ESR1 and p53 interactome alteration defines mechanisms of tamoxifen response in luminal breast cancer.

The canonical mechanism behind tamoxifen's therapeutic effect on estrogen receptor α/ESR1+ breast cancers is inhibition of ESR1-dependent estrogen signaling. Although ESR1+ tumors expressing wild-type p53 were reported to be more responsive to tamoxifen (Tam) therapy, p53 has not been factored into choice of this therapy and the mechanism underlying the role of p53 in Tam response remains unclear. In a window-of-opportunity trial on patients with newly diagnosed stage I-III ESR1+/HER2/wild-type p53 breast cancer who were randomized to arms with or without Tam prior to surgery, we reveal that the ESR1-p53 interaction in tumors was inhibited by Tam. This resulted in functional reactivation of p53 leading to transcriptional reprogramming that favors tumor-suppressive signaling, as well as downregulation of oncogenic pathways. These findings illustrating the convergence of ESR1 and p53 signaling during Tam therapy enrich mechanistic understanding of the impact of p53 on the response to Tam therapy.

In vitro and in vivo activity of ceftazidime/avibactam and aztreonam alone or in combination against mcr-9, serine- and metallo-ß-lactamases-co-producing carbapenem-resistant Enterobacter cloacae complex.

PURPOSE: Enterobacteriaceae carrying mcr-9, in particularly those also co-containing metallo-ß-lactamase (MBL) and TEM type ß-lactamase, present potential transmission risks and lack adequate clinical response methods, thereby posing a major threat to global public health. The aim of this study was to assess the antimicrobial efficacy of a combined ceftazidime/avibactam (CZA) and aztreonam (ATM) regimen against carbapenem-resistant Enterobacter cloacae complex (CRECC) co-producing mcr-9, MBL and TEM. METHODS: The in vitro antibacterial activity of CZA plus ATM was evaluated using a time-kill curve assay. Furthermore, the in vivo interaction between CZA plus ATM was confirmed using a Galleria mellonella (G. mellonella) infection model. RESULTS: All eight clinical strains of CRECC, co-carrying mcr-9, MBL and TEM, exhibited high resistance to CZA and ATM. In vitro time-kill curve analysis demonstrated that the combination therapy of CZA + ATM exerted significant bactericidal activity against mcr-9, MBL and TEM-co-producing Enterobacter cloacae complex (ECC) isolates with a 100% synergy rate observed in our study. Furthermore, in vivo survival assay using Galleria mellonella larvae infected with CRECC strains co-harboring mcr-9, MBL and TEM revealed that the CZA + ATM combination significantly improved the survival rate compared to the drug-treatment alone and untreated control groups. CONCLUSION: To our knowledge, this study represents the first report on the in vitro and in vivo antibacterial activity of CZA plus ATM against CRECC isolates co-harboring mcr-9, MBL and TEM. Our findings suggest that the combination regimen of CZA + ATM provides a valuable reference for clinicians to address the increasingly complex antibiotic resistance situation observed in clinical microorganisms.

Effect of an extraoral scavenging device on eliminating droplets and aerosols generated in ultrasonic supragingival scaling: A randomised clinical trial.

OBJECTIVES: Ultrasonic scaling is extensively applied as part of the initial therapy for periodontal diseases, which has been restricted since the outbreak of the COVID-19 pandemic due to droplets and aerosols generated by ultrasonic devices. An extraoral scavenging device (EOS) was designed for diminishing droplets and aerosols in dental clinics. The objective of this study is to evaluate the effect of EOS on eliminating droplets and aerosols during ultrasonic supragingival scaling. METHODS: This single-blinded, randomised controlled clinical trial enrolled 45 patients with generalised periodontitis (stage I or II, grade A or B) or plaque-induced gingivitis. The patients were randomly allocated and received ultrasonic supragingival scaling under three different intervention measures: only saliva ejector (SE), SE plus EOS and SE plus high-volume evacuation (HVE). The natural sedimentation method was applied to sample droplets and aerosols before or during supragingival scaling. After aerobic culturing, colony-forming units (CFUs) were counted and analysed. RESULTS: Compared with the level before treatment, more CFUs of samples throughout treatment could be obtained at the operator's chest and the patient's chest and the table surface when using SE alone (p < 0.05). Compared with the SE group, the SE + EOS group and the SE + HVE group obtained decreasing CFUs at the operator's chest and the patient's chest (p < 0.05), while no significant difference was determined between these two groups. CONCLUSIONS: The EOS effectively eliminated splatter contamination from ultrasonic supragingival scaling, which was an alternative precaution for nosocomial contamination in dental clinics.

