The primordial differentiation of tumor-specific memory CD8+ T cells as bona fide responders to PD-1/PD-L1 blockade in draining lymph nodes.

Blocking PD-1/PD-L1 signaling transforms cancer therapy and is assumed to unleash exhausted tumor-reactive CD8+ T cells in the tumor microenvironment (TME). However, recent studies have also indicated that the systemic tumor-reactive CD8+ T cells may respond to PD-1/PD-L1 immunotherapy. These discrepancies highlight the importance of further defining tumor-specific CD8+ T cell responders to PD-1/PD-L1 blockade. Here, using multiple preclinical tumor models, we revealed that a subset of tumor-specific CD8+ cells in the tumor draining lymph nodes (TdLNs) was not functionally exhausted but exhibited canonical memory characteristics. TdLN-derived tumor-specific memory (TTSM) cells established memory-associated epigenetic program early during tumorigenesis. More importantly, TdLN-TTSM cells exhibited superior anti-tumor therapeutic efficacy after adoptive transfer and were characterized as bona fide responders to PD-1/PD-L1 blockade. These findings highlight that TdLN-TTSM cells could be harnessed to potentiate anti-tumor immunotherapy.

An encoding generative modeling approach to dimension reduction and covariate adjustment in causal inference with observational studies.

In this article, we develop CausalEGM, a deep learning framework for nonlinear dimension reduction and generative modeling of the dependency among covariate features affecting treatment and response. CausalEGM can be used for estimating causal effects in both binary and continuous treatment settings. By learning a bidirectional transformation between the high-dimensional covariate space and a low-dimensional latent space and then modeling the dependencies of different subsets of the latent variables on the treatment and response, CausalEGM can extract the latent covariate features that affect both treatment and response. By conditioning on these features, one can mitigate the confounding effect of the high dimensional covariate on the estimation of the causal relation between treatment and response. In a series of experiments, the proposed method is shown to achieve superior performance over existing methods in both binary and continuous treatment settings. The improvement is substantial when the sample size is large and the covariate is of high dimension. Finally, we established excess risk bounds and consistency results for our method, and discuss how our approach is related to and improves upon other dimension reduction approaches in causal inference.

Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring.

Plasma cell-free DNA (cfDNA) is a noninvasive biomarker for cell death of all organs. Deciphering the tissue origin of cfDNA can reveal abnormal cell death because of diseases, which has great clinical potential in disease detection and monitoring. Despite the great promise, the sensitive and accurate quantification of tissue-derived cfDNA remains challenging to existing methods due to the limited characterization of tissue methylation and the reliance on unsupervised methods. To fully exploit the clinical potential of tissue-derived cfDNA, here we present one of the largest comprehensive and high-resolution methylation atlas based on 521 noncancer tissue samples spanning 29 major types of human tissues. We systematically identified fragment-level tissue-specific methylation patterns and extensively validated them in orthogonal datasets. Based on the rich tissue methylation atlas, we develop the first supervised tissue deconvolution approach, a deep-learning-powered model, cfSort, for sensitive and accurate tissue deconvolution in cfDNA. On the benchmarking data, cfSort showed superior sensitivity and accuracy compared to the existing methods. We further demonstrated the clinical utilities of cfSort with two potential applications: aiding disease diagnosis and monitoring treatment side effects. The tissue-derived cfDNA fraction estimated from cfSort reflected the clinical outcomes of the patients. In summary, the tissue methylation atlas and cfSort enhanced the performance of tissue deconvolution in cfDNA, thus facilitating cfDNA-based disease detection and longitudinal treatment monitoring.

Deep generative modeling and clustering of single cell Hi-C data.

Deciphering 3D genome conformation is important for understanding gene regulation and cellular function at a spatial level. The recent advances of single cell Hi-C technologies have enabled the profiling of the 3D architecture of DNA within individual cell, which allows us to study the cell-to-cell variability of 3D chromatin organization. Computational approaches are in urgent need to comprehensively analyze the sparse and heterogeneous single cell Hi-C data. Here, we proposed scDEC-Hi-C, a new framework for single cell Hi-C analysis with deep generative neural networks. scDEC-Hi-C outperforms existing methods in terms of single cell Hi-C data clustering and imputation. Moreover, the generative power of scDEC-Hi-C could help unveil the differences of chromatin architecture across cell types. We expect that scDEC-Hi-C could shed light on deepening our understanding of the complex mechanism underlying the formation of chromatin contacts.

