AI-based digital pathology provides newer insights into lifestyle intervention-induced fibrosis regression in MASLD: An exploratory study.

BACKGROUND AND AIMS: Lifestyle intervention is the mainstay of therapy for metabolic dysfunction-associated steatohepatitis (MASH), and liver fibrosis is a key consequence of MASH that predicts adverse clinical outcomes. The placebo response plays a pivotal role in the outcome of MASH clinical trials. Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) microscopy with artificial intelligence analyses can provide an automated quantitative assessment of fibrosis features on a continuous scale called qFibrosis. In this exploratory study, we used this approach to gain insight into the effect of lifestyle intervention-induced fibrosis changes in MASH. METHODS: We examined unstained sections from paired liver biopsies (baseline and end-of-intervention) from MASH individuals who had received either routine lifestyle intervention (RLI) (n = 35) or strengthened lifestyle intervention (SLI) (n = 17). We quantified liver fibrosis with qFibrosis in the portal tract, periportal, transitional, pericentral, and central vein regions. RESULTS: About 20% (7/35) and 65% (11/17) of patients had fibrosis regression in the RLI and SLI groups, respectively. Liver fibrosis tended towards no change or regression after each lifestyle intervention, and this phenomenon was more prominent in the SLI group. SLI-induced liver fibrosis regression was concentrated in the periportal region. CONCLUSION: Using digital pathology, we could detect a more pronounced fibrosis regression with SLI, mainly in the periportal region. With changes in fibrosis area in the periportal region, we could differentiate RLI and SLI patients in the placebo group in the MASH clinical trial. Digital pathology provides new insight into lifestyle-induced fibrosis regression and placebo responses, which is not captured by conventional histological staging.

Associations between cuprotosis-related genes and the spectrum of metabolic dysfunction-associated fatty liver disease: An exploratory study.

AIMS: To explore the associations between cuprotosis-related genes (CRGs) across different stages of liver disease in metabolic dysfunction-associated fatty liver disease (MAFLD), including hepatocellular carcinoma (HCC). MATERIALS AND METHODS: We analysed several bulk RNA sequencing datasets from patients with MAFLD (n = 331) and MAFLD-related HCC (n = 271) and two MAFLD single-cell RNA sequencing datasets. To investigate the associations between CRGs and MAFLD, we performed differential correlation, logistic regression and functional enrichment analyses. We also validated the findings in an independent Wenzhou PERSONS cohort of MAFLD patients (n = 656) used for a genome-wide association study (GWAS). RESULTS: GLS, GCSH and ATP7B genes showed significant differences across the MAFLD spectrum and were significantly associated with liver fibrosis stages. GLS was closely associated with fibrosis stages in patients with MAFLD and those with MAFLD-related HCC. GLS is predominantly expressed in monocytes and T cells in MAFLD. During the progression of metabolic dysfunction-associated fatty liver to metabolic-associated steatohepatitis, GLS expression in T cells decreased. GWAS revealed that multiple single nucleotide polymorphisms in GLS were associated with clinical indicators of MAFLD. CONCLUSIONS: GLS may contribute to liver inflammation and fibrosis in MAFLD mainly through cuprotosis and T-cell activation, promoting the progression of MAFLD to HCC. These findings suggest that cuprotosis may play a role in MAFLD progression, potentially providing new insights into MAFLD pathogenesis.

Operando Stable Palladium Hydride Nanoclusters Anchored on Tungsten Carbides Mediate Reverse Hydrogen Spillover for Hydrogen Evolution.

Proton exchange membrane (PEM) electrolysis holds great promise for green hydrogen production, but suffering from high loading of platinum-group metals (PGM) for large-scale deployment. Anchoring PGM-based materials on supports can not only improve the atomic utilization of active sites but also enhance the intrinsic activity. However, in practical PEM electrolysis, it is still challenging to mediate hydrogen adsorption/desorption pathways with high coverage of hydrogen intermediates over catalyst surface. Here, operando generated stable palladium (Pd) hydride nanoclusters anchored on tungsten carbide (WCx) supports were constructed for hydrogen evolution in PEM electrolysis. Under PEM operando conditions, hydrogen intercalation induces formation of Pd hydrides (PdHx) featuring weakened hydrogen binding energy (HBE), thus triggering reverse hydrogen spillover from WCx (strong HBE) supports to PdHx sites, which have been evidenced by operando characterizations, electrochemical results and theoretical studies. This PdHx-WCx material can be directly utilized as cathode electrocatalysts in PEM electrolysis with ultralow Pd loading of 0.022 mg cm-2, delivering the current density of 1 A cm-2 at the cell voltage of ~1.66 V and continuously running for 200 hours without obvious degradation. This innovative strategy via tuning the operando characteristics to mediate reverse hydrogen spillover provide new insights for designing high-performance supported PGM-based electrocatalysts.

