Inverted Pyramid Nanostructures Coupled with a Sandwich Immunoassay for SERS Biomarker Detection.

Cancer diagnostics often faces challenges, such as invasiveness, high costs, and limited sensitivity for early detection, emphasizing the need for improved approaches. We present a surface-enhanced Raman scattering (SERS)-based platform leveraging inverted pyramid SU-8 nanostructured substrates fabricated via nanoimprint lithography. These substrates, characterized by sharp apices and edges, are further functionalized with (3-aminopropyl)triethoxysilane (APTES), enabling the uniform self-assembly of AuNPs to create a highly favorable configuration for enhanced SERS analysis.

Enhancing generalization in a Kawasaki Disease prediction model using data augmentation: Cross-validation of patients from two major hospitals in Taiwan.

Kawasaki Disease (KD) is a rare febrile illness affecting infants and young children, potentially leading to coronary artery complications and, in severe cases, mortality if untreated. However, KD is frequently misdiagnosed as a common fever in clinical settings, and the inherent data imbalance further complicates accurate prediction when using traditional machine learning and statistical methods. This paper introduces two advanced approaches to address these challenges, enhancing prediction accuracy and generalizability.

Serum Potassium Monitoring Using AI-Enabled Smartwatch Electrocardiograms.

Background: Hyperkalemia, characterized by elevated serum potassium levels, heightens the risk of sudden cardiac death, particularly increasing risk for individuals with chronic kidney disease and end-stage renal disease (ESRD). Traditional laboratory test monitoring is resource-heavy, invasive, and unable to provide continuous tracking. Wearable technologies like smartwatches with electrocardiogram (ECG) capabilities are emerging as valuable tools for remote monitoring, potentially allowing for personalized monitoring with artificial intelligence (AI)-ECG interpretation.

Glycosyl oxazolines serve as active donors for iterative synthesis of type I oligosaccharides.

Synthesis of Galβ1 → 3GlcNAc-repeating saccharides is limited mainly by the formation of less-reactive oxazolines. We herein report an expeditious approach that requires trichloroacetyloxazolines as reactive glycosyl donors. Using only two disaccharide building blocks, the iterative oxazoline formation and glycosylation synthesized hexa- and octasaccharides with overall yields of 47% and 26% in four and six steps, respectively.

Defective N-glycosylation of IL6 induces metastasis and tyrosine kinase inhibitor resistance in lung cancer.

The IL6-GP130-STAT3 pathway facilitates lung cancer progression and resistance to tyrosine kinase inhibitors. Although glycosylation alters the stability of GP130, its effect on the ligand IL6 remains unclear. We herein find that N-glycosylated IL6, especially at Asn73, primarily stimulates JAK-STAT3 signaling and prolongs STAT3 phosphorylation, whereas N-glycosylation-defective IL6 (deNG-IL6) induces shortened STAT3 activation and alters the downstream signaling preference for the SRC-YAP-SOX2 axis.

B4GALT1-dependent galectin-8 binding with TGF-β receptor suppresses colorectal cancer progression and metastasis.

Transforming growth factor (TGF)-β signaling is critical for epithelial-mesenchymal transition (EMT) and colorectal cancer (CRC) metastasis. Disruption of Smad-depednent TGF-β signaling has been shown in CRC cells. However, TGF-β receptor remains expressed on CRC cells.

Galectins induced from hemocytes bridge phosphatidylserine and N-glycosylated Drpr/CED-1 receptor during dendrite pruning.

During neuronal pruning, phagocytes engulf shed cellular debris to avoid inflammation and maintain tissue homeostasis. How phagocytic receptors recognize degenerating neurites had been unclear. Here, we identify two glucosyltransferases Alg8 and Alg10 of the N-glycosylation pathway required for dendrite fragmentation and clearance through genetic screen.

Explainable Deep Learning Model for Predicting Serious Adverse Events in Hospitalized Geriatric Patients Within 72 Hours.

