Metabolomic screening of radioiodine refractory thyroid cancer patients and the underlying chemical mechanism of iodine resistance.

Radioiodine refractory (RAIR) patients do not benefit from iodine-131 therapy. Thus, timely identification of RAIR patients is critical for avoiding ineffective radioactive iodine therapy. In addition, determining the causes of iodine resistance will facilitate the development of novel treatment strategies. This study was comprised of 20 RAIR and 14 non-radioiodine refractory (non-RAIR) thyroid cancer patients. Liquid chromatography-mass spectrometry was used to identify differences in the serum metabolites of RAIR and non-RAIR patients. In addition, chemical assays were performed to determine the effects of the differential metabolites on iodine uptake. Metabolic pathway enrichment analysis of the differential metabolites revealed significant differences in the phenylalanine and tyrosine metabolic pathways. Notably, quinate and shikimic acid, metabolites of the tyrosine pathway, were significantly increased in the RAIR group. In contrast, the phenylalanine pathway metabolites, hippuric acid and 2-phenylacetamide, were markedly decreased in the RAIR group. Thyroid peroxidase plays an important role in catalyzing the iodination of tyrosine residues, while the ionic state of iodine promotes the iodination reaction. Quinate, shikimic acid, hippuric acid, and 2-phenylacetamide were found to be involved in the iodination of tyrosine, which is a key step in thyroid hormone synthesis. Specifically, quinate and shikimic acid were found to inhibit iodination, while hippuric acid and 2-phenylacetamide promoted iodination. Abnormalities in phenylalanine and tyrosine metabolic pathways are closely associated with iodine resistance. Tyrosine is required for thyroid hormone synthesis and could be a potential cause of iodine resistance.

GEnDDn: An lncRNA-Disease Association Identification Framework Based on Dual-Net Neural Architecture and Deep Neural Network.

Accumulating studies have demonstrated close relationships between long non-coding RNAs (lncRNAs) and diseases. Identification of new lncRNA-disease associations (LDAs) enables us to better understand disease mechanisms and further provides promising insights into cancer targeted therapy and anti-cancer drug design. Here, we present an LDA prediction framework called GEnDDn based on deep learning. GEnDDn mainly comprises two steps: First, features of both lncRNAs and diseases are extracted by combining similarity computation, non-negative matrix factorization, and graph attention auto-encoder, respectively. And each lncRNA-disease pair (LDP) is depicted as a vector based on concatenation operation on the extracted features. Subsequently, unknown LDPs are classified by aggregating dual-net neural architecture and deep neural network. Using six different evaluation metrics, we found that GEnDDn surpassed four competing LDA identification methods (SDLDA, LDNFSGB, IPCARF, LDASR) on the lncRNADisease and MNDR databases under fivefold cross-validation experiments on lncRNAs, diseases, LDPs, and independent lncRNAs and independent diseases, respectively. Ablation experiments further validated the powerful LDA prediction performance of GEnDDn. Furthermore, we utilized GEnDDn to find underlying lncRNAs for lung cancer and breast cancer. The results elucidated that there may be dense linkages between IFNG-AS1 and lung cancer as well as between HIF1A-AS1 and breast cancer. The results require further biomedical experimental verification. GEnDDn is publicly available at https://github.com/plhhnu/GEnDDn.

NADPH and NAC synergistically inhibits chronic ocular hypertension-induced neurodegeneration and neuroinflammation through regulating p38/MAPK pathway and peroxidation.

