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.

Development and external validation of a multidimensional deep learning model to dynamically predict kidney outcomes in IgA nephropathy.

BACKGROUND: Accurately predicting kidney outcomes in IgA nephropathy is crucial for clinical decision making. Insufficient use of longitudinal data in previous studies has limited the accuracy and interpretability of prediction models for failing to reflect the chronic nature of IgA nephropathy. This study aimed at establishing a multivariable dynamic deep learning model using comprehensive longitudinal data for the prediction of kidney outcomes in IgA nephropathy. METHODS: In this retrospective cohort study of 2,056 IgA nephropathy patients at 18 kidney centers, a total of 28,317 data points were collected by the sliding window method. Among them, 15,462 windows in a single center were randomly assigned to training (80%) and validation (20%) sets while 8797 windows in 18 kidney centers were assigned to an independently test set. Interpretable Multi-Variable Long Short-Term Memory (IMV-LSTM), a deep learning model, was implemented to predict kidney outcomes (kidney failure or 50% decline in kidney function) based on time-invariant variables measured at biopsy and time-variant variables measured during follow-up. Risk performance was evaluated using Kaplan-Meier analysis and the C statistic. Trajectory analysis was performed to assess the various trends of clinical variables during follow-up. RESULTS: The model achieved a higher C statistic (0.93; 95% CI, 0.92-0.95) on the test set than the XGBoost prediction model that we developed in a previous study using only baseline information (C statistic, 0.84; 95% CI, 0.80-0.88). Kaplan-Meier analysis showed that groups with lower predicted risks from the full model survived longer than groups with higher risks. Time-variant variables demonstrated higher importance scores than time-invariant variables. Within time-variant variables, more recent measurements showed higher importance scores. Further interpretation showed that certain trajectory groups of time-variant variables such as serum creatinine and urine protein were associated with elevated risks of adverse outcomes. CONCLUSIONS: In IgA nephropathy, a deep learning model can be used to accurately and dynamically predict kidney prognosis based on longitudinal data, and time-variant variables show strong ability to predict kidney outcome.

Molecular characteristics of Echinococcus multilocularis FABP1 and its regulatory functions on murine macrophages.

Fatty acid binding proteins (FABPs) have emerged as attractive vaccination candidates for several platyhelminth species. To explore the physiological functions of Echinococcus multilocularis (E. multilocularis) FABP, the molecular characteristics of EmFABP1 were analyzed by online software, and the regulatory roles of rEmFABP1 protein in murine macrophages were further investigated. The emfabp1 gene encodes 133 amino acids with the characteristic ß-barrel shape of the cytoplasmic FABP family. Natural EmFABP1 protein is predominantly expressed in protoscoleces tegument and germinal layer cells and is also detected in cyst fluid and exosomes of E. multilocularis. rEmFABP1 protein demonstrated a notable suppression of phagocytic activity and nitric oxide production in murine macrophages. Additionally, the protein was observed to promote apoptosis and regulate cytokine expression in macrophages. These findings suggested that E. multilocularis FABP1 is critical in modifying macrophage physiological processes and that this protein may have immunomodulatory roles during infection.

A novel score for early prediction of urinary tract infection risk in patients with acute ischemic stroke: a nomogram-based retrospective cohort study.

