Isolation and identification of a novel porcine-related recombinant mammalian orthoreovirus type 3 strain from cattle in Guangxi Province, China.

The Mammalian orthoreovirus (MRV) infects various mammals, including humans, and is linked to gastrointestinal, respiratory, and neurological diseases. A recent outbreak in Liuzhou, Guangxi, China, led to the isolation of a new MRV strain, GXLZ2301, from fecal samples. This strain replicates in multiple cell lines and forms lattice-like structures. Infected cells exhibit single-cell death and syncytia formation. The virus's titers peaked at 107.2 TCID50/0.1 mL in PK-15 and BHK cells, with the lowest at 103.88 TCID50/0.1 mL in A549 cells. Electron microscopy showed no envelope with a diameter of about 70 nm. Genetic analysis revealed GXLZ2301 as a recombinant strain with gene segments from humans, cows, and pigs, similar to type 3 MRV strains from Italy (2015-2016). Pathogenicity tests indicated that while the bovine MRV strain did not cause clinical symptoms in mice, it caused significant damage to the gut, lungs, liver, kidneys, and brain. The emergence of this MRV strain may pose a threat to the health of animals and humans, and it is recommended that its epidemiology and recombination be closely monitored.

Association between PER and CRY gene polymorphisms and the response to caffeine citrate treatment in infants with apnea of prematurity.

Background: Circadian rhythms impact metabolism and the therapeutic effects of drugs. The purpose of this study was to determine the association between PER and CRY polymorphisms and caffeine citrate treatment response in infants with apnea of prematurity. Methods: A total of 221 preterm infants of gestational age <34 weeks were included in this study (160 in the response group and 61 in the non-response group). The propensity score matching method was used to perform a 1:1 matching for all premature infants, and the general characteristics and clinical outcomes of the two groups were compared. The association between polymorphisms of the circadian transcription repressors PER and CRY and caffeine citrate treatment response in infants with apnea of prematurity was analyzed with co-dominant, dominant, recessive, and over-dominant models, as well as analysis of alleles. Generalized multifactor dimensionality reduction (GMDR) analysis was used to analyze the interaction between the PER and CRY genes. Results: After propensity score matching, 45 preterm infants were included in each of the response and non-response groups, and there were no statistically significant differences in general characteristics between the two groups (P > 0.05). Infants in the non-response groups had a higher incidence of moderate and severe bronchopulmonary dysplasia (BPD) (P = 0.043), retinopathy of prematurity (ROP) (P = 0.035), and invasive ventilation (P = 0.027), and their duration of oxygen use (P = 0.041) was longer. When corrected for false discovery rate, the PER3 rs228669 recessive model (P FDR = 0.045) and the over-dominant model (P FDR = 0.045) were both associated with caffeine citrate treatment response. Preterm infants with the rs228669 CC genotype had a significantly lower rate of caffeine citrate non-response in the recessive model (OR = 0.28, 95% CI = 0.12-0.66), which was significantly higher in preterm infants with the CT genotype in the over-dominant model (OR = 4.18, 95% CI = 1.64-10.66). GMDR analysis revealed an interaction between the PER and CRY genes (P < 0.05). Conclusions: Circadian rhythms may play a role in the response of premature infants to caffeine citrate, and polymorphisms of the PER and CRY genes may influence the effectiveness of caffeine citrate treatment for apnea of prematurity.

Placenta-specific CYP11A1 overexpression lead to autism-like symptom in offspring with altered steroid hormone biosynthesis in the placenta-brain axis and rescued by vitamin D intervention.