Glaucoma, the leading cause of irreversible blindness worldwide, is characterized by neurodegeneration and neuroinflammation with retinal NAD/NADP and GSH decline. Nicotinamide adenine dinucleotide (NAD)/NAD phosphate (NADP) and glutathione (GSH) are two redox reducers in neuronal and glial metabolism. However, therapeutic strategies targeting NAD/NADP or GSH do not exert ideal effects, and the underlying mechanisms are still poorly understood. We assessed morphological changes in retinal ganglion cells (RGCs), the affected neurons in glaucoma, and Müller cells, the major glial cells in the retina, as well as the levels of phosphorylated p38 (p-p38) and Caspase-3 in glaucoma patients. We constructed a modified chronic ocular hypertensive rat model and an oxygen-glucose deprivation (OGD) cell model. After applying NADPH and N-acetylcysteine (NAC), a precursor to cysteine, the rate-limiting substrate in GSH biosynthesis, to cells, apoptosis, axonal damage and peroxidation were reduced in the RGCs of the NAC group and p-p38 levels were decreased in the RGCs of the NADPH group, while in stimulated Müller cells cultured individually or cocultured with RGCs, gliosis and p38/MAPK, rather than JNK/MAPK, activation were inhibited. The results were more synergistic in the rat model, where either NADPH or NAC showed crossover effects on inhibiting peroxidation and p38/MAPK pathway activation. Moreover, the combination of NADPH and NAC ameliorated RGC electrophysiological function and prevented Müller cell gliosis to the greatest extent. These data illustrated conjoined mechanisms in glaucomatous RGC injury and Müller cell gliosis and suggested that NADPH and NAC collaborate as a neuroprotective and anti-inflammatory combination treatment for glaucoma and other underlying human neurodegenerative diseases.

Recurrence Characteristics on Optical Coherence Tomography and Treatments of Reis-Bücklers Corneal Dystrophy after Phototherapeutic Keratectomy or Penetrating Keratoplasty: Recurrence characteristics and treatments of RBCD.

PURPOSE: To evaluate the recurrence characteristics on optical coherence tomography and clinical outcomes after phototherapeutic keratectomy (PTK) or penetrating keratoplasty (PKP) in patients with Reis-Bücklers corneal dystrophy (RBCD). DESIGN: Retrospective interventional case series. METHODS: Seventeen patients with RBCD (31 eyes, including six surgery-naïve eyes and 25 surgical eyes) received 44 surgical interventions from 1996 through 2022. PTK or PKP was performed as the initial surgical procedure. Significant recurrence was determined when best spectacle-corrected visual acuity decreased at least two lines with increased opacity in the superficial cornea. Repeated PTK or PTK on the corneal graft (CG-PTK) was considered if patients could not endure poor vision due to significant recurrence. Recurrence depth and annual increase in thickness of the central cornea and subepithelial deposits were assessed by anterior segment optical coherence tomography. RESULTS: The mean follow-up time was 12.8±8.5 years (range, 2.0-25.5 years). The mean logMAR best spectacle-corrected visual acuity improved from 1.24±0.48 preoperatively to 0.27±0.09 postoperatively in the initial PTK group (13 eyes, P<0.001), from 1.84±0.69 to 0.40±0.13 in the PKP group (12 eyes, P<0.001), from 1.04±0.46 to 0.30±0.07 in the repeated PTK group (12 times in 7 eyes, P<0.001), and from 1.29±0.43 to 0.39±0.11 in the CG-PTK group (7 times in 5 eyes, P=0.001). The median significant recurrence time was 27 months (95% confidence interval 23.9-30.1), 96 months (84.1-107.9), 31 months (28.8-33.1), and 24 months (19.8-28.2), respectively (P<0.001). The depth of superficial deposits located between the epithelium and the anterior stroma was approximately 115µm (85-159µm). The annual thickening of subepithelial deposits was 14±2µm after initial PTK, 7±3µm after PKP, 14±3µm after repeated PTK, and 30±11µm after CG-PTK, compared to 4±2µm in surgery-naïve eyes (P=0.002, 0.515, 0.002, <0.001). The thickness of the central cornea increased by 15±2µm, 7±2µm, 15±3µm, and 31±10µm per year in the four surgery groups, respectively, compared to 5±2µm in surgery-naïve eyes (P=0.001, 0.469, 0.001, <0.001). CONCLUSIONS: Better visual acuity can be achieved after PTK than PKP for treatment of RBCD. The annual thickening of subepithelial deposits may approximate an increase in central corneal thickness. The superficial distribution of subepithelial deposits makes it feasible to perform repeated PTK, even on the corneal allograft, for recurrent RBCD.

