Catalytic pyrolysis and in situ carbonization of walnut shells: poly-generation and enhanced electrochemical performance of carbons.

In this study, walnut shell (WS) was used as feedstock, incorporating lithium carbonate (LC), sodium carbonate (SC), potassium carbonate (PC), and potassium hydroxide (PH) as pyrolysis catalysts and carbonization activators. A one-step method that allows catalytic pyrolysis and carbonization to be carried out consecutively under their respective optimal conditions is employed, enabling the concurrent production of high-quality pyrolysis oil, pyrolysis gas, and carbon materials from biomass conversion. The effects of LC, SC, PC, and PH on the yield and properties of products derived from WS pyrolysis as well as on the properties and performance of the resulting carbon materials were examined. The results indicated that the addition of LC, SC, PC, and PH enhanced the secondary cracking of tar, leading to increased solid and gas yields from WS. Additionally, it increased the production of phenolic compounds in bio-oil and H2 in syngas, concurrently yielding a walnut shell-based carbon material exhibiting excellent electrochemical performance. Specifically, when PC was used as an additive, the phenolic content in the pyrolysis oil increased by 27.64% compared to that without PC, reaching 74.9%, but the content of ketones, acids, aldehydes, and amines decreased. The hydrogen content increased from 2.5% (without the addition of PC) to 12.75%. The resulting carbon (WSC-PC) displayed a specific surface area of 598.6 m2 g-1 and achieved a specific capacitance of 245.18 F g-1 at a current density of 0.5 A g-1. Even after 5000 charge and discharge cycles at a current density of 2 A g-1, the capacitance retention rate remained at 98.16%. This method effectively enhances the quality of the biomass pyrolysis oil, gas, and char, contributing to the efficient and clean utilization of biomass in industrial applications.

Bis-tridentate Iridium(III) Complex with the N-Heterocyclic Carbene Ligand as a Novel Efficient Electrochemiluminescence Emitter for the Sandwich Immunoassay of the HHV-6A Virus.

Human herpesvirus type 6A (HHV-6A) can cause a series of immune and neurological diseases, and the establishment of a sensitive biosensor for the rapid detection of HHV-6A is of great significance for public health and safety. Herein, a bis-tridentate iridium complex (BisLT-Ir-NHC) comprising the N-heterocyclic carbene (NHC) ligand as a novel kind of efficient ECL luminophore has been unprecedently reported. Based on its excellent ECL properties, a new sensitive ECL-based sandwich immunosensor to detect the HHV-6A virus was successfully constructed by encapsulating BisLT-Ir-NHC into silica nanoparticles and embellishing ECL sensing interface with MXene@Au-CS. Notably, the immunosensor illustrated in this work not only had a wide linear range of 102 to 107 cps/µL but also showed outstanding recoveries (98.33-105.11%) in real human serum with an RSD of 0.85-3.56%. Undoubtedly, these results demonstrated the significant potential of the bis-tridentate iridium(III) complex containing an NHC ligand in developing ECL-based sensitive analytical methods for virus detection and exploring novel kinds of efficient iridium-based ECL luminophores in the future.

Influence of academic involution atmosphere on college students' stress response: the chain mediating effect of relative deprivation and academic involution.

BACKGROUND: In recent years, the phenomenon of academic involution atmosphere among college students has gradually attracted the focus of education and social circles. Thus, this study targets college students as the research object and constructs a hypothetical model to explore the relationship between academic involution atmosphere and college students' stress response, as well as the mediating role of relative deprivation and academic involution. METHODS: A survey was conducted on 1090 college students using the Academic Involution Atmosphere Scale, Relative Deprivation Scale, Personal Academic Involution Scale, and Stress Response Scale. RESULTS: The results show that: (1) Academic involution atmosphere, relative deprivation, and academic involution are significantly and positively correlated with stress response; (2) Academic involution atmosphere not only directly predicts college students' stress response, but also indirectly predicts them through relative deprivation and academic involution, respectively; (3) Relative deprivation and academic involution have a chain mediating effect between academic involution atmosphere and stress response. CONCLUSIONS: The findings of this study reveal the influence of academic involution atmosphere on college students' stress response and the mechanism, providing beneficial insights for reducing college students' stress response and maintaining their psychological well-being.

