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Computational and Mathematical Methods in Medicine publishes research and review articles focused on the application of mathematics to problems arising from the biomedical sciences.
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Sleep Deprivation and Heart Rate Variability in Healthy Volunteers: Effects of REM and SWS Sleep Deprivation
Objective. Using PSG-guided acute selective REM/SWS sleep deprivation in volunteers, this study examined the effects of sleep deprivation on the cardiovascular and autonomic nervous systems, as well as the relationship between cardiac neuromodulation homeostasis and cardiovascular disease. Methods. An experiment was conducted using 30 healthy volunteers (, aged years) divided into groups for sleep deprivation of SWS and REM sleep, and then, each group was crossed over for normal sleep (2 days) and repeated sleep deprivation (1 day, 3 times). During the study period, PSG and ELECTRO ECG monitoring were conducted, and five-minute frequency domain parameters and blood pressure values were measured before and after sleep deprivation. Results. Changes in VLF, LFnu, LF/HF, HF, and HFnu after SWS sleep deprivation were statistically significant (), but not LF (). Changes in VLF, LF, HF, LF/HF, LFnu, and HFnu after REM sleep deprivation were not statistically significant (). Conclusions. An increase in sympathetic nerve activity results from sleep deprivation and sudden awakening from SWS sleep is associated with a greater risk of cardiovascular disease.
Privacy Preserved Cervical Cancer Detection Using Convolutional Neural Networks Applied to Pap Smear Images
Image processing has enabled faster and more accurate image classification. It has been of great benefit to the health industry. Manually examining medical images like MRI and X-rays can be very time-consuming, more prone to human error, and way more costly. One such examination is the Pap smear exam, where the cervical cells are examined in laboratory settings to distinguish healthy cervical cells from abnormal cells, thus indicating early signs of cervical cancer. In this paper, we propose a convolutional neural network- (CNN-) based cervical cell classification using the publicly available SIPaKMeD dataset having five cell categories: superficial-intermediate, parabasal, koilocytotic, metaplastic, and dyskeratotic. CNN distinguishes between healthy cervical cells, cells with precancerous abnormalities, and benign cells. Pap smear images were segmented, and a deep CNN using four convolutional layers was applied to the augmented images of cervical cells obtained from Pap smear slides. A simple yet efficient CNN is proposed that yields an accuracy of 0.9113% and can be successfully used to classify cervical cells. A simple architecture that yields a reasonably good accuracy can increase the speed of diagnosis and decrease the response time, reducing the computation cost. Future researchers can build upon this model to improve the model’s accuracy to get a faster and more accurate prediction.
Study on the Effect of Bushen Zhuanggu Tablet Combined with Conventional Regimen on Bone Mineral Density Improvement, Functional Recovery and Fracture Risk Prevention in Patients with Postmenopausal Osteoporosis
Objective. This case-control study was to explore the effect of Bushen Zhuanggu tablet combined with routine regimen on bone mineral density (BMD) improvement, functional recovery, and fracture prevention in postmenopausal osteoporosis (PMOP) patients. Methods. 180 postmenopausal osteoporosis patients were randomly selected from communities A, B, and C cohorts as research subjects from January to May 2021. The study subjects were divided into three groups. The groups were in a 1 : 1 ratio according to the principles of nonrandomised, concurrent controlled trials, and methods. There were 60 participants in each group (group A, group B, and group C). Group A was treated with Bushen Zhuanggu tablet for antiosteoporosis + basic treatment (calcium supplement and vitamin D). Group C was given Bushen Zhuanggu tablet for antiosteoporosis intervention. Group B was given basic treatment (calcium supplement and vitamin D supplementation) as a control group. The follow-up time was 6 months after treatment. Finally, we compare the differences in calcium and phosphorus metabolism indexes, BMD, bone metabolism indexes, upper and lower limb muscle strength, and quality of life scores. Results. Group A, B, and C’s effective rate was 98.33%, 80.00%, and 93.33%, respectively. The group A’s effective rate was significantly higher than that in group B and C, and the difference was statistically significant (). After 6 months intervention, the levels of serum Ca2+, serum phosphorus (P), serum creatinine (Cr), and parathyroid hormone (PTH) in 3 groups decreased. Ca, P, Scr, and PTH levels in group A were the lowest among study groups, and the difference was statistically significant (). The increase in the BMD of lumbar spine, the left femoral neck, and Ward’s triangle area of the three groups were observed with the highest data in group A. After 6 months of treatment, the levels of serum N-terminal propeptide of type I procollagen, PINP, and serum osteocalcin (OC) increased, while the levels of β-cross-linked C-terminal telopeptide of type I collagen (β-CTX) and alkaline phosphatase (ALP) decreased in the three groups. The improvement of all bone metabolic indexes in group A was significantly better than that in B and C groups, and the difference was statistically significant (). The enhanced upper limb muscle strength and the shorter standing-walking timing test (TUGT) time were observed after 6 months of treatment. The improvement effect of upper and lower limb muscle strength in group A was significantly better than that in B and C groups, and the difference was statistically significant (). There were significant differences in physiological function, life function, general health status, physical pain, mental state, emotional function, vitality, and social function among the three groups after 6 months treatment, and the difference was statistically significant (). The score of quality of life in group A was higher than that in B and C groups, and the difference was statistically significant (). Conclusion. Bushen Zhuanggu tablet combined with conventional therapy is effective in the postmenopausal osteoporosis treatment, which effectively increase the BMD, regulate calcium and phosphorus metabolism, promote the recovery of limb function, prevent the recurrence of fracture, and improve the patients’ quality of life. This treatment scheme is worth popularizing.