Alterations in steroid hormone regulation have been implicated in the etiology and progression of autism spectrum disorders (ASD), with the enzyme cytochrome P450 family 11 subfamily A member 1 (CYP11A1)-a key catalyst in cholesterol side-chain cleavage, prominently expressed in the adrenal glands, ovaries, testes, and placenta-standing at the forefront of these investigations. The potential link between aberrations in placental Cyp11a1 expression and the resultant neurodevelopmental disorders, along with the mechanisms underpinning such associations, remains inadequately delineated. In this study, we employed a placental trophoblast-specific Cyp11a1 Hipp11 (H11) knock-in murine model to dissect the phenotypic manifestations within the placenta and progeny, thereby elucidating the underlying mechanistic pathways. Behavioral analyses revealed a diminution in social interaction capabilities alongside an augmented anxiety phenotype, as evidenced by open field and elevated plus maze assessments; both phenotypes were ameliorated after vitamin D3 supplementation. Electrophysiological assays underscored the augmented inhibition of paired-pulse facilitation, indicating impaired neuroplasticity in Cyp11a1 H11-modified mice. An elevation in progesterone concentrations was noted, alongside a significant upregulation of Th1-related cytokines (IL-6 and TNFα) across the plasma, placental, and frontal cortex-a pathological state mitigable through vitamin D3 intervention. Western blotting revealed a vitamin D-mediated rectification of vitamin D receptor and PGC-1α expression dysregulations. Immunofluorescence assays revealed microglial activation in the knock-in model, which was reversible upon vitamin D3 treatment. In conclusion, Cyp11a1 overexpression in the placenta recapitulated an autism-like phenotype in murine models, and vitamin D3 administration effectively ameliorated the resultant neurobehavioral and neuroinflammatory derangements. This study substantiates the application of Cyp11a1 as a biomarker in prenatal diagnostics and posits that prenatal vitamin D3 supplementation is a viable prophylactic measure against perturbations in steroid hormone metabolism associated with ASD pathogenesis.

A combined nutritional risk index and carcinoembryonic antigen score predicts the outcome in radically resected colorectal cancer.

BACKGROUND: Nutritional risk index (NRI) and carcinoembryonic antigen (CEA) are useful prognostic markers in colorectal cancer (CRC); however, the prognostic value of a combination of the NRI and CEA, namely, the NRI and CEA score (NCS), needs further investigation. METHODS: Stage I-III CRC patients were collected and then divided into three subgroups by counting the NCS: NCS 1: high NRI with normal CEA; NCS 2: high NRI with elevated CEA or low NRI with normal CEA; and NCS 3: low NRI with elevated CEA. The differences in outcome, counted as disease-free survival (DFS) and overall survival (OS), were tested among the subgroups. RESULTS: A total of 285 patients were enrolled, with 108 in NCS 1, 118 in NCS 2 and 59 in NCS 3. Patient features, including age, tumour deposit, T stage, N stage and TNM stage, were significantly different in the NCS subgroups. Both the DFS (log-rank = 26.06, P<0.001) and OS (log-rank = 39.10, P<0.001) were significant in different NCS subgroups, even in maximum tumour diameter ≤4 cm cases (DFS: log-rank = 21.42, P<0.001; OS: log-rank = 30.95, P<0.001), and NCS 1 patients displayed the best outcome compared with the rest of the subgroups. NCS was also found to be an independent risk factor for both DFS and OS. CONCLUSIONS: NCS was a useful prognostic indicator in stages I-III CRC patients.

DRML-Ensemble: drug repurposing method based on feature construction of multi-layer ensemble.

CONTEXT: Computational drug repurposing methods have been continuously developed in recent years to alleviate the high costs associated with drug development. As drug targets or the products of disease-related genes, proteins play an important role in drug repurposing. Although the potential has been demonstrated, heterogeneous graphs with proteins as independent nodes have yet to be studied, where extracting high-quality protein features from heterogeneous graphs poses a significant challenge. A novel drug repurposing model based on the feature construction of multi-layer ensemble (DRML-Ensemble) is proposed in this study. The performance of DRML-Ensemble, as evaluated on publicly available datasets, achieves an AUPR value of 0.93 and an AUROC value of 0.92, surpassing those of existing state-of-the-art methods. Additionally, DRML-Ensemble demonstrates its notable ability for drug repurposing in Alzheimer's disease. METHODS: DRML-Ensemble is primarily composed of multiple layers of heterogeneous graph feature construction (HGFC). Each HGFC can extract protein features by leveraging the relationships between drugs, diseases, and proteins. These protein features are then utilized in subsequent layers to build drug and disease features, facilitating drug repurposing. By stacking multiple layers, optimal protein features can be obtained from the heterogeneous graph, consequently improving the accuracy of drug repurposing. However, an excessive· stacking of layers usually affect the model's training process, for example, causing problems such as overfitting; a multi-layer ensemble prediction module is designed to further improve the model's performance.

