The efficacy and safety of adding PD-1 blockade to induction chemotherapy and concurrent chemoradiotherapy (IC-CCRT) for locoregionally advanced nasopharyngeal carcinoma: an observational, propensity score-matched analysis.

BACKGROUND: Despite the success of PD-1 blockade in recurrent/metastatic nasopharyngeal carcinoma (NPC), its effect for locoregionally advanced NPC (LANPC) remains unclear. This study aimed to evaluate the benefit of adding PD-1 blockade to the current standard treatment (gemcitabine and cisplatin IC  plus cisplatin CCRT ) for LANPC patients. METHODS: From January 2020 to November 2022, 347 patients with non-metastatic high-risk LANPC (stage III-IVA, excluding T3-4N0) were included. Of the 347 patients, 268 patients were treated with standard treatment (IC-CCRT), and 79 received PD-1 blockade plus IC-CCRT (PD-1 group). For the PD-1 group, PD-1 blockade was given intravenously once every 3 weeks for up to 9 cycles (3 induction and 6 adjuvant). The primary endpoint was disease-free survival (DFS) (i.e. freedom from local/regional/distant failure or death). The propensity score matching (PSM) with the ratio of 1:2 was performed to control confounding factors. RESULTS: After PSM analysis, 150 patients receiving standard treatment and 75 patients receiving additional PD-1 blockade remained in the current analysis. After three cycles of IC, the PD-1 group had significantly higher rates of complete response (defined as disappearance of all target lesions; 24% vs. 9%; P = 0.006) and complete biological response (defined as undetectable cell-free Epstein-Barr virus DNA, cfEBV DNA; 79% vs. 65%; P = 0.046) than that in the standard group. And the incidence of grade 3-4 toxicity during IC was 47% in the PD-1 group and 41% in the standard group, with no significant difference (P = 0.396). During follow-up period, additional PD-1 blockade to standard treatment improved 3-year DFS from 84 to 95%, with marginal statistical significance (HR, 0.28; 95%CI, 0.06-1.19; P = 0.064). CONCLUSION: Additiaonl PD-1 blockade to gemcitabine and cisplatin IC and adjuvant treatment results in significant improvement in tumor regression, cfEBV DNA clearance, superior DFS, and comparable toxicity profiles in high-risk LANPC patients.

Visible-light-induced C(sp3)-H bromination of 4-methylthiophene derivatives with HBr/H2O2.

A convenient method to synthesize ethyl 4-(bromomethyl)thiophene-3-carboxylate derivatives has been developed via a visible-light-induced radical process in good yields and with wide functional group tolerance under air conditions and at ambient temperature. The present protocol has the advantages of a high atom economy, easy purification, and environmental friendliness as it employs HBr as the bromine source and the cheap and low-toxic H2O2 as the oxidant. The synthetic utility of this method is demonstrated by a gram scale reaction and its application in the innovative synthesis of the clinical drug relugolix.

GETdb: A comprehensive database for genetic and evolutionary features of drug targets.

The development of an innovative drug is complex and time-consuming, and the drug target identification is one of the critical steps in drug discovery process. Effective and accurate identification of drug targets can accelerate the drug development process. According to previous research, evolutionary and genetic information of genes has been found to facilitate the identification of approved drug targets. In addition, allosteric proteins have great potential as targets due to their structural diversity. However, this information that could facilitate target identification has not been collated in existing drug target databases. Here, we construct a comprehensive drug target database named Genetic and Evolutionary features of drug Targets database (GETdb, http://zhanglab.hzau.edu.cn/GETdb/page/index.jsp). This database not only integrates and standardizes data from dozens of commonly used drug and target databases, but also innovatively includes the genetic and evolutionary information of targets. Moreover, this database features an effective allosteric protein prediction model. GETdb contains approximately 4000 targets and over 29,000 drugs, and is a user-friendly database for searching, browsing and downloading data to facilitate the development of novel targets.

