Advancing deep learning-based acoustic leak detection methods towards application for water distribution systems from a data-centric perspective.

Against the backdrop of severe leakage issue in water distribution systems (WDSs), numerous researchers have focused on the development of deep learning-based acoustic leak detection technologies. However, these studies often prioritize model development while neglecting the importance of data. This research explores the impact of data augmentation techniques on enhancing deep learning-based acoustic leak detection methods. Five random transformation-based methods-jittering, scaling, warping, iterated amplitude adjusted Fourier transform (IAAFT), and masking-are proposed. Jittering, scaling, warping, and IAAFT directly process original signals, while masking operating on time-frequency spectrograms. Acoustic signals from a real-world WDS are augmented, and the efficacy is validated using convolutional neural network classifiers to identify the spectrograms of acoustic signals. Results indicate the importance of implementing data augmentation before data splitting to prevent data leakage and overly optimistic outcomes. Among the techniques, IAAFT stands out, significantly increasing data volume and diversity, improving recognition accuracy by over 7%. Masking enhances performance mainly by compelling the classifier to learn global features of the spectrograms. Sequential application of IAAFT and masking further strengthens leak detection performance. Furthermore, when applying a complex model to acoustic leakage detection through transfer learning, data augmentation can also enhance the effectiveness of transfer learning. These findings advance artificial intelligence-driven acoustic leak detection technology from a data-centric perspective towards more mature applications.

A Network Pharmacology Approach and Validation Experiments to Investigate the Mechanism of Wen-Dan Decoction in the Treatment of SINFH.

INTRODUCTION: Steroid-induced necrosis of the femoral head (SINFH) is a femoral head necrotic disease caused by prolonged use of hormones. Wen-Dan decoction is used in Chinese clinical practice for the treatment of steroid-induced necrosis of the femoral head (SINFH). However, the mechanism and active compounds of Wen-Dan decoction used to treat SINFH are not well understood. OBJECTIVES: We studied the mechanism of action of Wen-Dan decoction in treating steroidinduced necrosis of the femoral head (SINFH) via network pharmacology and in vivo experiments. METHODS: The active compounds of Wen-Dan decoction and SINFH-related target genes were identified through public databases. Then, network pharmacological analysis was conducted to explore the potential key active compounds, core targets and biological processes of Wen-Dan decoction in SINFH. The potential mechanisms of Wen-Dan decoction in SINFH obtained by network pharmacology were validated through in vivo experiments. RESULTS: We identified 608 DEGs (differentially expressed genes) (230 upregulated, 378 downregulated) in SINFH. GO analysis revealed that the SINFH-related genes were mainly involved in neutrophil activation and the immune response. KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis showed that the SINFH-related genes were mainly associated with cytokine receptor interactions, lipids, atherosclerosis, and tuberculosis. We identified 147 active ingredients of Wen-Dan decoction; the core ingredient was quercetin, and licorice was an active ingredient. Moreover, 277 target genes in the treatment of SINFH with Wen-Dan decoction were identified, and NCF1, PTGS2, and RUNX2 were selected as core target genes. QRT-PCR of peripheral blood from SINFH patients showed higher levels of PGTS2 and NCF1 and showed lower levels of RUNX2 compared to controls. QRT-PCR analysis of peripheral blood and femoral bone tissue from a mouse model of SINFH showed higher levels of PGTS2 and NCF1 and lower levels of RUNX2 in the experimental animals than the controls, which was consistent with the bioinformatics results. HE, immunohistochemistry, and TUNEL staining confirmed a significant reduction in hormone-induced femoral head necrosis in the quercetintreated mice. HE, immunohistochemistry, and TUNEL staining confirmed significant improvement in hormone-induced femoral head necrosis in the quercetin-treated mice. CONCLUSION: We provide new insights into the genes and related pathways involved in SINFH and report that PTGS2, RUNX2, and NCF1 are potential drug targets. Quercetin improved SINFH by promoting osteogenesis and inhibiting apoptosis.

Antipyretic and anti-inflammatory effects of inosine, an active component of Kangfuxin.

