Biocybernetics and Biomedical Engineering最新文献_第7页

Contact Pressure, sliding distance and wear rate analysis at trunnion of hip implant for daily Activities: A finite element approach 髋关节耳轴日常活动接触压力、滑动距离及磨损率分析：有限元方法

IF 5.3 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biocybernetics and Biomedical Engineering

Pub Date : 2025-02-24 DOI: 10.1016/j.bbe.2025.02.001

Md Mohiuddin Soliman , Mohammad Tariqul Islam , Phumin Kirawanich , Muhammad E.H. Chowdhury , Touhidul Alam , Ayed M. Alrashdi , Norbahiah Misran , Mohamed S. Soliman

This research analyses contact pressure, sliding distance, and wear rate at the trunnion interface of hip implants during various activities to understand post-hip replacement outcomes. The study uses a numerical model and ISO-7206–6:2013 constraints with an AML hip implant. Greater F_x, F_y, and F_z forces broaden contact pressure distribution. The highest pressure occurs on the proximal superolateral surface, with the lowest on the anterior and posterior surfaces. The HIGH100 (individuals weighing above 100 kg) weight category demonstrates 2 times higher maximum and average contact pressure compared to AVG75 (individuals weighing 75 kg) for sit-down and knee bend activities. Force components and the duration of a full gait cycle influence sliding distance. Stance activities show the highest sliding distance due to rapid changes in force load during the gait cycle. For sit-down and knee bend activities, the total sliding distance is 2.5 times higher in the HIGH100 weight category compared to AVG75. Sliding distance primarily occurs at the proximal superolateral-inferomedial-anterior-posterior contact surface, decreasing distally. Based on contact pressure, sliding distance, and wear volume rate, jogging and stance activities pose the highest risk for hip replacement patients, while cycling is the safest. The HIGH100 weight group exhibits 5- and 4-times greater wear volume rates than AVG75 in sit-down and knee bend activities, respectively. The research findings align with wear degradation observed in retrieved hip implants, validating the study. These insights can assist patients in making informed decisions about performing activities after surgery while enabling physicians to provide accurate guidelines.

本研究分析了各种活动中髋关节植入体耳轴界面的接触压力、滑动距离和磨损率，以了解髋关节置换术后的结果。该研究使用数值模型和ISO-7206-6:2013对AML髋关节植入物的约束。较大的Fx， Fy和Fz力扩大接触压力分布。最大的压力发生在近外侧表面，最小的压力发生在前、后表面。与AVG75（体重75公斤）相比，HIGH100（体重超过100公斤的人）的最大接触压力和平均接触压力高出2倍。力的组成和一个完整的步态周期的持续时间影响滑动距离。由于在步态周期中力负荷的快速变化，站立活动显示出最高的滑动距离。对于坐下和屈膝活动，HIGH100重量类别的总滑动距离是AVG75的2.5倍。滑动距离主要发生在近侧上外侧-内侧内侧-前后接触面，远端逐渐减少。基于接触压力、滑动距离和磨损量率，慢跑和站立活动对髋关节置换术患者的风险最高，而骑自行车最安全。HIGH100重量组在坐下和膝盖弯曲活动中分别表现出比AVG75高5倍和4倍的磨损体积率。研究结果与回收髋关节植入物观察到的磨损退化一致，验证了研究结果。这些见解可以帮助患者在手术后进行活动时做出明智的决定，同时使医生能够提供准确的指导。

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引用次数: 0

Volumetric medical image segmentation via fully 3D adaptation of Segment Anything Model 通过完全三维适应分割任何模型的体积医学图像分割

IF 5.3 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biocybernetics and Biomedical Engineering

Pub Date : 2025-01-01 DOI: 10.1016/j.bbe.2024.11.001

Haoneng Lin , Jing Zou , Sen Deng , Ka Po Wong , Angelica I. Aviles-Rivero , Yiting Fan , Alex Pui-Wai Lee , Xiaowei Hu , Jing Qin

