Self-supervised learning on millions of primary RNA sequences from 72 vertebrates improves sequence-based RNA splicing prediction.

Language models pretrained by self-supervised learning (SSL) have been widely utilized to study protein sequences, while few models were developed for genomic sequences and were limited to single species. Due to the lack of genomes from different species, these models cannot effectively leverage evolutionary information. In this study, we have developed SpliceBERT, a language model pretrained on primary ribonucleic acids (RNA) sequences from 72 vertebrates by masked language modeling, and applied it to sequence-based modeling of RNA splicing. Pretraining SpliceBERT on diverse species enables effective identification of evolutionarily conserved elements. Meanwhile, the learned hidden states and attention weights can characterize the biological properties of splice sites. As a result, SpliceBERT was shown effective on several downstream tasks: zero-shot prediction of variant effects on splicing, prediction of branchpoints in humans, and cross-species prediction of splice sites. Our study highlighted the importance of pretraining genomic language models on a diverse range of species and suggested that SSL is a promising approach to enhance our understanding of the regulatory logic underlying genomic sequences.

Prioritizing genomic variants pathogenicity via DNA, RNA, and protein-level features based on extreme gradient boosting.

Genetic diseases are mostly implicated with genetic variants, including missense, synonymous, non-sense, and copy number variants. These different kinds of variants are indicated to affect phenotypes in various ways from previous studies. It remains essential but challenging to understand the functional consequences of these genetic variants, especially the noncoding ones, due to the lack of corresponding annotations. While many computational methods have been proposed to identify the risk variants. Most of them have only curated DNA-level and protein-level annotations to predict the pathogenicity of the variants, and others have been restricted to missense variants exclusively. In this study, we have curated DNA-, RNA-, and protein-level features to discriminate disease-causing variants in both coding and noncoding regions, where the features of protein sequences and protein structures have been shown essential for analyzing missense variants in coding regions while the features related to RNA-splicing and RBP binding are significant for variants in noncoding regions and synonymous variants in coding regions. Through the integration of these features, we have formulated the Multi-level feature Genomic Variants Predictor (ML-GVP) using the gradient boosting tree. The method has been trained on more than 400,000 variants in the Sherloc-training set from the 6th critical assessment of genome interpretation with superior performance. The method is one of the two best-performing predictors on the blind test in the Sherloc assessment, and is further confirmed by another independent test dataset of de novo variants.

Multifunctional Wearable Electronic Based on Fabric Modified by PPy/NiCoAl-LDH for Energy Storage, Electromagnetic Interference Shielding, and Photothermal Conversion.

With the rapid advancement of electronic technology, traditional textiles are challenged to keep up with the demands of wearable electronics. It is anticipated that multifunctional textile-based electronics incorporating energy storage, electromagnetic interference (EMI) shielding, and photothermal conversion are expected to alleviate this problem. Herein, a multifunctional cotton fabric with hierarchical array structure (PPy/NiCoAl-LDH/Cotton) is fabricated by the introduction of NiCoAl-layered double hydroxide (NiCoAl-LDH) nanosheet arrays on cotton fibers, followed by polymerization and growth of continuous dense polypyrrole (PPy) conductive layers. The multifunctional cotton fabric shows a high specific areal capacitance of 754.72 mF cm-2 at 5 mA cm-2 and maintains a long cycling life (80.95% retention after 1000 cycles). The symmetrical supercapacitor assembled with this fabric achieves an energy density of 20.83 Wh cm-2 and a power density of 0.23 mWcm-2. Moreover, the excellent electromagnetic interference shielding (38.83 dB), photothermal conversion (70.2 °C at 1000 mW cm-2), flexibility and durability are also possess by the multifunctional cotton fabric. Such a multifunctional cotton fabric has great potential for using in new energy, smart electronics, and thermal management applications.

Irisin attenuates type 1 diabetic cardiomyopathy by anti-ferroptosis via SIRT1-mediated deacetylation of p53.

