Exceptional green hydrogen production performance of a ruthenium-modulated nickel selenide.

Developing low-cost, high-efficiency and stable electrocatalysts for the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER) is crucial but highly challenging. Density functional theory (DFT) calculations reveal that doping ruthenium (Ru) into catalysts can effectively optimize their electronic structure, hence leading to an optimal Gibbs free energy on the catalyst surface. Herein, an ultra-low Ru (about 2.34 wt%)-doped Ni3Se2 nanowire catalyst (i.e., Ru/Ni3Se2) supported on nickel foam has been fabricated by a hydrothermal reaction followed by a chemical etching process. The unique three-dimensional (3D) interconnected nanowires not only endow Ru and Ni3Se2 with uniform distribution and coupling, but also provide higher electrical conductivity, more active sites, an optimized electronic structure and favorable reaction kinetics. Therefore, the as-obtained Ru/Ni3Se2 catalyst exhibits excellent electrocatalytic performance, with low overpotentials of 24 and 211 mV to supply a current density value of 10 mA cm-2 towards the HER and OER in an alkaline environment, respectively. Notably, the as-fabricated Ru/Ni3Se2 catalyst only requires a low voltage of 1.476 V to derive a current density of 10 mA cm-2 in the constructed two-electrode alkaline electrolyzer and exhibits exceptionally high stability. This work will provide a novel strategy for the design and fabrication of low-cost and high-performance bifunctional electrocatalysts for hydrogen production by water electrolysis.

Rare-earth hydroxide/MXene hybrid: a promising agent for near-infrared photothermy and magnetic resonance imaging.

Layered gadolinium hydroxide (LGdH) and Ti3C2 monolayers were assembled into a LGdH/Ti3C2 (GTC) hybrid. The hybrid demonstrated enhanced near-infrared (NIR) light absorption properties and superior photothermal performance. Moreover, the GTC hybrid achieved an excellent T1-weighted magnetic resonance imaging (MRI) effect.

Effect of laparoscopic sleeve gastrectomy on mobilization of site-specific body adipose depots: a prospective cohort study.

BACKGROUND: Effect of bariatric surgery on mobilization of site-specific body adipose depots is not well investigated. Herein, the authors conducted a prospective cohort study to assess whether bariatric surgery can differentially affect specific fat storage pools and to further investigate correlations between site-specific fat mobilization and clinical outcomes. MATERIALS AND METHODS: In this single-centre prospective cohort study, 49 participants underwent laparoscopic sleeve gastrectomy (LSG) from 24 May 2022 to 20 October 2022 and underwent MRI to estimate subcutaneous fat area, visceral fat area (VFA), hepatic and pancreatic proton density fat fraction (PDFF) at baseline and 3 months after surgery. The protocol for this study was registered on clinicaltrials.gov. RESULTS: Among 49 patients who met all inclusion criteria, the median [interquartile range (IQR)] age was 31.0 (23.0-37.0) years, the median (IQR) BMI was 38.1 (33.7-42.2) kg/m 2 and 36.7% were male. Median (IQR) percentage hepatic PDFF loss was the greatest after bariatric surgery at 68.8% (47.3-79.7%), followed by percentage pancreatic PDFF loss at 51.2% (37.0-62.1%), percentage VFA loss at 36.0% (30.0-42.4%), and percentage subcutaneous fat area loss at 22.7% (17.2-32.4%) ( P <0.001). By calculating Pearson correlation coefficient and partial correlation coefficient, the positive correlations were confirmed between change in VFA and change in glycated haemoglobin ( r =0.394, P =0.028; partial r =0.428, P =0.042) and between change in hepatic PDFF and change in homoeostatic model assessment of insulin resistance ( r =0.385, P =0.025; partial r =0.403, P =0.046). CONCLUSIONS: LSG preferentially mobilized hepatic fat, followed by pancreatic fat and visceral adipose tissue, while subcutaneous adipose tissue was mobilized to the least extent. Reduction in visceral adipose tissue and hepatic fat is independently associated with the improvement of glucose metabolism after LSG.

Topological abnormality of structural covariance network in MRI-negative frontal lobe epilepsy.

Background: Frontal lobe epilepsy (FLE) is the second most common type of focal epilepsy, however, imaging studies of FLE have been far less than Temporal lobe epilepsy (TLE) and the structural findings were not consistent in previous literature. Object: Investigate the changes in cortical thickness in patients with FLE and the alteration of the structural covariance networks (SCNs) of cortical thickness with graph-theory. Method: Thirty patients with FLE (18 males/12 females; 28.33 ± 11.81 years) and 27 demographically matched controls (15 males/12 females; 29.22 ± 9.73 years) were included in this study with high-resolution structural brain MRI scans. The cortical thickness was calculated, and structural covariance network (SCN) of cortical thickness were reconstructed using 68 × 68 matrix and analyzed with graph-theory approach. Result: Cortical thickness was not significantly different between two groups, but path length and node betweenness were significantly increased in patients with FLE, and the regional network alterations were significantly changed in right precentral gyrus and right temporal pole (FDR corrected, p < 0.05). Comparing to HC group, network hubs were decreased and shifted away from frontal lobe. Conclusion: The topological properties of cortical thickness covariance network were significantly altered in patients with FLE, even without obvious surface-based morphological damage. Graph-theory based SCN analysis may provide sensitive neuroanatomical biomarkers for FLE.

