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The discovery of a two-dimensional electron gas (2DEG) at the [Formula: see text] interface has set a new platform for all-oxide electronics which could potentially exhibit the interplay among charge, spin, orbital, superconductivity, ferromagnetism and ferroelectricity. In this work, by using angle-resolved photoemission spectroscopy and conductivity measurement, we found the reduction of 2DEGs and the changes of the conductivity nature of some ferroelectric oxides including insulating Nb-lightly-substituted [Formula: see text], [Formula: see text] (BTO) and (Ca,Zr)-doped BTO across paraelectric-ferroelectric transition. We propose that these behaviours could be due to the increase of space-charge screening potential at the 2DEG/ferroelectric regions which is a result of the realignment of ferroelectric polarisation upon light irradiation. This finding suggests an opportunity for controlling the 2DEG at a bare oxide surface (instead of interfacial system) by using both light and ferroelectricity.

Light-sensitive capacitance variation of Bi0.95La0.05FeO3 (BLFO) ceramics has been studied under violet to UV irradiation. The reversible capacitance enhancement up to 21% under 405 nm violet laser irradiation has been observed, suggesting a possible degree of freedom to dynamically control this in high dielectric materials for light-sensitive capacitance applications. By using ultraviolet photoemission spectroscopy (UPS), we show here that exposure of BLFO surfaces to UV light induces a counterintuitive shift of the O2p valence state to lower binding energy of up to 243 meV which is a direct signature of negative electronic compressibility (NEC). A decrease of BLFO electrical resistance agrees strongly with the UPS data suggesting the creation of a thin conductive layer on its insulating bulk under light irradiation. By exploiting the quantum capacitance model, we find that the negative quantum capacitance due to this NEC effect plays an important role in this capacitance enhancement.

Charge density wave (CDW) formation, a key physics issue for materials, arises from interactions among electrons and phonons that can also lead to superconductivity and other competing or entangled phases. The prototypical system TiSe2, with a particularly simple (2 × 2 × 2) transition and no Kohn anomalies caused by electron-phonon coupling, is a fascinating but unsolved case after decades of research. Our angle-resolved photoemission measurements of the band structure as a function of temperature, aided by first-principles calculations, reveal a hitherto undetected but crucial feature: a (2 × 2) electronic order in each layer sets in at ~232 K before the widely recognized three-dimensional structural order at ~205 K. The dimensional crossover, likely a generic feature of such layered materials, involves renormalization of different band gaps in two stages.

The interaction between two different materials can present novel phenomena that are quite different from the physical properties observed when each material stands alone. Strong electronic correlations, such as magnetism and superconductivity, can be produced as the result of enhanced Coulomb interactions between electrons. Two-dimensional materials are powerful candidates to search for the novel phenomena because of the easiness of arranging them and modifying their properties accordingly. In this work, we report magnetic effects in graphene, a prototypical non-magnetic two-dimensional semi-metal, in the proximity with sulfur, a diamagnetic insulator. In contrast to the well-defined metallic behaviour of clean graphene, an energy gap develops at the Fermi energy for the graphene/sulfur compound with decreasing temperature. This is accompanied by a steep increase of the resistance, a sign change of the slope in the magneto-resistance between high and low fields, and magnetic hysteresis. A possible origin of the observed electronic and magnetic responses is discussed in terms of the onset of low-temperature magnetic ordering. These results provide intriguing insights on the search for novel quantum phases in graphene-based compounds.

Charge density wave (CDW) formation, a key physics issue for materials, arises from interactions among electrons and phonons that can also lead to superconductivity and other competing or entangled phases. The prototypical system TiSe2, with a particularly simple (2 × 2 × 2) transition and no Kohn anomalies caused by electron-phonon coupling, is a fascinating but unsolved case after decades of research. Our angle-resolved photoemission measurements of the band structure as a function of temperature, aided by first-principles calculations, reveal a hitherto undetected but crucial feature: a (2 × 2) electronic order in each layer sets in at ~232 K before the widely recognized three-dimensional structural order at ~205 K. The dimensional crossover, likely a generic feature of such layered materials, involves renormalization of different band gaps in two stages. [ABSTRACT FROM AUTHOR]