Construction of microgravity biological knowledge graph and its applications in anti-osteoporosis drug prediction.

Microgravity in the space environment can potentially have various negative effects on the human body, one of which is bone loss. Given the increasing frequency of human space activities, there is an urgent need to identify effective anti-osteoporosis drugs for the microgravity environment. Traditional microgravity experiments conducted in space suffer from limitations such as time-consuming procedures, high costs, and small sample sizes. In recent years, the in-silico drug discovery method has emerged as a promising strategy due to the advancements in bioinformatics and computer technology. In this study, we first collected a total of 184,915 literature articles related to microgravity and bone loss. We employed a combination of dependency path extraction and clustering techniques to extract data from the text. Afterwards, we conducted data cleaning and standardization to integrate data from several sources, including The Global Network of Biomedical Relationships (GNBR), Curated Drug-Drug Interactions Database (DDInter), Search Tool for Interacting Chemicals (STITCH), DrugBank, and Traditional Chinese Medicines Integrated Database (TCMID). Through this integration process, we constructed the Microgravity Biology Knowledge Graph (MBKG) consisting of 134,796 biological entities and 3,395,273 triplets. Subsequently, the TransE model was utilized to perform knowledge graph embedding. By calculating the distances between entities in the model space, the model successfully predicted potential drugs for treating osteoporosis and microgravity-induced bone loss. The results indicate that out of the top 10 ranked western medicines, 7 have been approved for the treatment of osteoporosis. Additionally, among the top 10 ranked traditional Chinese medicines, 5 have scientific literature supporting their effectiveness in treating bone loss. Among the top 20 predicted medicines for microgravity-induced bone loss, 15 have been studied in microgravity or simulated microgravity environments, while the remaining 5 are also applicable for treating osteoporosis. This research highlights the potential application of MBKG in the field of space drug discovery.

Transcranial alternating current stimulation improves quality of life in Parkinson's disease: study protocol for a randomized, double-blind, controlled trial.

BACKGROUND: The neural cells in the brains of patients with Parkinson's disease (PWP) display aberrant synchronized oscillatory activity within the beta frequency range. Additionally, enhanced gamma oscillations may serve as a compensatory mechanism for motor inhibition mediated by beta activity and also reinstate plasticity in the primary motor cortex affected by Parkinson's disease. Transcranial alternating current stimulation (tACS) can synchronize endogenous oscillations with exogenous rhythms, thereby modulating cortical activity. The objective of this study is to investigate whether the addition of tACS to multidisciplinary intensive rehabilitation treatment (MIRT) can improve symptoms of PWP so as to enhance the quality of life in individuals with Parkinson's disease based on the central-peripheral-central theory. METHODS: The present study was a randomized, double-blind trial that enrolled 60 individuals with Parkinson's disease aged between 45 and 70 years, who had Hoehn-Yahr scale scores ranging from 1 to 3. Participants were randomly assigned in a 1:1 ratio to either the tACS + MIRT group or the sham-tACS + MIRT group. The trial consisted of a two-week double-blind treatment period followed by a 24-week follow-up period, resulting in a total duration of twenty-six weeks. The primary outcome measured the change in PDQ-39 scores from baseline (T0) to 4 weeks (T2), 12 weeks (T3), and 24 weeks (T4) after completion of the intervention. The secondary outcome assessed changes in MDS-UPDRS III scores at T0, the end of intervention (T1), T2, T3, and T4. Additional clinical assessments and mechanistic studies were conducted as tertiary outcomes. DISCUSSION: The objective of this study is to demonstrate that tACS can enhance overall functionality and improve quality of life in PWP, based on the framework of MIRT. Additionally, it seeks to establish a potential correlation between these therapeutic effects and neuroplasticity alterations in relevant brain regions. The efficacy of tACS will be assessed during the follow-up period in order to optimize neuroplasticity and enhance its potential impact on rehabilitation efficiency for PWP. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2300071969. Registered on 30 May 2023.

