Salvia miltiorrhiza in osteoporosis: a review of its phytochemistry, traditional clinical uses and preclinical studies (2014-2024).

Osteoporosis becomes a global public health concern due to its rising prevalence and substantial impact on life quality. Salvia miltiorrhiza Bunge (Salviae Miltiorrhizae Radix et Rhizoma, SM) has been firstly recorded in Shen Nong's Herbal Classic, and is frequently prescribed in conjunction with other herbs for the management of osteoporosis. This systematic review aims to comprehensively analyze the recent advances of SM on osteoporosis in traditional Chinese clinical uses and preclinical investigations. Literature encompassing pertinent studies were systematically retrieved across multiple databases, including the PubMed, Web of Science, Chinese National Knowledge Infrastructure, Chinese VIP Database, and Chinese Biomedical Literature Database. Original investigations spanning from February 2014 to March 2024, including traditional Chinese medicine (TCM) clinical trials and preclinical studies, were employed to analyze the effects and actions of SM on osteoporosis. Thirty-eight TCM clinical trials were identified to employ SM in combination with other herbs for the management of primary and secondary osteoporosis. The overall efficacy was between 77% and 96.67%. Forty preclinical studies were identified to investigate the effects and actions of SM and/or its ingredients on osteoporosis. The anti-osteoporosis actions of this herb may be attributed to inhibit osteoclastogenesis/bone resorption and promote osteoblastogenesis/osteogenesis. The ethanol extracts and its ingredients (tanshinones) inhibit osteoclastogenesis/bone resorption by inhibiting the MAPK/NF-κB/NFATc1 signaling pathway and cathepsin K-induced collagen degradation. Both ethanol extracts (tanshinones) and water extracts (Sal B and tanshinol) contribute to osteoblastogenesis by promoting osteogenesis and angiogenesis via activation of the Wnt/ß-catenin/VEGF and ERK/TAZ pathways, and eliminating ROS production targeting Nrf2/ARE/HO-1 pathway. In conclusions, SM may offer a novel strategy for osteoporosis management. Well-designed clinical trials are still needed to evaluate the actions of this herb and its ingredients on bone remodeling.

Virtual Inspection System for Pumping Stations with Multimodal Feedback.

Pumping stations have undergone significant modernization and digitalization in recent decades. However, traditional virtual inspections often prioritize the visual experience and fail to effectively represent the haptic physical properties of devices during inspections, resulting in poor immersion and interactivity. This paper presents a novel virtual inspection system for pumping stations, incorporating virtual reality interaction and haptic force feedback technology to enhance immersion and realism. The system leverages a 3D model, crafted in 3Ds Max, to provide immersive visualizations. Multimodal feedback is achieved through a combination of haptic force feedback provided by a haptic device and visual information delivered by a VR headset. The system's data platform integrates with external databases using Unity3D to display relevant information. The system provides immersive 3D visualizations and realistic force feedback during simulated inspections. We compared this system to a traditional virtual inspection method that demonstrated statistically significant improvements in task completion rates and a reduction in failure rates when using the multimodal feedback approach. This innovative approach holds the potential to enhance inspection safety, efficiency, and effectiveness in the pumping station industry.

Mapping autophagy-related membrane contact site proteins and complexes with AutoMCS Navigator.

In eukaryotic cells, membrane contact sites (MCSs) mediate interactions and communication between organelles by bringing their membranes into close proximity without fusion. These sites play crucial roles in intracellular transport, signal transduction, and the regulation of organelle functions. In a recent study, we compiled data on MCS proteins and complexes from publications to create the MCSdb database. During data compilation, we discovered that many MCSs, their associated proteins, and complexes are highly relevant to macroautophagy/autophagy. To elucidate the role of MCSs in autophagy, we reorganized the autophagy-related MCS proteins and complexes from MCSdb, creating a data map called AutoMCS Navigator. The current version of this map includes 30 complexes and 84 proteins, covering 13 different MCSs and 7 species. Meanwhile, we embedded a dedicated webpage for AutoMCS Navigator on the MCSdb website. This webpage features an orchestrated visual guide that hierarchically displays MCS proteins and complexes involved in autophagy. In summary, our research has developed a user-friendly visual map for querying, browsing, and visualizing detailed information on autophagy-related MCS proteins and complexes. This tool offers researchers easy access to understand autophagy-related MCS structure, assembly, functions, and therapeutic strategies for related diseases. AutoMCS Navigator is freely available at https://cellknowledge.com.cn/mcsdb/autophagy.html.

