful mixture of various chemical components that pose a challenge in determining their individual and combined health effects due to multicollinearity issues with traditional linear regression models. This study aimed to develop an analytical methodology combining traditional and novel machine learning models to evaluate PM2.5's combined effects on blood pressure (BP) and identify the most toxic components. METHODS: We measured late-pregnancy BP of 1138 women from the Heshan cohort while simultaneously analyzing 31 PM2.5 components. We utilized multiple linear regression modeling to establish the relationship between PM2.5 components and late-pregnancy BP and applied Random Forest (RF) and generalized Weighted Quantile Sum (gWQS) regression to identify the most toxic components contributing to elevated BP and to quantitatively evaluate the cumulative effect of the PM2.5 component mixtures. RESULTS: The results revealed that 16 PM2.5 components, such as EC, OC, Ti, Fe, Mn, Cu, Cd, Mg, K, Pb, Se, Na+, K+, Cl-, NO3-, and F-, contributed to elevated systolic blood pressure (SBP), while 26 components, including two carbon components (EC, OC), fourteen metallics (Ti, Fe, Mn, Cr, Mo, Co, Cu, Zn, Cd, Na, Mg, Al, K, Pb), one metalloid (Se), and nine water-soluble ions (Na+, K+, Mg2+, Ca2+, NH4+, Cl-, NO3-, SO42-, F-), contributed to elevated diastolic blood pressure (DBP). Mn and Cr were the most toxic components for elevated SBP and DBP, respectively, as analyzed by RF and gWQS models and verified against each other. Exposure to PM2.5 component mixtures increased SBP by 1.04 mmHg (95% CI: 0.33-1.76) and DBP by 1.13 mmHg (95% CI: 0.47-1.78). CONCLUSIONS: Our study highlights the effectiveness of combining traditional and novel models as an analytical strategy to quantify the health effects of PM2.5 constituent mixtures.

Convolutional neural network-based magnetic resonance image differentiation of filum terminale ependymomas from schwannomas.

PURPOSE: Preoperative diagnosis of filum terminale ependymomas (FTEs) versus schwannomas is difficult but essential for surgical planning and prognostic assessment. With the advancement of deep-learning approaches based on convolutional neural networks (CNNs), the aim of this study was to determine whether CNN-based interpretation of magnetic resonance (MR) images of these two tumours could be achieved. METHODS: Contrast-enhanced MRI data from 50 patients with primary FTE and 50 schwannomas in the lumbosacral spinal canal were retrospectively collected and used as training and internal validation datasets. The diagnostic accuracy of MRI was determined by consistency with postoperative histopathological examination. T1-weighted (T1-WI), T2-weighted (T2-WI) and contrast-enhanced T1-weighted (CE-T1) MR images of the sagittal plane containing the tumour mass were selected for analysis. For each sequence, patient MRI data were randomly allocated to 5 groups that further underwent fivefold cross-validation to evaluate the diagnostic efficacy of the CNN models. An additional 34 pairs of cases were used as an external test dataset to validate the CNN classifiers. RESULTS: After comparing multiple backbone CNN models, we developed a diagnostic system using Inception-v3. In the external test dataset, the per-examination combined sensitivities were 0.78 (0.71-0.84, 95% CI) based on T1-weighted images, 0.79 (0.72-0.84, 95% CI) for T2-weighted images, 0.88 (0.83-0.92, 95% CI) for CE-T1 images, and 0.88 (0.83-0.92, 95% CI) for all weighted images. The combined specificities were 0.72 based on T1-WI (0.66-0.78, 95% CI), 0.84 (0.78-0.89, 95% CI) based on T2-WI, 0.74 (0.67-0.80, 95% CI) for CE-T1, and 0.81 (0.76-0.86, 95% CI) for all weighted images. After all three MRI modalities were merged, the receiver operating characteristic (ROC) curve was calculated, and the area under the curve (AUC) was 0.93, with an accuracy of 0.87. CONCLUSIONS: CNN based MRI analysis has the potential to accurately differentiate ependymomas from schwannomas in the lumbar segment.

