Lower psoas mass indicates worse prognosis in percutaneous vertebroplasty-treated osteoporotic vertebral compression fracture.

The correlation between lower psoas mass and the prognosis of osteoporotic vertebral compression fractures (OVCF) is still unclear. This study aims to investigate the impact of lower psoas mass on the prognosis of patients undergoing percutaneous vertebroplasty (PVP). One hundred and sixty-three elderly patients who underwent single-segment PVP from January 2018 to December 2021 were included. The psoas to L4 vertebral index (PLVI) via MRI were measured to assess psoas mass. Patients were divided into high PLVI (> 0.79) and low PLVI (≤ 0.79) groups based on the median PLVI in the cohort. The basic information (age, gender, body mass index (BMI) and bone mineral density (BMD)), surgical intervention-related elements (duration of operation, latency to ambulation, period of hospital stay, and surgical site), postoperative clinical outcomes (Visual Analog Scale (VAS) scores, Oswestry Disability Index (ODI) scores, Japanese Orthopaedic Association (JOA) scores), and incidence of secondary fractures) were compared. Patients showed no statistically significant differences in terms of age, gender, surgical sute, BMI, BMD and preoperative VAS, ODI, JOA scores (P > 0.05) between the two groups. However, there were significant differences in terms of latency to ambulation, period of hospital stay (P < 0.05). VAS, ODI, and JOA scores at 1, 6, and 12 months after surgery showed that the high PLVI group had significantly better outcomes than the low PLVI group (P < 0.05). Additionally, the low PLVI group had a significantly higher incidence of recurrent fracture (P < 0.05). Lower psoas mass can reduce the clinical effect of PVP in patients with osteoporotic vertebral compression fractures, and is a risk factor for recurrent vertebral fracture.

SOFB is a comprehensive ensemble deep learning approach for elucidating and characterizing protein-nucleic-acid-binding residues.

Proteins and nucleic-acids are essential components of living organisms that interact in critical cellular processes. Accurate prediction of nucleic acid-binding residues in proteins can contribute to a better understanding of protein function. However, the discrepancy between protein sequence information and obtained structural and functional data renders most current computational models ineffective. Therefore, it is vital to design computational models based on protein sequence information to identify nucleic acid binding sites in proteins. Here, we implement an ensemble deep learning model-based nucleic-acid-binding residues on proteins identification method, called SOFB, which characterizes protein sequences by learning the semantics of biological dynamics contexts, and then develop an ensemble deep learning-based sequence network to learn feature representation and classification by explicitly modeling dynamic semantic information. Among them, the language learning model, which is constructed from natural language to biological language, captures the underlying relationships of protein sequences, and the ensemble deep learning-based sequence network consisting of different convolutional layers together with Bi-LSTM refines various features for optimal performance. Meanwhile, to address the imbalanced issue, we adopt ensemble learning to train multiple models and then incorporate them. Our experimental results on several DNA/RNA nucleic-acid-binding residue datasets demonstrate that our proposed model outperforms other state-of-the-art methods. In addition, we conduct an interpretability analysis of the identified nucleic acid binding residue sequences based on the attention weights of the language learning model, revealing novel insights into the dynamic semantic information that supports the identified nucleic acid binding residues. SOFB is available at https://github.com/Encryptional/SOFB and https://figshare.com/articles/online_resource/SOFB_figshare_rar/25499452 .

Improving stroke prognosis by TLR4 KO to enhance N2 neutrophil infiltration and reduce M1 macrophage polarization.