Kangfuxin has been widely recognized for its use in treating ulcerative conditions and mucositis, primarily due to its anti-inflammatory properties, which promote cell proliferation, granulation tissue growth, and angiogenesis. However, the exact mechanisms underlying these effects remain poorly understood. In this study, we employed high-throughput mass spectrometry to identify 11 compounds in Kangfuxin, including uracil, hypoxanthine, xanthine, inosine, glutamic acid, glycine, alanine, valine, isoleucine, leucine, and lysine. Notably, the antipyretic and anti-inflammatory properties of inosine, one of these compounds, have not been well characterized. To address this gap, we induced fever in vivo using lipopolysaccharide (LPS) and conducted various experiments, including the analysis of endogenous mediators, inflammatory factors, quantitative polymerase chain reaction (QPCR), Western blotting, and hematoxylin and eosin (HE) staining. Our findings indicate that inosine significantly reduces LPS-induced fever, inhibits the expression of inflammatory factors, and alleviates the inflammatory response. These results suggest that inosine may serve as a potential therapeutic target for inflammatory diseases.

Comprehensive Gene Analysis Reveals Cuproptosis-Related Gene Signature Associated with M2 Macrophage in Staphylococcus aureus-Infected Osteomyelitis.

Objective: Osteomyelitis is a challenging disease in the field of bone infections, with its immune and molecular regulatory mechanisms still poorly understood. The aim of this study is to explore the value and potential mechanisms of cuproptosis-related genes (CRGs) in Staphylococcus aureus (S. aureus)-infected osteomyelitis from an immunological perspective. Methods: Initially, three transcriptomic datasets from public databases were integrated and analyzed, and consistent expression of CRGs in S. aureus-infected osteomyelitis was identified. Subsequently, immune infiltration analysis was performed, and M2 macrophage-related CRGs (M2R-CRGs) were further identified. Their potential molecular mechanisms were evaluated using Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA). Finally, distinct osteomyelitis subtypes and diagnostic models based on characteristic M2R-CRGs were constructed. Results: Through correlation analysis with immune cell infiltration, three characteristic M2R-CRGs (SLC31A1, DLD, and MTF1) were identified. Further analysis using unsupervised clustering and immune microenvironment analysis indicated that cluster 1 might activate pro-inflammatory responses, while cluster 2 was shown to exhibit anti-inflammatory effects in osteomyelitis. Compared to Cluster A, Cluster B demonstrated higher levels and a greater diversity of immune cell infiltrations in CRG-related molecular patterns, suggesting a potential anti-inflammatory role in osteomyelitis. A diagnostic model for S. aureus-infected osteomyelitis, based on the three M2R-CRGs, was constructed, exhibiting excellent diagnostic performance and validated with an independent dataset. Significant upregulation in mRNA and protein expression levels of the three M2R-CRGs was observed in rat models of S. aureus-infected osteomyelitis, aligning with bioinformatic results. Conclusion: The M2R-CRGs (SLC31A1, DLD, and MTF1) may be considered characteristic genes for early diagnosis and personalized immune therapy in patients with S. aureus-infected osteomyelitis.

A review of graph and complex network theory in water distribution networks: Mathematical foundation, application and prospects.

Graph theory (GT) and complex network theory play an increasingly important role in the design, operation, and management of water distribution networks (WDNs) and these tasks were originally often heavily dependent on hydraulic models. Facing the general reality of the lack of high-precision hydraulic models in water utilities, GT has become a promising surrogate or assistive technology. However, there is a lack of a systematic review of how and where the GT techniques are applied to the field of WDNs, along with an examination of potential directions that GT can contribute to addressing WDNs' challenges. This paper presents such a review and first summarizes the graph construction methods and topological properties of WDNs, which are mathematical foundations for the application of GT in WDNs. Then, main application areas, including state estimation, performance evaluation, partitioning, optimal design, optimal sensor placement, critical components identification, and interdependent networks analysis, are identified and reviewed. GT techniques can provide acceptable results and valuable insights while having a low computational burden compared with hydraulic models. Combining GT with hydraulic model significantly enhances the performance of analysis methods. Four research challenges, namely reasonable abstraction, data availability, tailored topological indicators, and integration with Graph Neural Networks (GNNs), have been identified as key areas for advancing the application and implementation of GT in WDNs. This paper would have a positive impact on promoting the use of GT for optimal design and sustainable management of WDNs.

