The clinical scenario, which is based on systolic blood pressure (SBP) upon admission, is useful for classifying and determining initial treatment for acute heart failure (HF). However, the ...prognostic significance of SBP following the initial treatment is unclear.The Japanese Heart Failure Syndrome with Preserved Ejection Fraction (JASPER) registry is a nationwide, observational, and prospective registration of consecutive Japanese patients hospitalized with HF with preserved ejection fraction (HFpEF) and left ventricular ejection fraction ≥ 50%. We divided 525 patients into three groups based on their SBP on the day following hospitalization: high (SBP > 140 mmHg, n = 72, 13.7%); normal (100 ≤ SBP ≤ 140 mmHg, n = 379, 72.2%); and low (SBP < 100 mmHg, n = 74, 14.1%) groups. This analysis had two primary endpoints: (1) all-cause death and (2) all-cause death or rehospitalization for HF. In the Kaplan-Meier analysis, both of the endpoints were the highest in the low group (Log-Rank < 0.05, respectively). Compared to the normal and high groups, the low group demonstrated a higher prevalence of atrial fibrillation (67.1%, 63.9%, and 47.8%, P = 0.026) and the lowest left ventricular outflow tract velocity time integral determined by echocardiography (16.4 cm, 19.4 cm, and 23.3 cm, P = 0.001). In the multivariable Cox proportional hazard analysis, low SBP on the day following hospitalization was an independent predictor of all-cause death (hazard ratio 1.868, 95% confidence interval 1.024-3.407, P = 0.042) and the composite endpoint (hazard ratio 1.660, 95% confidence interval 1.103-2.500, P = 0.015).Classification based on SBP on the day following initial treatment predicts post-discharge prognosis in hospitalized patients with HFpEF.
Neuroblastoma (NB) is one of the primary causes of death for pediatric malignancies. Given the high heterogeneity in NB's mutation landscape, optimizing individualized therapies is still challenging. ...In the context of genomic alterations, MYCN amplification is the most correlated event with poor outcomes. MYCN is involved in the regulation of several cellular mechanisms, including cell cycle. Thus, studying the influence of MYCN overexpression in the G1/S transition checkpoint of the cell cycle may unveil novel druggable targets for the development of personalized therapeutical approaches. Here, we show that high expression of E2F3 and MYCN correlate with poor prognosis in NB despite the RB1 mRNA levels. Moreover, we demonstrate through luciferase reporter assays that MYCN bypasses RB function by incrementing E2F3-responsive promoter activity. We showed that MYCN overexpression leads to RB inactivation by inducing RB hyperphosphorylation during the G1 phase through cell cycle synchronization experiments. Moreover, we generated two MYCN-amplified NB cell lines conditionally knockdown (cKD) for the RB1 gene through a CRISPRi approach. Indeed, RB KD did not affect cell proliferation, whereas cell proliferation was strongly influenced when a non-phosphorylatable RB mutant was expressed. This finding revealed the dispensable role of RB in regulating MYCN-amplified NB's cell cycle. The described genetic interaction between MYCN and RB1 provides the rationale for using cyclin/CDK complexes inhibitors in NBs carrying MYCN amplification and relatively high levels of RB1 expression.
Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment process is long and very costly due to its high recurrence and mortality rates. Accurate early diagnosis and ...prognosis prediction of cancer are essential to enhance the patient's survival rate. Developments in statistics and computer engineering over the years have encouraged many scientists to apply computational methods such as multivariate statistical analysis to analyze the prognosis of the disease, and the accuracy of such analyses is significantly higher than that of empirical predictions. Furthermore, as artificial intelligence (AI), especially machine learning and deep learning, has found popular applications in clinical cancer research in recent years, cancer prediction performance has reached new heights. This article reviews the literature on the application of AI to cancer diagnosis and prognosis, and summarizes its advantages. We explore how AI assists cancer diagnosis and prognosis, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. We also demonstrate ways in which these methods are advancing the field. Finally, opportunities and challenges in the clinical implementation of AI are discussed. Hence, this article provides a new perspective on how AI technology can help improve cancer diagnosis and prognosis, and continue improving human health in the future.
•Artificial intelligence (AI) has reached new heights in clinical cancer research in recent years.•AI is applied to assist cancer diagnosis and prognosis, given its unprecedented accuracy level, which is even higher than that of general statistical expert.•An overview of how AI applied in clinical cancer could be leveraged in this area and thereby contribute to improved human health.
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