Identification of Molecular Subtypes Associated with PCD in Esophageal Squamous Cell Carcinoma and Analysis of Immune Microenvironment.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a highly fatal malignancy with increasing incidence, and programmed cell death (PCD) plays an important role in homeostasis. AIMS: This study aimed to explore the ESCC of heterogeneity based on the PCD signatures for the diagnosis and treatment of patients. METHODS: The clinical information and RNA-seq data of patients with ESCC and the PCD-related genes set were used to identify PCD signatures.The "limma" package was used to identify the differentially expressed genes (DEGs). "Clusterprofiler" package was used for function enrichment analysis, and the "ConsensusClusterPlus" package was performed for consensus clustering. Finally, the "GSVA" package and the Cibersort algorithm were used for the immune infiltration analysis. RESULTS: We performed differential expression analysis between ESCC and normal samples and identified 1659 DEGs, of which 124 DEGs were PCD genes. Then, the patients were divided into cluster1 and cluster2 based on the expression of 124 PCD genes. There was a significant difference in immune infiltration between the two clusters. The patients in cluster 1 had a higher immune score and more CD56dim natural killer cells, monocytes, activated CD4 T cells, eosinophil, and activated B cells infiltration, while cluster2 had a higher stromal score, more immune regulation, and immune checkpoint genes expression. CONCLUSION: We identified two clusters based on PCD gene expression and characterized their tumor microenvironment and immune checkpoint difference. Our findings may provide some new insight into the treatment of ESCC.

Autophagy in hepatic progenitor cells modulates exosomal miRNAs to inhibit liver fibrosis in schistosomiasis.

Schistosoma infection is one of the major causes of liver fibrosis. Emerging roles of hepatic progenitor cells (HPCs) in the pathogenesis of liver fibrosis have been identified. Nevertheless, the precise mechanism underlying the role of HPCs in liver fibrosis in schistosomiasis remains unclear. This study examined how autophagy in HPCs affects schistosomiasis-induced liver fibrosis by modulating exosomal miRNAs. The activation of HPCs was verified by immunohistochemistry (IHC) and immunofluorescence (IF) staining in fibrotic liver from patients and mice with Schistosoma japonicum infection. By coculturing HPCs with hepatic stellate cells (HSCs) and assessing the autophagy level in HPCs by proteomic analysis and in vitro phenotypic assays, we found that impaired autophagy degradation in these activated HPCs was mediated by lysosomal dysfunction. Blocking autophagy by the autophagy inhibitor chloroquine (CQ) significantly diminished liver fibrosis and granuloma formation in S. japonicum-infected mice. HPC-secreted extracellular vehicles (EVs) were further isolated and studied by miRNA sequencing. miR-1306-3p, miR-493-3p, and miR-34a-5p were identified, and their distribution into EVs was inhibited due to impaired autophagy in HPCs, which contributed to suppressing HSC activation. In conclusion, we showed that the altered autophagy process upon HPC activation may prevent liver fibrosis by modulating exosomal miRNA release and inhibiting HSC activation in schistosomiasis. Targeting the autophagy degradation process may be a therapeutic strategy for liver fibrosis during Schistosoma infection.

Rare Earth Nanoprobes for Targeted Delineation of Triple Negative Breast Cancer and Enhancement of Radioimmunotherapy.

