Bone regeneration after injury or after surgical bone removal due to disease is a serious medical challenge. A variety of materials are being tested to replace a missing bone or tooth. Regeneration requires cells capable of proliferation and differentiation in bone tissue. Although there are many possible human cell types available for use as a model for each phase of this process, no cell type is ideal for each phase. Osteosarcoma cells are preferred for initial adhesion assays due to their easy cultivation and fast proliferation, but they are not suitable for subsequent differentiation testing due to their cancer origin and genetic differences from normal bone tissue. Mesenchymal stem cells are more suitable for biocompatibility testing, because they mimic natural conditions in healthy bone, but they proliferate more slowly, soon undergo senescence, and some subpopulations may exhibit weak osteodifferentiation. Primary human osteoblasts provide relevant results in evaluating the effect of biomaterials on cellular activity; however, their resources are limited for the same reasons, like for mesenchymal stem cells. This review article provides an overview of cell models for biocompatibility testing of materials used in bone tissue research.

BACKGROUND: The progression of Alzheimer's dementia (AD) can be classified into three stages: cognitive unimpairment (CU), mild cognitive impairment (MCI), and AD. The purpose of this study was to implement a machine learning (ML) framework for AD stage classification using the standard uptake value ratio (SUVR) extracted from 18F-flortaucipir positron emission tomography (PET) images. We demonstrate the utility of tau SUVR for AD stage classification. We used clinical variables (age, sex, education, mini-mental state examination scores) and SUVR extracted from PET images scanned at baseline. Four types of ML frameworks, such as logistic regression, support vector machine (SVM), extreme gradient boosting, and multilayer perceptron (MLP), were used and explained by Shapley Additive Explanations (SHAP) to classify the AD stage. RESULTS: Of a total of 199 participants, 74, 69, and 56 patients were in the CU, MCI, and AD groups, respectively; their mean age was 71.5 years, and 106 (53.3%) were men. In the classification between CU and AD, the effect of clinical and tau SUVR was high in all classification tasks and all models had a mean area under the receiver operating characteristic curve (AUC) > 0.96. In the classification between MCI and AD, the independent effect of tau SUVR in SVM had an AUC of 0.88 (p < 0.05), which was the highest compared to other models. In the classification between MCI and CU, the AUC of each classification model was higher with tau SUVR variables than with clinical variables independently, which yielded an AUC of 0.75(p < 0.05) in MLP, which was the highest. As an explanation by SHAP for the classification between MCI and CU, and AD and CU, the amygdala and entorhinal cortex greatly affected the classification results. In the classification between MCI and AD, the para-hippocampal and temporal cortex affected model performance. Especially entorhinal cortex and amygdala showed a higher effect on model performance than all clinical variables in the classification between MCI and CU. CONCLUSIONS: The independent effect of tau deposition indicates that it is an effective biomarker in classifying CU and MCI into clinical stages using MLP. It is also very effective in classifying AD stages using SVM with clinical information that can be easily obtained at clinical screening.

BACKGROUND: Ballet is a highly technical and physically demanding dance form involving extensive end-range lumbar movements and emphasizing movement smoothness and gracefulness. A high prevalence of non-specific low back pain (LBP) is found in ballet dancers, which may lead to poor controlled movement and possible pain occurrence and reoccurrence. The power spectral entropy of time-series acceleration is a useful indicator of random uncertainty information, and a lower value indicates a greater smoothness or regularity. The current study thus applied a power spectral entropy method to analyze the movement smoothness in lumbar flexion and extension in healthy dancers and dancers with LBP, respectively. METHOD: A total of 40 female ballet dancers (23 in the LBP group and 17 in the control group) were recruited in the study. Repetitive end-range lumbar flexion and extension tasks were performed and the kinematic data were collected using a motion capture system. The power spectral entropy of the time-series acceleration of the lumbar movements was calculated in the anterior-posterior (AP), medial-lateral (ML), vertical (VT), and three-directional (3D) vectors. The entropy data were then used to conduct receiver operating characteristic curve analyses to evaluate the overall distinguishing performance and thus cutoff value, sensitivity, specificity, and area under the curve (AUC) were calculated. RESULTS: The power spectral entropy was significantly higher in the LBP group than the control group in the 3D vector in both lumbar flexion and lumber extension (flexion: p = 0.005; extension: p < 0.001). In lumbar extension, the AUC in the 3D vector was 0.807. In other words, the entropy provides an 80.7% probability of distinguishing between the two groups (i.e., LBP and control) correctly. The optimal cutoff entropy value was 0.5806 and yielded a sensitivity of 75% and specificity of 73.3%. In lumbar flexion, the AUC in the 3D vector was 0.777, and hence the entropy provided a probability of 77.7% of distinguishing between the two groups correctly. The optimal cutoff value was 0.5649 and yielded a sensitivity of 90% and a specificity of 73.3%. CONCLUSIONS: The LBP group showed significantly lower lumbar movement smoothness than the control group. The lumbar movement smoothness in the 3D vector had a high AUC and thus provided a high differentiating capacity between the two groups. It may therefore be potentially applied in clinical contexts to screen dancers with a high risk of LBP.