Metabolites of pathogenic microorganisms database (MPMdb) and its seed metabolite applications.

Seed metabolites are the combination of essential compounds required by an organism across various potential environmental conditions. The seed metabolites screening framework based on the network topology approach can capture important biological information of species. This study aims to identify comprehensively the relationship between seed metabolites and pathogenic bacteria. A large-scale data set was compiled, describing the seed metabolite sets and metabolite sets of 124,192 pathogenic strains from 34 genera, by constructing genome-scale metabolic models. The enrichment analysis method was used to screen the specific seed metabolites of each species/genus of pathogenic bacteria. The metabolites of pathogenic microorganisms database (MPMdb) (http://qyzhanglab.hzau.edu.cn/MPMdb/) was established for browsing, searching, predicting, or downloading metabolites and seed metabolites of pathogenic microorganisms. Based on the MPMdb, taxonomic and phylogenetic analyses of pathogenic bacteria were performed according to the function of seed metabolites and metabolites. The results showed that the seed metabolites could be used as a feature for microorganism chemotaxonomy, and they could mirror the phylogeny of pathogenic bacteria. In addition, our screened specific seed metabolites of pathogenic bacteria can be used not only for further tapping the nutritional resources and identifying auxotrophies of pathogenic bacteria but also for designing targeted bactericidal compounds by combining with existing antimicrobial agents.IMPORTANCEMetabolites serve as key communication links between pathogenic microorganisms and hosts, with seed metabolites being crucial for microbial growth, reproduction, external communication, and host infection. However, the large-scale screening of metabolites and the identification of seed metabolites have always been the main technical bottleneck due to the low throughput and costly analysis. Genome-scale metabolic models have become a recognized research paradigm to investigate the metabolic characteristics of species. The developed metabolites of pathogenic microorganisms database in this study is committed to systematically predicting and identifying the metabolites and seed metabolites of pathogenic microorganisms, which could provide a powerful resource platform for pathogenic bacteria research.

Neoadjuvant chemotherapy plus concurrent chemoradiotherapy versus concurrent chemoradiotherapy alone in elderly patients with stage III-IVa nasopharyngeal carcinoma: A real-world study based on medical comorbidities.

PURPOSE: To evaluate the outcomes and toxicities of adding neoadjuvant chemotherapy (NAC) to concurrent chemoradiotherapy (CCRT) in elderly (≥65 years) patients with locoregionally advanced nasopharyngeal carcinoma (LANPC, stage III-IVa). METHODS AND MATERIALS: Using an NPC-specific database, 245 elderly patients with stage III-IVa NPC, receiving CCRT +/- NAC, and an Adult Co-morbidity Evaluation 27 (ACE-27) score <2 were included. Recursive partitioning analysis (RPA) based on TNM stage and Epstein-Barr virus (EBV) DNA were applied for risk stratification. The primary end point was disease-free survival (DFS). RESULTS: Two risk groups were generated by the RPA model. In the high-risk group (EBV DNA < 4000 copy/ml with stage IVa & EBV DNA ≥4000 copy/ml with stage III-IVa), patients treated with NAC plus CCRT achieved improved 5-year DFS rates compared to those who received CCRT alone (56.9% vs. 29.4%; p = 0.003). But we failed to observe the survival benefit of additional NAC in the low-risk group (EBV DNA <4000 copy/ml with stage III). The most common severe acute toxic effects were leucopenia (46.8% vs. 24.4%) and neutropenia (43.7% vs. 20.2%) in the NAC plus CCRT group versus CCRT group with statistically significant differences. CONCLUSIONS: The addition of NAC to CCRT was associated with better DFS for the high-risk group of elderly LANPC patients with ACE-27 score <2. However, the survival benefit of additional NAC was not observed in low-risk patients.

Interaction between adolescent sleep rhythms and gender in an obese population.