Charge density wave (CDW) formation, a key physics issue for materials, arises from interactions among electrons and phonons that can also lead to superconductivity and other competing or entangled phases. The prototypical system TiSe2, with a particularly simple (2 × 2 × 2) transition and no Kohn anomalies caused by electron-phonon coupling, is a fascinating but unsolved case after decades of research. Our angle-resolved photoemission measurements of the band structure as a function of temperature, aided by first-principles calculations, reveal a hitherto undetected but crucial feature: a (2 × 2) electronic order in each layer sets in at ~232 K before the widely recognized three-dimensional structural order at ~205 K. The dimensional crossover, likely a generic feature of such layered materials, involves renormalization of different band gaps in two stages. [ABSTRACT FROM AUTHOR]

To retrospectively develop and validate an interpretable deep learning model and nomogram utilizing endoscopic ultrasound (EUS) images to predict pancreatic neuroendocrine tumors (PNETs). Following confirmation via pathological examination, a retrospective analysis was performed on a cohort of 266 patients, comprising 115 individuals diagnosed with PNETs and 151 with pancreatic cancer. These patients were randomly assigned to the training or test group in a 7:3 ratio. The least absolute shrinkage and selection operator algorithm was employed to reduce the dimensionality of deep learning (DL) features extracted from pre-standardized EUS images. The retained nonzero coefficient features were subsequently applied to develop predictive eight DL models based on distinct machine learning algorithms. The optimal DL model was identified and used to establish a clinical signature, which subsequently informed the construction and evaluation of a nomogram. Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP) were implemented to interpret and visualize the model outputs. A total of 2048 DL features were initially extracted, from which only 27 features with coefficients greater than zero were retained. The support vector machine (SVM) DL model demonstrated exceptional performance, achieving area under the curve (AUC) values of 0.948 and 0.795 in the training and test groups, respectively. Additionally, a nomogram was developed, incorporating both DL and clinical signatures, and was visually represented for practical application. Finally, the calibration curves, decision curve analysis (DCA) plots, and clinical impact curves (CIC) exhibited by the DL model and nomogram indicated high accuracy. The application of Grad-CAM and SHAP enhanced the interpretability of these models. These methodologies contributed substantial net benefits to clinical decision-making processes. A novel interpretable DL model and nomogram were developed and validated using EUS images, cooperating with machine learning algorithms. This approach demonstrates significant potential for enhancing the clinical applicability of EUS in predicting PNETs from pancreatic cancer, thereby offering valuable insights for future research and implementation., Competing Interests: Declarations. Ethics approval and consent to participate: Following the Declaration of Helsinki, this retrospective study was approved by the Medical Ethics Committee of The First Affiliated Hospital of Guangxi Medical University (No. 2023-K346-01, 2023-12-29). Considering the retrospective nature of the study, informed consent was not required. All experiments were performed in accordance with relevant guidelines and regulations. We confirm that all research was performed in accordance with relevant guidelines/regulations. Informed consent was obtained from all participants and/or their legal guardians. The retrospective study have been performed in accordance with the Declaration of Helsinki. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Soluble vector family member 6 (SLC2A6) has been implicated in the aggressiveness and poor prognosis of various cancers, yet its specific role in hepatocellular carcinoma (HCC) remains to be fully elucidated. This study utilized multiple databases to investigate the relationship between SLC2A6 expression and clinical stage, methylation status, drug sensitivity, immune infiltration, and immune checkpoint regulation. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Furthermore, in vitro and in vivo experiments were performed to assess the impact of SLC2A6 knockout on the proliferation, migration, invasion, and underlying mechanisms in hepatocellular carcinoma (LIHC) cells. SLC2A6 expression was significantly correlated with tumor prognosis, clinical stage, and methylation levels, and was found to influence immune cell infiltration and immune checkpoint gene expression. In LIHC, SLC2A6 was associated with key biological processes, including the cell cycle, P53 signaling, and ferroptosis. Knockdown of SLC2A6 markedly suppressed the proliferation, migration, and invasion of HCC cells, with this inhibition being closely tied to the ferroptosis pathway. SLC2A6 plays a pivotal role in the regulation of pan-cancer processes, particularly in tumor prognosis and immune-related mechanisms. In LIHC, it emerges as a potential prognostic biomarker and therapeutic target for the regulation of ferroptosis, offering new insights for targeted cancer therapies., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Animal experiments statement: All animal experiments were sanctioned by the Ethical Review Committee for Experimental Animal Welfare of Guangxi Medical University, and the study was conducted in accordance with ARRIVE guidelines., (© 2025. The Author(s).)