Electric Field Switching of Magnon Spin Current in a Compensated Ferrimagnet.

Manipulation of directional magnon propagation, known as magnon spin current, is essential for developing magnonic devices featuring nonvolatile functionalities and ultralow power consumption. Magnon spin current can usually be modulated by magnetic field or current-induced spin torques. However, these approaches may lead to energy dissipation due to Joule heating. Electric-field switching of magnon spin current without charge current is highly preferred but challenging to realize. By integrating magnonic and piezoelectric materials, the manipulation of the magnon spin current generated by the spin Seebeck effect in the ferrimagnetic insulator Gd3 Fe5 O12 (GdIG) film on a piezoelectric substrate is demonstrated. Reversible electric-field switching of magnon polarization without applied charge current is observed. Through strain-mediated magnetoelectric coupling, the electric field induces the magnetic compensation transition between two magnetic states of the GdIG, resulting in its magnetization reversal and the simultaneous switching of magnon spin current. This work establishes a prototype material platform that paves the way for developing magnon logic devices characterized by all electric field reading and writing and reveals the underlying physics principles of their functions.

DataColor: unveiling biological data relationships through distinctive color mapping.

In the era of rapid advancements in high-throughput omics technologies, the visualization of diverse data types with varying orders of magnitude presents a pressing challenge. To bridge this gap, we introduce DataColor, an all-encompassing software solution meticulously crafted to address this challenge. Our aim is to empower users with the ability to handle a wide array of data types through an assortment of tools, while simultaneously streamlining parameter selection for rapid insights and detailed enhancements. DataColor stands as a robust toolkit, encompassing 23 distinct tools coupled with over 600 parameters. The defining characteristic of this toolkit is its adept utilization of the color spectrum, allowing for the representation of data spanning diverse types and magnitudes. Through the integration of advanced algorithms encompassing data clustering, normalization, squarified layouts, and customizable parameters, DataColor unveils an abundance of insights that lay hidden within the intricate relationships embedded in the data. Whether you find yourself navigating the analysis of expansive datasets or embarking on the quest to visualize intricate patterns, DataColor stands as the comprehensive and potent solution. We extend the availability of DataColor to all users at no cost, accessible through the following link: https://github.com/frankgenome/DataColor.

Corrigendum: Integrated metabolomics and proteomics reveal biomarkers associated with hemodialysis in end-stage kidney disease.

[This corrects the article DOI: 10.3389/fphar.2023.1243505.].

Beyond Glycolysis: Aldolase A is a Novel Effector in Reelin Mediated Dendritic Development.

Reelin, a secreted glycoprotein, plays a crucial role in guiding neocortical neuronal migration, dendritic outgrowth and arborization, and synaptic plasticity in the adult brain. Reelin primarily operates through the canonical lipoprotein receptors apolipoprotein E receptor 2 (Apoer2) and very low-density lipoprotein receptor (Vldlr). Reelin also engages with non-canonical receptors and unidentified co-receptors; however, the effects of which are less understood. Using high-throughput tandem mass tag LC-MS/MS-based proteomics and gene set enrichment analysis, we identified both shared and unique intracellular pathways activated by Reelin through its canonical and non-canonical signaling in primary murine neurons during dendritic growth and arborization. We observed pathway crosstalk related to regulation of cytoskeleton, neuron projection development, protein transport, and actin filament-based process. We also found enriched gene sets exclusively by the non-canonical Reelin pathway including protein translation, mRNA metabolic process and ribonucleoprotein complex biogenesis suggesting Reelin fine-tunes neuronal structure through distinct signaling pathways. A key discovery is the identification of aldolase A, a glycolytic enzyme and actin binding protein, as a novel effector of Reelin signaling. Reelin induced de novo translation and mobilization of aldolase A from the actin cytoskeleton. We demonstrated that aldolase A is necessary for Reelin-mediated dendrite growth and arborization in primary murine neurons and mouse brain cortical neurons. Interestingly, the function of aldolase A in dendrite development is independent of its known role in glycolysis. Altogether, our findings provide new insights into the Reelin-dependent signaling pathways and effector proteins that are crucial for actin remodeling and dendritic development. Significance: Reelin is an extracellular glycoprotein and exerts its function primarily by binding to the canonical lipoprotein receptors Apoer2 and Vldlr. Reelin is best known for its role in neuronal migration during prenatal brain development. Reelin also signals through a non-canonical pathway outside of Apoer2/Vldlr; however, these receptors and signal transduction pathways are less defined. Here, we examined Reelin's role during dendritic outgrowth in primary murine neurons and identified shared and distinct pathways activated by canonical and non-canonical Reelin signaling. We also found aldolase A as a novel effector of Reelin signaling, that functions independently of its known metabolic role, highlighting Reelin's influence on actin dynamics and neuronal structure and growth.

