Inulin Reduces Kidney Damage in Type 2 Diabetic Mice by Decreasing Inflammation and Serum Metabolomics.

This study is aimed at assessing the impact of soluble dietary fiber inulin on the treatment of diabetes-related chronic inflammation and kidney injury in mice with type 2 diabetes (T2DM). The T2DM model was created by feeding the Institute of Cancer Research (ICR) mice a high-fat diet and intraperitoneally injecting them with streptozotocin (50 mg/kg for 5 consecutive days). The thirty-six ICR mice were divided into three dietary groups: the normal control (NC) group, the T2DM (DM) group, and the DM + inulin diet (INU) group. The INU group mice were given inulin at the dose of 500 mg/kg gavage daily until the end of the 12th week. After 12 weeks, the administration of inulin resulted in decreased serum levels of fasting blood glucose (FBG), low-density lipoprotein cholesterol (LDL-C), blood urea nitrogen (BUN), and creatinine (CRE). The administration of inulin not only ameliorated renal injury but also resulted in a reduction in the mRNA expressions of inflammatory factors in the spleen and serum oxidative stress levels, when compared to the DM group. Additionally, inulin treatment in mice with a T2DM model led to a significant increase in the concentrations of three primary short-chain fatty acids (SCFAs) (acetic acid, propionic acid, and butyric acid), while the concentration of advanced glycation end products (AGEs), a prominent inflammatory factor in diabetes, exhibited a significant decrease. The results of untargeted metabolomics indicate that inulin has the potential to alleviate inflammatory response and kidney damage in diabetic mice. This beneficial effect is attributed to its impact on various metabolic pathways, including glycerophospholipid metabolism, taurine and hypotaurine metabolism, arginine biosynthesis, and tryptophan metabolism. Consequently, oral inulin emerges as a promising treatment option for diabetes and kidney injury.

A bisulfite-assisted and ligation-based qPCR amplification technology for locus-specific pseudouridine detection at base resolution.

Over 150 types of chemical modifications have been identified in RNA to date, with pseudouridine (Ψ) being one of the most prevalent modifications in RNA. Ψ plays vital roles in various biological processes, and precise, base-resolution detection methods are fundamental for deep analysis of its distribution and function. In this study, we introduced a novel base-resolution Ψ detection method named pseU-TRACE. pseU-TRACE relied on the fact that RNA containing Ψ underwent a base deletion after treatment of bisulfite (BS) during reverse transcription, which enabled efficient ligation of two probes complementary to the cDNA sequence on either side of the Ψ site and successful amplification in subsequent real-time quantitative PCR (qPCR), thereby achieving selective and accurate Ψ detection. Our method accurately and sensitively detected several known Ψ sites in 28S, 18S, 5.8S, and even mRNA. Moreover, pseU-TRACE could be employed to measure the Ψ fraction in RNA and explore the Ψ metabolism of different pseudouridine synthases (PUSs), providing valuable insights into the function of Ψ. Overall, pseU-TRACE represents a reliable, time-efficient and sensitive Ψ detection method.

SCTC: inference of developmental potential from single-cell transcriptional complexity.

Inferring the developmental potential of single cells from scRNA-Seq data and reconstructing the pseudo-temporal path of cell development are fundamental but challenging tasks in single-cell analysis. Although single-cell transcriptional diversity (SCTD) measured by the number of expressed genes per cell has been widely used as a hallmark of developmental potential, it may lead to incorrect estimation of differentiation states in some cases where gene expression does not decrease monotonously during the development process. In this study, we propose a novel metric called single-cell transcriptional complexity (SCTC), which draws on insights from the economic complexity theory and takes into account the sophisticated structure information of scRNA-Seq count matrix. We show that SCTC characterizes developmental potential more accurately than SCTD, especially in the early stages of development where cells typically have lower diversity but higher complexity than those in the later stages. Based on the SCTC, we provide an unsupervised method for accurate, robust, and transferable inference of single-cell pseudotime. Our findings suggest that the complexity emerging from the interplay between cells and genes determines the developmental potential, providing new insights into the understanding of biological development from the perspective of complexity theory.