Trend Prediction and Operation Alarm Model Based on PCA-Based MTL and AM for the Operating Parameters of a Water Pumping Station.

In order to effectively predict the changing trend of operating parameters in the pump unit and carry out fault diagnosis and alarm processes, a trend prediction model is proposed in this paper based on PCA-based multi-task learning (MTL) and an attention mechanism (AM). The multi-task learning method based on PCA was used to process the operating data of the pump unit to make full use of the historical data to extract the key common features reflecting the operating state of the pump unit. The attention mechanism (AM) is introduced to dynamically allocate the weight coefficient of common feature mapping for highlighting the key common features and improving the prediction accuracy of the model when predicting the trend of data change for new working conditions. The model is tested with the actual operating data of a pumping station unit, and the calculation results of different models are compared and analyzed. The results show that the introduction of multi-task learning and attention mechanisms can improve the stability and accuracy of the trend prediction model compared with traditional single-task learning and static common feature mapping weights. According to the threshold analysis of the monitoring statistical parameters of the model, a multi-stage alarm model of pump unit operation condition monitoring can be established, which provides a theoretical basis for optimizing operation and maintenance management strategy in the process of pump station management.

High-throughput fluorescence quantification method based on inner filter effect and fluorescence imaging analysis.

The application of the inner filter effect (IFE) in fluorescent substance determination is gaining popularity. In this paper, a theory of the fluorescence distribution along with the excitation light path is derived from our previous research about the spatial micro-element method. According to the relationship between the summation of fluorescence intensities along the vertical direction at a certain position on the excitation light path and the position, a high-concentration and wide-range fluorescent substance quantification method based on the IFE and fluorescence imaging analysis is proposed. Correspondingly, a high-throughput fluorescent substance quantification detection system is constructed. In order to validate the method, solutions of rhodamine B in different concentrations are used for principle validation, concentration prediction, and experimental investigation on the influence of integration time and lens distortion. The high-throughput system enables the simultaneous measurement of six samples, realizing the high-concentration and wide-range quantification of rhodamine B (100-600 mg/L) with high precision (R2 = 0.9992, MRE = 2.34 %). By setting the filter wheel, the system can measure the concentration of fluorescent substances with different emission wavelengths. The improvement of experimental device is expected to reduce the single sample capacity to tens of microliters and increase the overall sample quantity to tens or even hundreds. The proposed method and system are beneficial to fluorescence measurement in fields such as biomedicine and dye research and to the improvement of high-throughput fluorescence quantitative PCR instruments.

Alarmin S100A8 imparts chemoresistance of esophageal cancer by reprogramming cancer-associated fibroblasts.

Chemotherapy remains the first-line treatment for advanced esophageal cancer. However, durable benefits are achieved by only a limited subset of individuals due to the elusive chemoresistance. Here, we utilize patient-derived xenografts (PDXs) from esophageal squamous-cell carcinoma to investigate chemoresistance mechanisms in preclinical settings. We observe that activated cancer-associated fibroblasts (CAFs) are enriched in the tumor microenvironment of PDXs resistant to chemotherapy. Mechanistically, we reveal that cancer-cell-derived S100A8 triggers the intracellular RhoA-ROCK-MLC2-MRTF-A pathway by binding to the CD147 receptor of CAFs, inducing CAF polarization and leading to chemoresistance. Therapeutically, we demonstrate that blocking the S100A8-CD147 pathway can improve chemotherapy efficiency. Prognostically, we found the S100A8 levels in peripheral blood can serve as an indicator of chemotherapy responsiveness. Collectively, our study offers a comprehensive understanding of the molecular mechanisms underlying chemoresistance in esophageal cancer and highlights the potential value of S100A8 in the clinical management of esophageal cancer.

Lycopene Promotes Osteogenesis and Reduces Adipogenesis through Regulating FoxO1/PPARγ Signaling in Ovariectomized Rats and Bone Marrow Mesenchymal Stem Cells.

