Prediction of mortality in intensive care unit with short-term heart rate variability: Machine learning-based analysis of the MIMIC-III database.

Background: Prognosis prediction in the intensive care unit (ICU) traditionally relied on physiological scoring systems based on clinical indicators at admission. Electrocardiogram (ECG) provides easily accessible information, with heart rate variability (HRV) derived from ECG showing prognostic value. However, few studies have conducted a comprehensive analysis of HRV-based prognostic model against established standards, which limits the application of HRV's prognostic value in clinical settings.

Enhanced Reaction Kinetics in Stationary Two-Phase Flow through Porous Media.

Understanding the interaction between multiphase flow and reactive transport in porous media is critical for many environmental and industrial applications. When a nonwetting immiscible phase is present within the pore space, it can remain immobile, which we call unsaturated flow, or move, resulting in multiphase flow. Previous studies under unsaturated flow conditions have shown that, for a given flow rate, the product of a mixing-driven reaction increases as wetting phase saturation decreases.

Key Node Identification Method Based on Multilayer Neighbor Node Gravity and Information Entropy.

In the field of complex network analysis, accurately identifying key nodes is crucial for understanding and controlling information propagation. Although several local centrality methods have been proposed, their accuracy may be compromised if interactions between nodes and their neighbors are not fully considered. To address this issue, this paper proposes a key node identification method based on multilayer neighbor node gravity and information entropy (MNNGE).

PreTKcat: A pre-trained representation learning and machine learning framework for predicting enzyme turnover number.

The enzyme turnover number (k) is crucial for understanding enzyme kinetics and optimizing biotechnological processes. However, experimentally measured k values are limited due to the high cost and labor intensity of wet-lab measurements, necessitating robust computational methods. To address this issue, we propose PreTKcat, a framework that integrates pre-trained representation learning and machine learning to predict k values.

Effects of intermittent theta burst stimulation on cognitive and swallowing function in patients with MCI and dysphagia risk: a randomized controlled trial.

Background: Mild cognitive impairment (MCI) is a high-risk factor for dementia and dysphagia; therefore, early intervention is vital. The effectiveness of intermittent theta burst stimulation (iTBS) targeting the right dorsal lateral prefrontal cortex (rDLPFC) remains unclear.

Methods: Thirty-six participants with MCI were randomly allocated to receive real (n = 18) or sham (n = 18) iTBS.

Tetrahydroberberrubine improves hyperlipidemia by activating the AMPK/SREBP2/PCSK9/LDL receptor signaling pathway.

Hyperlipidemia is a major risk factor for hypertension, coronary heart disease, diabetes and stroke, triggering an intensified research efforts into its prevention and treatment. Tetrahydroberberrubine (THBru) is a derivative of berberine (BBR) that has been shown to have higher bioavailability and lower toxicity compared to its parent compound. However, its impact on hyperlipidemia has not been fully explored.

Development of an anti-LAIR1 antibody-drug conjugate for acute myeloid leukemia therapy.

Acute myeloid leukemia (AML) is a severe blood cancer with an urgent need for novel therapies for refractory or relapsed patients. Leukocyte-associated immunoglobulin-like receptor 1 (LAIR1), an immune suppressive receptor expressed on immune cells and AML blasts but minimally on hematopoietic stem cells (HSCs), represents a potential therapeutic target. But there has been limited research on therapies targeting LAIR1 for AML and no published reports on LAIR1 antibody-drug conjugate (ADC).

Predicting upper limb motor recovery in subacute stroke patients via fNIRS-measured cerebral functional responses induced by robotic training.

Background: Neural activation induced by upper extremity robot-assisted training (UE-RAT) helps characterize adaptive changes in the brains of poststroke patients, revealing differences in recovery potential among patients. However, it remains unclear whether these task-related neural activities can effectively predict rehabilitation outcomes. In this study, we utilized functional near-infrared spectroscopy (fNIRS) to measure participants' neural activity profiles during resting and UE-RAT tasks and developed models via machine learning to verify whether task-related functional brain responses can predict the recovery of upper limb motor function.

The Respiratory-Swallow Coordination may be Related to Aspiration in Infratentorial Stroke Patients.

To investigate the characteristics of respiratory-swallow coordination (RSC) in poststroke dysphagia (PSD) patients following infratentorial stroke (IS) and further explore their association with aspiration. PSD patients after IS and age-matched healthy controls were recruited. Nasal airflow and sound signals were recorded using a nasal cannula-type flow sensor and contact microphone, which were synchronized with videofluoroscopic swallowing studies (VFSS).

Winter climate change mediates the sensitivity of vegetation leaf-out to spring warming in high latitudes in China.

Global warming has significantly altered plant phenology by advancing the timing of leaf emergence, impacting vegetation productivity and adaptability. Winter and spring temperatures have commonly been used to explain spring phenology shifts, but we still lack a solid understanding of the effects of interactions between conditions in different seasons. This study utilizes normalized difference vegetation index (NDVI) and meteorological data to examine the effects of changes in winter and spring temperatures and precipitation on the start of the vegetation growing season (SOS) at high latitudes in China from 1982 to 2015.

Impact of extreme temperature on congenital heart disease mortality: a population-based nationwide case-crossover study.

Background: The association between congenital heart disease (CHD) and non-optimal temperatures has received limited investigation. We aimed to investigate the impact of extreme temperatures on CHD mortality.

Methods: We reviewed the National Mortality Surveillance System of China and retrieved death records attributable to CHD from 2013 to 2021.

Carbon Nanocage-in-Microcage Structure With Tunable Carbon-Coated Nickel as a Microwave Absorber With Infrared Stealth Property.