BACKGROUND: Diabetic cardiomyopathy (DCM) is a serious complication in patients with type 1 diabetes mellitus (T1DM), which still lacks adequate therapy. Irisin, a cleavage peptide off fibronectin type III domain-containing 5, has been shown to preserve cardiac function in cardiac ischemia-reperfusion injury. Whether or not irisin plays a cardioprotective role in DCM is not known. METHODS AND RESULTS: T1DM was induced by multiple low-dose intraperitoneal injections of streptozotocin (STZ). Our current study showed that irisin expression/level was lower in the heart and serum of mice with STZ-induced TIDM. Irisin supplementation by intraperitoneal injection improved the impaired cardiac function in mice with DCM, which was ascribed to the inhibition of ferroptosis, because the increased ferroptosis, associated with increased cardiac malondialdehyde (MDA), decreased reduced glutathione (GSH) and protein expressions of solute carrier family 7 member 11 (SLC7A11) and glutathione peroxidase 4 (GPX4), was ameliorated by irisin. In the presence of erastin, a ferroptosis inducer, the irisin-mediated protective effects were blocked. Mechanistically, irisin treatment increased Sirtuin 1 (SIRT1) and decreased p53 K382 acetylation, which decreased p53 protein expression by increasing its degradation, consequently upregulated SLC7A11 and GPX4 expressions. Thus, irisin-mediated reduction in p53 decreases ferroptosis and protects cardiomyocytes against injury due to high glucose. CONCLUSION: This study demonstrated that irisin could improve cardiac function by suppressing ferroptosis in T1DM via the SIRT1-p53-SLC7A11/GPX4 pathway. Irisin may be a therapeutic approach in the management of T1DM-induced cardiomyopathy.

Forecasting acute kidney injury and resource utilization in ICU patients using longitudinal, multimodal models.

BACKGROUND: Advances in artificial intelligence (AI) have realized the potential of revolutionizing healthcare, such as predicting disease progression via longitudinal inspection of Electronic Health Records (EHRs) and lab tests from patients admitted to Intensive Care Units (ICU). Although substantial literature exists addressing broad subjects, including the prediction of mortality, length-of-stay, and readmission, studies focusing on forecasting Acute Kidney Injury (AKI), specifically dialysis anticipation like Continuous Renal Replacement Therapy (CRRT) are scarce. The technicality of how to implement AI remains elusive. OBJECTIVE: This study aims to elucidate the important factors and methods that are required to develop effective predictive models of AKI and CRRT for patients admitted to ICU, using EHRs in the Medical Information Mart for Intensive Care (MIMIC) database. METHODS: We conducted a comprehensive comparative analysis of established predictive models, considering both time-series measurements and clinical notes from MIMIC-IV databases. Subsequently, we proposed a novel multi-modal model which integrates embeddings of top-performing unimodal models, including Long Short-Term Memory (LSTM) and BioMedBERT, and leverages both unstructured clinical notes and structured time series measurements derived from EHRs to enable the early prediction of AKI and CRRT. RESULTS: Our multimodal model achieved a lead time of at least 12 h ahead of clinical manifestation, with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.888 for AKI and 0.997 for CRRT, as well as an Area Under the Precision Recall Curve (AUPRC) of 0.727 for AKI and 0.840 for CRRT, respectively, which significantly outperformed the baseline models. Additionally, we performed a SHapley Additive exPlanation (SHAP) analysis using the expected gradients algorithm, which highlighted important, previously underappreciated predictive features for AKI and CRRT. CONCLUSION: Our study revealed the importance and the technicality of applying longitudinal, multimodal modeling to improve early prediction of AKI and CRRT, offering insights for timely interventions. The performance and interpretability of our model indicate its potential for further assessment towards clinical applications, to ultimately optimize AKI management and enhance patient outcomes.

Interpretable Spatial Gradient Analysis for Spatial Transcriptomics Data.