It is well known that hyperthermia greatly impairs neuromuscular function and dynamic balance. However, whether a greater level of hyperthermia could potentially alter the lower limb simulated muscle activation when crossing an obstacle in female participants remains unknown. Therefore we examined the effect of a systematic increase in oral temperature on lower limb simulated muscle activation when crossing an obstacle in female participants. Eighteen female participants were recruited where they underwent a control trial (Con) and two progressive passive heating trials with Δ 1°C and Δ 2°C increase of oral temperature (T oral ) using a 45°C water bath. In each trial, we assessed lower limb simulated muscle activation when crossing an obstacle height of 10%, 20%, and 30% of the participant's leg length and toe-off, toe-above-obstacle and heel-strike events were identified and analyzed. In all events, the lower limb simulated muscle activation were greater in Δ2°C than Δ1°C and Con when both leading and trailing limbs crossed the obstacle height of 20% and 30% leg length (all p < 0.001). However, the lower limb simulated muscle activation were not different between Δ1°C and Con across all obstacle heights (p > 0.05). This study concluded that a greater level of hyperthermia resulted in a greater lower limb simulated muscle activation to ensure safety and stability when females cross an obstacle height of 20% leg length or higher., (© 2024. The Author(s).)

Dual-phenotype hepatocellular carcinoma (DPHCC) is a new subtype of hepatocellular carcinoma (HCC). This study aimed to investigate the relationship between the computerized tomography scan (CT) imaging and clinicopathologic features of DPHCC. The CT imaging and clinicopathologic data of 97 HCC cases who underwent radical resection were collected retrospectively. The CT imaging feature was evaluated by the ratio of the average CT value of tumor to liver (TLR) in the plain scan, arterial, portal vein and delayed phases. The association between CT imaging and clinicopathologic features was analyzed using the t-test or chi-square test. Univariate and multivariate recurrence-free survival (RFS) analysis and overall survival (OS) were performed. The positive rates of cytokeratin 7 (CK7) and CK19 were 35.1% and 20.6% respectively. The positive rate of CK19 was significantly higher in cases with age < 47 years (P = 0.005), tumor diameter > 4 cm (P = 0.016) or AFP ≥ 400 ng/ml (P = 0.007). The TLR in the portal vein phase was significantly lower in CK19 positive group (P = 0.024). The recurrence risk was significantly higher in cases with CK19 positive (HR: 2.17, 95% CI 1.16 to 4.04, P = 0.013), tumor diameter > 4 cm (HR: 2.05, 95% CI 1.11 to 3.78, P = 0.019), AFP ≥ 400 ng/ml (HR: 2.50, 95% CI 1.37 to 4.54, P = 0.002) or CA199 ≥ 37 U/ml (HR: 2.23, 95% CI 1.12 to 4.42, P = 0.020). However, imaging features, pathological subtype, CK7 or CK19 expression were not significantly related to HCC OS in the univariate and multivariate analysis (all P > 0.05). The expression of CK19 may be associated with the enhancement feature of the portal vein phase CT image, and CK19 positive may suggest a worse RFS., (© 2024. The Author(s).)