This study aimed to construct and externally validate a user-friendly nomogram-based scoring model for predicting the risk of urinary tract infections (UTIs) in patients with acute ischemic stroke (AIS). A retrospective real-world cohort study was conducted on 1748 consecutive hospitalized patients with AIS. Out of these patients, a total of 1132 participants were ultimately included in the final analysis, with 817 used for model construction and 315 utilized for external validation. Multivariate regression analysis was applied to develop the model. The discriminative capacity, calibration ability, and clinical effectiveness of the model were evaluated. The overall incidence of UTIs was 8.13% (92/1132), with Escherichia coli being the most prevalent causative pathogen in patients with AIS. After multivariable analysis, advanced age, female gender, National Institute of Health Stroke Scale (NIHSS) score ≥ 5, and use of urinary catheters were identified as independent risk factors for UTIs. A nomogram-based SUNA model was constructed using these four factors (Area under the receiver operating characteristic curve (AUC) = 0.810), which showed good discrimination (AUC = 0.788), calibration, and clinical utility in the external validation cohort. Based on four simple and readily available factors, we derived and externally validated a novel and user-friendly nomogram-based scoring model (SUNA score) to predict the risk of UTIs in patients with AIS. The model has a good predictive value and provides valuable information for timely intervention in patients with AIS to reduce the occurrence of UTIs.

A rare case of TFEB/6p21/VEGFA-amplified renal cell carcinoma diagnosed by whole-exome sequencing: clinicopathological and genetic feature report and literature review.

BACKGROUND: TFEB/6p21/VEGFA-amplified renal cell carcinoma (RCC) is rare and difficult to diagnose, with diverse histological patterns and immunohistochemical and poorly defined molecular genetic characteristics. CASE PRESENTATION: We report a case of a 63-year-old male admitted in 2017 with complex histomorphology, three morphological features of clear cell, eosinophilic and papillary RCC and resembling areas of glomerular and tubular formation. The immunophenotype also showed a mixture of CD10 and P504s. RCC with a high suspicion of collision tumors was indicated according to the 2014 WHO classification system; no precise diagnosis was possible. The patient was diagnosed at a different hospital with poorly differentiated lung squamous cell carcinoma one year after RCC surgery. We exploited molecular technology advances to retrospectively investigate the patient's molecular genetic alterations by whole-exome sequencing. The results revealed a 6p21 amplification in VEGFA and TFEB gene acquisition absent in other RCC subtypes. Clear cell, papillary, chromophobe, TFE3-translocation, eosinophilic solid and cystic RCC were excluded. Strong TFEB and Melan-A protein positivity prompted rediagnosis as TFEB/6p21/VEGFA-amplified RCC as per 2022 WHO classification. TMB-L (low tumor mutational load), CCND3 gene acquisition and MRE11A and ATM gene deletion mutations indicated sensitivity to PD-1/PD-L1 inhibitor combinations and the FDA-approved targeted agents Niraparib (Grade C), Olaparib (Grade C), Rucaparib (Grade C) and Talazoparib (Class C). GO (Gene Ontology) and KEGG enrichment analyses revealed major mutations and abnormal CNVs in genes involved in biological processes such as the TGF-ß, Hippo, E-cadherin, lysosomal biogenesis and autophagy signaling pathways, biofilm synthesis cell adhesion substance metabolism regulation and others. We compared TFEB/6p21/VEGFA-amplified with TFEB-translocated RCC; significant differences in disease onset age, histological patterns, pathological stages, clinical prognoses, and genetic characteristics were revealed. CONCLUSION: We clarified the patient's challenging diagnosis and discussed the clinicopathology, immunophenotype, differential diagnosis, and molecular genetic information regarding TFEB/6p21/VEGFA-amplified RCC via exome analysis and a literature review.

Competitive Inhibition of Okanin against Plasmodium falciparum Tyrosyl-tRNA Synthetase.