Cerebral ischemic stroke remains a leading cause of mortality and morbidity worldwide. Toll-like receptor 4 (TLR4) has been implicated in neuroinflammatory responses poststroke, particularly in the infiltration of immune cells and polarization of macrophages. This study aimed to elucidate the impact of TLR4 deficiency on neutrophil infiltration and subsequent macrophage polarization after middle cerebral artery occlusion (MCAO), exploring its role in stroke prognosis. The objective was to investigate how TLR4 deficiency influences neutrophil behavior poststroke, its role in macrophage polarization, and its impact on stroke prognosis using murine models. Wild-type and TLR4-deficient adult male mice underwent MCAO induction, followed by various analyses, including flow cytometry to assess immune cell populations, bone marrow transplantation experiments to evaluate TLR4-deficient neutrophil behaviors, and enzyme-linked immunosorbent assay and Western blot analysis for cytokine and protein expression profiling. Neurobehavioral tests and infarct volume analysis were performed to assess the functional and anatomical prognosis poststroke. TLR4-deficient mice exhibited reduced infarct volumes, increased neutrophil infiltration, and reduced M1-type macrophage polarization post-MCAO compared to wild-type mice. Moreover, the depletion of neutrophils reversed the neuroprotective effects observed in TLR4-deficient mice, suggesting the involvement of neutrophils in mediating TLR4's protective role. Additionally, N1-type neutrophils were found to promote M1 macrophage polarization via neutrophil gelatinase-associated lipocalin (NGAL) secretion, a process blocked by TLR4 deficiency. The study underscores the protective role of TLR4 deficiency in ischemic stroke, delineating its association with increased N2-type neutrophil infiltration, diminished M1 macrophage polarization, and reduced neuroinflammatory responses. Understanding the interplay between TLR4, neutrophils, and macrophages sheds light on potential therapeutic targets for stroke management, highlighting TLR4 as a promising avenue for intervention in stroke-associated neuroinflammation and tissue damage.

[A study on the regulation of motor behavior in mouse based on temporal interference].

Temporal interference (TI) as a new neuromodulation technique can be applied to non-invasive deep brain stimulation. In order to verify its effectiveness in the regulation of motor behavior in animals, this paper uses the TI method to focus the envelope electric field to the ventral posterior lateral nucleus (VPL) of the thalamus in the deep brain of mouse to regulate left- and right-turning motor behavior. The focusability of TI in the mouse VPL was analyzed by finite element method, and the focus area and volume were obtained by numerical calculation. A stimulator was used to generate TI current to stimulate the mouse VPL to verify the effectiveness of the TI stimulation method, and the accuracy of the focus location was further determined by c-Fos immunofluorescence experiments. The results showed that the electric field generated by TI stimulation was able to focus on the VPL nuclei when the stimulation current reached 800 µA; the mouse were able to make corresponding left and right turns according to the stimulation position; and the c-Fos positive cell markers in the VPL nuclei increased significantly after stimulation. This study confirms the feasibility of TI in regulating animal motor behavior and provides a non-invasive stimulation method for brain tissue for animal robots.

Spatiotemporal distribution, interactions and toxic effect of microorganisms and ARGs/MGEs from the bioreaction tank in hospital sewage treatment facility.

Antibiotic resistance genes (ARGs) can be emitted from wastewater to ambient air and impose unignorable inhalable hazards, which could be exacerbated in antibiotic-concentrated hospital sewage. However, whether the ARG-carrying pathogens are more likely to infect cells remains largely unknown. Here, this study investigated and analyzed the spatiotemporal distribution, interaction, and toxicity of airborne microorganisms and their hosting ARGs in a hospital sewage treatment facility. The average concentration of ARGs/MGEs in sewage of bioreaction tank (BRT-W) was 2.27 × 104 gene copies/L. In the air of bioreaction tank (BRT-A), the average concentration of ARGs/MGEs was 15.86 gene copies/m3. In the four seasons, the ARGs concentration of sewage gradually decreased over time; The concentration of ARGs in the air first decreased and then increased. In spring, the concentration of ARGs/MGEs (qacedelta1-01) in BRT-W was highest (1.05 × 105 gene copies/L); The concentration of ARGs/MGEs (strB) in BRT-A in winter was higher than other seasons (26.18 gene copies/m3). Different from the past, this study also paid attention to the pathogenic potential of ARGs/MGEs in the air. The results of cell experiments showed that the cytotoxicity of drug-resistant Escherichia coli could reach Grade V. This suggested that the longer the drug-resistant E. coli were exposed to cells, the greater the cytotoxicity. Moreover, the cytotoxicity of bacteria increased with the increase in exposure time. In spring, the toxic effect of ARGs/MGEs in sewage of BRT-W was highest. Traceability analysis proved that BRT-W was an essential source of microorganisms and ARGs/MGEs in BRT-A. Furthermore, the combined risk of people exposed to the air of BRT in spring was higher than that in other seasons.