Radiotherapy demonstrates a synergistic effect with immunotherapy by inducing a transformation of "immune cold" tumors into "immune hot" tumors in triple negative breast cancer (TNBC). Nevertheless, the effectiveness of immunotherapy is constrained by low expression of tumor-exposed antigens, inadequate inflammation, and insufficient tumor infiltrating lymphocyte (TILs). To address this predicament, novel lutecium-based rare earth nanoparticles (RENPs) are synthesized with the aim of amplifying radiation effect and tumor immune response. The nanoprobe is characterized by neodymium-based down-conversion fluorescence, demonstrating robust photostability, biocompatibility, and targetability. The conjugation of RENPs with a CXCR4 targeted drug enables precise delineation of breast tumors using a near-infrared imaging system and improves radiation efficacy via lutetium-based radio-sensitizer in vivo. Furthermore, the study shows a notable enhancement of immune response through the induction of immunogenic cell death and recruitment of TILs, resulting in the inhibition of tumor progression both in vitro and in vivo models following the administration of nanoparticles. Hence, the novel multifunctional nanoprobes incorporating various lanthanide elements offer the potential for imaging-guided tumor delineation, radio-sensitization, and immune activation post-radiation, thus presenting an efficient radio-immunotherapeutic approach for TNBC.

Design, Synthesis, Antifungal Activity, and Action Mechanism of Pyrazole-4-carboxamide Derivatives Containing Oxime Ether Active Fragment As Succinate Dehydrogenase Inhibitors.

The dearomatization at the hydrophobic tail of the boscalid was carried out to construct a series of novel pyrazole-4-carboxamide derivatives containing an oxime ether fragment. By using fungicide-likeness analyses and virtual screening, 24 target compounds with theoretical strong inhibitory effects against fungal succinate dehydrogenase (SDH) were designed and synthesized. Antifungal bioassays showed that the target compound E1 could selectively inhibit the in vitro growth of R. solani, with the EC50 value of 1.1 µg/mL that was superior to that of the agricultural fungicide boscalid (2.2 µg/mL). The observations by scanning electron microscopy (SEM) and transmission electron microscopy (TEM) demonstrated that E1 could reduce mycelial density and significantly increase the mitochondrial number in mycelia cytoplasm, which was similar to the phenomenon treated with boscalid. Enzyme activity assay showed that the E1 had the significant inhibitory effect against the SDH from R. solani, with the IC50 value of 3.3 µM that was superior to that of boscalid (7.9 µM). The mode of action of the target compound E1 with SDH was further analyzed by molecular docking and molecular dynamics simulation studies. Among them, the number of hydrogen bonds was significantly more in the SDH-E1 complex than that in the SDH-boscalid complex. This research on the dearomatization strategy of the benzene ring for constructing pyrazole-4-carboxamides containing an oxime ether fragment provides a unique thought to design new antifungal drugs targeting SDH.

SEnSCA: Identifying possible ligand-receptor interactions and its application in cell-cell communication inference.

Multicellular organisms have dense affinity with the coordination of cellular activities, which severely depend on communication across diverse cell types. Cell-cell communication (CCC) is often mediated via ligand-receptor interactions (LRIs). Existing CCC inference methods are limited to known LRIs. To address this problem, we developed a comprehensive CCC analysis tool SEnSCA by integrating single cell RNA sequencing and proteome data. SEnSCA mainly contains potential LRI acquisition and CCC strength evaluation. For acquiring potential LRIs, it first extracts LRI features and reduces the feature dimension, subsequently constructs negative LRI samples through K-means clustering, finally acquires potential LRIs based on Stacking ensemble comprising support vector machine, 1D-convolutional neural networks and multi-head attention mechanism. During CCC strength evaluation, SEnSCA conducts LRI filtering and then infers CCC by combining the three-point estimation approach and single cell RNA sequencing data. SEnSCA computed better precision, recall, accuracy, F1 score, AUC and AUPR under most of conditions when predicting possible LRIs. To better illustrate the inferred CCC network, SEnSCA provided three visualization options: heatmap, bubble diagram and network diagram. Its application on human melanoma tissue demonstrated its reliability in CCC detection. In summary, SEnSCA offers a useful CCC inference tool and is freely available at https://github.com/plhhnu/SEnSCA.

