Unraveling the intricate cargo-BBSome coupling mechanism at the ciliary tip.

Certain ciliary transmembrane and membrane-tethered signaling proteins migrate from the ciliary tip to base via retrograde intraflagellar transport (IFT), essential for maintaining their ciliary dynamics to enable cells to sense and transduce extracellular stimuli inside the cell. During this process, the BBSome functions as an adaptor between retrograde IFT trains and these signaling protein cargoes. The Arf-like 13 (ARL13) small GTPase resembles ARL6/BBS3 in facilitating these signaling cargoes to couple with the BBSome at the ciliary tip prior to loading onto retrograde IFT trains for transporting towards the ciliary base, while the molecular basis for how this intricate coupling event happens remains elusive. Here, we report that Chlamydomonas ARL13 only in a GTP-bound form (ARL13GTP) anchors to the membrane for diffusing into cilia. Upon entering cilia, ARL13 undergoes GTPase cycle for shuttling between the ciliary membrane (ARL13GTP) and matrix (ARL13GDP). To achieve this goal, the ciliary membrane-anchored BBS3GTP binds the ciliary matrix-residing ARL13GDP to activate the latter as an ARL13 guanine nucleotide exchange factor. At the ciliary tip, ARL13GTP recruits the ciliary matrix-residing and post-remodeled BBSome as an ARL13 effector to anchor to the ciliary membrane. This makes the BBSome spatiotemporally become available for the ciliary membrane-tethered phospholipase D (PLD) to couple with. Afterward, ARL13GTP hydrolyzes GTP for releasing the PLD-laden BBSome to load onto retrograde IFT trains. According to this model, hedgehog signaling defects associated with ARL13b and BBS3 mutations in humans could be satisfactorily explained, providing us a mechanistic understanding behind BBSome-cargo coupling required for proper ciliary signaling.

Multi-task prediction-based graph contrastive learning for inferring the relationship among lncRNAs, miRNAs and diseases.

MOTIVATION: Identifying the relationships among long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and diseases is highly valuable for diagnosing, preventing, treating and prognosing diseases. The development of effective computational prediction methods can reduce experimental costs. While numerous methods have been proposed, they often to treat the prediction of lncRNA-disease associations (LDAs), miRNA-disease associations (MDAs) and lncRNA-miRNA interactions (LMIs) as separate task. Models capable of predicting all three relationships simultaneously remain relatively scarce. Our aim is to perform multi-task predictions, which not only construct a unified framework, but also facilitate mutual complementarity of information among lncRNAs, miRNAs and diseases. RESULTS: In this work, we propose a novel unsupervised embedding method called graph contrastive learning for multi-task prediction (GCLMTP). Our approach aims to predict LDAs, MDAs and LMIs by simultaneously extracting embedding representations of lncRNAs, miRNAs and diseases. To achieve this, we first construct a triple-layer lncRNA-miRNA-disease heterogeneous graph (LMDHG) that integrates the complex relationships between these entities based on their similarities and correlations. Next, we employ an unsupervised embedding model based on graph contrastive learning to extract potential topological feature of lncRNAs, miRNAs and diseases from the LMDHG. The graph contrastive learning leverages graph convolutional network architectures to maximize the mutual information between patch representations and corresponding high-level summaries of the LMDHG. Subsequently, for the three prediction tasks, multiple classifiers are explored to predict LDA, MDA and LMI scores. Comprehensive experiments are conducted on two datasets (from older and newer versions of the database, respectively). The results show that GCLMTP outperforms other state-of-the-art methods for the disease-related lncRNA and miRNA prediction tasks. Additionally, case studies on two datasets further demonstrate the ability of GCLMTP to accurately discover new associations. To ensure reproducibility of this work, we have made the datasets and source code publicly available at https://github.com/sheng-n/GCLMTP.

Human cytomegalovirus pUL97 upregulates SOCS3 expression via transcription factor RFX7 in neural progenitor cells.