Airborne ARGs/MGEs from two sewage types during the COVID-21: Population, microbe interactions, cytotoxicity, formation mechanism, and dispersion.

During the COVID-2021 epidemic, a large number of antibiotics were used for clinical treatment in hospitals or daily prevention. Sewage from hospital sewage treatment centers (HSTC) and wastewater treatment plants (WWTP) produced a lot of antibiotic-resistance genes/mobile genetic elements (ARGs/MGEs). In this study, the sewage and bioaerosol in the biochemical tank (BT) of an HSTC and a WWTP were sampled throughout the year. The results showed that the average absolute abundance of sewage in BT of WWTP (BTW-W) was higher than sewage in BT of HSTC (BTW-H). Sewage was an important source of microorganisms and ARGs/MGEs in the air of BT. Microorganisms and MGEs were the factors affecting the differences in ARGs/MGEs. Cytotoxicity experiment proved that the cytotoxicity changed from Grade III to Grade IV with the increase in drug-resistant Escherichia coli concentration. According to the formation mechanism formula, the average generation rate of ARGs/MGEs in BT of HSTC was lower than that in WWTP. The diffusion range of ARGs/MGEs of HSTC was larger than that of WWTP. According to the above results, this study found that when people were far away from BT, the health risk of HSTC caused by the diffusion of bioaerosol was higher than WWTP; When people were close to BT, the health risk of WWTP was higher than HSTC due to the aeration of BT. This study provided a basis for public protection of ARGs. In the future, the elimination of airborne ARGs and crowd protection can be further studied in detail.

Extracellular vesicles released by transforming growth factor-beta 1-preconditional mesenchymal stem cells promote recovery in mice with spinal cord injury.

Spinal cord injury (SCI) causes neuroinflammation, neuronal death, and severe axonal connections. Alleviating neuroinflammation, protecting residual cells and promoting neuronal regeneration via endogenous neural stem cells (eNSCs) represent potential strategies for SCI treatment. Extracellular vesicles (EVs) released by mesenchymal stem cells have emerged as pathological mediators and alternatives to cell-based therapies following SCI. In the present study, EVs isolated from untreated (control, C-EVs) and TGF-ß1-treated (T-EVs) mesenchymal stem cells were injected into SCI mice to compare the therapeutic effects and explore the underlying mechanisms. Our study demonstrated for the first time that the application of T-EVs markedly enhanced the proliferation and antiapoptotic ability of NSCs in vitro. The infusion of T-EVs into SCI mice increased the shift from the M1 to M2 polarization of reactive microglia, alleviated neuroinflammation, and enhanced the neuroprotection of residual cells during the acute phase. Moreover, T-EVs increased the number of eNSCs around the epicenter. Consequently, T-EVs further promoted neurite outgrowth, increased axonal regrowth and remyelination, and facilitated locomotor recovery in the chronic stage. Furthermore, the use of T-EVs in Rictor-/- SCI mice (conditional knockout of Rictor in NSCs) showed that T-EVs failed to increase the activation of eNSCs and improve neurogenesis sufficiently, which suggested that T-EVs might induce the activation of eNSCs by targeting the mTORC2/Rictor pathway. Taken together, our findings indicate the prominent role of T-EVs in the treatment of SCI, and the therapeutic efficacy of T-EVs for SCI treatment might be optimized by enhancing the activation of eNSCs via the mTORC2/Rictor signaling pathway.

MRI-based central sarcopenia negatively impacts the therapeutic effectiveness of single-segment lumbar fusion surgery in the elderly.

