Computer methods and programs in biomedicine最新文献

{"title":"Linguistic-grammar profile of Polish patients with anorexia nervosa","authors":"Stella Maćkowska ,&nbsp;Katarzyna Rojewska ,&nbsp;Dominik Spinczyk","doi":"10.1016/j.cmpb.2025.108717","DOIUrl":"10.1016/j.cmpb.2025.108717","url":null,"abstract":"<div><h3>Background and objective</h3><div>The process of diagnosing and treating anorexia is fraught with many challenges. Physiologically unstable patient status in the first period of treatment, the barrier between patient-therapist, and patient's resistance constitute an essential negative element in accurate diagnosis and appropriate therapy selection. For this reason, there was a need to create a tool using elements of natural language processing to support the psychologist's work in the diagnostic process to verify and validate the expert hypotheses.</div></div><div><h3>Methods</h3><div>The research proposed that linguistic-grammatical profiles be created among the research and control groups using elements of natural language processing. After the general part of speech tagging, the rules for detailed analysis were developed for adjectives, verbs (including the verb “to be”), pronoun “I” and the possessive pronoun “my”, cognitive words and characteristic terms related to body image. The choice of rules was dictated by the state of art and literature review. The obtained results were subjected to statistical analysis.</div></div><div><h3>Results</h3><div>A detailed analysis showed a strong negative sentiment associated with body image among patients with anorexia. In the control group, the same analysis revealed opposite results. In this group, people are aware of their physical imperfections, but it does not distort their body image. Statistically significant differences were observed in all concept categories except for the noun group. Statistical analysis was not conducted for the following concept classes: personal pronoun “I”, verb “to be” in the past form, verb “to be” in the future form, and general verbs in past form due to the insufficient number of occurrences of these concepts in the written notes.</div></div><div><h3>Conclusion</h3><div>The adopted NLP methods and the tools used in the designed projective method may be helpful in the psychological diagnosis of anorexia, due to the demonstrated differentiation between healthy and people with anorexia, providing detailed information about the patient and its required minimally invasive character.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"264 ","pages":"Article 108717"},"PeriodicalIF":4.9,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611159","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":"Advancing hierarchical neural networks with scale-aware pyramidal feature learning for medical image dense prediction","authors":"Xiang Liu ,&nbsp;James Liang ,&nbsp;Jianwei Zhang ,&nbsp;Zihan Qian ,&nbsp;Phoebe Xing ,&nbsp;Taige Chen ,&nbsp;Shanchieh Yang ,&nbsp;Chijioke Chukwudi ,&nbsp;Liang Qiu ,&nbsp;Dongfang Liu ,&nbsp;Junhan Zhao","doi":"10.1016/j.cmpb.2025.108705","DOIUrl":"10.1016/j.cmpb.2025.108705","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Hierarchical neural networks are pivotal in medical imaging for multi-scale representation, aiding in tasks such as object detection and segmentation. However, their effectiveness is often limited by the loss of intra-scale information and misalignment of inter-scale features. Our study introduces the Integrated-Scale Pyramidal Interactive Reconfiguration to Enhance feature learning (INSPIRE).</div></div><div><h3>Methods:</h3><div>INSPIRE focuses on intra-scale semantic enhancement and precise inter-scale spatial alignment, integrated with a novel spatial-semantic back augmentation technique. We evaluated INSPIRE’s efficacy using standard hierarchical neural networks, such as UNet and FPN, across multiple medical segmentation challenges including brain tumors and polyps. Additionally, we extended our evaluation to object detection and semantic segmentation in natural images to assess generalizability.</div></div><div><h3>Results:</h3><div>INSPIRE demonstrated superior performance over standard baselines in medical segmentation tasks, showing significant improvements in feature learning and alignment. In identifying brain tumors and polyps, INSPIRE achieved enhanced precision, sensitivity, and specificity compared to traditional models. Further testing in natural images confirmed the adaptability and robustness of our approach.</div></div><div><h3>Conclusions:</h3><div>INSPIRE effectively enriches semantic clarity and aligns multi-scale features, achieving integrated spatial-semantic coherence. This method seamlessly integrates with existing frameworks used in medical image analysis, thereby promising to significantly enhance the efficacy of computer-aided diagnostics and clinical interventions. Its application could lead to more accurate and efficient imaging processes, essential for improved patient outcomes.