Congenital human cytomegalovirus (HCMV) infection causes severe damage to the fetal brain, and the underlying mechanisms remain elusive. Cytokine signaling is delicately controlled in the fetal central nervous system to ensure proper development. Here we show that suppressor of cytokine signaling 3 (SOCS3), a negative feedback regulator of the IL-6 cytokine family signaling, was upregulated during HCMV infection in primary neural progenitor cells (NPCs) with a biphasic expression pattern. From viral protein screening, pUL97 emerged as the viral factor responsible for prolonged SOCS3 upregulation. Further, by proteomic analysis of the pUL97-interacting host proteins, regulatory factor X 7 (RFX7) was identified as the transcription factor responsible for the regulation. Depletion of either pUL97 or RFX7 prevented the HCMV-induced SOCS3 upregulation in NPCs. With a promoter-luciferase activity assay, we demonstrated that the pUL97 kinase activity and RFX7 were required for SOCS3 upregulation. Moreover, the RFX7 phosphorylation level was increased by either UL97-expressing or HCMV-infection in NPCs, suggesting that pUL97 induces RFX7 phosphorylation to drive SOCS3 transcription. We further revealed that elevated SOCS3 expression impaired NPC proliferation and migration in vitro and caused NPCs migration defects in vivo. Taken together, these findings uncover a novel regulatory mechanism of sustained SOCS3 expression in HCMV-infected NPCs, which perturbs IL-6 cytokine family signaling, leads to NPCs proliferation and migration defects, and consequently affects fetal brain development.

The N-Myc-responsive lncRNA MILIP promotes DNA double-strand break repair through non-homologous end joining.

The protooncoprotein N-Myc, which is overexpressed in approximately 25% of neuroblastomas as the consequence of MYCN gene amplification, has long been postulated to regulate DNA double-strand break (DSB) repair in neuroblastoma cells, but experimental evidence of this function is presently scant. Here, we show that N-Myc transcriptionally activates the long noncoding RNA MILIP to promote nonhomologous end-joining (NHEJ) DNA repair through facilitating Ku70-Ku80 heterodimerization in neuroblastoma cells. High MILIP expression was associated with poor outcome and appeared as an independent prognostic factor in neuroblastoma patients. Knockdown of MILIP reduced neuroblastoma cell viability through the induction of apoptosis and inhibition of proliferation, retarded neuroblastoma xenograft growth, and sensitized neuroblastoma cells to DNA-damaging therapeutics. The effect of MILIP knockdown was associated with the accumulation of DNA DSBs in neuroblastoma cells largely due to decreased activity of the NHEJ DNA repair pathway. Mechanistical investigations revealed that binding of MILIP to Ku70 and Ku80 increased their heterodimerization, and this was required for MILIP-mediated promotion of NHEJ DNA repair. Disrupting the interaction between MILIP and Ku70 or Ku80 increased DNA DSBs and reduced cell viability with therapeutic potential revealed where targeting MILIP using Gapmers cooperated with the DNA-damaging drug cisplatin to inhibit neuroblastoma growth in vivo. Collectively, our findings identify MILIP as an N-Myc downstream effector critical for activation of the NHEJ DNA repair pathway in neuroblastoma cells, with practical implications of MILIP targeting, alone and in combination with DNA-damaging therapeutics, for neuroblastoma treatment.

Enhanced Selective Ion Transport in Highly Charged Bacterial Cellulose/Boron Nitride Composite Membranes for Thermo-Osmotic Energy Harvesting.

Significant untapped energy exists within low-grade heat sources and salinity gradients. Traditional nanofluidic membranes exhibit inherent limitations, including low ion selectivity, high internal resistance, reliance on nonrenewable resources, and instability in aqueous solutions, invariably constraining their practical application. Here, an innovative composite membrane-based nanofluidic system is reported, involving the strategy of integrating tailor-modified bacterial nanofibers with boron nitride nanosheets, enabling high surface charge densities while maintaining a delicate balance between ion selectivity and permeability, ultimately facilitating effective thermo-osmotic energy harvesting. The device exhibits an impressive output power density of 10 W m-2 with artificial seawater and river water at a 50 K temperature gradient. Furthermore, it demonstrates robust power density stability under prolonged exposure to salinity gradients or even at elevated temperatures. This work opens new avenues for the development of nanofluidic systems utilizing composite materials and presents promising solutions for low-grade heat recovery and osmotic energy harvesting.

Polarity-Mediated Antisolvent Control Enables Efficient Lanthanide-Based near-Infrared Perovskite LEDs.

