Discovery of novel glucagon-like peptide 1/cholecystokinin 1 receptor dual agonists.

The combined use of gastrointestinal hormones for treating metabolic diseases is gaining increasing attention. The potential of developing novel dual agonists targeting both cholecystokinin 1 (CCK-1) receptor and glucagon-like peptide 1 (GLP-1) receptor to improve the treatment of type 2 diabetes and obesity have not been fully explored. In this investigation, we reported a series of novel GLP-1/CCK-1 receptor co-agonists constructed by linking the C-terminus of a GLP-1 receptor agonist (bullfrog GLP-1) to the N-terminus of a CCK-1 receptor selective agonist NN9056.

Blood urea nitrogen to serum albumin ratio as a new prognostic indicator in type 2 diabetes mellitus patients with chronic kidney disease.

Chronic kidney disease (CKD) is often a common comorbidity in critically ill patients with type 2 diabetes mellitus (T2DM). This study explored the relationship between blood urea nitrogen to serum albumin ratio (BAR) and mortality in T2DM patients with CKD in intensive care unit (ICU). Patients were recruited from the Medical Information Mart database, retrospectively.

1000 °C High-Temperature Wetting Behaviors of Molten Metals on Laser-Microstructured Metal Surfaces.

The melting of metals at high temperatures is common and important in many fields, e.g., metallurgy, refining, casting, welding, brazing, even newly developed batteries, and nuclear fusion, which is thus of great value in modern industrialization.

The necroptosis signature and molecular mechanism of lung squamous cell carcinoma.

Background: Given the poor prognosis of lung squamous cell carcinoma (LUSC), the aim of this study was to screen for new prognostic biomarkers.

Methods: The TGCA_LUSC dataset was used as the training set, and GSE73403 was used as the validation set. The genes involved in necroptosis-related pathways were acquired from the KEGG database, and the differential genes between the LUSC and normal samples were identified using the GSEA.

Case Report: A rare case of non-small cell lung cancer with STRN-ALK fusion in a patient in very poor condition treated with first-line ensartinib.

Several cases of STRN-ALK fusion have been reported, and some anaplastic lymphoma kinase (ALK) inhibitors have been shown to be effective for treatment. Nevertheless, no cases of COVID-19 leading to heart failure and respiratory failure have been reported in people older than 70 years treated with ALK inhibitors. The present case report describes a 70-year-old patient with usual chronic obstructive pulmonary disease, diabetes, depression, and carotid plaque disease.

Prognostic value of monocyte-to-lymphocyte ratio for 90-day all-cause mortality in type 2 diabetes mellitus patients with chronic kidney disease.

The role of inflammation and the correlation between inflammatory markers and type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD) have been studied. In clinical work, a large number of T2DM patients complicated with CKD, but the cause of CKD was not clear. Our study aimed to evaluate the relationship between monocyte-to-lymphocyte ratio (MLR) and mortality in T2DM patients with CKD.

Comprehensive analysis of the clinical and prognostic significance of SFRP1 and PRKCB expression in non-small cell lung cancer: a retrospective analysis.

Objectives: Secreted frizzled-related protein 1 (SFRP1) and protein kinase C-B (PRKCB) contribute to cancer progression and angiogenesis. This study intended to detect SFRP1 and PRKCB expression in non-small-cell lung cancer (NSCLC) patients and analyze its association with clinicopathological features.

Methods: A total of 108 NSCLC patients who underwent surgical resection in our hospital between 2012 and 2017 were retrospectively analyzed.

Enhancing Unsupervised Anomaly Detection With Score-Guided Network.

Anomaly detection plays a crucial role in various real-world applications, including healthcare and finance systems. Owing to the limited number of anomaly labels in these complex systems, unsupervised anomaly detection methods have attracted great attention in recent years. Two major challenges faced by the existing unsupervised methods are as follows: 1) distinguishing between normal and abnormal data when they are highly mixed together and 2) defining an effective metric to maximize the gap between normal and abnormal data in a hypothesis space, which is built by a representation learner.