H6N2 reassortant avian influenza virus isolate in wild birds in Jiangxi Province, China.

H6 avian influenza virus is widely prevalent in wild birds and poultry and has caused human infection in 2013 in Taiwan, China. During our active influenza surveillance program in wild waterfowl at Poyang Lake, Jiangxi Province, an H6N2 AIV was isolated and named A/bean goose/JiangXi/452-4/2013(H6N2). The isolate was characterized as a typical low pathogenic avian influenza virus (LPAIV) due to the presence of the amino acid sequence PQIETR↓GLFGAI at the cleavage site of the hemagglutinin (HA) protein. The genetic evolution analysis revealed that the NA gene of the isolate originated from North America and exhibited the highest nucleotide identity (99.29%) with a virus recovered from wild bird samples in North America, specifically A/bufflehead/California/4935/2012(H11N2). Additionally, while the HA and PB1 genes belonged to the Eurasian lineage, they displayed frequent genetic interactions with the North American lineage. The remaining genes showed close genetic relationships with Eurasian viruses. The H6N2 isolate possessed a complex genome, indicating it is a multi-gene recombinant virus with genetic material from both Eurasian and North American lineages.

A Pump-Free Strategy for the Controllable Generation of Alginate Microgels as Cellular Microcarriers.

Microgels are advanced scaffolds for tissue engineering due to their proper biodegradability, good biocompatibility, and high specific surface area for effective oxygen and nutrient transfer. However, most of the current monodispersed microgel fabrication systems rely heavily on various precision pumps, which highly increase the cost and complexity of their downstream application. In this work, we developed a simple and facile system for the controllable generation of uniform alginate microgels by integrating a gas-shearing strategy into a glass microfluidic device. Importantly, the cell-laden microgels can be rapidly prepared in a pump-free manner under an all-aqueous environment. The three-dimensional cultured green fluorescent protein-human A549 cells in alginate microgels exhibited enhanced stemness and drug resistance compared to those under two-dimensional conditions. The pancreatic cancer organoids in alginate microgels exhibited some of the key features of pancreatic cancer. The proposed microgels showed decent monodispersity, biocompatibility, and versatility, providing great opportunities in various biomedical applications such as microcarrier fabricating, organoid engineering, and high-throughput drug screening.

Comprehensive understanding of glioblastoma molecular phenotypes: classification, characteristics, and transition.

Among central nervous system-associated malignancies, glioblastoma (GBM) is the most common and has the highest mortality rate. The high heterogeneity of GBM cell types and the complex tumor microenvironment frequently lead to tumor recurrence and sudden relapse in patients treated with temozolomide. In precision medicine, research on GBM treatment is increasingly focusing on molecular subtyping to precisely characterize the cellular and molecular heterogeneity, as well as the refractory nature of GBM toward therapy. Deep understanding of the different molecular expression patterns of GBM subtypes is critical. Researchers have recently proposed tetra fractional or tripartite methods for detecting GBM molecular subtypes. The various molecular subtypes of GBM show significant differences in gene expression patterns and biological behaviors. These subtypes also exhibit high plasticity in their regulatory pathways, oncogene expression, tumor microenvironment alterations, and differential responses to standard therapy. Herein, we summarize the current molecular typing scheme of GBM and the major molecular/genetic characteristics of each subtype. Furthermore, we review the mesenchymal transition mechanisms of GBM under various regulators.

Solid phase extraction technology combined with UPLC-MS/MS: a method for detecting 20 ß-lactamase antibiotics traces in goat's milk.

We develop and validate a method for the rapid determination and identification of 20 ß-lactamase antibiotics traces in goat's milk by combining the solid phase extraction technology with ultra-high performance liquid chromatography-tandem mass spectrometry. Goat milk samples were extracted with acetonitrile twice. The supernatant was then extracted and cleaned by solid-phase extraction using divinylbenzene and N-vinylpyrrolidone copolymer. The method was validated, with limits of quantification (LOQs) of 0.3 µg kg-1, specificities of 1/3 LOQ, linearities (R2) > 0.99, recoveries of 80-110%, repeatabilities <10.0%, and intermediate precisions <10.0%. The developed method was suitable for the routine analysis of ß-lactamase antibiotics residues in goat's milk and was used to test 76 goat milk samples produced in China.