2,2- dimethylbenzopyran derivatives containing pyridone structural fragments as selective dual-targeting inhibitors of HIF-1α and EZH2 for the treatment of lung cancer.

We formerly reported that EZH2 inhibitors sensitized HIF-1 inhibitor-resistant cells and inhibited HIF-1α to promote SUZ12 transcription, leading to enhanced EZH2 enzyme activity and elevated H3K27me3 levels, and conversely, inhibition of EZH2 promoted HIF-1α transcription. HIF-1α and EZH2 interacted to form a negative feedback loop that reinforced each other's activity. In this paper, a series of 2,2- dimethylbenzopyran derivatives containing pyridone structural fragments were designed and synthesized with DYB-03, a HIF-1α inhibitor previously reported by our group, and Tazemetostat, an EZH2 inhibitor approved by FDA, as lead compounds. Among these compounds, D-01 had significant inhibitory activities on HIF-1α and EZH2. In vitro experiments showed that D-01 significantly inhibited the migration of A549 cells, clone, invasion and angiogenesis. Moreover, D-01 had good pharmacokinetic profiles. All the results about compound D-01 could lay a foundation for the research and development of HIF-1α and EZH2 dual-targeting compounds.

A causal examination of the correlation between hormonal and reproductive factors and low back pain.

Background: The relationship between hormonal fluctuations in the reproductive system and the occurrence of low back pain (LBP) has been widely observed. However, the causal impact of specific variables that may be indicative of hormonal and reproductive factors, such as age at menopause (ANM), age at menarche (AAM), length of menstrual cycle (LMC), age at first birth (AFB), age at last live birth (ALB) and age first had sexual intercourse (AFS) on low back pain remains unclear. Methods: This study employed Bidirectional Mendelian randomization (MR) using publicly available summary statistics from Genome Wide Association Studies (GWAS) and FinnGen Consortium to investigate the causal links between hormonal and reproductive factors on LBP. Various MR methodologies, including inverse-variance weighted (IVW), MR-Egger regression, and weighted median, were utilized. Sensitivity analysis was conducted to ensure the robustness and validity of the findings. Subsequently, Multivariate Mendelian randomization (MVMR) was employed to assess the direct causal impact of reproductive and hormone factors on the risk of LBP. Results: After implementing the Bonferroni correction and conducting rigorous quality control, the results from MR indicated a noteworthy association between a decreased risk of LBP and AAM (OR=0.784, 95% CI: 0.689-0.891; p=3.53E-04), AFB (OR=0.558, 95% CI: 0.436-0.715; p=8.97E-06), ALB (OR=0.396, 95% CI: 0.226-0.692; p=0.002), and AFS (OR=0.602, 95% CI: 0.518-0.700; p=3.47E-10). Moreover, in the reverse MR analysis, we observed no significant causal effects of LBP on ANM, AAM, LMC and AFS. MVMR analysis demonstrated the continued significance of the causal effect of AFB on LBP after adjusting for BMI. Conclusion: Our study explored the causal relationship between ANM, AAM, LMC, AFB, AFS, ALB and the prevalence of LBP. We found that early menarche, early age at first birth, early age at last live birth and early age first had sexual intercourse may decrease the risk of LBP. These insights enhance our understanding of LBP risk factors, offering valuable guidance for screening, prevention, and treatment strategies for at-risk women.

MEF2D Functions as a Tumor Suppressor in Breast Cancer.