Recent interest in preventing the development of osteoporosis has focused on the regulation of redox homeostasis. However, the action of lycopene (LYC), a strong natural antioxidant compound, on osteoporotic bone loss remains largely unknown. Here, we show that oral administration of LYC to OVX rats for 12 weeks reduced body weight gain, improved lipid metabolism, and preserved bone quality. In addition, LYC treatment inhibited ROS overgeneration in serum and bone marrow in OVX rats, and in BMSCs upon H2O2 stimulation, leading to inhibiting adipogenesis and promoting osteogenesis during bone remodeling. At the molecular level, LYC improved bone quality via an increase in the expressions of FoxO1 and Runx2 and a decrease in the expressions of PPARγ and C/EBPα in OVX rats and BMSCs. Collectively, these findings suggest that LYC attenuates osteoporotic bone loss through promoting osteogenesis and inhibiting adipogenesis via regulation of the FoxO1/PPARγ pathway driven by oxidative stress, presenting a novel strategy for osteoporosis management.

CodLncScape Provides a Self-Enriching Framework for the Systematic Collection and Exploration of Coding LncRNAs.

Recent studies have revealed that numerous lncRNAs can translate proteins under specific conditions, performing diverse biological functions, thus termed coding lncRNAs. Their comprehensive landscape, however, remains elusive due to this field's preliminary and dispersed nature. This study introduces codLncScape, a framework for coding lncRNA exploration consisting of codLncDB, codLncFlow, codLncWeb, and codLncNLP. Specifically, it contains a manually compiled knowledge base, codLncDB, encompassing 353 coding lncRNA entries validated by experiments. Building upon codLncDB, codLncFlow investigates the expression characteristics of these lncRNAs and their diagnostic potential in the pan-cancer context, alongside their association with spermatogenesis. Furthermore, codLncWeb emerges as a platform for storing, browsing, and accessing knowledge concerning coding lncRNAs within various programming environments. Finally, codLncNLP serves as a knowledge-mining tool to enhance the timely content inclusion and updates within codLncDB. In summary, this study offers a well-functioning, content-rich ecosystem for coding lncRNA research, aiming to accelerate systematic studies in this field.

Cnidii Fructus: A traditional Chinese medicine herb and source of antiosteoporotic drugs.

BACKGROUND: Osteoporosis (OP) is a prevalent chronic metabolic bone disease for which limited countermeasures are available. Cnidii Fructus (CF), primarily derived from Cnidium monnieri (L.) Cusson., has been tested in clinical trials of traditional Chinese medicine for the management of OP. Accumulating preclinical studies indicate that CF may be used against OP. MATERIALS AND METHODS: Comprehensive documentation and analysis were conducted to retrieve CF studies related to its main phytochemical components as well as its pharmacokinetics, safety and pharmacological properties. We also retrieved information on the mode of action of CF and, in particular, preclinical and clinical studies related to bone remodeling. This search was performed from the inception of databases up to the end of 2022 and included PubMed, China National Knowledge Infrastructure, the National Science and Technology Library, the China Science and Technology Journal Database, Weipu, Wanfang, the Web of Science and the China National Patent Database. RESULTS: CF contains a wide range of natural active compounds, including osthole, bergapten, imperatorin and xanthotoxin, which may underlie its beneficial effects on improving bone metabolism and quality. CF action appears to be mediated via multiple processes, including the osteoprotegerin (OPG)/receptor activator of nuclear factor-κB ligand (RANKL)/receptor activator of nuclear factor-κB (RANK), Wnt/ß-catenin and bone morphogenetic protein (BMP)/Smad signaling pathways. CONCLUSION: CF and its ingredients may provide novel compounds for developing anti-OP drugs.

SQANTI3: curation of long-read transcriptomes for accurate identification of known and novel isoforms.

SQANTI3 is a tool designed for the quality control, curation and annotation of long-read transcript models obtained with third-generation sequencing technologies. Leveraging its annotation framework, SQANTI3 calculates quality descriptors of transcript models, junctions and transcript ends. With this information, potential artifacts can be identified and replaced with reliable sequences. Furthermore, the integrated functional annotation feature enables subsequent functional iso-transcriptomics analyses.

