Prognostic values of serum lactate-to-bicarbonate ratio and lactate for predicting 28-day in-hospital mortality in children with dengue shock syndrome.

This study aimed to assess the clinical utility of blood lactate-to-bicarbonate (L/B) ratio, as a prognostic factor for 28-day in-hospital mortality in children with dengue shock syndrome (DSS), admitted to the pediatric intensive care unit (PICU). This single-center retrospective study was conducted at a tertiary children hospital in southern Vietnam from 2013 to mid-2022. Prognostic models for DSS mortality were developed, using a predefined set of covariates in the first 24 hours of PICU admission. Area under the curves (AUCs), multivariable logistic and Least Absolute Shrinkage and Selection Operator (LASSO) regressions, bootstrapping and calibration slope were performed. A total of 492 children with DSS and complete clinical and biomarker data were included in the analysis, and 26 (5.3%) patients died. The predictive values for DSS mortality, regarding lactate showing AUC 0.876 (95% CI, 0.807-0.944), and that of L/B ratio 0.867 (95% CI, 0.80-0.934) (P values of both biomarkers < .001). The optimal cutoff point of the L/B ratio was 0.25, while that of lactate was 4.2 mmol/L. The multivariable model showed significant clinical predictors of DSS fatality including severe bleeding, cumulative amount of fluid infused and vasoactive-inotropic score (>30) in the first 24 hours of PICU admission. Combined with the identified clinical predictors, the L/B ratio yielded higher prognostic values (odds ratio [OR] = 8.66, 95% confidence interval [CI], 1.96-38.3; P < .01) than the lactate-based model (OR = 1.35, 95% CI, 1.15-1.58; P < .001). Both the L/B and lactate models showed similarly good performances. Considering that the L/B ratio has a better prognostic value than the lactate model, it may be considered a potential prognostic biomarker in clinical use for predicting 28-day mortality in PICU-admitted children with DSS.

Biogenic fabrication of a gold nanoparticle sensor for detection of Fe3+ ions using a smartphone and machine learning.

In recent years, smartphones have been integrated into rapid colorimetric sensors for heavy metal ions, but challenges persist in accuracy and efficiency. Our study introduces a novel approach to utilize biogenic gold nanoparticle (AuNP) sensors in conjunction with designing a lightbox with a color reference and machine learning for detection of Fe3+ ions in water. AuNPs were synthesized using the aqueous extract of Eleutherine bulbosa leaf as reductants and stabilizing agents. Physicochemical analyses revealed diverse AuNP shapes and sizes with an average size of 19.8 nm, with a crystalline structure confirmed via SAED and XRD techniques. AuNPs exhibited high sensitivity and selectivity in detection of Fe3+ ions through UV-vis spectroscopy and smartphones, relying on nanoparticle aggregation. To enhance image quality, we developed a lightbox and implemented a reference color value for standardization, significantly improving performance of machine learning algorithms. Our method achieved approximately 6.7% higher evaluation metrics (R 2 = 0.8780) compared to non-normalized approaches (R 2 = 0.8207). This work presented a promising tool for quantitative Fe3+ ion analysis in water.

Associations of obesity and dengue-associated mortality, acute liver failure and mechanical ventilation in children with dengue shock syndrome.

Dengue-associated complications, including dengue shock syndrome, severe respiratory distress, and pediatric acute liver failure (PALF), are associated with high mortality rates in patients with dengue. There is increasing prevalence of overweight and obesity among children worldwide. Obesity may activate inflammatory mediators, leading to increased capillary permeability and plasma leakage in patients with dengue. Several studies have shown a correlation between obesity and DSS, but did not include dengue fatality or PALF. Therefore, we hypothesized possible associations between obesity and critical dengue-associated clinical outcomes among PICU-admitted children with DSS, including dengue-related mortality, mechanical ventilation (MV) requirements, and dengue-associated PALF. The nutritional status of the participants was assessed using World Health Organization growth charts. A total of 858 participants with complete nutritional data were enrolled in this study. Obesity was significantly associated with risk of severe respiratory failure and MV support (odds ratio = 2.3, 95% CI: 1.31-4.06, P < .01); however, it was not associated with dengue-associated mortality or acute liver failure. Obese pediatric patients with DSS should be closely monitored for severe respiratory distress and the need for high-flow oxygenation support, particularly MV, soon after hospitalization.

