b-MAR: bidirectional artifact representations learning framework for metal artifact reduction in dental CBCT.

OBJECTIVE: Metal artifacts in computed tomography (CT) images hinder diagnosis and treatment significantly. Specifically, dental cone-beam computed tomography (Dental CBCT) images are seriously contaminated by metal artifacts due to the widespread use of low tube voltages and the presence of various high-attenuation materials in dental structures. Existing supervised metal artifact reduction (MAR) methods mainly learn the mapping of artifact-affected images to clean images, while ignoring the modeling of the metal artifact generation process. Therefore, we propose the bidirectional artifact representations learning framework to adaptively encode metal artifacts caused by various dental implants and model the generation and elimination of metal artifacts, thereby improving MAR performance. Approach. we introduce an efficient artifact encoder to extract multi-scale representations of metal artifacts from artifact-affected images. These extracted metal artifact representations are then bidirectionally embedded into both the metal artifact generator and the metal artifact eliminator, which can simultaneously improve the performance of artifact removal and artifact generation. The artifact eliminator learns artifact removal in a supervised manner, while the artifact generator learns artifact generation in an adversarial manner. To further improve the performance of the bidirectional task networks, we propose artifact consistency loss to align the consistency of images generated by the eliminator and the generator with or without embedding artifact representations. Main results. To validate the effectiveness of our algorithm, experiments are conducted on simulated and clinical datasets containing various dental metal morphologies. Quantitative metrics are calculated to evaluate the results of the simulation tests,which demonstrate b-MAR improvements of > 1.4131 dB in PSNR, > 0.3473 HU decrements in RMSE, and > 0.0025 promotion in SSIM over the current state-of-the-art MAR methods. All results indicate that the proposed b-MAR method can remove artifacts caused by various metal morphologies and restore the structural integrity of dental tissues effectively. Significance. The proposed b-MAR method strengthens the joint learning of the artifact removal process and the artifact generation process by bidirectionally embedding artifact representations, thereby improving the model's artifact removal performance. Compared with other comparison methods, b-MAR can robustly and effectively correct metal artifacts in dental CBCT images caused by different dental metals.

Enhancing cross-subject EEG emotion recognition through multi-source manifold metric transfer learning.

Transfer learning (TL) has demonstrated its efficacy in addressing the cross-subject domain adaptation challenges in affective brain-computer interfaces (aBCI). However, previous TL methods usually use a stationary distance, such as Euclidean distance, to quantify the distribution dissimilarity between two domains, overlooking the inherent links among similar samples, potentially leading to suboptimal feature mapping. In this study, we introduced a novel algorithm called multi-source manifold metric transfer learning (MSMMTL) to enhance the efficacy of conventional TL. Specifically, we first selected the source domain based on Mahalanobis distance to enhance the quality of the source domains and then used manifold feature mapping approach to map the source and target domains on the Grassmann manifold to mitigate data drift between domains. In this newly established shared space, we optimized the Mahalanobis metric by maximizing the inter-class distances while minimizing the intra-class distances in the target domain. Recognizing that significant distribution discrepancies might persist across different domains even on the manifold, to ensure similar distributions between the source and target domains, we further imposed constraints on both domains under the Mahalanobis metric. This approach aims to reduce distributional disparities and enhance the electroencephalogram (EEG) emotion recognition performance. In cross-subject experiments, the MSMMTL model exhibits average classification accuracies of 88.83 % and 65.04 % for SEED and DEAP, respectively, underscoring the superiority of our proposed MSMMTL over other state-of-the-art methods. MSMMTL can effectively solve the problem of individual differences in EEG-based affective computing.

Enhancing Iron(III) Oxide Photoelectrochemical Water Splitting Performance Using Defect Engineering and Heterostructure Construction.

Fe2O3 is a promising semiconductor for photoelectrochemical (PEC) water decomposition. However, severe charge recombination problems limit its applications. In this study, a F-Fe2O3-x/MoS2 nanorod array photoanode was designed and prepared to facilitate charge separation. Detailed characterization and experimental results showed that F doping in Fe2O3 regulated the electronic structure to improve the conductivity of Fe2O3 and induced abundant oxygen vacancies to increase the carrier concentration and promote charge separation in bulk. In addition, the internal electric field between F-Fe2O3-x and MoS2 facilitated the qualitative transfer of the photogenerated charge, thus inhibiting their recombination. The synergistic effect between the oxygen vacancy and F-Fe2O3-x/MoS2 heterojunction significantly enhanced the PEC performance of Fe2O3. This study provides a universal strategy for designing other photoanode materials with high-efficiency charge separation.