Early onset of pathological polyploidization and cellular senescence in hepatocytes lacking RAD51 creates a pro-fibrotic and pro-tumorigenic inflammatory microenvironment.

BACKGROUND AND AIMS: RAD51 recombinase (RAD51) is a highly conserved DNA repair protein and is indispensable for embryonic viability. As a result, the role of RAD51 in liver development and function is unknown. Our aim was to characterize the function of RAD51 in postnatal liver development. APPROACH AND RESULTS: RAD51 is highly expressed during liver development and during regeneration following hepatectomy and hepatic injury, and is also elevated in chronic liver diseases. We generated a hepatocyte-specific Rad51 deletion mouse model using Alb -Cre ( Rad51 -conditional knockout (CKO)) and Adeno-associated virus 8-thyroxine-binding globulin-cyclization recombination enzyme to evaluate the function of RAD51 in liver development and regeneration. The phenotype in Rad51 -CKO mice is dependent on CRE dosage, with Rad51fl/fl ; Alb -Cre +/+ manifesting a more severe phenotype than the Rad51fl/fl ; Alb -Cre +/- mice. RAD51 deletion in postnatal hepatocytes results in aborted mitosis and early onset of pathological polyploidization that is associated with oxidative stress and cellular senescence. Remarkable liver fibrosis occurs spontaneously as early as in 3-month-old Rad51fl/fl ; Alb -Cre +/+ mice. While liver regeneration is compromised in Rad51 -CKO mice, they are more tolerant of carbon tetrachloride-induced hepatic injury and resistant to diethylnitrosamine/carbon tetrachloride-induced HCC. A chronic inflammatory microenvironment created by the senescent hepatocytes appears to activate ductular reaction the transdifferentiation of cholangiocytes to hepatocytes. The newly derived RAD51 functional immature hepatocytes proliferate vigorously, acquire increased malignancy, and eventually give rise to HCC. CONCLUSIONS: Our results demonstrate a novel function of RAD51 in liver development, homeostasis, and tumorigenesis. The Rad51 -CKO mice represent a unique genetic model for premature liver senescence, fibrosis, and hepatocellular carcinogenesis.

HiChIPdb: a comprehensive database of HiChIP regulatory interactions.

Elucidating the role of 3D architecture of DNA in gene regulation is crucial for understanding cell differentiation, tissue homeostasis and disease development. Among various chromatin conformation capture methods, HiChIP has received increasing attention for its significant improvement over other methods in profiling of regulatory (e.g. H3K27ac) and structural (e.g. cohesin) interactions. To facilitate the studies of 3D regulatory interactions, we developed a HiChIP interactions database, HiChIPdb (http://health.tsinghua.edu.cn/hichipdb/). The current version of HiChIPdb contains ∼262M annotated HiChIP interactions from 200 high-throughput HiChIP samples across 108 cell types. The functionalities of HiChIPdb include: (i) standardized categorization of HiChIP interactions in a hierarchical structure based on organ, tissue and cell line and (ii) comprehensive annotations of HiChIP interactions with regulatory genes and GWAS Catalog SNPs. To the best of our knowledge, HiChIPdb is the first comprehensive database that utilizes a unified pipeline to map the functional interactions across diverse cell types and tissues in different resolutions. We believe this database has the potential to advance cutting-edge research in regulatory mechanisms in development and disease by removing the barrier in data aggregation, preprocessing, and analysis.

High-performance K-ion half/full batteries with superb rate capability and cycle stability.

SignificanceThe present work might be significant for exploring advanced K-ion batteries with superb rate capability and cycle stability toward practical applications. The as-assembled K-ion half cell exhibits an excellent rate capability of 428 mA h g-1 at 100 mA g-1 and a high reversible specific capacity of 330 mA h g-1 with 120% specific capacity retention after 2,000 cycles at 2,000 mA g-1, which is the best among those based on carbon materials. The as-constructed full cell delivers 98% specific capacity retention over 750 cycles at 500 mA g-1, superior to most of those based on carbon materials that have been reported thus far.