Identification and validation of oxidative stress-related genes in sepsis-induced myopathy.

BACKGROUND: Sepsis-induced myopathy (SIM) a complication of sepsis that results in prolonged mechanical ventilation, long-term functional disability, and increased patient mortality. This study was performed to identify potential key oxidative stress-related genes (OS-genes) as biomarkers for the diagnosis of SIM using bioinformatics. METHODS: The GSE13205 was obtained from the Gene Expression Omnibus (GEO) database, including 13 SIM samples and 8 healthy samples, and the differentially expressed genes (DEGs) were identified by limma package in R language. Simultaneously, we searched for the genes related to oxidative stress in the Gene Ontology (GO) database. The intersection of the genes selected from the GO database and the genes from the GSE13205 was considered as OS-genes of SIM, where the differential genes were regarded as OS-DEGs. OS-DEGs were analyzed using GO enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) networks. Hub genes in OS-DEGs were selected based on degree, and diagnostic genes were further screened by gene expression and receiver operating characteristic (ROC) curve. Finally, a miRNA-gene network of diagnostic genes was constructed. RESULTS: A total of 1089 DEGs were screened from the GSE13205, and 453 OS-genes were identified from the GO database. The overlapping DEGs and OS-genes constituted 25 OS-DEGs, including 15 significantly upregulated and 10 significantly downregulated genes. The top 10 hub genes, including CD36, GPX3, NQO1, GSR, TP53, IDH1, BCL2, HMOX1, JAK2, and FOXO1, were screened. Furthermore, 5 diagnostic genes were identified: CD36, GPX3, NQO1, GSR, and TP53. The ROC analysis showed that the respective area under the curves (AUCs) of CD36, GPX3, NQO1, GSR, and TP53 were 0.990, 0.981, 0.971, 0.971, and 0.971, which meant these genes had very high diagnostic values of SIM. Finally, based on these 5 diagnostic genes, we found that miR-124-3p and miR-16-5p may be potential targets for the treatment of SIM. CONCLUSIONS: The results of this study suggest that OS-genes might play an important role in SIM. CD36, GPX3, NQO1, GSR, and TP53 have potential as specific biomarkers for the diagnosis of SIM.

Identification of sepsis-associated mitochondrial genes through RNA and single-cell sequencing approaches.

BACKGROUND: Sepsis ranks among the most formidable clinical challenges, characterized by exorbitant treatment costs and substantial demands on healthcare resources. Mitochondrial dysfunction emerges as a pivotal risk factor in the pathogenesis of sepsis, underscoring the imperative to identify mitochondrial-related biomarkers. Such biomarkers are crucial for enhancing the accuracy of sepsis diagnostics and prognostication. METHODS: In this study, adhering to the SEPSIS 3.0 criteria, we collected peripheral blood within 24 h of admission from 20 sepsis patients at the ICU of the Southwest Medical University Affiliated Hospital and 10 healthy volunteers as a control group for RNA-seq. The RNA-seq data were utilized to identify differentially expressed RNAs. Concurrently, mitochondrial-associated genes (MiAGs) were retrieved from the MitoCarta3.0 database. The differentially expressed genes were intersected with MiAGs. The intersected genes were then subjected to GO (Gene Ontology), and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses and core genes were filtered using the PPI (Protein-Protein Interaction) network. Subsequently, relevant sepsis datasets (GSE65682, GSE28750, GSE54514, GSE67652, GSE69528, GSE95233) were downloaded from the GEO (Gene Expression Omnibus) database to perform bioinformatic validation of these core genes. Survival analysis was conducted to assess the prognostic value of the core genes, while ROC (Receiver Operating Characteristic) curves determined their diagnostic value, and a meta-analysis confirmed the accuracy of the RNA-seq data. Finally, we collected 5 blood samples (2 normal controls (NC); 2 sepsis; 1 SIRS (Systemic Inflammatory Response Syndrome), and used single-cell sequencing to assess the expression levels of the core genes in the different blood cell types. RESULTS: Integrating high-throughput sequencing with bioinformatics, this study identified two mitochondrial genes (COX7B, NDUFA4) closely linked with sepsis prognosis. Survival analysis demonstrated that patients with lower expression levels of COX7B and NDUFA4 exhibited a higher day survival rate over 28 days, inversely correlating with sepsis mortality. ROC curves highlighted the significant sensitivity and specificity of both genes, with AUC values of 0.985 for COX7B and 0.988 for NDUFA4, respectively. Meta-analysis indicated significant overexpression of COX7B and NDUFA4 in the sepsis group in contrast to the normal group (P < 0.01). Additionally, single-cell RNA sequencing revealed predominant expression of these core genes in monocytes-macrophages, T cells, and B cells. CONCLUSION: The mitochondrial-associated genes (MiAGs) COX7B and NDUFA4 are intimately linked with the prognosis of sepsis, offering potential guidance for research into the mechanisms underlying sepsis.