The Segment Anything Model (SAM) exhibits exceptional generalization capabilities in diverse domains, owing to its interactive learning mechanism designed for precise image segmentation. However, applying SAM to out-of-distribution domains, especially in 3D medical image segmentation, poses challenges. Existing methods for adapting 2D segmentation models to 3D medical data treat 3D volumes as a mere stack of 2D slices. The essential inter-slice information, which is pivotal to faithful 3D medical image segmentation tasks, is unfortunately neglected. In this work, we present the 3D Medical SAM-Adapter (3DMedSAM), a pioneer cross-dimensional adaptation, leveraging SAM’s pre-trained knowledge while accommodating the unique characteristics of 3D medical data. Firstly, to bridge the dimensional gap from 2D to 3D, we design a novel module to replace SAM’s patch embedding, ensuring a seamless transition into 3D image processing and recognition. Besides, we incorporate a 3D Adapter while maintaining the majority of pre-training parameters frozen, enriching deep features with abundant 3D spatial information and achieving efficient fine-tuning. Given the diverse scales of anomalies present in medical images, we also devised a multi-scale 3D mask decoder to elevate the network’s proficiency in medical image segmentation. Through various experiments, we showcase the effectiveness of 3DMedSAM in achieving accurate and robust 3D segmentation on both single-target segmentation and multi-organ segmentation tasks, surpassing the limitations of current methods.

基于交互式学习机制的任意分割模型（SAM）在不同领域表现出卓越的泛化能力。然而，将SAM应用于非分布域，特别是在三维医学图像分割中，面临着挑战。现有的将二维分割模型用于三维医疗数据的方法将三维体仅仅视为二维切片的堆栈。重要的切片间信息，这是关键的忠实的三维医学图像分割任务，不幸的是被忽略了。在这项工作中，我们提出了3D医疗SAM适配器（3DMedSAM），这是一种跨维度适应的先驱，利用SAM的预训练知识，同时适应3D医疗数据的独特特征。首先，为了弥补从2D到3D的尺寸差距，我们设计了一个新的模块来取代SAM的补丁嵌入，确保无缝过渡到3D图像处理和识别。此外，我们在保持大部分预训练参数冻结的同时，加入了一个3D适配器，以丰富的3D空间信息丰富深度特征，实现了高效的微调。鉴于医学图像中存在不同尺度的异常，我们还设计了一个多尺度的3D掩模解码器，以提高网络对医学图像分割的熟练程度。通过各种实验，我们证明了3DMedSAM在单目标分割和多器官分割任务上实现准确和鲁棒的3D分割的有效性，超越了现有方法的局限性。

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引用次数: 0

Impact of aging on anterior segment morphology and aqueous humor dynamics in human Eyes: Advanced imaging and computational techniques 老化对人眼前段形态学和房水动力学的影响：先进的成像和计算技术

IF 5.3 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biocybernetics and Biomedical Engineering

Pub Date : 2025-01-01 DOI: 10.1016/j.bbe.2025.01.004

Alireza Karimi , Marie Darche , Ansel Stanik , Reza Razaghi , Iman Mirafzal , Kamran Hassani , Mojtaba Hassani , Elizabeth White , Ivana Gantar , Stéphane Pagès , Laura Batti , Ted S. Acott , Michel Paques

Objective

Aging results in significant structural and functional changes in the anterior segment of the eye, influencing intraocular pressure (IOP) and overall ocular health. Although aging is a well-established risk factor for primary open-angle glaucoma, a leading cause of irreversible blindness, the specific mechanisms through which aging drives morphological changes in anterior segment tissues and affects aqueous humor dynamics remain incompletely understood.

Methods

In this study, we employed cutting-edge light sheet fluorescence microscopy (LSFM) to capture high-resolution, volumetric images of cleared human donor eyes’ anterior segment tissues. This advanced imaging enabled a comprehensive morphological analysis of key parameters, including central and peripheral corneal thickness (CCT and PCT), iris thickness, anterior chamber area (ACA), and ciliary body area (CBA). By integrating these morphological parameters with computational fluid dynamics (CFD) models, we analyzed aqueous humor dynamics across n = 6 female human donor eyes, spanning a wide age range of 5 to 94 years (all of Caucasian descent).

