Superior Capability Research Articles

Machine learning assisted prediction of absorption maxima in cyclohexene: A comparison using molecular descriptors and fingerprints

Light absorption plays important role in different photovoltaics devices. Easy and fast prediction of absorption properties is essential for fast screening of efficient materials. In our pursuit of identifying the optimal model for predicting absorption maxima, we systematically evaluated over 40 machine learning models, employing both molecular descriptors and fingerprints as input features. Notably, models trained on molecular descriptors demonstrated superior predictive capabilities as compared to those relying on molecular fingerprints. This not only showcased the efficacy of molecular descriptors but also highlighted the potential of these models as rapid and efficient alternatives to the density functional theory (DFT) based approaches. The use of machine learning models based on molecular descriptors introduced a level of simplicity and speed in predictions, surpassing the computational demands associated with traditional DFT-based methods. Our introduced framework that is based on machine learning, offers a valuable tool for the easy and fast prediction of properties.

Mapping coastal resilience: a Gis-based Bayesian network approach to coastal hazard identification for Queensland’s dynamic shorelines

Coastal regions worldwide face increasing threats from climate change-induced hazards, necessitating more accurate and comprehensive vulnerability assessment tools. This study introduces an innovative approach to coastal vulnerability assessment by integrating Bayesian Networks (BN) with the modern coastal vulnerability (CV) framework. The resulting BN-CV model was applied to Queensland's coastal regions, with a particular focus on tide-modified and tide-dominated beaches, which constitute over 85% of the studied area. The research methodology involved beach classification based on morphodynamic characteristics, spatial subdivision of Queensland's coast into 78 sections, and the application of the BN-CV model to analyze interactions between geomorphological features and oceanic dynamics. This approach achieved over 90% accuracy in correlating beach types with vulnerability factors, significantly outperforming traditional CVI applications. Key findings include the identification of vulnerability hotspots and the creation of detailed exposure and sensitivity maps for Gold Coast City, Redland City, Brisbane City, and the Sunshine Coast Regional area. The study revealed spatial variability in coastal vulnerability, providing crucial insights for targeted management strategies. The BN-CV model demonstrates superior precision and customization capabilities, offering a more nuanced understanding of coastal vulnerability in regions with diverse beach typologies. This research advocates for the adoption of the BN-CV approach to inform tailored coastal planning and management strategies, emphasizing the need for regular reassessments and sustained stakeholder engagement to build resilience against climate change impacts.Recommendations include prioritizing adaptive infrastructure in high-exposure areas like the Gold Coast, enhancing flood management in Brisbane, improving socio-economic adaptive capacity in Redland, and maintaining natural defences in Moreton Bay. This study contributes significantly to the field of coastal risk management, providing a robust tool for policymakers and coastal managers to develop more effective strategies for building coastal resilience in the face of climate change.

A HOF-101@AgNPs-based dual-signal mode aptasensor for electrochemiluminescence and fluorescence detection of E. coli O157:H7

Escherichia coli O157:H7 (E. coli O157:H7) emerges as a foodborne pathogen, emphasizing the imperative for creating precise detection tools. An ultrasensitive electrochemiluminescence/fluorescence (ECL/FL) dual-signal mode aptasensor was constructed for the detection of E. coli O157:H7. Ultrafine silver nanoparticles were loaded on the surface of hydrogen-bonded organic skeleton materials by in-situ photoreduction method, and then combined with aptamers that can identify specific targets to prepare HOF-101@AgNPs@Apt, greatly simplifying the ECL/FL dual-signal mode probe fabrication process and improving sensing reliability. HOF-101@AgNPs@Apt had both strong ECL luminescence and fluorescence, resulting in a high detection sensitivity. The low limit of detection (LOD) for ECL and FL were 0.48 CFU mL−1 and 2.39 CFU mL−1, respectively. Moreover, the proposed dual-signal mode aptasensor has been successfully applied to determine E. coli O157:H7 levels in tap water and milk with superior accuracy and high antiinterference capability, providing a promising method for food safety monitoring.