Cm-siRPred: Predicting chemically modified siRNA efficiency based on multi-view learning strategy.

The rational modification of siRNA molecules is crucial for ensuring their drug-like properties. Machine learning-based prediction of chemically modified siRNA (cm-siRNA) efficiency can significantly optimize the design process of siRNA chemical modifications, saving time and cost in siRNA drug development. However, existing in-silico methods suffer from limitations such as small datasets, inadequate data representation capabilities, and lack of interpretability. Therefore, in this study, we developed the Cm-siRPred algorithm based on a multi-view learning strategy. The algorithm employs a multi-view strategy to represent the double-strand sequences, chemical modifications, and physicochemical properties of cm-siRNA. It incorporates a cross-attention model to globally correlate different representation vectors and a two-layer CNN module to learn local correlation features. The algorithm demonstrates exceptional performance in cross-validation experiments, independent dataset, and case studies on approved siRNA drugs, and showcasing its robustness and generalization ability. In addition, we developed a user-friendly webserver that enables efficient prediction of cm-siRNA efficiency and assists in the design of siRNA drug chemical modifications. In summary, Cm-siRPred is a practical tool that offers valuable technical support for siRNA chemical modification and drug efficiency research, while effectively assisting in the development of novel small nucleic acid drugs. Cm-siRPred is freely available at https://cellknowledge.com.cn/sirnapredictor/.

AdipoRon ameliorates the progression of heart failure with preserved ejection fraction via mitigating lipid accumulation and fibrosis.

INTRODUCTION: Obesity and imbalance in lipid homeostasis contribute greatly to heart failure with preserved ejection fraction (HFpEF), the dominant form of heart failure. Few effective therapies exist to control metabolic alterations and lipid homeostasis. OBJECTIVES: We aimed to investigate the cardioprotective roles of AdipoRon, the adiponectin receptor agonist, in regulating lipid accumulation in the two-hit HFpEF model. METHODS: HFpEF mouse model was induced using 60 % high-fat diet plus L-NAME drinking water. Then, AdipoRon (50 mg/kg) or vehicle were administered by gavage to the two-hit HFpEF mouse model once daily for 4 weeks. Cardiac function was evaluated using echocardiography, and Postmortem analysis included RNA-sequencing, untargeted metabolomics, transmission electron microscopy and molecular biology methods. RESULTS: Our study presents the pioneering evidence that AdipoR was downregulated and impaired fatty acid oxidation in the myocardia of HFpEF mice, which was associated with lipid metabolism as indicated by untargeted metabolomics. AdipoRon, orally active synthetic adiponectin receptor agonist, could upregulate AdipoR1/2 (independently of adiponectin) and reduce lipid droplet accumulation, and alleviate fibrosis to restore HFpEF phenotypes. Finally, AdipoRon primarily exerted its effects through restoring the balance of myocardial fatty acid intake, transport, and oxidation via the downstream AMPKα or PPARα signaling pathways. The protective effects of AdipoRon in HFpEF mice were reversed by compound C and GW6471, inhibitors of AMPKα and PPARα, respectively. CONCLUSIONS: AdipoRon ameliorated the HFpEF phenotype by promoting myocardial fatty acid oxidation, decreasing fatty acid transport, and inhibiting fibrosis via the upregulation of AdipoR and the activation of AdipoR1/AMPKα and AdipoR2/PPARα-related downstream pathways. These findings underscore the therapeutic potential of AdipoRon in HFpEF. Importantly, all these parameters get restored in the context of continued mechanical and metabolic stressors associated with HFpEF.

SQANTI-SIM: a simulator of controlled transcript novelty for lrRNA-seq benchmark.

Long-read RNA sequencing has emerged as a powerful tool for transcript discovery, even in well-annotated organisms. However, assessing the accuracy of different methods in identifying annotated and novel transcripts remains a challenge. Here, we present SQANTI-SIM, a versatile tool that wraps around popular long-read simulators to allow precise management of transcript novelty based on the structural categories defined by SQANTI3. By selectively excluding specific transcripts from the reference dataset, SQANTI-SIM effectively emulates scenarios involving unannotated transcripts. Furthermore, the tool provides customizable features and supports the simulation of additional types of data, representing the first multi-omics simulation tool for the lrRNA-seq field.