Results

The CCT and PCT demonstrated thinning with age, accompanied by a reduction in ACA. In contrast, the CBA remained relatively stable across all age groups. Computational fluid dynamics analysis showed a decline in aqueous humor velocity and wall shear stress, with younger eyes exhibiting higher velocities and shear stress, compared to older eyes.

Conclusion

These findings emphasize the value of integrating LSFM and CFD approaches to provide a detailed understanding of how aging impacts the anterior segment and its fluid dynamics. This study contributes to the understanding of age-related ocular changes, highlighting the importance of considering these changes in the diagnosis and management of age-related eye diseases.

目的衰老导致眼前段结构和功能发生显著变化，影响眼内压（IOP）和整体眼部健康。虽然衰老是原发性开角型青光眼（不可逆失明的主要原因）的一个公认的危险因素，但衰老驱动前节组织形态变化和影响房水动力学的具体机制仍不完全清楚。方法在本研究中，我们采用先进的光片荧光显微镜（LSFM）捕获清除后的人供眼前段组织的高分辨率、体积图像。这种先进的成像技术能够对关键参数进行全面的形态学分析，包括角膜中央和周围厚度（CCT和PCT）、虹膜厚度、前房面积（ACA）和睫状体面积（CBA）。通过将这些形态学参数与计算流体动力学（CFD）模型相结合，我们分析了n = 6个女性人类供体眼睛的房水动力学，这些眼睛的年龄范围从5岁到94岁不等（全部为高加索血统）。结果CCT和PCT随年龄的增长而变薄，并伴有ACA的减少。相比之下，CBA在所有年龄段都保持相对稳定。计算流体动力学分析显示，房水流速和壁面剪应力下降，年轻的眼睛比年长的眼睛表现出更高的速度和剪应力。结论这些发现强调了LSFM和CFD方法相结合的价值，可以详细了解衰老如何影响前段及其流体动力学。这项研究有助于理解与年龄相关的眼部变化，强调了在诊断和治疗与年龄相关的眼病时考虑这些变化的重要性。

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引用次数: 0

Imaging of retinal ganglion cells and photoreceptors using Spatio-Temporal Optical Coherence Tomography (STOC-T) without hardware-based adaptive optics 无硬件自适应光学的时空光学相干断层成像（stock - t）视网膜神经节细胞和光感受器成像

IF 5.3 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biocybernetics and Biomedical Engineering

Pub Date : 2025-01-01 DOI: 10.1016/j.bbe.2025.01.001

Marta Mikuła-Zdańkowska , Dawid Borycki , Piotr Węgrzyn , Karolis Adomavičius , Egidijus Auksorius , Maciej Wojtkowski

We demonstrate an experimental Spatio-Temporal Optical Coherence Tomography (STOC-T) system featuring optimized illumination and an increased lateral resolution of approximately 3 µm. The integration of high-speed phase randomization with a numerical averaging process facilitates a noticeable improvement in the signal-to-noise ratio. The effectiveness of this enhancement is demonstrated through volumetric imaging of a scattering object, and it enables in vivo imaging of the human retina at the cellular level. Additionally, the experiment is supported by computational aberration-correction techniques to achieve high-resolution in vivo imaging of the human retina. The visualization of retinal cone mosaics, and ganglion cell somas was achieved through contrast enhancement during the averaging process.

我们展示了一个实验性的时空光学相干断层扫描（stock - t）系统，该系统具有优化的照明和增加的横向分辨率约为3µm。将高速相位随机化与数值平均相结合，可显著提高信噪比。这种增强的有效性是通过散射物体的体积成像来证明的，它可以在细胞水平上对人体视网膜进行体内成像。此外，该实验得到了计算像差校正技术的支持，以实现人类视网膜的高分辨率体内成像。在平均过程中通过对比度增强实现视网膜锥体嵌合体和神经节细胞体的可视化。

引用次数: 0

Spatio-temporal matched filter adjustment for enhanced accuracy in brain responses classification 提高脑反应分类准确性的时空匹配滤波调整

IF 5.3 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biocybernetics and Biomedical Engineering

Pub Date : 2025-01-01 DOI: 10.1016/j.bbe.2024.12.003

Michal Piela, Marian P. Kotas

In this paper, we apply modified spatio-temporal matched filtering (MSTMF) to enhance electroencephalographic (EEG) signals in evoked potentials (EP) based brain–computer interfaces (BCI). Our focus is on the effective treatment of noise in the system under consideration.