The plant-sucking insect selects assembly of the gut microbiota from environment to enhance host reproduction.

Plant-sucking insects have intricate associations with a diverse array of microorganisms to facilitate their adaptation to specific ecological niches. The midgut of phytophagous true bugs is generally structured into four distinct compartments to accommodate their microbiota. Nevertheless, there is limited understanding regarding the origins of these gut microbiomes, the mechanisms behind microbial community assembly, and the interactions between gut microbiomes and their insect hosts. In this study, we conducted a comprehensive survey of microbial communities within the midgut compartments of a bean bug Riptortus pedestris, soybean plant, and bulk soil across 12 distinct geographical fields in China, utilizing high-throughput sequencing of the 16 S rRNA gene. Our findings illuminated that gut microbiota of the plant-sucking insects predominantly originated from the surrounding soil environment, and plants also play a subordinate role in mediating microbial acquisition for the insects. Furthermore, our investigation suggested that the composition of the insect gut microbiome was probably shaped by host selection and/or microbe-microbe interactions at the gut compartment level, with marginal influence from soil and geographical factors. Additionally, we had unveiled a noteworthy dynamic in the acquisition of core bacterial taxa, particularly Burkholderia, which were initially sourced from the environment and subsequently enriched within the insect midgut compartments. This bacterial enrichment played a significant role in enhancing insect host reproduction. These findings contribute to our evolving understanding of microbiomes within the insect-plant-soil ecosystem, shedding additional light on the intricate interactions between insects and their microbiomes that underpin the ecological significance of microbial partnerships in host adaptation.

Recognition of autism spectrum disorder in children based on electroencephalogram network topology.

Although our knowledge of autism spectrum disorder (ASD) has been deepened, the accurate diagnosis of ASD from normal individuals is still left behind. In this study, we proposed to apply the spatial pattern of the network topology (SPN) to identify children with ASD from normal ones. Based on two independent batches of electroencephalogram datasets collected separately, the accurate recognition of ASD from normal children was achieved by applying the proposed SPN features. Since decreased long-range connectivity was identified for children with ASD, the SPN features extracted from the distinctive topological architecture between two groups in the first dataset were used to validate the capacity of SPN in classifying ASD, and the SPN features achieved the highest accuracy of 92.31%, which outperformed the other features e.g., power spectrum density (84.62%), network properties (76.92%), and sample entropy (73.08%). Moreover, within the second dataset, by using the model trained in the first dataset, the SPN also acquired the highest sensitivity in recognizing ASD, when compared to the other features. These results consistently illustrated that the functional brain network, especially the intrinsic spatial network topology, might be the potential biomarker for the diagnosis of ASD.

EMPDTA: An End-to-End Multimodal Representation Learning Framework with Pocket Online Detection for Drug-Target Affinity Prediction.

Accurately predicting drug-target interactions is a critical yet challenging task in drug discovery. Traditionally, pocket detection and drug-target affinity prediction have been treated as separate aspects of drug-target interaction, with few methods combining these tasks within a unified deep learning system to accelerate drug development. In this study, we propose EMPDTA, an end-to-end framework that integrates protein pocket prediction and drug-target affinity prediction to provide a comprehensive understanding of drug-target interactions. The EMPDTA framework consists of three main modules: pocket online detection, multimodal representation learning for affinity prediction, and multi-task joint training. The performance and potential of the proposed framework have been validated across diverse benchmark datasets, achieving robust results in both tasks. Furthermore, the visualization results of the predicted pockets demonstrate accurate pocket detection, confirming the effectiveness of our framework.

MGNR: A Multi-Granularity Neighbor Relationship and Its Application in KNN Classification and Clustering Methods.

