Accurate identification of structural variations from cancer samples.

Structural variations (SVs) are commonly found in cancer genomes. They can cause gene amplification, deletion and fusion, among other functional consequences. With an average read length of hundreds of kilobases, nano-channel-based optical DNA mapping is powerful in detecting large SVs. However, existing SV calling methods are not tailored for cancer samples, which have special properties such as mixed cell types and sub-clones. Here we propose the Cancer Optical Mapping for detecting Structural Variations (COMSV) method that is specifically designed for cancer samples. It shows high sensitivity and specificity in benchmark comparisons. Applying to cancer cell lines and patient samples, COMSV identifies hundreds of novel SVs per sample.

HGCLAMIR: Hypergraph contrastive learning with attention mechanism and integrated multi-view representation for predicting miRNA-disease associations.

Existing studies have shown that the abnormal expression of microRNAs (miRNAs) usually leads to the occurrence and development of human diseases. Identifying disease-related miRNAs contributes to studying the pathogenesis of diseases at the molecular level. As traditional biological experiments are time-consuming and expensive, computational methods have been used as an effective complement to infer the potential associations between miRNAs and diseases. However, most of the existing computational methods still face three main challenges: (i) learning of high-order relations; (ii) insufficient representation learning ability; (iii) importance learning and integration of multi-view embedding representation. To this end, we developed a HyperGraph Contrastive Learning with view-aware Attention Mechanism and Integrated multi-view Representation (HGCLAMIR) model to discover potential miRNA-disease associations. First, hypergraph convolutional network (HGCN) was utilized to capture high-order complex relations from hypergraphs related to miRNAs and diseases. Then, we combined HGCN with contrastive learning to improve and enhance the embedded representation learning ability of HGCN. Moreover, we introduced view-aware attention mechanism to adaptively weight the embedded representations of different views, thereby obtaining the importance of multi-view latent representations. Next, we innovatively proposed integrated representation learning to integrate the embedded representation information of multiple views for obtaining more reasonable embedding information. Finally, the integrated representation information was fed into a neural network-based matrix completion method to perform miRNA-disease association prediction. Experimental results on the cross-validation set and independent test set indicated that HGCLAMIR can achieve better prediction performance than other baseline models. Furthermore, the results of case studies and enrichment analysis further demonstrated the accuracy of HGCLAMIR and unconfirmed potential associations had biological significance.

p53 regulation of ammonia metabolism through urea cycle controls polyamine biosynthesis.

Cancer cells exhibit altered and usually increased metabolic processes to meet their high biogenetic demands1,2. Under these conditions, ammonia is concomitantly produced by the increased metabolic processing. However, it is unclear how tumour cells dispose of excess ammonia and what outcomes might be caused by the accumulation of ammonia. Here we report that the tumour suppressor p53, the most frequently mutated gene in human tumours, regulates ammonia metabolism by repressing the urea cycle. Through transcriptional downregulation of CPS1, OTC and ARG1, p53 suppresses ureagenesis and elimination of ammonia in vitro and in vivo, leading to the inhibition of tumour growth. Conversely, downregulation of these genes reciprocally activates p53 by MDM2-mediated mechanism(s). Furthermore, the accumulation of ammonia causes a significant decline in mRNA translation of the polyamine biosynthetic rate-limiting enzyme ODC, thereby inhibiting the biosynthesis of polyamine and cell proliferation. Together, these findings link p53 to ureagenesis and ammonia metabolism, and further reveal a role for ammonia in controlling polyamine biosynthesis and cell proliferation.

Publisher Correction: p53 regulation of ammonia metabolism through urea cycle controls polyamine biosynthesis.

In Fig. 1c of this Letter, the labels p53+/+ and p53-/- were inadvertently swapped. The original figure has been corrected online.

rhFGF-21 accelerates corneal epithelial wound healing through the attenuation of oxidative stress and inflammatory mediators in diabetic mice.