The myocyte enhancer factor 2 (MEF2) gene family play fundamental roles in the genetic programs that control cell differentiation, morphogenesis, proliferation, and survival in a wide range of cell types. More recently, these genes have also been implicated as drivers of carcinogenesis, by acting as oncogenes or tumor suppressors depending on the biological context. Nonetheless, the molecular programs they regulate and their roles in tumor development and progression remain incompletely understood. The present study evaluated whether the MEF2D transcription factor functions as a tumor suppressor in breast cancer. The knockout of the MEF2D gene in mouse mammary epithelial cells resulted in phenotypic changes characteristic of neoplastic transformation. These changes included enhanced cell proliferation, a loss of contact inhibition, and anchorage-independent growth in soft agar, as well as the capacity for tumor development in mice. Mechanistically, the knockout of MEF2D induced the epithelial-to-mesenchymal transition (EMT) and activated several oncogenic signaling pathways, including AKT, ERK, and Hippo-YAP. Correspondingly, a reduced expression of MEF2D was observed in human triple-negative breast cancer cell lines, and a low MEF2D expression in tissue samples was found to be correlated with a worse overall survival and relapse-free survival in breast cancer patients. MEF2D may, thus, be a putative tumor suppressor, acting through selective gene regulatory programs that have clinical and therapeutic significance.

Exploring the Conformational Ensembles of Protein-Protein Complex with Transformer-Based Generative Model.

Protein-protein interactions are the basis of many protein functions, and understanding the contact and conformational changes of protein-protein interactions is crucial for linking the protein structure to biological function. Although difficult to detect experimentally, molecular dynamics (MD) simulations are widely used to study the conformational ensembles and dynamics of protein-protein complexes, but there are significant limitations in sampling efficiency and computational costs. In this study, a generative neural network was trained on protein-protein complex conformations obtained from molecular simulations to directly generate novel conformations with physical realism. We demonstrated the use of a deep learning model based on the transformer architecture to explore the conformational ensembles of protein-protein complexes through MD simulations. The results showed that the learned latent space can be used to generate unsampled conformations of protein-protein complexes for obtaining new conformations complementing pre-existing ones, which can be used as an exploratory tool for the analysis and enhancement of molecular simulations of protein-protein complexes.

Transcriptomic analysis of induced resistance to polymyxin in carbapenem-resistant Enterobacter cloacae complex isolate carrying mcr-9.

OBJECTIVES: Polymyxins are currently the last-resort treatment against multi-drug resistant Gram-negative bacterial infections, but plasmid-mediated mobile polymyxin resistance genes (mcr) threaten its efficacy, especially in carbapenem-resistant Enterobacter cloacae complex (CRECC). The objective of this study was to provide insights into the mechanism of polymyxin-induced bacterial resistance and the effect of overexpression of mcr-9. METHODS: The clinical strain CRECC414 carrying the mcr-9 gene was treated with a gradient concentration of polymyxin. Subsequently, the broth microdilution was used to determine the minimum inhibitory concentration (MIC) and RT-qPCR was utilized to assess mcr-9 expression. Transcriptome sequencing and whole genome sequencing (WGS) was utilized to identify alterations in strains resulting from increased polymyxin resistance, and significant transcriptomic differences were analysed alongside a comprehensive examination of metabolic networks at the genomic level. RESULTS: Polymyxin treatment induced the upregulation of mcr-9 expression and significantly elevated the MIC of the strain. Furthermore, the WGS and transcriptomic results revealed a remarkable up-regulation of arnBCADTEF gene cassette, indicating that the Arn/PhoPQ system-mediated L-Ara4N modification is the preferred mechanism for achieving high levels of resistance. Additionally, significant alterations in bacterial gene expression were observed with regards to multidrug efflux pumps, oxidative stress and repair mechanisms, cell membrane biosynthesis, as well as carbohydrate metabolic pathways. CONCLUSION: Polymyxin greatly disrupts the transcription of vital cellular pathways. A complete PhoPQ two-component system is a prerequisite for polymyxin resistance of Enterobacter cloacae, even though mcr-9 is highly expressed. These findings provide novel and important information for further investigation of polymyxin resistance of CRECC.

Construction of diagnostic models for the progression of hepatocellular carcinoma using machine learning.

