Functional Divergence of the Closely Related Genes PhARF5 and PhARF19a in Flower Formation and Hormone Signaling.

The ARF gene family plays a vital role in regulating multiple aspects of plant growth and development. However, detailed research on the role of the ARF family in regulating flower development in petunia and other plants remains limited. This study investigates the distinct roles of and in flower development.

Special Issue: "Vaccination and Global Health".

This Special Issue, titled 'Vaccination and Global Health,' compiles 11 broad-ranging papers, each exploring critical facets of vaccination, public health, and global healthcare systems [...

Pathomics models for CD40LG expression and prognosis prediction in glioblastoma.

Glioblastoma (GBM) is the most prevalent primary malignant tumor of the nervous system. In this study, we utilized pathomics analysis to explore the expression of CD40LG and its predictive value for the prognosis of GBM patients. We analyzed the expression differences of CD40LG in GBM tissue and normal brain tissue, along with performing survival prognosis analysis.

Regulator of calcineurin 3 as a novel predictor of diagnosis and prognosis in pan-cancer.

Aim: To assess diagnostic and prognostic value of regulator of calcineurin 3 (RCAN3) in various malignancies.

Methods: RCAN3 expression levels were assessed across pan-cancer data sets including various molecular and immune subtypes. Receiver operating characteristic (ROC) and Kaplan-Meier curves were employed to determine the diagnostic and prognostic value of RCAN3 in pan-cancer, respectively.

Artificial intelligence-assisted metastasis and prognosis model for patients with nodular melanoma.

Objective: The objective of this study was to identify the risk factors that influence metastasis and prognosis in patients with nodular melanoma (NM), as well as to develop and validate a prognostic model using artificial intelligence (AI) algorithms.

Methods: The Surveillance, Epidemiology, and End Results (SEER) database was queried for 4,727 patients with NM based on the inclusion/exclusion criteria. Their clinicopathological characteristics were retrospectively reviewed, and logistic regression analysis was utilized to identify risk factors for metastasis.

Predicting osteoporotic fractures post-vertebroplasty: a machine learning approach with a web-based calculator.

Purpose: The aim of this study was to develop and validate a machine learning (ML) model for predicting the risk of new osteoporotic vertebral compression fracture (OVCF) in patients who underwent percutaneous vertebroplasty (PVP) and to create a user-friendly web-based calculator for clinical use.

Methods: A retrospective analysis of patients undergoing percutaneous vertebroplasty: A retrospective analysis of patients treated with PVP between June 2016 and June 2018 at Liuzhou People's Hospital was performed. The independent variables of the model were screened using Boruta and modelled using 9 algorithms.

Interpretable machine learning-based clinical prediction model for predicting lymph node metastasis in patients with intrahepatic cholangiocarcinoma.

Objective: Prediction of lymph node metastasis (LNM) for intrahepatic cholangiocarcinoma (ICC) is critical for the treatment regimen and prognosis. We aim to develop and validate machine learning (ML)-based predictive models for LNM in patients with ICC.

Methods: A total of 345 patients with clinicopathological characteristics confirmed ICC from Jan 2007 to Jan 2019 were enrolled.

Photo-modulated activation of organic bases enabling microencapsulation and on-demand reactivity.

A method is developed for facile encapsulation of reactive organic bases with potential application for autonomous damage detection and self-healing polymers. Highly reactive chemicals such as bases and acids are challenging to encapsulate by traditional oil-water emulsion techniques due to unfavorable physical and chemical interactions. In this work, reactivity of the bases is temporarily masked with photo-removable protecting groups, and the resulting inactive payloads are encapsulated via an in situ emulsion-templated interfacial polymerization method.

Unveiling research trends in the prognosis of osteosarcoma: A bibliometric analysis from 2000 to 2022.

Background: Osteosarcoma (OSA) is the most prevalent form of malignant bone tumor in children and adolescents, producing osteoid and immature bone. Numerous high quality studies have been published in the OSA field, however, no bibliometric study related to this area has been reported thus far. Therefore, the present study retrieved the published data from 2000 to 2022 to reveal the dynamics, development trends, hotspots and future directions of the OSA.

DNA methylome analysis reveals novel insights into active hypomethylated regulatory mechanisms of temperature-dependent flower opening in .

Short-term ambient low temperature (ALT) stimulation is necessary for to facilitate continued flower opening after floral bud development reaches maturity. DNA methylation, a vital epigenetic modification, regulates various biological processes in response to temperature fluctuations. However, its role in temperature-driven flower opening remains elusive.

Nanoparticle-Empowered Core-Shell Microcapsules: From Architecture Design to Fabrication and Functions.