Integrated metabolomics and proteomics reveal biomarkers associated with hemodialysis in end-stage kidney disease.

Background: We hypothesize that the poor survival outcomes of end-stage kidney disease (ESKD) patients undergoing hemodialysis are associated with a low filtering efficiency and selectivity. The current gold standard criteria using single or several markers show an inability to predict or disclose the treatment effect and disease progression accurately. Methods: We performed an integrated mass spectrometry-based metabolomic and proteomic workflow capable of detecting and quantifying circulating small molecules and proteins in the serum of ESKD patients. Markers linked to cardiovascular disease (CVD) were validated on human induced pluripotent stem cell (iPSC)-derived cardiomyocytes. Results: We identified dozens of elevated molecules in the serum of patients compared with healthy controls. Surprisingly, many metabolites, including lipids, remained at an elevated blood concentration despite dialysis. These molecules and their associated physical interaction networks are correlated with clinical complications in chronic kidney disease. This study confirmed two uremic toxins associated with CVD, a major risk for patients with ESKD. Conclusion: The retained molecules and metabolite-protein interaction network address a knowledge gap of candidate uremic toxins associated with clinical complications in patients undergoing dialysis, providing mechanistic insights and potential drug discovery strategies for ESKD.

Physiological Properties of Perennial Rice Regenerating Cultivation in Two Years with Four Harvests.

Crop perennialization has garnered global attention recently due to its role in sustainable agriculture. However, there is still a lack of detailed information regarding perennial rice's regenerative characteristics and physiological mechanisms in crop ratooning systems with different rice stubble heights. In addition, the response of phytohormones to varying stubble heights and how this response influences the regenerative characteristics of ratoon rice remains poorly documented. Here, we explored the regenerative characteristics and physiological mechanisms of an annual hybrid rice, AR2640, and a perennial rice, PR25, subjected to different stubble heights (5, 10, and 15 cm). The response of phytohormones to varying stubble heights and how this response influences the regenerative characteristics of ratoon rice were also investigated. The results show that PR25 overwintered successfully and produced the highest yield, especially in the second ratoon season, mainly due to its extended growth duration, higher number of mother stems, tillers at the basal nodes, higher number of effective panicles, and heavier grain weight when subjected to lower stubble heights. Further analysis revealed that PR25 exhibited a higher regeneration rate from the lower-position nodes in the stem with lower stubble heights. this was primarily due to the higher contents of phytohormones, especially auxin (IAA) and gibberellin (GA3) at an early stage and abscisic acid (ABA) at a later stage after harvesting of the main crop. Our findings reveal how ratoon rice enhances performance based on different stubble heights, which provides valuable insights and serves as crucial references for delving deeper into cultivating high-yielding perennial rice.

Loss of ADAR1 in macrophages in combination with interferon gamma suppresses tumor growth by remodeling the tumor microenvironment.