The rational design of microwave absorption (MA) material featuring light weight, wide absorption bandwidth, and infrared stealth property is crucial for military stealth and health protection but remains challenging. Herein, an innovative N-doped carbon nanocage-in-microcage structure with tunable carbon-coated Ni (NC/Ni(HS)) is reported via a reliable Ni-catalyzed and Ni-templated method. The hierarchically hollow structure of nanocage-in-microcage composites can optimize the impedance matching and respond to multiple reflections and scattering of incident microwaves and infrared waves.

An ultrasonic degraded polysaccharide extracted from Pueraria lobata ameliorate ischemic brain injury in mice by regulating the gut microbiota and LPS-TLR4 pathway.

Ischemia brain injury is closely associated with the gut microbiota. Polysaccharides, as a typical prebiotic, have been extensively employed in stroke treatment. In our previous study, Pueraria lobata polysaccharide (PLP-3) with antioxidant activity was prepared via water extraction and alcohol precipitation combined with ultrasonic degradation.

Efficient generation of multi-order Raman radiation in aqueous solutions derived from -CH and -NH vibrations via cascaded four-wave mixing and Stokes processes.

Multiple coherent radiations are achieved in a water-3-aminopropanol (3AP) mixed solution through cascaded four-wave mixing (C-FWM) and cascaded Stokes (C-Stokes) processes, both driven by stimulated Raman scattering (SRS) in this work. The O-H vibration peak from water is replaced by the emergence of the -NH symmetric stretching Raman peaks from 3AP, with intensity approaching that of the -CH symmetric stretching peak. The dual-wavelength SRS signals for the -NH and -CH stretching vibrations have a relatively small frequency interval of about 400 cm (16 nm).

Comparison of postoperative survival prognosis between early-onset and late-onset esophageal cancer: A Population-based study.

The prognosis of non-distant metastatic early-onset esophageal cancer (EC) patients undergoing surgical treatment remains unclear, this study aims to compare the prognosis of early-onset and late-onset EC. Information on non-distant metastatic EC patients who underwent surgical treatment and were initially diagnosed between 2004 and 2015 was collected from the Surveillance, Epidemiology, and End Results (SEER) database. Propensity score matching (PSM) was used to balance the baseline differences between early-onset and late-onset EC patients.

Global burden of non-rheumatic valvular heart disease in older adults (60-89 years old), 1990-2019: Systematic analysis of the Global Burden of Disease Study 2019.

Background: Understanding the global burden and risk factors of non-rheumatic valvular heart disease (NRVHD) in older adults is important for effective disease control. We wanted to analyze the prevalence, incidence, disability-adjusted life years (DALY) rate, mortality rate, and risk factors of NRVHD in older adults aged 60-89 years.

Methods: Global Burden of Disease Study (GBD) 2019 was used as the data source.

Development and validation of a nomogram for obesity and related factors to detect gastric precancerous lesions in the Chinese population: a retrospective cohort study.

Objectives: The purpose of this study was to construct a nomogram to identify patients at high risk of gastric precancerous lesions (GPLs). This identification will facilitate early diagnosis and treatment and ultimately reduce the incidence and mortality of gastric cancer.

Methods: In this single-center retrospective cohort study, 563 participants were divided into a gastric precancerous lesion (GPL) group (n=322) and a non-atrophic gastritis (NAG) group (n=241) based on gastroscopy and pathology results.

A Novel Ultrasonographic Method to Quickly and Accurately Access the C2 Dorsal Root Ganglion.

Background: Occipital neuralgia manifests as pain in the cutaneous distribution of occipital nerves, with the greater occipital nerve stemming from the C2 spinal nerve and the lesser occipital nerve originating from the C2 and C3 spinal nerves. While pulsed radiofrequency ablation of the C2 dorsal root ganglion (DRG) is an effective treatment for refractory occipital neuralgia, accessing the C2 DRG remains a clinical challenge even under fluoroscopic guidance.

Objective: We aimed to develop an ultrasonographic method for quickly and accurately accessing the C2 DRG.

Diversity and Anti-Infectious Components of Cultivable Rhizosphere Fungi Derived from Three Species of Plants in Northwestern Yunnan, China.

, a group of legume plants, has a pronounced rhizosphere effect. Many species of with limited resource reserves are distributed in the high-altitude area of northern Yunnan, China. Although some of these plants have high medicinal value, the recognition of them is still at a low level.

Stimulated Raman scattering and enhancement mechanisms in alkali metal hydroxide aqueous solutions under laser-induced dynamic pressure.

Stimulated Raman scattering (SRS) in lithium hydroxide-water/heavy water (LiOH-HO/DO) solutions with varying concentrations was investigated under laser-induced high-pressure conditions using an Nd: YAG laser. The spectra revealed a significant enhancement in SRS signals, characterized by the emergence of low-wavenumber Raman peaks and a shift of the main SRS peak of liquid water to lower frequencies, evolving from a single peak to two or three peaks, which suggested the formation of an ice-like structure. Additionally, the normalized SRS intensity was higher than that of pure water.

Learning multi-level topology representation for multi-view clustering with deep non-negative matrix factorization.

Clustering of multi-view data divides objects into groups by preserving structure of clusters in all views, requiring simultaneously takes into consideration diversity and consistency of various views, corresponding to the shared and specific components of various views. Current algorithms fail to fully characterize and balance diversity and consistency of various views, resulting in the undesirable performance. Here, a novel Multi-View Clustering with Deep non-negative matrix factorization and Multi-Level Representation (MVC-DMLR) learning is proposed, which integrates feature learning, multi-level topology representation, and clustering of multi-view data.