MiR-760 exerts a critical regulatory role in glioma proliferation, migration, and invasion by modulating MMP2 expression.

Background: Glioma represents the predominant subtype of brain tumor, characterized by an unfavorable prognosis. Current evidence indicates the involvement of microRNAs (miRNAs) in the initiation and progression of glioma malignancies. While miR-760 has been recognized in the context of tumorigenesis, its precise role in gliomas remains insufficiently explored. Methods: In this investigation, we harnessed the GSE25631 database to scrutinize the aberrant expression profiles of microRNAs, whereby the diminished expression of miR-760 in glioblastoma was validated. Our aim was to delineate the expression patterns of microRNA-760 (miR-760) and probe its prognostic significance within the realm of glioma. Employing quantitative real-time polymerase chain reaction, we ascertained the relative expression levels of miR-760 and MMP2 in glioma cell lines. The impact of miR-760 on cell proliferation, migration, and invasion was assessed through Cell Counting Kit-8 (CCK-8), 5-ethynyl-2'-deoxyuridine (EdU), and Transwell assays. Bioinformatics analysis corroborated the downstream target gene of miR-760. Furthermore, a luciferase reporter experiment was conducted to pinpoint MMP2 as the direct target gene of miR-760. The assessment of MMP2 protein levels was accomplished through Western blotting and immunofluorescence techniques. Result: Our data unequivocally revealed a substantial reduction in miR-760 expression within glioma tissues and cell lines. Heightened miR-760 levels exerted a restraining influence on the proliferation, migration, and invasion capabilities of glioma cell lines. The outcomes of our bioinformatics analysis unveiled the ability of miR-760 to engage with and curtail MMP2 expression. Collectively, these findings posit that miR-760 exerts a restraining influence on glioma growth by orchestrating the upregulation of miR-760 along the miR-760/MMP2 axis. Conclusion: The delineation of the miR-760/MMP2 axis promises to broaden our comprehension of the intricate molecular mechanisms underpinning glioma proliferation and may unveil prospective therapeutic avenues for the management of glioma.

AKIMLpred: An interpretable machine learning model for predicting acute kidney injury within seven days in critically ill patients based on a prospective cohort study.