BACKGROUND: ADAR1, the major enzyme for RNA editing, has emerged as a tumor-intrinsic key determinant for cancer immunotherapy efficacy through modulating interferon-mediated innate immunity. However, the role of ADAR1 in innate immune cells such as macrophages remains unknown. METHODS: We first analyzed publicly accessible patient-derived single-cell RNA-sequencing and perturbed RNA sequencing data to elucidate the ADAR1 expression and function in macrophages. Subsequently, we evaluated the combined effects of ADAR1 conditional knockout in macrophages and interferon (IFN)-γ treatment on tumor growth in three distinct disease mouse models: LLC for lung cancer, B16-F10 for melanoma, and MC38 for colon cancer. To gain the mechanistic insights, we performed human cytokine arrays to identify differentially secreted cytokines in response to ADAR1 perturbations in THP-1 cells. Furthermore, we examined the effects of ADAR1 loss and IFN-γ treatment on vessel formation through immunohistochemical staining of mouse tumor sections and tube-forming experiments using HUVEC and SVEC4-10 cells. We also assessed the effects on CD8+ T cells using immunofluorescent and immunohistochemical staining and flow cytometry. To explore the translational potential, we examined the consequences of injecting ADAR1-deficient macrophages alongside IFN-γ treatment on tumor growth in LLC-tumor-bearing mice. RESULTS: Our analysis on public data suggests that ADAR1 loss in macrophages promotes antitumor immunity as in cancer cells. Indeed, ADAR1 loss in macrophages combined with IFN-γ treatment results in tumor regression in diverse disease mouse models. Mechanistically, the loss of ADAR1 in macrophages leads to the differential secretion of key cytokines: it inhibits the translation of CCL20, GDF15, IL-18BP, and TIM-3 by activating PKR/EIF2α signaling but increases the secretion of IFN-γ through transcriptional upregulation and interleukin (IL)-18 due to the 5'UTR uORF. Consequently, decreased CCL20 and GDF15 and increased IFN-γ suppress angiogenesis, while decreased IL-18BP and TIM-3 and increased IL-18 induce antitumor immunity by enhancing cytotoxicity of CD8+ T cells. We further demonstrate that combination therapy of injecting ADAR1-deficient macrophages and IFN-γ effectively suppresses tumors in vivo. CONCLUSION: This study provides a comprehensive elucidation of how ADAR1 loss within macrophages contributes to the establishment of an antitumor microenvironment, suggesting the therapeutic potential of targeting ADAR1 beyond the scope of cancer cells.

Gestational age modifies the association between exposure to fine particles and fetal death: findings from a nationwide epidemiological study in the contiguous United States.