Establishment and validation of an artificial intelligence web application for predicting postoperative in-hospital mortality in patients with hip fracture: a National cohort study of 52,707 cases.

BACKGROUND: In-hospital mortality following hip fractures is a significant concern, and accurate prediction of this outcome is crucial for appropriate clinical management. Nonetheless, there is a lack of effective prediction tools in clinical practice. By utilizing artificial intelligence and machine learning techniques, this study aims to develop a predictive model that can assist clinicians in identifying geriatric hip fracture patients at a higher risk of in-hospital mortality. METHODS: A total of 52,707 geriatric hip fracture patients treated with surgery from 90 hospitals were included in this study. The primary outcome was postoperative in-hospital mortality. The patients were randomly divided into two groups, with a ratio of 7:3. The majority of patients, assigned to the training cohort, were used to develop the AI models. The remaining patients, assigned to the validation cohort, were used to validate the models. Various machine learning algorithms, including logistic regression (LR), decision tree (DT), naïve Bayesian (NB), neural network (NN), eXGBoosting machine (eXGBM), and random forest (RF), were employed for model development. A comprehensive scoring system, incorporating 10 evaluation metrics, was developed to assess the prediction performance, with higher scores indicating superior predictive capability. Based on the best machine learning-based model, an AI application was developed on the Internet. In addition, a comparative testing of prediction performance between doctors and the AI application. FINDINGS: The eXGBM model exhibited the best prediction performance, with an AUC of 0.908 (95% CI: 0.881-0.932), as well as the highest accuracy (0.820), precision (0.817), specificity (0.814), and F1 score (0.822), and the lowest Brier score (0.120) and log loss (0.374). Additionally, the model showed favorable calibration, with a slope of 0.999 and an intercept of 0.028. According to the scoring system incorporating 10 evaluation metrics, the eXGBM model achieved the highest score (56), followed by the RF model (48) and NN model (41). The LR, DT, and NB models had total scores of 27, 30, and 13, respectively. The AI application has been deployed online at https://in-hospitaldeathinhipfracture-l9vhqo3l55fy8dkdvuskvu.streamlit.app/ , based on the eXGBM model. The comparative testing revealed that the AI application's predictive capabilities significantly outperformed those of the doctors in terms of AUC values (0.908 vs. 0.682, P <0.001). CONCLUSIONS: The eXGBM model demonstrates promising predictive performance in assessing the risk of postoperative in-hospital mortality among geriatric hip fracture patients. The developed AI model serves as a valuable tool to enhance clinical decision-making.

Construction and validation of nomogram model for predicting the risk of ventricular arrhythmia after emergency PCI in patients with acute myocardial infarction.

OBJECTIVE: To make predictions about the risk of MVA (Malignant Ventricular Arrhythmia) after primary PCI (Percutaneous Coronary Intervention) in patients with AMI (Acute Myocardial Infarction) through constructing and validating the Nomogram model. METHODS: 311 AMI patients who suffered from emergency PCI in Hefei Second People's Hospital from January 2020 to May 2023 were selected as the training set; 253 patients suffering from the same symptom in Hefei First People's Hospital during the same period were selected as the validation set. Risk factors were further screened by means of multivariate logistic and stepwise regression. The nomogram model was constructed, and then validated by using C-index, ROC curve, decision curve and calibration curve. RESULTS: Multivariate logistic analysis revealed that urea, systolic pressure, hypertension, Killip class II-IV, as well as LVEF (Left Ventricular Ejection Fraction) were all unrelated hazards for MVA after emergency PCI for AMI (P<0.05); a risk prediction nomogram model was constructed. The C-index was calculated to evaluate the predictive ability of the model. Result showed that the index of the training and the validation set was 0.783 (95% CI: 0.726-0.84) and 0.717 (95% CI: 0.65-0.784) respectively, which suggested that the model discriminated well. Meanwhile, other tools including ROC curve, calibration curve and decision curve also proved that this nomogram plays an effective role in forecasting the risk for MVA after PCI in AMI patients. CONCLUSIONS: The study successfully built the nomogram model and made predictions for the development of MVA after PCI in AMI patients.