Modeling Indirect Greenhouse Gas Emissions Sources from Urban Wastewater Treatment Plants: Integrating Machine Learning Models to Compensate for Sparse Parameters with Abundant Observations.

Electricity consumption and sludge yield (SY) are important indirect greenhouse gas (GHG) emission sources in wastewater treatment plants (WWTPs). Predicting these byproducts is crucial for tailoring technology-related policy decisions. However, it challenges balancing mass balance models and mechanistic models that respectively have limited intervariable nexus representation and excessive requirements on operational parameters. Herein, we propose integrating two machine learning models, namely, gradient boosting tree (GBT) and deep learning (DL), to precisely pointwise model electricity consumption intensity (ECI) and SY for WWTPs in China. Results indicate that GBT and DL are capable of mining massive data to compensate for the lack of available parameters, providing a comprehensive modeling focusing on operation conditions and designed parameters, respectively. The proposed model reveals that lower ECI and SY were associated with higher treated wastewater volumes, more lenient effluent standards, and newer equipment. Moreover, ECI and SY showed different patterns when influent biochemical oxygen demand is above or below 100 mg/L in the anaerobic-anoxic-oxic process. Therefore, managing ECI and SY requires quantifying the coupling relationships between biochemical reactions instead of isolating each variable. Furthermore, the proposed models demonstrate potential economic-related inequalities resulting from synergizing water pollution and GHG emissions management.

Efficacy and Safety of Different Antibiotic Therapies for Bone and Joint Infections: A Network Meta-analysis of Randomized Controlled Trials.

BACKGROUND: Although an increasing number of antibiotics are being used to treat bone and joint infections, their specific efficacy remains controversial. Thus, we aimed to systematically compare the efficacy and safety of antibiotic therapies for orthopedic infections. METHODS: PubMed, Embase, The Cochrane Library, and Web of Science databases were searched from inception to April 2022. Two authors independently and rigorously conducted the screening, data extraction, and quality assessment of the relevant studies. All the extracted data were evaluated using traditional metaanalysis and network meta-analysis by STATA SE 16.0. RESULTS: A total of eleven randomized controlled trials (RCTs) involving 1,063 patients were included for data analysis. The analysis results from the NMA indicated that in terms of the clinical effectiveness rate, linezolid (OR: 1.75, 95% CI: 1.01 to 3.02) showed significant efficacy compared to ampicillin/sulbactam. With regard to the microbiological eradication rate, linezolid showed significant efficacy compared to cephalosporins (OR: 8.13, 95% CI: 1.16 to 57.09) and quinolones (OR: 3.51, 95% CI: 1.18 to 10.49). Similar findings were obtained for subgroup populations with diabetic foot infections (DFI). However, linezolid was significantly related to higher adverse events than ampicillin/sulbactam (OR: 3.25, 95% CI: 1.68 to 6.30) and cephalosporins (OR: 18.29, 95% CI: 1.59 to 209.76). CONCLUSION: Linezolid appeared to be the most promising treatment regimen for staphylococcal bone and joint infections. However, due to the overall limited evidence, the research results need further high-quality RCTs for confirmation.

MCM6 is a Poor Prognostic Biomarker and Promotes Progression in Breast Cancer.