BACKGROUND: The obesity rate of adolescents is gradually increasing, which seriously affects their mental health, and sleep plays an important role in adolescent obesity. AIM: To investigate the relationship between sleep rhythm and obesity among adolescents and further explores the interactive effect of sleep rhythm and gender on adolescent obesity, providing a theoretical basis for developing interventions for adolescent obesity. METHODS: Research data source Tianjin Mental Health Promotion Program for Students. From April to June 2022, this study selected 14201 students from 13 middle schools in a certain district of Tianjin as the research subject using the convenient cluster sampling method. Among these students, 13374 accepted and completed the survey, with an effective rate of 94.2%.The demographic data and basic information of adolescents, such as height and weight, were collected through a general situation questionnaire. The sleep rhythm of adolescents was evaluated using the reduced version of the morningness-eveningness questionnaire. RESULTS: A total of 13374 participants (6629 females, accounting for 49.56%; the average age is 15.21 ± 1.433 years) were analyzed. Among them, the survey showed that 2942 adolescent were obesity, accounting for 22% and 2104 adolescent were overweight, accounting for 15.7%. Among them, 1692 male adolescents are obese, with an obesity rate of 25.1%, higher than 18.9% of female adolescents. There is a statistically significant difference between the three groups (χ2 = 231.522, P < 0.000). The obesity group has the smallest age (14.94 ± 1.442 years), and there is a statistical difference in age among the three groups (F = 69.996, P < 0.000).Obesity rates are higher among individuals who are not-only-child, have residential experience within six months, have family economic poverty, and have evening-type sleep (P < 0.05). Logistic regression analysis shows a correlation between sleep rhythm and adolescent obesity. Evening-type sleep rhythm can increase the risk of obesity in male adolescents [1.250 (1.067-1.468)], but the effect on female obesity is not remarkable. Further logistic regression analysis in the overall population demonstrates that the interaction between evening-type sleep rhythm and the male gender poses a risk of adolescent obesity [1.122 (1.043-1.208)]. CONCLUSION: Among adolescents, the incidence of obesity in males is higher than in females. Evening-type sleep rhythm plays an important role in male obesity but has no significant effect on female obesity. Progressive analysis suggests an interactive effect of sleep rhythm and gender on adolescent obesity, and the combination of evening-type sleep and the male gender promotes the development of adolescent obesity. In formulating precautions against adolescent obesity, obesity in male adolescents with evening-type sleep should be a critical concern.

Associations between Serum Mineral Nutrients, Gut Microbiota, and Risk of Neurological, Psychiatric, and Metabolic Diseases: A Comprehensive Mendelian Randomization Study.

Recent observational studies have reported associations between serum mineral nutrient levels, gut microbiota composition, and neurological, psychiatric, and metabolic diseases. However, the causal effects of mineral nutrients on gut microbiota and their causal associations with diseases remain unclear and require further investigation. This study aimed to identify the associations between serum mineral nutrients, gut microbiota, and risk of neurological, psychiatric, and metabolic diseases using Mendelian randomization (MR). We conducted an MR study using the large-scale genome-wide association study (GWAS) summary statistics of 5 serum mineral nutrients, 196 gut microbes at the phylum, order, family, and genus levels, and a variety of common neurological, psychiatric, and metabolic diseases. Initially, the independent causal associations of mineral nutrients and gut microbiota with diseases were examined by MR. Subsequently, the causal effect of mineral nutrients on gut microbiota was estimated to investigate whether specific gut microbes mediated the association between mineral nutrients and diseases. Finally, we performed sensitivity analyses to assess the robustness of the study results. After correcting for multiple testing, we identified a total of 33 causal relationships among mineral nutrients, gut microbiota, and diseases. Specifically, we found 4 causal relationships between 3 mineral nutrition traits and 3 disease traits, 15 causal associations between 14 gut microbiota traits and 6 disease traits, and 14 causal associations involving 4 mineral nutrition traits and 15 gut microbiota traits. Meanwhile, 118 suggestive associations were identified. The current study reveals multiple causal associations between serum mineral nutrients, gut microbiota, risk of neurological, psychiatric, and metabolic diseases, and potentially provides valuable insights for subsequent nutritional therapies.