Millijoule Terahertz Radiation from Laser Wakefields in Nonuniform Plasmas.

We report the experimental measurement of millijoule terahertz (THz) radiation emitted in the backward direction from laser wakefields driven by a femtosecond laser pulse of few joules interacting with a gas target. By utilizing frequency-resolved energy measurement, it is found that the THz spectrum exhibits two peaks located at about 4.5 and 9.0 THz, respectively. In particular, the high frequency component emerges when the drive laser energy exceeds 1.26 J, at which electron acceleration in the forward direction is detected simultaneously. Theoretical analysis and particle-in-cell simulations indicate that the THz radiation is generated via mode conversion from the laser wakefields excited in plasma with an up-ramp profile, where radiations both at the local electron plasma frequency and its harmonics are produced. Such intense THz sources may find many applications in ultrafast science, e.g., manipulating the transient states of matter.

SCPLPA: An miRNA-disease association prediction model based on spatial consistency projection and label propagation algorithm.

Identifying the association between miRNA and diseases is helpful for disease prevention, diagnosis and treatment. It is of great significance to use computational methods to predict potential human miRNA disease associations. Considering the shortcomings of existing computational methods, such as low prediction accuracy and weak generalization, we propose a new method called SCPLPA to predict miRNA-disease associations. First, a heterogeneous disease similarity network was constructed using the disease semantic similarity network and the disease Gaussian interaction spectrum kernel similarity network, while a heterogeneous miRNA similarity network was constructed using the miRNA functional similarity network and the miRNA Gaussian interaction spectrum kernel similarity network. Then, the estimated miRNA-disease association scores were evaluated by integrating the outcomes obtained by implementing label propagation algorithms in the heterogeneous disease similarity network and the heterogeneous miRNA similarity network. Finally, the spatial consistency projection algorithm of the network was used to extract miRNA disease association features to predict unverified associations between miRNA and diseases. SCPLPA was compared with four classical methods (MDHGI, NSEMDA, RFMDA and SNMFMDA), and the results of multiple evaluation metrics showed that SCPLPA exhibited the most outstanding predictive performance. Case studies have shown that SCPLPA can effectively identify miRNAs associated with colon neoplasms and kidney neoplasms. In summary, our proposed SCPLPA algorithm is easy to implement and can effectively predict miRNA disease associations, making it a reliable auxiliary tool for biomedical research.

Plasma Metabolic Profiles-Based Prediction of Induction Chemotherapy Efficacy in Nasopharyngeal Carcinoma: Results of a Bidirectional Clinical Trial.

PURPOSE: The efficacy of induction chemotherapy (IC) as a primary treatment for advanced nasopharyngeal carcinoma (NPC) remains a topic of debate, with a lack of dependable biomarkers for predicting its efficacy. This study seeks to establish a predictive classifier utilizing plasma metabolomics profiling. EXPERIMENTAL DESIGN: A total of 166 NPC patients enrolled in the clinical trial NCT05682703 and undergoing IC were included in the study. Plasma lipoprotein profiles were obtained using 1H-NMR before and after IC treatment. An AI-assisted radiomics method was developed to effectively evaluate the efficacy. Metabolic biomarkers were identified through a machine learning approach based on a discovery cohort and subsequently validated in a validation cohort that mimicked the most unfavorable scenario in real-world. RESULTS: Our research findings indicate that the effectiveness of IC varies among individual patients, with a correlation observed between efficacy and changes in metabolite profiles. Utilizing machine learning techniques, it was determined that the XGB model exhibited notable efficacy, attaining an Area Under the Curve (AUC) value of 0.792 (95% CI, 0.668-0.913). In the validation cohort, the model exhibited strong stability and generalizability with an AUC of 0.786 (95%CI, 0.533-0.922). CONCLUSION: In this study, we found that dysregulation of plasma lipoprotein may result in resistance to IC in NPC patients. The prediction model constructed based on the plasma metabolites' profile as good predictive capabilities and potential for real-world generalization. This discovery has implications for the development of treatment strategies and may offer insight into potential targets for enhancing the effectiveness of IC.