Semi-Supervised Mixture Learning for Graph Neural Networks With Neighbor Dependence.

A graph neural network (GNN) is a powerful architecture for semi-supervised learning (SSL). However, the data-driven mode of GNNs raises some challenging problems. In particular, these models suffer from the limitations of incomplete attribute learning, insufficient structure capture, and the inability to distinguish between node attribute and graph structure, especially on label-scarce or attribute-missing data.

Altered temporoparietal junction activity during reflected self-evaluation in sub-clinical depression.

Negative self-schema is a core symptom of depression. According to social psychological theories, two types of self-evaluations play important roles in forming the negative self-view: direct self-evaluation (that is, evaluating the self directly through one's first-person perspective introspection) and reflected self-evaluation (which requires theory of mind (ToM) ability, and is evaluating the self through reflecting on a third person's perspective). Although many previous studies have investigated the processing of the direct self-evaluation in depression, few have extended research on the reflected self-evaluation.

A nomogram for predicting the mortality of patients with type 2 diabetes mellitus complicated with acute kidney injury in the intensive care unit.

Background: There is no predictive tool for type 2 diabetes mellitus (T2DM) patients with acute kidney injury (AKI). Our study aimed to establish an effective nomogram model for predicting mortality in T2DM patients with AKI.

Method: Data on T2DM patients with AKI were obtained from the Medical Information Mart for Intensive Care III.

SKA1/2/3 is a biomarker of poor prognosis in human hepatocellular carcinoma.

Background: Spindle and kinetochore-associated complex subunits 1-3 (SKA1-3) stabilize the kinetochore-attached spindle microtubules in metaphase. Due to the dysregulation in multiple cancers, SKA1-3 is considered a predictor for the prognosis of the patients. However, the potential clinical applications of SKA1-3, particularly in hepatocellular carcinoma (HCC) prognosis and progression, have completely unknown yet.

Discovering hidden brain network responses to naturalistic stimuli via tensor component analysis of multi-subject fMRI data.

The study of brain network interactions during naturalistic stimuli facilitates a deeper understanding of human brain function. To estimate large-scale brain networks evoked with naturalistic stimuli, a tensor component analysis (TCA) based framework was used to characterize shared spatio-temporal patterns across subjects in a purely data-driven manner. In this framework, a third-order tensor is constructed from the timeseries extracted from all brain regions from a given parcellation, for all participants, with modes of the tensor corresponding to spatial distribution, time series and participants.

LightSleepNet: A Lightweight Deep Model for Rapid Sleep Stage Classification with Spectrograms.

Deep learning has achieved unprecedented success in sleep stage classification tasks, which starts to pave the way for potential real-world applications. However, due to its enormous size, deployment of deep neural networks is hindered by high cost at various aspects, such as computation power, storage, network bandwidth, power consumption, and hardware complexity. For further practical applications (e.

An efficient functional magnetic resonance imaging data reduction strategy using neighborhood preserving embedding algorithm.

High dimensionality data have become common in neuroimaging fields, especially group-level functional magnetic resonance imaging (fMRI) datasets. fMRI connectivity analysis is a widely used, powerful technique for studying functional brain networks to probe underlying mechanisms of brain function and neuropsychological disorders. However, data-driven technique like independent components analysis (ICA), can yield unstable and inconsistent results, confounding the true effects of interest and hindering the understanding of brain functionality and connectivity.

The key role of pulse wave transit time to predict blood pressure variation during anaesthesia induction.

Objective: To establish the relationship between pulse wave transit time (PWTT) before anaesthesia induction and blood pressure variability (BPV) during anaesthesia induction.

Methods: This prospective observational cohort study enrolled consecutive patients that underwent elective surgery. Invasive arterial pressure, electrocardiography, pulse oximetry, heart rate and bispectral index were monitored.