Nacre‐Mimetic Multi‐Mechanical Synergistic Ceramic Aerogels with Interfacial Bridging and Stress Delocalization

AbstractElastic ceramic aerogels are exceptionally suitable for thermal insulating applications, but lacking multi‐mechanical synergistic high strength remains a deficiency. Inspired by the robust protective shells of mollusks in nature, which feature distinct hierarchical ordered structures, exhibiting strong and tough mechanical properties, a system construction and structural governing strategy is proposed to allow the nanostructure to assemble strong and flexible ceramic aerogels. Through the design of a hierarchical multi‐arched structure configurated binary‐soldering‐assemble interfacial topology interlocking, concentrated stress is effectively discrete. This biomimetic strategy enables the effective dissipation of mechanical energy, resulting in ceramic aerogels with exemplary compressive resilience, bendability, tensile resistance, and their strength exceeding that of other nanofibrous aerogels by up to tenfold. Additionally, the flexible aerogels also show remarkable thermal resistance from −196 to 1100 °C and superior thermal insulation capabilities (39.72 mW m−1 K−1), making them more suitable for the applications.

Functionalized LDH via intercalation of gluconate anion and surface assembly of ultrafine copper hydroxide for improving mechanical properties and the fire safety of epoxy resin

To impart epoxy resin (EP) with outstanding mechanical properties and fire safety, LDH was systematically functionalized through gluconate anion (GA) intercalation followed by surface assembly of ultrafine Cu(OH)2, resulting in the LDH-GA@Cu(OH)2 hybrid. Comprehensive characterizations confirmed the successful synthesis of the desired product, with a uniform distribution of ultrafine Cu(OH)2 nanoparticles on the GA-intercalated LDH surface. The findings revealed that LDH-GA@Cu(OH)2 improved the mechanical properties of EP due to the increased LDH layer spacing, facilitating EP chain insertion, and the hydrogen bonding between the –OH of GA and the EP matrix. Fire retardancy tests indicated that the EP composite containing 7.5 wt% LDH-GA@Cu(OH)2 achieved a limiting oxygen index (LOI) of 30.1%. Furthermore, compared to pure EP, the composite demonstrated significant reductions in the peak heat release rate (PHRR) and peak CO production rate (PCOP) by 47.4% and 60.5%, respectively, underscoring its superior flame-retardant and toxic smoke suppression capabilities. Mechanistic studies further indicated that ultrafine Cu(OH)2 enhanced char formation through catalytic charring at the interface, which promoted compact and cohesive char layers. Overall, this work proposes a promising functionalization strategy for LDH to endow EP with excellent comprehensive performance.

Significant improving magnetic properties and thermal conductivity of FeBSiPCNbCu nanocrystalline powder cores by designing novel ZnO insulating layer

As the application scenarios of soft magnetic composites (SMCs) tend to be high frequency, reducing their high-frequency core loss (Pcv) and improve the heat-sinking capability to avoiding thermal failure of electronic system are still greatly challenging. In present work, we successfully designed and prepared novel ZnO insulating layers on the surface of FeBSiPCNbCu nanocrystalline powders and discussed their growth processes. In addition, we investigated the effects of the ZnO insulating layers on the magnetic properties and heat-sinking capability of the FeBSiPCNbCu nanocrystalline magnetic powder cores (NMPCs) and explored the related mechanisms. The experimental results show that the hydrolysis of 1.75 wt% zinc acetate dihydrate under the coupling of 1 wt% polyvinylpyrrolidone forms uniform and thin ZnO insulating layers on the powders surface. The FeBSiPCNbCu@ZnO NMPCs annealed at 510 °C for 60 min exhibit excellent magnetic properties and superior heat-sinking capability with a low core loss of 698.4 mW/cm3 (20 mT, 2 MHz), high DC bias permeability of 68.3% (100 Oe) and heat conductivity coefficient of 2.22 W/(m•K) as compared with those of 859.8 mW/cm3, 57.1% and 1.86 W/(m•K), respectively, for the NMPCs without ZnO insulating layer.