The applied MSTMF is a spatio-temporal extension of generalized matched filtering, which allows for optimal enhancement of weak, repeatable signals embedded in colored Gaussian noise. However, since spontaneous EEG signals are often corrupted by high-energy super-Gaussian artifacts, which deviate from this distribution, we propose rejecting these artifacts before applying MSTMF. Particularly effective have been algorithms based on independent component analysis (ICA) and empirical mode decomposition (EMD). After artifacts rejection, performed locally within time segments they occupy, without disturbing other parts of the signal, the classification of brain responses becomes more accurate. Nevertheless, the nonstationarity of the EEG signal remains a challenge that must be addressed.

Therefore, we propose adjusting the MSTMF to the current noise properties to improve its performance in this demanding environment. This can be achieved by properly calculating the noise covariance matrix, which is necessary to determine the filter coefficients, using both the learning and currently processed signal segments.

As a result, we have developed an enhanced method based on MSTMF for improved discrimination of evoked potentials and verified its performance on two publicly available reference databases: BCIAUT-P300 (for IFMBE Scientific Challenge) and Speller (for the BCI Competition III Challenge 2004). For these databases, we have achieved overall accuracies of 92.67% and 99.5%, surpassing the reference methods presented in the literature.

本文采用改进的时空匹配滤波（MSTMF）增强基于诱发电位（EP）的脑机接口（BCI）中的脑电图（EEG）信号。我们的重点是如何有效地处理系统中的噪音。所应用的MSTMF是广义匹配滤波的时空扩展，它允许嵌入在彩色高斯噪声中的弱、可重复信号的最佳增强。然而，由于自发脑电信号经常被偏离这种分布的高能超高斯伪影破坏，我们建议在应用MSTMF之前拒绝这些伪影。特别有效的是基于独立成分分析（ICA）和经验模式分解（EMD）的算法。在排除伪影后，在不干扰信号的其他部分的情况下，在它们占据的时间段内局部执行，大脑反应的分类变得更加准确。然而，脑电信号的非平稳性仍然是一个必须解决的挑战。因此，我们建议调整MSTMF以适应当前的噪声特性，以改善其在这种苛刻环境中的性能。这可以通过适当地计算噪声协方差矩阵来实现，这是确定滤波器系数所必需的，同时使用学习和当前处理的信号段。因此，我们开发了一种基于MSTMF的增强方法来改进诱发电位的区分，并在两个公开可用的参考数据库上验证了其性能：BCIAUT-P300（用于IFMBE科学挑战赛）和Speller（用于2004年BCI竞赛III挑战赛）。对于这些数据库，我们达到了92.67%和99.5%的总体准确率，超过了文献中提出的参考方法。

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引用次数: 0

Advancing eye disease detection: A comprehensive study on computer-aided diagnosis with vision transformers and SHAP explainability techniques 推进眼病检测：视觉变形和SHAP可解释性技术在计算机辅助诊断中的综合研究

IF 5.3 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biocybernetics and Biomedical Engineering

Pub Date : 2025-01-01 DOI: 10.1016/j.bbe.2024.11.005

Hossam Magdy Balaha , Asmaa El-Sayed Hassan , Rawan Ayman Ahmed , Magdy Hassan Balaha