Upconversion fluorescence nanosensor based on enzymatic inhibited and copper-triggered o-phenylenediamine oxidation for the detection of dimethoate pesticides.

Pesticide residues in agricultural products pose a significant threat to human health. Herein, a sensitive fluorescence method employing upconversion nanoparticles was developed for detecting organophosphorus pesticides (OPs) based on the principle of enzyme inhibition and copper-triggered o-phenylenediamine (OPD) oxidation. Copper ions (Cu2+) oxidized the colorless OPD to a yellow 2,3-diaminophenazine (oxOPD). The yellow solution oxOPD quenched the fluorescence of upconversion nanoparticles due to the fluorescence resonance energy transfer. The high affinity of Cu2+ for thiocholine reduced the level of oxOPD, resulting in almost no fluorescence quenching. The addition of dimethoate led to the inhibition of acetylcholinesterase activity and thus prevented the formation of thiocholine. Subsequently, Cu2+ oxidized OPD to form oxOPD, which attenuated the fluorescence signal of the system. The detection system has a good linear range of 0.01 ng/mL to 50 ng/mL with a detection limit of 0.008 ng/mL, providing promising applications for rapid detection of dimethoate.

Ptehosides A-I: Nine undescribed iridoids with in vitro cytotoxicity from the whole plant of Pterocephalus hookeri (C.B. Clarke) Höeck.

Nine previously undescribed iridoids, ptehosides A-I (1-9), together with 12 known ones (10-21), were isolated from Pterocephalus hookeri (C.B. Clarke) Höeck. Their structures were elucidated using various spectroscopic methods including HR-ESI-MS, NMR, UV, IR and CD, etc. The cytotoxic activities of all isolates were evaluated using MTT method in three human cancer cell lines (Caco2, Huh-7, and SW982). As result, compound 9 exhibited substantial inhibitory activity on Caco2, Huh-7, and SW982 cells with IC50 values of 1.17 ± 0.05, 1.15 ± 0.05 and 1.14 ± 0.04 µM, respectively. A preliminary mechanism study showed that 9 arrested the cell cycle of SW982 cells in the G0/G1 phase and induced apoptosis by upregulating Bax expression and downregulating Bcl-2 expression.

Bioorthogonal labeling and enrichment of histone monoaminylation reveal its accumulation and regulatory function in cancer cell chromatin.

Histone monoaminylation ( i . e ., serotonylation and dopaminylation) is an emerging category of epigenetic mark occurring on the fifth glutamine (Q5) residue of H3 N-terminal tail, which plays significant roles in gene transcription. Current analysis of histone monoaminylation is mainly based on site-specific antibodies and mass spectrometry, which either lacks high resolution or is time-consuming. In this study, we report the development of chemical probes for bioorthogonal labeling and enrichment of histone serotonylation and dopaminylation. These probes were successfully applied for the monoaminylation analysis of in vitro biochemical assays, cells, and tissue samples. The enrichment of monoaminylated histones by the probes further confirmed the crosstalk between H3Q5 monoaminylation and H3K4 methylation. Finally, combining the ex vivo and in vitro analyses based on the developed probes, we have shown that both histone serotonylation and dopaminylation are highly enriched in tumor tissues that overexpress transglutaminase 2 (TGM2) and regulate the three-dimensional architecture of cellular chromatin.

STING agonist diABZI enhances the cytotoxicity of T cell towards cancer cells.