Diabetic keratopathy, commonly associated with a hyperactive inflammatory response, is one of the most common eye complications of diabetes. The peptide hormone fibroblast growth factor-21 (FGF-21) has been demonstrated to have anti-inflammatory and antioxidant properties. However, whether administration of recombinant human (rh) FGF-21 can potentially regulate diabetic keratopathy is still unknown. Therefore, in this work, we investigated the role of rhFGF-21 in the modulation of corneal epithelial wound healing, the inflammation response, and oxidative stress using type 1 diabetic mice and high glucose-treated human corneal epithelial cells. Our experimental results indicated that the application of rhFGF-21 contributed to the enhancement of epithelial wound healing. This treatment also led to advancements in tear production and reduction in corneal edema. Moreover, there was a notable reduction in the levels of proinflammatory cytokines such as TNF-α, IL-6, IL-1ß, MCP-1, IFN-γ, MMP-2, and MMP-9 in both diabetic mouse corneal epithelium and human corneal epithelial cells treated with high glucose. Furthermore, we found rhFGF-21 treatment inhibited reactive oxygen species production and increased levels of anti-inflammatory molecules IL-10 and SOD-1, which suggests that FGF-21 has a protective role in diabetic corneal epithelial healing by increasing the antioxidant capacity and reducing the release of inflammatory mediators and matrix metalloproteinases. Therefore, we propose that administration of FGF-21 may represent a potential treatment for diabetic keratopathy.

Neural correlates of semantic-driven syntactic parsing in sentence comprehension.

For sentence comprehension, information carried by semantic relations between constituents must be combined with other information to decode the constituent structure of a sentence, due to atypical and noisy situations of language use. Neural correlates of decoding sentence structure by semantic information have remained largely unexplored. In this functional MRI study, we examine the neural basis of semantic-driven syntactic parsing during sentence reading and compare it with that of other types of syntactic parsing driven by word order and case marking. Chinese transitive sentences of various structures were investigated, differing in word order, case making, and agent-patient semantic relations (i.e., same vs. different in animacy). For the non-canonical unmarked sentences without usable case marking, a semantic-driven effect triggered by agent-patient ambiguity was found in the left inferior frontal gyrus opercularis (IFGoper) and left inferior parietal lobule, with the activity not being modulated by naturalness factors of the sentences. The comparison between each type of non-canonical sentences with canonical sentences revealed that the non-canonicity effect engaged the left posterior frontal and temporal regions, in line with previous studies. No extra neural activity was found responsive to case marking within the non-canonical sentences. A word order effect across all types of sentences was also found in the left IFGoper, suggesting a common neural substrate between different types of parsing. The semantic-driven effect was also observed for the non-canonical marked sentences but not for the canonical sentences, suggesting that semantic information is used in decoding sentence structure in addition to case marking. The current findings illustrate the neural correlates of syntactic parsing with semantics, and provide neural evidence of how semantics facilitates syntax together with other information.

Oncofetal SNRPE promotes HCC tumorigenesis by regulating the FGFR4 expression through alternative splicing.

BACKGROUND: Due to insufficient knowledge about key molecular events, Hepatocellular carcinoma (HCC) lacks effective treatment targets. Spliceosome-related genes were significantly altered in HCC. Oncofetal proteins are ideal tumor therapeutic targets. Screening of differentially expressed Spliceosome-related oncofetal protein in embryonic liver development and HCC helps discover effective therapeutic targets for HCC. METHODS: Differentially expressed spliceosome genes were analysis in fetal liver and HCC through bioinformatics analysis. Small nuclear ribonucleoprotein polypeptide E (SNRPE) expression was detected in fetal liver, adult liver and HCC tissues. The role of SNRPE in HCC was performed multiple assays in vitro and in vivo. SNRPE-regulated alternative splicing was recognized by RNA-Seq and confirmed by multiple assays. RESULTS: We herein identified SNRPE as a crucial oncofetal splicing factor, significantly associated with the adverse prognosis of HCC. SOX2 was identified as the activator for SNRPE reactivation. Efficient knockdown of SNRPE resulted in the complete cessation of HCC tumorigenesis and progression. Mechanistically, SNRPE knockdown reduced FGFR4 mRNA expression by triggering nonsense-mediated RNA decay. A partial inhibition of SNRPE-induced malignant progression of HCC cells was observed upon FGFR4 knockdown. CONCLUSIONS: Our findings highlight SNRPE as a novel oncofetal splicing factor and shed light on the intricate relationship between oncofetal splicing factors, splicing events, and carcinogenesis. Consequently, SNRPE emerges as a potential therapeutic target for HCC treatment. Model of oncofetal SNRPE promotes HCC tumorigenesis by regulating the AS of FGFR4 pre-mRNA.