Cellular anatomy and signaling vary across niches, which can induce gradated gene expressions in subpopulations of cells. Such spatial transcriptomic gradient (STG) makes a significant source of intratumor heterogeneity and can influence tumor invasion, progression, and response to treatment. Here we report Local Spatial Gradient Inference (LSGI), a computational framework that systematically identifies spatial locations with prominent, interpretable STGs from spatial transcriptomic (ST) data. To achieve so, LSGI scrutinizes each sliding window employing non-negative matrix factorization (NMF) combined with linear regression. With LSGI, we demonstrated the identification of spatially proximal yet opposite directed pathway gradients in a glioblastoma dataset. We further applied LSGI to 87 tumor ST datasets reported from nine published studies and identified both pan-cancer and tumor-type specific pathways with gradated expression patterns, such as epithelial mesenchymal transition, MHC complex, and hypoxia. The local gradients were further categorized according to their association to tumor-TME (tumor microenvironment) interface, highlighting the pathways related to spatial transcriptional intratumoral heterogeneity. We conclude that LSGI enables highly interpretable STG analysis which can reveal novel insights in tumor biology from the increasingly reported tumor ST datasets.

Label-aware distance mitigates temporal and spatial variability for clustering and visualization of single-cell gene expression data.

Clustering and visualization are essential parts of single-cell gene expression data analysis. The Euclidean distance used in most distance-based methods is not optimal. The batch effect, i.e., the variability among samples gathered from different times, tissues, and patients, introduces large between-group distance and obscures the true identities of cells. To solve this problem, we introduce Label-Aware Distance (LAD), a metric using temporal/spatial locality of the batch effect to control for such factors. We validate LAD on simulated data as well as apply it to a mouse retina development dataset and a lung dataset. We also found the utility of our approach in understanding the progression of the Coronavirus Disease 2019 (COVID-19). LAD provides better cell embedding than state-of-the-art batch correction methods on longitudinal datasets. It can be used in distance-based clustering and visualization methods to combine the power of multiple samples to help make biological findings.

Gene Expression Profiles Perturbed by Injury to the Mouse Intervertebral Disc.

OBJECTIVES: Back pain subsequent to intervertebral disc (IVD) injury is a common clinical problem. Previous work examining early molecular changes post injury mainly used a candidate marker approach. In this study, gene expression in the injured and intact mouse tail IVDs was determined with a nonbiased whole transcriptome approach. DESIGN: Mouse tail IVD injury was induced by a needle puncture. Whole murine transcriptome was determined by RNASeq. Transcriptomes of injured IVDs were compared with those of intact controls by bioinformatic methods. RESULTS: Among the 18,078 murine genes examined, 592 genes were differentially expressed (P.adj < 0.01). Novel genes upregulated in injured compared with intact IVDs included Chl1, Lum, etc. Ontology study of upregulated genes revealed that leukocyte migration was the most enriched biological process, and network analysis showed that Tnfa had the most protein-protein interactions. Novel downregulated genes in the injured IVDs included 4833412C05Rik, Myoc, etc. The most enriched downregulated pathways were related to cytoskeletal organization. CONCLUSION: Novel genes highly regulated post disc injury were identified with an unbiased approach; they may serve as biomarkers of injury and response to treatments in future experiments. Enriched biological pathways and molecules with high numbers of connections may be targets for treatments post injury.

Forecasting Acute Kidney Injury and Resource Utilization in ICU patients using longitudinal, multimodal models.