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"265 ","pages":"Article 108705"},"PeriodicalIF":4.9,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759641","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":"Physiological modeling of autonomic regulation of cardiac system under graded exercise","authors":"Tao Wang ,&nbsp;JianKang Wu ,&nbsp;Fei Qin ,&nbsp;Hong Jiang ,&nbsp;Xiang Xiao ,&nbsp;YongGang Tong ,&nbsp;ChuChu Liao ,&nbsp;ZhiPei Huang","doi":"10.1016/j.cmpb.2025.108704","DOIUrl":"10.1016/j.cmpb.2025.108704","url":null,"abstract":"<div><h3>Background and Objective</h3><div>: Dysfunction of the autonomic nervous system (ANS) plays a critical role in the progression and assessment of cardiovascular diseases, neurological disorders, and various other pathologies. Therefore, a quantitative assessment of ANS function is vital for personalized medicine in these diseases. However, direct measurements of ANS activity can be costly and invasive, prompting researchers to adopt indirect methods for quantitative evaluation. These methods typically involve mathematical techniques, such as statistical analysis and mathematical modeling, to interpret cardiovascular fluctuations in response to external stimuli.The purpose of this study is to develop a non-invasive mathematical method that quantitatively assesses ANS function during graded exercise.</div></div><div><h3>Methods:</h3><div>In this study, we present a physiological mathematical model for autonomic regulation of the cardiac system under graded exercise, which recognizes the crucial role of the ANS in controlling heart rate during physical activity. The model utilizes the metabolic equivalent of walking as the input and heart rate as the output, with model parameters serving as quantitative measures of personalized ANS function. Experimental data were collected from groups with different health statuses and genders. Mann–Whitney U non-parametric tests were conducted on the model parameters to assess performance between individuals who frequently engage in aerobic exercise (15 participants, aerobic exercise frequency of more than 4 times/week) and those who barely exercise (15 participants, aerobic exercise frequency of 1 time per week or less), as well as between male and female participants.</div></div><div><h3>Results:</h3><div>The experimental results indicate that our model effectively quantitatively assesses ANS function across groups with different health statuses and genders (P <span><math><mo>&lt;</mo></math></span> 0.05). Additionally, the model provides precise estimations of heart rate, yielding a Root Mean Square Error of 2.79 beats per minute, a Mean Absolute Error of 2.18 beats per minute, and an R-squared value of 0.93.</div></div><div><h3>Conclusion:</h3><div>Our findings suggest that the proposed physiological mathematical model offers a non-invasive and user-friendly tool for measuring ANS function and monitoring cardiovascular health. This approach is feasible for home application, thereby reducing the need for professional supervision, and supports the early detection and personalized management of cardiovascular diseases. As a result, it enhances clinical decision-making and improves patient outcomes.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"264 ","pages":"Article 108704"},"PeriodicalIF":4.9,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636342","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":"A study on brain tumor dynamics in two-dimensional irregular domain with variable-order time-fractional derivative","authors":"Harshad Sakariya,&nbsp;Ravi Shankar Prasad,&nbsp;Sushil Kumar","doi":"10.1016/j.cmpb.2025.108700","DOIUrl":"10.1016/j.cmpb.2025.108700","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Understanding tumor growth in the brain is a crucial and complex challenge. This study aims to develop and analyze a brain tumor growth model that incorporates variable-order time-fractional derivatives within a two-dimensional irregular domain. The purpose is to explore the effects of time-fractional orders, mutation rates, and growth parameters on tumor dynamics.</div></div><div><h3>Methods:</h3><div>The model employs the finite difference method for temporal discretization and Gaussian radial basis functions based on Kansa’s method for spatial variables. Ulam–Hyers stability analysis is performed to ensure the model’s stability and the existence and uniqueness of the solution are established. Additionally, the stability and convergence of the scheme are analyzed. Code verification is conducted to confirm the accuracy and reliability of the computational approach. Key parameters, such as the mutation rate <span><math><msub><mrow><mi>K</mi></mrow><mrow><mi>c</mi></mrow></msub></math></span> and growth parameters <span><math><msub><mrow><mi>σ</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> and <span><math><msub><mrow><mi>σ</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>, are investigated under various time-fractional derivative orders, including variable orders.</div></div><div><h3>Results:</h3><div>The numerical simulations provide a detailed analysis of tumor cell dynamics, accounting for heterogeneity and fractional effects. Graphical representations reveal novel behaviors induced by variable-order time-fractional derivatives, including their impact on tumor cell population growth. Changes in the mutation rate and growth parameters significantly influence the results, demonstrating sensitivity to parameter variations.</div></div><div><h3>Conclusions:</h3><div>This study demonstrates that the integration of variable-order time-fractional derivatives into brain tumor models introduces memory effects, revealing new insights into tumor behavior. The findings highlight the importance of fractional-order parameters in accurately modeling brain tumor growth, which could have potential implications for predicting tumor progression and developing targeted treatments.