Development of a new Cox model for predicting long-term survival in hepatitis cirrhosis patients underwent transjugular intrahepatic portosystemic shunts.

BACKGROUND: Transjugular intrahepatic portosystemic shunt (TIPS) placement is a procedure that can effectively treat complications of portal hypertension, such as variceal bleeding and refractory ascites. However, there have been no specific studies on predicting long-term survival after TIPS placement. AIM: To establish a model to predict long-term survival in patients with hepatitis cirrhosis after TIPS. METHODS: A retrospective analysis was conducted on a cohort of 224 patients who underwent TIPS implantation. Through univariate and multivariate Cox regression analyses, various factors were examined for their ability to predict survival at 6 years after TIPS. Consequently, a composite score was formulated, encompassing the indication, shunt reasonability, portal venous pressure gradient (PPG) after TIPS, percentage decrease in portal venous pressure (PVP), indocyanine green retention rate at 15 min (ICGR15) and total bilirubin (Tbil) level. Furthermore, the performance of the newly developed Cox (NDC) model was evaluated in an internal validation cohort and compared with that of a series of existing models. RESULTS: The indication (variceal bleeding or ascites), shunt reasonability (reasonable or unreasonable), ICGR15, postoperative PPG, percentage of PVP decrease and Tbil were found to be independent factors affecting long-term survival after TIPS placement. The NDC model incorporated these parameters and successfully identified patients at high risk, exhibiting a notably elevated mortality rate following the TIPS procedure, as observed in both the training and validation cohorts. Additionally, in terms of predicting the long-term survival rate, the performance of the NDC model was significantly better than that of the other four models [Child-Pugh, model for end-stage liver disease (MELD), MELD-sodium and the Freiburg index of post-TIPS survival]. CONCLUSION: The NDC model can accurately predict long-term survival after the TIPS procedure in patients with hepatitis cirrhosis, help identify high-risk patients and guide follow-up management after TIPS implantation.

EEG multi-domain feature transfer based on sparse regularized Tucker decomposition.

Tensor analysis of electroencephalogram (EEG) can extract the activity information and the potential interaction between different brain regions. However, EEG data varies between subjects, and the existing tensor decomposition algorithms cannot guarantee that the features across subjects are distributed in the same domain, which leads to the non-objectivity of the classification result and analysis, In addition, traditional Tucker decomposition is prone to the explosion of feature dimensions. To solve these problems, combined with the idea of feature transfer, a novel EEG tensor transfer algorithm, Tensor Subspace Learning based on Sparse Regularized Tucker Decomposition (TSL-SRT), is proposed in this paper. In TSL-SRT, new EEG samples are considered as the target domain and original samples as the source domain. The target features can be obtained by projecting the target tensor to the source feature space to ensure that all features are in the same domain. Furthermore, to solve the problem of dimension explosion caused by TSL-SRT, a redundant EEG features screening algorithm is adopted to eliminate the redundant features, and achieves 77.8%, 73.2% and 75.3% accuracy on three BCI datasets. By visualizing the spatial basic matrix of the feature space, it can be seen that TSL-SRT is effective in extracting the features of active brain regions in the BCI task and it can extract the multi-domain features of different subjects in the same domain simultaneously, which provides a new method for the tensor analysis of EEG.

Two-dimensional correlation infrared spectroscopy study on vanadoborate anionic skeleton regulated by countercations.