Alloying lanthanide ions (Yb3+) into perovskite quantum dots (Yb3+:CsPb(Cl1-xBrx)3) is an effective method to achieve efficient near-infrared (NIR) luminescence (>950 nm). Increasing the Yb3+ alloying ratio in the perovskite matrix enhances the luminescence intensity of Yb3+ emission at 990 nm. However, high Yb3+ alloying (>15%) results in vacancy-induced inferior material stability. In this work, we developed a polarity-mediated antisolvent manipulation strategy to resolve the incompatibility between a high Yb3+ alloying ratio and inferior stability of Yb3+:CsPb(Cl1-xBrx)3. Precise control of solution polarity enables increased uniformity of the perovskite matrix with fewer trap densities. Employing this strategy, we obtain Yb3+:CsPb(Cl1-xBrx)3 with the highest Yb3+ alloying ratio of 30.2% and a 2-fold higher electroluminescence intensity at 990 nm. We lever the engineered Yb3+:CsPb(Cl1-xBrx)3 to fabricate NIR-LEDs, achieving a peak external quantum efficiency (EQE) of 8.5% at 990 nm: this represents the highest among perovskite NIR-LEDs with an emission wavelength above 950 nm.

MMGAT: a graph attention network framework for ATAC-seq motifs finding.

BACKGROUND: Motif finding in Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data is essential to reveal the intricacies of transcription factor binding sites (TFBSs) and their pivotal roles in gene regulation. Deep learning technologies including convolutional neural networks (CNNs) and graph neural networks (GNNs), have achieved success in finding ATAC-seq motifs. However, CNN-based methods are limited by the fixed width of the convolutional kernel, which makes it difficult to find multiple transcription factor binding sites with different lengths. GNN-based methods has the limitation of using the edge weight information directly, makes it difficult to aggregate the neighboring nodes' information more efficiently when representing node embedding. RESULTS: To address this challenge, we developed a novel graph attention network framework named MMGAT, which employs an attention mechanism to adjust the attention coefficients among different nodes. And then MMGAT finds multiple ATAC-seq motifs based on the attention coefficients of sequence nodes and k-mer nodes as well as the coexisting probability of k-mers. Our approach achieved better performance on the human ATAC-seq datasets compared to existing tools, as evidenced the highest scores on the precision, recall, F1_score, ACC, AUC, and PRC metrics, as well as finding 389 higher quality motifs. To validate the performance of MMGAT in predicting TFBSs and finding motifs on more datasets, we enlarged the number of the human ATAC-seq datasets to 180 and newly integrated 80 mouse ATAC-seq datasets for multi-species experimental validation. Specifically on the mouse ATAC-seq dataset, MMGAT also achieved the highest scores on six metrics and found 356 higher-quality motifs. To facilitate researchers in utilizing MMGAT, we have also developed a user-friendly web server named MMGAT-S that hosts the MMGAT method and ATAC-seq motif finding results. CONCLUSIONS: The advanced methodology MMGAT provides a robust tool for finding ATAC-seq motifs, and the comprehensive server MMGAT-S makes a significant contribution to genomics research. The open-source code of MMGAT can be found at https://github.com/xiaotianr/MMGAT , and MMGAT-S is freely available at https://www.mmgraphws.com/MMGAT-S/ .

Chronic Viral Hepatitis B and C Outweigh MASLD in the Associated Risk of Cirrhosis and HCC.

BACKGROUND & AIMS: The impact of metabolic dysfunction-associated steatotic liver disease (MASLD) on the development of cirrhosis and hepatocellular carcinoma (HCC) by chronic hepatitis B (CHB) or C infection and antiviral treatment statuses is not well-known. METHODS: A total of 336,866 adults aged ≥30 years were prospectively enrolled in a health screening program between 1997-2013. MASLD was identified by abdominal ultrasonography and cardiometabolic profiles. Data linkage was performed using 3 nationwide databases-National Health Insurance, Cancer Registry, and Death Certification System-to obtain information on antiviral treatment, vital status, and newly diagnosed cirrhosis and HCC. Follow-up was conducted until December 31, 2019. RESULTS: In the total population, 122,669 (36.4%) had MASLD. Over a mean follow-up of 15 years, 5562 new cases of cirrhosis and 2273 new cases of HCC were diagnosed. Although MASLD significantly increased the cumulative risks of cirrhosis or HCC (P < .0001), the associated risk was more pronounced when comparing CHB or C infection with the presence of MASLD. Stratifying the participants based on their MASLD and CHB or C statuses, hazard ratios (HRadj) with 95% confidence intervals for HCC were 8.81 (7.83-9.92) for non-steatotic liver disease (SLD) with CHB or C, 1.52 (1.32-1.74) for MASLD without CHB or C, and 8.86 (7.76-10.12) for MASLD with CHB or C, compared with non-SLD without CHB or C (all P < .0001). Among CHB or C patients who received antivirals during follow-up, MASLD was associated with increased risks of cirrhosis and HCC, with HRadj of 1.23 (1.01-1.49) and 1.32 (1.05-1.65), respectively. CONCLUSIONS: These findings underscore the need to prioritize treatment of chronic viral hepatitis before addressing MASLD.