Liver cancer is one of the most prevalent forms of cancer worldwide. A significant proportion of patients with hepatocellular carcinoma (HCC) are diagnosed at advanced stages, leading to unfavorable treatment outcomes. Generally, the development of HCC occurs in distinct stages. However, the diagnostic and intervention markers for each stage remain unclear. Therefore, there is an urgent need to explore precise grading methods for HCC. Machine learning has emerged as an effective technique for studying precise tumor diagnosis. In this research, we employed random forest and LightGBM machine learning algorithms for the first time to construct diagnostic models for HCC at various stages of progression. We categorized 118 samples from GSE114564 into three groups: normal liver, precancerous lesion (including chronic hepatitis, liver cirrhosis, dysplastic nodule), and HCC (including early stage HCC and advanced HCC). The LightGBM model exhibited outstanding performance (accuracy = 0.96, precision = 0.96, recall = 0.96, F1-score = 0.95). Similarly, the random forest model also demonstrated good performance (accuracy = 0.83, precision = 0.83, recall = 0.83, F1-score = 0.83). When the progression of HCC was categorized into the most refined six stages: normal liver, chronic hepatitis, liver cirrhosis, dysplastic nodule, early stage HCC, and advanced HCC, the diagnostic model still exhibited high efficacy. Among them, the LightGBM model exhibited good performance (accuracy = 0.71, precision = 0.71, recall = 0.71, F1-score = 0.72). Also, performance of the LightGBM model was superior to that of the random forest model. Overall, we have constructed a diagnostic model for the progression of HCC and identified potential diagnostic characteristic gene for the progression of HCC.

Leveraging the Fragment Molecular Orbital and MM-GBSA Methods in Virtual Screening for the Discovery of Novel Non-Covalent Inhibitors Targeting the TEAD Lipid Binding Pocket.

The Hippo pathway controls organ size and homeostasis and is linked to numerous diseases, including cancer. The transcriptional enhanced associate domain (TEAD) family of transcription factors acts as a receptor for downstream effectors, namely yes-associated protein (YAP) and transcriptional co-activator with PDZ-binding motif (TAZ), which binds to various transcription factors and is essential for stimulated gene transcription. YAP/TAZ-TEAD facilitates the upregulation of multiple genes involved in evolutionary cell proliferation and survival. TEAD1-4 overexpression has been observed in different cancers in various tissues, making TEAD an attractive target for drug development. The central drug-accessible pocket of TEAD is crucial because it undergoes a post-translational modification called auto-palmitoylation. Crystal structures of the C-terminal TEAD complex with small molecules are available in the Protein Data Bank, aiding structure-based drug design. In this study, we utilized the fragment molecular orbital (FMO) method, molecular dynamics (MD) simulations, shape-based screening, and molecular mechanics-generalized Born surface area (MM-GBSA) calculations for virtual screening, and we identified a novel non-covalent inhibitor-BC-001-with IC50 = 3.7 µM in a reporter assay. Subsequently, we optimized several analogs of BC-001 and found that the optimized compound BC-011 exhibited an IC50 of 72.43 nM. These findings can be used to design effective TEAD modulators with anticancer therapeutic implications.

One Fits Many: Class Confusion Loss for Versatile Domain Adaptation.

In the open world, various label sets and domain configurations give rise to a variety of Domain Adaptation (DA) setups, including closed-set, partial-set, open-set, and universal DA, as well as multi-source and multi-target DA. It is notable that existing DA methods are generally designed only for a specific setup, and may under-perform in setups they are not tailored to. This paper shifts the common paradigm of DA to Versatile Domain Adaptation (VDA), where one method can handle several different DA setups without any modification. Towards this goal, we first delve into a general inductive bias: class confusion, and then uncover that reducing such pairwise class confusion leads to significant transfer gains. With this insight, we propose one general class confusion loss (CC-Loss) to learn many setups. We estimate class confusion based only on classifier predictions and minimize the class confusion to enable accurate target predictions. Further, we improve the loss by enforcing the consistency of confusion matrices under different data augmentations to encourage its invariance to distribution perturbations. Experiments on 2D vision and 3D vision benchmarks show that the CC-Loss performs competitively in different mainstream DA setups. Code is available at https://github.com/thuml/Transfer-Learning-Library.

