Biocybernetics and Biomedical Engineering最新文献

{"title":"Two state quasi-LPV dynamic model for gas exchange dynamics using the cycle-ergometer test","authors":"J.D. Chiza-Ocaña ,&nbsp;G. Realpe ,&nbsp;C.A. López-Albán ,&nbsp;E. Rosero ,&nbsp;J.M. Ramírez-Scarpetta","doi":"10.1016/j.bbe.2025.01.005","DOIUrl":"10.1016/j.bbe.2025.01.005","url":null,"abstract":"<div><div>This paper presents a two state quasi-linear parameter varying <span><math><mrow><mo>(</mo><mi>q</mi><mi>u</mi><mi>a</mi><mi>s</mi><mi>i</mi><mo>−</mo><mi>L</mi><mi>P</mi><mi>V</mi><mo>)</mo></mrow></math></span> 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 <span><math><mrow><mi>V</mi><mo>−</mo><mi>s</mi><mi>l</mi><mi>o</mi><mi>p</mi><mi>e</mi></mrow></math></span> 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.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 1","pages":"Pages 105-113"},"PeriodicalIF":5.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel copolymers of poly(sebacic anhydride) and poly(ethylene glycol) as azithromycin carriers to the lungs","authors":"Konrad Kwiecień ,&nbsp;Karolina Knap ,&nbsp;Rick Heida ,&nbsp;Jonasz Czajkowski ,&nbsp;Alan Gorter ,&nbsp;Dorota Ochońska ,&nbsp;Przemysław Mielczarek ,&nbsp;Agata Dorosz ,&nbsp;Daria Niewolik ,&nbsp;Katarzyna Reczyńska-Kolman ,&nbsp;Katarzyna Jaszcz ,&nbsp;Monika Brzychczy-Włoch ,&nbsp;Tomasz R. Sosnowski ,&nbsp;Peter Olinga ,&nbsp;Elżbieta Pamuła","doi":"10.1016/j.bbe.2025.01.002","DOIUrl":"10.1016/j.bbe.2025.01.002","url":null,"abstract":"<div><div>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 <em>in vitro</em> 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 <em>ex vivo</em>. The microparticles showed bactericidal effects against various strains of <em>Staphylococcus aureus</em> in lower than cytotoxic concentrations.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 1","pages":"Pages 114-136"},"PeriodicalIF":5.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regional constraint consistency contrastive learning for automatic detection of urinary sediment in microscopic images","authors":"Fufei Li ,&nbsp;Li Chen ,&nbsp;Ge Song ,&nbsp;Lianzheng Su ,&nbsp;Shian Wang ,&nbsp;Qiuyue Fu ,&nbsp;Yongqi Nie ,&nbsp;Peng Wang","doi":"10.1016/j.bbe.2025.01.003","DOIUrl":"10.1016/j.bbe.2025.01.003","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 1","pages":"Pages 74-89"},"PeriodicalIF":5.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis on grading of lung nodule images with segmentation using u-net and classification with Convolutional Neural Network Fish Swarm Optimization","authors":"R. Sudha ,&nbsp;K.M. Uma Maheswari","doi":"10.1016/j.bbe.2024.12.002","DOIUrl":"10.1016/j.bbe.2024.12.002","url":null,"abstract":"<div><div>Lung malignant tumors are abnormal growths of cells in the lungs that have the potential to invade nearby tissues and spread to other parts of the body. Early detection of these malignant lung tumors is crucial to avoid complications and improve patient outcomes. However, manual processing consumes time and is a tedious process. This might result in poor estimation on cancer-prognosis, leading the patients into a higher risk of mortality. Many existing literatures have detected the malignant tumors, yet, found certain difficulties with the identification of size, appearance and spread of cancerous-cells in lung region to determine how far it has been occupied. Hence, the present study aims to overcome the existing complications through Deep Learning based Swarm Intelligence Algorithms. Implementation of the proposed work is involved with three stages such as pre-processing, segmentation and classification. Besides, CT scan possess the capability for giving a comprehensive view than X-rays. Data are collected from LIDC-IDRI (Lung Image Database Consortium-Image Database Resource Initiative) with lung CT-images and accomplishes pre-processing by removing noise efficiently using wiener filter. Further, changes in soft tissues of lungs are identified and segmented in the subsequent phase using U-Net and finally classification is performed using CFSO (Convolutional Neural Network Fish Swarm Optimization) to overcome the slight chance of misclassification error as proposed CFSO can lead to more efficient computational processes since FSO algorithms are designed to minimize computational costs while