CT Radiomics to Predict Macrotrabecular-Massive Subtype and Immune Status in Hepatocellular Carcinoma.

Background Macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) is an aggressive variant associated with angiogenesis and immunosuppressive tumor microenvironment, which is expected to be noninvasively identified using radiomics approaches. Purpose To construct a CT radiomics model to predict the MTM subtype and to investigate the underlying immune infiltration patterns. Materials and Methods This study included five retrospective data sets and one prospective data set from three academic medical centers between January 2015 and December 2021. The preoperative liver contrast-enhanced CT studies of 365 adult patients with resected HCC were evaluated. The Third Xiangya Hospital of Central South University provided the training set and internal test set, while Yueyang Central Hospital and Hunan Cancer Hospital provided the external test sets. Radiomic features were extracted and used to develop a radiomics model with machine learning in the training set, and the performance was verified in the two test sets. The outcomes cohort, including 58 adult patients with advanced HCC undergoing transarterial chemoembolization and antiangiogenic therapy, was used to evaluate the predictive value of the radiomics model for progression-free survival (PFS). Bulk RNA sequencing of tumors from 41 patients in The Cancer Genome Atlas (TCGA) and single-cell RNA sequencing from seven prospectively enrolled participants were used to investigate the radiomics-related immune infiltration patterns. Area under the receiver operating characteristics curve of the radiomics model was calculated, and Cox proportional regression was performed to identify predictors of PFS. Results Among 365 patients (mean age, 55 years ± 10 [SD]; 319 men) used for radiomics modeling, 122 (33%) were confirmed to have the MTM subtype. The radiomics model included 11 radiomic features and showed good performance for predicting the MTM subtype, with AUCs of 0.84, 0.80, and 0.74 in the training set, internal test set, and external test set, respectively. A low radiomics model score relative to the median value in the outcomes cohort was independently associated with PFS (hazard ratio, 0.4; 95% CI: 0.2, 0.8; P = .01). The radiomics model was associated with dysregulated humoral immunity involving B-cell infiltration and immunoglobulin synthesis. Conclusion Accurate prediction of the macrotrabecular-massive subtype in patients with hepatocellular carcinoma was achieved using a CT radiomics model, which was also associated with defective humoral immunity. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Yoon and Kim in this issue.

In vitro anti-PRV activity of dihydromyricetin from Ampelopsis grossedentata.

Pseudorabies (PR) is an acute infectious disease caused by pseudorabies virus (PRV). There are no available drugs due to the emergence of variant of PRV. Dihydromyricetin (DMY) is a flavonoid extracted from Ampelopsis grossedentata (A. grossedentata), which has a variety of pharmacological activities. In this study, we aim to investigate the in vitro anti-PRV activity of DMY extracted from A. grossedentata. MTT assay was used to detect the cytotoxicity and antiviral activity of DMY. The results detected by flow cytometry and qRT-PCR showed that DMY played anti-PRV role mainly by interfering with the process of virus invasion into host cell and inhibiting the occurrence of pyroptosis in vitro. This suggested that anti-pyroptosis may be an important antiviral mechanism for DMY which is expected to be a potential anti-PRV drug.

Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19.

OBJECTIVES: To develop and validate a machine learning model for the prediction of adverse outcomes in hospitalized patients with COVID-19. METHODS: We included 424 patients with non-severe COVID-19 on admission from January 17, 2020, to February 17, 2020, in the primary cohort of this retrospective multicenter study. The extent of lung involvement was quantified on chest CT images by a deep learning-based framework. The composite endpoint was the occurrence of severe or critical COVID-19 or death during hospitalization. The optimal machine learning classifier and feature subset were selected for model construction. The performance was further tested in an external validation cohort consisting of 98 patients. RESULTS: There was no significant difference in the prevalence of adverse outcomes (8.7% vs. 8.2%, p = 0.858) between the primary and validation cohorts. The machine learning method extreme gradient boosting (XGBoost) and optimal feature subset including lactic dehydrogenase (LDH), presence of comorbidity, CT lesion ratio (lesion%), and hypersensitive cardiac troponin I (hs-cTnI) were selected for model construction. The XGBoost classifier based on the optimal feature subset performed well for the prediction of developing adverse outcomes in the primary and validation cohorts, with AUCs of 0.959 (95% confidence interval [CI]: 0.936-0.976) and 0.953 (95% CI: 0.891-0.986), respectively. Furthermore, the XGBoost classifier also showed clinical usefulness. CONCLUSIONS: We presented a machine learning model that could be effectively used as a predictor of adverse outcomes in hospitalized patients with COVID-19, opening up the possibility for patient stratification and treatment allocation. KEY POINTS: • Developing an individually prognostic model for COVID-19 has the potential to allow efficient allocation of medical resources. • We proposed a deep learning-based framework for accurate lung involvement quantification on chest CT images. • Machine learning based on clinical and CT variables can facilitate the prediction of adverse outcomes of COVID-19.