Characterization of brain resilience in Alzheimer's disease using polygenic risk scores and further improvement by integrating mitochondria-associated loci.

INTRODUCTION: Identification of high-risk people for Alzheimer's disease (AD) is critical for prognosis and early management. Longitudinal epidemiologic studies have observed heterogeneity in the brain and cognitive aging. Brain resilience was described as above-expected cognitive function. The "resilience" framework has been shown to correlate with individual characteristics such as genetic factors and age. Besides, accumulative evidence has confirmed the association of mitochondria with the pathogenesis of AD. However, it is challenging to assess resilience through genetic metrics, in particular incorporating mitochondria-associated loci. OBJECTIVES: In this paper, we first demonstrated that polygenic risk scores (PRS) could characterize individuals' resilience levels. Then, we indicated that mitochondria-associated loci could improve the performance of PRSs, providing more reliable measurements for the prevention and diagnosis of AD. METHODS: The discovery (N = 1,550) and independent validation samples (N = 2,090) were used to construct nine types of PRSs containing mitochondria-related loci (PRSMT) from both biological and statistical aspects and combined them with known AD risk loci derived from genome-wide association studies (GWAS).Individuals' levels of brain resilience were comprehensively measured by linear regression models using eight pathological characteristics. RESULTS: It was found that PRSs could characterize brain resilience levels (e.g., Pearson correlation test Pmin = 7.96×10-9). Moreover, the performance of PRS models could be efficiently improved by incorporating a small number of mitochondria-related loci (e.g., Pearson correlation test P improved from 1.41×10-3 to 6.09×10-6). PRSs' ability to characterize brain resilience was validated. More importantly, by incorporating some mitochondria-related loci, the performance of PRSs in measuring brain resilience could be significantly improved. CONCLUSION: Our findings imply that mitochondria may play an important role in brain resilience, and targeting mitochondria may open a new door to AD prevention and therapy.

[Photodegradation of Plastic Blends in Seawater and Its Risk to the Marine Environment].

The production and use of plastic blends have been gradually increasing owing to their versatility and low cost. However, the photodegradation of plastic blends in seawater and the potential risk to the marine environment are still not well understood. In this study, plastic blends including polypropylene/thermoplastic starch blends(PP/TPS) and polylactic acid/poly(butylene adipate-co-terephthalate)/thermoplastic starch blends(PLA/PBAT/TPS) were investigated. The corresponding neat polymers, namely polypropylene(PP) and polylactic acid(PLA), were set as control groups. We investigated the formation of MPs and the changes in the physicochemical properties of plastic blends after photodegradation in seawater. The size distribution of MPs indicated that PP/TPS and PLA/PBAT/TPS were more likely to produce small-sized particles after photodegradation than PP and PLA owing to their poorer mechanical properties and lower resistance to UV irradiation. Noticeable surface morphology alterations, including cracks and wrinkles, were observed for plastic blends following photodegradation, whereas PP and PLA were relatively resistant. After photodegradation, the ATR-FTIR spectrum of PP/TPS and PLA/PBAT/TPS showed a significant decrease in the characteristic bands of thermoplastic starch(TPS), indicating the degradation of their starch fractions. The C 1s spectra demonstrated that aged plastic blends contained fewer -OH groups than the pristine MPs did, further confirming the photodegradation of TPS. These results indicate that PP/TPS and PLA/PBAT/TPS had a higher degree of photodegradation than PP and PLA and thereby generated more small-sized MPs. In summary, plastic blends may pose a higher risk to the marine environment than neat polymers, and caution should be taken in the production and use of plastic blends.