DeepAEG: a model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies.

MOTIVATION: The prediction of cancer drug response is a challenging subject in modern personalized cancer therapy due to the uncertainty of drug efficacy and the heterogeneity of patients. It has been shown that the characteristics of the drug itself and the genomic characteristics of the patient can greatly influence the results of cancer drug response. Therefore, accurate, efficient, and comprehensive methods for drug feature extraction and genomics integration are crucial to improve the prediction accuracy. RESULTS: Accurate prediction of cancer drug response is vital for guiding the design of anticancer drugs. In this study, we propose an end-to-end deep learning model named DeepAEG which is based on a complete-graph update mode to predict IC50. Specifically, we integrate an edge update mechanism on the basis of a hybrid graph convolutional network to comprehensively learn the potential high-dimensional representation of topological structures in drugs, including atomic characteristics and chemical bond information. Additionally, we present a novel approach for enhancing simplified molecular input line entry specification data by employing sequence recombination to eliminate the defect of single sequence representation of drug molecules. Our extensive experiments show that DeepAEG outperforms other existing methods across multiple evaluation parameters in multiple test sets. Furthermore, we identify several potential anticancer agents, including bortezomib, which has proven to be an effective clinical treatment option. Our results highlight the potential value of DeepAEG in guiding the design of specific cancer treatment regimens.

Hemodynamic force dictates endothelial angiogenesis through MIEN1-ERK/MAPK-signaling axis.

It is well-recognized that blood flow at branches and bends of arteries generates disturbed shear stress, which plays a crucial in driving atherosclerosis. Flow-generated fluid shear stress (FSS), as one of the key hemodynamic factors, is appreciated for its critical involvement in regulating angiogenesis to facilitate wound healing and tissue repair. Endothelial cells can directly sense FSS but the mechanobiological mechanism by which they decode different patterns of FSS to trigger angiogenesis remains unclear. In the current study, laminar shear stress (LSS, 15 dyn/cm2) was employed to mimic physiological blood flow, while disturbed shear stress (DSS, ranging from 0.5 ± 4 dyn/cm2) was applied to simulate pathological conditions. The aim was to investigate how these distinct types of blood flow regulated endothelial angiogenesis. Initially, we observed that DSS impaired angiogenesis and downregulated endogenous vascular endothelial growth factor B (VEGFB) expression compared to LSS. We further found that the changes in membrane protein, migration and invasion enhancer 1 (MIEN1) play a role in regulating ERK/MAPK signaling, thereby contributing to endothelial angiogenesis in response to FSS. We also showed the involvement of MIEN1-directed cytoskeleton organization. These findings suggest the significance of shear stress in endothelial angiogenesis, thereby enhancing our understanding of the alterations in angiogenesis that occur during the transition from physiological to pathological blood flow.

Rowell's syndrome with Condyloma acuminatum: A case report.

Rowell's syndrome is an autoimmune disease characterized by lupus erythematosus, erythema multiforme skin lesions, and speckled antinuclear antibody. We report the case of a woman who presented with erythema multiforme with target-type skin lesions and vulvar vegetation who fulfilled the criteria for Rowell's syndrome and condyloma acuminatum. The simultaneous occurrence of both conditions has rarely been reported in the literature.

Label-Free Light Scattering Imaging with Purified Brownian Motion Differentiates Small Extracellular Vesicles in Cell Microenvironments.

Small extracellular vesicles (sEVs) are heterogeneous biological nanoparticles (NPs) with wide biomedicine applications. Tracking individual nanoscale sEVs can reveal information that conventional microscopic methods may lack, especially in cellular microenvironments. This usually requires biolabeling to identify single sEVs. Here, we developed a light scattering imaging method based on dark-field technology for label-free nanoparticle diffusion analysis (NDA). Compared with nanoparticle tracking analysis (NTA), our method was shown to determine the diffusion probabilities of a single NP. It was demonstrated that accurate size determination of NPs of 41 and 120 nm in diameter is achieved by purified Brownian motion (pBM), without or within the cell microenvironments. Our pBM method was also shown to obtain a consistent size estimation of the normal and cancerous plasma-derived sEVs without and within cell microenvironments, while cancerous plasma-derived sEVs are statistically smaller than normal ones. Moreover, we showed that the velocity and diffusion coefficient are key parameters for determining the diffusion types of the NPs and sEVs in a cancerous cell microenvironment. Our light scattering-based NDA and pBM methods can be used for size determination of NPs, even in cell microenvironments, and also provide a tool that may be used to analyze sEVs for many biomedical applications.

