At present, no single indicator could be used as a golden index to estimate aging process. The biological age (BA), which combines several important biomarkers with mathematical modeling, has been proposed for >50 years as an aging estimation method to replace chronological age (CA). The common methods used for BA estimation include the multiple linear regression (MLR), the principal component analysis (PCA), the Hochschild’s method, and the Klemera and Doubal’s method (KDM). The fundamental differences in these four methods are the roles of CA and the selection criteria of aging biomarkers. In MLR and PCA, CA is treated as the selection criterion and an independent index. The Hochschild’s method and KDM share a similar concept, making CA an independent variable. Previous studies have either simply constructed the BA model by one or compared the four methods together. However, reviews have yet to illustrate and compare the four methods systematically. Since the BA model is a potential estimation of aging for clinical use, such as predicting onset and prognosis of diseases, improving the elderly’s living qualities, and realizing successful aging, here we summarize previous BA studies, illustrate the basic statistical steps, and thoroughly discuss the comparisons among the four common BA estimation methods.

The burgeoning accumulation of large-scale biomedical data in oncology, alongside significant strides in deep learning (DL) technologies, has established multimodal DL (MDL) as a cornerstone of precision oncology. This review provides an overview of MDL applications in this field, based on an extensive literature survey. In total, 651 articles published before September 2024 are included. We first outline publicly available multimodal datasets that support cancer research. Then, we discuss key DL training methods, data representation techniques, and fusion strategies for integrating multimodal data. The review also examines MDL applications in tumor segmentation, detection, diagnosis, prognosis, treatment selection, and therapy response monitoring. Finally, we critically assess the limitations of current approaches and propose directions for future research. By synthesizing current progress and identifying challenges, this review aims to guide future efforts in leveraging MDL to advance precision oncology.

Vascular calcification (VC) is an intricate active process, significantly controlled by vascular smooth muscle cells (VSMCs). Mitochondrial dysfunction plays a pivotal role in VC and VSMCs osteoblastic transformation. We previously reported that decreased levels of Irisin were independently associated with VC in hemodialysis patients. The present study aimed to investigate the role of Irisin in VC, especially in VSMCs osteoblastic transformation and mitochondrial function. In vitro, VSMCs calcification was induced by β-glycerophosphate, while in vivo VC was triggered by adenine and high phosphorus diet. Alizarin red, Von Kossa staining, and calcium and Alp activity were performed to test VC. Western blot and immunohistochemical staining were employed to analyze the expression of proteins associated with VSMCs osteoblastic transformation and AMPK signaling. Mitochondrial membrane potential (MMP) and structures were observed by immunofluorescence staining. Irisin alleviated VSMCs calcification induced by β-glycerophosphate. Mechanistically, Irisin activated AMPK and downregulated the expression of Drp1, further alleviating mitochondria fission and VSMCs osteoblastic transformation. In vivo, Irisin decreased serum creatinine, urea and phosphorous levels in chronic kidney disease (CKD) mice. Importantly, Irisin treatment postponed CKD-associated VC with the upregulation of α-Sma and p-AMPK expression, and the downregulation of Runx2 and Drp1 expression. Our results firstly reveal that Irisin inhibits CKD-associated VC. Irisin suppresses VSMCs osteoblastic transformation and mitochondria dysfunction via AMPK/Drp1 signaling.

Background/Aims: Dysbiosis of the intestinal microbiota may accelerate the progression of chronic kidney disease (CKD) by increasing the levels of urea toxins. In recent years, probiotics have been recognized to maintain the physiological balance of the intestinal microbiota. In this study, we aim to assess the therapeutic effects of probiotics on CKD patients with and without dialysis via meta-analysis. Methods: We conducted a meta-analysis of randomized controlled trials (RCTs) by searching the databases of Pubmed, EMBASE and Cochrane Library (No. CRD42018093080). Studies on probiotics for treatment of CKD adults lasting for at least 4 weeks were selected. The primary outcomes were the levels of urea toxins, and the second outcomes were the levels of interleukin (IL)-6, C-reactive protein (CRP) and hemoglobin (Hb). The risk of bias was assessed by Cochrane Collaboration’ tool, and the quality of evidence was appraised with the Grading of Recommendation Assessment. Means and standard deviations were analyzed by random effects analysis. Stratified analysis was done and sensitivity analysis was performed when appropriate. Results: Totally, eight studies with 261 patients at CKD stage 3 to 5 with and without dialysis were included. We found a decrease of p-cresyl sulfate (PCS) of 3 studies with 125 subjects (P = 0.01, SMD -0.57, 95% CI, -0.99 to -0.14, I² = 25%) and an increase of IL-6 in 3 studies with 134 subjects (P = 0.03, 95% CI, SMD 0.37, 0.03 to 0.72, I² = 0%) in the probiotics groups. Analysis of serum creatinine (P = 0.47), blood urine nitrogen (P = 0.73), CRP (P = 0.55) and Hb (P = 0.49) yielded insignificant difference. Conclusion: Limited number of studies and small sample size are limitations of our study. Probiotics supplementation may reduce the levels of PCS and elevate the levels of IL-6 whereby protecting the intestinal epithelial barrier of patients with CKD.