Bimetal doped Cu-Fe-ZIF-8/g-C3N4 nanocomposites for the adsorption of tetracycline hydrochloride from water.

Bimetal doped Cu-Fe-zeolitic imidazole framework-8 (ZIF-8)/graphitic carbon nitride (GCN) (Cu-Fe-ZIF-8/GCN) nanocomposites were prepared via one-pot and ion-exchange methods. The main influencing factors, such as adsorbent concentration, TC concentration, initial pH, and coexisting ions, were evaluated in detail. Due to the suitable pore structures and the presence of multiple interactions on the surface, the nanocomposite showed a high adsorption capacity up to 932 mg g-1 for tetracycline hydrochloride (TC), outperforming ZIF-8 by 4.8 times. The adsorption kinetics and adsorption isotherm were depicted in good detail using pseudo-second-order kinetic and Langmuir models, respectively. Thermodynamic calculation revealed that the adsorption of the nanocomposite under experimental conditions was a spontaneous heat absorption process, and was primarily driven by chemisorption. After four cycles of use, the nanocomposite retained 87.2% of its initial adsorption capacity, confirming its high reusability and broad application prospects in removing tetracycline-type pollutants from wastewater.

CRISPR-Cas12a based target recognition initiated duplex-specific nuclease enhanced fluorescence and colorimetric analysis of cell-free DNA (cfDNA).

The significant role of cell-free DNA (cfDNA) for disease diagnosis, including cancer, has garnered a lot of attention. The challenges of creating target-specific primers and the possibility of false-positive signals make amplification-based detection methods problematic. Fluorescent biosensors based on CRISPR-Cas have been widely established, however they still require an amplification step before they can be used for detection. To detect cfDNA, researchers have created a CRISPR-Cas12a-based nucleic acid amplification-free fluorescent biosensor that uses a combination of fluorescence and colorimetric signaling improved by duplex-specific nuclease (DSN). DSN-assisted signal recycling is initiated in H1@MBs when the target cfDNA activates the CRISPR-Cas12a complex, leading to the degradation of single-strand DNA (ssDNA) sequences. This method has an extremely high detection limit for the BRCA-1 breast cancer gene. In addition to measuring viral DNA in a field-deployable and point-of-care testing (POCT) platform, this fast and highly selective sensor can be used to evaluate additional nucleic acid biomarkers.

Ultrasensitive Electrochemiluminescence Biosensor to Detect Ampicillin Resistance Gene (ARGAMP) Based on a Novel Near-Infrared Ruthenium Carbene Complex/TPrA/PEI Ternary ECL System.

The establishment of rapid target identification and analysis methods for antibiotic resistance genes (ARGs) is urgently needed. In this study, we unprecedently designed a target-catalyzed hairpin assembly (CHA) electrochemiluminescent (ECL) biosensor for the ultrasensitive detection of ampicillin resistance genes (ARGAMP) based on a novel, efficient near-infrared ruthenium carbene complex/TPrA/PEI ternary ECL system with low oxidation potential. The ternary NIR-ECL system illustrated in this work displayed double ECL intensity in comparison with their corresponding traditional binary ECL system. The as-prepared ECL biosensor illustrated in this work demonstrates highly selective and sensitive determination of ARGAMP from 1 fM to 1 nM and a low detection limit of 0.23 fM. Importantly, it also exhibits good accuracy and stabilities to identify ARGAMP in plasmid and bacterial genome DNA, which demonstrates its excellent reliability and great potential in detecting ARGAMP in real environmental samples.

A model for the diagnosis of anxiety in patients with epilepsy based on phase locking value and Lempel-Ziv complexity features of the electroencephalogram.