To assess the effect of atosiban in pregnancy outcomes of the fresh embryo transfer (ET), the retrospective cohort study was conducted. Six hundred and eighty-nine cases (using atosiban) and 1377 age and ET cycle-matched controls were collected from the January 2016 to May 2022 to perform the fresh IVF-ET cycle. The essential characteristics and pregnancy outcomes were analyzed. Conditional logistic regression analysis and subgroup analysis were performed. In the whole samples, atosiban had no effects in the pregnancy outcomes. Subgroup analyses suggested that atosiban could improve the clinical pregnancy in more than 3 ET cycles (OR 1.667, 95% CI 1.108-2.509, P = 0.014). Moreover, the improvement of clinical pregnancy was mainly present in the advanced-age women (age ≥ 35 years: OR 1.851, 95% CI 1.136-3.014, P = 0.013), obesity (BMI ≥ 24 kg/m 2 : OR 2.550, 95% CI 1.105-5.883, P = 0.028) and cleavage stage embryo (D3 embryo: OR 1.721, 95% CI 1.098-2.696, P = 0.018) among the repeated implantation failures (RIF). Atosiban could also improve the live birth for the obese women. Further, in the RIF, atosiban application was strongly recommended for the advanced-age infertility women, who also had the risk of obesity with the implantation of the cleavage stage embryo. In conclusion, atosiban could improve pregnancy outcomes for the advanced-age and obese women in RIF, especially while implanting the cleavage stage embryo in fresh ET cycle., (© 2023. The Author(s).)

Even after debulking surgery combined with chemotherapy or new adjuvant chemotherapy paired with internal surgery, the average year of disease free survival in advanced ovarian cancer was approximately 1.7 years 1 . The development of a molecular predictor of early recurrence would allow for the identification of ovarian cancer (OC) patients with high risk of relapse. The Ovarian Cancer Disease Free Survival Predictor (ODFSP), a predictive model constructed from a special set of 1580 OC tumors in which gene expression was assessed using both microarray and sequencing platforms, was created by our team. To construct gene expression barcodes that were resistant to biases caused by disparate profiling platforms and batch effects, we employed a meta-analysis methodology that was based on the binary gene pair technique. We demonstrate that ODFSP is a reliable single-sample predictor of early recurrence (1 year or less) using the largest pool of OC transcriptome data sets available to date. The ODFSP model showed significantly high prognostic value for binary recurrence prediction unaffected by clinicopathologic factors, with a meta-estimate of the area under the receiver operating curve of 0.64 (P  =  4.6E-05) and a D-index (robust hazard ratio) of 1.67 (P  =  9.2E-06), respectively. GO analysis of ODFSP's 2040 gene pairs (collapsed to 886 distinct genes) revealed the involvement in small molecular catabolic process, sulfur compound metabolic process, organic acid catabolic process, sulfur compound biosynthetic process, glycosaminoglycan metabolic process and aminometabolic process. Kyoto encyclopedia of genes and genomes pathway analysis of ODFSP's signature genes identified prominent pathways that included cAMP signaling pathway and FoxO signaling pathway. By identifying individuals who might benefit from a more aggressive treatment plan or enrolment in a clinical trial but who will not benefit from standard surgery or chemotherapy, ODFSP could help with treatment decisions., (© 2023. The Author(s).)

Shotgun metagenome sequencing provides the opportunity to recover underexplored rare populations and identify difficult-to-elucidate biochemical pathways. However, information on sulfur genes, including their sequences, is scattered in public databases. Here, we introduce SMDB ( https://smdb.gxu.edu.cn/ )-a manually curated database of sulfur genes based on an in-depth review of the scientific literature and orthology database. The SMDB contained a total of 175 genes and covered 11 sulfur metabolism processes with 395,737 representative sequences affiliated with 110 phyla and 2340 genera of bacteria/archaea. The SMDB was applied to characterize the sulfur cycle from five habitats and compared the microbial diversity of mangrove sediments with that of other habitats. The structure and composition of microorganism communities and sulfur genes were significantly different among the five habitats. Our results show that microorganism alpha diversity in mangrove sediments was significantly higher than in other habitats. Genes involved in dissimilatory sulfate reduction were abundant in subtropical marine mangroves and deep-sea sediments. The neutral community model results showed that microbial dispersal was higher in the marine mangrove ecosystem than in others habitats. The Flavilitoribacter of sulfur-metabolizing microorganism becomes a reliable biomarker in the five habitats. SMDB will assist researchers to analyze genes of sulfur cycle from the metagenomic efficiently., (© 2023. The Author(s).)

