Calmodulin Triggers Activity-Dependent rRNA Biogenesis via Interaction with DDX21.

Protein synthesis in response to neuronal activity, known as activity-dependent translation, is critical for synaptic plasticity and memory formation. However, the signaling cascades that couple neuronal activity to the translational events remain elusive. In this study, we identified the role of calmodulin (CaM), a conserved Ca2+-binding protein, in ribosomal RNA (rRNA) biogenesis in neurons. We found the CaM-regulated rRNA synthesis is Ca2+-dependent and necessary for nascent protein synthesis and axon growth in hippocampal neurons. Mechanistically, CaM interacts with nucleolar DEAD (Asp-Glu-Ala-Asp) box RNA helicase (DDX21) in a Ca2+-dependent manner to regulate nascent rRNA transcription within nucleoli. We further found CaM alters the conformation of DDX21 to liberate the DDX21-sequestered RPA194, the catalytic subunit of RNA polymerase I, to facilitate transcription of ribosomal DNA. Using high-throughput screening, we identified the small molecules batefenterol and indacaterol that attenuate the CaM-DDX21 interaction and suppress nascent rRNA synthesis and axon growth in hippocampal neurons. These results unveiled the previously unrecognized role of CaM as a messenger to link the activity-induced Ca2+ influx to the nucleolar events essential for protein synthesis. We thus identified the ability of CaM to transmit information to the nucleoli of neurons in response to stimulation.

High-throughput precision MRI assessment with integrated stack-ensemble deep learning can enhance the preoperative prediction of prostate cancer Gleason grade.

BACKGROUND: To develop and test a Prostate Imaging Stratification Risk (PRISK) tool for precisely assessing the International Society of Urological Pathology Gleason grade (ISUP-GG) of prostate cancer (PCa). METHODS: This study included 1442 patients with prostate biopsy from two centres (training, n = 672; internal test, n = 231 and external test, n = 539). PRISK is designed to classify ISUP-GG 0 (benign), ISUP-GG 1, ISUP-GG 2, ISUP-GG 3 and ISUP GG 4/5. Clinical indicators and high-throughput MRI features of PCa were integrated and modelled with hybrid stacked-ensemble learning algorithms. RESULTS: PRISK achieved a macro area-under-curve of 0.783, 0.798 and 0.762 for the classification of ISUP-GGs in training, internal and external test data. Permitting error ±1 in grading ISUP-GGs, the overall accuracy of PRISK is nearly comparable to invasive biopsy (train: 85.1% vs 88.7%; internal test: 85.1% vs 90.4%; external test: 90.4% vs 94.2%). PSA ≥ 20 ng/ml (odds ratio [OR], 1.58; p = 0.001) and PRISK ≥ GG 3 (OR, 1.45; p = 0.005) were two independent predictors of biochemical recurrence (BCR)-free survival, with a C-index of 0.76 (95% CI, 0.73-0.79) for BCR-free survival prediction. CONCLUSIONS: PRISK might offer a potential alternative to non-invasively assess ISUP-GG of PCa.

Artificial intelligence performance in image-based ovarian cancer identification: A systematic review and meta-analysis.

Background: Accurate identification of ovarian cancer (OC) is of paramount importance in clinical treatment success. Artificial intelligence (AI) is a potentially reliable assistant for the medical imaging recognition. We systematically review articles on the diagnostic performance of AI in OC from medical imaging for the first time. Methods: The Medline, Embase, IEEE, PubMed, Web of Science, and the Cochrane library databases were searched for related studies published until August 1, 2022. Inclusion criteria were studies that developed or used AI algorithms in the diagnosis of OC from medical images. The binary diagnostic accuracy data were extracted to derive the outcomes of interest: sensitivity (SE), specificity (SP), and Area Under the Curve (AUC). The study was registered with the PROSPERO, CRD42022324611. Findings: Thirty-four eligible studies were identified, of which twenty-eight studies were included in the meta-analysis with a pooled SE of 88% (95%CI: 85-90%), SP of 85% (82-88%), and AUC of 0.93 (0.91-0.95). Analysis for different algorithms revealed a pooled SE of 89% (85-92%) and SP of 88% (82-92%) for machine learning; and a pooled SE of 88% (84-91%) and SP of 84% (80-87%) for deep learning. Acceptable diagnostic performance was demonstrated in subgroup analyses stratified by imaging modalities (Ultrasound, Magnetic Resonance Imaging, or Computed Tomography), sample size (≤300 or >300), AI algorithms versus clinicians, year of publication (before or after 2020), geographical distribution (Asia or non Asia), and the different risk of bias levels (≥3 domain low risk or < 3 domain low risk). Interpretation: AI algorithms exhibited favorable performance for the diagnosis of OC through medical imaging. More rigorous reporting standards that address specific challenges of AI research could improve future studies. Funding: This work was supported by the Natural Science Foundation of China (No. 82073647 to Q-JW and No. 82103914 to T-TG), LiaoNing Revitalization Talents Program (No. XLYC1907102 to Q-JW), and 345 Talent Project of Shengjing Hospital of China Medical University (No. M0268 to Q-JW and No. M0952 to T-TG).