Eye diseases such as age-related macular degeneration (AMD) and diabetic retinopathy are common worldwide and affect millions of people. These conditions can cause severe vision problems and even lead to blindness if not treated promptly. Therefore, accurate and timely diagnosis is crucial to manage these diseases effectively and prevent irreversible vision loss. This study introduces a computer-aided diagnosis (CAD) framework for automatically detecting various eye diseases via advanced methodologies and datasets. The main focus is on classifying fundus images, which is essential for precise diagnosis and prognosis. By incorporating cutting-edge techniques such as Vision Transformers (ViTs), this study aims to improve the performance and interpretability of traditional Convolutional Neural Networks (CNNs). ViTs can capture complex patterns and long-range dependencies in fundus images, helping distinguish between different eye diseases and healthy conditions. Furthermore, the study integrates SHapley additive exPlanations (SHAP) explainability techniques to provide insights into the model’s decision-making process, enhancing trust and understanding of its predictions. The results demonstrate significant performance enhancements compared with the baseline models, with an overall accuracy of 95%. This method outperforms previous state-of-the-art methods by a considerable margin. Additionally, metrics such as precision, recall, intersection over union (IoU), and the Matthews correlation coefficient (MCC) show superior performance across various eye diseases, such as diabetic retinopathy, glaucoma, and age-related macular degeneration. These findings underscore the effectiveness and reliability of the proposed approach in automated eye disease detection, indicating its potential for clinical integration and widespread adoption in healthcare settings.

诸如年龄相关性黄斑变性（AMD）和糖尿病性视网膜病变等眼病在世界范围内很常见，影响着数百万人。如果不及时治疗，这些情况会导致严重的视力问题，甚至导致失明。因此，准确、及时的诊断对于有效地控制这些疾病，防止不可逆的视力丧失至关重要。本研究介绍一种电脑辅助诊断（CAD）架构，透过先进的方法及资料，自动侦测各种眼疾。重点是对眼底图像进行分类，这对准确诊断和预后至关重要。通过结合视觉变换（ViTs）等前沿技术，本研究旨在提高传统卷积神经网络（cnn）的性能和可解释性。ViTs可以捕获眼底图像中的复杂模式和长期依赖关系，有助于区分不同的眼病和健康状况。此外，该研究整合了SHapley加性解释（SHAP）可解释性技术，以提供对模型决策过程的见解，增强对其预测的信任和理解。与基线模型相比，结果显示了显著的性能增强，总体准确率为95%。这种方法比以前最先进的方法要好得多。此外，精度、查全率、交叉超过联合（IoU）和马修斯相关系数（MCC）等指标在各种眼病（如糖尿病视网膜病变、青光眼和年龄相关性黄斑变性）中表现优异。这些发现强调了所提出的方法在自动眼病检测中的有效性和可靠性，表明其在临床整合和医疗保健机构中广泛采用的潜力。

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引用次数: 0

Multi-scale neural networks classification of mild cognitive impairment using functional near-infrared spectroscopy 基于功能近红外光谱的轻度认知障碍多尺度神经网络分类

IF 5.3 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biocybernetics and Biomedical Engineering

Pub Date : 2025-01-01 DOI: 10.1016/j.bbe.2024.12.001

Min-Kyoung Kang , Keum-Shik Hong , Dalin Yang , Ho Kyung Kim

Mild cognitive impairment (MCI) is recognized as an early stage preceding Alzheimer’s disease. Functional near-infrared spectroscopy (fNIRS) has recently been used to differentiate MCI patients from healthy controls (HCs) by analyzing their hemodynamic responses. This paper proposes a new method that uses the entire time series data from all fNIRS channels, skipping the feature extraction step. It involves a multi-scale convolutional neural network (CNN) integrated with long short-term memory (LSTM) layers to extract spatial and temporal features simultaneously. The study involves 64 participants (37 MCI patients and 27 HCs) performing three mental tasks: N-back, Stroop, and verbal fluency tests (VFT). The algorithm’s performance was assessed using 10-fold cross-validation across oxyhemoglobin (HbO), deoxyhemoglobin (HbR), and total hemoglobin (HbT). The highest classification accuracies were achieved with HbT, reaching 93.22 % for the N-back task, 91.14 % for the Stroop task, and 89.58 % for the VFT. It was found that using all types of hemodynamic signals from all channels provides better results than analyzing the region of interest data, eliminating the need for data segmentation and feature extraction procedures. Additionally, HbR (or HbT) gives better classification accuracy than HbO. The developed method can be implemented online for clinical applications and real-time monitoring of cognitive disorders.