Malaria is a severe disease that presents a significant threat to human health. As resistance to current drugs continues to increase, there is an urgent need for new antimalarial medications. Aminoacyl-tRNA synthetases (aaRSs) represent promising targets for drug development. In this study, we identified Plasmodium falciparum tyrosyl-tRNA synthetase (PfTyrRS) as a potential target for antimalarial drug development through a comparative analysis of the amino acid sequences and three-dimensional structures of human and plasmodium TyrRS, with particular emphasis on differences in key amino acids at the aminoacylation site. A total of 2141 bioactive compounds were screened using a high-throughput thermal shift assay (TSA). Okanin, known as an inhibitor of LPS-induced TLR4 expression, exhibited potent inhibitory activity against PfTyrRS, while showing limited inhibition of human TyrRS. Furthermore, bio-layer interferometry (BLI) confirmed the high affinity of okanin for PfTyrRS. Molecular dynamics (MD) simulations highlighted the stable conformation of okanin within PfTyrRS and its sustained binding to the enzyme. A molecular docking analysis revealed that okanin binds to both the tyrosine and partial ATP binding sites of the enzyme, preventing substrate binding. In addition, the compound inhibited the production of Plasmodium falciparum in the blood stage and had little cytotoxicity. Thus, okanin is a promising lead compound for the treatment of malaria caused by P. falciparum.

Coronary sodium [18F]fluoride activity predicts outcomes post-CABG: a comparative evaluation with conventional metrics.

PURPOSE: The value of preoperative multidisciplinary approach remains inadequately delineated in forecasting postoperative outcomes of patients undergoing coronary artery bypass grafting (CABG). Herein, we aimed to ascertain the efficacy of multi-modality cardiac imaging in predicting post-CABG cardiovascular outcomes. METHODS: Patients with triple coronary artery disease underwent cardiac sodium [18F]fluoride ([18F]NaF) positron emission tomography/computed tomography (PET/CT), coronary angiography, and CT-based coronary artery calcium scoring before CABG. The maximum coronary [18F]NaF activity (target-to-blood ratio [TBR]max) and the global coronary [18F]NaF activity (TBRglobal) was determined. The primary endpoint was perioperative myocardial infarction (PMI) within 7-day post-CABG. Secondary endpoint included major adverse cardiac and cerebrovascular events (MACCEs) and recurrent angina. RESULTS: This prospective observational study examined 101 patients for a median of 40 months (interquartile range: 19-47 months). Both TBRmax (odds ratio [OR] = 1.445; p = 0.011) and TBRglobal (OR = 1.797; P = 0.018) were significant predictors of PMI. TBRmax>3.0 (area under the curve [AUC], 0.65; sensitivity, 75.0%; specificity, 56.8%; p = 0.036) increased PMI risk by 3.661-fold, independent of external confounders. Kaplan-Meier test revealed a decrease in MACCE survival rate concomitant with an escalating TBRmax. TBRmax>3.6 (AUC, 0.70; sensitivity, 76.9%; specificity, 73.9%; p = 0.017) increased MACCEs risk by 5.520-fold. Both TBRmax (hazard ratio [HR], 1.298; p = 0.004) and TBRglobal (HR = 1.335; p = 0.011) were significantly correlated with recurrent angina. No significant associations were found between CAC and SYNTAX scores and between PMI occurrence and long-term MACCEs. CONCLUSION: Quantification of coronary microcalcification activity via [18F]NaF PET displayed a strong ability to predict early and long-term post-CABG cardiovascular outcomes, thereby outperforming conventional metrics of coronary macrocalcification burden and stenosis severity. TRIAL REGISTRATION: The trial was registered with the Chinese Clinical Trial Committee (number: ChiCTR1900022527; URL: www.chictr.org.cn/showproj.html?proj=37933 ).

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.

Risk Factors of New Symptomatic Fractures after Vertebroplasty: A Retrospective Cohort Study of 268 Patients with Painful Osteoporotic Vertebral Compression Fracture.