Background: Advances in artificial intelligence (AI) have realized the potential of revolutionizing healthcare, such as predicting disease progression via longitudinal inspection of Electronic Health Records (EHRs) and lab tests from patients admitted to Intensive Care Units (ICU). Although substantial literature exists addressing broad subjects, including the prediction of mortality, length-of-stay, and readmission, studies focusing on forecasting Acute Kidney Injury (AKI), specifically dialysis anticipation like Continuous Renal Replacement Therapy (CRRT) are scarce. The technicality of how to implement AI remains elusive. Objective: This study aims to elucidate the important factors and methods that are required to develop effective predictive models of AKI and CRRT for patients admitted to ICU, using EHRs in the Medical Information Mart for Intensive Care (MIMIC) database. Methods: We conducted a comprehensive comparative analysis of established predictive models, considering both time-series measurements and clinical notes from MIMIC-IV databases. Subsequently, we proposed a novel multi-modal model which integrates embeddings of top-performing unimodal models, including Long Short-Term Memory (LSTM) and BioMedBERT, and leverages both unstructured clinical notes and structured time series measurements derived from EHRs to enable the early prediction of AKI and CRRT. Results: Our multimodal model achieved a lead time of at least 12 hours ahead of clinical manifestation, with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.888 for AKI and 0.997 for CRRT, as well as an Area Under the Precision Recall Curve (AUPRC) of 0.727 for AKI and 0.840 for CRRT, respectively, which significantly outperformed the baseline models. Additionally, we performed a SHapley Additive exPlanation (SHAP) analysis using the expected gradients algorithm, which highlighted important, previously underappreciated predictive features for AKI and CRRT. Conclusion: Our study revealed the importance and the technicality of applying longitudinal, multimodal modeling to improve early prediction of AKI and CRRT, offering insights for timely interventions. The performance and interpretability of our model indicate its potential for further assessment towards clinical applications, to ultimately optimize AKI management and enhance patient outcomes.

Irisin-Encapsulated Mitochondria-Targeted Biomimetic Nanotherapeutics for Alleviating Acute Kidney Injury.

Acute kidney injury (AKI) is the sudden decrease in renal function that can be attributed to dysregulated reactive oxygen species (ROS) production and impaired mitochondrial function. Irisin, a type I membrane protein secreted by skeletal muscles in response to physical activity, has been reported to alleviate kidney damage through regulation of mitochondrial biogenesis and oxidative metabolism. In this study, a macrophage membrane-coated metal-organic framework (MCM@MOF) is developed as a nanocarrier for encapsulating irisin to overcome the inherent characteristics of irisin, including a short circulation time, limited kidney-targeting ability, and low membrane permeability. The engineered irisin-mediated biomimetic nanotherapeutics have extended circulation time and enhanced targeting capability toward injured kidneys due to the preservation of macrophage membrane proteins. The irisin-encapsulated biomimetic nanotherapeutics effectively mitigate acute ischemia-reperfusion injury by protecting mitochondrial function and modulating SOD2 levels in renal tubular epithelial cells. The present study provides novel insights to advance the development of irisin as a potential therapeutic approach for AKI.

Free buccal fat pad graft for the bone defect filling of medication-related osteonecrosis of the jaws: A novel surgical approach.

Surgical intervention for medication-related jaw osteonecrosis (MRONJ) is currently the main treatment method, offering a higher healing rate than conservative approaches. However, the management of bony defects after sequestrectomy remains a challenging issue due to poor vascularization from the drug effect. The use of pedicled buccal fat pad (PBFP) for filling bone defects has become common and effective but is limited to the posterior maxillary region. To add to the advantages of the buccal fat pad, we explored a novel treatment approach using a free buccal fat pad (FBFP) to fill bone defects other than the posterior maxilla. While the FBFP has been employed in oral defect reconstruction, currently published cases have been utilized in recipient sites with good blood supply. There has yet to be any usage in poor vascularization defects like MRONJ. This article describes that the FBFP was used to fill the surgical defects of 8 patients who were diagnosed with MRONJ and who underwent sequestrectomy and saucerization. During follow-up visits, there was excellent wound healing and no significant tissue depression. Based on successful treatment experiences, FBFP is a reliable therapeutic option for the management of poor vascularization defects like MRONJ treated through surgical intervention.

Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection.