Imaging and reconstruction of asymmetric wrinkles in carbon fibre composites using high-frequency eddy current and full matrix capture-based ultrasound

Ply wrinkles are a common defect found in Carbon Fiber Reinforced Polymer (CFRP) laminates that can lead to failure in composite structures if not correctly evaluated during manufacture stages. The mechanical performance of a structure depends on multiple parameters of a wrinkle, such as amplitude, wavelength, and asymmetry, which ideally need to all be measured to characterise the wrinkle properly. To address this issue, the authors designed and manufactured a set of complex wrinkles with various asymmetry offsets and amplitude and obtained raw data by testing them with a high-frequency eddy-current system. A unique trajectory in the complex Nyquist plot was found on asymmetric wrinkles, demonstrating the dominant influence of wrinkle asymmetry on electrical resistance. The authors also used full matrix capture ultrasound to characterise the wrinkles and measured the profiles by extracting texture phase randomness. The study revealed the superior capability of FMC UT for characterising and retrieving the profile in wrinkle coupons, while HF ECT was more adaptable for identifying the region of interest effectively. This work also implements a state-of-the-art deep fusion strategy to demonstrate a lightweight architecture can be easily used to combine multiple sources of wrinkle profiles and generate a more accurate prediction than traditional methods. Overall, the demonstrated work can be further applied to CFRPs with increasing geometric complexity and defects to facilitate the design efficiency and testing of CFRP components.

Vision transformer introduces a new vitality to the classification of renal pathology

Recent advancements in computer vision within the field of artificial intelligence (AI) have made significant inroads into the medical domain. However, the application of AI for classifying renal pathology remains challenging due to the subtle variations in multiple renal pathological classifications. Vision Transformers (ViT), an adaptation of the Transformer model for image recognition, have demonstrated superior capabilities in capturing global features and providing greater explainability. In our study, we developed a ViT model using a diverse set of stained renal histopathology images to evaluate its effectiveness in classifying renal pathology. A total of 1861 whole slide images (WSI) stained with HE, MASSON, PAS, and PASM were collected from 635 patients. Renal tissue images were then extracted, tiled, and categorized into 14 classes on the basis of renal pathology. We employed the classic ViT model from the Timm library, utilizing images sized 384 × 384 pixels with 16 × 16 pixel patches, to train the classification model. A comparative analysis was conducted to evaluate the performance of the ViT model against traditional convolutional neural network (CNN) models. The results indicated that the ViT model demonstrated superior recognition ability (accuracy: 0.96–0.99). Furthermore, we visualized the identification process of the ViT models to investigate potentially significant pathological ultrastructures. Our study demonstrated that ViT models outperformed CNN models in accurately classifying renal pathology. Additionally, ViT models are able to focus on specific, significant structures within renal histopathology, which could be crucial for identifying novel and meaningful pathological features in the diagnosis and treatment of renal disease.

Cluster-guided denoising graph auto-encoder for enhanced traffic data imputation and fault detection

In an era of increasing digitalization, the integration of diverse sensors has become commonplace. Within the transportation sector, state Departments of Transportation have extensively deployed traffic sensors to support various engineering endeavors. However, these sensors are prone to faults such as data loss, random noise, bias, and drift due to sensor aging, defects, or environmental influences. Therefore, detecting these faults is imperative to maintain data integrity. This paper presents a novel approach for detecting faults in traffic data based on a cluster-guided data reconstruction process. First, a traffic sensor clustering module is designed using a dual-encoding attention graph auto-encoder (DA-GAE) to identify clusters of traffic sensors. This module leverages a joint embedding of node and edge features in a low-dimensional vector space. Subsequently, utilizing the identified clusters, a cluster-guided denoising graph auto-encoder (CG-DGAE) is devised and trained for data reconstruction. The CG-DGAE employs a diffusion graph convolutional network (DGCN) and is trained with a cluster-wise sampling strategy. Extensive experiments were conducted using traffic data obtained from a real-world large sensor network, demonstrating superior performance of the CG-DGAE model in data reconstruction compared to various baseline methods. For fault detection, a score function is devised to discern potential faults by contrasting the sensor data sequence and the reconstructed data sequence. The fault detection results show that our proposed CG-DGAE model achieved an impressive overall accuracy of 99.09%, a precision of 99.13%, a recall of 99.53%, and a F1 score of 99.53%. The superior capability of CG-DGAE in data reconstruction and fault detection is attributable to the cluster-wise spatial–temporal context leveraged by the model.

Sparse-View Photoacoustic Reconstruction Method for Diabetic Retinopathy Using Feature Fusion Network.