Photonic spin Hall effect driven broadband multi-focus dielectric metalens.

The multi-focus metalens can couple the light into multiple channels in optical interconnections, which is beneficial to the development of planar, miniaturized, and integrated components. We propose broadband photonic spin Hall effect (PSHE) driven multi-focus metalenses, in which each nanobrick plays a positive role for all focal points. Three PSHE driven metalenses with four, six, and eight focal points have been designed and investigated, respectively. Under the incidences of left-/right-handed circularly polarized (LCP/RCP) light, these metalenses can generate regularly distributed two, three, and four RCP/LCP focal points, respectively. The uniformity of the focusing intensity has been investigated in detail by designing an additional four six-focus metalenses with different focus distributions. The uniqueness of these metalenses makes this design philosophy very attractive for applications in spin photonics, compact polarization detection, multi-imaging systems, and information processing systems.

Genomic alterations driving precancerous to cancerous lesions in esophageal cancer development.

Esophageal squamous cell carcinoma (ESCC) develops through a series of increasingly abnormal precancerous lesions. Previous studies have revealed the striking differences between normal esophageal epithelium and ESCC in copy number alterations (CNAs) and mutations in genes driving clonal expansion. However, due to limited data on early precancerous lesions, the timing of these transitions and which among them are prerequisites for malignant transformation remained unclear. Here, we analyze 1,275 micro-biopsies from normal esophagus, early and late precancerous lesions, and esophageal cancers to decipher the genomic alterations at each stage. We show that the frequency of TP53 biallelic inactivation increases dramatically in early precancerous lesion stage while CNAs and APOBEC mutagenesis substantially increase at late stages. TP53 biallelic loss is the prerequisite for the development of CNAs of genes in cell cycle, DNA repair, and apoptosis pathways, suggesting it might be one of the earliest steps initiating malignant transformation.

Aberrant epithelial cell interaction promotes esophageal squamous-cell carcinoma development and progression.

Epithelial-mesenchymal transition (EMT) and proliferation play important roles in epithelial cancer formation and progression, but what molecules and how they trigger EMT is largely unknown. Here we performed spatial transcriptomic and functional analyses on samples of multistage esophageal squamous-cell carcinoma (ESCC) from mice and humans to decipher these critical issues. By investigating spatiotemporal gene expression patterns and cell-cell interactions, we demonstrated that the aberrant epithelial cell interaction via EFNB1-EPHB4 triggers EMT and cell cycle mediated by downstream SRC/ERK/AKT signaling. The aberrant epithelial cell interaction occurs within the basal layer at early precancerous lesions, which expands to the whole epithelial layer and strengthens along the cancer development and progression. Functional analysis revealed that the aberrant EFNB1-EPHB4 interaction is caused by overexpressed ΔNP63 due to TP53 mutation, the culprit in human ESCC tumorigenesis. Our results shed new light on the role of TP53-TP63/ΔNP63-EFNB1-EPHB4 axis in EMT and cell proliferation in epithelial cancer formation.

Conflict with children, psychological depression, and problematic internet use among Chinese older adults: The moderating effect of sociability and living situation.

Introduction: Problematic internet use among the elderly is an emerging area as previous studies focused more among the young people. Only a few studies focused on problematic internet use at the level of individual characteristics of older adults or on mitigating factors at the level of the older adult's family, ignoring family-level disruptive factors. Objective: The purpose of study is to investigate the relationship between conflict with children and problematic internet use among the elderly, as well as the mediating mechanisms and boundary conditions of the relationship. Methods: The valid sample of study composed of 428 older adults from 39 different villages and communities in central China. Data analyses were conducted by SPSS, MPLUS, and SmartPLS software. To test our hypotheses, we implement several quantitative methods, including confirmatory factor analysis (CFA), correlations analysis, and ordinary least squares (OLS) regression. Also, we employed partial least square structural equation modeling (PLS-SEM) for robustness testing. Results: The results indicated that conflict with children was positively associated with problematic internet use of old people; psychological depression mediated the relationship between conflict with children and old adults' problematic internet use; sociability moderated the effect of conflict with children on psychological depression; and living situation moderated the effect of psychological depression on problematic internet use among the elderly. Conclusion: The current research improved the understanding of the mechanisms that produce problematic internet use among the elderly and helped prevent or reduce problematic internet use in older adults in terms of family support systems and individual ability characteristics.