Background: The global aging population presents a significant challenge, with older adults experiencing declining physical and cognitive abilities and increased vulnerability to chronic diseases and adverse health outcomes. This study aims to develop an interpretable deep learning (DL) model to predict adverse events in geriatric patients within 72 hours of hospitalization.

Methods: The study used retrospective data (2017-2020) from a major medical center in Taiwan.

Utilizing Nearest-Neighbor Clustering for Addressing Imbalanced Datasets in Bioengineering.

Imbalance classification is common in scenarios like fault diagnosis, intrusion detection, and medical diagnosis, where obtaining abnormal data is difficult. This article addresses a one-class problem, implementing and refining the One-Class Nearest-Neighbor (OCNN) algorithm. The original inter-quartile range mechanism is replaced with the K-means with outlier removal (KMOR) algorithm for efficient outlier identification in the target class.

Robotic exoskeletons and total knee arthroplasty: The future of knee rehabilitation and replacement - A meta-analysis.

Objective: Exoskeletons can play a crucial role in post-TKA rehabilitation by accelerating recovery, improving mobility, and reducing further injury risk. This meta-analysis evaluated the effectiveness of exoskeletons in post-total knee replacement (TKR) rehabilitation.

Design: Comprehensive searches were conducted on PubMed, OVID Medline, Cochrane Collaboration Library, and Embase (period: database inception to March 2023).

Ultrasensitive Upconversion Nanoparticle Immunoassay for Human Serum Cardiac Troponin I Detection Achieved with Resonant Waveguide Grating.

Selective detection of biomarkers at low concentrations in blood is crucial for the clinical diagnosis of many diseases but remains challenging. In this work, we aimed to develop an ultrasensitive immunoassay that can detect biomarkers in serum with an attomolar limit of detection (LOD). We proposed a sandwich-type heterogeneous immunosensor in a 3 × 3 well array format by integrating a resonant waveguide grating (RWG) substrate with upconversion nanoparticles (UCNPs).

Molecular subtypes of breast cancer predicting clinical benefits of radiotherapy after breast-conserving surgery: a propensity-score-matched cohort study.

Background: Based on the molecular expression of cancer cells, molecular subtypes of breast cancer have been applied to classify patients for predicting clinical outcomes and prognosis. However, further evidence is needed regarding the influence of molecular subtypes on the efficacy of radiotherapy (RT) after breast-conserving surgery (BCS), particularly in a population-based context. Hence, the present study employed a propensity-score-matched cohort design to investigate the potential role of molecular subtypes in stratifying patient outcomes for post-BCS RT and to identify the specific clinical benefits that may emerge.

Characterization and catalytic investigation of fungal single-module nonribosomal peptide synthetase in terpene-amino acid meroterpenoid biosynthesis.

Unlabelled: Hybrid natural products are compounds that originate from diverse biosynthetic pathways and undergo a conjugation process, which enables them to expand their chemical diversity and biological functionality. Terpene-amino acid meroterpenoids have garnered increasing attention in recent years, driven by the discovery of noteworthy examples such as the anthelmintic CJ-12662, the insecticidal paeciloxazine, and aculene A (1). In the biosynthesis of terpene-amino acid natural products, single-module nonribosomal peptide synthetases (NRPSs) have been identified to be involved in the esterification step, catalyzing the fusion of modified terpene and amino acid components.

Sulfoglycolipids and Related Analogues of Mycobacterium tuberculosis: Chemical Synthesis and Immunological Studies.

Mycobacterium tuberculosis (Mtb) causes tuberculosis as one major threat to human health, which has been deteriorated owing to the emerging multidrug resistance. Mtb contains a complex lipophilic cell wall structure that is important for bacterial persistence. Among the lipid components, sulfoglycolipids (SGLs), known to induce immune cell responses, are composed of a trehalose core attached with a conserved sulfate group and 1-4 fatty acyl chains in an asymmetric pattern.

Development and validation of a deep learning pipeline to measure pericardial effusion in echocardiography.

Objectives: The aim of this study was to develop a deep-learning pipeline for the measurement of pericardial effusion (PE) based on raw echocardiography clips, as current methods for PE measurement can be operator-dependent and present challenges in certain situations.