Diabetic retinopathy is one of the most prevalent microvascular complications of diabetes mellitus, and photoacoustic imaging is an effective method for imaging diabetic retinal vessels. Photoacoustic imaging is an emerging noninvasive imaging method based on the photoacoustic effect, which offers advantages of contrast, resolution, and depth imaging. Appropriate photoacoustic reconstruction methods are essential for obtaining high-quality photoacoustic images. In this study, a multi-input self-attention multiscale feature fusion network (SAMF-Net) is proposed for photoacoustic reconstruction. The algorithm accepts two inputs, namely the original photoacoustic signal and the traditional reconstructed image. Furthermore, a global feature extraction module based on the self-attention mechanism is employed to focus on the global information. The results demonstrate that the proposed method exhibits superior reconstruction capability under different sparse detection views. The method has instructive value for photoacoustic image reconstruction and has the potential for further application in the diagnosis of diabetic retinopathy.

Machine learning-based analytical approach for mechanical analysis of composite hydrogen storage tanks under internal pressure

This study is a practical exploration of the application of machine learning for the mechanical analysis of filament-wound thin-composite hydrogen storage tanks under internal pressure. Our innovative approach seamlessly integrates classical laminate theory, comprehensive parametric analysis, and machine learning to advance the state-of-the-art in composite tank design. This versatile framework holds promise for addressing challenges in other structurally complex systems. A comprehensive parametric study was then conducted to explore the influence of key design parameters, such as internal pressure, tank radius, order of layer, and layer orientation. Pearson's correlation analysis was used to determine the parameters that had the most significant impact on the mechanical behavior of the tank. In addition, five machine learning models, namely, Extreme Gradient Boosting, Adaptive Boosting, Artificial Neural Network, Support Vector Machine, and Random Forest, were examined to determine their predictive capability for the mechanical performance of these composite tanks. Key findings demonstrate XGBoost's superior predictive capabilities, achieving a remarkable 99% accuracy in predicting stress in the x-direction compared to Random Forest's 96%. XGBoost also outperformed Random Forest in terms of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) with values of 26 and 0.02, respectively, versus 207 and 0.16. These results underscore XGBoost's potential to revolutionize hydrogen storage tank design. The development of a user-friendly GUI application further enhances the practicality of this research, allowing engineers and composite designers to efficiently simulate the mechanical behavior of these tanks in real time by adjusting the cursors for variables such as the pressure, radius, layer order, and layer orientation. This tool, with its intuitive interface and elimination of manual calculations, promises significant enhancements in the design process, providing a platform for the rapid assessment and optimization of tank architectures, thereby improving the efficiency and reliability of the design process.

Modified furosemide responsiveness index and biomarkers for AKI progression and prognosis: a prospective observational study.

Modified furosemide responsiveness index (mFRI) is a novel biomarker for assessing diuretic response and AKI progression in patients with early AKI. However, the comparative predictive performance of mFRI and novel renal biomarkers for adverse renal outcomes remains unclear. In a single-center prospective study, we aimed to evaluate the discriminatory abilities of mFRI and other novel renal biomarkers in predicting AKI progression and prognosis in patients with initial mild and moderate AKI (KDIGO stage 1 to 2). Patients with initial mild and moderate AKI within 48h following cardiac surgery were included in this study. The mFRI, renal biomarkers (including serum or urinary neutrophil gelatinase-associated lipocalin [sNGAL or uNGAL], serum cystatin C, urinary N-acetyl-beta-D-glycosaminidase [uNAG], urinary albumin-to-creatinine ratio) and cytokines (TNF, IL-1β, IL-2R, IL-6, IL-8, and IL-10) were measured at AKI diagnosis. The mFRI was calculated for each patient, which was defined as 2-hour urine output divided by furosemide dose and body weight. Of 1013 included patients, 154 (15.2%) experienced AKI progression, with 59 (5.8%) progressing to stage 3 and 33 (3.3%) meeting the composite outcome of hospital mortality or receipt of renal replacement therapy (RRT). The mFRI showed non-inferiority or potential superiority to renal biomarkers and cytokines in predicting AKI progression (area under the curve [AUC] 0.80, 95% confidence interval [CI] 0.77-0.82), progression to stage 3 (AUC 0.87, 95% CI 0.85-0.89), and composite outcome of death and receipt of RRT (AUC 0.85, 95% CI 0.82-0.87). Furthermore, the combination of a functional biomarker (mFRI) and a urinary injury biomarker (uNAG or uNGAL) resulted in a significant improvement in the prediction of adverse renal outcomes than either individual biomarker (all P < 0.05). Moreover, incorporating these panels into clinical model significantly enhanced its predictive capacity for adverse renal outcomes, as demonstrated by the C index, integrated discrimination improvement, and net reclassification improvement (all P < 0.05). As a rapid, cost-effective and easily accessible biomarker, mFRI, exhibited superior or comparable predictive capabilities for AKI progression and prognosis compared to renal biomarkers in cardiac surgical patients with mild to moderate AKI. Clinicaltrials.gov, NCT04962412. Registered July 15, 2021, https://clinicaltrials.gov/ct2/show/NCT04962412?cond=NCT04962412&draw=2&rank=1 .