OBJECTIV: To evaluate the risk factors of new osteoporotic vertebral compression fractures (OVCFs) after percutaneous vertebroplasty (PVP). METHODS: From January 2016 to November 2019, patients suffering from OVCFs were retrospectively reviewed. The independent influence factors for new OVCFs after PVP were assessed, from following variables: age, sex, BMI, BMD, history of alcoholism, smoking, hypertension, diabetes, glucocorticoid use, and prior vertebral fractures, the number of initial fractures, mean cement volume, method of puncture, D-type of cement leakage and regular anti-osteoporosis treatment. RESULTS: A total of 268 patients with 347 levels met the inclusion criteria and were finally included in this study. 49 levels of new OVCFs among 33 patients (12.31%) were observed during the follow-up period. It indicated that female (Adjusted OR: 6.812, 95%CI: [1.096, 42.337], P = 0.040), lower BMD (Adjusted OR: 0.477, 95%CI: [0.300, 0.759], P = 0.002), prior vertebral fractures (Adjusted OR: 16.145, 95%CI: [5.319, 49.005], P = 0.000), and regular anti-osteoporosis treatment (Adjusted OR: 0.258, 95%CI: [0.086, 0.774], P = 0.016) were independent influence factors for new OVCF. The cut-off value of BMD to reach new OVCF was -3.350, with a sensitivity of 0.660 and a specificity of 0.848. CONCLUSION: Female, lower BMD (T- score of lumbar), prior vertebral fractures and regular anti-osteoporosis treatment were independent influencing factors. BMD (T- score of lumbar) lower than -3.350 would increase risk for new OVCF, and none osteoporotic treatment has detrimental effect on new onset fractures following PVP.

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.

Electric Polarization from a Many-Body Neural Network Ansatz.

Ab initio calculation of dielectric response with high-accuracy electronic structure methods is a long-standing problem, for which mean-field approaches are widely used and electron correlations are mostly treated via approximated functionals. Here we employ a neural network wave function ansatz combined with quantum Monte Carlo method to incorporate correlations into polarization calculations. On a variety of systems, including isolated atoms, one-dimensional chains, two-dimensional slabs, and three-dimensional cubes, the calculated results outperform conventional density functional theory and are consistent with the most accurate calculations and experimental data. Furthermore, we have studied the out-of-plane dielectric constant of bilayer graphene using our method and reestablished its thickness dependence. Overall, this approach provides a powerful tool to accurately describe electron correlation in the modern theory of polarization.

miR-455-3p regulates lymphangiogenesis in silicosis by regulating VEGF-C/VEGFR3.

Silicosis is a disease characterized by lung inflammation and fibrosis caused by long-term inhalation of free silicon dioxide (SiO2). Recent studies have found that a large number of lymphatic hyperplasia occurs during the occurrence and development of silicosis. miRNAs play an important role in lymphangiogenesis. However, the regulation and mechanism of miRNAs on lymphangiogenesis in silicosis remain unclear. In this study, lymphangiogenesis was observed in silicosis rats, and VEGF-C-targeted miRNAs were screened, and the effect of miRNAs on the formation of human lymphatic endothelial cells (HLECs) tubular structure was investigated in vitro. The results showed that SiO2 promoted the expressions of Collagen Ι and α-SMA, TNF-α, IL-6 and VEGF-C increased first and then decreased, and promoted the formation of lymphatic vessels. Bioinformatics methods screened miR-455-3p for targeted binding to VEGF-C, and dual luciferase reporter genes confirmed VEGF-C as the target gene of miR-455-3p, and miR-455-3p was down-regulated in the lung tissue of silicosis rats. Transfection of miR-455-3p Inhibitors down-regulated the expression level of miR-455-3p and up-regulated the expression levels of VEGF-C and VEGFR-3 in HLECs, enhanced migration ability and increased tube formation. Transfection of miR-455-3p Mimics showed an opposite trend. These results suggest that miR-455-3p further regulates the tubular structure formation of HLECs by regulating VEGF-C/VEGFR3. Therefore, targeting miR-455-3p may provide a new therapeutic strategy for SiO2-induced silicosis injury.

Integrated machine learning reveals aquatic biological integrity patterns in semi-arid watersheds.