BACKGROUND: Early recognition and timely intervention for AKI in critically ill patients were crucial to reduce morbidity and mortality. This study aimed to use biomarkers to construct a optimal machine learning model for early prediction of AKI in critically ill patients within seven days. METHODS: The prospective cohort study enrolled 929 patients altogether who were admitted in ICU including 680 patients in training set (Jiefang Campus) and 249 patients in external testing set (Binjiang Campus). After performing strict inclusion and exclusion criteria, 421 patients were selected in training set for constructing predictive model and 167 patients were selected in external testing for evaluating the predictive performance of resulting model. Urine and blood samples were collected for kidney injury associated biomarkers detection. Baseline clinical information and laboratory data of the study participants were collected. We determined the average prediction efficiency of six machine learning models through 10-fold cross validation. RESULTS: In total, 78 variables were collected when admission in ICU and 43 variables were statistically significant between AKI and non-AKI cohort. Then, 35 variables were selected as independent features for AKI by univariate logistic regression. Spearman correlation analysis was used to remove two highly correlated variables. Three ranking methods were used to explore the influence of 33 variables for further determining the best combination of variables. The gini importance ranking method was found to be applicable for variables filtering. The predictive performance of AKIMLpred which constructed by the XGBoost algorithm was the best among six machine learning models. When the AKIMLpred included the nine features (NGAL, IGFBP7, sCysC, CAF22, KIM-1, NT-proBNP, IL-6, IL-18 and L-FABP) with the highest influence ranking, its model had the best prediction performance, with an AUC of 0.881 and an accuracy of 0.815 in training set, similarly, with an AUC of 0.889 and an accuracy of 0.846 in validation set. Moreover, the performace was slightly outperformed in testing set with an AUC of 0.902 and an accuracy of 0.846. The SHAP algorithm was used to interpret the prediction results of AKIMLpred. The web-calculator of AKIMLpred was shown for predicting AKI with more convenient(https://www.xsmartanalysis.com/model/list/predict/model/html?mid=8065&symbol=11gk693982SU6AE1ms21). AKIMLpred was better than the optimal model built with only routine tests for predicting AKI in critically ill patients within 7 days. CONCLUSION: The model AKIMLpred constructed by the XGBoost algorithm with selecting the nine most influential biomarkers in the gini importance ranking method had the best performance in predicting AKI in critically ill patients within 7 days. This data-driven predictive model will help clinicians to make quick and accurate diagnosis.

MH2AFormer: An Efficient Multiscale Hierarchical Hybrid Attention with a Transformer for Bladder Wall and Tumor Segmentation.

Achieving accurate bladder wall and tumor segmentation from MRI is critical for diagnosing and treating bladder cancer. However, automated segmentation remains challenging due to factors such as comparable density distributions, intricate tumor morphologies, and unclear boundaries. Considering the attributes of bladder MRI images, we propose an efficient multiscale hierarchical hybrid attention with a transformer (MH2AFormer) for bladder cancer and wall segmentation. Specifically, a multiscale hybrid attention and transformer (MHAT) module in the encoder is designed to adaptively extract and aggregate multiscale hybrid feature representations from the input image. In the decoder stage, we devise a multiscale hybrid attention (MHA) module to generate high-quality segmentation results from multiscale hybrid features. Combining these modules enhances the feature representation and guides the model to focus on tumor and wall regions, which helps to solve bladder image segmentation challenges. Moreover, MHAT utilizes the Fast Fourier Transformer with a large kernel (e.g., 224*******224) to model global feature relationships while reducing computational complexity in the encoding stage. The model performance was evaluated on two datasets. As a result, the model achieves relatively best results regarding the intersection over union (IoU) and dice similarity coefficient (DSC) on both datasets (Dataset A: IoU=80.26%, DSC=88.20%; Dataset B: IoU=89.74%, DSC=94.48%). These advantageous outcomes substantiate the practical utility of our approach, highlighting its potential to alleviate the workload of radiologists when applied in clinical settings.

Ethylene-responsive VviERF003 modulates glycosylated monoterpenoid synthesis by upregulating VviGT14 in grapes.

Terpenoids are important contributors to the aroma of grapes and wines. Grapes contain terpenoids in both volatile free form and non-volatile glycosidic form, with the latter being more abundant. Glycosylated terpenoids are deemed as latent aromatic potentials for their essential role in adding to the flowery and fruity bouquet of wines. However, the transcriptional regulatory mechanism underlying glycosylated terpenoid biosynthesis remains poorly understood. Our prior study identified an AP2/ERF transcription factor, VviERF003, through DNA pull-down screening using the promoter of terpenoid glycosyltransferase VviGT14 gene. This study demonstrated that both genes were co-expressed and synchronized with the accumulation of glycosylated monoterpenoids during grape maturation. VviERF003 can bind to the VviGT14 promoter and promote its activity according to yeast one-hybrid and dual-luciferase assays. VviERF003 upregulated VviGT14 expression in vivo, leading to increased production of glycosylated monoterpenoids based on the evidence from overexpression or RNA interference in leaves, berry skins, and calli of grapes, as well as tomato fruits. Additionally, VviERF003 and VviGT14 expressions and glycosylated monoterpenoid levels were induced by ethylene in grapes. The findings suggest that VviERF003 is ethylene-responsive and stimulates glycosylated monoterpenoid biosynthesis through upregulating VviGT14 expression.