BACKGROUNDS: The vulnerability of fetuses differs at different developmental stages, in response to environmental stressors such as fine particulate matter (PM2.5), a ubiquitous air pollutant. Whether gestational age (GA) modifies the association between prenatal fine particulate matter (PM2.5) exposure and fetal death remains unclear. METHODS: We selected approximately 47.8 million eligible United States (US) livebirth and fetal death (defined as a termination at a GA of 20-43 weeks) records from 1989 to 2004. For each record, we took the level of prenatal exposure to PM2.5 as the average concentration in the mother's residential county during the entire gestational period, or a specific trimester (i.e., GA-specific exposure), according to well-established estimates of monthly levels across the contiguous US. First, we evaluated the associations between PM2.5 exposure and fetal death at a specific GA (i.e., GA-specific outcome) using five different logit models (unadjusted, covariate-adjusted, propensity-score, double robust, and diagnostic-score models). Double robust model was selected as the main model due to its advantages in causal inference. Then, we conducted meta-analyses to pool the estimated GA-specific associations, and explored how the pooled estimates varied with GA. RESULTS: According to the meta-analysis, all models suggested gestational PM2.5 exposure was associated with fetal death. However, there was slight heterogeneity in the estimated effects, as different models revealed a range of 3.6-10.7% increase in the odds of fetal death per 5-µg/m3 increment of PM2.5. Each 5-µg/m3 increase in PM2.5 exposure during the entire gestation period significantly increased the odds of fetal death, by 8.1% (95% confidence interval [CI]: 5.1-11.2%). In terms of GA-specific outcomes, the odds of fetal death at a GA of 20-27, 28-36, or ≥ 37 weeks increased by 11.0% (5.9-16.4%), 5.2% (0.4-10.1%), and 8.3% (2.5-14.5%), respectively. In terms of GA-specific exposure, the odds of fetal death increased by 6.0% (3.9-8.2%), 4.1% (3.9-8.2%), and 4.3% (0.5-8.2%) with 5-µg/m3 increases in PM2.5 exposure during the first, second, and third trimester, respectively. The association had the largest effect size (odds ratio = 1.098, 95% CI: 1.061-1.137) between PM2.5 exposure during early gestation (i.e., first trimester) and early fetal death (i.e., 20-27 weeks). CONCLUSIONS: Prenatal exposure to PM2.5 was significantly associated with an increased risk of fetal death. The association was varied by gestational-age-specific exposures or outcomes, suggesting gestation age as a potential modifier on the effect of PM2.5. The fetus was most vulnerable during the early stage of development to death associated with PM2.5 exposure.

Role of antenatal anxiety in the relationship between maternal exposure to nitrogen dioxide and small for gestational age: A birth cohort study.

BACKGROUND: Both Nitrogen dioxide (NO2) exposure and antenatal anxiety have individually been associated with small for gestational age (SGA). Little is known, however, about whether there is effect modification of antenatal anxiety on NO2-related SGA. METHODS: The prospective birth cohort study included 1823 mother-newborn pairs in Guangzhou, China, from January 2017 to April 2020. Exposure to NO2 during the pre-conceptional and prenatal periods was estimated using an inverse distance weighted method. Antenatal anxiety was assessed by Trait Anxiety Inventory. SGA was determined by the Chinese gestational age- and sex-specific birthweight standards. Cox proportional hazards regression models was used to estimate hazard ratios (HRs) and 95 % confidence intervals (CIs) for SGA as per 10 µg/m3 increase in NO2. Modifying effects of trait anxiety on NO2-related SGA were identified by stratified analyses, and three-dimensional response surface plots and two-dimensional heat maps. RESULTS: Each 10 µg/m3 increase in NO2 exposure during the third trimester was significantly associated with SGA risk among overall participants (HR = 1.221, 95 % CI: 1.014-1.471) and primipara (HR = 1.271, 95 % CI: 1.023-1.579). We found significant effect modification of anxiety level for NO2-related SGA in the third trimester (Pinteraction < 0.05). Pregnant women with higher levels of trait anxiety were more likely to deliver SGA newborns, particularly for those with high trait anxiety (HR = 1.781, 95 % CI: 1.007-2.945). Primiparous women were more susceptible. CONCLUSIONS: This study provides evidence that antenatal trait anxiety may modify the effects of maternal NO2 exposure on SGA risk. The third trimester could be a critical window of susceptibility.

The effects of prenatal PM2.5 oxidative potential exposure on feto-placental vascular resistance and fetal weight: A repeated-measures study.