Semi-arid regions present unique challenges for maintaining aquatic biological integrity due to their complex evolutionary mechanisms. Uncovering the spatial patterns of aquatic biological integrity in these areas is a challenging research task, especially under the compound environmental stress. Our goal is to address this issue with a scientifically rigorous approach. This study aims to explore the spatial analysis and diagnosis method of aquatic biological based on the combination of machine learning and statistical analysis, so as to reveal the spatial differentiation patterns and causes of changes of aquatic biological integrity in semi-arid regions. To this end, we have introduced an innovative approach that combines XGBoost-SHAP and Fuzzy C-means clustering (FCM), we successfully identified and diagnosed the spatial variations of aquatic biological integrity in the Wei River Basin (WRB). The study reveals significant spatial variations in species number, diversity, and aquatic biological integrity of phytoplankton, serving as a testament to the multifaceted responses of biological communities under the intricate tapestry of environmental gradients. Delving into the depths of the XGBoost-SHAP algorithm, we discerned that Annual average Temperature (AT) stands as the pivotal driver steering the spatial divergence of the Phytoplankton Integrity Index (P-IBI), casting a positive influence on P-IBI when AT is below 11.8 °C. The intricate interactions between hydrological variables (VF and RW) and AT, as well as between water quality parameters (WT, NO3-N, TP, COD) and AT, collectively sculpt the spatial distribution of P-IBI. The fusion of XGBoost-SHAP with FCM unveils pronounced north-south gradient disparities in aquatic biological integrity across the watershed, segmenting the region into four distinct zones. This establishes scientific boundary conditions for the conservation strategies and management practices of aquatic ecosystems in the region, and its flexibility is applicable to the analysis of spatial heterogeneity in other complex environmental contexts.

The impact of frailty on clinical outcomes of older patients undergoing enhanced recovery after lumbar fusion surgery: A prospective cohort study.

BACKGROUND: Frailty is recognized as a surrogate for physiological age and has been established as a valid and independent predictor of postoperative morbidity, mortality, and complications. ERAS can enhance surgical safety by minimizing stress responses in frail patients, enabling surgeons to discharge patients earlier. However, the question of whether and to what extent the frailty impacts the post-ERAS outcomes in older patients remains. MATERIALS AND METHODS: An evidence-based ERAS program was implemented in our center from January 2019. This is a prospective cohort study of patients aged ≥75 years who underwent open transforaminal lumbar interbody fusion (TLIF) for degenerative spine disease from April 2019 to October 2021. Frailty was assessed with the Fried frailty scale (FP scale), and patients were categorized as non/prefrail (FP 0-2) or frail (FP ≥ 3). The preoperative variables, operative data, postoperative outcomes and follow-up information were compared between the two groups. Univariate and multivariate logistic regression analyses were used to identify risk factors for 90-day major complications and prolonged length of hospital stay (LOS) after surgery. RESULTS: A total of 245 patients (age of 79.8 ± 3.4 yr) who had a preoperative FP score recorded and underwent scheduled TLIF surgery were included in the final analysis. Comparisons between non-frail and prefrail/frail patients revealed no significant difference in age, sex, and surgery-related variables. Even after adjusting for multiple comparisons, the association between Fried frailty and ADL-dependency, IADL-dependency, and malnutrition remained significant. Preoperative frailty was associated with increased rates of postoperative adverse events. A higher CCI grade was an independent predictor for 90-day major complications, while Fried frailty and MNA-SF scores <12 were predictive of poor postoperative recovery. CONCLUSION: Frail older patients had more adverse post-ERAS outcomes after TLIF compared to non/prefrail older patients. Continued research and multidisciplinary collaboration will be essential to refine and optimize protocols for surgical care in frail older adults.

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.

InSb all-dielectric metasurface for ultrahigh efficient Si-based mid-infrared detection.