A kinetic/thermodynamic study of transparent co-adsorbents and colored dye molecules in visible light based on microgravimetric quartz-crystal microbalance on porous TiO2 films for dye-sensitized solar cells.

In this study, a quartz crystal microbalance (QCM) in situ method is used to study the kinetic and thermodynamic processes of the adsorption of ruthenium-based dyes (N719, N3, N749), and the co-adsorbent chenodeoxycholic acid (CDCA) on the TiO2 film surface. The results of the kinetic studies show that the adsorption rate of N749 is slightly higher than the other two dyes, and the adsorption rate of CDCA is more sensitive to temperature change. The adsorption mechanism of the dye and CDCA on the surface of TiO2 can be reasonably inferred based on the result of the activation energy. The isotherm adsorption model studies show that the ratio of the number of surface molecules (296 K) is n(N719) : n(N3) : n(N749) : n(CDCA) = 0.69 : 1.48 : 0.50 : 1. The Keq value of CDCA is about two orders of magnitude smaller than that of all the dye molecules, which indicates that the adsorption strength of CDCA is much weaker than that of the dye molecules. Thermodynamic studies show that the adsorption reaction is an endothermic reaction. The ΔS is ΔS(N3 = 143.11 J mol-1) > ΔS(N719 = 112.72 J mol-1) > ΔS(N749 = 109.43 J mol-1) > ΔS(CDCA = 96.14 J mol-1). The Gibbs free energy ΔG is negative, and indicates that the adsorption reaction of the four molecules on the surface of the TiO2 film is spontaneous. The results of this paper show that the tedious and lengthy experimental process of the traditional method can be simplified by QCM. In addition, the development of this study provides a certain theoretical and experimental basis for future studies on the interaction mechanism between dyes and co-adsorbents.

Early prediction of disease progression in COVID-19 pneumonia patients with chest CT and clinical characteristics.

The outbreak of coronavirus disease 2019 (COVID-19) has rapidly spread to become a worldwide emergency. Early identification of patients at risk of progression may facilitate more individually aligned treatment plans and optimized utilization of medical resource. Here we conducted a multicenter retrospective study involving patients with moderate COVID-19 pneumonia to investigate the utility of chest computed tomography (CT) and clinical characteristics to risk-stratify the patients. Our results show that CT severity score is associated with inflammatory levels and that older age, higher neutrophil-to-lymphocyte ratio (NLR), and CT severity score on admission are independent risk factors for short-term progression. The nomogram based on these risk factors shows good calibration and discrimination in the derivation and validation cohorts. These findings have implications for predicting the progression risk of COVID-19 pneumonia patients at the time of admission. CT examination may help risk-stratification and guide the timing of admission.

Single crystal SnO2 zigzag nanobelts.

Zigzag SnO2 nanobelts have been synthesized by evaporating tin grain in air. XRD, SEM, and TEM were employed to characterize the prepared samples. SEM images showed large amounts of zigzag nanobelts with periodic morphology. XRD result showed a pure tetragonal SnO2 phase. HRTEM and SAED revealed a single crystal structure with [010] zone axis on the whole zigzag zone. The zigzag structure is deduced to be formed by shifting the growth direction from [101] to [10] or vice versa. Growing nanobelts along different equivalent directions opens a new avenue for the preparation of novel nanostructured materials.

Rapid synthesis and visible photoluminescence of ZnS nanobelts.

Belt-like ZnS with visible photoluminescence was synthesized rapidly with Zn and S in the vapor phase.

Controllable assembly of aligned ZnO nanowires/belts arrays.

We describe a simple method to assemble ZnO nanowires/belts into highly ordered arrays. ZnCu(2) alloy was used as the Zn source, which reacted with water vapor to generate ZnO nanocrystals. The reaction was performed in a mild way, which facilitated the easy control of the reaction conditions. By simply controlling the water bath temperature and carrier gas flux in our experiments, we obtained ZnO nanowires/belts aligned to form ordered arrays. The highly ordered nature of the ZnO arrays is suggested to be related both with the polarities of the H(2)O molecule and the ZnO (0001) surface. Photoluminescence (PL) microscopy revealed that the comblike structures had waveguide properties, where green light enhancement was observed at the tips of the branches. The light enhancement property reveals their promising applications as light source arrays.