OBJECTIVE: Anxiety disorders (AD) are critical factors that significantly (about one-fifth) impact the quality of life (QoL) in patients with epilepsy (PWE). Objective diagnostic methods have contributed to the identification of PWE susceptible to AD. This study aimed to identify AD in PWE by constructing a diagnostic model based on the phase locking value (PLV) and Lempel-Ziv Complexity (LZC) features of the electroencephalogram (EEG). METHODS: EEG data from 131 patients with epilepsy (PWE) were enrolled in this study. Patients were divided into two groups, anxiety disorder (AD, n = 61) and non-anxiety disorder (NAD, n = 70), according to the Hamilton Rating Scale for Anxiety (HAM-A). Support vector machine (SVM) and K-Nearest-Neighbor(KNN) algorithms were used to construct three models - the PLVEEG, LZCEEG, and PLVEEG + LZCEEG feature models. Finally, the area under the receiver operating characteristic curve (AUC) and statistical analyses were performed to evaluate the model performance. RESULTS: The efficiency of the KNN-based PLCEEG + LZCEEG feature model was the best, and the accuracy, precision, recall, F1-score, and AUC of the model after five-fold cross-validations scores were 87.89 %, 82.27 %, 98.33 %, 88.95 %, and 0.89, respectively. When the model efficiency was optimal, 29 EEG features were suggested. Further analysis of these features indicated 22 EEG features that were significantly different between the two groups, including 50 % features of the alpha (α)-band. CONCLUSIONS: The PLVEEG + LZCEEG model features can identify AD in PWE. The PLVEEG and LZCEEG characteristics of the α-band may further be explored as potential biomarkers for AD in PWE.

Active natural compounds perturb the melanoma risk-gene network.

Cutaneous melanoma is an aggressive type of skin cancer with a complex genetic landscape caused by the malignant transformation of melanocytes. This study aimed at providing an in silico network model based on the systematic profiling of the melanoma-associated genes considering germline mutations, somatic mutations, and genome-wide association study signals accounting for a total of 232 unique melanoma risk genes. A protein-protein interaction network was constructed using the melanoma risk genes as seeds and evaluated to describe the functional landscape in which the melanoma genes operate within the cellular milieu. Not only were the majority of the melanoma risk genes able to interact with each other at the protein level within the core of the network, but this showed significant enrichment for genes whose expression is altered in human melanoma specimens. Functional annotation showed the melanoma risk network to be significantly associated with processes related to DNA metabolism and telomeres, DNA damage and repair, cellular ageing, and response to radiation. We further explored whether the melanoma risk network could be used as an in silico tool to predict the efficacy of anti-melanoma phytochemicals, that are considered active molecules with potentially less systemic toxicity than classical cytotoxic drugs. A significant portion of the melanoma risk network showed differential expression when SK-MEL-28 human melanoma cells were exposed to the phytochemicals harmine and berberine chloride. This reinforced our hypothesis that the network modeling approach not only provides an alternative way to identify molecular pathways relevant to disease but it may also represent an alternative screening approach to prioritize potentially active compounds.

Simultaneous high-resolution whole-brain MR spectroscopy and [18F]FDG PET for temporal lobe epilepsy.

PURPOSE: Precise lateralizing the epileptogenic zone in patients with drug-resistant mesial temporal lobe epilepsy (mTLE) remains challenging, particularly when routine MRI scans are inconclusive (MRI-negative). This study aimed to investigate the synergy of fast, high-resolution, whole-brain MRSI in conjunction with simultaneous [18F]FDG PET for the lateralization of mTLE. METHODS: Forty-eight drug-resistant mTLE patients (M/F 31/17, age 12-58) underwent MRSI and [18F]FDG PET on a hybrid PET/MR scanner. Lateralization of mTLE was evaluated by visual inspection and statistical classifiers of metabolic mappings against routine MRI. Additionally, this study explored how disease status influences the associations between altered N-acetyl aspartate (NAA) and FDG uptake using hierarchical moderated multiple regression. RESULTS: The high-resolution whole-brain MRSI data offers metabolite maps at comparable resolution to [18F]FDG PET. Visual examinations of combined MRSI and [18F]FDG PET showed an mTLE lateralization accuracy rate of 91.7% in a 48-patient cohort, surpassing routine MRI (52.1%). Notably, out of 23 MRI-negative mTLE, combined MRSI and [18F]FDG PET helped detect 19 cases. Logistical regression models combining hippocampal NAA level and FDG uptake improved lateralization performance (AUC=0.856), while further incorporating extrahippocampal regions such as amygdala, thalamus, and superior temporal gyrus increased the AUC to 0.939. Concurrent MRSI/PET revealed a moderating influence of disease duration and hippocampal atrophy on the association between hippocampal NAA and glucose uptake, providing significant new insights into the disease's trajectory. CONCLUSION: This paper reports the first metabolic imaging study using simultaneous high-resolution MRSI and [18F]FDG PET, which help visualize MRI-unidentifiable lesions and may thus advance diagnostic tools and management strategies for drug-resistant mTLE.