Two novel vanadoborate compounds, [Cu(en)2]3[Li(H2O)]4[Li(H2O)3]2[V12B18O50(OH)10(H2O)]2·33.5H2O (1) and (H2en)4[Li(H2O)]4[V12B18O55(OH)5(H2O)]·14H2O (2), were synthesized via hydrothermal synthesis under identical conditions except for temperature. Structural analysis revealed that although both contain [V12B18O60]n- cluster anion, the different countercations potentially lead to variations in the [V12B18O60]n- cluster anion skeletons. In compound 1, the V4+/V5+ ratio was 10:2; while in compound 2 the ratio was 11:1. It is speculated that different countercations may influence the valence states of cluster anions. In this study, quantum chemical calculations revealed that the aromaticity and activity of the two compounds were different, and two-dimensional correlation infrared spectroscopy (2D-COS-IR) under magnetic perturbation confirmed that distinct response peaks of functional group vibrations to the magnetic field due to the different V4+/V5+ ratios and aromaticity of the two compounds. An electrochemical analysis revealed that compound 2 exhibits higher electrocatalytic activity. The results of quantum chemical calculations are aligned not only with the changes in the 2D-COS-IR spectra but also with the conclusions obtained from experiments on electrochemical properties. Overall, this work proposes a novel strategy for interpreting the alteration of vanadoborate anionic skeleton due to the introduction of different countercations by combining 2D-COS-IR with quantum chemical calculations.

Three-stage transfer learning for motor imagery EEG recognition.

Motor imagery (MI) paradigms have been widely used in neural rehabilitation and drowsiness state assessment. The progress in brain-computer interface (BCI) technology has emphasized the importance of accurately and efficiently detecting motor imagery intentions from electroencephalogram (EEG). Despite the recent breakthroughs made in developing EEG-based algorithms for decoding MI, the accuracy and efficiency of these models remain limited by technical challenges posed by cross-subject heterogeneity in EEG data processing and the scarcity of EEG data for training. Inspired by the optimal transport theory, this study aims to develop a novel three-stage transfer learning (TSTL) method, which uses the existing labeled data from a source domain to improve classification performance on an unlabeled target domain. Notably, the proposed method comprises three components, namely, the Riemannian tangent space mapping (RTSM), source domain transformer (SDT), and optimal subspace mapping (OSM). The RTSM maps a symmetric positive definite matrix from the Riemannian space to the tangent space to minimize the marginal probability distribution drift. The SDT transforms the source domain to a target domain by finding the optimal transport mapping matrix to reduce the joint probability distribution differences. The OSM finally maps the transformed source domain and original target domain to the same subspace to further mitigate the distribution discrepancy. The performance of the proposed method was validated on two public BCI datasets, and the average accuracy of the algorithm on two datasets was 72.24% and 69.29%. Our results demonstrated the improved performance of EEG-based MI detection in comparison with state-of-the-art algorithms.

Independent and reproducible hippocampal radiomics biomarkers for multisite multiple sclerosis and neuromyelitis optica spectrum disorders.

OBJECTIVE: To investigate the abnormal radiomics features of the hippocampus in patients with multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSD) and to explore the clinical implications of these features. METHODS: 752 participants were recruited in this retrospective multicenter study (7 centers), which included 236 MS, 236 NMOSD, and 280 normal controls (NC). Radiomics features of each side of the hippocampus were extracted, including intensity, shape, texture, and wavelet features (N = 431). To identify the variations in these features, two-sample t-tests were performed between the NMOSD vs. NC, MS vs. NC, and NMOSD vs. MS groups at each site. The statistical results from each site were then integrated through meta-analysis. To investigate the clinical significance of the hippocampal radiomics features, we conducted further analysis to examine the correlations between these features and clinical measures such as Expanded Disability Status Scale (EDSS), Brief Visuospatial Memory Test (BVMT), California Verbal Learning Test (CVLT), and Paced Auditory Serial Addition Task (PASAT). RESULTS: Compared with NC, patients with MS exhibited significant differences in 78 radiomics features (P < 0.05/862), with the majority of these being texture features. Patients with NMOSD showed significant differences in 137 radiomics features (P < 0.05/862), most of which were intensity features. The difference between MS and NMOSD patients was observed in 47 radiomics features (P < 0.05/862), mainly texture features. In patients with MS and NMOSD, the most significant features related to the EDSS were intensity and textural features, and the most significant features related to the PASAT were intensity features. Meanwhile, both disease groups observed a weak correlation between radiomics data and BVMT. CONCLUSION: Variations in the microstructure of the hippocampus can be detected through radiomics, offering a new approach to investigating the abnormal pattern of the hippocampus in MS and NMOSD.

Analysis Of Multiscale Corticomuscular Coupling Networks Based On Ordinal Patterns.