Effect of Dapagliflozin on Clinical Outcome after Drug-Eluting Stent Implantation in Elderly T2DM Patients: A Real-World Study
Objective. To investigate the effect of dapagliflozin therapy on the clinical outcome of drug-eluting stent (DES) implantation in elderly patients with type 2 diabetes mellitus (T2DM). Methods. We retrospectively studied the real-world data of patients with coronary heart disease who received DES implantation in our hospital from May 2019 to May 2021. Baseline general data and laboratory results of patients were collected. All patients were followed up for two years after PCI, and the follow-up endpoints included in-stent restenosis, major adverse cardiovascular events (MACE), revascularization, rehospitalization, and all-cause mortality. Results. Compared with those before treatment, body mass index, systolic blood pressure, diastolic blood pressure, TG, TC, LDL-C, FBG, and HbA1c were decreased in both groups after treatment, while HDL-C was increased after treatment (). There were significant differences in BMI, systolic blood pressure, diastolic blood pressure, FBG, and HbA1c between the two groups before and after treatment. At the end of follow-up, the incidence of in-stent restenosis and MACE in the dapagliflozin group was lower than that in the nondapagliflozin group. K-M curve analysis showed a significant difference in in-stent restenosis and MACE after DES implantation between the dapagliflozin group and the nondapagliflozin group (, 4.524; , 0.033). Conclusion. In accurate clinical data, dapagliflozin could significantly improve the postoperative BMI, blood pressure, and blood glucose outcome of patients with T2DM complicated with coronary heart disease and positively impact in-stent restenosis and MACE.
Finite Element Analysis of Acetabulum Prosthesis’ Lining Damage Zone with Different Implanting Angle
Objective. Research the acetabular component’s construction method of a three-dimensional finite element model in THA with different angles and study the influence of polyethylene liner wearing with finite element analysis. Methods. Build a model in the 3D modeling software system HyperMesh according to the artificial hip joint prosthesis’ entities and data. Using a finite element analysis system, ABAQUS 6.11 reconstitute acetabular prosthesis after hip replacement joints under different implanting position angles. Simulation and load the joint load when sheet foot touchdown state. Calculate the plastic volume strain and fatigue fracture. Results. The two groups of combinations of abduction angle 50° vs. anteversion angle 10° and abduction angle 55° vs. anteversion angle 15° have been found to have relatively smaller interface plastic strain and fatigue fracture volume value ( m3, m3), respectively. Conclusion. The groups of combinations of abduction angle 50° vs. anteversion angle 10° have been found to have relatively smallest interface plastic strain and fatigue fracture volume value in the total hip arthroplasty.
Early and High-Accuracy Diagnosis of Parkinson’s Disease: Outcomes of a New Model
Parkinson’s disease (PD) is one of the significant common neurological disorders of the current age that causes uncontrollable movements like shaking, stiffness, and difficulty. The early clinical diagnosis of this disease is essential for preventing the progression of PD. Hence, an innovative method is proposed here based on combining the crow search algorithm and decision tree (CSADT) for the early PD diagnosis. This approach is used on four crucial Parkinson’s datasets, including meander, spiral, voice, and speech-Sakar. Using the presented method, PD is effectively diagnosed by evaluating each dataset’s critical features and extracting the primary practical outcomes. The used algorithm was compared with other machine learning algorithms of k-nearest neighbor (KNN), support vector machine (SVM), naive Baye (NB), multilayer perceptron (MLP), decision tree (DT), random tree, logistic regression, support vector machine of radial base functions (SVM of RBFs), and combined classifier in terms of accuracy, recall, and combination measure F1. The analytical results emphasize the used algorithm’s superiority over the other selected ones. The proposed model yields nearly 100% accuracy through various trials on the datasets. Notably, a high detection speed achieved the lowest detection time of 2.6 seconds. The main novelty of this paper is attributed to the accuracy of the presented PD diagnosis method, which is much higher than its counterparts.