Traffic time series anomaly detection has been intensively studied for years because of its potential applications in intelligent transportation. However, classical traffic anomaly detection methods often overlook the evolving dynamic associations between road network nodes, which leads to challenges in capturing the long-term temporal correlations, spatial characteristics, and abnormal node behaviors in datasets with high periodicity and trends, such as morning peak travel periods. In this paper, we propose a mirror temporal graph autoencoder (MTGAE) framework to explore anomalies and capture unseen nodes and the spatiotemporal correlation between nodes in the traffic network. Specifically, we propose the mirror temporal convolutional module to enhance feature extraction capabilities and capture hidden node-to-node features in the traffic network. Morever, we propose the graph convolutional gate recurrent unit cell (GCGRU CELL) module. This module uses Gaussian kernel functions to map data into a high-dimensional space, and enables the identification of anomalous information and potential anomalies within the complex interdependencies of the traffic network, based on prior knowledge and input data. We compared our work with several other advanced deep-learning anomaly detection models. Experimental results on the NYC dataset illustrate that our model works best compared to other models for traffic anomaly detection.

Clinical characteristics of choledochal cysts with intrahepatic bile duct dilatations: an observational study.

Purpose: Whether a dilated intrahepatic bile duct (IHBD) has any effect on the prognosis of choledochal cyst (CC) remains controversial. We aimed to summarize the clinical characteristics and prognosis of CC with IHBD dilatation. Methods: One hundred ninety-two children diagnosed with CC were identified, including 127 without IHBD dilatation (group A) and 65 with IHBD dilatation (group B). A retrospective analysis was performed to explore the clinical characteristics and prognosis of CC with IHBD dilatation based on clinical indices, symptoms, and complications. Results: Compared with group A, incidences of jaundice and fever were higher in group B (P = 0.010 and P = 0.033). Preoperative total bilirubin, direct bilirubin, and indirect bilirubin were increased in group B compared to group A (P = 0.005, P < 0.001, and P = 0.014), as were preoperative ALT, AST, γ-GT, and total bile acid (P = 0.006, P = 0.025, P < 0.001, and P = 0.024). The risk of liver fibrosis or cirrhosis was significantly increased for group B compared with group A (P = 0.012) and also occurred earlier in group B (P = 0.006). In the dilated IHBDs, 95.4% (62 of 65) recovered to normal, and more than half of dilated IHBDs (37 of 65) recovered to normal in 1 week. Conclusion: Most IHBDs can recover to normal postoperatively in a short time, and proactive treatment is recommended for CC patients with IHBD dilatation for significant abnormal liver functions.

Metagenomics profiling of the microbial community and functional differences in solid-state fermentation vinegar starter (seed Pei) from different Chinese regions.

Introduction: The starter used in solid-state fermentation (SSF) vinegar, known as seed Pei is a microbial inoculant from the previous batch that is utilized during the acetic acid fermentation stage. The seed Pei, which has a notable impact on vinegar fermentation and flavor, is under-researched with comparative studies on microorganisms. Methods: Herein metagenomics was employed to reveal the microbes and their potential metabolic functions of four seed Pei from three regions in China. Results: The predominant microbial taxa in all four starters were bacteria, followed by viruses, eukaryotes, and archaea, with Lactobacillus sp. or Acetobacter sp. as main functional taxa. The seed Pei used in Shanxi aged vinegar (SAV) and Sichuan bran vinegar (SBV) exhibited a higher similarity in microbial composition and distribution of functional genes, while those used in two Zhenjiang aromatic vinegar (ZAV) differed significantly. Redundancy analysis (RDA) of physicochemical factors and microbial communities indicated that moisture content, pH, and reducing sugar content are significant factors influencing microbial distribution. Moreover, seven metagenome-assembled genomes (MAGs) that could potentially represent novel species were identified. Conclusions: There are distinctions in the microbiome and functional genes among different seed Pei. The vinegar starters were rich in genes related to carbohydrate metabolism. This research provides a new perspective on formulating vinegar fermentation starters and developing commercial fermentation agents for vinegar production.