NeRSI: Neural Implicit Representations for 5D Seismic Data Interpolation

Due to challenging field operations and resource constraints, seismic data acquisition often requires coping with missing traces. Interpolation algorithms are crucial for reconstructing these missing traces to enable improved subsurface analysis and interpretation. While deep learning has made exciting advances in seismic reconstruction, its focus has predominantly been on 2D and 3D datasets with relatively low rates of missing data. Five-dimensional (5D) seismic data reconstruction entails considering simultaneous sources and receivers deployed in areal arrays to solve the reconstruction problem. The latter offers greater data redundancy, which can be leveraged to enhance interpolation quality. Traditional 5D deep learning interpolation methods rely heavily on synthetic training pairs, posing challenges when applied to real-world data. This necessitates transfer learning techniques, which can be cumbersome. Addressing this, we introduce a self-supervised, coordinate-based deep interpolation algorithm that obviates the need for labeled data. Employing a multi-layer perceptron (MLP) network can effectively encode the continuous seismic wavefield inherent in 5D data. Once trained, the MLP can infer missing trace amplitudes from their coordinates. We contribute to boosting the MLP, enabling it to generate seismic profiles rather than single-point predictions. This enhancement significantly strengthens the model’s performance and efficiency. Moreover, we apply nuclear norm regularization to the output profiles, improving the reconstruction quality. The effectiveness of our algorithm is supported by both synthetic and field data experiments, demonstrating its superior reconstruction capabilities.

Detection of Avian Leukosis Virus Subgroup J (ALV-J) Using RAA and CRISPR-Cas13a Combined with Fluorescence and Lateral Flow Assay.

Avian Leukosis Virus (ALV) is a retrovirus that induces immunosuppression and tumor formation in poultry, posing a significant threat to the poultry industry. Currently, there are no effective vaccines or treatments for ALV. Therefore, the early diagnosis of infected flocks and farm sanitation are crucial for controlling outbreaks of this disease. To address the limitations of traditional diagnostic methods, which require sophisticated equipment and skilled personnel, a dual-tube detection method for ALV-J based on reverse transcription isothermal amplification (RAA) and the CRISPR-Cas13a system has been developed. This method offers the advantages of high sensitivity, specificity, and rapidity; it is capable of detecting virus concentrations as low as 5.4 × 100 copies/μL without cross-reactivity with other avian viruses, with a total testing time not exceeding 85 min. The system was applied to 429 clinical samples, resulting in a positivity rate of 15.2% for CRISPR-Cas13a, which was higher than the 14.7% detected by PCR and 14.2% by ELISA, indicating superior detection capability and consistency. Furthermore, the dual-tube RAA-CRISPR detection system provides visually interpretable results, making it suitable for on-site diagnosis in remote farms lacking laboratory facilities. In conclusion, the proposed ALV-J detection method, characterized by its high sensitivity, specificity, and convenience, is expected to be a vital technology for purification efforts against ALV-J.