A comprehensive study of oxidative stress-related effects on the prognosis and drug therapy of cervical cancer.

BACKGROUND: Cervical cancer (CC) is a serious global disease with poor prognoses and a significant recurrence rate in patients with advanced disease. Oxidative stress (OS) greatly influences many types of human cancers, making it crucial to understand the functional mechanisms of OS-related genes in CC. METHODS: The transcriptome and clinical data of three normal samples and 306 patients with CC were obtained from The Cancer Genome Atlas dataset. The GSE44001 dataset was acquired from the Gene Expression Omnibus database. OS-related subtypes in the cohort with CC were identified using unsupervised hierarchical clustering, univariate Cox analysis, gene set enrichment analysis (GSEA), and least absolute shrinkage and selection operator regression analysis. Additionally, molecular pathways that differ across subtypes were determined and OS-related genes linked to the prognosis of patients of CC were determined. Finally, a clinical prognostic gene signature was developed and validated. The relative infiltration level of immune cell subpopulations in different risk groups and subtypes was evaluated using the cell-type identification by estimating relative subsets of RNA transcripts (CIBERPORT) algorithm and single-sample GSEA (ssGSEA) techniques. RESULTS: The present study established two distinct OS subtypes (OS clusters A and B). Analysis using ssGSEA and CIBERSPORT revealed that OS cluster B exhibited a significant level of immune infiltration. A clinical prognostic gene signature was established using OS-related characteristic genes identified by examining the differentially expressed genes across both subtypes. Furthermore, patients with CC were grouped into high- and low-risk groups, with the low-risk group showing higher survival rates. Additionally, these individuals exhibited significant advantages in terms of survival and immunotherapy. Receiver operating characteristic curve analysis demonstrated the higher predictive value of the clinical prognostic gene signature. The outcomes of the validation group depicted congruence with those recorded in the training group. CONCLUSIONS: A new model was constructed based on eight OS-related characteristic genes to aid the prediction of the survival rates of individuals with CC. The present study contributes to the existing literature on the mechanisms of OS genes in CC and offers a fresh perspective for future advancements in immunotherapy for such individuals.

Long-term Risks of Cirrhosis and Hepatocellular Carcinoma Across Steatotic Liver Disease Subtypes.

INTRODUCTION: The prospective study aimed to investigate the long-term associated risks of cirrhosis and hepatocellular carcinoma (HCC) across various subtypes of steatotic liver disease (SLD). METHODS: We enrolled 332,175 adults who participated in a health screening program between 1997 and 2013. Participants were categorized into various subtypes, including metabolic dysfunction-associated SLD (MASLD), MASLD with excessive alcohol consumption (MetALD), and alcohol-related liver disease (ALD), based on ultrasonography findings, alcohol consumption patterns, and cardiometabolic risk factors. We used computerized data linkage with nationwide registries from 1997 to 2019 to ascertain the incidence of cirrhosis and HCC. RESULTS: After a median follow-up of 16 years, 4,458 cases of cirrhosis and 1,392 cases of HCC occurred in the entire cohort, resulting in an incidence rate of 86.1 and 26.8 per 100,000 person-years, respectively. The ALD group exhibited the highest incidence rate for cirrhosis and HCC, followed by MetALD, MASLD, and non-SLD groups. The multivariate adjusted hazard ratios for HCC were 1.92 (95% confidence interval [CI] 1.51-2.44), 2.91 (95% CI 2.11-4.03), and 2.59 (95% CI 1.93-3.48) for MASLD, MetALD, and ALD, respectively, when compared with non-SLD without cardiometabolic risk factors. The pattern of the associated risk of cirrhosis was similar to that of HCC (all P value <0.001). The associated risk of cirrhosis for ALD increased to 4.74 (95% CI 4.08-5.52) when using non-SLD without cardiometabolic risk factors as a reference. DISCUSSION: This study highlights elevated risks of cirrhosis and HCC across various subtypes of SLD compared with non-SLD, emphasizing the importance of behavioral modifications for early prevention.