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"264 ","pages":"Article 108700"},"PeriodicalIF":4.9,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629115","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":"Optimization of primary screw stability in Trabecular bone using neural network-based models","authors":"Yijun Zhou ,&nbsp;Benedikt Helgason ,&nbsp;Stephen J. Ferguson ,&nbsp;Cecilia Persson","doi":"10.1016/j.cmpb.2025.108720","DOIUrl":"10.1016/j.cmpb.2025.108720","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Screw implant stability in bone is crucial to the success of many orthopaedic procedures, yet the relationship between screw design parameters and specific bone characteristics remains underexplored. This study aims to optimize screw designs to enhance primary stability by leveraging subject-specific bone data and advanced surrogate modelling techniques.</div></div><div><h3>Methods</h3><div>In this study, 2880 screw pull-out simulations were conducted to assess primary screw stability by analysing pull-out stiffness and strength. The resulting dataset was used to develop surrogate models using multiple linear regression, random forest, and neural networks (NN). An optimization process was then applied to find optimal screw designs for 80 distinct trabecular bone specimens, in terms of inner diameter, pitch, and thread angle.</div></div><div><h3>Results</h3><div>The models, trained with various input parameters, including bone morphological parameters and computed tomography images, promisingly predicted the results of the simulations. The prediction errors varied by model type, with multiple linear regression yielding approximately 12 % error, while non-linear machine learning models achieved lower errors, ranging between 2–6 %. The series of subsequent optimization tasks provided optimized screw designs showing statistically significant improvements in pull-out stiffness and strength compared to the average screw designs (approximately 16 and 14 %, respectively). This even though our study focused only on screw design parameters that generally have a smaller impact on stability compared to factors such as screw outer diameter and insertion depth.</div></div><div><h3>Conclusions</h3><div>Multiple linear regression models were found to be insufficient for generating optimized screw configurations, and more complex surrogate models, such as NN, are needed. It could be concluded that different trabecular bone morphologies can benefit from distinct optimal screw designs. The insights gained from this study could have implications for the development of patient-specific orthopaedic treatments.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"264 ","pages":"Article 108720"},"PeriodicalIF":4.9,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143641154","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":"The value of MRI radiomics in distinguishing different types of spinal infections","authors":"Chao Qin ,&nbsp;Li-ping Dai ,&nbsp;Ye-lei Zhang ,&nbsp;Rong-can Wu ,&nbsp;Kai-li Du ,&nbsp;Chun-qiang Zhang ,&nbsp;Wen-ge Liu","doi":"10.1016/j.cmpb.2025.108719","DOIUrl":"10.1016/j.cmpb.2025.108719","url":null,"abstract":"<div><h3>Background</h3><div>In clinical practice, the three most prevalent forms of infectious spondylitis are tuberculous spondylitis (TS), brucellosis spondylitis (BS), and pyogenic spondylitis (PS). It is possible to successfully lessen neurological and spinal damage by detecting them early. In the medical field, radiomics has been applied extensively. It is crucial to find out if MRI imaging can be used to diagnose spinal infections early.</div></div><div><h3>Purpose</h3><div>To explore the diagnostic value of establishing models based on MRI radiomics for different spinal infections.</div></div><div><h3>Methods</h3><div>This retrospective study collected clinical and magnetic resonance imaging information on a total of 136 patients diagnosed with spondylitis in April 2019 and August 2023, who were classified into specific spinal infections (TS or BS) and non-specific spinal infections (PS) based on treatment. 3D Slicer software was used to outline the region of interest (ROI) and extracted ROI features. All patients were randomly divided into a training set and a test set (7:3), and after standardized, the <em>t</em>-test and LASSO were sequentially performed in the training set to extract the optimal radiomic features. These features were used to calculate the Radscore and construct the features classifier model and evaluated by test set. Univariate and multivariate logistic regression of Radscore and clinical features to identify predictors contributing to the diagnosis were used to plot nomograms, the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA) to assess the nomogram. The same approach described above was used to diagnose both subgroups of BS and TS in SSI.</div></div><div><h3>Results</h3><div>321 radiological features were extracted from the three different sequences. The remaining 7 optimal radiomics features were used to calculate the Radscore and establish three feature classifier models, with RF having the best performance (AUC=1 and 0.86). And after univariate and multivariate logistic regression, the final nomogram constructed by Radscore and had good discriminatory performance in the training set and the test set (AUC =0.924 and 0.868), and the calibration curve and DCA showed good clinical efficacy. In the subgroup, the AUC of the training and test sets was 0.929and0.863.