FedDRL: Trustworthy Federated Learning Model Fusion Method Based on Staged Reinforcement Learning

Federated learning facilitates collaborative data analysis among multiple participants while preserving user privacy. However, conventional federated learning approaches, typically employing weighted average techniques for model fusion, confront two significant challenges: 1. The inclusion of malicious models in the fusion process can drastically undermine the accuracy of the aggregated global model. 2. Due to the heterogeneity problem of devices and data, the number of client samples does not determine the weight value of the model. To solve those challenges, we propose a trustworthy model fusion method based on reinforcement learning (FedDRL), which includes two stages. In the first stage, we propose a reliable client selection mechanism to exclude malicious models from the fusion process. In the second stage, we propose an adaptive model fusion method that dynamically assigns weights based on model quality to aggregate the best global models. Finally, we validate our approach against five distinct model fusion scenarios, demonstrating that our algorithm significantly enhanced reliability without compromising accuracy

Determination of seven nicotinamide compounds in health products

Objective： This study aimed to establish an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method for simultaneous determination of 7 nicotinamide compounds in health food. Methods： The samples were dissolved in 10% methanol water, extracted by ultrasonic, and 10 mmol/L ammonium acetate-acetonitrile was used as the mobile phase of gradient elution. The seven nicotinamide compounds were monitored by ESI, positive and negative ion scanning mode, and multiple reaction monitoring (MRM) mode. Results： The 7 nicotinamide compounds showed good linearity in the mass concentration range, and the correlation coefficients were greater than 0.996. The limit of detection (LOD) was 0.075~0.600 mg/kg, the recovery was 84.6%~108.6% with the relative standard deviation of 2.1%~8.7% (n=6). Conclusion： The method has the advantages of simple operation, high speed, high efficiency, high recovery rateand good precision, and can be used for qualitative and quantitative analysis of nicotinamide and other analogues in health care foods

Determination of eight anions in liquid milk

Objective： A rapid method was established for the simultaneous determination of variety anions in liquid milk. Methods： The macromolecular protein in liquid milk was precipitated by acetonitrile, and a variety of anions were extracted. After purification by C18 column, the material was separated by Dionex IonPac AS19-HC ion column with gradient elution of potassium hydroxide and conductivity detection. Finally, a method for the determination of bromate, thiocyanate, nitrite, nitrate, chlorate, perchlorate, phosphate and iodine ion in liquid milk was established using ion chromatograph. Results： The results showed that the correlation coefficients （R2）of eight kinds of anion range from 0.999 0 to 0.999 8 in the linear range, and the limits of detection were between 0.065 mg/kg and 0.417 mg/kg. The recoveries were in the range 81.8%~102.0% with the relative standard deviations in the range 1.1%~6.7%（n=6）. Conclusion： The method has the advantages of simple operation, high accuracy and sensitivity, and can be used for simultaneous determination of various anion residues in liquid milk

Studying trabecular bone samples demonstrates a power law relation between deteriorated structure and mechanical properties - a study combining 3D printing with the finite element method

IntroductionThe bone volume fraction (BV/TV) significantly contributes to the mechanical properties of trabecular bone. However, when studies compare normal trabeculae against osteoporotic trabeculae (in terms of BV/TV decrease), only an “average” mechanical result has been determined because of the limitation that no two trabecular structures are the same and that each unique trabecular structure can be mechanically tested only once. The mathematic relation between individual structural deterioration and mechanical properties during aging or the osteoporosis process has yet to be further clarified. Three-dimensional (3D) printing and micro-CT-based finite element method (μFEM) can assist in overcoming this issue.MethodsIn this study, we 3D printed structural-identical but BV/TV value-attenuated trabecular bones (scaled up ×20) from the distal femur of healthy and ovariectomized rats and performed compression mechanical tests. Corresponding μFEM models were also established for simulations. The tissue modulus and strength of 3D printed trabecular bones as well as the effective tissue modulus (denoted as Ez) derived from μFEM models were finally corrected by the side-artifact correction factor.ResultsThe results showed that the tissue modulus corrected, strength corrected and Ez corrected exhibited a significant power law function of BV/TV in structural-identical but BV/TV value-attenuated trabecular samples. DiscussionUsing 3D printed bones, this study confirms the long-known relationship measured in trabecular tissue with varying volume fractions. In the future, 3D printing may help us attain better bone strength evaluations and even personal fracture risk assessments for patients who suffer from osteoporosis

Measurement of the relationship between maxillary premolar roots and the maxillary sinus floor using cone beam CT and analysis of the impact on immediate implantation