The coupled analysis of corticomuscular function based on physiological electrical signals can identify differences in causal relationships between electroencephalogram (EEG) and surface electromyogram (sEMG) in different motor states. The existing methods are mainly devoted to the analysis in the same frequency band, while ignoring the cross-band coupling, which plays an active role in motion control. Considering the inherent multiscale characteristics of physiological signals, a method combining Ordinal Partition Transition Networks (OPTNs) and Multivariate Variational Modal Decomposition (MVMD) was proposed in this paper. The EEG and sEMG were firstly decomposed on a time-frequency scale using MVMD, and then the coupling strength was calculated by the OPTNs to construct a corticomuscular coupling network, which was analyzed with complex network parameters. Experimental data were obtained from a self-acquired dataset consisting of EEG and sEMG of 16 healthy subjects at different sizes of constant grip force. The results showed that the method was superior in representing changes in the causal link among multichannel signals characterized by different frequency bands and grip strength patterns. Complex information transfer between the cerebral cortex and the corresponding muscle groups during constant grip force output from the human upper limb. Furthermore, the sEMG of the flexor digitorum superficialis (FDS) in the low frequency band is the hub in the effective information transmission between the cortex and the muscle, while the importance of each frequency component in this transmission network becomes more dispersed as the grip strength grows, and the increase in coupling strength and node status is mainly in the γ band (30~60Hz). This study provides new ideas for deconstructing the mechanisms of neural control of muscle movements.

High-sensitivity self-powered photodetector based on an in-situ prepared CsPbBr3 microwire/InGaN heterojunction.

Low-dimensional CsPbBr3 perovskite materials have gained widespread attention, derived from their remarkable properties and potential for numerous optoelectronic applications. Herein, the sample of CsPbBr3 microwires were prepared horizontally onto n-type InGaN film substrate using an in-plane solution growth method. The resulting CsPbBr3 microwire/InGaN heterojunction allows for the achievement of a highly sensitive and broadband photodetector. Particularly for the implementation in a self-supplying manner, the best-performing photodetector can achieve a superior On/Off ratio of 4.6×105, the largest responsivity ∼ 800.0 mA/W, a maximum detectivity surpassing 4.6× 1012 Jones, and a high external quantum efficiency approaching 86.5% upon 405 nm light illumination. A rapid response time (∼ 4.48 ms/7.68 ms) was also achieved. The as-designed CsPbBr3 microwire/InGaN heterojunction device without any encapsulation exhibits superior comprehensive stability. Besides, the device featuring as a single pixel imaging unit can readily detect simple images under broadband light illumination with a high spatial resolution, acknowledging its outstanding imaging capability. The robust photodetection properties could be derived from the intense absorption of CsPbBr3 MWs and high-efficiency charge carriers transporting toward the in-situ formed CsPbBr3/InGaN heterointerface. The results may offer an available strategy for the in-situ construction of best-performing low-dimensional perovskite heterojunction optoelectronic devices.

A novel nomogram predicting overt hepatic encephalopathy after transjugular intrahepatic portosystemic shunt in portal hypertension patients.

We aim to develop a nomogram to predict overt hepatic encephalopathy (OHE) after transjugular intrahepatic portosystemic shunt (TIPS) in patients with portal hypertension, according to demographic/clinical indicators such as age, creatinine, blood ammonia, indocyanine green retention rate at 15 min (ICG-R15) and percentage of Portal pressure gradient (PPG) decline. In this retrospective study, 296 patients with portal hypertension who received elective TIPS in Beijing Shijitan Hospital from June 2018 to June 2020 were included. These patients were randomly divided into a training cohort (n = 207) and a validation cohort (n = 89). According to the occurrence of OHE, patients were assigned to OHE group and non-OHE group. Both univariate and multivariate analyses were performed to determine independent variables for predicting OHE after TIPS. Accordingly, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to compare the accuracy and superiority of a novel model with conventional Child-Pugh and MELD scoring model. Age (OR 1.036, 95% CI 1.002-1.070, p = 0.037), Creatinine (OR 1.011, 95% CI 1.003-1.019, p = 0.009), Blood ammonia (OR 1.025, 95% CI 1.006-1.044, p = 0.011), ICG-R15 (OR 1.030, 95% CI 1.009-1.052, p = 0.004) and Percentage decline in PPG (OR 1.068, 95% CI 1.029-1.109, p = 0.001) were independent risk factors for OHE after TIPS using multifactorial analysis. A nomogram was constructed using a well-fit calibration curve for each of these five covariates. When compared to Child-Pugh and MELD score, this new nomogram has a better predictive value (C-index = 0.828, 95% CI 0.761-0.896). Consistently, this finding was reproduceable in validation cohort and confirmed with DCA. A unique nomogram was developed to predict OHE after TIPS in patients with PHT, with a high prediction sensitivity and specificity performance than commonly applied scoring systems.