BACKGROUND: Breast cancer is the commonest global malignancy and the primary cause of carcinoma death. MCM6 is vital to carcinogenesis, but the pathogenesis of MCM6 remains unclear. METHODS: MCM6 expression in patients with breast cancer was examined through The Cancer Genome Atlas (TCGA) database, immunohistochemistry, Quantitative Real-Time PCR (qRT‒PCR) and Western blotting. The prognostic factors were assessed by the Kaplan‒Meier method and Cox regression. On the basis of the key factors selected by multivariable Cox regression analysis, a nomogram risk prediction model was adopted for clinical risk assessment. The TCGA database was utilized to determine how MCM6 is correlated with chemotherapy sensitivity, immune checkpoint-related genes (ICGs), tumor-infiltrating immune cells, along with tumor mutation burden (TMB) and methylation. The impact of MCM6 on carcinoma cells was investigated in terms of proliferation, cell cycle as well as migrating and invasive behavior through CCK assays, flow cytometry, wound healing assays, Transwell assays and xenotransplantation experiments. RESULTS: MCM6 expression was upregulated, which is closely associated with the size of the tumor (p = 0.001) and lymph node metastasis (p = 0.012) in patients with breast cancer. Multivariate analysis revealed MCM6 to be an independent risk factor for prognosis in patients with breast carcinoma. The nomograph prediction model included MCM6, age, ER, M and N stage, which displayed good discrimination with a C index of 0.817 and good calibration. Overexpression of MCM6 correlated with chemotherapy sensitivity, immune checkpoint-related genes (ICGs), tumor-infiltrating immune cells, tumor mutation burden (TMB), and methylation. Silencing MCM6 significantly inhibited proliferation, prolonged the G1 phase of the cell cycle, and restrained the proliferation, migration and invasive behavior of cancerous cells and inhibited tumor growth in vivo. CONCLUSIONS: Our research shows that MCM6 is highly expressed in breast cancer and can be used as an independent prognostic factor, which is expected to become a new target for the treatment of breast cancer in the future.

Pmepa1 knockdown alleviates SpA-induced pyroptosis and osteogenic differentiation inhibition of hBMSCs via p38MAPK/NLRP3 axis.

BACKGROUND: Osteomyelitis is a refractory bone infectious disease, which usually results in progressive bone destruction and bone loss. The invasion of pathogens and subsequent inflammatory response could damage bone marrow mesenchymal stem cells (BMSCs) and inhibit osteogenic differentiation, and finally aggravate uncontrolled bone remodeling in osteomyelitis by affecting bone formation. Exploring the mechanisms of BMSCs injury and osteogenic differentiation inhibition may would help us to find potential therapeutic targets. METHOD: Firstly, staphylococcal protein A (SpA)-treated human bone marrow mesenchymal stem cells (hBMSCs) were used to construct cell models of osteomyelitis. Secondly, transcriptome sequencing was performed to screen differentially expressed genes and then verified the expression of target genes. Next, in vitro experiments were conducted to explore the functions and mechanisms of prostate transmembrane protein androgen induced 1 (Pmepa1) in SpA-treated hBMSCs. Finally, the rat model of osteomyelitis was established to provide an auxiliary validation of the in vitro experimental results. RESULTS: We found that SpA treatment induced inflammatory injury and inhibited osteogenic differentiation in hBMSCs, then the transcriptome sequencing and further detection results showed that Pmepa1 was significantly upregulated in this process. Functionally, Pmepa1 knockdown alleviated inflammatory injury and promoted osteogenic differentiation in SpA-treated hBMSCs. Among them, it was demonstrated that Pmepa1 knockdown exerted cytoprotective effects by alleviating pyroptosis of SpA-infected hBMSCs. Furthermore, recovery experiments revealed that Pmepa1 knockdown reversed SpA-mediated adverse effects by downregulating the p38MAPK/NLRP3 axis. Finally, the detection results of rat femoral osteomyelitis showed that the expression of Pmepa1 was up-regulated, and the expression trends of other indicators including p38MAPK, NLRP3, and caspase-1 were also consistent with the in vitro model. CONCLUSION: Pmepa1 knockdown alleviates SpA-induced pyroptosis and inhibition of osteogenic differentiation in hBMSCs by downregulating p38MAPK/NLRP3 signaling axis. Modulating the expression of Pmepa1 may be a potential strategy to ameliorate osteomyelitis.

Resilience evaluation for water distribution system based on partial nodes' hydraulic information.