Current liver fibrosis scoring by computer-assisted image analytics is not fully automated as it requires manual preprocessing (segmentation and feature extraction) typically based on domain knowledge in liver pathology. Deep learning-based algorithms can potentially classify these images without the need for preprocessing through learning from a large dataset of images. We investigated the performance of classification models built using a deep learning-based algorithm pre-trained using multiple sources of images to score liver fibrosis and compared them against conventional non-deep learning-based algorithms - artificial neural networks (ANN), multinomial logistic regression (MLR), support vector machines (SVM) and random forests (RF). Automated feature classification and fibrosis scoring were achieved by using a transfer learning-based deep learning network, AlexNet-Convolutional Neural Networks (CNN), with balanced area under receiver operating characteristic (AUROC) values of up to 0.85-0.95 versus ANN (AUROC of up to 0.87-1.00), MLR (AUROC of up to 0.73-1.00), SVM (AUROC of up to 0.69-0.99) and RF (AUROC of up to 0.94-0.99). Results indicate that a deep learning-based algorithm with transfer learning enables the construction of a fully automated and accurate prediction model for scoring liver fibrosis stages that is comparable to other conventional non-deep learning-based algorithms that are not fully automated.

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Dysfunction of cholinergic signaling in the brain has long been believed to be associated with depressive disorders. However, the functional impact of habenular cholinergic signaling on the specified depressive behaviors is not well understood. Here, we demonstrated that the expression levels of cholinergic signaling genes (CHAT, VACHT, CHT, CHRNA3, CHRNB3 and CHRNB4) were down-regulated in a chronic restraint stress (CRS) rat model of depression, in which rats display depression-like behaviors such as anhedonia and mood despair. Moreover, knockdown of CHAT in the rat habenula was sufficient to evoke anhedonia-like behavior. The anhedonia-like behavior induced by CHAT knockdown was not reversed by chronic administration of the selective serotonin reuptake inhibitor fluoxetine. To determine whether habenular cholinergic signaling is associated with regulation of dopamine neurons in the ventral tegmental area (VTA) and serotonin neurons in the dorsal raphe nucleus (DRN), we used CHAT::cre transgenic mice expressing the Designer Receptors Exclusively Activated by Designer Drugs (DREADD). Pharmacogenetic activation of habenular cholinergic neurons induces the excitation of dopamine neurons in the VTA and reduces the immunoreactivity of 5-hydroxytryptamine (5-HT) in the DRN. Habenular cholinergic gene down-regulation was recapitulated in the postmortem habenula of suicide victims diagnosed with major depressive disorder (MDD).

The aim of this study is to explore the differentially expressed lncRNAs, which may have potential biological function and diagnostic value in colorectal cancer (CRC). Through integrated data mining, we finally identified nine differentially expressed lncRNAs and their potential mRNA targets. After a series of bioinformatics analyses, we screened significant pathways and GO terms that are related to the up-regulated and down-regulated transcripts respectively. Meanwhile, the nine lncRNAs were validated in 30 paired tissues and cell lines by qRT-PCR and the results were basically consistent with the microarray data. We also tested the nine lncRNAs in the serum of 30 CRC patients matched with the CRC tissue, 30 non-cancer patients and 30 health controls. Finally, we found that BLACAT1 was significant for the diagnosis of CRC. The area under the curve (AUC), sensitivity and specificity were 0.858 (95% CI: 0.765-0.951), 83.3% and 76.7% respectively between CRC patients and health controls. Moreover, BLACAT1 also had distinct value to discriminate CRC from other non-cancer diseases. The results indicated that the differentially expressed lncRNAs and their potential target transcripts could be considered as potential therapeutic targets for CRC patients. Meanwhile, lncRNA BLACAT1 might represent a new supplementary biomarker for the diagnosis of CRC.

Stability is of paramount importance in colloidal applications. Attraction between colloidal particles is believed to lead to particle aggregation and phase separation; hence, stability improvement can be achieved through either increasing repulsion or reducing attraction by modifying the fluid medium or by using additives. Two traditional mechanisms for colloidal stability are electrostatic stabilization and steric stabilization. However, stability improvement by mixing attractive and unstable particles has rarely been considered. Here, we emphasize the function of mixing entropy in colloidal stabilization. Dispersion stability improvement is demonstrated by mixing suspensions of attractive nanosized titania spheres and platelets. A three-dimensional phase diagram is proposed to illustrate the collaborative effects of particle mixing and particle attraction on colloidal stability. This discovery provides a novel method for enhancing colloidal stability and opens a novel opportunity for engineering applications.