Spatial distribution prediction of soil heavy metals based on sparse sampling and multi-source environmental data.

Predicting the precise spatial distribution of heavy metals in soil is crucial, especially in the fields of environmental management and remediation. However, achieving accurate spatial predictions of soil heavy metals becomes quite challenging when the number of soil sampling points is relatively limited. To address this challenge, this study proposes a hybrid approach, namely, Light Gradient Boosting Machine plus Ordinary Kriging (LGBK), for predicting the spatial distribution of soil heavy metals. A total of 137 soil samples were collected from the Shengli Coal-mine Base in Inner Mongolia, China, and their heavy metal concentrations were measured. Leveraging environmental covariates and soil heavy metal data, we constructed the predictive model. Experimental results demonstrate that, in comparison to traditional models, LGBK exhibits superior predictive performance. For copper (Cu), zinc (Zn), chromium (Cr), and arsenic (As), the coefficients of determination (R²) from the cross-validation results are 0.65, 0.52, 0.57, and 0.63, respectively. Moreover, the LGBK model excels in capturing intricate spatial features in heavy metal distribution. It accurately forecasts trends in heavy metal distribution that closely align with actual measurements. ENVIRONMENTAL IMPLICATION: This study introduces a novel method, LGBK, for predicting the spatial distribution of soil heavy metals. This method yields higher-precision predictions even with a limited number of sampling points. Furthermore, the study analyzes the spatial distribution characteristics of Cu, Zn, Cr, and As in the grassland coal-mine base, along with the key environmental factors influencing their spatial distribution. This research holds significant importance for the environmental regulation and remediation of heavy metal pollution.

Novel Electrochemiluminescent Biosensor to Ultrasensitively Detect U94 Gene in Human Herpesvirus 6 Using Metal-Organic Framework-Based Nanoemitters Comprising Iridium(III) Complexes via One-Pot Coordination Reaction Strategy.

The detection of the U94 gene in human herpesvirus 6 is crucial for early diagnosis of HHV-6 infections, which could induce acute febrile illness in infants. In this work, the first ultrasensitive electrochemiluminescence (ECL) biosensor for detecting U94 gene in Human Herpesvirus 6 was successfully designed by utilizing efficient novel metal-organic framework (MOF)-based ECL nanoemitters comprising iridium(III) complexes (Ir-ZIF-8-NH2) synthesized via one-pot coordination reaction strategy as an ECL indicator and a target-catalyzed hairpin assembly (CHA) signal amplification strategy. The as-prepared ECL indicator Ir-ZIF-8-NH2 exhibited an approximately 2.7-fold ECL intensity compared with its small molecular analogue of emissive iridium(III) complex named IrppymIM formed by in situ coordination reaction between iridium(III) solvent complex and imidazole ligands. In addition, a target-catalyzed hairpin assembly (CHA) strategy was employed to further improve the sensitivity of the proposed ECL biosensor, which demonstrated a wide linear range from 1 fM to 1 µM and the limit of detection as low as 0.113 fM (S/N = 3). Significantly, this biosensor was successfully applied to detect U94 gene in plasmids and real virus samples. The recoveries were in the range of 97.0-109.0% for plasmids and 95.7-107.5% for real virus samples with a relative standard deviation (RSD) of 1.87-2.53%. These satisfactory experimental results from the proposed ECL biosensor in this work would inevitably promote the development of new time/cost-effective and sensitive methods to detect HHV-6 with a major global health threat and substantial burden on healthcare in the future.

Activation extending based on long-range dependencies for weakly supervised semantic segmentation.