DockingGA: enhancing targeted molecule generation using transformer neural network and genetic algorithm with docking simulation.

Generative molecular models generate novel molecules with desired properties by searching chemical space. Traditional combinatorial optimization methods, such as genetic algorithms, have demonstrated superior performance in various molecular optimization tasks. However, these methods do not utilize docking simulation to inform the design process, and heavy dependence on the quality and quantity of available data, as well as require additional structural optimization to become candidate drugs. To address this limitation, we propose a novel model named DockingGA that combines Transformer neural networks and genetic algorithms to generate molecules with better binding affinity for specific targets. In order to generate high quality molecules, we chose the Self-referencing Chemical Structure Strings to represent the molecule and optimize the binding affinity of the molecules to different targets. Compared to other baseline models, DockingGA proves to be the optimal model in all docking results for the top 1, 10 and 100 molecules, while maintaining 100% novelty. Furthermore, the distribution of physicochemical properties demonstrates the ability of DockingGA to generate molecules with favorable and appropriate properties. This innovation creates new opportunities for the application of generative models in practical drug discovery.

Race-specific coregulatory and transcriptomic profiles associated with DNA methylation and androgen receptor in prostate cancer.

BACKGROUND: Prostate cancer is a significant health concern, particularly among African American (AA) men who exhibit higher incidence and mortality compared to European American (EA) men. Understanding the molecular mechanisms underlying these disparities is imperative for enhancing clinical management and achieving better outcomes. METHODS: Employing a multi-omics approach, we analyzed prostate cancer in both AA and EA men. Using Illumina methylation arrays and RNA sequencing, we investigated DNA methylation and gene expression in tumor and non-tumor prostate tissues. Additionally, Boolean analysis was utilized to unravel complex networks contributing to racial disparities in prostate cancer. RESULTS: When comparing tumor and adjacent non-tumor prostate tissues, we found that DNA hypermethylated regions are enriched for PRC2/H3K27me3 pathways and EZH2/SUZ12 cofactors. Olfactory/ribosomal pathways and distinct cofactors, including CTCF and KMT2A, were enriched in DNA hypomethylated regions in prostate tumors from AA men. We identified race-specific inverse associations of DNA methylation with expression of several androgen receptor (AR) associated genes, including the GATA family of transcription factors and TRIM63. This suggests that race-specific dysregulation of the AR signaling pathway exists in prostate cancer. To investigate the effect of AR inhibition on race-specific gene expression changes, we generated in-silico patient-specific prostate cancer Boolean networks. Our simulations revealed prolonged AR inhibition causes significant dysregulation of TGF-ß, IDH1, and cell cycle pathways specifically in AA prostate cancer. We further quantified global gene expression changes, which revealed differential expression of genes related to microtubules, immune function, and TMPRSS2-fusion pathways, specifically in prostate tumors of AA men. Enrichment of these pathways significantly correlated with an altered risk of disease progression in a race-specific manner. CONCLUSIONS: Our study reveals unique signaling networks underlying prostate cancer biology in AA and EA men, offering potential insights for clinical management strategies tailored to specific racial groups. Targeting AR and associated pathways could be particularly beneficial in addressing the disparities observed in prostate cancer outcomes in the context of AA and EA men. Further investigation into these identified pathways may lead to the development of personalized therapeutic approaches to improve outcomes for prostate cancer patients across different racial backgrounds.

TCRαß-depleted hematopoietic stem cell transplant and third-party CD45RA+ depleted adoptive cell therapy for treatment of post-transplant parvovirus B19 aplastic crisis.