Central sarcopenia is associated with the prognosis of various orthopedic surgeries in the elderly. This study aims to investigate its impact on the outcomes of single-segment lumbar fusion surgery in elderly patients. Retrospective analysis was conducted on 314 patients aged 60 to 80 who underwent single-segment posterior lumbar fusion surgery due to degenerative lumbar diseases. Patients were categorized into high psoas and L4 vertebral index (PLVI) and low PLVI groups according to the MRI-measured PLVI for central sarcopenia. Basic patient data, surgery-related parameters, functional assessments at preoperative and postoperative 3, 6, and 12 months, and X-ray-based fusion status were compared. The basic data of the two groups showed no significant differences. Parameters including the operative segment, preoperative hemoglobin levels, surgical duration, and intraoperative blood loss exhibited no significant variances. However, notable differences were observed in postoperative initial hemoglobin levels, transfusion requirements, and length of hospital stay between the two groups. During the postoperative follow-ups at 3, 6, and 12 months, the VAS scores for lower back pain and ODI scores in the lower PLVI group were significantly higher compared to the high PLVI group. Additionally, the EuroQoL 5D scores were notably lower in the low PLVI group. There were no significant differences between the groups in terms of leg pain VAS scores at each time point and the fusion status at 12 months postoperatively. MRI-based central sarcopenia has a negative impact on the therapeutic effectiveness following single-segment lumbar fusion surgery in elderly patients.

miTDS: Uncovering miRNA-mRNA interactions with deep learning for functional target prediction.

MicroRNAs (miRNAs) are vital in regulating gene expression through binding to specific target sites on messenger RNAs (mRNAs), a process closely tied to cancer pathogenesis. Identifying miRNA functional targets is essential but challenging, due to incomplete genome annotation and an emphasis on known miRNA-mRNA interactions, restricting predictions of unknown ones. To address those challenges, we have developed a deep learning model based on miRNA functional target identification, named miTDS, to investigate miRNA-mRNA interactions. miTDS first employs a scoring mechanism to eliminate unstable sequence pairs and then utilizes a dynamic word embedding model based on the transformer architecture, enabling a comprehensive analysis of miRNA-mRNA interaction sites by harnessing the global contextual associations of each nucleotide. On this basis, miTDS fuses extended seed alignment representations learned in the multi-scale attention mechanism module with dynamic semantic representations extracted in the RNA-based dual-path module, which can further elucidate and predict miRNA and mRNA functions and interactions. To validate the effectiveness of miTDS, we conducted a thorough comparison with state-of-the-art miRNA-mRNA functional target prediction methods. The evaluation, performed on a dataset cross-referenced with entries from MirTarbase and Diana-TarBase, revealed that miTDS surpasses current methods in accurately predicting functional targets. In addition, our model exhibited proficiency in identifying A-to-I RNA editing sites, which represents an aberrant interaction that yields valuable insights into the suppression of cancerous processes.

RCAd-LTH-shPD-L1, a double-gene recombinant oncolytic adenovirus with enhanced antitumor immunity, increases lymphocyte infiltration and reshapes the tumor microenvironment.

BACKGROUND: With the successful development of modern immunotherapy, immune checkpoint inhibitors (ICIs) are currently considered potential therapeutic options for patients with cancer. However, the therapeutic potential of ICIs in human cancer is mainly limited by their systemic toxicity and low response rate, which suggests the necessity of local drug delivery with an effective vector and reshaping the immunosuppressive tumor microenvironment (TME) to enhance ICI therapy. Here, we constructed a novel double-gene recombinant oncolytic adenovirus named RCAd-LTH-shPD-L1 based on the RCAd virus platform armed with a DNA fragment encoding an anti-VEGF antibody and shRNA to inhibit PD-L1 expression. METHODS: The correct assembly of RCAd-LTH-shPD-L1 was characterized by analyzing its secretion, antigen specificity, and replication using western blotting, ELISA and quantitative PCR, respectively. The in vitro effects of RCAd-LTH-shPD-L1 on cell proliferation, vasculogenic, and cell migration were assessed. Antitumor effects and therapeutic mechanisms were evaluated in vivo using immunodeficient and humanized immune system mouse models. The TME was studied by ELISA, immunohistochemistry and flow cytometry. RESULTS: RCAd-LTH-shPD-L1 cells secreted anti-VEGF antibodies and inhibited the expression of PD-L1 in cancer cells. Moreover, RCAd-LTH-shPD-L1 exerted a specific cytotoxic effect on human cancer cells, but not on murine cancer cells or normal human cells. RCAd-LTH-shPD-L1 elicited a more potent antitumor effect in an immunodeficient mouse model and a humanized immune system mouse model than RCAd-shPD-L1, as demonstrated by the significant decrease in tumor growth. Furthermore, RCAd-LTH-shPD-L1 modulated the TME, which led to lymphocyte infiltration and alteration of their immune phenotype, as characterized by downregulation of anoxic factor HIF-1α and angiogenesis marker CD31, upregulation of cytokine such as IFN-γ, IL-6 and IL-12. CONCLUSIONS: In summary, our data demonstrated that the localized delivery of anti-VEGF antibodies and shPD-L1 by engineered RCAd-LTH-shPD-L1 is a highly effective and safe strategy for cancer immunotherapy. Moreover, the data underscore the potential of combining local virotherapy and anti-angiogenic therapy with ICIs as an effective TME therapy for poorly infiltrating tumors.