Absorbed Bioactive Compounds Replicate Guanxin II-Induced Endothelium-Associated in/ex vivo Vasodilation.

OBJECTIVE: To develop an interference-free and rapid method to elucidate Guanxin II (GX II)'s representative vasodilator absorbed bioactive compounds (ABCs) among enormous phytochemicals. METHODS: The contents of ferulic acid, tanshinol, and hydroxysafflor yellow A (FTA) in GX II/rat serum after the oral administration of GX II (30 g/kg) were detected using ultra-performance liquid chromatography-mass spectrometry. Totally 18 rats were randomly assigned to the control group (0.9% normal saline), GX II (30 g/kg) and FTA (5, 28 and 77 mg/kg) by random number table method. Diastolic coronary flow velocity-time integral (VTI), i.e., coronary flow or coronary flow-mediated dilation (CFMD), and endothelium-intact vascular tension of isolated aortic rings were measured. After 12 h of exposure to blank medium or 0.5 mmol/L H2O2, endothelial cells (ECs) were treated with post-dose GX II of supernatant from deproteinized serum (PGSDS, 300 µL PGSDS per 1 mL of culture medium) or FTA (237, 1539, and 1510 mg/mL) for 10 min as control, H2O2, PGSDS and FTA groups. Nitric oxide (NO), vascular endothelial growth factor (VEGF), endothelin-1 (ET-1), superoxide dismutase (SOD), malondialdehyde (MDA) and phosphorylated phosphoinositide 3 kinase (p-PI3K), phosphorylated protein kinase B (p-AKT), phosphorylated endothelial nitric oxide synthase (p-eNOS) were analyzed. PGSDS was developed as a GX II proxy of ex vivo herbal crude extracts. RESULTS: PGSDS effectively eliminates false responses caused by crude GX II preparations. When doses equaled the contents in GX II/its post-dose serum, FTA accounted for 98.17% of GX II -added CFMD and 92.99% of PGSDS-reduced vascular tension. In ECs, FTA/PGSDS was found to have significant antioxidant (lower MDA and higher SOD, P<0.01) and endothelial function-protective (lower VEGF, ET-1, P<0.01) effects. The increases in aortic relaxation, endothelial NO levels and phosphorylated PI3K/Akt/eNOS protein induced by FTA/PGSDS were markedly abolished by NG-nitro-L-arginine methyl ester (L-NA, eNOS inhibitor) and wortmannin (PI3K/AKT inhibitor), respectively, indicating an endothelium-dependent vasodilation via the PI3K/AKT-eNOS pathway (P<0.01). CONCLUSION: This study provides a strategy for rapidly and precisely elucidating GX II's representative in/ex vivo cardioprotective absorbed bioactive compounds (ABCs)-FTA, suggesting its potential in advancing precision ethnomedicine.

A Scalable and Robust Personal Health Management Textile with Multiple Desired Thermal Functions and Electromagnetic Shielding.

The development of functional textiles combining conventional apparel with advanced technologies for personal health management (PHM) has garnered widespread attention. However, the current PHM textiles often achieve multifunctionality by stacking functional modules, leading to poor durability and scalability. Herein, a scalable and robust PHM textile is designed by integrating electrical, radiative, and solar heating, electromagnetic interference (EMI) shielding, and piezoresistive sensing performance onto cotton fabric. This is achieved through an uncomplicated screen-printing process using silver paste. The conductivity of the PHM textile is ≈1.6  ×  104 S m-1, ensuring an electric heating temperature of ≈134 °C with a low voltage of 1.7 V, as well as an EMI shielding effectiveness of ≈56 dB, and human motion monitoring performance. Surprisingly, the radiative/solar heating capability of the PHM textile surpasses that of traditional warm leather. Even after undergoing rigorous physical and chemical treatments, the PHM textile maintains terrific durability. Additionally, the PHM textile possesses maneuverable scalability and comfortable wearability. This innovative work opens up new avenues for the strategic design of PHM textiles and provides an advantageous guarantee of mass production.

