Browse Articles

Treatments for Dupuytren’s Contracture

AHNAK2: a potential diagnostic biomarker for pancreatic cancer related to cellular motility

Abstract Pancreatic ductal adenocarcinoma lacks suitable biomarkers for early diagnosis of disease. In gene panels developed for early diagnosis of pancreatic cancer, high AHNAK2 mRNA expression was one possible biomarker. In silico analysis of published human sample datasets (n = 177) and ex vivo analysis of human plasma samples (n = 30 PDAC with matched 30 healthy control) suggested AHNAK2 could be a diagnostic biomarker. At a plasma level of 421.47 ng/ml, AHNAK2 could potentially diagnose PDAC with a specificity and sensitivity of 83.33% and 86.67%. In vitro analysis suggests that in cell lines with diffuse cytoplasmic distribution of AHNAK2, there was colocalization of AHNAK2 with Cortactin in filipodia. This colocalization increased when cells were cultured on substrates such as Fibronectin and Collagen, as well as in hypoxia, and resulted in an augmented invasion of cancer cells. However, in cell lines with a vesicular AHNAK2 staining, such changes were not observed. Our study posits AHNAK2 as a valuable diagnostic biomarker in PDAC, now demanding prospective validation. Determination of mechanisms regulating AHNAK2 subcellular localisation may help explain its biological role.

Neuromorphic computing at scale

The Role of Importation in Remediating U.S. Generic Drug Shortages

3D numerical modeling of the deformation and failure mechanisms of batter pile groups subjected to fault ruptures

Abstract Fault ruptures induced by earthquakes pose a significant threat to constructions, particularly underground structures such as pile foundations. Among various foundation types, batter pile foundations are widely used due to their ability to resist inclined forces. To gain new insights into the response of batter pile groups to fault ruptures caused by earthquakes, this study investigates the deformation and failure mechanisms of batter pile groups due to the propagation of normal and reverse fault ruptures using 3D numerical modeling. An advanced hypoplastic constitutive model for clay, which accounts for small-strain stiffness, and a concrete damage plasticity (CDP) model are employed to simulate the soil and the batter pile foundation, respectively. Results show that following fault propagation, nearly 10% tilting and significant displacement occurred at the pile cap, indicating a total failure of the batter pile foundation. It was also observed that the piles bent towards the slipping direction of the hanging wall. Tensile damage to the pile foundation was notably more severe than compression damage. The most severely damaged regions were not only located at the joints between the piles and the pile caps but were also found along the pile shafts.

Author Correction: Ab initio characterization of protein molecular dynamics with AI2BMD

Vaginal Varix in Pregnancy

InGaN multiquantum wells—problem of carrier injection

Cytomegalovirus Retinitis

De novo transcriptome assembly and analysis during agarwood induction in Gyrinops versteegii Gilg. seedling

Case 3-2025: A 54-Year-Old Man with Exertional Dyspnea and Chest Pain

TOPSIS prefabricated building construction evaluation based on interval-valued Pythagorean fuzzy numbers based on prospect theory

Author Correction: Oxidative cyclization reagents reveal tryptophan cation–π interactions

Targeting a Hormonal Cause of Hypertension

The influence of depth on the global deep-sea plasmidome

Superconductivity in 5.0° twisted bilayer WSe2

Platelet-Targeted Gene Therapy for Hemophilia A with Inhibitor History

Exploration of transfer learning techniques for the prediction of PM10

Abstract Modelling of pollutants provides valuable insights into air quality dynamics, aiding exposure assessment where direct measurements are not viable. Machine learning (ML) models can be employed to explore such dynamics, including the prediction of air pollution concentrations, yet demanding extensive training data. To address this, techniques like transfer learning (TL) leverage knowledge from a model trained on a rich dataset to enhance one trained on a sparse dataset, provided there are similarities in data distribution. In our experimental setup, we utilize meteorological and pollutant data from multiple governmental air quality measurement stations in Graz, Austria, supplemented by data from one station in Zagreb, Croatia to simulate data scarcity. Common ML models such as Random Forests, Multilayer Perceptrons, Long-Short-Term Memory, and Convolutional Neural Networks are explored to predict particulate matter in both cities. Our detailed analysis of PM10 suggests that similarities between the cities and the meteorological features exist and can be further exploited. Hence, TL appears to offer a viable approach to enhance PM10 predictions for the Zagreb station, despite the challenges posed by data scarcity. Our results demonstrate the feasibility of different TL techniques to improve particulate matter prediction on transferring a ML model trained from all stations of Graz and transferred to Zagreb. Through our investigation, we discovered that selectively choosing time spans based on seasonal patterns not only aids in reducing the amount of data needed for successful TL but also significantly improves prediction performance. Specifically, training a Random Forest model using data from all measurement stations in Graz and transferring it with only 20% of the labelled data from Zagreb resulted in a 22% enhancement compared to directly testing the trained model on Zagreb.

Ebola and a Decade of Disparities — Forging a Future for Global Health Equity

Contributing factors to postoperative surgical site infections in pituitary neuroendocrine tumors undergoing endonasal transsphenoidal resection