RGA1 regulates grain size, rice quality and seed germination in the small and round grain mutant srg5.

BACKGROUND: Generating elite rice varieties with high yield and superior quality is the main goal of rice breeding programs. Key agronomic traits, including grain size and seed germination characteristics, affect the final yield and quality of rice. The RGA1 gene, which encodes the α-subunit of rice G-protein, plays an important role in regulating rice architecture, seed size and abiotic stress responses. However, whether RGA1 is involved in the regulation of rice quality and seed germination traits is still unclear. RESULTS: In this study, a rice mutant small and round grain 5 (srg5), was identified in an EMS-induced rice mutant library. Systematic analysis of its major agronomic traits revealed that the srg5 mutant exhibited a semi-dwarf plant height with small and round grain and reduced panicle length. Analysis of the physicochemical properties of rice showed that the difference in rice eating and cooking quality (ECQ) between the srg5 mutant and its wild-type control was small, but the appearance quality was significantly improved. Interestingly, a significant suppression of rice seed germination and shoot growth was observed in the srg5 mutant, which was mainly related to the regulation of ABA metabolism. RGA1 was identified as the candidate gene for the srg5 mutant by BSA analysis. A SNP at the splice site of the first intron disrupted the normal splicing of the RGA1 transcript precursor, resulting in a premature stop codon. Additional linkage analysis confirmed that the target gene causing the srg5 mutant phenotype was RGA1. Finally, the introduction of the RGA1 mutant allele into two indica rice varieties also resulted in small and round rice grains with less chalkiness. CONCLUSIONS: These results indicate that RGA1 is not only involved in the control of rice architecture and grain size, but also in the regulation of rice quality and seed germination. This study sheds new light on the biological functions of RGA1, thereby providing valuable information for future systematic analysis of the G-protein pathway and its potential application in rice breeding programs.

Zn-Leaching Induced Rapid Self-Reconstruction of NiFe-Layered Double Hydroxides for Boosted Oxygen Evolution Reaction.

Optimizing the active centers through reconstruction is recognized as the key to construct high-performance oxygen evolution reaction (OER) catalysts. Herein, a simple and rapid in situ leaching strategy to promote the self-reconstruction of NiFe-layered double hydroxides (LDHs) catalysts is reported. The trace Zn dopants are introduced in advance by a facile and one-step hydrothermal method, followed by leaching over the electrochemical activation process, which can remarkably reduce the formation potential of NiFeOOH active centers to enable the deeper self-reconstruction for the formation of abundant highly active centers. Moreover, the self-restructured NiFeOOH-VZn cannot only significantly lower the dehydrogenation energy barrier for the transformation from Ni(OH)2 to NiOOH, but also decrease the free energy barrier of rate determining step for the *OH converted to *O through a deprotonation process, thus significantly boosting the OER behaviors. As a proof of concept, the obtained NiFeOOH-VZn catalyst just requires a low overpotential of 240 mV at 10 mA cm-2, and delivers robust stability at 50 mA cm-2 over 120 h, which outperforms the benchmark of noble metal RuO2 and those of most non-noble metal catalysts ever reported.

The protocol of enhanced recovery after cardiac surgery in adult patients: A stepped wedge cluster randomized trial.

BACKGROUND: The enhanced recovery after cardiac surgery is a bundle of measurements from preoperative to postoperative phases to improve patients' recovery. METHODS: This study is a multicenter, stepwise design, cluster randomized controlled trial. About 3,600 patients presenting during control and intervention periods are eligible if they are aged from 18 to 80 years old awaiting elective cardiac surgery with cardiopulmonary bypass (CPB). About 5 centers are randomly assigned to staggered start dates for one-way crossover from the control phase to the intervention phase. In the intervention periods, patients will receive ERAS strategy including preoperative, intraoperative, and postoperative approaches. During the control phase, patients receive usual care. The primary outcome consists of major adverse cardiac and cerebrovascular events (MACCEs), postoperative pulmonary complications (PPCs), and acute kidney injury (AKI). DISCUSSION: This study aims to compare the application of ERAS management protocol and traditional management protocol in adult cardiac surgery under extracorporeal circulation.