Objective To analyze the spatial relationship between the roots of maxillary anterior premolars and the maxillary sinus, thus providing an anatomical basis for timing, planning, surgical approaches, and implant selection at this site. Methods Cone beam CT (CBCT) images were collected from 264 patients (aged 20-65 years) who visited the Ruihua Dental Clinic between January 2017 and March 2023. The minimum distance from the apex of the maxillary anterior premolar roots to the lower wall of the maxillary sinus was measured on the coronal plane. The classification of the vertical relationship between the tooth root and the lower wall of the maxillary sinus was performed, and comparisons were made bilaterally, between genders, and among different age groups. Results The minimum distance (Q50) from the apex of the first maxillary premolar root to the lower wall of the maxillary sinus was 7.34 mm for the single-root type, 7.80 mm for the buccal root of the double-root type, and 7.36 mm for the palatal root. For the second maxillary premolar, the median distance was 2.56 mm for the single root type, 1.73 mm for the buccal root type, and 1.23 mm for the palatal root type. There was a significant difference in the shortest distance from the apex of the right second maxillary premolar single root to the lower wall of the maxillary sinus among the different age groups (P<0.05), with the 20-29-year-old group having the smallest median distance (1.52 mm) and the ≥ 40-year-old group having the largest (4.44 mm). There was no significant difference in the effect of sex or laterality on distance (P>0.05). The most common vertical relationship between the apex of the maxillary anterior premolar roots and the lower wall of the maxillary sinus was noncontact. There was no significant difference in the vertical relationship classification between the single-root and double-root types (P>0.05). Conclusion Most maxillary first premolar roots can provide sufficient bone height, which makes it easy to achieve immediate implantation. The maxillary second premolar root frequently involves insufficient bone, which is necessary to make full use of the bone wall of the extraction socket or the sinus floor cortical bone to achieve initial stability. The vertical relationship between the premolar root and maxillary sinus was influenced by age and dental position. Younger age groups often exhibit inadequate bone height, and the indication for immediate implantation should be carefully considered. The number of roots does not significantly affect the relationship between the sinus and root; however, double-rooted premolars offer more support for immediate implantation and socket healing due to the small root diameter and bony separation between the roots

KD_ConvNeXt: knowledge distillation-based image classification of lung tumor surgical specimen sections

Introduction: Lung cancer is currently among the most prevalent and lethal cancers in the world in terms of incidence and fatality rates. In clinical practice, identifying the specific subtypes of lung cancer is essential in diagnosing and treating lung lesions.Methods: This paper aims to collect histopathological section images of lung tumor surgical specimens to construct a clinical dataset for researching and addressing the classification problem of specific subtypes of lung tumors. Our method proposes a teacher-student network architecture based on a knowledge distillation mechanism for the specific subtype classification of lung tumor histopathological section images to assist clinical applications, namely KD_ConvNeXt. The proposed approach enables the student network (ConvNeXt) to extract knowledge from the intermediate feature layers of the teacher network (Swin Transformer), improving the feature extraction and fitting capabilities of ConvNeXt. Meanwhile, Swin Transformer provides soft labels containing information about the distribution of images in various categories, making the model focused more on the information carried by types with smaller sample sizes while training.Results: This work has designed many experiments on a clinical lung tumor image dataset, and the KD_ConvNeXt achieved a superior classification accuracy of 85.64% and an F1-score of 0.7717 compared with other advanced image classification method

Effects of hesperidin on the histological structure, oxidative stress, and apoptosis in the liver and kidney induced by NiCl2

The aim of this study was to investigate the effect of hesperidin on the liver and kidney dysfunctions induced by nickel. The mice were divided into six groups: nickel treatment with 80 mg/kg, 160 mg/kg, 320 mg/kg hesperidin groups, 0.5% CMC-Na group, nickel group, and blank control group. Histopathological techniques, biochemistry, immunohistochemistry, and the TUNEL method were used to study the changes in structure, functions, oxidative injuries, and apoptosis of the liver and kidney. The results showed that hesperidin could alleviate the weight loss and histological injuries of the liver and kidney induced by nickel, and increase the levels of lactate dehydrogenase (LDH), alanine aminotransferase (GPT), glutamic oxaloacetic transaminase (GOT) in liver and blood urea nitrogen (BUN), creatinine (Cr) and N-acetylglucosidase (NAG) in kidney. In addition, hesperidin could increase the activities of superoxide dismutase (SOD), catalase (CAT), glutathione (GSH), and glutathione peroxidase (GSH-Px) in the liver and kidney, decrease the content of malondialdehyde (MDA) and inhibit cell apoptosis. It is suggested that hesperidin could help inhibit the toxic effect of nickel on the liver and kidney

A comprehensive AI model development framework for consistent Gleason grading

Background: Artificial Intelligence(AI)-based solutions for Gleason grading hold promise for pathologists, while image quality inconsistency, continuous data integration needs, and limited generalizability hinder their adoption and scalability. Methods: We present a comprehensive digital pathology workflow for AI-assisted Gleason grading. It incorporates A!MagQC (image quality control), A!HistoClouds (cloud-based annotation), Pathologist-AI Interaction (PAI) for continuous model improvement, Trained on Akoya-scanned images only, the model utilizes color augmentation and image appearance migration to address scanner variations. We evaluate it on Whole Slide Images (WSI) from another five scanners and conduct validations with pathologists to assess AI efficacy and PAI. Results: Our model achieves an average F1 score of 0.80 on annotations and 0.71 Quadratic Weighted Kappa on WSIs for Akoya-scanned images. Applying our generalization solution increases the average F1 score for Gleason pattern detection from 0.73 to 0.88 on images from other scanners. The model accelerates Gleason scoring time by 43% while maintaining accuracy. Additionally, PAI improve annotation efficiency by 2.5 times and led to further improvements in model performance. Conclusions: This pipeline represents a notable advancement in AI-assisted Gleason grading for improved consistency, accuracy, and efficiency. Unlike previous methods limited by scanner specificity, our model achieves outstanding performance across diverse scanners. This improvement paves the way for its seamless integration into clinical workflows