In the real world, data distributions often exhibit multiple granularities. However, the majority of existing neighbor-based machine-learning methods rely on manually setting a single-granularity for neighbor relationships. These methods typically handle each data point using a single-granularity approach, which severely affects their accuracy and efficiency. This paper adopts a dual-pronged approach: it constructs a multi-granularity representation of the data using the granular-ball computing model, thereby boosting the algorithm's time efficiency. It leverages the multi-granularity representation of the data to create tailored, multi-granularity neighborhood relationships for different task scenarios, resulting in improved algorithmic accuracy. The experimental results convincingly demonstrate that the proposed multi-granularity neighbor relationship effectively enhances KNN classification and clustering methods. The source code has been publicly released and is now accessible on GitHub at https://github.com/xjnine/MGNR.

Association between blood ethylene oxide levels and the prevalence of periodontitis: evidence from NHANES 2013-2014.

BACKGROUND: The study aimed to establish a link between blood ethylene oxide (EO) levels and periodontitis, given the growing concern about EO's detrimental health effects. MATERIALS AND METHODS: The study included 1006 adults from the 2013-2014 National Health and Nutrition Examination Survey (NHANES) dataset. We assessed periodontitis prevalence across groups, used weighted binary logistic regression and restricted cubic spline fitting for HbEO-periodontitis association, and employed Receiver Operating Characteristic (ROC) curves for prediction. RESULTS: In the periodontitis group, HbEO levels were significantly higher (40.57 vs. 28.87 pmol/g Hb, P < 0.001). The highest HbEO quartile showed increased periodontitis risk (OR = 2.88, 95% CI: 1.31, 6.31, P = 0.01). A "J"-shaped nonlinear HbEO-periodontitis relationship existed (NL-P value = 0.0116), with an inflection point at ln-HbEO = 2.96 (EO = 19.30 pmol/g Hb). Beyond this, ln-HbEO correlated with higher periodontitis risk. A predictive model incorporating sex, age, education, poverty income ratio, alcohol consumption, and HbEO had 69.9% sensitivity and 69.2% specificity. The model achieved an area under the ROC curve of 0.761. CONCLUSIONS: These findings suggest a correlation between HbEO levels and an increased susceptibility to periodontitis.

Systemic Immune-Inflammation Index and Systemic Inflammation Response Index are Associated With Periodontitis: Evidence From NHANES 2009 to 2014.

INTRODUCTION AND AIMS: Periodontitis, a chronic inflammatory condition affecting the supporting structures of the teeth, is a substantial public health burrden whilst impacting the life quality of those affected. Elevated levels of systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI) have been implicated in various inflammatory conditions. This study aimed to investigate the relationship between SII and SIRI with periodontitis. METHODS: The study examined a total of 8666 participants in the 2009 to 2014 National Health and Nutrition Examination Survey (NHANES). The study compared the weighted prevalence of periodontitis among various groups. The association between SII, SIRI levels, and periodontitis was analyzed using binary logistic regression. Additionally, we explored nonlinear relationships between SII, SIRI, and the prevalence of periodontitis using restricted cubic spline (RCS) plots. RESULTS: Among participants in the fourth quartile (Q4) of SII and SIRI, the highest prevalence of periodontitis was observed, with rates of 44.87% and 48.41%, respectively. After adjusting for all covariates, the odds ratio (OR) for periodontitis associated with SII Q4 was 1.19 (95% CI 1.02, 1.39, P = .03), while for SIRI Q4, it was 1.18 (95% CI 1.01, 1.39, P = .04). In addition, the results of sensitivity analysis revealed consistent findings, indicating that after adjusting for all covariates, the OR for periodontitis associated with SII Q4 and SIRI Q4 remained statistically significant. Specifically, the OR for periodontitis associated with SII Q4 was 1.19 (95% CI 1.02, 1.39, P = .03), while for SIRI Q4, it was 1.19 (95% CI 1.01, 1.40, P = .04). CONCLUSIONS: These results indicate that elevated SII and SIRI levels are associated with an increased prevalence of periodontitis. CLINICAL RELEVANCE: These findings suggest a potential connection between systemic inflammation and periodontitis, highlighting the importance of periodontitis patients being aware of their systemic diseases that are inflammatory in nature such as chronic cardiovascular afflictions.