Global research trends in immunotherapy for glioma: a comprehensive visualization and bibliometric analysis.

Background: Glioma is a prevalent and lethal brain malignancy; despite current treatment options, the prognosis remains poor. Therefore, immunotherapy has emerged as a promising therapeutic strategy. However, research trends and hotspots in glioma immunotherapy have not been systematically analyzed. This study aimed to elucidate global research trends and knowledge structures regarding immunotherapy for glioma using bibliometric analysis. Methods: Publications related to immunotherapy for glioma from 2000-2023 were retrieved from Web of Science Core Collection database (WoSCC). We conducted quantitative analysis and visualization of research trends using various tools, including VOSviewer (1.6.18), CiteSpace (5.7 R3), Microsoft Charticulator, and the Bibliometrix package in R. Results: A total of 4910 publications were included. The number of annual publications exhibited an obvious upward trend since 2019. The USA was the dominant country in terms of publication output and centrality. Frontiers in Immunology published the most articles. Harvard Medical School ranked first in productivity among institutions. Sampson, John H. Ph.D. is the most prolific author in the field with 88 articles and a total of 7055 citations. Clinical Cancer Research has the largest total number and impact factor. Analysis of keywords showed immunotherapy, glioblastoma, immunotherapy, and clinical trials as hot topics. The tumor microenvironment, cell death pathways, chimeric antigen receptor engineering, tumor-associated macrophages, and nivolumab treatment represent indicating shifts in the direction of future glioma immunotherapy development. Conclusion: This bibliometric analysis systematically delineated global landscapes and emerging trends in glioma immunotherapy research. This study highlighted the prominence of Chimeric Antigen Receptor T-cell (CAR-T), Programmed Death-1 (PD-1), and nivolumab in current glioma immunotherapy research. The growing emphasis on specific neoantigens and prognostic tumor markers suggests potential avenues for future exploration. Furthermore, the data underscores the importance of strengthened international collaboration in advancing the field.

Lewis Acid Catalyzed [4 + 2] Annulation of Propargylic Alcohols with 2-Vinylanilines.

An elegant Lewis acid catalyzed, protection-free, and straightforward synthetic strategy for the assembly of a series of sophisticated polycyclic quinoline skeletons employing propargylic alcohols and 2-vinylanilines as the substrates in the presence of Yb(OTf)3 (10 mol %) and AgOTf (10 mol %) in tetrahydrofuran has been described. This annulation protocol, which proceeds through a sequential Meyer-Schuster rearrangement/nucleophilic substitution/deprotonation sequence, provides a versatile, practical, and atom-economical approach for accessing quinoline derivatives in moderate-to-good yields.

Dissecting Mitochondrial Mechanisms of Alzheimer's Disease Using Gene Dependency Network and Its Implications for Discovering Nutrients Combatting the Disease.

BACKGROUND: Alzheimer's disease (AD) is the leading cause of dementia, with its prevalence increasing as the global population ages. AD is a multifactorial and intricate neurodegenerative disease with pathological changes varying from person to person. Because the mechanism of AD is highly controversial, effective treatments remain a distant prospect. Currently, one of the most promising hypotheses posits mitochondrial dysfunction as an early event in AD diagnosis and a potential therapeutic target. OBJECTIVE: Here, we adopted a systems medicine strategy to explore the mitochondria-related mechanisms of AD. Then, its implications for discovering nutrients combatting the disease were demonstrated. METHODS: We employed conditional mutual information (CMI) to construct AD gene dependency networks. Furthermore, the GeneRank algorithm was applied to prioritize the gene importance of AD patients and identify potential anti-AD nutrients targeting crucial genes. RESULTS: The results suggested that two highly interconnected networks of mitochondrial ribosomal proteins (MRPs) play an important role in the regulation of AD pathology. The close association between mitochondrial ribosome dysfunction and AD was identified. Additionally, we proposed seven nutrients with potential preventive and ameliorative effects on AD, five of which have been supported by experimental reports. CONCLUSIONS: Our study explored the important regulatory role of MRP genes in AD, which has significant implications for AD prevention and treatment.