Tumor microenvironment characteristics association with clinical outcome in patients with resected intestinal-type gastric cancer.

BACKGROUND: Tumor microenvironment (TME) characteristics including tumor stroma ratio (TSR), tumor budding (TB), and tumor-infiltrating lymphocytes (TILs) were examined in resected gastric cancer. These TME features have been shown to indicate metastatic potential in colon cancer, and intestinal-type gastric cancer (IGC) has pathological similarities with that malignancy. METHODS: TSR, TB, and TILs were quantified in routine histological sections from 493 patients with IGC who underwent radical resection at 2 university hospitals in China from 2010 to 2016. TME variables were dichotomized as follows: TSR (50%), TILs (median), TB per international guidelines (4 buds/0.785mm2), and platelet-lymphocyte ratio (PLR) per survival ROC. Association of TME features with patient clinicopathological characteristics, time-to-recurrence (TTR), and cancer-specific-survival (CSS) were examined using univariate and multivariate analysis, including a relative contribution analysis by Cox regression. RESULTS: Patients whose tumors showed high TSR or high TB or low TILs were each significantly associated with increased T and N stage, higher histological grade, and poorer TTR and CSS at 5 years. Only TSR and N stage were independently associated with TTR and CSS after adjustment for covariates. PLR was only independently associated with TTR after adjustment for covariates. Among the variables examined, only TSR was significantly associated with both TTR (HR 1.72, 95% CI, 1.14-2.60, P = .01) and CSS (HR 1.62, 95% CI, 1.05-2.51, P = .03) multivariately. Relative contribution to TTR revealed that the top 3 contributors were N stage (45.1%), TSR (22.5%), and PLR (12.9%), while the top 3 contributors to CSS were N stage (59.9%), TSR (14.7%), and PLR (10.9%). CONCLUSIONS: Among the examined TME features, TSR was the most robust for prognostication and was significantly associated with both TTR and CSS. Furthermore, the relative contribution of TSR to patient TTR and CSS was second only to nodal status.

Copper Nanosheet-Based Wash-Free Fluorescence Imaging of Cancer Cells.

Near-infrared fluorescence (NIRF) probes greatly facilitate in vivo imaging of various biologically important species. However, there are several significant limitations such as consuming washing steps, photobleaching, and low signal intensity. Herein, we synthesized fluorescent copper nanosheets templated with DNA scaffolds (DNS/CuNSs). We employ them and Cy5.5 of the fluorescence resonance energy transfer (FRET) system, which have a larger Stokes shift (∼12-fold) than the traditional NIRF dye Cy5.5. Based on their excellent fluorescence properties, we employ DNS/CuNSs-Cy5.5 for fluorescence probes in cancer cell imaging. Compared with the free Cy5.5 fluorescence probe, the novel fluorescence imaging probe implements wash-free imaging and exhibits enhanced anti-photobleaching ability (∼5.5-fold). Moreover, the FRET system constructed by DNS/CuNSs has a higher signal amplification ability (∼4.17-fold), which is more similar to that of Cu nanoclusters prepared with DNA nanomonomers as a template. This work provides a new idea for cancer cell MCF-7 imaging and is expected to promote the development of cancer cell fluorescence imaging.

FeN3S1─OH Single-Atom Sites Anchored on Hollow Porous Carbon for Highly Efficient pH-Universal Oxygen Reduction Reaction.

