Multimodal data integration for predicting progression risk in castration-resistant prostate cancer using deep learning: a multicenter retrospective study.

Purpose: Patients with advanced prostate cancer (PCa) often develop castration-resistant PCa (CRPC) with poor prognosis. Prognostic information obtained from multiparametric magnetic resonance imaging (mpMRI) and histopathology specimens can be effectively utilized through artificial intelligence (AI) techniques. The objective of this study is to construct an AI-based CRPC progress prediction model by integrating multimodal data. Methods and materials: Data from 399 patients diagnosed with PCa at three medical centers between January 2018 and January 2021 were collected retrospectively. We delineated regions of interest (ROIs) from 3 MRI sequences viz, T2WI, DWI, and ADC and utilized a cropping tool to extract the largest section of each ROI. We selected representative pathological hematoxylin and eosin (H&E) slides for deep-learning model training. A joint combined model nomogram was constructed. ROC curves and calibration curves were plotted to assess the predictive performance and goodness of fit of the model. We generated decision curve analysis (DCA) curves and Kaplan-Meier (KM) survival curves to evaluate the clinical net benefit of the model and its association with progression-free survival (PFS). Results: The AUC of the machine learning (ML) model was 0.755. The best deep learning (DL) model for radiomics and pathomics was the ResNet-50 model, with an AUC of 0.768 and 0.752, respectively. The nomogram graph showed that DL model contributed the most, and the AUC for the combined model was 0.86. The calibration curves and DCA indicate that the combined model had a good calibration ability and net clinical benefit. The KM curve indicated that the model integrating multimodal data can guide patient prognosis and management strategies. Conclusion: The integration of multimodal data effectively improves the prediction of risk for the progression of PCa to CRPC.

Metformin improves nonalcoholic fatty liver disease in db/db mice by inhibiting ferroptosis.

Nonalcoholic fatty liver disease (NAFLD) is the primary complication of type 2 diabetes (T2DM)-related liver disease, lacking effective treatment options. Metformin (Met), a widely prescribed anti-hyperglycemic medication, has been found to protect against NAFLD. Ferroptosis, a newly discovered form of cell death, is associated with the development of NAFLD. Despite this association, the extent of Met's protective effects on NAFLD through the modulation of ferroptosis has yet to be thoroughly investigated. In the present study, the administration of erastin or Ras-selective lethal 3 (RSL3), both known ferroptosis inducers, resulted in elevated cell mortality and reduced cell viability in AML12 hepatocytes. Notably, Met treatment demonstrated the capacity to mitigate these effects. Furthermore, we observed increased ferroptosis levels in both AML12 hepatocytes treated with palmitate and oleate (PA/OA) and in the liver tissue of db/db mice. Met treatment demonstrated significant reductions in iron accumulation and lipid-related reactive oxygen species production, simultaneously elevating the glutathione/glutathione disulfide ratio in both PA/OA-treated AML12 hepatocytes and the liver tissue of db/db mice. Interestingly, the anti-ferroptosis effects of Met were significantly reversed with the administration of RSL3, both in vitro and in vivo. Mechanistically, Met treatment regulated the glutathione peroxidase 4/solute carrier family 7 member 11/acyl-CoA synthetase long-chain family member 4 axis to alleviate ferroptosis in NAFLD hepatocytes. Overall, our findings highlight the crucial role of ferroptosis in the development of T2DM-related NAFLD and underscore the potential of Met in modulating key factors associated with ferroptosis in the context of NAFLD.

Effects of microbial agent and microbial fertilizer input on soil microbial community structure and diversity in a peanut continuous cropping system.

INTRODUCTION: The soil harbors a diverse array of microorganisms, and these are essential components of terrestrial ecosystems. The presence of microorganisms in the soil, particularly in the rhizosphere, is closely linked to plant growth and soil fertility. OBJECTIVE: The primary objective of this study is to assess the potential advantages of integrating microbial inoculants with compound fertilizer in enhancing peanut yield. METHODS: We utilized Illumina MiSeq high-throughput sequencing technology to conduct our investigation. The experimental design consists of four treatment groups: compound fertilizers (CF), compound fertilizers supplemented with microbial agents (CF + MA), compound fertilizers supplemented with microbial fertilizers (CF + MF), and compound fertilizers supplemented with both microbial agents and microbial fertilizers (CF + MM). RESULTS: The experimental results demonstrated a significant increase in peanut yield upon application of CF + MA, CF + MF, and CF + MM treatments. During the blossom stage and pod-setting stage, the soil's catalase, urease, and acid phosphatase activities were significantly increased in the CF + MA, and CF + MM treatments compared to the CF treatment. The application of CF + MA resulted in an increase in bacterial richness in the rhizosphere soil of peanuts, as indicated by the sequencing results. The application of CF + MA, CF + MF, and CF + MM resulted in a reduction of fungal diversity. Proteobacteria, Actinobacteria, and Acidobacteria were the dominant bacterial phyla, while Ascomycota and Basidiomycota were the dominant phyla in the fungal component of the rhizosphere soil microbiome across all experimental treatments. CONCLUSION: Microbial agents and fertilizers modify the peanut rhizosphere soil's microbial community structure, as per our findings. The abundance of potentially beneficial bacteria (Bradyrhizobium, Rhizobium, and Burkholderia) and fungi (Trichoderma and Cladophialophora) could increase, while pathogenic fungi (Penicillium and Fusarium) decreased, thereby significantly promoting plant growth and yield of peanut.