Methods: The proposed pipeline consisted of three distinct steps: moving window view selection (MWVS), automated segmentation, and width calculation from a segmented mask. The MWVS model utilized the ResNet architecture to classify each frame of the extracted raw echocardiography files into selected view types.

Synthesis of Metal/SU-8 Nanocomposites through Photoreduction on SU-8 Substrates.

The paper presents a simple, fast, and cost-effective method for creating metal/SU-8 nanocomposites by applying a metal precursor drop onto the surface or nanostructure of SU-8 and exposing it to UV light. No pre-mixing of the metal precursor with the SU-8 polymer or pre-synthesis of metal nanoparticles is required. A TEM analysis was conducted to confirm the composition and depth distribution of the silver nanoparticles, which penetrate the SU-8 film and uniformly form the Ag/SU-8 nanocomposites.

Joint Data Transmission and Energy Harvesting for MISO Downlink Transmission Coordination in Wireless IoT Networks.

The advent of simultaneous wireless information and power (SWIPT) has been regarded as a promising technique to provide power supplies for an energy sustainable Internet of Things (IoT), which is of paramount importance due to the proliferation of high data communication demands of low-power network devices. In such networks, a multi-antenna base station (BS) in each cell can be utilized to concurrently transmit messages and energies to its intended IoT user equipment (IoT-UE) with a single antenna under a common broadcast frequency band, resulting in a multi-cell multi-input single-output (MISO) interference channel (IC). In this work, we aim to find the trade-off between the spectrum efficiency (SE) and energy harvesting (EH) in SWIPT-enabled networks with MISO ICs.

Using Machine Learning for the Risk Factors Classification of Glycemic Control in Type 2 Diabetes Mellitus.

Several risk factors are related to glycemic control in patients with type 2 diabetes mellitus (T2DM), including demographics, medical conditions, negative emotions, lipid profiles, and heart rate variability (HRV; to present cardiac autonomic activity). The interactions between these risk factors remain unclear. This study aimed to use machine learning methods of artificial intelligence to explore the relationships between various risk factors and glycemic control in T2DM patients.

Use of Machine Learning to Differentiate Children With Kawasaki Disease From Other Febrile Children in a Pediatric Emergency Department.

Importance: Early awareness of Kawasaki disease (KD) helps physicians administer appropriate therapy to prevent acquired heart disease in children. However, diagnosing KD is challenging and relies largely on subjective diagnosis criteria.

Objective: To develop a prediction model using machine learning with objective parameters to differentiate children with KD from other febrile children.

Explainable deep learning model to predict invasive bacterial infection in febrile young infants: A retrospective study.

Background: Machine learning models have demonstrated superior performance in predicting invasive bacterial infection (IBI) in febrile infants compared to commonly used risk stratification criteria in recent studies. However, the black-box nature of these models can make them difficult to apply in clinical practice. In this study, we developed and validated an explainable deep learning model that can predict IBI in febrile infants ≤ 60 days of age visiting the emergency department.

Cytosolic galectin-4 enchains bacteria, restricts their motility, and promotes inflammasome activation in intestinal epithelial cells.

Galectin-4, a member of the galectin family of animal glycan-binding proteins (GBPs), is specifically expressed in gastrointestinal epithelial cells and is known to be able to bind microbes. However, its function in host-gut microbe interactions remains unknown. Here, we show that intracellular galectin-4 in intestinal epithelial cells (IECs) coats cytosolic serovar Worthington and induces the formation of bacterial chains and aggregates.

Utilization of Personalized Machine-Learning to Screen for Dysglycemia from Ambulatory ECG, toward Noninvasive Blood Glucose Monitoring.

Blood glucose (BG) monitoring is important for critically ill patients, as poor sugar control has been associated with increased mortality in hospitalized patients. However, constant BG monitoring can be resource-intensive and pose a healthcare burden in clinical practice. In this study, we aimed to develop a personalized machine-learning model to predict dysglycemia from electrocardiogram (ECG) data.