Causal Association Between Childhood Body Mass Index and Phlebitis and Thrombophlebitis: An Analysis Using Mendelian Randomization.

Research has indicated a link between obesity and a greater likelihood of venous disorders. However, the specific relationship between obesity in children and conditions such as phlebitis and thrombophlebitis remains undetermined. To explore this, we undertook a two-sample Mendelian randomization (MR) study to investigate the possible causal impact of childhood body mass index (BMI) on the development of phlebitis and thrombophlebitis.

Overview of current research on traditional Chinese medicine in skin disease treatment: a bibliometric analysis from 2014 to 2024.

Context: Recent research has revealed significant advancements in the field of traditional Chinese medicine (TCM) for skin diseases. However, there is a lack of visualization analysis within this research domain.

Objective: To analyze the research directions and advancements in TCM research in skin diseases.

Mendelian randomization study reveals a causal relationship between body mass index in children and risk of autoimmune diseases.

Newly emerging evidence indicates that body mass index (BMI) is a potential risk factor for autoimmune diseases (ADs). Nevertheless, the exact causal connection between ADs and BMI in children remains uncertain. To investigate the relationship between BMI in children and ADs, a 2-sample Mendelian randomization (MR) analysis was conducted.

Development and trends in metabolomics studies in psoriasis: A bibliometric analysis of related research from 2011 to 2024.

Background: Psoriasis is a chronic, inflammatory skin disease with autoimmune characteristics. Recent research has made significant progress in the field of psoriasis metabolomics. However, there is a lack of bibliometric analysis on metabolomics of psoriasis.

Knowledge mapping of neonatal electroencephalogram: A bibliometric analysis (2004-2022).

Background: Electroencephalography (EEG), a widely used noninvasive neurophysiological diagnostic tool, has experienced substantial advancements from 2004 to 2022, particularly in neonatal applications. Utilizing a bibliometric methodology, this study delineates the knowledge structure and identifies emergent trends within neonatal EEG research.

Methods: An exhaustive literature search was conducted on the Web of Science Core Collection (WoSCC) database to identify publications related to neonatal EEG from 2004 to 2022.

Assessing causal relationships between gut microbiota and psoriasis: evidence from two sample Mendelian randomization analysis.

Mounting data hints that the gut microbiota's role may be pivotal in understanding the emergence of psoriasis. However, discerning a direct causal link is yet elusive. In this exploration, we adopted a Mendelian randomization (MR) strategy to probe the prospective causal interplay between the gut's microbial landscape and the predisposition to psoriasis.

Immune control in Kawasaki disease knowledge mapping: a bibliometric analysis.

Background: Kawasaki disease is a systemic vascular disease with an unclear pathophysiology that primarily affects children under the age of five. Research on immune control in Kawasaki disease has been gaining attention. This study aims to apply a bibliometric analysis to examine the present and future directions of immune control in Kawasaki disease.

Machine learning combined with single-cell analysis reveals predictive capacity and immunotherapy response of T cell exhaustion-associated lncRNAs in uterine corpus endometrial carcinoma.

Background: The exhaustion of T-cells is a primary factor contributing to immune dysfunction in cancer. Long non-coding RNAs (lncRNAs) play a significant role in the advancement, survival, and treatment of Uterine Corpus Endometrial Carcinoma (UCEC). Nevertheless, there has been no investigation into the involvement of lncRNAs associated with T-cell exhaustion (TEXLs) in UCEC.

Identification of potential biomarkers in the peripheral blood of neonates with bronchopulmonary dysplasia using WGCNA and machine learning algorithms.

Bronchopulmonary dysplasia (BPD) is often seen as a pulmonary complication of extreme preterm birth, resulting in persistent respiratory symptoms and diminished lung function. Unfortunately, current diagnostic and treatment options for this condition are insufficient. Hence, this study aimed to identify potential biomarkers in the peripheral blood of neonates affected by BPD.

Exploration of programmed cell death-associated characteristics and immune infiltration in neonatal sepsis: new insights from bioinformatics analysis and machine learning.

Background: Neonatal sepsis, a perilous medical situation, is typified by the malfunction of organs and serves as the primary reason for neonatal mortality. Nevertheless, the mechanisms underlying newborn sepsis remain ambiguous. Programmed cell death (PCD) has a connection with numerous infectious illnesses and holds a significant function in newborn sepsis, potentially serving as a marker for diagnosing the condition.

Genetic analysis of potential biomarkers and therapeutic targets associated with ferroptosis from bronchopulmonary dysplasia.

Ferroptosis is a recently identified form of cell death that is distinct from the conventional modes such as necrosis, apoptosis, and autophagy. Its role in bronchopulmonary dysplasia (BPD) remains inadequately understood. To address this gap, we obtained BPD-related RNA-seq data and ferroptosis-related genes (FRGs) from the GEO database and FerrDb, respectively.

Casual association between childhood body mass index and risk of psoriasis: A Mendelian randomization study.

Background: Observational studies have suggested that childhood body mass index (BMI) is associated with the risk of psoriasis. However, their causal relationship remains unclear. In this investigation, we aimed to determine whether an association exists between childhood BMI and psoriasis.

Identification and immunological characterization of endoplasmic reticulum stress-related molecular subtypes in bronchopulmonary dysplasia based on machine learning.

Bronchopulmonary dysplasia (BPD) is a life-threatening lung illness that affects premature infants and has a high incidence and mortality. Using interpretable machine learning, we aimed to investigate the involvement of endoplasmic reticulum (ER) stress-related genes (ERSGs) in BPD patients. We evaluated the expression profiles of endoplasmic reticulum stress-related genes and immune features in bronchopulmonary dysplasia using the GSE32472 dataset.

Genome-wide exploration of a pyroptosis-related gene module along with immune cell infiltration patterns in bronchopulmonary dysplasia.

Pyroptosis plays a crucial role in bronchopulmonary dysplasia (BPD) and is associated with various lung injury illnesses. However, the function of pyroptosis-related genes (PRGs) in BPD remains poorly understood. The gene expression omnibus (GEO) database was searched for information on genes associated with BPD.

Integrated bioinformatics analysis and experimental validation reveals fatty acid metabolism-related prognostic signature and immune responses for uterine corpus endometrial carcinoma.

Background: Uterine corpus endometrial carcinoma (UCEC) is the third most common gynecologic malignancy. Fatty acid metabolism (FAM) is an essential metabolic process in the immune microenvironment that occurs reprogramming in the presence of tumor signaling and nutrient competition. This study aimed to identify the fatty acid metabolism-related genes (FAMGs) to develop a risk signature for predicting UCEC.