Renal Fibrosis Is Alleviated through Targeted Inhibition of IL-11-Induced Renal Tubular Epithelial-to-Mesenchymal Transition.

Renal fibrosis is a pathologic process that leads to irreversible renal failure without effective treatment. Epithelial-to-mesenchymal transition (EMT) plays a key role in this process. The current study found that aberrant expression of IL-11 is critically involved in tubular EMT. IL-11 and its receptor subunit alpha-1 (IL-11Rα1) were significantly induced in renal tubular epithelial cells (RTECs) in unilateral ureteral obstruction (UUO) kidneys, co-localized with transforming growth factor-ß1. IL-11 knockdown ameliorated UUO-induced renal fibrosis in vivo and transforming growth factor-ß1-induced EMT in vitro. IL-11 intervention directly induced the transdifferentiation of RTECs to the mesenchymal phenotype and increased the synthesis of profibrotic mediators. The EMT response induced by IL-11 was dependent on the sequential activation of STAT3 and extracellular signal-regulated kinase 1/2 signaling pathways and the up-regulation of metadherin in RTECs. Micheliolide (MCL) competitively inhibited the binding of IL-11 with IL-11Rα1, suppressing the activation of STAT3 and extracellular signal-regulated kinase 1/2-metadherin pathways, ultimately inhibiting renal tubular EMT and interstitial fibrosis induced by IL-11. In addition, treatment with dimethylaminomicheliolide, a pro-drug of MCL for in vivo use, significantly ameliorated renal fibrosis exacerbated by IL-11 in the UUO model. These findings suggest that IL-11 is a promising target in renal fibrosis and that MCL/dimethylaminomicheliolide exerts its antifibrotic effect by suppressing IL-11/IL-11Rα1 interaction and blocking its downstream effects.

Research progress on the mechanism and signalling pathway of ferroptosis and its potential role in dermatosis research.

Ferroptosis is a novel type of cell death that is dependent on lipid peroxidation and iron accumulation, which distinguishes it from other types of programmed cell death. Current research indicates a significant association between ferroptosis and various pathological conditions, including cancer, neurological disorders, and cardiovascular diseases, albeit with a relatively unexplored role in dermatological afflictions. This paper elaborates on the mechanisms and signalling pathways of ferroptosis, summarizing the recent studies on ferroptosis and its related factors in dermatosis. Our objective is to shed light on novel perspectives and therapeutic strategies for dermatosis, enhancing the understanding of this under-researched area through this comprehensive review.

Global, regional and national trends and impacts of natural floods, 1990-2022.

Objective: To assess global, regional and national trends in the impact of floods from 1990 to 2022 and determine factors influencing flood-related deaths. Methods: We used data on flood disasters from the International Disaster Database for 1990-2022 from 168 countries. We calculated the annual percentage change to estimate trends in the rates of people affected and killed by floods by study period, World Health Organization (WHO) region, country income level and flood type. We used multivariable logistic regression analysis to assess the factors associated with death from floods. Findings: From 1990 to 2022, 4713 floods were recorded in 168 countries, which affected > 3.2 billion people, caused 218 353 deaths and were responsible for more than 1.3 trillion United States dollars of economic losses. The WHO Western Pacific Region had the most people affected by floods (> 2.0 billion), accounting for 63.19% (2 024 599 380/3 203 944 965) of all affected populations. The South-East Asia Region had the most deaths (71 713, 32.84%). The African and Eastern Mediterranean Regions had the highest number of people affected and killed by floods per 100 000 population in 2022. The odds of floods causing more than 50 deaths were significantly higher in low-income countries (adjusted odds ratio: 14.34; 95% confidence interval: 7.46 to 30.04) compared with high-income countries. Numbers of people affected and mortality due to floods declined over time. Conclusion: Despite the decreases in populations affected and deaths, floods still have a serious impact on people and economies globally, particularly in lower-income countries. Action is needed to improve disaster risk management and flood mitigation.

Metabolic subtypes and immune landscapes in esophageal squamous cell carcinoma: prognostic implications and potential for personalized therapies.

