Predictive value of prognostic nutritional index as prognostic biomarkers in patients with lymphoma: a systematic review and meta-analysis.

Background: Several research have indicated the significant potential of the Prognostic Nutritional Index (PNI) as a prognostic biomarker in lymphoma patients. However, there is some inconsistency in the findings of a few studies. Hence, to offer a thorough evaluation of the predictive significance of PNI in lymphoma patients, we performed a meta-analysis to examine the prognostic value of PNI for survival outcomes in lymphoma patients.

Value of circulating tumor cell assisting low-dose computed tomography in screening pulmonary nodules based on existing liquid biopsy techniques: a systematic review with meta-analysis and trial sequential analysis.

Objective: This study aims to assess the diagnostic utility of circulating tumor cells (CTCs) in conjunction with low-dose computed tomography (LDCT) for differentiating between benign and malignant pulmonary nodules and to substantiate the foundation for their integration into clinical practice.

Methods: A systematic literature review was performed independently by two researchers utilizing databases including PubMed, Web of Science, The Cochrane Library, Embase, and Medline, to collate studies up to September 15, 2023, that investigated the application of CTCs in diagnosing pulmonary nodules. A meta-analysis was executed employing Stata 15.

Comparison of deep learning models to traditional Cox regression in predicting survival of colon cancer: Based on the SEER database.

Background And Aim: In this study, a deep learning algorithm was used to predict the survival rate of colon cancer (CC) patients, and compared its performance with traditional Cox regression.

Methods: In this population-based cohort study, we used the characteristics of patients diagnosed with CC between 2010 and 2015 from the Surveillance, Epidemiology and End Results (SEER) database. The population was randomized into a training set (n = 10 596, 70%) and a test set (n = 4536, 30%).

Identifying depression in the United States veterans using deep learning algorithms, NHANES 2005-2018.

Background: Depression is a common mental health problem among veterans, with high mortality. Despite the numerous conducted investigations, the prediction and identification of risk factors for depression are still severely limited. This study used a deep learning algorithm to identify depression in veterans and its factors associated with clinical manifestations.

Diagnostic accuracy of optical coherence tomography for margin assessment in breast-conserving surgery: A systematic review and meta-analysis.

Background: Breast cancer is the most common malignant tumor among women, and its incidence is increasing annually. At present, the results of the study on whether optical coherence tomography (OCT) can be used as an intraoperative margin assessment method for breast-conserving surgery (BCS) are inconsistent. We herein conducted this systematic review and meta-analysis to assess the diagnostic value of OCT in BCS.

Association between anemia and depression: results from NHANES 2005-2018 and mendelian randomization analyses.

Background: The relationship between anemia and depression remains controversial.

Objective: To explore the association between anemia/hemoglobin and depression.

Methods: The data for our cross-sectional study were obtained from the National Health and Nutrition Examination Survey (NHANES) 2005-2018.

Using machine learning algorithms to identify chronic heart disease: National Health and Nutrition Examination Survey 2011-2018.

Objective: The number of heart disease patients is increasing. Establishing a risk assessment model for chronic heart disease (CHD) based on risk factors is beneficial for early diagnosis and timely treatment of high-risk populations.

Methods: Four machine learning models, including logistic regression, support vector machines (SVM), random forests, and extreme gradient boosting (XGBoost), were used to evaluate the CHD among 14 971 participants in the National Health and Nutrition Examination Survey from 2011 to 2018.

Impact factor assessment of the uptake and accumulation of polycyclic aromatic hydrocarbons by plant leaves: Morphological characteristics have the greatest impact.

Polycyclic aromatic hydrocarbons (PAHs) have toxic, teratogenic, mutagenic and carcinogenic effects on living organisms. Plants can function as pollutant bioindicators and bioaccumulators due to their wide surface distribution and specific responses to atmospheric pollutants. However, various plants exhibit significant differences in their capacities to accumulate PAHs.