Disruption and recovery of outdoor bioaerosols before, during, and after the COVID-19 outbreak at a campus in Central China: pathogen composition, particle size distribution, influencing factors, and exposure risk.

Before (2019), during (2020), and after (2021) the COVID-19 outbreak, different response methods and measures were taken on campuses to control the spread of COVID-19 within schools. These response methods may have changed the outdoor bioaerosol characteristics, which may affect staff and student health. Therefore, we analyzed the bacterial concentrations, particle size distribution, microbial populations, exposure risks, and environmental influences of bioaerosols at a campus before, during, and after the COVID-19 outbreak. This study used eight-stage Andersen samplers to collect and analyze culturable bacteria in bioaerosols from various locations, high-throughput sequencing to analyze microbial species, principal component analysis to compare differences in samples, RDA to investigate the effects of environmental factors on bioaerosols, and hazard quotient (HQ) and BugBase to evaluate human health risks. The study findings revealed that average bacterial concentrations before, during, and after COVID-19 were 75 CFU m-3, 136 CFU m-3, and 78 CFU m-3, respectively. Moreover, the average percentage of bacteria attached to PM2.5 was 49.2%, 42.7%, and 29.9%, respectively. High-throughput sequencing revealed that species composition changed significantly during the three years of COVID-19. The proportion of Pantoea and Bacillus increased with the development of COVID-19 and these became the dominant strains after COVID-19, whereas Pseudomonas had the maximum proportion during COVID-19. Both risk assessment and BugBase phenotype prediction results indicated that the potential pathogenic risk was the highest in the outdoor environment of the campus during COVID-19 and that bioaerosol contamination was the most severe compared to the outdoor bioaerosol characteristics of the campus recovered after COVID-19.

Biocompatibility and osteogenic potential of choline phosphate chitosan-coated biodegradable Zn1Mg.

