BACKGROUND: Cerebrovascular autoregulation (CVAR) is the mechanism that maintains constant cerebral blood flow by adjusting the caliber of the cerebral vessels. It is important to have an effective, contactless way to monitor and assess CVAR in patients with ischemia. METHODS: The adjustment of cerebral blood flow leads to changes in the conductivity of the whole brain. Here, whole-brain conductivity measured by the magnetic induction phase shift method is a valuable alternative to cerebral blood volume for non-contact assessment of CVAR. Therefore, we proposed the correlation coefficient between spontaneous slow oscillations in arterial blood pressure and the corresponding magnetic induction phase shift as a novel index called the conductivity reactivity index (CRx). In comparison with the intracranial pressure reactivity index (PRx), the feasibility of the conductivity reactivity index to assess CVAR in the early phase of cerebral ischemia has been preliminarily confirmed in animal experiments. RESULTS: There was a significant difference in the CRx between the cerebral ischemia group and the control group (p = 0.002). At the same time, there was a significant negative correlation between the CRx and the PRx (r = - 0.642, p = 0.002) after 40 min after ischemia. The Bland-Altman consistency analysis showed that the two indices were linearly related, with a minimal difference and high consistency in the early ischemic period. The sensitivity and specificity of CRx for cerebral ischemia identification were 75% and 20%, respectively, and the area under the ROC curve of CRx was 0.835 (SE = 0.084). CONCLUSION: The animal experimental results preliminarily demonstrated that the CRx can be used to monitor CVAR and identify CVAR injury in early ischemic conditions. The CRx has the potential to be used for contactless, global, bedside, and real-time assessment of CVAR of patients with ischemic stroke.

BACKGROUND: The worldwide society is currently facing an epidemiological shift due to the significant improvement in life expectancy and increase in the elderly population. This shift requires the public and scientific community to highlight successful aging (SA), as an indicator representing the quality of elderly people's health. SA is a subjective, complex, and multidimensional concept; thus, its meaning or measuring is a difficult task. This study seeks to identify the most affecting factors on SA and fed them as input variables for constructing predictive models using machine learning (ML) algorithms. METHODS: Data from 1465 adults aged ≥ 60 years who were referred to health centers in Abadan city (Iran) between 2021 and 2022 were collected by interview. First, binary logistic regression (BLR) was used to identify the main factors influencing SA. Second, eight ML algorithms, including adaptive boosting (AdaBoost), bootstrap aggregating (Bagging), eXtreme Gradient Boosting (XG-Boost), random forest (RF), J-48, multilayered perceptron (MLP), Naïve Bayes (NB), and support vector machine (SVM), were trained to predict SA. Finally, their performance was evaluated using metrics derived from the confusion matrix to determine the best model. RESULTS: The experimental results showed that 44 factors had a meaningful relationship with SA as the output class. In total, the RF algorithm with sensitivity = 0.95 ± 0.01, specificity = 0.94 ± 0.01, accuracy = 0.94 ± 0.005, and F-score = 0.94 ± 0.003 yielded the best performance for predicting SA. CONCLUSIONS: Compared to other selected ML methods, the effectiveness of the RF as a bagging algorithm in predicting SA was significantly better. Our developed prediction models can provide, gerontologists, geriatric nursing, healthcare administrators, and policymakers with a reliable and responsive tool to improve elderly outcomes.

BACKGROUND: The challenges in developing new bone replacement materials and procedures reside not solely in technological innovation and advancement, but also in a broader patient therapy acceptance. Therefore, there is a need to assess patients' perspectives on the materials and approaches in use as well as the ones being developed to better steer future progress in the field. METHODS: A self-initiating cross-sectional questionnaire aimed at people seeking treatment at the university hospital environment of Charité Berlin was formulated. The survey contained 15 close-ended questions directed toward the participant's epidemiological profile, willingness, acceptance, and agreement to receive different bone replacement materials, as well as, worries about the post-surgical consequences that can arise post bone replacement surgery. Descriptive and categorical analysis was performed to compare the observed number of subjects, their profile and each related response (Pearson's chi-square test or Fischer's test, p < 0.05). RESULTS: A total of 198 people engaged with the questionnaire, most of them Millennials. Overall patients trusted scientifically developed biomaterials designed for bone replacement, as demonstrated by their willingness to participate in a clinical trial, their acceptance of alloplastic materials, and the none/few worries about the presence of permanent implants. The data revealed the preferences of patients towards autologous sources of cells and blood to be used with a biomaterial. The data have also shown that both generation and education influenced willingness to participate in a clinical trial and acceptance of alloplastic materials, as well as, worries about the presence of permanent implants and agreement to receive a material with pooled blood and cells. CONCLUSION: Patients were open to the implantation of biomaterials for bone replacement, with a preference toward autologous sources of blood and/or tissue. Moreover, patients are concerned about strategies based on permanent implants, which indicates a need for resorbable materials. The knowledge gained in this study supports the development of new bone biomaterials.