Accurate resilience evaluation for water distribution systems generally requires all nodes' hydraulic data which are usually obtained from a well-calibrated hydraulic model. However, in reality, few utilities maintain a workable hydraulic model, making the resilience evaluation far more from practicability. Under this condition, whether resilience evaluation can be realized based on a small amount of monitoring nodes is still a research gap. Therefore, this paper investigates the possibility of accurate resilience evaluation using partial nodes by answering two problems: (1) whether the importance of nodes differs in resilience evaluation; (2) what proportion of nodes are indispensable in resilience evaluation. Accordingly, the Gini index of nodes' importance and the error distribution of partial node resilience evaluation are computed and analyzed. A database including 192 networks is used. Results show that the importance of nodes in the resilience evaluation varies. The Gini index of nodes' importance is 0.604 ± 0.106. The proportion of nodes that meet the accuracy requirement of resilience evaluation is 6.5% ± 2%. Further analysis shows that the importance of nodes is determined by the transmission efficiency between water sources and consumption nodes, and the degree of a node's influence on other nodes. The optimal proportion of required nodes is controlled by a network's centralization, centrality, and efficiency. These results show that accurate resilience evaluation using partial nodes' hydraulic data is feasible and provide some basis for the resilience evaluation-orientated selection of monitoring nodes.

Identification of molecular subgroups in osteomyelitis induced by staphylococcus aureus infection through gene expression profiles.

BACKGROUND: Staphylococcus aureus (S. aureus) infection-induced osteomyelitis (OM) is an inflammatory bone disease accompanied by persistent bone destruction, and the treatment is challenging because of its tendency to recur. Present study was aimed to explore the molecular subgroups of S. aureus infection-induced OM and to deepen the mechanistic understanding for molecularly targeted treatment of OM. METHODS: Integration of 164 OM samples and 60 healthy samples from three datasets of the Gene Expression Omnibus (GEO) database. OM patients were classified into different molecular subgroups based on unsupervised algorithms and correlations of clinical characteristics between subgroups were analyzed. Next, The CIBERSORT algorithm was used to evaluate the proportion of immune cell infiltration in different OM subgroups. Weighted gene co-expression analysis (WGCNA) was used to identify different gene modules and explore the relationship with clinical characteristics, and further annotated OM subgroups and gene modules by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. RESULTS: Two subgroups with excellent consistency were identified in this study, subgroup and hospital length of stay were independent predictors of OM. Compared with subgroup I, OM patients in subgroup II had longer hospital length of stay and more severe disease. Meanwhile, the infiltration proportions of monocytes and macrophages M0 were higher in patients of OM subgroup II. Finally, combined with the characteristics of the KEGG enrichment modules, the expression of osteoclast differentiation-related genes such as CTSK was upregulated in OM subgroup II, which may be closely associated with more severe OM patients. CONCLUSION: The current study showed that OM subgroup II had longer hospital length of stay and more severe disease, the osteoclast differentiation pathway and the main target CTSK contribute to our deeper understanding for the molecular mechanisms associated with S. aureus infection-induced OM, and the construction of molecular subgroups suggested the necessity for different subgroups of patients to receive individualized treatment.

Identification of Ferroptosis-Related Biomarkers for Diagnosis and Molecular Classification of Staphylococcus aureus-Induced Osteomyelitis.

Objective: Staphylococcus aureus (SA)-induced osteomyelitis (OM) is one of the most common refractory diseases in orthopedics. Early diagnosis is beneficial to improve the prognosis of patients. Ferroptosis plays a key role in inflammation and immune response, while the mechanism of ferroptosis-related genes (FRGs) in SA-induced OM is still unclear. The purpose of this study was to determine the role of ferroptosis-related genes in the diagnosis, molecular classification and immune infiltration of SA-induced OM by bioinformatics. Methods: Datasets related to SA-induced OM and ferroptosis were collected from the Gene Expression Omnibus (GEO) and ferroptosis databases, respectively. The least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms were combined to screen out differentially expressed-FRGs (DE-FRGs) with diagnostic characteristics, and gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to explore specific biological functions and pathways. Based on these key DE-FRGs, a diagnostic model was established, and molecular subtypes were divided to explore the changes in the immune microenvironment between molecular subtypes. Results: A total of 41 DE-FRGs were identified. After screening and intersecting with LASSO and SVM-RFE algorithms, 8 key DE-FRGs with diagnostic characteristics were obtained, which may regulate the pathogenesis of OM through the immune response and amino acid metabolism. The ROC curve indicated that the 8 DE-FRGs had excellent diagnostic ability for SA-induced OM (AUC=0.993). Two different molecular subtypes (subtype 1 and subtype 2) were identified by unsupervised cluster analysis. The CIBERSORT analysis revealed that the subtype 1 OM had higher immune cell infiltration rates, mainly in T cells CD4 memory resting, macrophages M0, macrophages M2, dendritic cells resting, and dendritic cells activated. Conclusion: We developed a diagnostic model related to ferroptosis and molecular subtypes significantly related to immune infiltration, which may provide a novel insight for exploring the pathogenesis and immunotherapy of SA-induced OM.