Zinc alloys have demonstrated considerable potentials as implant materials for biodegradable vascular and orthopedic applications. However, the high initial release of Zn2+ can trigger intense immune responses that impede tissue healing. To address this challenge and enhance the osteogenic capacity of zinc alloys, the surface of Zn1Mg was subjected to CO2 plasma modification (Zn1Mg-PP) followed by grafting with choline phosphate chitosan (Zn1Mg-PP-PCCs). This study aims to investigate the in vitro and in vivo biocompatibility of the surface-modified Zn1Mg. The effect of the surface modification on the inflammatory response and osteogenic repair process was investigated. Compared with unmodified Zn1Mg, the degradation rate of Zn1Mg-PP-PCCs was significantly decreased, avoiding the cytotoxicity triggered by the release of large amounts of Zn2+. Moreover, PCCs significantly enhanced the cell-material adhesion, promoted the proliferation of osteoblasts (MC3T3-E1) and upregulated the expression of key osteogenic factors in vitro. Notably, the in vivo experiments revealed that the surface modification of Zn1Mg suppressed inhibited the expression of inflammatory cytokines, promoting the secretion of anti-inflammatory factors, thereby reducing inflammation and promoting bone tissue repair. Furthermore, histological analysis of tissue sections exhibited strong integration between the material and the bone, along with well-defined new bone formation and reduced osteoclast aggregation on the surface. This was attributed to the improved immune microenvironment by PCCs, which promoted osteogenic differentiation of osteoblasts. These findings highlight that the preparation of PCCs coatings on zinc alloy surfaces effectively inhibited ion release and modulated the immune environment to promote bone tissue repair. STATEMENT OF SIGNIFICANCE: Surface modification of biodegradable Zn alloys facilitates the suppression of intense immune responses caused by excessive ion release concentrations from implants. We modified the surface of Zn1Mg with choline phosphate chitosan (PCCs) and investigated the effects of surface modification on the inflammatory response and osteogenic repair process. In vitro results showed that the PCCs coating effectively reduced the degradation rate of Zn1Mg to avoid cytotoxicity caused by high Zn2+ concentration, favoring the proliferation of osteoblasts. In addition, in vivo results indicated that Zn1Mg-PP-PCCs attenuated inflammation to promote bone repair by modulating the release of inflammation-related factors. The surface-modified Zn1Mg implants demonstrated strong osseointegration, indicating that the PCCs coating effectively modulated the immune microenvironment and promoted bone healing.

Pressure-Sensitive Capability of AgNPs Self-Sensing Cementitious Sensors.

Intelligent monitoring approaches for long-term, real-time digitalization in structural health monitoring (SHM) are currently attracting significant interest. Among these, self-sensing cementitious composites stand out due to their easy preparation, cost-effectiveness, and excellent compatibility with concrete structures. However, the current research faces challenges, such as excessive conductive filler, difficulties in filler dispersion, and insufficient stress sensitivity and instability. This study presents a novel approach to these challenges by fabricating self-sensing cementitious sensors using silver nanoparticles (AgNPs), a new type of conductive filler. The percolation threshold of AgNPs in these materials was determined to be 0.0066 wt%, marking a reduction of approximately 90% compared to traditional conductive fillers. Moreover, the absorbance test with a UV spectrophotometer showed that AgNPs were well dispersed in an aqueous solution, which is beneficial for the construction of conductive pathways. Through various cyclic loading tests, it was observed that the self-sensing cementitious sensors with AgNPs exhibited robust pressure-sensitive stability. Additionally, their stress sensitivity reached 11.736, a value significantly surpassing that of conventional fillers. Regarding the conductive mechanism, when encountering the intricate environment within the cementitious material, AgNPs can establish numerous conductive pathways, ensuring a stable response to stress due to their ample quantity. This study provides a significant contribution to addressing the existing challenges in self-sensing cementitious materials and offers a novel reference for further research in this domain.

[Characterization of Cell Subsets Associated With Prognosis of Osteosarcoma Based on Single-Cell Sequencing Data].

Objective To explore the cell subsets and characteristics related to the prognosis of osteosarcoma by analyzing the cellular composition of tumor tissue samples from different osteosarcoma patients.Methods The single-cell sequencing data and bulk sequencing data of different osteosarcoma patients were downloaded.We extracted the information of cell samples for dimensionality reduction,annotation,and cell function analysis,so as to identify the cell subsets and clarify the cell characteristics related to the prognosis of osteosarcoma.The development trajectory of macrophages with prognostic significance was analyzed,and the prognostic model of osteosarcoma was established based on the differentially expressed genes of macrophage differentiation.Results The cellular composition presented heterogeneity in the patients with osteosarcoma.The infiltration of mononuclear phagocytes in osteosarcoma had prognostic significance(P=0.003).Four macrophage subsets were associated with prognosis,and their signature transcription factors included RUNX3(+),ETS1(+),HOXD11(+),ZNF281(+),and PRRX1(+).Prog_Macro2 and Prog_Macro4 were located at the end of the developmental trajectory,and the prognostic ability of macrophage subsets increased with the progression of osteosarcoma.The prognostic model established based on the differentially expressed genes involved in macrophage differentiation can distinguish the survival rate of osteosarcoma patients with different risks(P<0.001).Conclusion Macrophage subsets are closely related to the prognosis of osteosarcoma and can be used as the key target cells for the immunotherapy of osteosarcoma.