Acute kidney injury (AKI) is a serious complication in the intensive care unit (ICU), which may increase the mortality of critically ill patients. The red blood cell distribution width (RDW) has proved useful as a predictor of short-term prognosis in critically ill patients with AKI. However, it remains unknown whether RDW has a prognostic value of long-term all-cause mortality in these patients. The data of 18279 critically ill patients with AKI at first-time hospital admission were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The tertiles of the RDW values were used to divide subjects into three groups, namely RDW < 13.6% for the low RDW group, 13.6% ≤ RDW < 15.2% for the middle RDW group and RDW ≥ 15.2% for the high RDW group. Demographic data, mortality, 4-year survival time and severity scale scores were compared among groups. The Kaplan-Meier analysis and the Cox regression analysis were performed to assess the impact of RDW on all-cause mortality in AKI patients. The receiver operating characteristic (ROC) curve analysis was done to evaluate the prognostic value of RDW on the long-term outcome of critically ill patients with AKI. The median age of the enrolled subjects was 65.6 years. AKI patients with a higher RDW value had significantly shorter survival time and higher death rate. By the Kaplan-Meier analysis, patients in the higher RDW group presented significantly shorter survival time and higher death rate. The Cox regression model indicated RDW as an independent risk factor of all-cause mortality of AKI patients (HR 1.219, 95% CI, 1.211 to 1.228). By the ROC analysis, RDW appeared more efficient in predicting long-term prognosis as compared with conventional severity scales. The AUC of RDW (95% CI, 0.712 to 0.725) was significantly higher than other severity scale scores. In conclusion, RDW is positively correlated to survival time of 4-year follow-up in critically ill patients with AKI, and RDW is an independent prognostic factor of long-term outcomes of these patients.

Background/Aims: Accumulating evidence has highlighted the importance of long non-coding RNAs (lncRNAs) as competing endogenous RNAs (ceRNAs) in tumor biology. Among others, actin filament-associated protein 1 antisense RNA 1 (AFAP1-AS1) has been associated with non-small cell lung cancer (NSCLC). However, it remains unclear how AFAP1-AS1 participates in the development and progression of NSCLC. Methods: The peripheral blood samples were collected from patients with NSCLC. White blood cell subsets were classified and levels of interleukin (IL)-10, IL-12 and IFN-γ in serum were measured. We then identified its target gene of AFAP1-AS1 via bioinformatics methods. NSCLC cell line with the highest expression of AFAP1-AS1, i.e. H1975 was selected for in vitro experiments. A series of inhibitor, vector and siRNA were employed to validate the regulatory mechanisms of AFAP1-AS1 in the development and progression of NSCLC. Cell proliferation was detected by MTT assay and EdU staining. Cell migration and invasion, and cell cycle and apoptosis were measured by transwell assay and flow cytometry, respectively. Results: A high expression of AFAP1-AS1 was identified in NSCLC, alongside with a reduced level of IL-12 and increased levels of IL-10 and interferon (IFN)-γ. Aberrant expressions of AFAP1-AS1 were associated with pathological grade, TNM staging and metastatic potential of NSCLC. AFAP1-AS1 could activate interferon regulatory factor (IRF)7, the retinoid-inducible protein (RIG)-I-like receptor signaling pathway and Bcl-2 in vitro. Over-expression of AFAP1-AS1 promoted NSCLC cell proliferation, invasion and migration while inhibiting cell apoptosis. Conclusion: lncRNA AFAP1-AS1 promotes migration and invasion of non-small cell lung cancer via up-regulating IRF7 and the RIG-I-like receptor signaling pathway.