Weakly supervised semantic segmentation (WSSS) principally obtains pseudo-labels based on the class activation maps (CAM) to handle expensive annotation resources. However, CAM easily involves false and local activation due to the the lack of annotation information. This paper suggests weakly supervised learning as semantic information mining to extend object mask. We proposes a novel architecture to mining semantic information by modeling through long-range dependencies from in-sample and inter-sample. Considering the confusion caused by the long-range dependencies, the images are divided into blocks and carried out self-attention operation on the premise of fewer classes to obtain long-range dependencies, to reduce false predictions. Moreover, we perform global to local weighted self-supervised contrastive learning among image blocks, and the local activation of CAM is transferred to different foreground area. Experiments verified that superior semantic details and more reliable pseudo-labels are captured through these suggested modules. Experiments on PASCAL VOC 2012 demonstrated the proposed model achieves 76.6% and 77.4% mIoU in val and test sets, which is superior to the comparison baselines.

Screening of immune-related biological markers for aneurysmal subarachnoid hemorrhage based on machine learning approaches.

Background: Aneurysmal subarachnoid hemorrhage (aSAH) is a common hemorrhagic condition frequently encountered in the emergency department, which is characterized by high mortality and disability rates. However, the precise molecular mechanisms underlying the rupture of an aneurysm are still not fully understood. The primary objective of this study is to elucidate the fundamental molecular mechanisms underlying aSAH and provide novel therapeutic targets for the treatment of aSAH. Methods: The gene expression matrix of aSAH was downloaded from the Gene Expression Omnibus (GEO) database. In this study, we employed weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis (DEGs) screening to identify crucial modules and genes associated with aSAH. Furthermore, the evaluation of immune cell infiltration was conducted through the utilization of the single-sample gene set enrichment analysis (ssGSEA) technique and the CIBERSORT algorithm. The study utilized Gene Set Variation Analysis (GSVA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) to investigate and comprehend the fundamental biological pathways and mechanisms. Results: Using WGCNA, six gene co-expression modules were constructed. Among the identified modules, the yellow module, which encompasses 184 genes, demonstrated the most significant correlation with aSAH. Consequently, it was determined to be the central module responsible for governing the pathogenesis of aSAH. Additionally, the application of WGCNA, LASSO regression, and multiple factor logistic regression analysis revealed ARHGAP26 and SLMAP as the key genes associated with aSAH. Furthermore, the diagnostic efficacy of these pivotal genes in aSAH was confirmed through the use of receiver operating characteristic (ROC) curve analysis, validating their discriminative potential. Moreover, the utilization of GO and KEGG pathway analysis revealed a significant enrichment of inflammation-related signaling in aSAH. Conclusion: The genes ARHGAP26 and SLMAP were identified as significant predictors of aSAH. Accordingly, these genes demonstrate significant potential to function as novel biological markers and therapeutic targets for aSAH.

Clinical and Epidemiological Characteristics of Rhabdomyolysis: A Retrospective Study.

Background: Rhabdomyolysis (RM) refers to a clinical syndrome in which muscle cells are damaged by various causes and the clinical manifestations are mainly muscle pain, weakness, and dark urine. Acute kidney injury (AKI) is a serious complication of RM with complex mechanisms and high mortality. Therefore, understanding the pathogenesis and clinical manifestations, early diagnosis and treatment of RM are crucial to improve its prognosis. Method: Analysis of medical records of RM patients admitted to Tianjin Medical University General Hospital from October 2019 to October 2022. Statistical software SPSS 25.0 was used to analyze the data. The risk factors of RM-complicated AKI were analyzed by logistic regression. The receiver operating characteristic (ROC) curve was plotted, the area under the curve (AUC) was calculated, and the optimal cutoff value was determined by the Youden index. P < 0.05 indicates a statistically significant difference between the groups. Result: Among the 71 patients, the median age of the patients was 53.0 (30.0, 71.0) years and was 2.5 times higher in men than in women. Infection was the most common etiology. History of alcohol consumption, CK, and creatinine were independent influencing factors for AKI due to RM. Logistic regression analysis showed that CK combined with creatinine had a better predictive value than the single index. Conclusion: Our study revealed the clinical and laboratory characteristics of RM in the population attending the Tianjin Medical University General Hospital in the last three years, which is a reference for future multicenter, prospective studies.