maximizing performance through their metaheuristic nature. This efficiency is particularly beneficial when dealing with large datasets typical in medical imaging, allowing faster processing times without sacrificing accuracy. Hence, amalgamation of CFSO can reduce the number of features, thus speeding up training and inference times. Through the performance assessment, IoU (Intersection over Union) value attained through the analysis is found to be 0.7822. Further, accuracy obtained by the proposed model is 97.80%, recall is 98.49%, precision is 96.8% and F1-score is 97.32%. Findings of the study exhibits the purposefulness of the study in clinical settings by potentially reducing false negatives in lung cancer screening, ultimately improving patient survival rates through earlier detection and treatment.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 1","pages":"Pages 90-104"},"PeriodicalIF":5.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of motor imagery EEG signals using wavelet scattering transform and Bi-directional long short-term memory networks","authors":"Hongyuan Zhang ,&nbsp;Zijian Zhao ,&nbsp;Chong Liu ,&nbsp;Miao Duan ,&nbsp;Zhiguo Lu ,&nbsp;Hong Wang","doi":"10.1016/j.bbe.2024.11.003","DOIUrl":"10.1016/j.bbe.2024.11.003","url":null,"abstract":"<div><div>A brain-computer interface (BCI) is a technology that creates a communication path between the brain and external devices. Raw EEG data in BCI contain a large amount of complex information, but only some of it needs to be focused on in research. So Feature extraction and classification play an important role in BCI by reducing the data dimensionality and improving the accuracy of subsequent classification. Wavelet scattering transform is an emerging feature extraction method that generates time-shift invariant representations of EEG signals. We applied the wavelet scattering transform to extract features from motor imagery EEG signals, and utilized these features for classification purposes. To achieve this, we proposed a new method that combines wavelet scattering transform with a bidirectional long short-term memory (BiLSTM) network in a fusion deep learning network. Wavelet scattering transform can deeply mine the feature information in EEG signals. In the classification stage, multiple time window features obtained in the scattering transform are sent to the BiLSTM network for classification. The final result will be determined by a vote. In addition, for the processing of raw EEG data, we proposed a time-step based time window strategy that can better utilize the small dataset. This operation can obtain EEG data of multiple time steps. The proposed method was validated using BCI competition II dataset III and BCI competition IV dataset 2b. The results show that the proposed method in this paper can effectively improve the accuracy of motor imagery EEG and provide a new idea for the feature extraction and classification research of motor imagery brain-computer interface.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 4","pages":"Pages 874-884"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilistic and explainable modeling of Phase–Phase Cross-Frequency Coupling patterns in EEG. Application to dyslexia diagnosis","authors":"Diego Castillo-Barnes ,&nbsp;Nicolás J. Gallego-Molina ,&nbsp;Marco A. Formoso ,&nbsp;Andrés Ortiz ,&nbsp;Patrícia Figueiredo ,&nbsp;Juan L. Luque","doi":"10.1016/j.bbe.2024.09.003","DOIUrl":"10.1016/j.bbe.2024.09.003","url":null,"abstract":"<div><div>This work explores the intricate neural dynamics associated with dyslexia through the lens of Cross-Frequency Coupling (CFC) analysis applied to electroencephalography (EEG) signals evaluated from 48 seven-year-old Spanish readers from the LEEDUCA research platform. The analysis focuses on CFS (Cross-Frequency phase Synchronization) maps, capturing the interaction between different frequency bands during low-level auditory processing stimuli. Then, making use of Gaussian Mixture Models (GMMs), CFS activations are quantified and classified, offering a compressed representation of EEG activation maps. The study unveils promising results specially at the Theta-Gamma coupling (Area Under the Curve = 0.821), demonstrating the method’s sensitivity to dyslexia-related neural patterns and highlighting potential applications in the early identification of dyslexic individuals.