A Low-Cost and Lightweight Real-Time Object-Detection Method Based on UAV Remote Sensing in Transportation Systems

Accurate detection of transportation objects is pivotal for enhancing driving safety and operational efficiency. In the rapidly evolving domain of transportation systems, the utilization of unmanned aerial vehicles (UAVs) for low-altitude detection, leveraging remotely-sensed images and videos, has become increasingly vital. Addressing the growing demands for robust, real-time object-detection capabilities, this study introduces a lightweight, memory-efficient model specifically engineered for the constrained computational and power resources of UAV-embedded platforms. Incorporating the FasterNet-16 backbone, the model significantly enhances feature-processing efficiency, which is essential for real-time applications across diverse UAV operations. A novel multi-scale feature-fusion technique is employed to improve feature utilization while maintaining a compact architecture through passive integration methods. Extensive performance evaluations across various embedded platforms have demonstrated the model’s superior capabilities and robustness in real-time operations, thereby markedly advancing UAV deployment in crucial remote-sensing tasks and improving productivity and safety across multiple domains.

Novel fracturing fluid made of low molecular weight polymer and surfactant achieves proppant full suspension and filling entire area of hydraulic fractures

In large-scale hydraulic fracturing of unconventional reservoirs, high viscosity friction reducers (HVFR) which employ high concentration polymers of high molecular weight can improve proppant suspension, but still lack long-distance and long-time proppant transport capability. Herein, for the first time, we report a fracturing fluid made of self- assembled low molecular weight polymer and a surfactant (LMPS). When using only 0.1 wt% of the polymer, LMPS has a viscosity of 29.6 mPa·s (170 s−1, 25 ℃) and can achieve full suspension of 40/70 mesh sand at 80 ℃. LMPS can form robust short molecular chain networks through numerous self-assembling association sites, rather than relying on entanglement. Notably, it achieves a minimum loss tangent as low as 0.1 in the viscoelastic response, demonstrating its strong elastic characteristics. According to the creep-recovery test, the strain recovery rate (87.9 %) of LMPS at 0.2 wt% polymer concentration far exceeds that of the HVFR with a concentration as high as 10.1 %. This indicates that LMPS has a stronger deformation-resisting ability, thereby preventing proppant settling. Taking advantage of LMPS’ superior proppant suspension capability, proppant can move with the slurry to the whole fracture for placement, while the HVFR provides only 48 % height support of the fractures in the form of dunes. A field trial shows that at a polymer concentration of 0.2 wt%, LMPS can stably transport 20/40 mesh quartz sand under a slurry rate of 2.8 m3/min and a proppant concentration of 635 kg/m3 during the fracturing process.

Conditional generative adversarial network-based predictive method for crack initiation in a dual-phase austenite stainless weld

A novel integration approach of a conditional generative adversarial network (cGAN) with an improved electro-chemo-mechanical (E−C−M) model was developed to calculate the localised electrochemistry and evaluate the stress corrosion cracking (SCC) crack initiation properties in high-temperature water. The results demonstrate the superior accuracy, computational efficiency and robust generalisation capabilities of the hybrid method. High-temperature electrochemical tests were conducted to validate and optimize the E−C−M model for calculating the stress induced corrosion behavior of austenite stainless steel (ASS) in high-temperature water. The SCC initiation test results corroborate the predicted corrosion current distribution on ASS with a dual-phase structure.

Liver Dysfunction and Systemic Inflammation Drive Organ Failures in Acute Decompensation of Cirrhosis: A Multi-Centric Study.

Hospitalized patients with acute decompensation (AD) of cirrhosis are at risk of progressing to acute-on-chronic liver failure (ACLF), significantly increasing their mortality. This study aimed to identify key predictors and patient trajectories predisposing to ACLF. In this multi-center, prospective study spanning two years, clinical, biochemical, and 90-day survival data were collected from 625 AD patients (EASL criteria) across North, South, and East India. We divided the cohort into a Derivation-cohort (DC: 318 patients) and a Validation-cohort (VC: 307 patients). Predictive models for pre-ACLF were derived, validated, and compared with established scores like MELD3.0 and CLIF-C AD. Of 625 patients, (mean age 49, 83% male, 77.5% with alcohol-related liver disease), 32.2% progressed to ACLF. Patients progressing to ACLF showed significantly higher bilirubin (10.9vs.8.1mg/dl), leukocyte counts (9400vs.8000 per mm3), INR (1.9 vs.1.8), and MELD3.0 (28vs.25), but lower sodium (131vs.134mEq/L) and survival (62% vs.86%) compared to those without progression (p<0.05) in the DC. Consistent results were noted with AH, infection and HE as additional risk factors in VC. Liver failure at presentation [OR: 2.4 (in DC), 6.9 (in VC)] and the seven-day trajectories of bilirubin, INR, and MELD3.0 significantly predicted ACLF progression (p<0.001). A new PRE-ACLF Model showed superior predictive capability (AUC of 0.71 in DC and 0.82 in VC) compared to MELD3.0 and CLIF-C AD scores (p<0.05). Approximately one-third of AD patients in this Indian cohort rapidly progressed to ACLF, resulting in high mortality. Early identification of patients at risk can guide targeted interventions to prevent ACLF.