Regulating the asymmetric active center of a single-atom catalyst to optimize the binding energy is critical but challenging to improve the overall efficiency of the electrocatalysts. Herein, an effective strategy is developed by introducing an axial hydroxyl (OH) group to the Fe─N4 center, simultaneously assisting with the further construction of asymmetric configurations by replacing one N atom with one S atom, forming FeN3S1─OH configuration. This novel structure can optimize the electronic structure and d-band center shift to reduce the reaction energy barrier, thereby promoting oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) catalytic activities. The optimal catalyst, FeSA-S/N-C (FeN3S1─OH anchored on hollow porous carbon) displays remarkable ORR performance with a half-wave potential of 0.92, 0.78, and 0.64 V versus RHE in 0.1 m KOH, 0.5 m H2SO4, and 0.1 m PBS, respectively. The rechargeable liquid Zn-air batteries (LZABs) equipped with FeSA-S/N-C display a higher power density of 128.35 mW cm-2, long-term operational stability of over 500 h, and outstanding reversibility. More importantly, the corresponding flexible solid-state ZABs (FSZABs@FeSA-S/N-C) display negligible voltage changes at different bending angles during the charging and discharging processes. This work provides a new perspective for the design and optimization of asymmetric configuration for single-atom catalysts applied to the area of energy conversion and storage.

Predicting multiple types of miRNA-disease associations using adaptive weighted nonnegative tensor factorization with self-paced learning and hypergraph regularization.

More and more evidence indicates that the dysregulations of microRNAs (miRNAs) lead to diseases through various kinds of underlying mechanisms. Identifying the multiple types of disease-related miRNAs plays an important role in studying the molecular mechanism of miRNAs in diseases. Moreover, compared with traditional biological experiments, computational models are time-saving and cost-minimized. However, most tensor-based computational models still face three main challenges: (i) easy to fall into bad local minima; (ii) preservation of high-order relations; (iii) false-negative samples. To this end, we propose a novel tensor completion framework integrating self-paced learning, hypergraph regularization and adaptive weight tensor into nonnegative tensor factorization, called SPLDHyperAWNTF, for the discovery of potential multiple types of miRNA-disease associations. We first combine self-paced learning with nonnegative tensor factorization to effectively alleviate the model from falling into bad local minima. Then, hypergraphs for miRNAs and diseases are constructed, and hypergraph regularization is used to preserve the high-order complex relations of these hypergraphs. Finally, we innovatively introduce adaptive weight tensor, which can effectively alleviate the impact of false-negative samples on the prediction performance. The average results of 5-fold and 10-fold cross-validation on four datasets show that SPLDHyperAWNTF can achieve better prediction performance than baseline models in terms of Top-1 precision, Top-1 recall and Top-1 F1. Furthermore, we implement case studies to further evaluate the accuracy of SPLDHyperAWNTF. As a result, 98 (MDAv2.0) and 98 (MDAv2.0-2) of top-100 are confirmed by HMDDv3.2 dataset. Moreover, the results of enrichment analysis illustrate that unconfirmed potential associations have biological significance.

Peroxidase gene TaPrx109-B1 enhances wheat tolerance to water deficit via modulating stomatal density.

Increasing the tolerance of crops to water deficit is crucial for the improvement of crop production in water-restricted regions. Here, a wheat peroxidase gene (TaPrx109-B1) belonging to the class III peroxidase gene family was identified and its function in water deficit tolerance was revealed. We demonstrated that overexpression of TaPrx109-B1 reduced leaf H2O2 level and stomatal density, increased leaf relative water content, water use efficiency, and tolerance to water deficit. The expression of TaEPF1 and TaEPF2, two key negative regulators of stomatal development, were significantly upregulated in TaPrx109-B1 overexpression lines. Furthermore, exogenous H2O2 downregulated the expression of TaEPF1 and TaEPF2 and increased stomatal density, while exogenous application of diphenyleneiodonium chloride, a potent NADPH oxidase inhibitor that repressed the synthesis of H2O2, upregulated the expression of TaEPF1 and TaEPF2, decreased stomatal density, and enhanced wheat tolerance to water deficit. These findings suggest that TaPrx109-B1 influences leaf stomatal density by modulation of H2O2 level, and consequently affecting the expression of TaEPF1 and TaEPF2. The results of the field trial showed that overexpressing TaPrx109-B1 increased grain number per spike, which reduced the yield loss caused by water deficiency. Therefore, TaPrx109-B1 has great potential in breeding wheat varieties with improved water deficit tolerance.