DRTerHGAT: A drug repurposing method based on the ternary heterogeneous graph attention network.

Drug repurposing is an effective method to reduce the time and cost of drug development. Computational drug repurposing can quickly screen out the most likely associations from large biological databases to achieve effective drug repurposing. However, building a comprehensive model that integrates drugs, proteins, and diseases for drug repurposing remains challenging. This study proposes a drug repurposing method based on the ternary heterogeneous graph attention network (DRTerHGAT). DRTerHGAT designs a novel protein feature extraction process consisting of a large-scale protein language model and a multi-task autoencoder, so that protein features can be extracted accurately and efficiently from amino acid sequences. The ternary heterogeneous graph of drug-protein-disease comprehensively considering the relationships among the three types of nodes, including three homogeneous and three heterogeneous relationships. Based on the graph and the extracted protein features, the deep features of the drugs and the diseases are extracted by graph convolutional networks (GCN) and heterogeneous graph node attention networks (HGNA). In the experiments, DRTerHGAT is proven superior to existing advanced methods and DRTerHGAT variants. DRTerHGAT's powerful ability for drug repurposing is also demonstrated in Alzheimer's disease.

Corrigendum: Roles of ubiquitin-specific proteases in inflammatory diseases.

[This corrects the article DOI: 10.3389/fimmu.2024.1258740.].

Causal effects of gut microbiota on autism spectrum disorder: A two-sample mendelian randomization study.

There is increasing evidence that alterations in gut microbiota (GM) composition are associated with autism spectrum disorder (ASD), but no reliable causal relationship has been established. Therefore, a 2-sample Mendelian randomization (MR) study was conducted to reveal a potential causal relationship between GM and ASD. Instrumental variables for 211 GM taxa were obtained from genome-wide association studies (GWAS) and Mendelian randomization studies to estimate their impact on ASD risk in the iPSYCH-PGC GWAS dataset (18,382 ASD cases and 27,969 controls). Inverse variance weighted (IVW) is the primary method for causality analysis, and several sensitivity analyses validate MR results. Among 211 GM taxa, IVW results confirmed that Tenericutes (P value = .0369), Mollicutes (P value = .0369), Negativicutes (P value = .0374), Bifidobacteriales (P value = .0389), Selenomonadales (P value = .0374), Bifidobacteriaceae (P value = .0389), Family XIII (P value = .0149), Prevotella7 (P value = .0215), Ruminococcaceae NK4A214 group (P value = .0205) were potential protective factors for ASD. Eisenbergiella (P value = .0159) was a possible risk factor for ASD. No evidence of heterogeneous, pleiotropic, or outlier single-nucleotide polymorphism was detected. Additionally, further sensitivity analysis verified the robustness of the above results. We confirm a potential causal relationship between certain gut microbes and ASD, providing new insights into how gut microbes mediate ASD. The association between them needs to be further explored and will provide new ideas for the prevention and treatment of ASD.

Roles of ubiquitin-specific proteases in inflammatory diseases.

Ubiquitin-specific proteases (USPs), as one of the deubiquitinating enzymes (DUBs) families, regulate the fate of proteins and signaling pathway transduction by removing ubiquitin chains from the target proteins. USPs are essential for the modulation of a variety of physiological processes, such as DNA repair, cell metabolism and differentiation, epigenetic modulations as well as protein stability. Recently, extensive research has demonstrated that USPs exert a significant impact on innate and adaptive immune reactions, metabolic syndromes, inflammatory disorders, and infection via post-translational modification processes. This review summarizes the important roles of the USPs in the onset and progression of inflammatory diseases, including periodontitis, pneumonia, atherosclerosis, inflammatory bowel disease, sepsis, hepatitis, diabetes, and obesity. Moreover, we highlight a comprehensive overview of the pathogenesis of USPs in these inflammatory diseases as well as post-translational modifications in the inflammatory responses and pave the way for future prospect of targeted therapies in these inflammatory diseases.