</div></div><div><h3>Conclusion</h3><div>The diagnostic model based on MR radiomics can gradually differentiate tuberculous spondylitis, brucellosis spondylitis, and pyogenic spondylitis.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"264 ","pages":"Article 108719"},"PeriodicalIF":4.9,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621125","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":"MCNEL: A multi-scale convolutional network and ensemble learning for Alzheimer’s disease diagnosis","authors":"Fei Yan ,&nbsp;Lixing Peng ,&nbsp;Fangyan Dong ,&nbsp;Kaoru Hirota","doi":"10.1016/j.cmpb.2025.108703","DOIUrl":"10.1016/j.cmpb.2025.108703","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Alzheimer’s disease (AD) significantly threatens community well-being and healthcare resource allocation due to its high incidence and mortality. Therefore, early detection and intervention are crucial for reducing AD-related fatalities. However, the existing deep learning-based approaches often struggle to capture complex structural features of magnetic resonance imaging (MRI) data effectively. Common techniques for multi-scale feature fusion, such as direct summation and concatenation methods, often introduce redundant noise that can negatively affect model performance. These challenges highlight the need for developing more advanced methods to improve feature extraction and fusion, aiming to enhance diagnostic accuracy.</div></div><div><h3>Methods:</h3><div>This study proposes a multi-scale convolutional network and ensemble learning (MCNEL) framework for early and accurate AD diagnosis. The framework adopts enhanced versions of the EfficientNet-B0 and MobileNetV2 models, which are subsequently integrated with the DenseNet121 model to create a hybrid feature extraction tool capable of extracting features from multi-view slices. Additionally, a SimAM-based feature fusion method is developed to synthesize key feature information derived from multi-scale images. To ensure classification accuracy in distinguishing AD from multiple stages of cognitive impairment, this study designs an ensemble learning classifier model using multiple classifiers and a self-adaptive weight adjustment strategy.</div></div><div><h3>Results:</h3><div>Extensive experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset validate the effectiveness of our solution, which achieves average accuracies of 96.67% for ADNI-1 and 96.20% for ADNI-2, respectively. The results indicate that the MCNEL outperforms recent comparable algorithms in terms of various evaluation metrics, demonstrating superior performance and robustness in AD diagnosis.</div></div><div><h3>Conclusions:</h3><div>This study markedly enhances the diagnostic capabilities for AD, allowing patients to receive timely treatments that can slow down disease progression and improve their quality of life.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"264 ","pages":"Article 108703"},"PeriodicalIF":4.9,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601086","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":"A comprehensive exploration of motion sickness process analysis from EEG signal and virtual reality","authors":"Naishi Feng ,&nbsp;Bin Zhou ,&nbsp;Qianqian Zhang ,&nbsp;Chengcheng Hua ,&nbsp;Yue Yuan","doi":"10.1016/j.cmpb.2025.108714","DOIUrl":"10.1016/j.cmpb.2025.108714","url":null,"abstract":"<div><h3>Background and objective</h3><div>Virtual reality motion sickness is a significant barrier to the widespread adoption of virtual reality technology. Current virtual reality motion sickness detection methods using EEG signals often fail to identify comprehensive neuro-markers and lack generalizability across multiple subjects.</div></div><div><h3>Methods</h3><div>To address this issue, we analyzed the pre- and post-induction phases of virtual reality motion sickness, as well as the induction process, from multiple domain features. The features were extracted from time domain, frequency domain, spatial domain and Riemann space across delta, theta, beta, and all frequency bands. The neuro-markers selected have a correlation greater than 0.5 with behaviors information and showed significant changes in both phases. Five kinds of traditional machine learning methods were used to classify VR motion sickness states in within-in subjects and cross-subjects by using neuro-markers.</div></div><div><h3>Results</h3><div>Traditional machine learning methods achieved a maximum accuracy of 92 % for within-subject classification and 68 % for cross-subject classification. Spectral entropy across all frequency bands yielded the highest classification accuracy during the pre- and post-induction phases, while spectral skew showed the most significant changes during the task phase.</div></div><div><h3>Conclusion</h3><div>These findings suggest that these features hold strong potential for future neurofeedback studies.