Simulation Investigation on the Influence Mechanism of Toluene and Heptane on the Aggregation of Asphaltene Molecules.

Asphaltenes are a group of compounds that are soluble in benzene and toluene but insoluble in nonpolar small molecule n-alkanes. The asphaltene aggregation in the asphaltene-heptane-toluene system was studied using molecular dynamics (MD) simulation, and the interaction between asphaltene molecules during this process was also revealed from the evolution of the density field, radial distribution function (RDF), and intermolecular distance of asphaltenes. Three main findings were made: (1) more asphaltene precipitates (heptane) were contained, and more asphaltene dimers or trimers were formed during the MD simulation; (2) asphaltene molecules interacted with each other to form aggregates in the form of π-π or H-bond interaction. The stable distance of the π-π interaction was 3.3-3.5 Å, and the stable distance of the H-bond connection was 1.7-1.9 Å. (3) The asphaltene interaction in the heptane-rich system was dominated by π-π interaction between asphaltene molecules. However, the asphaltene interactions in the toluene-rich system were mainly the π-π interaction between asphaltene molecules and toluene and the H-bond interaction between the side chains of asphaltene molecules. The results of this study can aid in understanding how asphaltene molecules aggregate and self-associate and can also offer theoretical support for flow assurance in systems used to produce crude oil.

Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNet.

RNA-binding proteins play crucial roles in the regulation of gene expression, and understanding the interactions between RNAs and RBPs in distinct cellular conditions forms the basis for comprehending the underlying RNA function. However, current computational methods pose challenges to the cross-prediction of RNA-protein binding events across diverse cell lines and tissue contexts. Here, we develop HDRNet, an end-to-end deep learning-based framework to precisely predict dynamic RBP binding events under diverse cellular conditions. Our results demonstrate that HDRNet can accurately and efficiently identify binding sites, particularly for dynamic prediction, outperforming other state-of-the-art models on 261 linear RNA datasets from both eCLIP and CLIP-seq, supplemented with additional tissue data. Moreover, we conduct motif and interpretation analyses to provide fresh insights into the pathological mechanisms underlying RNA-RBP interactions from various perspectives. Our functional genomic analysis further explores the gene-human disease associations, uncovering previously uncharacterized observations for a broad range of genetic disorders.

Allele-specific RNA N 6-methyladenosine modifications reveal functional genetic variants in human tissues.

An intricate network of cis- and trans-elements acts on RNA N 6-methyladenosine (m6A), which in turn may affect gene expression and, ultimately, human health. A complete understanding of this network requires new approaches to accurately measure the subtle m6A differences arising from genetic variants, many of which have been associated with common diseases. To address this gap, we developed a method to accurately and sensitively detect transcriptome-wide allele-specific m6A (ASm6A) from MeRIP-seq data and applied it to uncover 12,056 high-confidence ASm6A modifications from 25 human tissues. We also identified 1184 putative functional variants for ASm6A regulation, a subset of which we experimentally validated. Importantly, we found that many of these ASm6A-associated genetic variants were enriched for common disease-associated and complex trait-associated risk loci, and verified that two disease risk variants can change m6A modification status. Together, this work provides a tool to detangle the dynamic network of RNA modifications at the allelic level and highlights the interplay of m6A and genetics in human health and disease.

Analysis of Zinc and Stromal Immunity in Disuse Osteoporosis: Mendelian Randomization and Transcriptomic Analysis.