Constructing of thiophene-based covalent organic frameworks with electron rich system for enhanced iodine capture

To address the significant energy pollution created by nuclear waste and the environmental catastrophe resulting from rapid industrial expansion, electronic-rich materials are widely developed and applied. However, the stability and reproducibility of the material limit its practical application. Therefore, the development of highly stable electron-rich adsorbents remains a great challenge. Herein, we report two highly stable thiophene-based COFs (JLNU-308 and JLNU-309) for iodine capture. Thiophene groups enhance electron-rich interactions with iodine, boosting adsorption capacity. Both COFs exhibit strong iodine adsorption and stability. Notably, JLNU-309 achieves rapid adsorption within 50 h, reaching 3.840 g g-1 capacity. In addition, theoretical calculations unequivocally demonstrate that in addition to the influence of thiophene groups, the superior adsorption capability of JLNU-309 can be attributed to the robust interaction between the adsorbed iodine and the many triazine nitrogen-rich adsorption sites that are evenly dispersed throughout the pore. Therefore, this study offers a method for designing and developing electron-rich COFs with high stability.

Assessment of the antinociceptive effect of a single fentanyl bolus dose in children: A pharmacokinetic and pharmacodynamic analysis based on the nociception level index during sevoflurane general anesthesia.

The Nociception Level Index has shown benefits in estimating the nociception/antinociception balance in adults, but there is limited evidence in the pediatric population. Evaluating the index performance in children might provide valuable insights to guide opioid administration. To evaluate the Nociception Level Index ability to identify a standardized nociceptive stimulus and the analgesic effect of a fentanyl bolus. Additionally, to characterize the pharmacokinetic/pharmacodynamic relationship of fentanyl with the Nociception Level Index response during sevoflurane anesthesia. Nineteen children, 5.3 (4.1-6.7) years, scheduled for lower abdominal or urological surgery, were studied. After sevoflurane anesthesia and caudal block, a tetanic stimulus (50 Hz, 60 mA, 5 s) was performed in the forearm. Following the administration of fentanyl 2 μg/kg intravenous bolus, three similar consecutive tetanic stimuli were performed at 5-, 15-, and 30-min post-fentanyl administration. Changes in the Nociception Level Index, heart rate, mean arterial pressure, and bispectral index were compared in response to the tetanic stimuli. Fentanyl plasma concentrations and the Nociception Level Index data were used to elaborate a pharmacokinetic/pharmacodynamic model using a sequential modeling approach in NONMEM®. After the first tetanic stimulus, both the Nociception Level Index and the heart rate increased compared to baseline (8 ± 7 vs. 19 ± 10; mean difference (CI95) -12(-18--6) and 100 ± 10 vs. 102 ± 10; -2(-4--0.1)) and decrease following fentanyl administration (19 ± 10 vs. 8 ± 8; 12 (5-18) and 102 ± 10 vs. 91 ± 11; 11 (7-16)). In subsequent tetanic stimuli, heart rate remained unchanged, while the Nociception Level Index progressively increased within 15 min to values similar to those before fentanyl. An allometric weight-scaled, 3-compartment model best characterized the pharmacokinetic profile of fentanyl. The pharmacokinetic/pharmacodynamic modeling analysis revealed hysteresis between fentanyl plasma concentrations and the Nociception Level Index response, characterized by plasma effect-site equilibration half-time of 1.69 (0.4-2.9) min. The estimated fentanyl C50 was 1.93 (0.73-4.2) ng/mL. The Nociception Level Index showed superior capability compared to traditional hemodynamic variables in discriminating different nociception-antinociception levels during varying fentanyl concentrations in children under sevoflurane anesthesia.