Psychological interventions for the prevention of depression relapse: systematic review and network meta-analysis.

Depression is highly prevalent and easily relapses. Psychological interventions are effective for the prevention of depression relapse. This systematic review and network meta-analysis aimed to compare the efficacy at the same follow-up time points of psychological interventions in depression. We searched PubMed, Embase, and PsycINFO via OVID, and the Cochrane Library published up to December 12, 2021, and PubMed up to July 1, 2022. The primary outcome was depression relapse, considering the same time points that were extracted on survival curves or relapse curves. The study protocol was registered with PROSPERO, CRD42022343327. A total of 2,871 patients were included from 25 RCTs. Mindfulness-based cognitive therapy (MBCT) was significantly better than placebo at the 3 months, the 6 months, and the 9 months at follow-up. Cognitive behavioral therapy (CBT) was significantly better than treatment as usual at the 3 months, the 9 months, the 12 months, and the 15 months at follow-up. CBT was significantly better than placebo at the 21 months and the 24 months at follow-up. Behavioral activation therapy was significantly better than placebo at the 21 months and the 24 months at follow-up. Interpersonal psychotherapy was significantly better than placebo at the 24-month follow-up. All psychological interventions included in the study were significantly better than supportive counseling most of the time. The results were robust in various sensitivity and subgroup analyses. In conclusion, MBCT had a continuous effect in preventing relapse of depression. CBT had the longest but not continuous effect in preventing relapse of depression. The effects of behavioral activation therapy and interpersonal therapy for the prevention of depression appeared late. All psychological interventions included in the study were more effective than supportive counseling. More evidence is needed from large comparative trials that provide long-term follow-up data.

IL-7 promotes CD19-directed CAR-T cells proliferation through miRNA-98-5p by targeting CDKN1A.

CAR-T targeting CD19 have achieved significant effects in the treatment of B-line leukemia and lymphoma. However, the treated patients frequently relapsed and could not achieve complete remission. Therefore, improving the proliferation and cytotoxicity of CAR-T cells, reducing exhaustion and enhancing infiltration capacity are still issues to be solved. The IL-7 has been shown to enhance the memory characteristics of CAR-T cells, but the specific mechanism has yet to be elaborated. miRNAs play an important role in T cell activity. However, whether miRNA is involved in the activation of CAR-T cells by IL-7 has not yet been reported. Our previous study had established the 3rd generation CAR-T cells. The present study further found that IL-7 significantly increased the proliferation of anti-CD19 CAR-T cells, the ratio of CD4 + CAR + cells and the S phase of cell cycle. In vivo study NAMALWA xenograft model showed that IL-7-stimulated CAR-T cells possessed stronger tumoricidal efficiency. Further we validated that IL-7 induced CAR-T cells had low expression of CDKN1A and high expression of miRNA-98-5p. Additionally, CDKN1A was associated with miRNA-98-5p. Our results, for the first time, suggested IL-7 could conspicuously enhance the proliferation of CAR-T cells through miRNA-98-5p targeting CDKN1A expression, which should be applied to CAR-T production.

Integration and suppression interact in binocular vision.