OBJECTIVE: Disuse osteoporosis is known to be primarily caused by a lack of exercise. However, the causal relationships between zinc and immunity and disuse osteoporosis remain unknown. This study investigated these relationships and their potential mechanisms. METHODS: This study was an integrative study combining genome-wide association studies and transcriptomics. Two-sample Mendelian randomization analysis (MR) was used to analyze the causal relationships between exposures (zinc, immunity, physical activity) and the outcome (osteoporosis) with the aid of single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs). Four models, MR-Egger, inverse variance weighted, weighted median and MR-Pleiotrophy RESidual Sum and Outlier (MRPRESSO), were used to calculate odds ratio values. Sensitivity and heterogeneity analyses were also performed using MRPRESSO and MR-Egger methods. The mRNA transcriptomic analysis was subsequently conducted. Zinc metabolism scores were acquired through single-sample Gene Set Enrichment Analysis algorithms. Stromal scores were obtained using the R Package "estimate" algorithms. Important Kyoto Encyclopedia of Genes and Genomes and Gene Ontology pathways were also derived through gene set variation analysis. Cytoscape software helped construct the transcription factor (TF)-mRNA-microRNA (miRNA) network. Virtual screening and molecular docking were performed. Polymerase chain reaction validation was also carried out in vivo. RESULTS: Causal relationships were demonstrated between zinc and exercise (95% confidence interval [CI] = 1.30-2.95, p = 0.001), exercise and immunity (95% CI = 0.36-0.80, p = 0.002), exercise and osteoporosis (95% CI = 0.97-0.99, p = 0.0007), and immunity disorder and osteoporosis (95% CI = 1.30-2.03, p = 0.00002). One hundred and seventy-nine mRNAs in important modules were screened. Combining the differential expressional genes (DEGs) and the Boruta selection, six DEGs were screened (AHNAK, CSF2, ADAMTS12, SRA1, RUNX2, and SLC39A14). TF HOXC10 and miRNA hsa-miR-204 were predicted. Then, the TF-mRNA-miRNA network was successfully constructed. RUNX2 and SLC39A14 were identified as hub mRNAs in the TF-mRNA-miRNA network. Eventually, the novel small drug C6O4NH5 was designed according to the pharmacophore structure of SLC39A14. The docking energy for the novel drug was -5.83 kcal/mol. SLC39A14 and RUNX2 were downregulated (of statistical significance p-value < 0.05) in our animal experiment. CONCLUSION: This study revealed that zinc had a protective causal relationship with disuse osteoporosis by promoting exercise and immunity. SLC39A14 and RUNX2 mRNA participated in this zinc-related mechanism.

A novel m7G regulator-based methylation patterns in head and neck squamous cell carcinoma.

Abnormal RNA N7-methylguanosine (m7G) modification is known to contribute to effects on tumor occurrence and development. Nevertheless, the mechanisms of its function in immunoregulation, tumor microenvironment (TME) modulation, and tumor promotion remain largely unknown. A series of computer-aided bioinformatic analyses were conducted based on transcriptomic, single-cell sequence, and spatial transcriptomic data to determine the m7G modification patterns in head and neck squamous cell carcinoma (HNSCC). Consensus clustering approach was employed according to the expressions of 33 m7G regulators. ESTIMATE, CIBERSORT, and single sample gene set enrichment analysis algorithms were adopted to investigate the immune cell infiltration features. A prognostic model named m7Gscore was established. Seurat, SingleR, and Monocle2 were used to analyze the single-cell sequence profiling. STUtility was used to integrate multiple spatial transcriptomic datasets. Quantitative reverse transcription polymerase chain reaction, transwell, and wound-healing assay were performed to verify the oncogenes. Here, three different m7G modification patterns were highlighted in HNSCC patients, which were also related to various clinical manifestations and three representative immunophenotypes: immune-excluded, immune-desert, and inflamed, separately. Patients with lower m7Gscore were highlighted by higher immune cell infiltrations, better overall survival rates, lesser tumor mutation burden (TMB), lower sensitivities to target inhibitors therapies, and better immunotherapeutic response. Moreover, DCPS, EIF4E, EIF4E2, LSM1, NCBP2, NUDT1, and NUDT5 were identified to play critical roles in T-cell differentiation. Knockdown of LSM1/NUDT5 could restrain the malignancy of HNSCC cells. Collectively, quantitative assessment of m7G modification patterns in individual HNSCC patients could contribute to identifying more efficient immunotherapeutic approaches and improve the clinical outcome of HNSCC.