Ba4B14O25: A Deep Ultraviolet Transparent Nonlinear Optical Crystal with Strong Second Harmonic Generation Response Achieved by a Boron-Rich Closed-Loop Strategy.

Discovering new deep ultraviolet (DUV) nonlinear optical (NLO) materials is the current research hotspot. However, how to perfectly integrate several stringent performances into a crystal is a great challenge because of the natural incompatibility among them, particularly wide band gap and large NLO coefficient. To tackle the challenge, a boron-rich closed-loop strategy is supposed, based on which a new barium borate, Ba4B14O25, is designed and synthesized successfully via the high-temperature solid-state melting method. It features a highly polymeric 3D geometry with the closed-loop anionic framework [B14O25]8- constructed by the fundamental building blocks [B14O33]24-. The high-density π-conjugated [BO3]3- groups and the fully closed-loop B-O-B connections make Ba4B14O25 possess excellent NLO properties, including short UV cutoff edge (<200 nm), large second harmonic generation response (3.0 × KDP) and phase-matching capability, being a promising DUV-transparent NLO candidate material. The work provides a creative design strategy for the exploration of DUV NLO crystals.

Human Cytomegalovirus IE1 Impairs Neuronal Migration by Downregulating Connexin 43.

Human cytomegalovirus (HCMV) is a leading cause of congenital birth defects. Though the underlying mechanisms remain poorly characterized, mouse models of congenital CMV infection have demonstrated that the neuronal migration process is damaged. In this study, we evaluated the effects of HCMV infection on connexin 43 (Cx43), a crucial adhesion molecule mediating neuronal migration. We show in multiple cellular models that HCMV infection downregulated Cx43 posttranslationally. Further analysis identified the immediate early protein IE1 as the viral protein responsible for the reduction of Cx43. IE1 was found to bind the Cx43 C terminus and promote Cx43 degradation through the ubiquitin-proteasome pathway. Deletion of the Cx43-binding site in IE1 rendered it incapable of inducing Cx43 degradation. We validated the IE1-induced loss of Cx43 in vivo by introducing IE1 into the fetal mouse brain. Noteworthily, ectopic IE1 expression induced cortical atrophy and neuronal migration defects. Several lines of evidence suggest that these damages result from decreased Cx43, and restoration of Cx43 levels partially rescued IE1-induced interruption of neuronal migration. Taken together, the results of our investigation reveal a novel mechanism of HCMV-induced neural maldevelopment and identify a potential intervention target. IMPORTANCE Congenital CMV (cCMV) infection causes neurological sequelae in newborns. Recent studies of cCMV pathogenesis in animal models reveal ventriculomegaly and cortical atrophy associated with impaired neural progenitor cell (NPC) proliferation and migration. In this study, we investigated the mechanisms underlying these NPC abnormalities. We show that Cx43, a critical adhesion molecule mediating NPC migration, is downregulated by HCMV infection in vitro and HCMV-IE1 in vivo. We provide evidence that IE1 interacts with the C terminus of Cx43 to promote its ubiquitination and consequent degradation through the proteasome. Moreover, we demonstrate that introducing IE1 into mouse fetal brains led to neuronal migration defects, which was associated with Cx43 reduction. Deletion of the Cx43-binding region in IE1 or ectopic expression of Cx43 rescued the IE1-induced migration defects in vivo. Our study provides insight into how cCMV infection impairs neuronal migration and reveals a target for therapeutic interventions.

Multi-channel graph attention autoencoders for disease-related lncRNAs prediction.

MOTIVATION: Predicting disease-related long non-coding RNAs (lncRNAs) can be used as the biomarkers for disease diagnosis and treatment. The development of effective computational prediction approaches to predict lncRNA-disease associations (LDAs) can provide insights into the pathogenesis of complex human diseases and reduce experimental costs. However, few of the existing methods use microRNA (miRNA) information and consider the complex relationship between inter-graph and intra-graph in complex-graph for assisting prediction. RESULTS: In this paper, the relationships between the same types of nodes and different types of nodes in complex-graph are introduced. We propose a multi-channel graph attention autoencoder model to predict LDAs, called MGATE. First, an lncRNA-miRNA-disease complex-graph is established based on the similarity and correlation among lncRNA, miRNA and diseases to integrate the complex association among them. Secondly, in order to fully extract the comprehensive information of the nodes, we use graph autoencoder networks to learn multiple representations from complex-graph, inter-graph and intra-graph. Thirdly, a graph-level attention mechanism integration module is adopted to adaptively merge the three representations, and a combined training strategy is performed to optimize the whole model to ensure the complementary and consistency among the multi-graph embedding representations. Finally, multiple classifiers are explored, and Random Forest is used to predict the association score between lncRNA and disease. Experimental results on the public dataset show that the area under receiver operating characteristic curve and area under precision-recall curve of MGATE are 0.964 and 0.413, respectively. MGATE performance significantly outperformed seven state-of-the-art methods. Furthermore, the case studies of three cancers further demonstrate the ability of MGATE to identify potential disease-correlated candidate lncRNAs. The source code and supplementary data are available at https://github.com/sheng-n/MGATE. CONTACT: huanglan@jlu.edu.cn, wy6868@jlu.edu.cn.

