Nudge theories and strategies influencing adult health behaviors and outcomes in COPD management: a systematic review.

Objective: Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease with high prevalence and mortality, and self-management is a key component for better outcomes of COPD. Recently, nudging has shown promising potential in COPD management. In the present study, we conducted a systematic review to collate the list of nudges and identified the variables that influence nudging.

Prediction of sepsis among patients with major trauma using artificial intelligence: a multicenter validated cohort study.

Background: Sepsis remains a significant challenge in patients with major trauma in the ICU. Early detection and treatment are crucial for improving outcomes and reducing mortality rates. Nonetheless, clinical tools for predicting sepsis among patients with major trauma are limited.

Interpretable machine learning models for predicting the incidence of antibiotic- associated diarrhea in elderly ICU patients.

Background: Antibiotic-associated diarrhea (AAD) can prolong hospitalization, increase medical costs, and even lead to higher mortality rates. Therefore, it is essential to predict the incidence of AAD in elderly intensive care unit(ICU) patients. The objective of this study was to create a prediction model that is both interpretable and generalizable for predicting the incidence of AAD in elderly ICU patients.

A retrospective study on intracranial mixed infection with tuberculous meningitis in Shenzhen, China.

Unlabelled: Tuberculous meningitis (TBM) is a prevalent global intracranial infection and the most lethal and disabling form of tuberculosis. TBM with mixed intracranial infections is clinically rare but has a higher mortality rate. To investigate the clinical characteristics of TBM with mixed intracranial infections, demographic and clinical data of TBM and pulmonary tuberculosis (PTB) patients admitted to Shenzhen Third People's Hospital between January 2015 and October 2022 were collected anonymously.

Establishment and validation of an artificial intelligence web application for predicting postoperative in-hospital mortality in patients with hip fracture: a national cohort study of 52 707 cases.

Background: In-hospital mortality following hip fractures is a significant concern, and accurate prediction of this outcome is crucial for appropriate clinical management. Nonetheless, there is a lack of effective prediction tools in clinical practice. By utilizing artificial intelligence (AI) and machine learning techniques, this study aims to develop a predictive model that can assist clinicians in identifying geriatric hip fracture patients at a higher risk of in-hospital mortality.

Chinese experts' consensus on the application of intensive care big data.

The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible.

Accurate malocclusion tooth segmentation method based on a level set with adaptive edge feature enhancement.

Objective: This study aimed to accurately segment teeth under complex oral conditions, including complex structural interference among adjacent teeth or malocclusion conditions, such as tooth rotation and displacement caused by dental crowding.

Study Design: Cone-beam computed tomography (CBCT) images were obtained from 19 patients with complex oral conditions, and a three-step solution was proposed. This study used a global convex level-set model to extract bony tissue and developed a flexible curve extraction method for separating neighbouring teeth under complex structural interference.

Artificial intelligence and cognitive diagnosis based teaching resource recommendation algorithm.

In the realm of advanced technology, deep learning capabilities are harnessed to analyze and predict novel data, once it has absorbed existing information. When applied to the sphere of education, this transformative technology becomes a catalyst for innovation and reform, leading to advancements in teaching modes, methodologies, and curricula. In light of these possibilities, the application of deep learning technology to teaching resource recommendations is explored in this article.

How is occupational identity affected by the family environment of publicly funded students in local normal colleges? A moderated mediation model.

The occupational identity of local publicly funded students is associated with the stability of rural teachers. To discuss the influence mechanisms of family environment, psychological capital, and professional commitment on occupational identity, this study examined 395 local publicly funded students with the occupational identity scale, psychological capital scale, professional commitment scale compiled, and family environment scale in China. We found no significant difference in terms of sex, source of birth, only children or not, and from teachers' families or not.

Association between serum uric acid, hyperuricemia and periodontitis: a cross-sectional study using NHANES data.

Objectives: Diabetes and other metabolic diseases have been linked to the development of periodontitis, but little research has been done to determine whether serum uric acid (SUA) levels and hyperuricemia play a role. This study aimed to investigate the relationship between SUA, hyperuricemia, and periodontitis.

Methods: Using data from the National Health and Nutrition Examination Survey (NHANES) 2011-2014, we created a nationally representative data set.

Illness severity assessment of older adults in critical illness using machine learning (ELDER-ICU): an international multicentre study with subgroup bias evaluation.

Background: Comorbidity, frailty, and decreased cognitive function lead to a higher risk of death in elderly patients (more than 65 years of age) during acute medical events. Early and accurate illness severity assessment can support appropriate decision making for clinicians caring for these patients. We aimed to develop ELDER-ICU, a machine learning model to assess the illness severity of older adults admitted to the intensive care unit (ICU) with cohort-specific calibration and evaluation for potential model bias.

Multi-stage Unet segmentation and automatic measurement of pharyngeal airway based on lateral cephalograms.

Objectives: Orthodontic treatment profoundly impact the pharyngeal airway (PA) of patients. Airway examination is an integral part of daily orthodontic diagnosis, and lateral cephalograms (LC) are reliable to reveal PA structures. This study attempted to develop a simple method to help clinicians make a preliminary judgement of patients' PA conditions and assess the impact of orthodontic treatment on their airways.

Signal Property Information-Based Target Detection with Dual-Output Neural Network in Complex Environments.