PET-CT radiomics by integrating primary tumor and peritumoral areas predicts E-cadherin expression and correlates with pelvic lymph node metastasis in early-stage cervical cancer.

OBJECTIVES: To explore the role of radiomics in integrating primary tumor and peritumoral areas based on PET-CT scans for predicting E-cadherin (E-cad) expression in early-stage cervical cancer (ESCC) and its correlation with pelvic lymph node metastasis (PLNM). METHODS: Ninety-seven ESCC patients who had undergone PET-CT scans were retrospectively analyzed. The ROI of primary tumors, peritumoral areas, and plus tumors were semi-automatically segmented on PET-CT images. A total of 1188 radiomics features were extracted, selected, and eventually integrated into radiomics score (rad-score). The rad-score difference between patients with E-cad expression of high and low was analyzed using Mann-Whitney tests. Characteristic correlation was tested using a Spearman analysis. Four models were established using logistic regression algorithms and evaluated using ROC and calibration curves. A DeLong test was used to perform pairwise comparisons of AUCs. RESULTS: The rad-score of patients with low E-cad expression was higher than that of patients with high E-cad expression in both training and testing cohorts (p < 0.001 and p = 0.027, respectively). A significant correlation was observed between the rad-score and E-cad (p < 0.001). PLNM correlated slightly with rad-score and E-cad values (p = 0.01 and p < 0.001, respectively). The ROC curve and calibration curve of the rad-score model performed best in both training and testing cohorts (AUC = 0.915, 0.844, p < 0.001, respectively). CONCLUSIONS: The radiomics of integrating primary tumor and peritumoral areas based on PET-CT showed correlations with PLNM. It was also able to predict E-cad expression in ESCC patients, allowing for evaluation of those patients' prognosis and more individualized medical treatment. KEY POINTS: • By integrating the primary tumor and peritumoral area based on PET-CT, radiomics was feasible. • The rad-score was associated with E-cad expression and PLNM in patients with ESCC. • Radiomics that integrated the primary tumor and peritumoral areas based on PET-CT could predict E-cad expression in patients with ESCC.

Hybrid PET/MRI-based delineation of gross tumor volume in head and neck cancer and tumor parameter analysis.

OBJECTIVES: Accurate target delineation allows an increase in radiation dose to the target tumor while reducing damage to the surrounding normal tissue. However, there is currently no standard for evaluating volumes measured by different imaging modalities. The aim of this study is to evaluate the feasibility of contouring gross tumor volume (GTV) by PET/MRI in head and neck cancer, and to define an adaptive threshold level (aTL) for delineating the biological target volume (BTV). PATIENTS AND METHODS: Eighteen head and neck cancer patients underwent time of flight PET/MRI before chemoradiotherapy. Different GTVs of primary tumors and metastatic lymph nodes were manually contoured on MRI (GTVMRI), PET (GTVVIS), and fused PET/MRI (GTVFUS). An MRI-based GTV contour was substituted for the pathologic GTV. The percentile threshold boundary of the maximum standardized uptake value (SUVmax) for the BTV was determined when the volume of BTV approached that of GTVMRI. RESULTS: All GTVs were highly correlated (all Pearson's r>0.85, all P<0.001). Tumor diameter strongly correlated with GTVs (r=0.7-0.8 for all lesions and primary tumor; r=0.8-0.9 for lymph node metastases). aTL and SUVmax were moderately correlated for all lesions (r=-0.692, P<0.001) and were strongly correlated for primary tumors (r=-0.866, P<0.001). CONCLUSION: Delineating GTV on hybrid PET/MRIs is feasible, and aTL, the threshold boundary of BTV, was correlated inversely with the SUVmax.

[Progress in the study of the chemical synapse in glioma-associated epileptogenesis].

Seizures may be the first or sometimes the only manifestation of patients with glioma in clinics. The aim of operation is to eliminate epilepsy far beyond mere resection of tumor mass. The underlying mechanisms of glioma-associated epileptogenesis are poorly understood. Recently the theory of amino-acid like neurotransmitters in chemical synapse is gradually accepted. However, the molecular mechanisms remain to be further investigated on how glutamate release is regulated and how synaptic homeostasis in peripheral neurons is kept or disturbed. So detailed studies are needed to clarify specific molecular target and provide proper evidence for optimal antiepileptic drugs in glioma-associated epileptogenesis.