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"264 ","pages":"Article 108714"},"PeriodicalIF":4.9,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592918","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":"Pattern formation of network epidemic model and its application in oral medicine","authors":"Linhe Zhu ,&nbsp;Yue Li ,&nbsp;Le He ,&nbsp;Shuling Shen","doi":"10.1016/j.cmpb.2025.108688","DOIUrl":"10.1016/j.cmpb.2025.108688","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>The prevention and control of infectious diseases is one of the major public safety issues in the 21 st century. In this paper, a Susceptible–Infected–Recovered (SIR) epidemic model with disease recurrence behavior is established based on continuous space and network environment. The Turing pattern, optimal control and parameter identification of infectious disease models under different network structures are studied.</div></div><div><h3>Methods:</h3><div>We analyze the sufficient conditions for the existence of the disease equilibrium point of the system, and discuss the necessary conditions of Turing instability of the system on homogeneous and heterogeneous networks, respectively. Our work further derives the global optimal solution of the parameters under the target pattern based on optimal control theorem.</div></div><div><h3>Results:</h3><div>The validity of the theoretical analysis is verified by a series of numerical simulations. Meanwhile, we have explored the impact of disease recurrence rate on the spread of infectious diseases on three network structures. It is found that when the recurrence rate <span><math><mi>α</mi></math></span> increases, it will result in a decrease in the recovered population <span><math><mi>R</mi></math></span> as well as an increase in the infected population <span><math><mi>I</mi></math></span>. Furthermore, the public Corona Virus Disease 2019 data are used for fitting verification. The verification results are basically consistent with the development trend of the epidemic, as well as the validity of the model is visually demonstrated.</div></div><div><h3>Conclusions:</h3><div>The complex network model can more accurately simulate the dynamic propagation process of infectious diseases. Combined with optimal control and parameter identification methods, it can provide theoretical support for public health departments to prevent and control infectious diseases. In particular, optimization parameter identification technology can be successfully applied to oral image recognition and adjuvant therapy.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"264 ","pages":"Article 108688"},"PeriodicalIF":4.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578502","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":"Targeted drug delivery to the deviated regions of the human nasal cavities: An in silico investigation and in vitro validation","authors":"Kartika Chandra Tripathy,&nbsp;Ajay Bhandari","doi":"10.1016/j.cmpb.2025.108706","DOIUrl":"10.1016/j.cmpb.2025.108706","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Delivering drugs to the deviated regions in patients with nasal septal deviation is vital for the treatment but challenging due to the complex shape of the nasal cavity and the intersubject variability in the nasal anatomies. While previous studies have focused on drug delivery to standard areas like the olfactory region, none have specifically looked at how drugs are deposited in the deviated regions. The current study numerically investigates six drug administration parameters aiming to control and maximize drug deposition in the deviated regions of the nasal cavity in three types of septal deviations.</div></div><div><h3>Methods</h3><div>Three-dimensional models are created using CT scans from three patients, with S-shaped, C-shaped, and reverse C-shaped septal deviations, and the deviated regions are identified by the detailed slice-by-slice inspection method. Eulerian and Lagrangian simulations are performed for the fluid flow and drug delivery, incorporating all six drug administration parameter variations. Further, in-house experiments are performed on a three-dimensional printed transparent nasal cavity to validate the pressure drop and drug deposition patterns.</div></div><div><h3>Results</h3><div>It is observed that increasing the spray half-cone angle decreases the targeted deposition in the C, reverse C, and left side of the S-shaped models. On the contrary, a wider half-cone angle (20°) and cone radius (2 mm) enhances targeted deposition on the right side of the S-shaped model. The ideal particle size range for all models is 10 to 16 µm. Lower initial particle velocities (1 m/s and 3 m/s) lead to maximum targeted deposition in the C and left side of the S-shaped models, while higher ones (14 m/s and 8m/s) enhance targeted deposition in the reverse C and right side of the S-shaped model, respectively. Optimized administration angles accurately direct particles to deviated regions, with patient-specific adjustments achieving better outcomes. Increased flow rates enhance both total and targeted depositions. Head orientation adjustments are effective for deviations in the middle and lower regions but have a limited impact when deviation presents in the anterior regions.</div></div><div><h3>Conclusions</h3><div>The study's findings help unveil the effective targeted drug delivery strategies, provide insights for the better design of the nasal spray device, and improve personalized treatment efficacy for patients with septal deviations.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"264 ","pages":"Article 108706"},"PeriodicalIF":4.9,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601084","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}