Motion correction and super-resolution for multi-slice cardiac magnetic resonance imaging via an end-to-end deep learning approach.

Accurate reconstruction of a high-resolution 3D volume of the heart is critical for comprehensive cardiac assessments. However, cardiac magnetic resonance (CMR) data is usually acquired as a stack of 2D short-axis (SAX) slices, which suffers from the inter-slice misalignment due to cardiac motion and data sparsity from large gaps between SAX slices. Therefore, we aim to propose an end-to-end deep learning (DL) model to address these two challenges simultaneously, employing specific model components for each challenge. The objective is to reconstruct a high-resolution 3D volume of the heart (VHR) from acquired CMR SAX slices (VLR). We define the transformation from VLR to VHR as a sequential process of motion correction and super-resolution. Accordingly, our DL model incorporates two distinct components. The first component conducts motion correction by predicting displacement vectors to re-position each SAX slice accurately. The second component takes the motion-corrected SAX slices from the first component and performs the super-resolution to fill the data gaps. These two components operate in a sequential way, and the entire model is trained end-to-end. Our model significantly reduced inter-slice misalignment from originally 3.33±0.74 mm to 1.36±0.63 mm and generated accurate high resolution 3D volumes with Dice of 0.974±0.010 for left ventricle (LV) and 0.938±0.017 for myocardium in a simulation dataset. When compared to the LAX contours in a real-world dataset, our model achieved Dice of 0.945±0.023 for LV and 0.786±0.060 for myocardium. In both datasets, our model with specific components for motion correction and super-resolution significantly enhance the performance compared to the model without such design considerations. The codes for our model are available at https://github.com/zhennongchen/CMR_MC_SR_End2End.

Expression of visfatin in gingival crevicular fluid and gingival tissues in different periodontal conditions: a cross-sectional study.

BACKGROUND: Studies have shown that visfatin is an inflammatory factor closely related to periodontitis. We examined the levels of visfatin in gingival crevicular fluid (GCF) and gingival tissues under different periodontal conditions, in order to provide more theoretical basis for exploring the role of visfatin in the pathogenesis of periodontitis. METHODS: We enrolled 87 subjects, with 43 in the chronic periodontitis (CP) group, 21 in the chronic gingivitis (CG) group, and 23 in the periodontal health (PH) group. Periodontal indexes (PD, AL, PLI, and BI) were recorded. GCF samples were collected for visfatin quantification, and gingival tissues were assessed via immunohistochemical staining. RESULTS: Visfatin levels in GCF decreased sequentially from CP to CG and PH groups, with statistically significant differences (P < 0.05). The CP group exhibited the highest visfatin levels, while the PH group had the lowest. Gingival tissues showed a similar trend, with significant differences between groups (P < 0.001). Periodontal indexes were positively correlated with visfatin levels in both GCF and gingival tissues (P < 0.001). A strong positive correlation was observed between visfatin levels in GCF and gingival tissues (rs = 0.772, P < 0.001). CONCLUSION: Greater periodontal destruction corresponded to higher visfatin levels in GCF and gingival tissues, indicating their potential collaboration in damaging periodontal tissues. Visfatin emerges as a promising biomarker for periodontitis and may play a role in its pathogenesis.

Traffic trajectory data analysis technology based on HMM model map matching algorithm.