轻度认知障碍（MCI）被认为是阿尔茨海默病的早期阶段。功能近红外光谱（fNIRS）最近被用于通过分析MCI患者的血流动力学反应来区分他们与健康对照（hc）。本文提出了一种利用所有fNIRS通道的整个时间序列数据，跳过特征提取步骤的新方法。该方法采用多尺度卷积神经网络（CNN）和长短期记忆（LSTM）层相结合的方法同时提取时空特征。这项研究涉及64名参与者（37名轻度认知障碍患者和27名hcc患者），他们执行三项心理任务：N-back、Stroop和语言流畅性测试（VFT）。该算法的性能通过氧合血红蛋白（HbO）、脱氧血红蛋白（HbR）和总血红蛋白（HbT）的10倍交叉验证进行评估。HbT的分类准确率最高，N-back任务为93.22%，Stroop任务为91.14%，VFT为89.58%。研究发现，使用来自所有通道的所有类型的血流动力学信号比分析感兴趣区域的数据提供了更好的结果，消除了对数据分割和特征提取过程的需要。此外，HbR（或HbT）提供比HbO更好的分类精度。所开发的方法可以在线实施，用于临床应用和实时监测认知障碍。

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引用次数: 0

Two state quasi-LPV dynamic model for gas exchange dynamics using the cycle-ergometer test 基于循环工力计试验的气体交换动力学的两态准lpv动力学模型

IF 5.3 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biocybernetics and Biomedical Engineering

Pub Date : 2025-01-01 DOI: 10.1016/j.bbe.2025.01.005

J.D. Chiza-Ocaña , G. Realpe , C.A. López-Albán , E. Rosero , J.M. Ramírez-Scarpetta

This paper presents a two state quasi-linear parameter varying

(q u a s i - L P V)

dynamic model for gas exchange dynamics using the cycle-ergometer test. The obtained model, is based on the analysis of stationary and dynamic energy flow, and the

V - s l o p e

method analysis, applies to both oxidative and glycolytic physical activities performed by an individual. The model parameters were identified by a power meter measuring the mechanical power at the pedal level on an ergometer bicycle (input signal), a commercial gas analyzer measuring the flow of oxygen uptake and the flow of carbon dioxide excreted (output signals), with data generated from two test protocols: a mixed protocol and an incremental cycling protocol. The model’s parameters are obtained in parts, from the measurements taken in the oxidative stage, the glycolytic stage, and the transition stage between the two, using the mixed protocol. The resulting model is validated using data from the incremental cycling protocol of nine individuals: six males and three females. The validated models obtained an accuracy of above 84.8% for the flow of oxygen and 89.1% for the flow of carbon dioxide. The dynamic model could be used to aid in creating personalized physical exercise programs for overweight individuals, simulating training plans within the operational thresholds of the human body or in structuring high performance training for athletes.

本文提出了一种基于循环工力计试验的气体交换动力学的两态准线性参数变化（quasi - LPV）动态模型。所获得的模型是基于静态和动态能量流的分析，以及V -斜率法分析，适用于个体进行的氧化和糖酵解物理活动。模型参数由测量计力计自行车踏板水平机械功率的功率计（输入信号），测量吸氧流量和排出二氧化碳流量的商用气体分析仪（输出信号）确定，数据来自两种测试方案：混合方案和增量循环方案。采用混合方案，从氧化阶段、糖酵解阶段和两者之间的过渡阶段的测量中获得了模型的部分参数。所得到的模型使用来自9个个体（6个雄性和3个雌性）的增量循环协议的数据进行验证。经过验证的模型对氧气流量和二氧化碳流量的预测精度分别达到84.8%和89.1%以上。该动态模型可用于帮助超重个体创建个性化的体育锻炼计划，模拟人体操作阈值内的训练计划，或为运动员构建高性能训练。