A surface metal ion-modified 3D-printed Ti-6Al-4V implant with direct and immunoregulatory antibacterial and osteogenic activity.

The high concentration of antibacterial metal ions may exhibit unavoidable toxicity to cells and normal tissues. The application of antibacterial metal ions to activate the immune response and induce macrophages to attack and phagocytose bacteria is a new antimicrobial strategy. Herein, 3D-printed Ti-6Al-4V implants modified by copper, and strontium ions combined with natural polymers were designed to treat implant-related infections and osseointegration disorders. The polymer-modified scaffolds rapidly released a large amount of copper and strontium ions. During the release process, copper ions were employed to promote the polarization of M1 macrophages, thus inducing a proinflammatory immune response to inhibit infection and achieve the immune antibacterial activity. Meanwhile, copper and strontium ions promoted the secretion of bone-promoting factors by macrophages, induced osteogenesis and showed immunomodulatory osteogenesis. This study proposed immunomodulatory strategies based on the immunological characteristics of target diseases and provided ideas for the design and synthesis of new immunoregulatory biomaterials.

Downregulated annexin A1 expression correlates with poor prognosis, metastasis, and immunosuppressive microenvironment in Ewing's sarcoma.

OBJECTIVE: Ewing's sarcoma (ES) is a common bone malignancy in children and adolescents that severely affects the prognosis of patients. The aim of this study was to identify novel biomarkers and potential therapeutic targets for ES. METHODS: Highly prognosis-related hub genes were identified by independent prognostic analysis in the GSE17679 dataset. We then performed survival analysis, Cox regression analysis and clinical correlation analysis on the key gene and validated them with the GSE63157, GSE45544 and GSE73166 datasets. Differentially expressed genes (DEGs) were screened based on the high and low expression of key gene, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA) were performed to explore the underlying mechanisms of ES, and significant module genes were established based on protein-protein interaction (PPI) networks. Furthermore, the correlations between module genes and the immune microenvironment were analyzed and the correlations between the key gene and immune infiltration levels in sarcoma were investigated using TIMER and TISIDB. Finally, the expression levels of these key genes in ES cell lines (RD-ES and A673 cells) were further validated by real-time quantitative PCR (RT-qPCR). CCK-8 and EdU assays were performed to assess the effect of ANXA1 knockdown on RD-ES cell proliferation. RESULTS: ANXA1 was identified as a key gene for ES prognosis. The overall survival (OS) time of patients with low ANXA1 expression was shorter, and the expression level of ANXA1 in the metastatic group was significantly lower than that in the primary group (P<0.01). Additionally, the abundance of 12 immune cells in the ANXA1 low-expression group was significantly lower than that in the high-expression group (all P<0.05), which may be related to the inhibition of the immune microenvironment. A PPI network was constructed based on 96 DEGs to further identify the five ANXA1-related module genes (COL1A2, MMP9, VIM, S100A11 and S100A4). The expression levels of ANXA1, COL1A2, MMP9, VIM, S100A11 and S100A4 were significantly different between ES cell lines and mesenchymal stem cells after validation in two ES cell lines (all P<0.01). Among these genes, ANXA1, COL1A2, MMP9, VIM and S100A4 were significantly associated with the prognosis of ES patients (all P<0.05). Importantly, ANXA1 knockdown significantly promoted the proliferation of RD-ES cells, which may explain the susceptibility to ES metastasis in the ANXA1 low-expression group. CONCLUSIONS: ANXA1 may serve as an independent prognostic biomarker for ES patients and is associated with metastasis and the immunosuppressive microenvironment in ES, which needs to be validated in further studies.

Dual-Mode Integrated Janus Films with Highly Efficient NaH2 PO2 -Enhanced Infrared Radiative Cooling and Solar Heating for Year-Round Thermal Management.