Innovative rapid liquid concentration measurement based on thermal lens effect and machine learning.

This study addresses the critical need for rapid and online measurement of liquid concentrations in industrial applications. Although the thermal lens effect (TLE) is extensively explored in laser systems for determining thermal lens focal lengths, its application in quantifying solution concentrations remains underexplored. This research explores the relationship between various liquid concentrations and the interference fringes induced by the TLE. A novel approach is introduced, utilizing TLE to measure solution concentrations, with integration of image processing and discrete Fourier transform (DFT) techniques for feature extraction from interference rings. Further, machine learning, specifically backpropagation artificial neural network (BP-ANN), is employed to model concentration measurement. The model demonstrates high accuracy, evidenced by low root mean square error (RMSE) values of 3.055 and 5.396 for the training and test sets, respectively. This enables precise, real-time determination of soy sauce concentration, offering significant implications for industrial testing, environmental monitoring, and other related fields.

Relationship between stress hyperglycemia ratio and acute kidney injury in patients with congestive heart failure.

BACKGROUND: The stress hyperglycemia ratio (SHR) has been demonstrated as an independent risk factor for acute kidney injury (AKI) in certain populations. However, this relationship in patients with congestive heart failure (CHF) remains unclear. Our study sought to elucidate the relationship between SHR and AKI in patients with CHF. METHODS: A total of 8268 patients with CHF were included in this study. We categorized SHR into distinct groups and evaluated its association with mortality through logistic or Cox regression analyses. Additionally, we applied the restricted cubic spline (RCS) analysis to explore the relationship between SHR as a continuous variable and the occurrence of AKI. The primary outcome of interest in this investigation was the incidence of AKI during hospitalization. RESULTS: Within this patient cohort, a total of 5,221 (63.1%) patients experienced AKI during their hospital stay. Upon adjusting for potential confounding variables, we identified a U-shaped correlation between SHR and the occurrence of AKI, with an inflection point at 0.98. When the SHR exceeded 0.98, for each standard deviation (SD) increase, the risk of AKI was augmented by 1.32-fold (odds ratio [OR]: 1.32, 95% CI: 1.22 to 1.46). Conversely, when SHR was below 0.98, each SD decrease was associated with a pronounced increase in the risk of AKI. CONCLUSION: Our study reveals a U-shaped relationship between SHR and AKI in patients with CHF. Notably, we identified an inflection point at an SHR value of 0.98, signifying a critical threshold for evaluating AKI in this population.

Associations of 50 modifiable risk factors with atrial fibrillation using Mendelian randomization analysis.

BACKGROUND: Substantial focus has been placed on atrial fibrillation (AF) treatment and associated stroke prevention rather than preventing AF itself. We employed Mendelian randomization (MR) approach to examine the causal relationships between 50 modifiable risk factors (RFs) and AF. METHODS: Instrumental variables for genetically predicted exposures were derived from corresponding genome-wide association studies (GWASs). Summary-level statistical data for AF were obtained from a GWAS meta-analysis (discovery dataset, N = 1,030,836) and FinnGen (validation dataset, N = 208,594). Univariable and multivariable MR analyses were performed, primarily using inverse variance weighted method with a series of robust sensitivity analyses. RESULTS: Genetic predisposition to insomnia, daytime naps, apnea, smoking initiation, moderate to vigorous physical activity and obesity traits, including body mass index, waist-hip ratio, central and peripheral fat/fat-free mass, exhibited significant associations with an increased risk of AF. Coffee consumption and ApoB had suggestive increased risks. Hypertension (odds ratio (OR) 95% confidence interval (CI): 5.26 (4.42, 6.24)), heart failure (HF) (OR 95% CI, 4.77 (2.43, 9.37)) and coronary artery disease (CAD) (OR 95% CI: 1.20 (1.16, 1.24)) were strongly associated with AF, while college degree, higher education attachment and HDL levels were associated with a decreased AF risk. Reverse MR found a bidirectional relationship between genetically predicted AF and CAD, HF and ischemic stroke. Multivariable analysis further indicated that obesity-related traits, systolic blood pressure and lower HDL levels independently contributed to the development of AF. CONCLUSIONS: This study identified several lifestyles and cardiometabolic factors that might be causally related to AF, underscoring the importance of a holistic approach to AF management and prevention.