Prediction of drug-disease associations by integrating common topologies of heterogeneous networks and specific topologies of subnets.

MOTIVATION: The development process of a new drug is time-consuming and costly. Thus, identifying new uses for approved drugs, named drug repositioning, is helpful for speeding up the drug development process and reducing development costs. Existing drug-related disease prediction methods mainly focus on single or multiple drug-disease heterogeneous networks. However, heterogeneous networks, and drug subnets and disease subnet contained in heterogeneous networks cover the common topology information between drug and disease nodes, the specific information between drug nodes and the specific information between disease nodes, respectively. RESULTS: We design a novel model, CTST, to extract and integrate common and specific topologies in multiple heterogeneous networks and subnets. Multiple heterogeneous networks composed of drug and disease nodes are established to integrate multiple kinds of similarities and associations among drug and disease nodes. These heterogeneous networks contain multiple drug subnets and a disease subnet. For multiple heterogeneous networks and subnets, we then define the common and specific representations of drug and disease nodes. The common representations of drug and disease nodes are encoded by a graph convolutional autoencoder with sharing parameters and they integrate the topological relationships of all nodes in heterogeneous networks. The specific representations of nodes are learned by specific graph convolutional autoencoders, respectively, and they fuse the topology and attributes of the nodes in each subnet. We then propose attention mechanisms at common representation level and specific representation level to learn more informative common and specific representations, respectively. Finally, an integration module with representation feature level attention is built to adaptively integrate these two representations for final association prediction. Extensive experimental results confirm the effectiveness of CTST. Comparison with six latest methods and case studies on five drugs further verify CTST has the ability to discover potential candidate diseases.

Boosting Hydrogen Production of a MOF-based Multicomponent Photocatalyst with Clean Interface via Facile One-pot Electrosynthesis.

Hydrogen production from photocatalysis via the usage of multicomponent photocatalysts represents a promising pathway for carbon peaking and carbon neutrality, owing to their structural advantages in dealing with the three crucial processes in photocatalysis, namely, light harvesting, charge transfer, and surface redox reactions. We demonstrate the fabrication of a MOF-based multicomponent photocatalyst, denoted as semiconductor/MOF/cocatalyst, by a one-pot electrochemical synthetic route. The as-fabricated multicomponent photocatalyst has a clean interface among the components, leading to close connections that contribute to high-quality heterojunction and facilitate photogenerated charge transfer and separation, thereby the efficient hydrogen evolution. The hydrogen production rate of the resultant ZrO2 /Zr-MOF/Pt is 1327 µmol ⋅ g-1 ⋅ h-1 , which is much higher than that of ZrO2 /Zr-MOF (15 µmol ⋅ g-1 ⋅ h-1 ) and pure Zr-MOF (10.1 µmol ⋅ g-1 ⋅ h-1 ), as well as the photodeposited-Pt products ZrO2 /Zr-MOF/PtPD (287 µmol ⋅ g-1 ⋅ h-1 ) and Zr-MOF/PtPD (192 µmol ⋅ g-1 ⋅ h-1 ) obtained by the step-wise synthetic approach. The work gives a good inspiration for the rational design and construction of MOF-based multicomponent photocatalysts through the one-pot electrosynthesis.

Hepatitis B relapse after entecavir or tenofovir alafenamide cessation under anti-viral prophylaxis for cancer chemotherapy.