Association between sleep apnea-specific hypoxic burden and severity of coronary artery disease.

PURPOSE: Sleep apnea-specific hypoxic burden (SASHB) is a polysomnographic metric that comprehensively measures the degree of nocturnal desaturation caused by obstructive sleep apnea. This research was conducted to elucidate the relationship between SASHB and coronary artery disease (CAD) severity. METHODS: We carried out a prospective study of hospitalized patients with CAD of unstable angina who were expected to undergo invasive coronary angiography at Beijing Anzhen Hospital from February to September 2023. SASHB values were calculated using a self-programmed C + + program. Multivariable logistic regression analysis was applied to identify the association between SASHB and the prevalence of severe CAD, documented by the Gensini Score, and the SYNTAX (Synergy between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery) Score. RESULTS: This study enrolled 137 patients with a median age of 59 years, 96 (70.1%) of whom were male. A total of 125 (91.2%) patients had coronary stenosis of ≥ 50% in at least one location. Patients with a high SASHB of ≥ 18% min/h had a significantly higher Gensini Score (32.0 vs. 18.5, P = 0.002) and SYNTAX Score (14.0 vs. 7.0, P = 0.002) than those with a low SASHB. After adjusting for multiple covariates, a high SASHB was significantly associated with the prevalence of severe CAD, determined by a Gensini Score ≥ 21 (OR 2.67, P = 0.008) or a SYNTAX Score > 22 (OR 4.03, P = 0.016). CONCLUSION: Our findings revealed a robust and independent association between SASHB and CAD severity in patients with unstable angina, highlighting the potential value of SASHB as a predictor of risk and a target for interventions aimed at preventing cardiovascular diseases. TRIAL REGISTRATION: Chinese Clinical Trial Registry No. ChiCTR2300067991 on February 2, 2023.

Values of prognostic nutritional index for predicting Kawasaki disease: a systematic review and meta-analysis.

Objective: This systematic review and meta-analysis aimed to evaluate the relationship between the prognostic nutritional index (PNI) and intravenous immunoglobulin (IVIG) resistance and coronary artery lesion (CAL) in Kawasaki disease (KD). Methods: The relevant literature was searched on PubMed, Embase, Cochrane Library, Web of Science, and Google Scholar up to August 5, 2023. A pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under curve (AUC) were calculated to assess the predicted values of PNI in KD patients with IVIG resistance and CAL. Results: A total of 8 articles containing 10 studies involving 7,047 participants were included. The pooled results revealed a pooled sensitivity of 0.44 (0.25-0.65), a pooled specificity of 0.87 (0.73-0.94), a pooled PLR of 3.4 (2.0-5.9), a pooled NLR of 0.65 (0.48-0.87), a pooled DOR of 5.26 (2.76-10.02), and a pooled AUC of 0.75 (0.71-0.78) in the diagnosis of KD with CAL. The pooled results suggested that a pooled sensitivity was 0.69 (0.60-0.77), specificity was 0.76 (0.69-0.82), PLR was 2.9 (2.1-4.1), NLR was 0.40 (0.29-0.56), DOR was 7.27 (3.89-13.59), and AUC was 0.79 (0.75-0.82) in the diagnosis of KD with IVIG resistance. The combined results revealed the pooled sensitivity was 0.63 (0.58-0.67), specificity was 0.82 (0.80-0.83), PLR was 3.09 (1.06-8.98), NLR was 0.38 (0.07-2.02), DOR was 8.23 (0.81-83.16) in differentiating KD from febrile patients. These findings demonstrated low sensitivity and relatively high specificity of PNI for KD, KD-CAL, and IVIG-resistant KD. Conclusion: In conclusion, this study was the first systematic review and meta-analysis of the diagnostic value of PNI in KD with IVIG resistance and CAL. The results suggested that PNI could be used as biomarkers for distinguish KD, KD with CAL, and KD with IVIG resistance.

Osteosarcoma neutrophil extracellular trap network-associated gene recurrence and metastasis model.