BACKGROUND: Feto-placental hemodynamic deterioration is a critical contributing factor to fetal growth restriction. Whether PM2.5 oxidative potential (OP) affects feto-placental hemodynamics and what impact is on estimated fetal weight (EFW) have not been fully elucidated. We sought to evaluate the association of PM2.5 OP with EFW and to explore whether feto-placental vascular impedance hemodynamic change is a possible mediator in this association. METHODS: A repeated-measures study was conducted involving sixty pregnant women with at least 26 weeks of follow-up during pregnancy in Guangzhou, China, from September 2017 to October 2018. Daily filter-based PM2.5 samples were prospectively collected from ground monitors, and estimates of OP for PM2.5 and its metallic (OPv-metal) and non-metallic constituents (OPv-nonmental) were determined by dithiothreitol assay. Ultrasound data of fetal growth and umbilical arterial resistance, including estimated fetal weight (EFW), pulsatility index, resistance index, and systolic-to-diastolic ratio, were also obtained during gestation. Generalized estimating equations and polynomial distribution lag models were applied to analyze the associations of maternal exposure to PM2.5 OP with EFW and umbilical artery indices. Causal mediation analysis was used to evaluate the mediating role of umbilical arterial resistance. RESULTS: Prenatal exposure to ambient PM2.5 OP was significantly inversely associated with EFW. The magnitudes of effects of OPv-nonmetal on EFW were larger than those of OPv-metal. Significant mediation for the relationship between PM2.5-related OP and EFW by increased impedance in the umbilical artery was observed, with the estimated percent mediated ranging from 31% to 61%. The estimated percent mediated for OPv-nonmetal was higher than those for OPv-metal. CONCLUSIONS: Findings suggest that increased impedance in the umbilical artery may be one of the potential mediators of the relationship between PM2.5 oxidative potential exposure and low fetal weight.

Integration of Metabolomic and Proteomic Data to Uncover Actionable Metabolic Pathways.

Mass spectrometry (MS) is an important tool for biological studies because it is capable of interrogating a diversity of biomolecules (proteins, drugs, metabolites) not captured via alternate genomic platforms. Unfortunately, downstream data analysis becomes complicated when attempting to evaluate and integrate measurements of different molecular classes and requires the aggregation of expertise from different relevant disciplines. This complexity represents a significant bottleneck that limits the routine deployment of MS-based multi-omic methods, despite the unmatched biological and functional insight the data can provide. To address this unmet need, our group introduced Omics Notebook as an open-source framework for facilitating exploratory analysis, reporting and integrating MS-based multi-omic data in a way that is automated, reproducible and customizable. By deploying this pipeline, we have devised a framework for researchers to more rapidly identify functional patterns across complex data types and focus on statistically significant and biologically interesting aspects of their multi-omic profiling experiments. This chapter aims to describe a protocol which leverages our publicly accessible tools to analyze and integrate data from high-throughput proteomics and metabolomics experiments and produce reports that will facilitate more impactful research, cross-institutional collaborations, and wider data dissemination.

Generic User Behavior: A User Behavior Similarity-Based Recommendation Method.

Recommender system (RS) plays an important role in Big Data research. Its main idea is to handle huge amounts of data to accurately recommend items to users. The recommendation method is the core research content of the whole RS. However, the existing recommendation methods still have the following two shortcomings: (1) Most recommendation methods use only one kind of information about the user's interaction with items (such as Browse or Purchase), which makes it difficult to model complete user preference. (2) Most mainstream recommendation methods only consider the final consistency of recommendation (e.g., user preferences) but ignore the process consistency (e.g., user behavior), which leads to the biased final result. In this article, we propose a recommendation method based on the Entity Interaction Knowledge Graph (EIKG), which draws on the idea of collaborative filtering and innovatively uses the similarity of user behaviors to recommend items. The method first extracts fact triples containing interaction relations from relevant data sets to generate the EIKG; then embeds the entities and relations in the EIKG; finally, uses link prediction techniques to recommend items for users. The proposed method is compared with other recommendation methods on two publicly available data sets, Scholat and Lizhi, and the experimental result shows that it exceeds the state of the art in most metrics, verifying the effectiveness of the proposed method.

A Time-Domain Signal Processing Algorithm for Data-Driven Drive-by Inspection Methods: An Experimental Study.