BACKGROUND: No study has comparing hepatitis B virus (HBV) relapse rates among patients with both cancer and hepatitis B e antigen (HBeAg)-negative chronic hepatitis B (CHB) who completed anti-viral prophylaxis for chemotherapy and then stopped taking entecavir or tenofovir alafenamide (TAF). METHODS: A total of 227 HBeAg-negative cancer patients without cirrhosis who previously took entecavir (n = 144) or TAF (n = 83) for antiviral prophylaxis were enrolled. RESULTS: The cumulative incidence of virological and clinical relapse at 2 years was 37% and 10.4%, respectively, in the entecavir group, and 46.7% and 19.5%, respectively, in the TAF group. The multivariate analysis revealed that the use of hematologic malignancy, TAF use, and high-viremia group at baseline were independent risk factors for virological relapse, and use of rituximab, TAF use, higher FIB-4 index and high-viremia group at baseline were independent risk factors for clinical relapse. After propensity score-matching, the patients who discontinued TAF therapy still exhibited higher virological (P = 0.031) and clinical relapse rates (P = 0.012) than did those who discontinued entecavir therapy. The patients were allocated to high- (> 2000 IU/mL), moderate- (between 20 and 2000 IU/mL) and low- (< 20 IU/mL) viremia groups. In the high-viremia group, those who had taken TAF for antiviral prophylaxis had higher rates of virological and clinical relapse than did those who had taken entecavir; in the moderate- and low-viremia groups, no significant difference in virological and clinical relapse rates was detected between the entecavir and TAF groups. Three patients experienced hepatic decompensation upon clinical relapse. All three patients were lymphoma and underwent rituximab therapy. One patient developed acute on chronic liver failure and died even though timely retreatment. CONCLUSIONS: In patients with both cancer and CHB who underwent antiviral prophylaxis, TAF use was associated with a higher chance of HBV relapse than entecavir use after nucleos(t)ide analogue cessation, particularly in the high-viremia group. Patients who are hematologic malignancy and undergo a rituximab-containing cytotoxic therapy should be monitored closely after withdrawal from prophylactic NA treatment.

Circadian Rhythm Disturbance in Acute Ischemic Stroke Patients and Its Effect on Prognosis.

INTRODUCTION: Poststroke sleep disturbances are common and can affect stroke outcomes, but the clinical studies mainly focus on breathing-related sleep disorders, while the bidirectional impact of circadian rhythm dysfunction in ischemic stroke remains unknown. This study observed the characteristics of melatonin secretion in acute ischemic stroke patients and evaluated whether melatonin rhythm impacts the prognosis after stroke by assessing the neurological function, cognition, emotion, and quality of life 3 months after stroke. METHODS: Acute ischemic stroke patients were selected from the Department of Neurology Inpatients of the Second Hospital affiliated with Soochow University from October 2019 to July 2021. Healthy control subjects were recruited at the same time. Demographic and clinical data were collected, and relevant scale scores (including neurological function, cognition, emotion, and sleep) were assessed within 2 weeks of onset and followed up 3 months later. All participants collected salivary melatonin samples on the 4th day of hospitalization and dim light melatonin onset (DLMO) was calculated according to melatonin concentration. Stroke patients were then divided into three groups based on their DLMO values. RESULTS: A total of 74 stroke patients and 33 control subjects were included in this analysis. Compared with healthy controls, stroke patients exhibited a delayed melatonin rhythm during the acute phase of stroke (21:36 vs. 20:38, p = 0.004). Stroke patients were then divided into three groups, namely normal (n = 36), delayed (n = 28), or advanced DLMO (n = 10), based on their DLMO values. A χ2 test showed that there were significant differences in the rate of poor prognosis (p = 0.011) and depression tendency (p = 0.028) among the three groups. A further pairwise comparison revealed that stroke patients with delayed DLMO were more likely to experience poor short-term outcomes than normal DLMO group (p = 0.003). The average melatonin concentration of stroke patients at 5 time points was significantly lower than that of the control group (3.145 vs. 7.065 pg/mL, p < 0.001). Accordingly, we split stroke patients into three groups, namely low melatonin level (n = 14), normal melatonin level (n = 54), or high melatonin level (n = 6). Unfortunately, there were no great differences in the clinical characteristics, cognition, emotion, sleep quality, and short-term outcome among groups. CONCLUSIONS: This is a preliminary study, and our results indicate that changes in melatonin secretion phase of stroke patients may have effect on their short-term prognosis.

Mechanistic Investigation on the Regioselectivity of Electrochemical Co(II)-Catalyzed [2 + 2 + 2] Cycloaddition of Terminal Acetylenes.