Polar Aromatic Two-dimensional Dion-Jacobson Halide Perovskites for Efficient Photocatalytic H2 Evolution.

Polar materials with spontaneous polarization (Ps) have emerged as highly promising photocatalysts for efficient photocatalytic H2 evolution owing to the Ps-enhanced photogenerated carrier separation. However, traditional inorganic polar materials often suffer from limitations such as wide band gaps and poor carrier transport, which hinders their photocatalytic H2 evolution efficiency. Here, we rationally synthesized a series of isostructural two-dimensional (2D) aromatic Dion-Jacobson (DJ) perovskites, namely (2-(2-Aminoethyl)pyridinium)PbI4 (2-APDPI), (3-(2-Aminoethyl)pyridinium)PbI4 (3-APDPI), and (4-(2-Aminoethyl)pyridinium)PbI4 (4-APDPI), where 2-APDPI and 4-APDPI crystalize in polar space groups with piezoelectric constants (d33) of approximately 40 pm V-1 and 3-APDPI adopts a centrosymmetric structure. Strikingly, owing to the Ps-facilitated separation of photogenerated carriers, polar 2-APDPI and 4-APDPI exhibit a 3.9- and 2.8-fold increase, respectively, in photocatalytic H2 evolution compared to the centrosymmetric 3-APDPI. As a pioneering study, this work provides an efficient approach for exploring new polar photocatalysts and highlights their potential in promoting photocatalytic H2 evolution.

Isolation of Highly Reactive Cobalt Phthalocyanine via Electrochemical Activation for Enhanced CO2 Reduction Reaction.

Electrochemical CO2 -to-CO conversion offers an attractive and efficient route to recycle CO2 greenhouse gas. Molecular catalysts, like CoPc, are proved to be possible replacement for precious metal-based catalysts. These molecules, a combination of metal center and organic ligand molecule, may evolve into single atom structure for enhanced performance; besides, the manipulation of molecules' behavior also plays an important role in mechanism research. Here, in this work, the structure evolution of CoPc molecules is investigated via electrochemical-induced activation process. After numbers of cyclic voltammetry scanning, CoPc molecular crystals become cracked and crumbled, meanwhile the released CoPc molecules migrate to the conductive substrate. Atomic-scale HAADF-STEM proves the migration of CoPc molecules, which is the main reason for the enhancement in CO2 -to-CO performance. The as-activated CoPc exhibits a maximum FECO of 99% in an H-type cell and affords a long-term durability at 100 mA cm-2 for 29.3 h in a membrane electrode assembly reactor. Density-functional theory (DFT) calculation also demonstrates a favorable CO2 activation energy with such an activated CoPc structure. This work provides a different perspective for understanding molecular catalysts as well as a reliable and universal method for practical utilization.

Positive Valent Metal Sites in Electrochemical CO2 Reduction Reaction.

The discovery of high-performance catalysts for the electrochemical CO2 reduction reaction (CO2 RR) has faced an enormous challenge for years. The lack of cognition about the surface active structures or centers of catalysts in complex conditions limits the development of advanced catalysts for CO2 RR. Recently, the positive valent metal sites (PVMS) are demonstrated as a kind of potential active sites, which can facilitate carbon dioxide (CO2 ) activation and conversation but are always unstable under reduction potentials. Many advanced technologies in theory and experiment have been utilized to understand and develop excellent catalysts with PVMS for CO2 RR. Here, we present an introduction of some typical catalysts with PVMS in CO2 RR and give some understanding of the activity and stability for these related catalysts.

Novel urinary protein panels for the non-invasive diagnosis of non-alcoholic fatty liver disease and fibrosis stages.

BACKGROUND & AIMS: There is an unmet clinical need for non-invasive tests to diagnose non-alcoholic fatty liver disease (NAFLD) and individual fibrosis stages. We aimed to test whether urine protein panels could be used to identify NAFLD, NAFLD with fibrosis (stage F ≥ 1) and NAFLD with significant fibrosis (stage F ≥ 2). METHODS: We collected urine samples from 100 patients with biopsy-confirmed NAFLD and 40 healthy volunteers, and proteomics and bioinformatics analyses were performed in this derivation cohort. Diagnostic models were developed for detecting NAFLD (UPNAFLD model), NAFLD with fibrosis (UPfibrosis model), or NAFLD with significant fibrosis (UPsignificant fibrosis model). Subsequently, the derivation cohort was divided into training and testing sets to evaluate the efficacy of these diagnostic models. Finally, in a separate independent validation cohort of 100 patients with biopsy-confirmed NAFLD and 45 healthy controls, urinary enzyme-linked immunosorbent assay analyses were undertaken to validate the accuracy of these new diagnostic models. RESULTS: The UPfibrosis model and the UPsignificant fibrosis model showed an AUROC of .863 (95% CI: .725-1.000) and 0.858 (95% CI: .712-1.000) in the training set; and .837 (95% CI: .711-.963) and .916 (95% CI: .825-1.000) in the testing set respectively. The UPNAFLD model showed an excellent diagnostic performance and the area under the receiver operator characteristic curve (AUROC) exceeded .90 in the derivation cohort. In the independent validation cohort, the AUROC for all three of the above diagnostic models exceeded .80. CONCLUSIONS: Our newly developed models constructed from urine protein biomarkers have good accuracy for non-invasively diagnosing liver fibrosis in NAFLD.