Collagen 1-mediated CXCL1 secretion in tumor cells activates fibroblasts to promote radioresistance of esophageal cancer.

Esophageal squamous-cell carcinoma (ESCC) is commonly treated with radiotherapy; however, radioresistance hinders its clinical effectiveness, and the underlying mechanism remains elusive. Here, we develop patient-derived xenografts (PDXs) from 19 patients with ESCC to investigate the mechanisms driving radioresistance. Using RNA sequencing, cytokine arrays, and single-cell RNA sequencing, we reveal an enrichment of cancer-associated fibroblast (CAF)-derived collagen type 1 (Col1) and tumor-cell-derived CXCL1 in non-responsive PDXs. Col1 not only promotes radioresistance by augmenting DNA repair capacity but also induces CXCL1 secretion in tumor cells. Additionally, CXCL1 further activates CAFs via the CXCR2-STAT3 pathway, establishing a positive feedback loop. Directly interfering with tumor-cell-derived CXCL1 or inhibiting the CXCL1-CXCR2 pathway effectively restores the radiosensitivity of radioresistant xenografts in vivo. Collectively, our study provides a comprehensive understanding of the molecular mechanisms underlying radioresistance and identifies potential targets to improve the efficacy of radiotherapy for ESCC.

Brain Functional Representation of Highly Occluded Object Recognition.

Recognizing highly occluded objects is believed to arise from the interaction between the brain's vision and cognition-controlling areas, although supporting neuroimaging data are currently limited. To explore the neural mechanism during this activity, we conducted an occlusion object recognition experiment using functional magnetic resonance imaging (fMRI). During magnet resonance examinations, 66 subjects engaged in object recognition tasks with three different occlusion degrees. Generalized linear model (GLM) analysis showed that the activation degree of the occipital lobe (inferior occipital gyrus, middle occipital gyrus, and occipital fusiform gyrus) and dorsal anterior cingulate cortex (dACC) was related to the occlusion degree of the objects. Multivariate pattern analysis (MVPA) further unearthed a considerable surge in classification precision when dACC activation was incorporated as a feature. This suggested the combined role of dACC and the occipital lobe in occluded object recognition tasks. Moreover, psychophysiological interaction (PPI) analysis disclosed that functional connectivity (FC) between the dACC and the occipital lobe was enhanced with increased occlusion, highlighting the necessity of FC between these two brain regions in effectively identifying exceedingly occluded objects. In conclusion, these findings contribute to understanding the neural mechanisms of highly occluded object recognition, augmenting our appreciation of how the brain manages incomplete visual data.

Real-Time Classification of Motor Imagery Using Dynamic Window-Level Granger Causality Analysis of fMRI Data.

This article presents a method for extracting neural signal features to identify the imagination of left- and right-hand grasping movements. A functional magnetic resonance imaging (fMRI) experiment is employed to identify four brain regions with significant activations during motor imagery (MI) and the effective connections between these regions of interest (ROIs) were calculated using Dynamic Window-level Granger Causality (DWGC). Then, a real-time fMRI (rt-fMRI) classification system for left- and right-hand MI is developed using the Open-NFT platform. We conducted data acquisition and processing on three subjects, and all of whom were recruited from a local college. As a result, the maximum accuracy of using Support Vector Machine (SVM) classifier on real-time three-class classification (rest, left hand, and right hand) with effective connections is 69.3%. And it is 3% higher than that of traditional multivoxel pattern classification analysis on average. Moreover, it significantly improves classification accuracy during the initial stage of MI tasks while reducing the latency effects in real-time decoding. The study suggests that the effective connections obtained through the DWGC method serve as valuable features for real-time decoding of MI using fMRI. Moreover, they exhibit higher sensitivity to changes in brain states. This research offers theoretical support and technical guidance for extracting neural signal features in the context of fMRI-based studies.