This is a case report of a 6-year-old girl with relapsed B cell acute lymphoblastic leukemia in which adoptive cell therapy was applied successfully to treat refractory human parvovirus (HPV) B19 infection. Allogenic chimeric antigen receptor (CAR) T-cell therapy (bispecific CD19/CD22) was bridged to hematopoietic stem cell transplantation (HSCT) using a haploidentical paternal donor. However, HPV B19 DNAemia progressed and transfusion-related graft versus host disease occurred. After finding a third-party related donor with a better HLA match, haploidentical HPV B19-seropositive CD45RA+ depleted cells (16.5 × 106/kg) were administered and paternal TCRαß+ depleted stem cell were retransplanted. The HPV B19 DNAemia became negative within 1 week and the reticulocyte, neutrophil, hemoglobin, and platelet counts gradually normalized. The patient remained stable during the 1-year outpatient follow-up period. Thus, our case report highlights that persistent B19 infection can lead to pancytopenia, aplastic crisis, and graft rejection and TCRαß+ depleted haplo-HSCT is an effective means of hematopoiesis recovery. CD45RO memory T-cell therapy is the key to treating and preventing the development of refractory severe HPV B19 infection.

FraHMT: A Fragment-Oriented Heterogeneous Graph Molecular Generation Model for Target Proteins.

The molecular generation task stands as a pivotal step in the domains of computational chemistry and drug discovery, aiming to computationally generate molecular structures for specific properties. In contrast to previous models that focused primarily on SMILES strings or molecular graphs, our model placed a special emphasis on the substructure information on molecules, enabling the model to learn richer chemical rules and structure features from fragments and chemical reaction information on molecules. To accomplish this, we fragmented the molecules to construct heterogeneous graph representations based on atom and fragment information. Then our model mapped the heterogeneous graph data into a latent vector space by using an encoder and employed a self-regressive generative model as a decoder for molecular generation. Additionally, we performed transfer learning on the model using a small set of ligand molecules known to be active against the target protein to generate molecules that bind better to the target protein. Experimental results demonstrate that our model is highly competitive with state-of-the-art models. It can generate valid and diverse molecules with favorable physicochemical properties and drug-likeness. Importantly, they produce novel molecules with high docking scores against the target proteins.

Predictive value of glycoprotein DKK3 for early neurological deterioration after ischemic stroke.

OBJECTIVE: To explore the value of serum Dickkopf-3 (sDKK3) in predicting Early Neurological Deterioration (END) and in-hospital adverse outcomes in acute ischemic stroke (AIS) patients. METHODS: AIS patients (n = 200) were included and assessed by the National Institutes of Health Stroke Rating Scale. Serum Dkk3 levels were assessed by ELISA. END was defined as an increase of ≥ 4 points in NIHSS score within 72h. The biological threshold of sDKK3 level and END occurrence were predicted based on X-tile software. Primary outcomes were END and all-cause death, and the secondary outcome was ICU admission during hospitalization. The logistic regression model and Cox risk regression model were applied to evaluate the relationship between DKK3 level and END incidence, all-cause in-hospital mortality, and in-hospital adverse outcomes (ICU admission). RESULTS: During hospitalization, the incidence of END in patients with AIS was 13.0 %, and the mortality rate within 7 days after END was 11.54 % (3/26). In patients below the serum DKK3 cutoff (93.0 pg/mL), the incidence of END was 43.5 % (20/48). Patients with lower sDKK3 levels were associated with a 1.188-fold increased risk of developing END (OR = 1.188, 95 % CI 1.055‒1.369, p < 0.0001). However, there was no significant association with admission to the ICU. sDKK3 below the threshold (93.0 pg/mL) was a risk factor for death. CONCLUSION: Predictive threshold levels of serum DKK3 based on X-tile software may be a potential predictive biomarker of in-hospital END in patients with AIS, and low levels of DKK3 are independently associated with increased in-hospital mortality.

Spatiotemporal Single-Photon Airy Bullets.

Uninhibited control of the complex spatiotemporal quantum wave function of a single photon has so far remained elusive even though it can dramatically increase the encoding flexibility and thus the information capacity of a photonic quantum link. By fusing temporal waveform generation in an atomic ensemble and spatial single-photon shaping, we hereby demonstrate for the first time complete spatiotemporal control of a propagation invariant (2+1)D Airy single-photon optical bullet. These correlated photons are not only self-accelerating and impervious to spreading as their classical counterparts, but can be concealed and revealed in the presence of strong classical stray light.

Droplet digital PCR: An effective method for monitoring and prognostic evaluation of minimal residual disease in JMML.