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引用次数: 0

Novel copolymers of poly(sebacic anhydride) and poly(ethylene glycol) as azithromycin carriers to the lungs 新型聚（癸二酸酐）和聚（乙二醇）共聚物作为肺部阿奇霉素载体

IF 5.3 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biocybernetics and Biomedical Engineering

Pub Date : 2025-01-01 DOI: 10.1016/j.bbe.2025.01.002

Konrad Kwiecień , Karolina Knap , Rick Heida , Jonasz Czajkowski , Alan Gorter , Dorota Ochońska , Przemysław Mielczarek , Agata Dorosz , Daria Niewolik , Katarzyna Reczyńska-Kolman , Katarzyna Jaszcz , Monika Brzychczy-Włoch , Tomasz R. Sosnowski , Peter Olinga , Elżbieta Pamuła

By many chronic lung diseases, there is a problem of recurrent bacterial infections that require frequent usage of antibiotics. They can be more effective and cause fewer side effects when administrated directly via the pulmonary route. For such purposes, various types of inhalers are used of which dry powder inhalers (DPIs) are one of the most common. Formulations such as dry powders usually consist of an active pharmaceutical ingredient (API) and a carrier material that is supposed to provide adequate properties to deliver the bioactive molecules to the site of action, effectively. Copolymers of sebacic acid (SA) and poly(ethylene glycol) (PEG) have been regarded as suitable materials for such formulations. Here, we present a study about the manufacturing of microparticles from such materials dedicated to inhalation which have been loaded with azithromycin (AZM). The microparticles (MPs) were 0.5 to 5 µm in size, presenting either a spherical or elongated shape depending on the material type and composition. The encapsulation efficiency (EE) of the MPs were almost complete with the drug loading up to 23.1 %. The powders had fair or good flowability based on Carr’s index and Hausner ratio. Due to the presence of the drug, the tendency to agglomerate decreased. As a result, up to 90 % of the obtained powders showed diameters below 5 µm. Also, the fine particles fraction (FPF) of the chosen formulation reached 66.3 ± 4.5 % and the mass median aerodynamic diameter was 3.8 ± 0.4 µm. The microparticles degraded quickly in vitro losing up to 50 % of their mass within 24 h and up to 80 % within 96 h of their incubation in phosphate-buffered saline (PBS). They were also nontoxic up to 100 µg/ml when added to cultures of A549 and BEAS-2B lung epithelial cells as well as to rat lung tissue slices tested ex vivo. The microparticles showed bactericidal effects against various strains of Staphylococcus aureus in lower than cytotoxic concentrations.

许多慢性肺部疾病都存在反复细菌感染的问题，需要经常使用抗生素。当直接经肺途径给药时，它们更有效，副作用更少。为此，使用了各种类型的吸入器，其中干粉吸入器（dpi）是最常见的吸入器之一。诸如干粉之类的制剂通常由活性药物成分（API）和载体材料组成，该载体材料应提供足够的特性，以有效地将生物活性分子递送到作用部位。己二酸（SA）和聚乙二醇（PEG）的共聚物被认为是这种配方的合适材料。在这里，我们提出了一项研究，从这些专门用于吸入的材料中制造含有阿奇霉素（AZM）的微粒。微颗粒（MPs）的尺寸为0.5至5µm，根据材料类型和组成呈现球形或细长形状。MPs的包封效率（EE）基本完全，载药量高达23.1%。根据卡尔指数和豪斯纳比，粉末具有一般或良好的流动性。由于药物的存在，凝结的倾向减少了。结果，高达90%的所得粉末的直径低于5µm。所选配方的细颗粒分数（FPF）达到66.3±4.5%，质量中位数气动直径为3.8±0.4µm。微颗粒在体外迅速降解，在24小时内损失高达50%的质量，在磷酸盐缓冲盐水（PBS）中孵育96小时内损失高达80%的质量。当添加到A549和BEAS-2B肺上皮细胞培养物以及离体测试的大鼠肺组织切片中，其毒性高达100µg/ml。在低于细胞毒浓度的条件下，微颗粒对多种金黄色葡萄球菌均有杀菌作用。