BACKGROUND: Aging is associated with a decline in postural control and an increased risk of falls. The Center of Pressure (CoP) trajectory analysis is a commonly used method to assess balance. In this study, we proposed a new method to identify balance impairments in older adults by analyzing their CoP trajectory frequency components, sensory inputs, reaction time, motor functions, and Fall-related Concerns (FrC). METHODS: The study includes 45 older adults aged [Formula: see text] years who were assessed for sensory and motor functions. FrC and postural control in a quiet stance with open and closed eyes on stable and unstable surfaces. A Discrete Wavelet Transform (DWT) was used to detect features in frequency scales, followed by the K-means algorithm to detect different clusters. The multinomial logistic model was used to identify and predict the association of each group with the sensorimotor tests and FrC. RESULTS: The study results showed that by DWT, three distinct groups of subjects could be revealed. Group 2 exhibited the broadest use of frequency scales, less decline in sensorimotor functions, and lowest FrC. The study also found that a decline in sensorimotor functions and fall-related concern may cause individuals to rely on either very low-frequency scales (group 1) or higher-frequency scales (group 3) and that those who use lower-frequency scales (group 1) can manage their balance more successfully than group 3. CONCLUSIONS: Our study provides a new, cost-effective method for detecting balance impairments in older adults. This method can be used to identify people at risk and develop interventions and rehabilitation strategies to prevent falls in this population.

BACKGROUND: In this study, we proposed a deep learning technique that can simultaneously detect suspicious positions of benign vocal cord tumors in laparoscopic images and classify the types of tumors into cysts, granulomas, leukoplakia, nodules and polyps. This technique is useful for simplified home-based self-prescreening purposes to detect the generation of tumors around the vocal cord early in the benign stage. RESULTS: We implemented four convolutional neural network (CNN) models (two Mask R-CNNs, Yolo V4, and a single-shot detector) that were trained, validated and tested using 2183 laryngoscopic images. The experimental results demonstrated that among the four applied models, Yolo V4 showed the highest F1-score for all tumor types (0.7664, cyst; 0.9875, granuloma; 0.8214, leukoplakia; 0.8119, nodule; and 0.8271, polyp). The model with the lowest false-negative rate was different for each tumor type (Yolo V4 for cysts/granulomas and Mask R-CNN for leukoplakia/nodules/polyps). In addition, the embedded-operated Yolo V4 model showed an approximately equivalent F1-score (0.8529) to that of the computer-operated Yolo-4 model (0.8683). CONCLUSIONS: Based on these results, we conclude that the proposed deep-learning-based home screening techniques have the potential to aid in the early detection of tumors around the vocal cord and can improve the long-term survival of patients with vocal cord tumors.