Antigen-specific T cell receptor-engineered T cell (TCR-T) based immunotherapy has proven to be an effective method to combat cancer. In recent years, cross-talk between the innate and adaptive immune systems may be requisite to optimize sustained antigen-specific immunity, and the stimulator of interferon genes (STING) is a promising therapeutic target for cancer immunotherapy. The level of expression or presentation of antigen in tumor cells affects the recognition and killing of tumor cells by TCR-T. This study aimed at investigating the potential of innate immune stimulation of T cells and engineered T cells to enhance immunotherapy for low-expression antigen cancer cells. We systematically investigated the function and mechanism of cross-talk between STING agonist diABZI and adaptive immune systems. We established NY-ESO-1 full knockout Mel526 cells for this research and found that diABZI activated STING media and TCR signaling pathways. In addition, the results of flow cytometry showed that antigens presentation from cancer cells induced by STING agonist diABZI also improved the affinity of TCR-T cells function against tumor cells in vitro and in vivo. Our findings revealed that diABZI enhanced the immunotherapy efficacy of TCR-T by activating STING media and TCR signaling pathways, improving interferon-γ expression, and increasing antigens presentation of tumor cells. This indicates that STING agonist could be used as a strategy to promote TCR-T cancer immunotherapy.

Neutralization of Rubella Vaccine Virus and Immunodeficiency-Related Vaccine-Derived Rubella Viruses by Intravenous Immunoglobulins.

The association between granulomas and vaccine-derived rubella virus (VDRV) in people with primary immune deficiencies (PID) has raised concerns about the ability of immunoglobulin (IG) preparations to neutralize VDRVs. We investigated the capacity of IG to neutralize rubella vaccine virus and four VDRV strains. As expected, the rubella vaccine virus itself was potently neutralized by IG preparations; however, the VDRV isolates from patients after intra-host evolution, 2-6 times less so. Diagnosis of immune deficiencies before possible live-virus vaccination is thus of critical importance, while IG replacement therapy can be expected to provide protection from rubella virus infection.

The occurrence of granulomas associated with vaccine derived rubella viruses (VDRV) in people with primary immune deficiencies (PID) challenges immunoglobulin (IG) preparations regarding their rubella neutralizing ability. This study confirmed potent rubella virus neutralization capacity of IG preparations and thus suggests protection of IG-treated PID patients against rubella. The study also highlights the importance of early diagnosis and timely given IG to prevent possible systemic spread of VDRV persisting locally in granulomas.

Pre-Diagnosis Diet Predicts Response to Exclusive Enteral Nutrition and Correlates with Microbiome in Pediatric Crohn Disease.

Exclusive enteral nutrition (EEN) is effective in inducing remission in pediatric Crohn disease (CD). EEN alters the intestinal microbiome, but precise mechanisms are unknown. We hypothesized that pre-diagnosis diet establishes a baseline gut microbiome, which then mediates response to EEN. We analyzed prospectively recorded food frequency questionnaires (FFQs) for pre-diagnosis dietary patterns. Fecal microbiota were sequenced (16SrRNA) at baseline and through an 18-month follow-up period. Dietary patterns, Mediterranean diet adherence, and stool microbiota were associated with EEN treatment outcomes, disease flare, need for anti-tumor necrosis factor (TNF)-α therapy, and long-term clinical outcomes. Ninety-eight patients were included. Baseline disease severity and microbiota were associated with diet. Four dietary patterns were identified by FFQs; a "mature diet" high in fruits, vegetables, and fish was linked to increased baseline microbial diversity, which was associated with fewer disease flares (p < 0.05) and a trend towards a delayed need for anti-TNF therapy (p = 0.086). Baseline stool microbial taxa were increased (Blautia and Faecalibacterium) or decreased (Ruminococcus gnavus group) with the mature diet compared to other diets. Surprisingly, a "pre-packaged" dietary pattern (rich in processed foods) was associated with delayed flares in males (p < 0.05). Long-term pre-diagnosis diet was associated with outcomes of EEN therapy in pediatric CD; diet-microbiota and microbiota-outcome associations may mediate this relationship.