Deciphering cell types by integrating scATAC-seq data with genome sequences.

The single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) technology provides insight into gene regulation and epigenetic heterogeneity at single-cell resolution, but cell annotation from scATAC-seq remains challenging due to high dimensionality and extreme sparsity within the data. Existing cell annotation methods mostly focus on the cell peak matrix without fully utilizing the underlying genomic sequence. Here we propose a method, SANGO, for accurate single-cell annotation by integrating genome sequences around the accessibility peaks within scATAC data. The genome sequences of peaks are encoded into low-dimensional embeddings, and then iteratively used to reconstruct the peak statistics of cells through a fully connected network. The learned weights are considered as regulatory modes to represent cells, and utilized to align the query cells and the annotated cells in the reference data through a graph transformer network for cell annotations. SANGO was demonstrated to consistently outperform competing methods on 55 paired scATAC-seq datasets across samples, platforms and tissues. SANGO was also shown to be able to detect unknown tumor cells through attention edge weights learned by the graph transformer. Moreover, from the annotated cells, we found cell-type-specific peaks that provide functional insights/biological signals through expression enrichment analysis, cis-regulatory chromatin interaction analysis and motif enrichment analysis.

Characteristics and biological responses of selective laser melted Ti6Al4V modified by micro-arc oxidation.

Background/purpose: Additive manufacturing (AM) technology, such as selective laser melting (SLM), has been used to fabricate medical devices of Ti-6wt.% Al-4wt.%V (Ti6Al4V) alloys in dentistry. Strontium (Sr) has been shown to have the potential to treat osteoporosis. The aim of this study was to investigate the physicochemical and biological properties of strontium-containing coatings on selective laser melted Ti6Al4V (SLM-Ti6Al4V) substrate. Materials and methods: The disk of Ti6Al4V was prepared by SLM method. The strontium-containing coatings were prepared by micro-arc oxidation (MAO) in aqueous electrolytes. The surface topography, chemical composition, and phase of strontium-containing MAO (SrMAO) coatings were performed by scanning electron microscope (SEM), energy dispersive X-ray spectrometer (EDS), and thin film X-ray diffraction (TF-XRD), respectively. The apatite-forming ability of the MAO coatings was conducted in simulating body fluid (SBF), and the cell proliferation was determined by methylthiazoletetrazolium (MTT) assay. Results: The microstructure of SLM-Ti6Al4V displays acicular α-phase organization. The TF-XRD results indicated that the phase of SrMAO coating was anatase, rutile, and titanium. The calcium, phosphorus, and strontium were detected in the coatings by EDS. Using the SEM, the surface morphology of SrMAO coatings exhibited a uniform 3D porous structure. The SrMAO coatings could induce a bone-like apatite layer after immersion in SBF, and presented significantly higher cell proliferation than untreated specimens in in-vitro experiments. Conclusion: All findings in this study indicate that SrMAO coatings formed on SLM-Ti6Al4V surfaces exhibit a benefit on biological responses and thereby are suitable for biomedical applications.

Enhancing surgical outcomes of posterior iliac bone harvesting through a pivotal modification.

Traditionally, patients are positioned in the prone position to access the donor site during the posterior iliac bone graft harvesting procedure. However, this well-established method is associated with complications such as pressure injuries, displacement of the endotracheal tube and intravenous catheter, and blindness. Moreover, the process of turning patients 180° between the supine and prone positions is both laborious and time consuming. However, no updates have been made in the approaches published in the literature to counteract these problems. Therefore, to overcome these challenges and improve patient outcomes, we proposed a pivotal modification: change prone position to the lateral decubitus position. This approach allowed us to effectively avoid the aforementioned complications. In addition, this modification offered significant advantages, including ease of implementation and timesaving benefits. The article presented results of the modification and a comprehensive evaluation of clinical and anesthetic considerations comparing the two methods.