Whole genome analysis of Bacillus amyloliquefaciens TA-1, a promising biocontrol agent against Cercospora arachidicola pathogen of early leaf spot in Arachis hypogaea L.

BACKGROUND: Early leaf spot disease, caused by Cercospora arachidicola, is a devastating peanut disease that has severely impacted peanut production and quality. Chemical fungicides pollute the environment; however, Bacillus bacteria can be used as an environmentally friendly alternative to chemical fungicides. To understand the novel bacterial strain and unravel its molecular mechanism, De novo whole-genome sequencing emerges as a rapid and efficient omics approach. RESULTS: In the current study, we identified an antagonistic strain, Bacillus amyloliquefaciens TA-1. In-vitro assay showed that the TA-1 strain was a strong antagonist against C. arachidicola, with an inhibition zone of 88.9 mm. In a greenhouse assay, results showed that the TA-1 strain had a significant biocontrol effect of 95% on peanut early leaf spot disease. De novo whole-genome sequencing analysis, shows that strain TA-1 has a single circular chromosome with 4172 protein-coding genes and a 45.91% guanine and cytosine (GC) content. Gene function was annotated using non-redundant proteins from the National Center for Biotechnology Information (NCBI), Swiss-Prot, the Kyoto Encyclopedia of Genes and Genomes (KEGG), clusters of orthologous groups of proteins, gene ontology, pathogen-host interactions, and carbohydrate-active enZYmes. antiSMASH analysis predicted that strain TA-1 can produce the secondary metabolites siderophore, tailcyclized peptide, myxochelin, bacillibactin, paenibactin, myxochelin, griseobactin, benarthin, tailcyclized, and samylocyclicin. CONCLUSION: The strain TA-1 had a significant biological control effect against peanut early leaf spot disease in-vitro and in greenhouse assays. Whole genome analysis revealed that, TA-1 strain belongs to B. amyloliquefaciens and could produce the antifungal secondary metabolites.

Adipocyte-derived extracellular vesicles: bridging the communications between obesity and tumor microenvironment.

By the year 2035 more than 4 billion people might be affected by obesity and being overweight. Adipocyte-derived Extracellular Vesicles (ADEVs/ADEV-singular) are essential for communication between the tumor microenvironment (TME) and obesity, emerging as a prominent mechanism of tumor progression. Adipose tissue (AT) becomes hypertrophic and hyperplastic in an obese state resulting in insulin resistance in the body. This modifies the energy supply to tumor cells and simultaneously stimulates the production of pro-inflammatory adipokines. In addition, obese AT has a dysregulated cargo content of discharged ADEVs, leading to elevated amounts of pro-inflammatory proteins, fatty acids, and carcinogenic microRNAs. ADEVs are strongly associated with hallmarks of cancer (proliferation and resistance to cell death, angiogenesis, invasion, metastasis, immunological response) and may be useful as biomarkers and antitumor therapy strategy. Given the present developments in obesity and cancer-related research, we conclude by outlining significant challenges and significant advances that must be addressed expeditiously to promote ADEVs research and clinical applications.

Bowl Inversion in an Exo-type Supramolecule in the Solid State.

Bowl inversion is a unique property of buckybowls. The polarity and assembly configuration of buckybowls are reversed after bowl inversion. So far, this unique phenomenon has been studied in solution and on surface, but not in solid state due to spatial constraint. Now a series of exo-type supramolecular assemblies of trithiasumanene and nanographene are investigated. Tuning the electron density of the nanogaphene component was found to directly affect the binding constant of the complex. Reversible bowl inversion in the solid state was then successfully achieved by subjecting the trithiasumanene-nanographene assembly with the weakest binding strength to repeated heating-cooling cycles, which was unambiguously observed by single crystal X-ray diffraction.