Constructional material deterioration and member damage can cause changes in the dynamic characteristics of bridge structures, and such changes can be tracked in the responses of passing vehicles via the vehicle-bridge interaction (VBI). Though data-driven methods have shown promising results in damage inspection for drive-by methods, there is still much room for improvement in their performance. Given this background, this paper proposes a novel time-domain signal processing algorithm for the raw vehicle acceleration data of data-driven drive-by inspection methods. To achieve the best data processing performance, an optimizing strategy is designed to automatically search for the optimal parameters, tuning the algorithm. The proposed method intentionally overcomes the difficulties in the application of drive-by methods, such as measurement noise, speed variance, and enormous data volumes. Meanwhile, the use of this method can greatly improve the accuracy and efficiency of Machine Learning (ML) models in vehicle-based damage detection. It consists of a filtering process to denoise the data, a pooling process to reduce data redundancy, and an optimizing procedure to maximize algorithm performance. A dataset is obtained to validate the proposed algorithm through laboratory experiments with a scale truck model and a steel beam. The results show that, compared to using raw data, the present algorithm can increase the average accuracy by 12.2-15.0%, and the average efficiency by 35.7-96.7% for different damaged cases and ML models. Additionally, the functions of filtering and pooling operations, the influence of window function parameters, as well as the performance of different sensor locations, are also investigated in the paper. The goal is to present a signal processing algorithm for data-driven drive-by inspection methods to improve their detection performance of bridge damage caused by material deterioration or structural change.

IGFBP5 is an ROR1 ligand promoting glioblastoma invasion via ROR1/HER2-CREB signaling axis.

Diffuse infiltration is the main reason for therapeutic resistance and recurrence in glioblastoma (GBM). However, potential targeted therapies for GBM stem-like cell (GSC) which is responsible for GBM invasion are limited. Herein, we report Insulin-like Growth Factor-Binding Protein 5 (IGFBP5) is a ligand for Receptor tyrosine kinase like Orphan Receptor 1 (ROR1), as a promising target for GSC invasion. Using a GSC-derived brain tumor model, GSCs were characterized into invasive or non-invasive subtypes, and RNA sequencing analysis revealed that IGFBP5 was differentially expressed between these two subtypes. GSC invasion capacity was inhibited by IGFBP5 knockdown and enhanced by IGFBP5 overexpression both in vitro and in vivo, particularly in a patient-derived xenograft model. IGFBP5 binds to ROR1 and facilitates ROR1/HER2 heterodimer formation, followed by inducing CREB-mediated ETV5 and FBXW9 expression, thereby promoting GSC invasion and tumorigenesis. Importantly, using a tumor-specific targeting and penetrating nanocapsule-mediated delivery of CRISPR/Cas9-based IGFBP5 gene editing significantly suppressed GSC invasion and downstream gene expression, and prolonged the survival of orthotopic tumor-bearing mice. Collectively, our data reveal that IGFBP5-ROR1/HER2-CREB signaling axis as a potential GBM therapeutic target.

Low-intensity pulsed ultrasound promotes skeletal muscle regeneration via modulating the inflammatory immune microenvironment.