BACKGROUND: Cerebral edema is an extremely common secondary disease in post-stroke. Point-of-care testing for cerebral edema types has important clinical significance for the precise management to prevent poor prognosis. Nevertheless, there has not been a fully accepted bedside testing method for that. METHODS: A symmetric cancellation near-field coupling phase shift (NFCPS) monitoring system is established based on the symmetry of the left and right hemispheres and the fact that unilateral lesions do not affect healthy hemispheres. For exploring the feasibility of this system to reflect the occurrence and development of cerebral edema, 13 rabbits divided into experimental group (n = 8) and control group (n = 5) were performed 24-h NFCPS continuous monitoring experiments. After time difference offset and feature band averaging processing, the changing trend of NFCPS at the stages dominated by cytotoxic edema (CE) and vasogenic edema (VE), respectively, was analyzed. Furthermore, the features under the different time windows were extracted. Then, a discriminative model of cerebral edema types based on support vector machines (SVM) was established and performance of multiple feature combinations was compared. RESULTS: The NFCPS monitoring outcomes of experimental group endured focal ischemia modeling by thrombin injection show a trend of first decreasing and then increasing, reaching the lowest value of - 35.05° at the 6th hour. Those of control group do not display obvious upward or downward trend and only fluctuate around the initial value with an average change of - 0.12°. Furthermore, four features under the 1-h and 2-h time windows were extracted. Based on the discriminative model of cerebral edema types, the classification accuracy of 1-h window is higher than 90% and the specificity is close to 1, which is almost the same as the performance of the 2-h window. CONCLUSION: This study proves the feasibility of NFCPS technology combined with SVM to distinguish cerebral edema types in a short time, which is promised to become a new solution for immediate and precise management of dehydration therapy after ischemic stroke.

In order to study the local interactions between facial soft-tissues and a Silhouette Soft® suspension suture, a CE marked medical device designed for the repositioning of soft tissues in the face and the neck, Finite element simulations were run, in which a model of the suture was embedded in a three-layer Finite Element structure that accounts for the local mechanical organization of human facial soft tissues. A 2D axisymmetric model of the local interactions was designed in ANSYS, in which the geometry of the tissue, the boundary conditions and the applied loadings were considered to locally mimic those of human face soft tissue constrained by the suture in facial tissue repositioning. The Silhouette Soft suture is composed of a knotted thread and sliding cones that are anchored in the tissue. Hence, simulating these interactions requires special attention for an accurate modelling of contact mechanics. As tissue is modelled as a hyper-elastic material, the displacement of the facial soft tissue changes in a nonlinear way with the intensity of stress induced by the suture and the number of the cones. Our simulations show that for a 4-cone suture a displacement of 4.35 mm for a 2.0 N external loading and of 7.6 mm for 4.0 N. Increasing the number of cones led to the decrease in the equivalent local strain (around 20%) and stress (around 60%) applied to the tissue. The simulated displacements are in general agreement with experimental observations.

BACKGROUND: Nano-photothermal therapy (NPTT) has gained wide attention in cancer treatment due to its high efficiency and selective treatment strategy. The biggest challenges in the clinical application are the lack of (i) a reliable platform for mapping the thermal dose and (ii) efficient photothermal agents (PTAs). This study developed a 3D treatment planning for NPTT to reduce the uncertainty of treatment procedures, based on our synthesized nanohybrid. METHODS: This study aimed to develop a three-dimensional finite element method (FEM) model for in vivo NPTT in mice using magneto-plasmonic nanohybrids, which are complex assemblies of superparamagnetic iron oxide nanoparticles and gold nanorods. The model was based on Pennes' bio-heat equation and utilized a geometrically correct mice whole-body. CT26 colon tumor-bearing BALB/c mice were injected with nanohybrids and imaged using MRI (3 Tesla) before and after injection. MR images were segmented, and STereoLithography (STL) files of mice bodies and nanohybrid distribution in the tumor were established to create a realistic geometry for the model. The accuracy of the temperature predictions was validated by using an infrared (IR) camera. RESULTS: The photothermal conversion efficiency of the nanohybrids was experimentally determined to be approximately 30%. The intratumoral (IT) injection group showed the highest temperature increase, with a maximum of 17 °C observed at the hottest point on the surface of the tumor-bearing mice for 300 s of laser exposure at a power density of 1.4 W/cm2. Furthermore, the highest level of tissue damage, with a maximum value of Ω = 0.4, was observed in the IT injection group, as determined through a simulation study. CONCLUSIONS: Our synthesized nanohybrid shows potential as an effective agent for MRI-guided NPTT. The developed model accurately predicted temperature distributions and tissue damage in the tumor. However, the current temperature validation method, which relies on limited 2D measurements, may be too lenient. Further refinement is necessary to improve validation. Nevertheless, the presented FEM model holds great promise for clinical NPTT treatment planning.