A remote sensing method for mapping alpine grasslines based on graph-cut.

Climate change has induced substantial shifts in vegetation boundaries such as alpine treelines and shrublines, with widespread ecological and climatic influences. However, spatial and temporal changes in the upper elevational limit of alpine grasslands ("alpine grasslines") are still poorly understood due to lack of field observations and remote sensing estimates. In this study, taking the Tibetan Plateau as an example, we propose a novel method for automatically identifying alpine grasslines from multi-source remote sensing data and determining their positions at 30-m spatial resolution. We first identified 2895 mountains potentially having alpine grasslines. On each mountain, we identified a narrow area around the upper elevational limit of alpine grasslands where the alpine grassline was potentially located. Then, we used linear discriminant analysis to adaptively generate from Landsat reflectance features a synthetic feature that maximized the difference between vegetated and unvegetated pixels in each of these areas. After that, we designed a graph-cut algorithm to integrate the advantages of the Otsu and Canny approaches, which was used to determine the precise position of the alpine grassline from the synthetic feature image. Validation against alpine grasslines visually interpreted from a large number of high-spatial-resolution images showed a high level of accuracy (R2 , .99 and .98; mean absolute error, 22.6 and 36.2 m, vs. drone and PlanetScope images, respectively). Across the Tibetan Plateau, the alpine grassline elevation ranged from 4038 to 5380 m (5th-95th percentile), lower in the northeast and southeast and higher in the southwest. This study provides a method for remotely sensing alpine grasslines for the first-time at large scale and lays a foundation for investigating their responses to climate change.

Synthesis of Bench-stable Polycyclic Organophosphorus Heterocycles via Staudinger-type Annulations of ortho-Azidophenols.

The formation of a five- or six-membered ring is known to stabilize unstable molecular structures such as hemiacetals. This idea can also be extended to stabilize other high-coordinated p-block element species. Herein, we synthesized two novel polycyclic organophosphorus heterocycles via Staudinger-type annulations. Reactions of either ortho-phosphinoarenesulfonyl fluorides 1 or ortho-phosphinobenzoic acid methyl esters 4 with ortho-azidophenols 2 gave rise to penta-coordinated P(V) heterocycles, benzo-benzo-1,2,3-thiazaphospholo-1,3,2-oxazaphosphole (B-B-TAP-OAP) 3 and benzo-benzo-1,2-azaphospholo-1,3,2-oxazaphosphol-12-one (B-B-AP-OAP) 5 in satisfactory yields. It is remarkable that heterocycles 3 and 5 are both bench-stable and exhibit considerable stability in a 10 % aqueous tetrahydrofuran solution. Preliminary computational studies disclosed that the formation of nitrogen gas is the key driving force for the annulations. In addition, the formation of a strong Si-F bond is another contributor to the annulation of 1 and 2.

A novel surgical scheme for hepatectomy in hepatocellular carcinoma patients with clinically significant portal hypertension.