The early diagnosis and pathogenic mechanisms of sepsis-related acute kidney injury.

Sepsis is a syndrome caused by an imbalance in the inflammatory response of the body caused by an infection that leads to organ dysfunction, with the kidney being one of the most commonly affected organs. Sepsis-related acute kidney injury (SAKI) is strongly linked to increased mortality and poor clinical outcomes. Early diagnosis and treatment can significantly reduce patient mortality. On the other hand, the pathogenesis of SAKI is not fully understood, and early diagnosis of SAKI is a clinical challenge. Therefore, the current review describes biomarkers of acute kidney injury in sepsis and discusses the various pathogenic mechanisms involved in the progression of acute kidney injury in sepsis to develop new clinical treatment avenues.

A novel efficient intramolecular self-enhanced water-soluble iridium-based electrochemiluminescence reagent and its analytical application in aptamer sensor.

In order to overcome the poor solubilities of iridium-based ECL luminophores and explore self-enhanced ECL luminophores, polyethyleneimine (PEI) covalently linked with iridium complex via amide bonds (abbreviated as Ir-PEI) as a new novel intramolecular self-enhanced water-soluble ECL reagent has been unprecedently designed and successfully synthesized in this work. The chemical structure data, FT-IR spectra, photophysical, electrochemical and electrochemiluminescence of this new ECL reagent have been well characterized. In addition, in order to investigate its properties in the real applications, a corresponding new sensitive and specific ECL-based aptasensor to monitor tetracycline (TET) residues in honey and lake water has been further constructed based on this novel self-enhanced reagent of Ir-PEI in this work. This as-prepared intramolecular self-enhanced water-soluble of Ir-PEI illustrated in this work would pave a new avenue to promote the analytical applications of iridium-based ECL luminophores in the future.

Delayed 18F-FDG PET imaging provides better metabolic asymmetry in potential epileptogenic zone in temporal lobe epilepsy.

Objective: To investigate the value of 18F-FDG positron emission tomography/computed tomography (PET/CT) two time point imaging for the identification of the potential epileptogenic zone (EZ) in temporal lobe epilepsy (TLE). Methods: Fifty-two patients with TLE were prospectively enrolled in the 18F-FDG PET/CT two time point imaging study. The early imaging was obtained approximately 40 min (43.44 ± 18.04 min) after 18F-FDG injection, and the delayed imaging was obtained about 2 to 3 h (160.46 ± 28.70 min) after the injection. Visual and semi-quantitative analysis of 18F-FDG uptake were performed at the two time points in EZ and contralateral symmetrical region. The mean standardized uptake value (SUVmean) of EZ and contralateral symmetrical region was calculated to determine the asymmetry index (AI) of the early and delayed images, as well as in the MRI positive and negative patient groups. Results: Semi-quantitative analysis demonstrated that AI of the early and delayed 18F-FDG PET/CT images was 13.47 ± 6.10 and 16.43 ± 6.66, respectively. The ΔAI was 2.95 ± 3.05 in 52 TLE patients between the two time points. The AI of the EZ was significantly elevated in delayed images compared to the early images (p < 0.001). The AI of delayed imaging was also significantly elevated compared to the early imaging in both MRI positive (ΔAI = 2.81 ± 2.54, p < 0.001) and MRI negative (ΔAI = 3.21 ± 3.91, p < 0.003) groups, and more pronounced in MRI negative group. Visual analysis also showed that the delayed imaging appeared to be superior to the early imaging for identification of potential EZ. Conclusion: Delayed 18F-FDG PET imaging provided significantly better than the early imaging in the identification of potential EZ, which can be valuable during epilepsy pre-surgical evaluation in patients with TLE.

The roles of interleukin-17A in risk stratification and prognosis of patients with sepsis-associated acute kidney injury.