BACKGROUND: This study aimed to identify metabolic subtypes in ESCA, explore their relationship with immune landscapes, and establish a metabolic index for accurate prognosis assessment. METHODS: Clinical, SNP, and RNA-seq data were collected from 80 ESCA patients from the TCGA database and RNA-seq data from the GSE19417 dataset. Metabolic genes associated with overall survival (OS) and progression-free survival (PFS) were selected, and k-means clustering was performed. Immune-related pathways, immune infiltration, and response to immunotherapy were predicted using bioinformatic algorithms. Weighted gene co-expression network analysis (WGCNA) was conducted to identify metabolic genes associated with co-expression modules. Lastly, cell culture and functional analysis were performed using patient tissue samples and ESCA cell lines to verify the identified genes and their roles. RESULTS: Molecular subtypes were identified based on the expression profiles of metabolic genes, and univariate survival analysis revealed 163 metabolic genes associated with ESCA prognosis. Consensus clustering analysis classified ESCA samples into three distinct subtypes, with MC1 showing the poorest prognosis and MC3 having the best prognosis. The subtypes also exhibited significant differences in immune cell infiltration, with MC3 showing the highest scores. Additionally, the MC3 subtype demonstrated the poorest response to immunotherapy, while the MC1 subtype was the most sensitive. WGCNA analysis identified gene modules associated with the metabolic index, with SLC5A1, NT5DC4, and MTHFD2 emerging as prognostic markers. Gene and protein expression analysis validated the upregulation of MTHFD2 in ESCA. MTHFD2 promotes the progression of ESCA and may be a potential therapeutic target for ESCA. CONCLUSION: The established metabolic index and identified metabolic genes offer potential for prognostic assessment and personalized therapeutic interventions for ESCA, underscoring the importance of targeting metabolism-immune interactions in ESCA. MTHFD2 promotes the progression of ESCA and may be a potential therapeutic target for ESCA.

Computer-aided designing of a novel multi­epitope DNA vaccine against severe fever with thrombocytopenia syndrome virus.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne viral disease caused by the SFTS virus (Dabie bandavirus), which has become a substantial risk to public health. No specific treatment is available now, that calls for an effective vaccine. Given this, we aimed to develop a multi-epitope DNA vaccine through the help of bioinformatics. The final DNA vaccine was inserted into a special plasmid vector pVAX1, consisting of CD8+ T cell epitopes, CD4+ T cell epitopes and B cell epitopes (six epitopes each) screened from four genome-encoded proteins--nuclear protein (NP), glycoprotein (GP), RNA-dependent RNA polymerase (RdRp), as well as nonstructural protein (NSs). To ascertain if the predicted structure would be stable and successful in preventing infection, an immunological simulation was run on it. In conclusion, we designed a multi-epitope DNA vaccine that is expected to be effective against Dabie bandavirus, but in vivo trials are needed to verify this claim.

Spatial-temporal distribution characteristics of pulmonary tuberculosis in eastern China from 2011 to 2021.

China is still among the 30 high-burden tuberculosis (TB) countries in the world. Few studies have described the spatial epidemiological characteristics of pulmonary TB (PTB) in Jiangsu Province. The registered incidence data of PTB patients in 95 counties of Jiangsu Province from 2011 to 2021 were collected from the Tuberculosis Management Information System. Three-dimensional spatial trends, spatial autocorrelation, and spatial-temporal scan analysis were conducted to explore the spatial clustering pattern of PTB. From 2011 to 2021, a total of 347,495 newly diagnosed PTB cases were registered. The registered incidence rate of PTB decreased from 49.78/100,000 in 2011 to 26.49/100,000 in 2021, exhibiting a steady downward trend (χ2 = 414.22, P < 0.001). The average annual registered incidence rate of PTB was higher in the central and northern regions. Moran's I indices of the registered incidence of PTB were all >0 (P< 0.05) except in 2016, indicating a positive spatial correlation overall. Local autocorrelation analysis showed that 'high-high' clusters were mainly distributed in northern Jiangsu, and 'low-low' clusters were mainly concentrated in southern Jiangsu. The results of this study assist in identifying settings and locations of high TB risk and inform policy-making for PTB control and prevention.