BACKGROUND: To investigate the application effect of artificial intelligence (AI)-based fundus screening system in real-world clinical environment. METHODS: A total of 637 color fundus images were included in the analysis of the application of the AI-based fundus screening system in the clinical environment and 20,355 images were analyzed in the population screening. RESULTS: The AI-based fundus screening system demonstrated superior diagnostic effectiveness for diabetic retinopathy (DR), retinal vein occlusion (RVO) and pathological myopia (PM) according to gold standard referral. The sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of three fundus abnormalities were greater (all > 80%) than those for age-related macular degeneration (ARMD), referable glaucoma and other abnormalities. The percentages of different diagnostic conditions were similar in both the clinical environment and the population screening. CONCLUSIONS: In a real-world setting, our AI-based fundus screening system could detect 7 conditions, with better performance for DR, RVO and PM. Testing in the clinical environment and through population screening demonstrated the clinical utility of our AI-based fundus screening system in the early detection of ocular fundus abnormalities and the prevention of blindness.

BACKGROUND: Although titanium plates/screws are effective fixation methods (FM) after L-shaped osteotomy reduction malarplasty (LORM), the ideal FM remains controversial. This first finite element analysis (FEA) aimed to study the effect of various zygomatic body/zygomatic arch FM combinations and their placement vectors on the zygoma complex stability after virtual LORM under the effect of both average (150 N/mm2) and maximum (750 N/mm2) forces and three-dimensional (3D) mapping of stress and strain parameters distribution over the zygomatic bone, fixation methods, and total model. RESULTS: The fixation methods about the short-arm of the L-shaped osteotomy showed lower stress, strain, and displacement values than those across the long-arm osteotomy site. Combined with any zygomatic arch fixation methods (ZAFm), the two bicortical screws group (2LS) on the zygomatic body osteotomy site resulted in smaller displacements and the lowest zygoma bone stress and displacement when combined with Mortice-Tenon structure (MT) as zygomatic arch fixation method. Applied forces caused statistically significant differences in zygomatic bone stress (P < 0.001 and P = 0.001) and displacement (P = 0.001 and P = 0.002). CONCLUSION: All FMs both on the zygomatic body and zygomatic arch provide adequate zygomatic complex stability after LORM. The 2LS group showed better resistance than rectangular plate (RP) and square plate (SP) with lower stress concentrations. The L-shaped plate with short-wing on the maxilla (LPwM) is more stable than having the short-wing on the zygoma bone (LPwZ). Future prospective clinical studies are required to validate the current findings.

INTRODUCTION: In recent times, an upsurge in the investigation related to the effects of meditation in reconditioning various cardiovascular and psychological disorders is seen. In majority of these studies, heart rate variability (HRV) signal is used, probably for its ease of acquisition and low cost. Although understanding the dynamical complexity of HRV is not an easy task, the advances in nonlinear analysis has significantly helped in analyzing the impact of meditation of heart regulations. In this review, we intend to present the various nonlinear approaches, scientific findings and their limitations to develop deeper insights to carry out further research on this topic. RESULTS: Literature have shown that research focus on nonlinear domain is mainly concentrated on assessing predictability, fractality, and entropy-based dynamical complexity of HRV signal. Although there were some conflicting results, most of the studies observed a reduced dynamical complexity, reduced fractal dimension, and decimated long-range correlation behavior during meditation. However, techniques, such as multiscale entropy (MSE) and multifractal analysis (MFA) of HRV can be more effective in analyzing non-stationary HRV signal, which were hardly used in the existing research works on meditation. CONCLUSIONS: After going through the literature, it is realized that there is a requirement of a more rigorous research to get consistent and new findings about the changes in HRV dynamics due to the practice of meditation. The lack of adequate standard open access database is a concern in drawing statistically reliable results. Albeit, data augmentation technique is an alternative option to deal with this problem, data from adequate number of subjects can be more effective. Multiscale entropy analysis is scantily employed in studying the effect of meditation, which probably need more attention along with multifractal analysis. METHODS: Scientific databases, namely PubMed, Google Scholar, Web of Science, Scopus were searched to obtain the literature on "HRV analysis during meditation by nonlinear methods". Following an exclusion criteria, 26 articles were selected to carry out this scientific analysis.

BACKGROUND: Physics-based cardiovascular models are only recently being considered for disease diagnosis or prognosis in clinical settings. These models depend on parameters representing the physical and physiological properties of the modeled system. Personalizing these parameters may give insight into the specific state of the individual and etiology of disease. We applied a relatively fast model optimization scheme based on common local optimization methods to two model formulations of the left ventricle and systemic circulation. One closed-loop model and one open-loop model were applied. Intermittently collected hemodynamic data from an exercise motivation study were used to personalize these models for data from 25 participants. The hemodynamic data were collected for each participant at the start, middle and end of the trial. We constructed two data sets for the participants, both consisting of systolic and diastolic brachial pressure, stroke volume, and left-ventricular outflow tract velocity traces paired with either the finger arterial pressure waveform or the carotid pressure waveform. RESULTS: We examined the feasibility of separating parameter estimates for the individual from population estimates by assessing the variability of estimates using the interquartile range. We found that the estimated parameter values were similar for the two model formulations, but that the systemic arterial compliance was significantly different ([Formula: see text]) depending on choice of pressure waveform. The estimates of systemic arterial compliance were on average higher when using the finger artery pressure waveform as compared to the carotid waveform. CONCLUSIONS: We found that for the majority of participants, the variability of parameter estimates for a given participant on any measurement day was lower than the variability both across all measurement days combined for one participant, and for the population. This indicates that it is possible to identify individuals from the population, and that we can distinguish different measurement days for the individual participant by parameter values using the presented optimization method.