Characteristics in patients with unexplained recurrent spontaneous abortion and the impact on immune, coagulation, and inflammatory profiles.

Objective: Unexplained Recurrent Spontaneous Abortion (RSA) impacts the physical and psychological well-being of women of reproductive age. This study aimed to reveal the potential mechanisms behind unexplained RSA from the perspective of glucose and lipid metabolic profiles before conception and their relationship with immune, coagulation, and inflammatory markers.

Methods: A retrospective analysis was conducted on 100 patients with unexplained recurrent spontaneous abortion at our gynecology department from June 2022 to June 2023.

The reduction effect on sensitization of sesame protein Ses i 3 of ultrasound-assisted glycation treatment through modulation of T cell differentiation.

This study aimed to investigate the potential reduction on the sensitization of sesame protein Ses i 3 through ultrasound-assisted glycation. Ses i 3 was extracted and purified using an immunoaffinity column, and the allergenicity changes of Ses i 3 were assessed by a comprehensive strategy, and T cell polarization was also assessed in vivo. Results showed ultrasound-assisted glycation treated Ses i 3 was more easily digestible; and the cell degranulation model showed the histamine, tryptase, and β-hexosaminidase induced by ultrasound-assisted glycation treatment were significantly decreased; besides, the serological results demonstrated that a notable decrease in the binding ability of immunoglobulin E (IgE) and IgG; finally, a BALB/c mice model demonstrated an alleviation of allergic responses induced by ultrasonic-assisted glycation treatment.

Scale-Adaptive viable tumor burden estimation via histopathological microscopy image segmentation.

Cancer segmentation in whole-slide images is a fundamental step for estimating tumor burden, which is crucial for cancer assessment. However, challenges such as vague boundaries and small regions dissociated from viable tumor areas make it a complex task. Considering the usefulness of multi-scale features in various vision-related tasks, we present a structure-aware, scale-adaptive feature selection method for efficient and accurate cancer segmentation.

An ultra-sensitive, intelligent platform for food safety monitoring: Label-free detection of illegal additives using self-assembled SERS substrates and machine learning.

To overcome the limitations of SERS in food safety monitoring, particularly significant interference from citrate ions, this study introduces an intelligent SERS-based platform for food safety monitoring. The platform utilizes sodium borohydride to activate silver nanoparticles, and calcium ions can facilitate the nanoparticles aggregation to promote self-assembly and the form of "hotspots", but will also amplify citrate ions signal. Iodine ions was introduced to eliminate the interference of citrate signals and background fluorescence interference.

Enhanced dose prediction for head and neck cancer artificial intelligence-driven radiotherapy based on transfer learning with limited training data.

Purpose: Training deep learning dose prediction models for the latest cutting-edge radiotherapy techniques, such as AI-based nodal radiotherapy (AINRT) and Daily Adaptive AI-based nodal radiotherapy (DA-AINRT), is challenging due to limited data. This study aims to investigate the impact of transfer learning on the predictive performance of an existing clinical dose prediction model and its potential to enhance emerging radiotherapy approaches for head and neck cancer patients.

Method: We evaluated the impact and benefits of transfer learning by fine-tuning a Hierarchically Densely Connected U-net on both AINRT and DA-AINRT patient datasets, creating Model (Study 1) and Model (Study 2).