Juvenile myelomonocytic leukaemia (JMML) is a rare myeloproliferative neoplasm requiring haematopoietic stem cell transplantation (HSCT) for potential cure. Relapse poses a significant obstacle to JMML HSCT treatment, as the lack of effective minimal residual disease (MRD)-monitoring methods leads to delayed interventions. This retrospective study utilized the droplet digital PCR (ddPCR) technique, a highly sensitive nucleic acid detection and quantification technique, to monitor MRD in 32 JMML patients. The results demonstrated that ddPCR detected relapse manifestations earlier than traditional methods and uncovered molecular insights into JMML MRD dynamics. The findings emphasized a critical 1- to 3-month window post-HSCT for detecting molecular relapse, with 66.7% (8/12) of relapses occurring within this period. Slow MRD clearance post-HSCT was observed, as 65% (13/20) of non-relapse patients took over 6 months to achieve ddPCR-MRD negativity. Furthermore, bone marrow ddPCR-MRD levels at 1-month post-HSCT proved to be prognostically significant. Relapsed patients exhibited significantly elevated ddPCR-MRD levels at this time point (p = 0.026), with a cut-off of 0.465% effectively stratifying overall survival (p = 0.007), event-free survival (p = 0.035) and cumulative incidence of relapse (p = 0.035). In conclusion, this study underscored ddPCR's superiority in JMML MRD monitoring post-HSCT. It provided valuable insights into JMML MRD dynamics, offering guidance for the effective management of JMML.

Predicting potential therapeutic targets and small molecule drugs for early-stage lung adenocarcinoma.

Lung cancer is a leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) constituting the majority, and its main subtype being lung adenocarcinoma (LUAD). Despite substantial advances in LUAD diagnosis and treatment, early diagnostic biomarkers inadequately fulfill clinical requirements. Thus, we conducted bioinformatics analysis to identify potential biomarkers and corresponding therapeutic drugs for early-stage LUAD patients. Here we identified a total of 10 differentially expressed genes (DEGs) with survival significance through the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Subsequently, we identified a promising small molecule drug, Aminopurvalanol A, based on the 10 key genes using the L1000FWD application, which was validated by molecular docking followed by in vivo and in vitro experiments. The results highlighted TOP2A, CDH3, ASPM, CENPF, SLC2A1, and PRC1 as potential detection biomarkers for early LUAD. We confirmed the efficacy and safety of Aminopurvalanol A, providing valuable insights for the clinical management of LUAD.

A bidirectional interpretable compound-protein interaction prediction framework based on cross attention.

The identification of compound-protein interactions (CPIs) plays a vital role in drug discovery. However, the huge cost and labor-intensive nature in vitro and vivo experiments make it urgent for researchers to develop novel CPI prediction methods. Despite emerging deep learning methods have achieved promising performance in CPI prediction, they also face ongoing challenges: (i) providing bidirectional interpretability from both the chemical and biological perspective for the prediction results; (ii) comprehensively evaluating model generalization performance; (iii) demonstrating the practical applicability of these models. To overcome the challenges posed by current deep learning methods, we propose a cross multi-head attention oriented bidirectional interpretable CPI prediction model (CmhAttCPI). First, CmhAttCPI takes molecular graphs and protein sequences as inputs, utilizing the GCW module to learn atom features and the CNN module to learn residue features, respectively. Second, the model applies cross multi-head attention module to compute attention weights for atoms and residues. Finally, CmhAttCPI employs a fully connected neural network to predict scores for CPIs. We evaluated the performance of CmhAttCPI on balanced datasets and imbalanced datasets. The results consistently show that CmhAttCPI outperforms multiple state-of-the-art methods. We constructed three scenarios based on compound and protein clustering and comprehensively evaluated the model generalization ability within these scenarios. The results demonstrate that the generalization ability of CmhAttCPI surpasses that of other models. Besides, the visualizations of attention weights reveal that CmhAttCPI provides chemical and biological interpretation for CPI prediction. Moreover, case studies confirm the practical applicability of CmhAttCPI in discovering anticancer candidates.