Mid-infrared (MIR) Si-based optoelectronics has wide potential applications, and its design requires simultaneous consideration of device performance optimization and the feasibility of heterogeneous integration. The emerging interest in all-dielectric metasurfaces for optoelectronic applications stems from their exceptional ability to manipulate light. In this Letter, we present our research on an InSb all-dielectric metasurface designed to achieve ultrahigh absorptivity within the 5-5.5 µm wavelength range. By integrating an InSb nanodisk array layer on a Si platform using wafer bonding and heteroepitaxial growth, we demonstrate three kinds of metasurface with high absorptivity of 98.36%, 99.28%, and 99.18%. The enhanced absorption is mainly contributed by the Kerker effect and the anapole state and the peak, with the added flexibility of tuning both the peak and bandwidth of absorption by altering the metasurface parameters. Our findings provide an alternative scheme to develop high-performance detectors and absorbers for MIR silicon photonics.

Comprehensive Glycosylation Characterization of Recombinant Human Erythropoietin by Electron-Activated Dissociation Mass Spectrometry.

Recombinant human erythropoietin (rhEPO) is a glycoprotein that acts as the main hormone involved in regulating red blood cell production to treat anemia caused by chronic kidney disease or chemotherapy, which has three N-glycosylation sites and one O-glycosylation site. It contains a variety of different glycosylation modifications, such as sialyation, O-acetylation on sialic acids, etc., which causes a big challenge for the glycosylation analysis of rhEPO. In this study, a liquid chromatography-mass spectrometry (LC-MS) method combined with electron-activated dissociation (EAD) technology was used in qualitative and quantitative characterization of rhEPO N-glycosylation and O-glycosylation in just one injection. The usage of EAD not only generated abundant MS/MS fragment ions of glycopeptides and improved the MS/MS sequence coverage but also preserved the glycan structures in the MS/MS fragment ions and the integrity of the glycosidic bond between the glycans and peptides. Three N-glycosylation sites (N24, N38, and N83) and one O-glycosylation site (S126) of rhEPO samples were successfully identified. Among them, the glycosylation ratios of N24, N38, and N83 sites were 82.7%, 100%, and 100% respectively, and 15, 10, and 12 different N-glycans could be identified at the glycopeptide level. The total average number of sialic acids, N-hydroxyacetylneuraminoic acid, and O-acetylation on sialic acid were 7.28, 4.21, and 0.66 at the intact protein level, respectively. For O-glycosylation site S126, O-glycosylation ratios analyzed at the intact protein level and the glycopeptide level were 80.2% and 80.3%, respectively, and two O-glycans were identified, including Core1_S1 and Core1_S2. This study also compared the difference of the glycans and their relative contents in batch-to-batch rhEPO samples. The results proved that the workflow using EAD fragmentation in LC-MS method could be effectively applied for characterizing the glycosylation analysis of rhEPO samples and batch-to-batch consistency analysis, which would help to reasonably guide the optimization of rhEPO production process.

HAP40 modulates mutant Huntingtin aggregation and toxicity in Huntington's disease mice.

Huntington's disease (HD) is a monogenic neurodegenerative disease, caused by the CAG trinucleotide repeat expansion in exon 1 of the Huntingtin (HTT) gene. The HTT gene encodes a large protein known to interact with many proteins. Huntingtin-associated protein 40 (HAP40) is one that shows high binding affinity with HTT and functions to maintain HTT conformation in vitro. However, the potential role of HAP40 in HD pathogenesis remains unknown. In this study, we found that the expression level of HAP40 is in parallel with HTT but inversely correlates with mutant HTT aggregates in mouse brains. Depletion of endogenous HAP40 in the striatum of HD140Q knock-in (KI) mice leads to enhanced mutant HTT aggregation and neuronal loss. Consistently, overexpression of HAP40 in the striatum of HD140Q KI mice reduced mutant HTT aggregation and ameliorated the behavioral deficits. Mechanistically, HAP40 preferentially binds to mutant HTT and promotes Lysine 48-linked ubiquitination of mutant HTT. Our results revealed that HAP40 is an important regulator of HTT protein homeostasis in vivo and hinted at HAP40 as a therapeutic target in HD treatment.