Scientific Reports - Serum microRNA-24-based nomogram predicts prognosis for patients with resected pancreatic cancer

Purpose: The challenges of language assessment in bilinguals include a lack of assessment tools and bilingual speech-language pathology services....

A nomogram based on the relationship between plasma renin activity and urinary sodium excretion in normal subjects has been used to classify 956 patients with essential hypertension into low, medium a...

Ischemic stroke is a cerebrovascular disease with high mortality and disability. Due to similar physiological characteristics, ocular vascular characteristics are important indicators for monitoring cerebrovascular diseases. This study aimed to develop a…

Background Surgical site infection (SSI) represents a significant postoperative complication in colorectal cancer (CRC). Identifying associated factors is therefore critical. We evaluated the predictive value of clinicopathological features and inflammation-based prognostic scores (IBPSs) for SSI occurrence in CRC patients. Methods We retrospectively analyzed data from 1445 CRC patients who underwent resection surgery at Wuhan Union Hospital between January 2015 and December 2018. We applied two algorithms, least absolute shrinkage and selector operation (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), to identify key predictors. Participants were randomly divided into training (n = 1043) and validation (n = 402) cohorts. A nomogram was constructed to estimate SSI risk, and its performance was assessed by calibration, discrimination, and clinical utility. Results Combining the 30 clinicopathological features identified by LASSO and SVM-RFE, we pinpointed seven variables as optimal predictors for a pathology-based nomogram: obstruction, dNLR, ALB, HGB, ALT, CA199, and CA125. The model demonstrated strong calibration and discrimination, with an area under the curve (AUC) of 0.838 (95% CI 0.799–0.876) in the training cohort and 0.793 (95% CI 0.732–0.865) in the validation cohort. Decision curve analysis (DCA) showed that our models provided greater predictive benefit than individual clinical markers. Conclusion The model based on simplified clinicopathological features in combination with IBPSs is useful in predicting SSI for CRC patients.

[This corrects the article DOI: 10.3389/fped.2026.1777432.].

The results of this study indicate that NLR and PLR are significantly associated with the onset of IgAV in children. The combined nomogram model based on these two indicators serves only as a preliminary exploratory tool for a single center. It relies solely on routine complete blood counts for quan …

BackgroundThe subacute phase is a key period for stroke recovery, yet there is a lack of simple and effective indicators to predict rehabilitation outcomes. ...

Background This study aims to construct a nutrition-based nomogram for predicting the risk of intra-abdominal infection (IAI) after D2 radical gastrectomy for gastric cancer (GC). Methods We retrospectively analyzed the clinical data of 404 individuals who received D2 radical gastrectomy for GC. Four preoperative nutrition-related indicators, the nutritional risk screening (NRS) 2002 score, albumin (ALB), prognostic nutritional index (PNI), and controlling nutritional status (CONUT) score, were collected and calculated. Multivariate logistic regression analysis was utilized to screen the independent risk factors for IAI following D2 radical gastrectomy for GC. The area under the receiver operating characteristics (ROC) curve (AUROC) was computed. A nomogram was established to forecast postoperative IAI using the independent risk factors. Results The NRS2002 score, ALB, PNI, CONUT score, fasting blood glucose (FBG), American Society of Anesthesiologists (ASA) score, type of resection, multi-visceral resection, perioperative blood transfusion, and the tumor, node, metastasis (TNM) stage were significantly associated with postoperative IAI. Considering the collinearity between these nutrition-related variables, four multivariate logistic regression analyses were separately performed, and four independent nutrition-based models were constructed. Of these, the best one was the model based on the three indicators of NRS2002 score, FBG, and multi-visceral resection, which had an AUROC of 0.744 (0.657–0.830), with a specificity of 75.6% and a sensitivity of 62.9%. Further, a nomogram was constructed to estimate the probability of IAI following D2 radical gastrectomy. The internal validation was carried out using the bootstrap method with self-help repeated sampling 1000 times, and the concordance index (c-index) was determined at 0.742 (95% CI = 0.739–0.745). The calibration curve revealed that the predictive results of the nomogram were in excellent concordance with the actual observations. The decision curve analysis (DCA) indicates that the nomogram has excellent clinical benefit. Conclusion The nomogram constructed based on NRS2002 score, FBG, and multi-visceral resection has good predictive capacity for the incidence of IAI following D2 radical gastrectomy and provides a reference value for clinicians to assess the risk of IAI occurrence.

Objective To develop and validate a body composition parameters (BCPs)-based nomogram for predicting recurrence in T1-stage clear cell renal cell carcinoma (ccRCC), comparing its performance with…

With advancements in medical technology and a deeper understanding of the pathogenesis of MPE, novel therapies and drugs are continually being developed. For example, research is exploring the use of tumor-derived microparticles to encapsulate chemotherapeutic drugs, allowing for targeted tumor...

Objectives To develop a nomogram that assists in selecting nasopharyngeal carcinoma (NPC) patients suitable for induction chemotherapy. Materials and methods This retrospective study included NPC…

Background We developed a deep learning radiomics nomogram (DLRN) using CT scans to improve clinical decision-making and risk stratification for early recurrence of hepatocellular carcinoma (HCC)…