The currently available materials cannot meet the requirements of human thermal comfort against the hot and cold seasonal temperature fluctuations. In this study, a dual-mode Janus film with a bonded interface to gain dual-mode functions of both highly efficient radiative cooling and solar heating for year-round thermal management is designed and prepared. The cooling side is achieved by embedding NaH2 PO2 particles with high infrared radiation (IR) emittance into a porous polymethyl methacrylate (PMMA) film during pore formation process, which is reported for the first time to the knowledge. A synergistic enhancement of NaH2 PO2 and 3D porous structure leads to efficient radiant cooling with high solar reflectance (R̅solar ≈ 92.6%) and high IR emittance (ε̅IR ≈ 97.2%), especially the ε̅IR value is much greater than that of the reported best porous polymer films. In outdoor environments under 750 mW cm-2 solar radiation, the dual-mode Janus film shows subambient cooling temperature of ≈8.8 °C and heating temperature reaching ≈39.3 °C, indicating excellent thermal management capacity. A wide temperature range is obtained only by flipping the dual-mode Janus film for thermal management. This work provides an advanced zero-energy-consumption human thermal management technique based on the high-performance dual-mode integrated Janus film material.

LncRNA SNHG5 Suppresses Cell Migration and Invasion of Human Lung Adenocarcinoma via Regulation of Epithelial-Mesenchymal Transition.

Long noncoding RNAs (lncRNAs) are gradually being annotated as important regulators of multiple cellular processes. The goal of our study was to investigate the effects of the lncRNA small nucleolar RNA host gene 5 (SNHG5) in lung adenocarcinoma (LAD) and its underlying mechanisms. The findings revealed a substantial drop in SNHG5 expression in LAD tissues, which correlated with clinical-pathological parameters. Transcriptome sequencing analysis demonstrated that the inhibitory effect of SNHG5 was associated with cell adhesion molecules. Moreover, the expression of SNHG5 was shown to be correlated with epithelial-mesenchymal transition (EMT) markers in western blots and immunofluorescence. SNHG5 also had significant effects of antimigration and anti-invasion on LAD cells in vitro. Furthermore, the migration and invasion of A549 cells were suppressed by overexpressed SNHG5 in the EMT progress induced by transforming growth factor ß1 (TGF-ß1), and this might be due to the inhibition of the expression of EMT-associated transcription factors involving Snail, SLUG, and ZEB1. In LAD tissues, the expression of SNHG5 exhibited a positive association with E-cadherin protein expression but a negative correlation with N-cadherin and vimentin, according to the results of quantitative real-time PCR (qRT-PCR). In summary, the current work demonstrated that the lncRNA SNHG5 might limit cell migration and invasion of LAD cancer via decreasing the EMT process, indicating that SNHG5 might be used as a target for LAD therapeutic methods.

Macrophage migration inhibitory factor (MIF) inhibitor iSO-1 promotes staphylococcal protein A-induced osteogenic differentiation by inhibiting NF-κB signaling pathway.

BACKGROUND: Osteomyelitis is among the most difficult to treat diseases in the field of orthopedics, and there is a lack of effective treatment modalities. Exploring the mechanisms of its development is beneficial for finding molecular targets for treatment. Increasing evidence suggests that macrophage migration inhibitory factor (MIF), as a proinflammatory mediator, is not only involved in various pathophysiological processes of inflammation but also plays an important role in osteogenic differentiation, while its specific regulatory mechanism in osteomyelitis remains unclear. METHODS: In the present study, staphylococcal protein A (SPA)-treated rat bone marrow mesenchymal stem cells (rBMSCs) were used to construct cell models of osteomyelitis. Rat and cell models of osteomyelitis were used to validate the expression levels of MIF, and to further explore the regulatory mechanisms of the MIF inhibitor methyl ester of (S, R)-3-(4-hydroxyphenyl)-4,5-dihydro-5-isoxazole acetic acid (iSO-1) and MIF knockdown on cell model of osteomyelitis toward osteogenic differentiation. RESULTS: We found that the expression level of MIF was upregulated in rat and cell models of osteomyelitis and subsequently demonstrated by the GSE30119 dataset that the expression level of MIF was also significantly upregulated in patients with osteomyelitis. Furthermore, SPA promotes MIF expression in rBMSCs while inhibiting the expression of osteogenic-related genes such as Runt-related transcription factor 2 (RUNX2), osteocalcin (OCN), osteopontin (OPN) and collagen type-1 (COL-1) through activation of the nuclear factor kappa-B (NF-κB) pathway. In vivo, we further demonstrated that local injection of iSO-1 significantly increased the osteogenic activity in rat model of osteomyelitis. Importantly, we also demonstrated that MIF knockdown and the MIF inhibitor iSO-1 reversed the SPA-mediated inhibition of osteogenic differentiation of rBMSCs by inhibiting the activation of the NF-κB pathway, as evidenced by the upregulation of osteogenic-related gene expression and enhanced bone mineralization. CONCLUSION: ISO-1 and MIF knockdown can reverse the SPA-mediated inhibition of osteogenic differentiation in the rBMSCs model of osteomyelitis by inhibiting the NF-κB signaling pathway, providing a potential target for the treatment of osteomyelitis.