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 4","pages":"Pages 814-823"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of a body sensor network for cardiorespiratory monitoring during dynamic activities","authors":"Alessandra Angelucci ,&nbsp;Federica Camuncoli ,&nbsp;Federica Dotti ,&nbsp;Filippo Bertozzi ,&nbsp;Manuela Galli ,&nbsp;Marco Tarabini ,&nbsp;Andrea Aliverti","doi":"10.1016/j.bbe.2024.09.002","DOIUrl":"10.1016/j.bbe.2024.09.002","url":null,"abstract":"<div><div>One of the major challenges in the field of wearable devices is to accurately measure physiological parameters during dynamic activities. The aim of this work is to present a completely wearable Wireless Body Sensor Network (WBSN) for cardio-respiratory monitoring during dynamic activities and a validation of the devices composing the WBSN against reference measurement systems. The WBSN is composed of three inertial measurement units (IMUs) to detect the respiratory rate (RR), and of a fourth unit to detect the pulse rate (PR). 30 healthy volunteers (17 men, mean age 25.9 ± 6.0 years, mean weight 68.7 ± 9.7 kg, mean height 170.9 ± 9.5 cm) were enrolled in a validation protocol consisting in walking, running, and cycling. The participants had to simultaneously wear the devices of the WBSN and reference instruments. The IMU-based system proved to be particularly effective in monitoring RR during cycling, with a RMSE of 3.77 bpm for the complete cohort, and during running. The respiratory signal during walking exhibited a frequency content like the stride, making it difficult to properly filter the desired signal content. PR showed good agreement with the reference heart rate monitor. The system exploits information regarding motion to improve RR estimation during dynamic activities thanks to an ad hoc signal processing algorithm.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 4","pages":"Pages 794-803"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0208521624000676/pdfft?md5=7b1f1f42608cbb77aef909206d34b316&pid=1-s2.0-S0208521624000676-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automating synaptic plasticity analysis: A deep learning approach to segmenting hippocampal field potential signal","authors":"Sabri Altunkaya","doi":"10.1016/j.bbe.2024.09.005","DOIUrl":"10.1016/j.bbe.2024.09.005","url":null,"abstract":"<div><div>Hippocampal field potentials are widely used in research on neurodegenerative diseases, epilepsy, neuropharmacology, and particularly long- and short-term synaptic plasticity. To conduct these studies, it is necessary to identify specific components within hippocampal field potential signals. However, manually marking the relevant signal points for analysis is a time-consuming, error-prone, and subjective process. Currently, there is no specialized software dedicated to automating this task. In this study, three different recurrent neural network-based deep learning architectures were examined for the automatic segmentation of hippocampal field potential signals in two separate experimental studies. In the first experimental study, 10,836 epochs of field potential signals recorded from 54 rats were used, and in the second experimental study, field potential signals with noise added to the above data at different rates were used. The best model achieved an average f-score of 98.1% on noise-free data and 97.15% on data with noise, highlighting its robustness in real-world scenarios. Furthermore, we assessed system stability using the repeated holdout method, which randomly split the data into training and testing sets 100 times, and each time trained a new version of the system. As a result, the proposed system was proven to be reliable and generalizable by showing similar average scores and low variability across all 100 iterations of the test.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 4","pages":"Pages 804-813"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inference-enabled tracking of acute mental stress via multi-modal wearable physiological sensing: A proof-of-concept study","authors":"Yuanyuan Zhou ,&nbsp;Azin S. Mousavi ,&nbsp;Yekanth R. Chalumuri ,&nbsp;Jesse D. Parreira ,&nbsp;Mihir Modak ,&nbsp;Jesus Antonio Sanchez-Perez ,&nbsp;Asim H. Gazi ,&nbsp;Omer T. Inan ,&nbsp;Jin-Oh Hahn","doi":"10.1016/j.bbe.2024.09.004","DOIUrl":"10.1016/j.bbe.2024.09.004","url":null,"abstract":"<div><h3>Objective</h3><p>To develop a novel algorithm for tracking acute mental stress which can infer acute mental stress state from multi-modal digital signatures of physiological parameters compatible with wearable-enabled sensing.