Delay-Dependent Distributed Kalman Fusion Estimation With Dimensionality Reduction in Cyber-Physical Systems.

This article studies the distributed dimensionality reduction fusion estimation problem with communication delays for a class of cyber-physical systems (CPSs). The raw measurements are preprocessed in each sink node to obtain the local optimal estimate (LOE) of a CPS, and the compressed LOE under dimensionality reduction encounters with communication delays during the transmission. Under this case, a mathematical model with compensation strategy is proposed to characterize the dimensionality reduction and communication delays.

Formation Tracking of Multiagent Systems With Time-Varying Actuator Faults and Its Application to Task-Space Cooperative Tracking of Manipulators.

This article is concerned with a fault-tolerant formation tracking problem of nonlinear systems under unknown faults, where the leader's states are only accessible to a small set of followers via a directed graph. Under these faults, not only the amplitudes but also the signs of control coefficients become time-varying and unknown. The current setting will enhance the investigated problem's practical relevance and at the same time, it poses nontrivial design challenges of distributed control algorithms and convergence analysis.

Distributed Generalized Nash Equilibrium Seeking for Monotone Generalized Noncooperative Games by a Regularized Penalized Dynamical System.

In this work, we study the generalized Nash equilibrium (GNE, see Definition 1) seeking problem for monotone generalized noncooperative games with set constraints and shared affine inequality constraints. A novel projected gradient-based regularized penalized dynamical system is proposed to solve this issue. The idea is to use a differentiable penalty function with a time-varying penalty parameter to deal with the inequality constraints.

Frequency specific co-activation pattern analysis via sparse nonnegative tensor decomposition.

Background: Traditionally, the diagnosis of Parkinson's disease (PD) has been made based on symptoms. Extensive studies have demonstrated that PD may lead to variation of brain activity in different frequency bands. However, frequency specific dynamic alterations of PD have not yet been explored.

Two effective clinical prediction models to screen for obstructive sleep apnoea based on body mass index and other parameters.

Background And Objective: The diagnosis of obstructive sleep apnea (OSA) relies on polysomnography which is time-consuming and expensive. We therefore aimed to develop two simple, non-invasive models to screen adults for OSA.

Methods: The effectiveness of using body mass index (BMI) and a new visual prediction model to screen for OSA was evaluated using a development set (1769 participants) and confirmed using an independent validation set (642 participants).

Asymmetric Input-Output Constraint Control of a Flexible Variable-Length Rotary Crane Arm.

This article demonstrates the realization of angle tracking and deformation suppression by developing two boundary controllers for a flexible variable-length rotary crane arm with extraneous disturbances and asymmetric input-output constraints. The dynamic model description of this kind of crane arm system is several partial differential equations integrated into few ordinary differential equations. The S-curve acceleration and deceleration scheme is utilized to adjust the elongation rate of the arm.

Time-Varying Optimization-Based Approach for Distributed Formation of Uncertain Euler-Lagrange Systems.

We investigate a distributed time-varying formation control problem for an uncertain Euler-Lagrange system. A time-varying optimization-based approach is proposed. Based on this approach, the robots can achieve the expected formation configuration and meanwhile optimize a global objective function using only neighboring and local information.

Consistency of independent component analysis for FMRI.

Background: Independent component analysis (ICA) has been widely used for blind source separation in the field of medical imaging. However, despite of previous substantial efforts, the stability of ICA components remains a critical issue which has not been adequately addressed, despite numerous previous efforts. Most critical is the inconsistency of some of the extracted components when ICA is run with different model orders (MOs).

Snowball ICA: A Model Order Free Independent Component Analysis Strategy for Functional Magnetic Resonance Imaging Data.

In independent component analysis (ICA), the selection of model order (i.e., number of components to be extracted) has crucial effects on functional magnetic resonance imaging (fMRI) brain network analysis.