Contingent on stereo compatibility, two images presented dichoptically can lead to either binocular integration, thus generating stable stereopsis, or interocular suppression that induces binocular rivalry with bistable perception that alternates between the two images. The relationship between binocular integration and interocular suppression concerns how our brain processes binocular inputs to form unified visual awareness but remains unclear. Here, a series of psychophysical experiments were conducted to address this question, revealing that these collaborative and competitive binocular interactions are interconnected and would mediate one another according to their strength. Specifically, Experiments 1a and 1b showed that the presence of binocular rivalry inhibited peripheral stereopsis, significantly elevating the stereo threshold, with higher elevation resulting from increasing rivalry contrast. Experiments 2a and 2b showed that existing stereopsis with increasing binocular disparity balanced the dynamics of peripheral binocular rivalry, rendering more equivalent eye dominance. Based on these interactions, we suggest that binocular integration and interocular suppression may mediate one another through an overlapping mechanism for regulating eye dominance, with strong stereo percepts tending to reduce eye dominance and strong rivalry tending to increase eye dominance.

Development and validation of an online calculator to predict the pathological nature of colorectal tumors.

BACKGROUND: No single endoscopic feature can reliably predict the pathological nature of colorectal tumors (CRTs). AIM: To establish and validate a simple online calculator to predict the pathological nature of CRTs based on white-light endoscopy. METHODS: This was a single-center study. During the identification stage, 530 consecutive patients with CRTs were enrolled from January 2015 to December 2021 as the derivation group. Logistic regression analysis was performed. A novel online calculator to predict the pathological nature of CRTs based on white-light images was established and verified internally. During the validation stage, two series of 110 images obtained using white-light endoscopy were distributed to 10 endoscopists [five highly experienced endoscopists and five less experienced endoscopists (LEEs)] for external validation before and after systematic training. RESULTS: A total of 750 patients were included, with an average age of 63.6 ± 10.4 years. Early colorectal cancer (ECRC) was detected in 351 (46.8%) patients. Tumor size, left semicolon site, rectal site, acanthosis, depression and an uneven surface were independent risk factors for ECRC. The C-index of the ECRC calculator prediction model was 0.906 (P = 0.225, Hosmer-Lemeshow test). For the LEEs, significant improvement was made in the sensitivity, specificity and accuracy (57.6% vs 75.5%; 72.3% vs 82.4%; 64.2% vs 80.2%; P < 0.05), respectively, after training with the ECRC online calculator prediction model. CONCLUSION: A novel online calculator including tumor size, location, acanthosis, depression, and uneven surface can accurately predict the pathological nature of ECRC.

[Difference in Brain Age Between Alzheimer's Disease and Mild Cognitive Impairment].

Objective To investigate the brain age differences between Alzheimer's disease(AD)and mild cognitive impairment(MCI)patients,and further explore the correlations between brain age gap(BAG)and clinical features.Methods The clinical data and radiologic findings of 132 probable AD and AD-derived MCI patients diagnosed at Beijing Tiantan Hospital,Capital Medical University from December 2018 to July 2021 were retrospectively analyzed.According to the diagnostic criteria for AD and MCI,the patients were assigned into AD and MCI groups.In addition,156 volunteers without neurological diseases and other severe diseases were recruited as the control group.The general data,Montreal cognitive assessment(MoCA)score,and mini-mental state examination(MMSE)score were compared among the three groups.The deep learning-based brain age prediction model was employed to calculate the BAGs of the three groups.Spearman correlation analysis was conducted to explore the correlations between BAG and clinical features.Results The 132 patients included 106 patients in the AD group and 26 patients in the MCI group.The MoCA and MMSE scores followed an ascending trend of AD group

Unconscious face processing shows a different lateralisation pattern.

Faces contain rich information and play a pivotal role in human daily social interactions. Previous studies have provided evidence for the left-visual-field-advantage in face perception. The current study investigated lateralisation when face stimuli were unconsciously presented using interocular suppression methods. The results showed that when an unconscious face stimulus was presented in one side of the visual field without other face stimuli, the unconscious face in the right visual field could break through interocular suppression faster than that in the left (Experiments 1 and 2). However, this right visual field processing advantage reversed to the left when a face stimulus either with (Experiment 2) or without (Experiment 3) visual awareness was presented concurrently with or prior to (Experiment 4) the unconscious face stimuli. These results showed that the unconscious face might have a different processing pattern compared with the conscious face. The relationship between this novel behavioural observation and known functional lateralisation of the fusiform face areas is discussed, suggesting a dynamic interaction between the two cortical hemispheres may underlie the formation of visual awareness for faces. The current study expands our understanding of face processing lateralisation and provided evidence on the asymmetric inter-hemisphere interaction patterns in forming visual awareness.