Tuning the Microenvironment in Monolayer MgAl Layered Double Hydroxide for CO2 -to-Ethylene Electrocatalysis in Neutral Media.

The electrocatalytic reduction of carbon dioxide provides a feasibility to achieve a carbon-neutral energy cycle. However, there are a number of bottleneck issues to be resolved before industrial application, such as the low conversion efficiency, selectivity and reaction rate, etc. Engineering local environment is a critical way to address these challenges. Here, a monolayer MgAl-LDH was proposed to optimize the local environment of Cu for stimulating industrial-current-density CO2 -to-C2 H4 electroreduction in neutral media. In situ spectroscopic results and theoretical study demonstrated that the Cu electrode modified by MgAl-LDH (MgAl-LDH/Cu) displayed a much higher surface pH value compared to the bare Cu, which could be attributed to the decreased energy barrier for hydrolysis on MgAl-LDH sites with more OH- ions on the surface of the electrode. As a result, MgAl-LDH/Cu achieved a C2 H4 Faradaic efficiency of 55.1 % at a current density up to 300 mA cm-2 in 1.0 M KHCO3 electrolyte.

Operando High-Valence Cr-Modified NiFe Hydroxides for Water Oxidation.

High-valence metal-doped multimetal (oxy)hydroxides outperform noble metal electrocatalysts for the oxygen evolution reaction (OER) owing to the modified energetics between 3d metals and high-valence dopants. However, the rational design of sufficient and subtle modulators is still challenging. With a multimetal layered double hydroxide (LDH) as the OER catalyst, this study introduces a series of operando high-valence dopants (Cr, Ru, Ce, and V), which can restrict the 3+ valence states in the LDH template to prevent phase separation and operando transfer to the >3+ valence states for sufficient electronic interaction during the OER process. Through density functional theory simulations, ultrathin Cr-doped NiFe (NiFeCr) LDH is synthesized with strong electronic interaction between Cr dopants and NiFe bimetallic sites, evidenced by X-ray absorption spectroscopy. The resulting NiFeCr-LDH catalyzes the OER with ultralow overpotentials of 189 and 284 mV, obtaining current densities of 10 and 1000 mA cm-2 , respectively. Further, a NiFeCr-LDH anode is coupled in the anion exchange membrane electrolyzers to promote alkaline water splitting and CO2 -to-CO electrolysis, which achieves low full cell voltages at high current densities.

Machine learning algorithms based on proteomic data mining accurately predicting the recurrence of hepatitis B-related hepatocellular carcinoma.

BACKGROUND AND AIM: Over 10% of hepatocellular carcinoma (HCC) cases recur each year, even after surgical resection. Currently, there is a lack of knowledge about the causes of recurrence and the effective prevention. Prediction of HCC recurrence requires diagnostic markers endowed with high sensitivity and specificity. This study aims to identify new key proteins for HCC recurrence and to build machine learning algorithms for predicting HCC recurrence. METHODS: The proteomics data for analysis in this study were obtained from the Clinical Proteomics Tumor Analysis Consortium (CPTAC) database. We analyzed different proteins based on cases with or without recurrence of HCC. Survival analysis, Cox regression analysis, and area under the ROC curves (AUROC > 0.7) were used to screen for more significant differential proteins. Predictive models for HCC recurrence were developed using four machine learning algorithms. RESULTS: A total of 690 differentially expressed proteins between 50 relapsed and 77 non-relapsed hepatitis B-related HCC patients were identified. Seven of these proteins had an AUROC > 0.7 for 5-year survival in HCC, including BAHCC1, ESF1, RAP1GAP, RUFY1, SCAMP3, STK3, and TMEM230. Among the machine learning algorithms, the random forest algorithm showed the highest AUROC values (AUROC: 0.991, 95% CI 0.962-0.999) for identifying HCC recurrence, followed by the support vector machine (AUROC: 0.893, 95% Cl 0.824-0.956), the logistic regression (AUROC: 0.774, 95% Cl 0.672-0.868), and the multi-layer perceptron algorithm (AUROC: 0.571, 95% Cl 0.459-0.682). CONCLUSIONS: Our study identifies seven novel proteins for predicting HCC recurrence and the random forest algorithm as the most suitable predictive model for HCC recurrence.