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引用次数: 0

Regional constraint consistency contrastive learning for automatic detection of urinary sediment in microscopic images 区域约束一致性对比学习在显微图像尿液沉积物自动检测中的应用

IF 5.3 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biocybernetics and Biomedical Engineering

Pub Date : 2025-01-01 DOI: 10.1016/j.bbe.2025.01.003

Fufei Li , Li Chen , Ge Song , Lianzheng Su , Shian Wang , Qiuyue Fu , Yongqi Nie , Peng Wang

Diagnosing renal and urinary system illnesses usually entails analysing the sediment found in urine. The components in microscopic urine images are diverse and show high similarity, with low contrast due to noise, impeding the progress of automated urine analysis. In order to tackle this difficulty, we propose a region-constrained consistency contrastive learning approach for automated urine analysis. In the first stage, we tackle the complex overlap phenomena in microscopic urine images by innovating the Urine Sediment Paste (US-Paste) positive sample construction method based on supervised contrastive learning. This method uses label information to apply regional constraints and improves the performance of out-of-distribution detection. We also rebuilt the Global Guidance Module (GG Module) and the Enhanced Supervision Module(ES Module). The former improves contrast in urine sediment images by restoring important image details guided by an encoder–decoder structure, while the latter achieves strong feature consistency by combining the most pertinent feature responses from four sets of attention feature maps, which are further mapped via a projection network. In the second phase, we enhance the representations acquired in the initial phase by incorporating a linear classification layer. Our region-constrained consistency contrastive learning algorithm attained an average classification accuracy of 98.30%, precision of 98.33%, recall of 98.30%, and F1-score of 98.30% on the private dataset. Furthermore, in the public urine sediment dataset, the approach achieved an average classification accuracy of 96.19%, precision of 95.79%, recall of 96.19%, and F1-score of 95.94%. The public chromosomal dataset yielded an average classification accuracy of 95.46%, precision of 94.84%, recall of 95.47%, and F1-score of 95.15%. Our methodology surpasses the most advanced methods and demonstrates exceptional performance in urine analysis. This showcases the efficiency of our label-based regional limitations, the outstanding out-of-distribution detection performance of US-Paste, and the robust feature consistency achieved by the Guided Supervision Encoder (GS Encoder). This substantially enhances diagnostic efficiency for clinicians and significantly advances the progress of automated urine sediment analysis.

诊断肾脏和泌尿系统疾病通常需要分析尿液中的沉淀物。尿液显微图像中成分多样，相似度高，但由于噪声的影响，对比度较低，阻碍了尿液自动分析的进展。为了解决这一困难，我们提出了一种区域约束一致性对比学习方法用于自动尿液分析。在第一阶段，我们通过创新基于监督对比学习的尿液沉积物粘贴（US-Paste）阳性样本构建方法来解决微观尿液图像中复杂的重叠现象。该方法利用标签信息应用区域约束，提高了超出分布检测的性能。我们还重建了全局引导模块（GG模块）和增强监督模块（ES模块）。前者通过在编码器-解码器结构的引导下恢复重要的图像细节，提高了尿液沉积物图像的对比度，而后者通过结合四组注意力特征图中最相关的特征响应，通过投影网络进一步映射，实现了强特征一致性。在第二阶段，我们通过加入一个线性分类层来增强在初始阶段获得的表示。我们的区域约束一致性对比学习算法在私有数据集上的平均分类准确率为98.30%，精密度为98.33%，召回率为98.30%，f1分数为98.30%。此外，在公共尿液沉积物数据集中，该方法的平均分类准确率为96.19%，精密度为95.79%，召回率为96.19%，f1评分为95.94%。公开的染色体数据集平均分类准确率为95.46%，准确率为94.84%，召回率为95.47%，f1评分为95.15%。我们的方法超越了最先进的方法，在尿液分析中表现出卓越的性能。这展示了我们基于标签的区域限制的效率，US-Paste出色的分布外检测性能，以及引导监督编码器（GS编码器）实现的鲁棒特征一致性。这大大提高了临床医生的诊断效率，并显著推进了自动尿液沉积物分析的进展。

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