Two-dimensional lateral anatase-rutile TiO2 phase junctions with oxygen vacancies for robust photoelectrochemical water splitting.

Exploiting the photoelectrode materials with broad solar light response, high-efficient separation of photogenerated charges and abundant active sites is extremely vital yet enormously challenging. Herein, an innovative two-dimensional (2D) lateral anatase-rutile TiO2 phase junctions with controllable oxygen vacancies perpendicularly aligned on Ti mesh is presented. Our experimental observations and theoretical calculations corroborate explicitly that the 2D lateral phase junctions together with three-dimensional arrays not only exhibit the high-efficient photogenerated charges separation guaranteed by the build-in electric field at the side-to-side interface, but also furnish enriching active sites. Moreover, the interfacial oxygen vacancies generate new defect energy levels and serve as electron donors, hence extending visible light response and further accelerating the separation and transfer of photogenerated charges. Profiting from these merits, the optimized photoelectrode yield a pronounced photocurrent density of 1.2 mA/cm2 at 1.23 V vs. RHE with Faradic efficiency of 100%, which is approximately 2.4 times larger than that of pristine 2D TiO2 nanosheets. Furthermore, the incident photon to current conversion efficiency (IPCE) of the optimized photoelectrode is also boosted within both ultraviolet and visible light regions. This research is envisioned deliver the new insight in developing the novel 2D lateral phase junctions for PEC applications.

Construction of PdSe2/ZnIn2S4 heterojunctions with covalent interface for highly efficient photocatalytic hydrogen evolution.

Constructing semiconductor heterojunctions can enable novel schemes for highly efficient photocatalytic activity. However, introducing strong covalent bonding at the interface remains an open challenge. Herein, ZnIn2S4 (ZIS) with abundant sulfur vacancies (Sv) is synthesized with the presence of PdSe2 as an additional precursor. The sulfur vacancies of Sv-ZIS are filled by Se atoms of PdSe2, leading to the Zn-In-Se-Pd compound interface. Our density functional theory (DFT) calculations reveal the increased density of states at the interface, which will increase the local carrier concentration. Moreover, the length of the Se-H bond is longer than that of the SH bond, which is good for the evolution of H2 from the interface. In addition, the charge redistribution at the interface results in a built-in field, providing the driving force for efficient separation of photogenerated electron-hole. Therefore, the PdSe2/Sv-ZIS heterojunction with strong covalent interface exhibits an excellent photocatalytic hydrogen evolution performance (4423 µmol g-1h-1) with an apparent quantum efficiency (λ > 420 nm) of 9.1 %. This work will provide new inspirations to improve photocatalytic activity by engineering the interfaces of semiconductor heterojunctions.

LINC00978 regulates metabolic rewiring to promote the malignancy of glioblastoma through AKR1B1.

Glioma is a fatal primary brain tumor. Improved glioma treatment effectiveness depends on a better understanding of its underlying mechanisms. Glioblastoma (GBM), was classified as high-grade glioma with the most lethality and therapeutic resistance. Herein, we reported LINC00978 overexpressed in high-grade gliomas. Down-regulation of LINC00978 in glioblastoma cells inhibited cell proliferation, invasion, migration, and induced apoptosis. In vivo experiments confirmed that the CamK-A siRNA of LINC00978 could effectively inhibit the proliferation of glioblastoma cells. The main pathway and genes regulated by LINC00978 were detected using RNA sequencing to elucidate the molecular mechanism. The results suggest that LINC00978 regulates the expression of genes related to metabolic pathways, including aldo-keto reductase family 1 member B (AKR1B1), which mediates the cytotoxicity of 2-deoxyglucose. LINC00978 positively regulated AKR1B1 expression, and 2-deoxyglucose induced AKR1B1 expression via a LINC00978-dependent mechanism. This research has revealed that LINC00978 promotes the sensitivity of glioblastoma cells to 2DG. LINC00978 is highly expressed in most high-grade glioma patients. Thus, understanding the anticancer mechanism identified in this study may contribute to treating the majority of glioma patients. This study clarified the function and molecular mechanism of LINC00978 in glioblastoma and provided a study basis for LINC00978 to guide the clinical treatment of glioblastoma.