A life-threatening bleeding prediction model for immune thrombocytopenia based on personalized machine learning: a nationwide prospective cohort study.

Rare but critical bleeding events in primary immune thrombocytopenia (ITP) present life-threatening complications in patients with ITP, which severely affect their prognosis, quality of life, and treatment decisions. Although several studies have investigated the risk factors related to critical bleeding in ITP, large sample size data, consistent definitions, large-scale multicenter findings, and prediction models for critical bleeding events in patients with ITP are unavailable. For the first time, in this study, we applied the newly proposed critical ITP bleeding criteria by the International Society on Thrombosis and Hemostasis for large sample size data and developed the first machine learning (ML)-based online application for predict critical ITP bleeding. In this research, we developed and externally tested an ML-based model for determining the risk of critical bleeding events in patients with ITP using large multicenter data across China. Retrospective data from 8 medical centers across the country were obtained for model development and prospectively tested in 39 medical centers across the country over a year. This system exhibited good predictive capabilities for training, validation, and test datasets. This convenient web-based tool based on a novel algorithm can rapidly identify the bleeding risk profile of patients with ITP and facilitate clinical decision-making and reduce the occurrence of adversities.

A new pathway for the transformation of AML in MDS: APA mechanism regulated by NUDT21 and RUNX1.

We have identified that NUDT21 plays a vital role in MDS transformations, while the transcription factor RUNX1 is essential for normal hematopoiesis, which is a high expression in acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS), and we aim to explore the linkage between the two genes and new pathways for MDS transformation to AML. Prediction of RUNX1 expression levels and its relationship with NUDT21 in AML and MDS patients was performed using bioinformatics techniques and validated in patients. Using lentiviral packaging technology, NUDT21 knockdown and overexpression models were developed in AML and MDS cell lines. These models were validated using quantitative polymerase chain reaction (qPCR) and western blotting. The cell cycle, apoptosis, differentiation, and cytokines were examined by flow cytometry, CCK-8 analyzed proliferation, and the intracellular localization of NUDT21 and RUNX1 was examined by immunofluorescence. mRNA transcriptome sequencing was performed on THP-1, MUTZ-1, and Dapars analyzed SKM-1 cell lines and the sequencing data to observe the knockdown effect of NUDT21 on RUNX1. qPCR and western blot revealed a positive correlation between NUDT21 and RUNX1; both were located in the nucleus. Overexpression of NUDT21 reduced apoptosis, promoted cell proliferation, and possibly increased the invasive ability of cells. It also altered the APA site in the RUNX1 3'-UTRs region. NUDT21 regulates RUNX1 gene expression and promotes AML transformation in MDS through an APA mechanism.

Deciphering tissue heterogeneity from spatially resolved transcriptomics by the autoencoder-assisted graph convolutional neural network.

Spatially resolved transcriptomics (SRT) provides an unprecedented opportunity to investigate the complex and heterogeneous tissue organization. However, it is challenging for a single model to learn an effective representation within and across spatial contexts. To solve the issue, we develop a novel ensemble model, AE-GCN (autoencoder-assisted graph convolutional neural network), which combines the autoencoder (AE) and graph convolutional neural network (GCN), to identify accurate and fine-grained spatial domains. AE-GCN transfers the AE-specific representations to the corresponding GCN-specific layers and unifies these two types of deep neural networks for spatial clustering via the clustering-aware contrastive mechanism. In this way, AE-GCN accommodates the strengths of both AE and GCN for learning an effective representation. We validate the effectiveness of AE-GCN on spatial domain identification and data denoising using multiple SRT datasets generated from ST, 10x Visium, and Slide-seqV2 platforms. Particularly, in cancer datasets, AE-GCN identifies disease-related spatial domains, which reveal more heterogeneity than histological annotations, and facilitates the discovery of novel differentially expressed genes of high prognostic relevance. These results demonstrate the capacity of AE-GCN to unveil complex spatial patterns from SRT data.