OBJECTIVE: Clinically significant portal hypertension (CSPH) seriously affects the feasibility and safety of surgical treatment for hepatocellular carcinoma (HCC) patients. The aim of this study was to establish a new surgical scheme defining risk classification of post-hepatectomy liver failure (PHLF) to facilitate the surgical decision-making and identify suitable candidates for individual hepatectomy among HCC patients with CSPH. BACKGROUNDS: Hepatectomy is the preferred treatment for HCC. Surgeons must maintain a balance between the expected oncological outcomes of HCC removal and short-term risks of severe PHLF and morbidity. CSPH aggravates liver decompensation and increases the risk of severe PHLF thus complicating hepatectomy for HCC. METHODS: Multivariate logistic regression and stochastic forest algorithm were performed, then the independent risk factors of severe PHLF were included in a nomogram to determine the risk of severe PHLF. Further, a conditional inference tree (CTREE) through recursive partitioning analysis validated supplement the misdiagnostic threshold of the nomogram. RESULTS: This study included 924 patients, of whom 137 patients (14.8%) suffered from mild-CSPH and 66 patients suffered from (7.1%) with severe-CSPH confirmed preoperatively. Our data showed that preoperative prolonged prothrombin time, total bilirubin, indocyanine green retention rate at 15 min, CSPH grade, and standard future liver remnant volume were independent predictors of severe PHLF. By incorporating these factors, the nomogram achieved good prediction performance in assessing severe PHLF risk, and its concordance statistic was 0.891, 0.850 and 0.872 in the training cohort, internal validation cohort and external validation cohort, respectively, and good calibration curves were obtained. Moreover, the calculations of total points of diagnostic errors with 95% CI were concentrated in 110.5 (range 76.9-178.5). It showed a low risk of severe PHLF (2.3%), indicating hepatectomy is feasible when the points fall below 76.9, while the risk of severe PHLF is extremely high (93.8%) and hepatectomy should be rigorously restricted at scores over 178.5. Patients with points within the misdiagnosis threshold were further examined using CTREE according to a hierarchic order of factors represented by the presence of CSPH grade, ICG-R15, and sFLR. CONCLUSION: This new surgical scheme established in our study is practical to stratify risk classification in assessing severe PHLF, thereby facilitating surgical decision-making and identifying suitable candidates for individual hepatectomy.

Brain functional changes across mood states in bipolar disorder: from a large-scale network perspective.

BACKGROUND: Exploring the neural basis related to different mood states is a critical issue for understanding the pathophysiology underlying mood switching in bipolar disorder (BD), but research has been scarce and inconsistent. METHODS: Resting-state functional magnetic resonance imaging data were acquired from 162 patients with BD: 33 (hypo)manic, 64 euthymic, and 65 depressive, and 80 healthy controls (HCs). The differences of large-scale brain network functional connectivity (FC) between the four groups were compared and correlated with clinical characteristics. To validate the generalizability of our findings, we recruited a small longitudinal independent sample of BD patients (n = 11). In addition, we examined topological nodal properties across four groups as exploratory analysis. RESULTS: A specific strengthened pattern of network FC, predominantly involving the default mode network (DMN), was observed in (hypo)manic patients when compared with HCs and bipolar patients in other mood states. Longitudinal observation revealed an increase in several network FCs in patients during (hypo)manic episode. Both samples evidenced an increase in the FC between the DMN and ventral attention network, and between the DMN and limbic network (LN) related to (hypo)mania. The altered network connections were correlated with mania severity and positive affect. Bipolar depressive patients exhibited decreased FC within the LN compared with HCs. The exploratory analysis also revealed an increase in degree in (hypo)manic patients. CONCLUSIONS: Our findings identify a distributed pattern of large-scale network disturbances in the unique context of (hypo)mania and thus provide new evidence for our understanding of the neural mechanism of BD.

Recent Advances in Graphene-Based Materials for Zinc-Ion Batteries.

Zinc-ion batteries (ZIBs) are a promising alternative for large-scale energy storage due to their advantages of environmental protection, low cost, and intrinsic safety. However, the utilization of their full potential is still hindered by the sluggish electrode reaction kinetics, poor structural stability, severe Zn dendrite growth, and narrow electrochemical stability window of the whole battery. Graphene-based materials with excellent physicochemical properties hold great promise for addressing the above challenges foe ZIBs. In this review, the energy storage mechanisms and challenges faced by ZIBs are first discussed. Key issues and recent progress in design strategies for graphene-based materials in optimizing the electrochemical performance of ZIBs (anode, cathode, electrolyte, separator and current collector) are then discussed. Finally, some potential challenges and future research directions of graphene-based materials in high-performance ZIBs are proposed for practical applications.