Background: Low-intensity pulsed ultrasound (LIPUS, a form of mechanical stimulation) can promote skeletal muscle functional repair, but a lack of mechanistic understanding of its relationship and tissue regeneration limits progress in this field. We investigated the hypothesis that specific energy levels of LIPUS mediates skeletal muscle regeneration by modulating the inflammatory microenvironment. Methods: To address these gaps, LIPUS irritation was applied in vivo for 5 min at two different intensities (30mW/cm2 and 60mW/cm2) in next 7 consecutive days, and the treatment begun at 24h after air drop-induced contusion injury. In vitro experiments, LIPUS irritation was applied at three different intensities (30mW/cm2, 45mW/cm2, and 60mW/cm2) for 2 times 24h after introduction of LPS in RAW264.7. Then, we comprehensively assessed the functional and histological parameters of skeletal muscle injury in mice and the phenotype shifting in macrophages through molecular biological methods and immunofluorescence analysis both in vivo and in vitro. Results: We reported that LIPUS therapy at intensity of 60mW/cm2 exhibited the most significant differences in functional recovery of contusion-injured muscle in mice. The comprehensive functional tests and histological analysis in vivo indirectly and directly proved the effectiveness of LIPUS for muscle recovery. Through biological methods and immunofluorescence analysis both in vivo and in vitro, we found that this improvement was attributable in part to the clearance of M1 macrophages populations and the increase in M2 subtypes with the change of macrophage-mediated factors. Depletion of macrophages in vivo eliminated the therapeutic effects of LIPUS, indicating that improvement in muscle function was the result of M2-shifted macrophage polarization. Moreover, the M2-inducing effects of LIPUS were proved partially through the WNT pathway by upregulating FZD5 expression and enhancing ß-catenin nuclear translocation in macrophages both in vitro and in vivo. The inhibition and augment of WNT pathway in vitro further verified our results. Conclusion: LIPUS at intensity of 60mW/cm2 could significantly promoted skeletal muscle regeneration through shifting macrophage phenotype from M1 to M2. The ability of LIPUS to direct macrophage polarization may be a beneficial target in the clinical treatment of many injuries and inflammatory diseases.

Effects of propofol, benzodiazepines, and opioids on survival in cancer patients: a retrospective cohort study based on MIMIC-III.

Sedative and analgesic drugs are commonly used in the diagnosis and treatment of cancer patients. Analyzing the impact of these drugs on the prognosis of cancer patients can help improve patient outcomes. This study aimed to analyze the use of propofol, benzodiazepines, and opioids on the survival of cancer patients in the intensive care unit (ICU) based on the Medical Information Mart for Intensive Care III (MIMIC-III) database. A total of 2,567 cancer patients from the MIMIC-III database between 2001 and 2012 were included in this retrospective cohort study. Logistic regression analyses were utilized to assess the relationship between propofol, benzodiazepine, and opioid and survival in cancer patients. The follow-up was 1 year from the patient's first admission to the ICU. Outcomes were ICU mortality, 28-day mortality, and 1-year mortality. Stratified analyses were based on patients' metastatic status. The use of propofol [odds ratio (OR) = 0.66; 95% confidence interval (CI), 0.53-0.80] and opioids (OR = 0.65; 95%CI, 0.54-0.79) were associated with a decreased risk of 1-year mortality. Both benzodiazepines and opioids use were related to an increased risk of ICU mortality and 28-day mortality (all P < 0.05), whereas propofol use was associated with a decreased risk of 28-day mortality (OR = 0.59; 95%CI, 0.45-0.78). Compared with the use of benzodiazepines combined with opioids, patients who used propofol and opioids were related to a decreased risk of 1-year mortality (OR = 0.74; 95%CI, 0.55-0.98). Similar results were found in patients with metastasis and metastasis-free. Cancer patients who used propofol may experience a lower risk of mortality than benzodiazepine use.

Multi-Omics Profiling of Hypertrophic Cardiomyopathy Reveals Altered Mechanisms in Mitochondrial Dynamics and Excitation-Contraction Coupling.

Hypertrophic cardiomyopathy is one of the most common inherited cardiomyopathies and a leading cause of sudden cardiac death in young adults. Despite profound insights into the genetics, there is imperfect correlation between mutation and clinical prognosis, suggesting complex molecular cascades driving pathogenesis. To investigate this, we performed an integrated quantitative multi-omics (proteomic, phosphoproteomic, and metabolomic) analysis to illuminate the early and direct consequences of mutations in myosin heavy chain in engineered human induced pluripotent stem-cell-derived cardiomyocytes relative to late-stage disease using patient myectomies. We captured hundreds of differential features, which map to distinct molecular mechanisms modulating mitochondrial homeostasis at the earliest stages of pathobiology, as well as stage-specific metabolic and excitation-coupling maladaptation. Collectively, this study fills in gaps from previous studies by expanding knowledge of the initial responses to mutations that protect cells against the early stress prior to contractile dysfunction and overt disease.