</p></div><div><h3>Methods</h3><p>We derived prominent digital signatures of physiological responses to mental stress using cross-integration of multi-modal physiological signals including the electrocardiogram (ECG), photoplethysmogram (PPG), seismocardiogram (SCG), ballistocardiogram (BCG), electrodermal activity (EDA), and respiratory effort. Then, we developed an algorithm for tracking acute mental stress that can continuously classify stress vs no stress states by computing an aggregated likelihood computed with respect to a priori probability density distributions associated with the digital signatures of mental stress under stress and no stress states.</p></div><div><h3>Results</h3><p>Our algorithm could adequately infer mental stress state (average classification accuracy: 0.85, sensitivity: 0.85, specificity: 0.86) using a small number of prominent digital signatures derived from cross-integration of multi-modal physiological signals. The digital signatures in our work significantly outperformed the digital signatures employed in the state-of-the-art in tracking acute mental stress. Its exploitation of collective inference allowed for improved inference of mental stress state relative to naïve data mining techniques.</p></div><div><h3>Conclusion</h3><p>Our algorithm for tracking acute mental stress has the potential to make a leap in continuous, high-accuracy, and high-confidence inference of mental stress via convenient wearable-enabled physiological sensing. <u>Significance</u>: The ability to continuously monitor and track mental stress can collectively improve human wellbeing.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 4","pages":"Pages 771-781"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Profiled delivery of bicarbonate during weekly cycle of hemodialysis","authors":"Mauro Pietribiasi ,&nbsp;John K. Leypoldt ,&nbsp;Monika Wieliczko ,&nbsp;Malgorzata Twardowska-Kawalec ,&nbsp;Malgorzata Debowska ,&nbsp;Jolanta Malyszko ,&nbsp;Jacek Waniewski","doi":"10.1016/j.bbe.2024.10.002","DOIUrl":"10.1016/j.bbe.2024.10.002","url":null,"abstract":"<div><h3>Background</h3><div>Delivery of bicarbonate during hemodialysis (HD) is aimed at correcting metabolic acidosis in end-stage renal disease patients. We tested modified prescriptions of bicarbonate concentration in dialysis fluid (C_D,bic), aimed to achieve an optimal pre-dialytic bicarbonate plasma concentration (C_P,bic).</div></div><div><h3>Methods</h3><div>We used a mathematical model to prescribe individualized HD treatments consisting of 1) adjustment of C_D,bic to get the pre-dialytic C_P,bic in a prescribed range, 2) increase of bicarbonate load before the long interdialytic break, and 3) a single step of increase in C_D,bic after two hours. The outcomes were tested in 24 stable HD patients, monitored during a week of standard HD (Test Week) and a week of modified treatment (Intervention Week).</div></div><div><h3>Results</h3><div>The response to the model-based prescription was different whether the average C_D,bic during the Intervention Week was higher or lower than the constant value used for the Test Week. For patients with lower average C_D,bic during the Intervention Week, a significant fraction achieved the target (22 ≤ C_P,bic ≤ 24 mEq/L). In the group with higher average C_D,bic, the interventions were effective only in increasing post-dialytic C_P,bic. The simple step-increase profile was effective in linearizing the intradialytic increase in bicarbonate and decreasing the amount of time spent by patients at high plasma C_P,bic.</div></div><div><h3>Conclusions</h3><div>The interventions were effective mostly in patients who needed to lower their pre-dialytic CP_,bic. The resistance of the system to increasing pre-dialytic C_P,bic in other patients might be caused by modifications of breathing or in hydrogen generation that were not accounted for by our model.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 4","pages":"Pages 836-843"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}