In this paper, the regioselectivity of electrochemical Co(II)-catalyzed [2 + 2 + 2] cycloaddition of terminal alkynes was investigated using density functional theory. We explored in detail the energy profiles for both 1,2,4- and 1,3,5-regioselectivity pathways and revealed the origin of the regioselectivity. Two kinds of conformational isomers derived from the different coordination modes of alkynes with cobaltacyclopentadiene have been found, which were formed through electrochemically mediated redox processes. The regioselectivity of the reaction depends on the two coordination modes. When the Co(II) center attacks α-C of the third alkyne, while ß2-C in cyclopentadiene bonds to ß-C of the alkyne, the reaction favors the formation of 1,2,4-products. In contrast, when the Co(II) center connects to ß-C of the alkyne, it forms only the 1,3,5-products via [4 + 2] cycloaddition because of the steric repulsion between the bulky ligand on Co(II) and the phenyl group in the alkyne.

Kynurenic Acid Plays a Protective Role in Hepatotoxicity Induced by HFPO-DA in Male Mice.

Following its introduction as an alternative to perfluorooctanoic acid, hexafluoropropylene oxide dimer acid (HFPO-DA) has been extensively detected in various environmental matrices. Despite this prevalence, limited information is available regarding its hepatotoxicity biomarkers. In this study, toxicokinetic simulations indicated that under repeated treatment, HFPO-DA in mice serum reached a steady state by the 4th day. To assess its subacute hepatic effects and identify potential biomarkers, mice were administered HFPO-DA orally at doses of 0, 0.1, 0.5, 2.5, 12.5, or 62.5 mg/kg/d for 7 d. Results revealed that the lowest observed adverse effect levels were 0.5 mg/kg/d for hepatomegaly and 2.5 mg/kg/d for hepatic injury. Serum metabolomics analysis identified 34, 58, and 118 differential metabolites in the 0.1, 0.5, and 2.5 mg/kg/d groups, respectively, compared to the control group. Based on weighted gene coexpression network analysis, eight potential hepatotoxicity-related metabolites were identified; among them, kynurenic acid (KA) in mouse serum exhibited the highest correlation with liver injury. Furthermore, liver-targeted metabolomics analysis demonstrated that HFPO-DA exposure induced metabolic migration of the kynurenine pathway from KA to nicotinamide adenine dinucleotide, resulting in the activation of endoplasmic reticulum stress and the nuclear factor kappa-B signaling pathway. Notably, pretreatment with KA significantly attenuated liver injury induced by HFPO-DA exposure in mice, highlighting the pivotal roles of KA in the hepatotoxicity of HFPO-DA.

Carbon dots with enhanced red emission for ratiometric sensing and encryption applications.

The excitation-dependent emission properties of carbon dots (Cdots) are extensively reported, but their red emission is often weak, limiting their wider application. Here we introduce ethidium bromide, as a functional precursor with red emission, to enhance the red emission for Cdots, with comparable intensity at a broad wavelength range to multi-emission Cdots (M-Cdots). We found that Cdots prepared with ethidium bromide/ethylenediamine exhibited strong blue and red emission at 440 and 615 nm, with optimal excitation at 360 and 470 nm as M-Cdots, respectively, but the Cdots from single ethidium bromide (EB-Cdots) possessed weak red emission. M-Cdots exhibited a broad absorption band at 478 nm, but a band blue-shifted to 425 nm was observed for EB-Cdots, while no absorption was observed at 478-425 nm for the Cdots prepared with citric acid and ethylenediamine. Thus, we proposed that C=O and C=N formed a π-conjugation structure as the absorption band at 478 nm for the red emission of M-Cdots, as also confirmed with the excitation at 470 nm. Moreover, the π-conjugation structure is fragile and sensitive to harsh conditions, so red emission was difficult to observe for the Cdots prepared with citric acid/ethylenediamine or single ethidium bromide. M-Cdots possess two centers for blue and red emission with different structures. The dual emission was therefore used for ratiometric sensing with dichromate (Cr2O72-) and formaldehyde (HCHO) as the targets using the intensity ratio of the emissions at 615 and 440 nm. Due to the comparable intensity at a broad wavelength range, we designed encryption codes with five excitations at 360, 400, 420, 450, and 470 nm as the inputs, and the emission colors were used for information decoding. Thus, we determined why red emission was difficult to realize for Cdots, and our results could motivate the design of red-emission Cdots for extensive applications.