Diagnostic signature, subtype classification, and immune infiltration of key m6A regulators in osteomyelitis patients.

Background: As a recurrent inflammatory bone disease, the treatment of osteomyelitis is always a tricky problem in orthopaedics. N6-methyladenosine (m6A) regulators play significant roles in immune and inflammatory responses. Nevertheless, the function of m6A modification in osteomyelitis remains unclear. Methods: Based on the key m6A regulators selected by the GSE16129 dataset, a nomogram model was established to predict the incidence of osteomyelitis by using the random forest (RF) method. Through unsupervised clustering, osteomyelitis patients were divided into two m6A subtypes, and the immune infiltration of these subtypes was further evaluated. Validating the accuracy of the diagnostic model for osteomyelitis and the consistency of clustering based on the GSE30119 dataset. Results: 3 writers of Methyltransferase-like 3 (METTL3), RNA-binding motif protein 15B (RBM15B) and Casitas B-lineage proto-oncogene like 1 (CBLL1) and three readers of YT521-B homology domain-containing protein 1 (YTHDC1), YT521-B homology domain-containing family 3 (YTHDF2) and Leucine-rich PPR motif-containing protein (LRPPRC) were identified by difference analysis, and their Mean Decrease Gini (MDG) scores were all greater than 10. Based on these 6 significant m6A regulators, a nomogram model was developed to predict the incidence of osteomyelitis, and the fitting curve indicated a high degree of fit in both the test and validation groups. Two m6A subtypes (cluster A and cluster B) were identified by the unsupervised clustering method, and there were significant differences in m6A scores and the abundance of immune infiltration between the two m6A subtypes. Among them, two m6A regulators (METTL3 and LRPPRC) were closely related to immune infiltration in patients with osteomyelitis. Conclusion: m6A regulators play key roles in the molecular subtypes and immune response of osteomyelitis, which may provide assistance for personalized immunotherapy in patients with osteomyelitis.

Mapping the self-generated magnetic fields due to thermal Weibel instability.

The origin of the seed magnetic field that is amplified by the galactic dynamo is an open question in plasma astrophysics. Aside from primordial sources and the Biermann battery mechanism, plasma instabilities have also been proposed as a possible source of seed magnetic fields. Among them, thermal Weibel instability driven by temperature anisotropy has attracted broad interests due to its ubiquity in both laboratory and astrophysical plasmas. However, this instability has been challenging to measure in a stationary terrestrial plasma because of the difficulty in preparing such a velocity distribution. Here, we use picosecond laser ionization of hydrogen gas to initialize such an electron distribution function. We record the 2D evolution of the magnetic field associated with the Weibel instability by imaging the deflections of a relativistic electron beam with a picosecond temporal duration and show that the measured [Formula: see text]-resolved growth rates of the instability validate kinetic theory. Concurrently, self-organization of microscopic plasma currents is observed to amplify the current modulation magnitude that converts up to ~1% of the plasma thermal energy into magnetic energy, thus supporting the notion that the magnetic field induced by the Weibel instability may be able to provide a seed for the galactic dynamo.