Hydroxyapatite Derived from Salmon Bone As Green Ecoefficient Support for Ceria-Doped Nickel Catalyst for CO2 Methanation.

Hydroxyapatite (HA) derived from salmon bone byproducts is used as a green support for the nanostructured nickel catalysts applied in the methanation of carbon dioxide (CO2). Undoped nickel catalysts and various ceria-doped nickel supported on hydroxyapatite (HA) were prepared by coimpregnation. Characteristics of the as-prepared catalysts were investigated by the various techniques, including X-ray diffraction (XRD), scanning electron microscopy (SEM), Brunauer-Emmett-Teller (BET), hydrogen temperature-programmed reduction (H2-TPR), carbon dioxide temperature-programmed desorption (CO2-TPD), and energy-dispersive X-ray spectroscopy (EDX). The catalyst activity was assessed throughout CO2 methanation in the low-temperature range of 225-350 °C with the molar ratio of H2/CO2 = 4/1. The function of HA and ceria provided a high dispersity of nickel particles over the catalyst surface with the size range of 24.5-25.8 nm, leading to improvement in the reduction and CO2 adsorption capacity of the catalysts as well as enhancing the catalytic efficiency in CO2 methanation. The 10Ni/HA catalyst reduced at suitable conditions of 400 °C for 2 h showed the highest catalytic performance among the tested catalysts. CO2 conversion and CH4 selectivity reached 76.6 and 100% at a reaction temperature of 350 °C, respectively. The results show that the Ni/HA sample doped with 6.0 wt % ceria was the best, with the CO2 conversion and the CH4 selectivity reaching 92.5% and 100%, respectively, at a reaction temperature of 325 °C.

High Efficiency Water Splitting using Ultrasound Coupled to a BaTiO3 Nanofluid.

To date, a number of studies have reported the use of vibrations coupled to ferroelectric materials for water splitting. However, producing a stable particle suspension for high efficiency and long-term stability remains a challenge. Here, the first report of the production of a nanofluidic BaTiO3 suspension containing a mixture of cubic and tetragonal phases that splits water under ultrasound is provided. The BaTiO3 particle size reduces from approximately 400 nm to approximately 150 nm during the application of ultrasound and the fine-scale nature of the particulates leads to the formation of a stable nanofluid consisting of BaTiO3 particles suspended as a nanofluid. Long-term testing demonstrates repeatable H2 evolution over 4 days with a continuous 24 h period of stable catalysis. A maximum rate of H2 evolution is found to be 270 mmol h-1 g-1 for a loading of 5 mg l-1 of BaTiO3 in 10% MeOH/H2 O. This work indicates the potential of harnessing vibrations for water splitting in functional materials and is the first demonstration of exploiting a ferroelectric nanofluid for stable water splitting, which leads to the highest efficiency of piezoelectrically driven water splitting reported to date.

Automatic Acne Object Detection and Acne Severity Grading Using Smartphone Images and Artificial Intelligence.

Skin image analysis using artificial intelligence (AI) has recently attracted significant research interest, particularly for analyzing skin images captured by mobile devices. Acne is one of the most common skin conditions with profound effects in severe cases. In this study, we developed an AI system called AcneDet for automatic acne object detection and acne severity grading using facial images captured by smartphones. AcneDet includes two models for two tasks: (1) a Faster R-CNN-based deep learning model for the detection of acne lesion objects of four types, including blackheads/whiteheads, papules/pustules, nodules/cysts, and acne scars; and (2) a LightGBM machine learning model for grading acne severity using the Investigator's Global Assessment (IGA) scale. The output of the Faster R-CNN model, i.e., the counts of each acne type, were used as input for the LightGBM model for acne severity grading. A dataset consisting of 1572 labeled facial images captured by both iOS and Android smartphones was used for training. The results show that the Faster R-CNN model achieves a mAP of 0.54 for acne object detection. The mean accuracy of acne severity grading by the LightGBM model is 0.85. With this study, we hope to contribute to the development of artificial intelligent systems to help acne patients better understand their conditions and support doctors in acne diagnosis.

Novel cryptomelane nanosheets for the superior catalytic combustion of oxygenated volatile organic compounds.