Osteosarcoma (OS) is the most common malignancy in children and adolescents and has a high probability of recurrence and metastasis. A growing number of studies have shown that neutrophil extracellular traps (NETs) are strongly associated with cancer metastasis, but in osteosarcoma, genes associated with NETs that promote osteosarcoma recurrence and metastasis remain to be explored. We systematically investigated the gene expression patterns of NETs in OS samples from the GEO database. NETs molecular typing was evaluated based on NETs expression profiles, and the association between NETs molecular subtypes and immune microenvironment and metastatic features were explored. Ultimately, we constructed a signature model and column line graph associated with metastasis prediction and screened possible potential drugs for metastatic osteosarcoma. We established two different molecular subtypes of NETs, which showed significant differences in metastatic status, metastasis time, tumor immune microenvironment, and biological effects. We also constructed a NETs-related gene metastasis signature(NRGMS) to assess the expression pattern of NETs in patients to predict metastatic recurrence in osteosarcoma patients. We screened for TOMM40 and FH associated with metastatic recurrence in osteosarcoma patients. Overall, this study constructs a predictive model for osteosarcoma metastasis of NETs-related genes, which is expected to provide new insights into the metastasis of osteosarcoma.

Penicimides A and B, two novel diels-alder [4 + 2] cycloaddition ergosteroids from Penicillium herquei.

Two novel naturally occurring [4 + 2] Diels-Alder cycloaddition ergosteroids (1 and 2), three undescribed oxidized ergosteroids (3-5), and eleven known analogs (6-16) were isolated from Penicillium herquei. Compounds 1 and 2 represent the first reported cycloadducts of a steroid with 1,4,6-trimethyl-1,6-dihydropyridine-2,5-dione or 4,6-dimethyl-1,6-dihydropyridine-2,5-dione to date. Compound 3 is the C-15 epimer of (22E,24R)-9α,11ß-dihydroxyergosta-4,6,8(14),22-tetraen-3-one (14). The chemical structures of these compounds were elucidated through widespread spectroscopic analyses, mainly including HRESIMS and 1D and 2D NMR data, calculated 13C NMR-DP4+ analysis, and electronic circular dichroism (ECD) data analyses. Biological evaluations of Compounds 1-16 revealed that 3, 9-11, and 15 inhibited the production of NO in LPS-induced RAW264.7 cells with an IC50 value from 7.37 ± 0.69 to 38.9 ± 2.25 µM (the positive control dexamethasone IC50: 9.54 ± 0.71 µM). In addition, Compound 3 exhibited a potent inhibitory effect on the secretion of the proinflammatory cytokines TNF-α and IL-6, the transcription level of the proinflammatory macrophage markers TNF-α, and the expression of the iNOS protein.

MSFF-MA-DDI: Multi-Source Feature Fusion with Multiple Attention blocks for predicting Drug-Drug Interaction events.

The interaction of multiple drugs could lead to severe events, which cause medical injuries and expenses. Accurate prediction of drug-drug interaction (DDI) events can help clinicians make effective decisions and establish appropriate therapy programs. However, there exist two issues worthy of further consideration. (i) The global features of drug molecules should be paid attention to, rather than just their local characteristics. (ii) The fusion of multi-source features should also be studied to capture the comprehensive features of the drug. This study designs a Multi-Source Feature Fusion framework with Multiple Attention blocks named MSFF-MA-DDI that utilizes multimodal data for DDI event prediction. MSFF-MA-DDI can (i) encode global correlations between long-distance atoms in drug molecular sequences by a self-attention layer based on a position embedding block and (ii) fuse drug sequence features and heterogeneous features (chemical substructure, target, and enzyme) through a multi-head attention block to better represent the features of drugs. Experiments on real-world datasets show that MSFF-MA-DDI can achieve performance that is close to or even better than state-of-the-art models. Especially in cold start scenarios, the model can achieve the best performance. The effectiveness of the model is also supported by the case study on nervous system drugs. The source codes and data are available at https://github.com/BioCenter-SHU/MSFF-MA-DDI.