The performance of traditional model-based constant false-alarm ratio (CFAR) detection algorithms can suffer in complex environments, particularly in scenarios involving multiple targets (MT) and clutter edges (CE) due to an imprecise estimation of background noise power level. Furthermore, the fixed threshold mechanism that is commonly used in the single-input single-output neural network can result in performance degradation due to changes in the scene. To overcome these challenges and limitations, this paper proposes a novel approach, a single-input dual-output network detector (SIDOND) using data-driven deep neural networks (DNN).

Intelligent Intensive Care Unit: Current and Future Trends.

There is a growing demand for intensive care units, but there is a relative shortage of medical staff. Intensive care work is heavy and stressful. Optimizing the working conditions and processes of the intensive care unit is of great significance for improving the work efficiency and the level of diagnosis and treatment in the intensive care unit.

Identification of AKI signatures and classification patterns in ccRCC based on machine learning.

Background: Acute kidney injury can be mitigated if detected early. There are limited biomarkers for predicting acute kidney injury (AKI). In this study, we used public databases with machine learning algorithms to identify novel biomarkers to predict AKI.

A Simple Nomogram for Predicting Hospital Mortality of Patients Over 80 Years in ICU: An International Multicenter Retrospective Study.

Objectives: This study aimed to develop and validate an easy-to-use intensive care unit (ICU) illness scoring system to evaluate the in-hospital mortality for very old patients (VOPs, over 80 years old).

Methods: We performed a multicenter retrospective study based on the electronic ICU (eICU) Collaborative Research Database (eICU-CRD), Medical Information Mart for Intensive Care Database (MIMIC-III CareVue and MIMIC-IV), and the Amsterdam University Medical Centers Database (AmsterdamUMCdb). Least Absolute Shrinkage and Selection Operator regression was applied to variables selection.

Activation of glucagon-like peptide-1 receptor in microglia exerts protective effects against sepsis-induced encephalopathy via attenuating endoplasmic reticulum stress-associated inflammation and apoptosis in a mouse model of sepsis.

Sepsis-induced encephalopathy (SAE) is a detrimental complication in patients with severe sepsis, while there is still no effective treatment. Previous studies have elucidated the neuroprotective effects of glucagon-like peptide-1 receptor (GLP-1R) agonists. However, the role of GLP-1R agonists in the pathological process of SAE is unclear.

Bibliometric analysis of ferroptosis in acute kidney injury from 2014 to 2022.

Introduction: Acute kidney injury (AKI) is a global disease with high morbidity and mortality. At present, the treatment of AKI still lacks targeted measures. Ferroptosis, a form of regulated cell death, plays an essential role in the initiation and progression of AKI.

Association between admission serum phosphate and risk of acute kidney injury in critically ill patients with rhabdomyolysis: A retrospective study based on MIMIC-Ⅲ.

Background: The incidence of acute kidney injury (AKI) is high in critically ill patients with rhabdomyolysis. Limited evidence was proved of the association between serum phosphate levels at intensive care unit (ICU) admission and the subsequent risk of AKI. Our study aims to assess if serum phosphate levels at admission were independently associated with AKI risk in these patients.

Clinical utility and future direction of speaking valve: A review.

This paper summarizes and analyzes the clinical research progress of the speaking valve in recent years, including the structure and function of the speaking valve, the impact of the speaking valve on the patient's vocalization or speech, the impact on ventilator weaning and tracheal intubation and extubation, and the effect on aspiration and swallowing function, the impact on patient mobility and quality of life. Related issues in clinical use are also described.

Evaluation of the impact of reference tooth morphology and alignment on model measurement accuracy.

Background: The development of personalized and high-precision dental treatment is inseparable from the accurate measurement and analysis of the model. Compared with traditional plaster models, digital models allow dentists to obtain richer and more detailed inspection results. However, the measurement of digital models in clinical practice usually ignores the influence of the overall three-dimensional (3D) structure of teeth and tooth arrangement on the measurement results.

Geriatric Nutritional Risk Index is Associated with Hospital Death in Elderly Patients with Multiple Organ Dysfunction Syndrome: A Retrospective Study Based on the MIMIC-III Database.

Purpose: Elderly patients with multiple organ dysfunction syndrome (MODS) have a higher mortality during hospitalization in the intensive care unit (ICU). Elderly patients often suffer from malnutrition. On the basis of the MIMIC-III database, this study analyzed the effect of the baseline nutritional status on the death of elderly patients with MODS during hospitalization.

Delayed neutrophil apoptosis may enhance NET formation in ARDS.

Background: Acute respiratory distress syndrome (ARDS) is a neutrophil-associated disease. Delayed neutrophil apoptosis and increased levels of neutrophil extracellular traps (NETs) have been described in ARDS. We aimed to investigate the relationship between these phenomena and their potential as inflammation drivers.

Clinically Interpretable Machine Learning Models for Early Prediction of Mortality in Older Patients with Multiple Organ Dysfunction Syndrome: An International Multicenter Retrospective Study.

Background: Multiple organ dysfunction syndrome (MODS) is associated with a high risk of mortality among older patients. Current severity scores are limited in their ability to assist clinicians with triage and management decisions. We aim to develop mortality prediction models for older patients with MODS admitted to the ICU.

Relationship between periodontitis and diabetes: a bibliometrics analysis.

Background: Diabetes and periodontitis are common chronic diseases, and the pathogeneses of the 2 diseases both involve chronic inflammation. The presence of either of the 2 diseases increases the risk of the other, while the treatment of 1 can reduce the risk of the other. This study sought to summarize the current status of research in this field via a bibliometric analysis.