This work offers a novel pathway to prepare cryptomelane manganese oxides nanosheets as an superior catalyst for the catalytic combustion of oxygenated volatile organic compounds. The tunnel cryptomelane manganese oxides nanosheets were prepared from layered birnessite via simultaneously tuning pH and molar ratio (ROK) of the redox-precipitation between oxalic acid and KMnO4. Thus, few-layered cryptomelane nanosheets possessing the most predominantly exposed (211) facet are generated at low pH (5.2-5.6), which intensifies the surface area of thin crystal cryptomelane nanosheets up to 177 m2g-1 and weakens Mn-O bonds. Moreover, high ROK results in low manganese average oxidation state (AOS), greater oxygen vacancies and better low-temperature reduction and oxygen mobility. Such features significantly maneuver the catalytic activity of the cryptomelane nanosheets catalysts for the complete oxidation of oxygenated volatile organic compound (e.g., 2-propanol, acetone) at low temperature (170-230 °C). Moreover, the catalysts show high stability for 48 h. The presented catalyst discloses an avenue to address current obstacles in the catalytic oxidation of volatile organic compounds.

A novel biosensing system for rapid and sensitive detection of heavy metal toxicity in water.

Toxicity biosensors have recently gained significant attention due to their potential use in online monitoring. However, the effects of toxicants and the influence of dose, exposure time, and type and concentration of respiration substrate (RS) on the performance of a bioreactor are species-specific. Although these factors need to be investigated case-by-case as they can lead either to damage or self-repair of the affected microorganisms, they have seldom been considered in previous studies. Therefore, this work examined, for the first time, the effects of resting time and RS concentration on the performance of the biosensing system for toxicity of Cr6+ in water. In addition, it is also the first time that a novel non-contact fluid delivery system was applied to a toxicity biosensing system to prevent unstable responses. By choosing the best RS concentration and balancing the resting and exposure times, the proposed procedure exhibits promising results in terms of minimum detectable concentration (MDC), limit of detection (LOD), detection range, linearity, sensitivity, reproducibility and accuracy. The recovery time was only a few hours and the coefficients of variation of inhibition and recovery were only 12% and 9.6%, respectively, during six times reuse over one month of storage.

Simple approach for the rapid estimation of BOD5 in food processing wastewater.

A simple approach was developed for the rapid and accurate estimation of 5-day biochemical oxygen demand (BOD5) in food processing wastewater. Immobilization of the natural microbial consortium that was collected from an aerobic compartment of a food processing wastewater treatment plant was simply performed by adhesion using a low-cost porous carrier. Pseudomonas aeruginosa, Bacillus cereus, and Streptomyces, whose salt-tolerance and ability to break down organic compounds have been widely reported, were found to be predominant. These microorganisms may cause an enhancement of the bioreactor response in the presence of sodium chloride. Consequently, a modified glucose-glutamic acid (GGA) calibration standard was proposed in which an appropriate amount of NaCl was added; this solution was found to be more effective in terms of accuracy and practicality than both conventional GGA and the synthetic wastewater recipe from the Organisation for Economic Cooperation and Development (OECD). The calibrated self-built packed-bed bioreactor exhibited good precision of 3% or less in predicting BOD5 in influent, which is similar to the performance of the most common commercial biochemical oxygen demand (BOD) bioreactors. There was a statistical agreement between the results obtained from this rapid BOD biosensor and the conventional methods, even when testing treated wastewater samples.

Self-build packed-bed bioreactor for rapid and effective BOD estimation.

This work demonstrated a simple, low-cost, rapid, and effective biochemical oxygen demand (BOD) estimation system based on a packed-bed bioreactor that can be easily self-built on-site at a particular wastewater treatment plant for continuous monitoring of the influent and effluent. The use of natural microbial consortium that were collected from the target wastewater and immobilized on a cheap porous carrier simply by adhesion resulted in an acceptable accuracy of over 95%. The newly developed semi-continuous operating mode with peak-type signals was shown to be able to continuously estimate BOD at a high flow rate to overcome the flow dependence of the oxygen electrode, limit clogging issues, enhance the response time, and lower the limit of detection. The resulting packed-bed bioreactors could work continuously for 22 h with a coefficient of variance (CoV) of only 1.8% or for 13 h a day for several days with a maximum CoV of 1.4% and their response was observed to be stable over 80 consecutive measurements. They exhibited stable responses at a wide pH range of 6.5-8.5, which is also the recommended range for aerobic wastewater treatment, emphasizing the greater ease of use of natural microorganisms for BOD estimation.