BACKGROUND: The aim of this study was to evaluate the roles of interleukin (IL)-17A in risk stratification and prognosis of patients with sepsis-associated acute kidney injury (SAKI). METHODS: We enrolled 146 sepsis patients (84 non-SAKI and 62 SAKI patients) admitted to the emergency department from November 2020 to November 2021. Patients with SAKI were differentiated based on the severity of acute kidney injury. All clinical parameters were evaluated upon admission before administering antibiotic treatment. Inflammatory cytokines were assessed using flow cytometry and the Pylon 3D automated immunoassay system (ET Healthcare). In addition, a receiver operating characteristic (ROC) curve was utilized to determine the prognostic values of IL-17A in SAKI. RESULTS: The levels of creatinine, IL-2, IL-4, IL-6, IL-17A, tumor necrosis factor alpha, C-reactive protein, and procalcitonin (PCT) were significantly higher in the SAKI group than in the non-SAKI group (p < 0.05). The level of IL-17A revealed significant differences among stages 1, 2, and 3 in SAKI patients (p < 0.05). The mean levels of PCT, IL-4, and IL-17A were significantly higher in the non-survival group than in the survival group in SAKI patients (p < 0.05). In addition, the area under the ROC curve of IL-17A was 0.811. Moreover, the IL-17A cutoff for differentiating survivors from non-survivors was 4.7 pg/mL, of which the sensitivity and specificity were 77.4% and 71.0%, respectively. CONCLUSION: Elevated levels of IL-17A could predict that SAKI patients are significantly prone to worsening kidney injury with higher mortality. The usefulness of IL-17A in treating SAKI requires further research.

SCFusion: Infrared and Visible Fusion Based on Salient Compensation.

The aim of infrared and visible image fusion is to integrate the complementary information of the two modalities for high-quality fused images. However, many deep learning fusion algorithms have not considered the characteristics of infrared images in low-light scenes, leading to the problems of weak texture details, low contrast of infrared targets and poor visual perception in the existing methods. Therefore, in this paper, we propose a salient compensation-based fusion method that makes sufficient use of the characteristics of infrared and visible images to generate high-quality fused images under low-light conditions. First, we design a multi-scale edge gradient module (MEGB) in the texture mainstream to adequately extract the texture information of the dual input of infrared and visible images; on the other hand, the salient tributary is pre-trained by salient loss to obtain the saliency map based on the salient dense residual module (SRDB) to extract salient features, which is supplemented in the process of overall network training. We propose the spatial bias module (SBM) to fuse global information with local information. Finally, extensive comparison experiments with existing methods show that our method has significant advantages in describing target features and global scenes, the effectiveness of the proposed module is demonstrated by ablation experiments. In addition, we also verify the facilitation of this paper's method for high-level vision on a semantic segmentation task.

B1 mapping using pre-learned subspaces for quantitative brain imaging.

PURPOSE: To develop a machine learning-based method for estimation of both transmitter and receiver B1 fields desired for correction of the B1 inhomogeneity effects in quantitative brain imaging. THEORY AND METHODS: A subspace model-based machine learning method was proposed for estimation of B1t and B1r fields. Probabilistic subspace models were used to capture scan-dependent variations in the B1 fields; the subspace basis and coefficient distributions were learned from pre-scanned training data. Estimation of the B1 fields for new experimental data was achieved by solving a linear optimization problem with prior distribution constraints. We evaluated the performance of the proposed method for B1 inhomogeneity correction in quantitative brain imaging scenarios, including T1 and proton density (PD) mapping from variable-flip-angle spoiled gradient-echo (SPGR) data as well as neurometabolic mapping from MRSI data, using phantom, healthy subject and brain tumor patient data. RESULTS: In both phantom and healthy subject data, the proposed method produced high-quality B1 maps. B1 correction on SPGR data using the estimated B1 maps produced significantly improved T1 and PD maps. In brain tumor patients, the proposed method produced more accurate and robust B1 estimation and correction results than conventional methods. The B1 maps were also applied to MRSI data from tumor patients and produced improved neurometabolite maps, with better separation between pathological and normal tissues. CONCLUSION: This work presents a novel method to estimate B1 variations using probabilistic subspace models and machine learning. The proposed method may make correction of B1 inhomogeneity effects more robust in practical applications.