Integrating Enrichment, Reduction, and Oxidation Sites in One System for Artificial Photosynthetic Diluted CO2 Reduction.

Artificial photosynthetic diluted CO2 reduction directly driven by natural sunlight is a challenging, but promising way to realize carbon-resources recycling utilization. Herein, a three-in-one photocatalytic system of CO2 enrichment, CO2 reduction and H2 O oxidation sites is designed for diluted CO2 reduction. A Zn-Salen-based covalent organic framework (Zn-S-COF) with oxidation and reductive sites is synthesized; then, ionic liquids (ILs) are loaded into the pores. As a result, [Emim]BF4 @Zn-S-COF shows a visible-light-driven CO2 -to-CO conversion rate of 105.88 µmol g-1 h-1 under diluted CO2 (15%) atmosphere, even superior than most photocatalysts in high concentrations CO2 . Moreover, natural sunlight driven diluted CO2 reduction rate also reaches 126.51 µmol g-1 in 5 h. Further experiments and theoretical calculations reveal that the triazine ring in the Zn-S-COF promotes the activity of H2 O oxidation and CO2 reduction sites, and the loaded ILs provide an enriched CO2 atmosphere, realizing the efficient photocatalytic activity in diluted CO2 reduction.

Evolution-strengthened knowledge graph enables predicting the targetability and druggability of genes.

Identifying promising targets is a critical step in modern drug discovery, with causative genes of diseases that are an important source of successful targets. Previous studies have found that the pathogeneses of various diseases are closely related to the evolutionary events of organisms. Accordingly, evolutionary knowledge can facilitate the prediction of causative genes and further accelerate target identification. With the development of modern biotechnology, massive biomedical data have been accumulated, and knowledge graphs (KGs) have emerged as a powerful approach for integrating and utilizing vast amounts of data. In this study, we constructed an evolution-strengthened knowledge graph (ESKG) and validated applications of ESKG in the identification of causative genes. More importantly, we developed an ESKG-based machine learning model named GraphEvo, which can effectively predict the targetability and the druggability of genes. We further investigated the explainability of the ESKG in druggability prediction by dissecting the evolutionary hallmarks of successful targets. Our study highlights the importance of evolutionary knowledge in biomedical research and demonstrates the potential power of ESKG in promising target identification. The data set of ESKG and the code of GraphEvo can be downloaded from https://github.com/Zhankun-Xiong/GraphEvo.

Transporter proteins knowledge graph construction and its application in drug development.

Transporters are the main determinant for pharmacokinetics characteristics of drugs, such as absorption, distribution, and excretion of drugs in humans. However, it is difficult to perform drug transporter validation and structure analysis of membrane transporter proteins by experimental methods. Many studies have demonstrated that knowledge graphs (KG) could effectively excavate potential association information between different entities. To improve the effectiveness of drug discovery, a transporter-related KG was constructed in this study. Meanwhile, a predictive frame (AutoInt_KG) and a generative frame (MolGPT_KG) were established based on the heterogeneity information obtained from the transporter-related KG by the RESCAL model. Natural product Luteolin with known transporters was selected to verify the reliability of the AutoInt_KG frame, its ROC-AUC (1:1), ROC-AUC (1:10), PR-AUC (1:1), PR-AUC (1:10) are 0.91, 0.94, 0.91 and 0.78, respectively. Subsequently, the MolGPT_KG frame was constructed to implement efficient drug design based on transporter structure. The evaluation results showed that the MolGPT_KG could generate novel and valid molecules and that these molecules were further confirmed by molecular docking analysis. The docking results showed that they could bind to important amino acids at the active site of the target transporter. Our findings will provide rich information resources and guidance for the further development of the transporter-related drugs.