Compartmentalization is a powerful concept to integrate multiscale components with diverse functionalities into miniature architectures. Inspired by evolution-optimized cell compartments, synthetic core-shell capsules enable storage of actives and on-demand delivery of programmed functions, driving scientific progress across various fields including adaptive materials, sustainable electronics, soft robotics, and precision medicine. To simultaneously maximize structural stability and environmental sensitivity, which are the two most critical characteristics dictating performance, diverse nanoparticles are incorporated into microcapsules with a dense shell and a liquid core.

Ensemble learning-assisted prediction of prolonged hospital length of stay after spine correction surgery: a multi-center cohort study.

Purpose: This research aimed to develop a machine learning model to predict the potential risk of prolonged length of stay in hospital before operation, which can be used to strengthen patient management.

Methods: Patients who underwent posterior spinal deformity surgery (PSDS) from eleven medical institutions in China between 2015 and 2022 were included. Detailed preoperative patient data, including demographics, medical history, comorbidities, preoperative laboratory results, and surgery details, were collected from their electronic medical records.

Pneumonia detection based on RSNA dataset and anchor-free deep learning detector.

Pneumonia is a highly lethal disease, and research on its treatment and early screening tools has received extensive attention from researchers. Due to the maturity and cost reduction of chest X-ray technology, and with the development of artificial intelligence technology, pneumonia identification based on deep learning and chest X-ray has attracted attention from all over the world. Although the feature extraction capability of deep learning is strong, existing deep learning object detection frameworks are based on pre-defined anchors, which require a lot of tuning and experience to guarantee their excellent results in the face of new applications or data.

Early-life antibiotic exposure promotes house dust mite-induced allergic airway inflammation by impacting gut microbiota and lung lipid metabolism.

Asthma is a chronic inflammatory respiratory disease. Early-life antibiotic exposure is a unique risk factor for the incidence and severity of asthma later in life. Perturbations in microbial-metabolite-immune interaction caused by antibiotics are closely associated with the pathogenesis of allergy and asthma.

Machine learning predictors of risk of death within 7 days in patients with non-traumatic subarachnoid hemorrhage in the intensive care unit: A multicenter retrospective study.

Non-traumatic subarachnoid hemorrhage (SAH) is a critical neurosurgical emergency with a high mortality rate, imposing a significant burden on both society and families. Accurate prediction of the risk of death within 7 days in SAH patients can provide valuable information for clinicians, enabling them to make better-informed medical decisions. In this study, we developed six machine learning models using the MIMIC III database and data collected at our institution.

Development and validation of a clinical prediction model for the risk of distal metastasis in intrahepatic cholangiocarcinoma: a real-world study.

Background: Cholangiocarcinoma (CCA) is a highly malignant and easily metastatic bile duct tumor with poor prognosis. We aimed at studying the associated risk factors affecting distal metastasis of CCA and using nomogram to guide clinicians in predicting distal metastasis of CCA.

Methods: Based on inclusion and exclusion criteria, 345 patients with CCA were selected from the Fifth Medical Center of Chinese PLA General Hospital and were divided into distal metastases (N = 21) and non-distal metastases (N = 324).

A machine learning-based model for clinical prediction of distal metastasis in chondrosarcoma: a multicenter, retrospective study.

Background: The occurrence of distant metastases (DM) limits the overall survival (OS) of patients with chondrosarcoma (CS). Early diagnosis and treatment of CS remains a great challenge in clinical practice. The aim of this study was to investigate metastatic factors and develop a risk stratification model for clinicians' decision-making.

Co-doped RuO nanoparticles with enhanced catalytic activity and stability for the oxygen evolution reaction.

Efficient and durable electrocatalysts for the oxygen evolution reaction (OER) play an important role in the use of hydrogen energy. Rutile RuO, despite being considered as an advanced electrocatalyst for the OER, performs poorly in stability due to its easy oxidative dissolution at very positive (oxidizing) potentials. Herein, we report a type of Co-doped RuO nanoparticle for boosting OER catalytic activity and stability in alkaline solutions.

A clinical prediction model based on interpretable machine learning algorithms for prolonged hospital stay in acute ischemic stroke patients: a real-world study.

Objective: Acute ischemic stroke (AIS) brings an increasingly heavier economic burden nowadays. Prolonged length of stay (LOS) is a vital factor in healthcare expenditures. The aim of this study was to predict prolonged LOS in AIS patients based on an interpretable machine learning algorithm.

Facile synthesis of Ag lattice doped mesoporous InO nanocubes for high performance ethanol sensing.

Ag lattice doped InO with a mesoporous structure was synthesized through a combination of hydrothermal and calcination methods. The structural and morphological characteristics were assessed using XRD, SEM, TEM, TGA, BET, and XPS analyses. Gas sensing measurements revealed that the 7.