Conectar
by Ayesha Bibi, Muhammad Hamza Afandi, Azra Mehmood, Usman Ali Ashfaq, Muhammad Shareef Masoud, Mohsin Ahmad Khan, Rashid Bhatti
Hepatocellular carcinoma (HCC) has a very significant mortality rate and is one of the most common cancers worldwide. Jacaranda mimosifolia is reported to have potential antitumor activities against various human cancers. However, the effects of J. mimosifolia on HCC are yet elusive. This study aimed to investigate the anti-HCC potential of methanolic extract of J. mimosifolia leaves using in vitro and in vivo studies and a network pharmacology approach. The effect of J. mimosifolia extract was assessed on Huh-7.5 cells using MTT assay, wound healing assay, and DNA fragmentation assay. These experiments found that J. mimosifolia extract significantly suppressed Huh-7.5 cell proliferation, impaired cell migration, and induced cell apoptosis. The real-time PCR validated the upregulation of p53 and Bax, alongside the downregulation of AFP and GPC3 in Huh-7.5 cells after treatment with J. mimosifolia extract. In vivo experiments confirmed the hepatoprotective effects of J. mimosifolia extract in mice models with CCl4-induced hepatic injury. In addition, through network pharmacological analysis, J. mimosifolia was found to play a critical role against HCC via targeting multiple potential targets and pathways. Docking analysis identified apigenin and kaempferol with the lowest binding energy against PTGS2 and EGFR, respectively, while flavonol glycoside showed the lowest binding energy against MMP9. However, detailed research is needed to isolate the potential phytochemicals from J. mimosifolia against HCC.This paper discusses data errors and offers guidance on data cleaning techniques, with a particular focus on handling missing values and outliers in quantitative datasets.
Methodological discussion.
This paper provides an overview of various techniques for identifying and addressing data anomalies, which can arise from incomplete, noisy, and inconsistent data. These anomalies can significantly affect data quality, leading to biased model parameter estimates and evidence-based decisions. Data cleaning, particularly the appropriate handling of missing values and outliers, is essential to improving data quality before analysis. Data cleaning includes screening for anomalies, diagnosing errors, and applying appropriate corrective measures.
Proper handling of missing values and the identification and correction of outliers are crucial aspects of data cleaning in ensuring data quality and the reliability of statistical analyses. Effective data cleaning enhances the validity and accuracy of research findings for evidence-based decision making that leads to optimal patient outcomes.
The quality of study results depends on how a dataset and its complexities are processed or handled before the analysis. Nursing researchers must use a framework to identify and address important data anomalies and produce reliable results.
This paper describes data cleaning, often overlooked during the data mining process, as a crucial step before conducting data analysis. By addressing missing values and outliers, identifying and fixing data anomalies, and enhancing data quality prior to analysis, data cleaning techniques can produce precise research findings for evidence-based decision making.
In this methodological paper, no new data were generated.
No patient or public contribution.
To externally validate and subsequently repurpose/recalibrate the easily accessible kidney failure risk equation (KFRE) for a prevalent transplant population with an estimated glomerular filtration rate (eGFR)
Retrospective cohort study using UK Renal Registry data.
68 adult UK kidney centres.
4092 patients with grafts at least 2 years old and eGFR2 from 2009 to 2018.
Death-censored allograft failure at 2 years, defined as dialysis initiation, re-transplantation or, in the absence of the former two, the recorded date of transplant failure.
The KFRE was calculated at baseline using the 2-year, 8-variable non-North American KFRE, and performance was assessed using Harrell’s C-statistic and calibration plots. The model was recalibrated using Cox Regression (2009–2013 cohort) and temporally validated using the 2014–2018 cohort. Clinical utility was assessed using decision-curve analysis, estimating per-100-patient gains in timely planning and reductions in unnecessary interventions compared with eGFR triggers.
The original KFRE had excellent discrimination but was miscalibrated, underpredicting graft failure. Temporal validation demonstrated that the performance of the recalibrated KFRE could be maintained across time periods (Harrell’s C-index of 0.81 (95% CI 0.80 to 0.83); O/E (Observed/Expected events) ratio 1.00 (95% CI 0.93 to 1.07). It identified 9/100 more patients for timely intervention and 13/100 more for whom intervention could be delayed compared with a late clinical trigger of an eGFR2.
While there are other prognostic models, this is the first study to focus on the understudied and clinically important cohort of patients with an eGFR
FreshRSS 