The rapid growth of traffic trajectory data and the development of positioning technology have driven the demand for its analysis. However, in the current application scenarios, there are some problems such as the deviation between positioning data and real roads and low accuracy of existing trajectory data traffic prediction models. Therefore, a map matching algorithm based on hidden Markov models is proposed in this study. The algorithm starts from the global route, selects K nearest candidate paths, and identifies candidate points through the candidate paths. It uses changes in speed, angle, and other information to generate a state transition matrix that match trajectory points to the actual route. When processing trajectory data in the experiment, K = 5 is selected as the optimal value, the algorithm takes 51 ms and the accuracy is 95.3%. The algorithm performed well in a variety of road conditions, especially in parallel and mixed road sections, with an accuracy rate of more than 96%. Although the time loss of this algorithm is slightly increased compared with the traditional algorithm, its accuracy is stable. Under different road conditions, the accuracy of the algorithm is 98.3%, 97.5%, 94.8% and 96%, respectively. The accuracy of the algorithm based on traditional hidden Markov models is 95.9%, 95.7%, 95.4% and 94.6%, respectively. It can be seen that the accuracy of the algorithm designed has higher precision. The experiment proves that the map matching algorithms based on hidden Markov models is superior to other algorithms in terms of matching accuracy. This study makes the processing of traffic trajectory data more accurate.

Stromal lenticule addition keratoplasty with corneal crosslinking for corneal ectasia secondary to FS-LASIK: a case series.

AIM: To explore the clinical efficacy and safety of stromal lenticule addition keratoplasty (SLAK) with corneal crosslinking (CXL) on patients with corneal ectasia secondary to femtosecond laser-assisted in situ keratomileusis (FS-LASIK). METHODS: A series of 5 patients undertaking SLAK with CXL for the treatment of corneal ectasia secondary to FS-LASIK were followed for 4-9mo. The lenticules were collected from patients undertaking small incision lenticule extraction (SMILE) for the correction of myopia. Adding a stromal lenticule was aimed at improving the corneal thickness for the safe application of crosslinking and compensating for the thin cornea to improve its mechanical strength. RESULTS: All surgeries were conducted successfully with no significant complications. Their best corrected visual acuity (BCVA) ranged from 0.05 to 0.8-2 before surgery. The pre-operational total corneal thickness ranged from 345-404 µm and maximum keratometry (Kmax) ranged from 50.8 to 86.3. After the combination surgery, both the corneal keratometry (range 55.9 to 92.8) and total corneal thickness (range 413-482 µm) significantly increased. Four out of 5 patients had improvement of corneal biomechanical parameters (reflected by stiffness parameter A1 in Corvis ST). However, 3 patients showed decreased BCVA after surgery due to the development of irregular astigmatism and transient haze. Despite the onset of corneal edema right after SLAK, the corneal topography and thickness generally stabilized after 3mo. CONCLUSION: SLAK with CXL is a potentially beneficial and safe therapy for advanced corneal ectasia. Future work needs to address the poor predictability of corneal refractometry and compare the outcomes of different surgical modes.

Superfast Zincophilic Ion Conductor Enables Rapid Interfacial Desolvation Kinetics for Low-Temperature Zinc Metal Batteries.

Low-temperature rechargeable aqueous zinc metal batteries (AZMBs) as highly promising candidates for energy storage are largely hindered by huge desolvation energy barriers and depressive Zn2+ migration kinetics. In this work, a superfast zincophilic ion conductor of layered zinc silicate nanosheet (LZS) is constructed on a metallic Zn surface, as an artificial layer and ion diffusion accelerator. The experimental and simulation results reveal the zincophilic ability and layer structure of LZS not only promote the desolvation kinetics of [Zn(H2O)6]2+ but also accelerate the Zn2+ transport kinetics across the anode/electrolyte interface, guiding uniform Zn deposition. Benefiting from these features, the LZS-modified Zn anodes showcase long-time stability (over 3300 h) and high Coulombic efficiency with ≈99.8% at 2 mA cm-2, respectively. Even reducing the environment temperature down to 0 °C, ultralong cycling stability up to 3600 h and a distinguished rate performance are realized. Consequently, the assembled Zn@LZS//V2O5-x full cells deliver superior cyclic stability (344.5 mAh g-1 after 200 cycles at 1 A g-1) and rate capability (285.3 mAh g-1 at 10 A g-1) together with a low self-discharge rate, highlighting the bright future of low-temperature AZMBs.