Unobtrusive Cognitive Assessment in Smart-Homes: Leveraging Visual Encoding and Synthetic Movement Traces Data Mining.

The ubiquity of sensors in smart-homes facilitates the support of independent living for older adults and enables cognitive assessment. Notably, there has been a growing interest in utilizing movement traces for identifying signs of cognitive impairment in recent years. In this study, we introduce an innovative approach to identify abnormal indoor movement patterns that may signal cognitive decline. This is achieved through the non-intrusive integration of smart-home sensors, including passive infrared sensors and sensors embedded in everyday objects. The methodology involves visualizing user locomotion traces and discerning interactions with objects on a floor plan representation of the smart-home, and employing different image descriptor features designed for image analysis tasks and synthetic minority oversampling techniques to enhance the methodology. This approach distinguishes itself by its flexibility in effortlessly incorporating additional features through sensor data. A comprehensive analysis, conducted with a substantial dataset obtained from a real smart-home, involving 99 seniors, including those with cognitive diseases, reveals the effectiveness of the proposed functional prototype of the system architecture. The results validate the system's efficacy in accurately discerning the cognitive status of seniors, achieving a macro-averaged F1-score of 72.22% for the two targeted categories: cognitively healthy and people with dementia. Furthermore, through experimental comparison, our system demonstrates superior performance compared with state-of-the-art methods.

State of the Art of Non-Invasive Technologies for Bladder Monitoring: A Scoping Review.

Bladder monitoring, including urinary incontinence management and bladder urinary volume monitoring, is a vital part of urological care. Urinary incontinence is a common medical condition affecting the quality of life of more than 420 million people worldwide, and bladder urinary volume is an important indicator to evaluate the function and health of the bladder. Previous studies on non-invasive techniques for urinary incontinence management technology, bladder activity and bladder urine volume monitoring have been conducted. This scoping review outlines the prevalence of bladder monitoring with a focus on recent developments in smart incontinence care wearable devices and the latest technologies for non-invasive bladder urine volume monitoring using ultrasound, optical and electrical bioimpedance techniques. The results found are promising and their application will improve the well-being of the population suffering from neurogenic dysfunction of the bladder and the management of urinary incontinence. The latest research advances in bladder urinary volume monitoring and urinary incontinence management have significantly improved existing market products and solutions and will enable the development of more effective future solutions.

Automatic lesion segmentation using atrous convolutional deep neural networks in dermoscopic skin cancer images.

BACKGROUND: Melanoma is the most dangerous and aggressive form among skin cancers, exhibiting a high mortality rate worldwide. Biopsy and histopathological analysis are standard procedures for skin cancer detection and prevention in clinical settings. A significant step in the diagnosis process is the deep understanding of the patterns, size, color, and structure of lesions based on images obtained through dermatoscopes for the infected area. However, the manual segmentation of the lesion region is time-consuming because the lesion evolves and changes its shape over time, making its prediction challenging. Moreover, it is challenging to predict melanoma at the initial stage as it closely resembles other skin cancer types that are not malignant as melanoma; thus, automatic segmentation techniques are required to design a computer-aided system for accurate and timely detection. METHODS: As deep learning approaches have gained significant attention in recent years due to their remarkable performance, therefore, in this work, we proposed a novel design of a convolutional neural network (CNN) framework based on atrous convolutions for automatic lesion segmentation. This architecture is built based on the concept of atrous/dilated convolutions which are effective for semantic segmentation. A deep neural network is designed from scratch employing several building blocks consisting of convolutional, batch normalization, leakyReLU layer, and fine-tuned hyperparameters contributing altogether towards higher performance. CONCLUSION: The network was tested on three benchmark datasets provided by International Skin Imaging Collaboration (ISIC), i.e., ISIC 2016, ISIC 2017, and ISIC 2018. The experimental results showed that the proposed network achieved an average Jaccard index of 90.4% on ISIC 2016, 81.8% on ISIC 2017, and 89.1% on ISIC 2018 datasets, respectively which is recorded as higher than the top three winners of the ISIC challenge and other state-of-the-art methods. Also, the model successfully extracts lesions from the whole image in one pass in less time, requiring no pre-processing step. The conclusions yielded that network is accurate in performing lesion segmentation on adopted datasets.

A Systematic Review of Wearable Sensors for Monitoring Physical Activity.

This article reviews the use of wearable sensors for the monitoring of physical activity (PA) for different purposes, including assessment of gait and balance, prevention and/or detection of falls, recognition of various PAs, conduction and assessment of rehabilitation exercises and monitoring of neurological disease progression. The article provides in-depth information on the retrieved articles and discusses study shortcomings related to demographic factors, i.e., age, gender, healthy participants vs patients, and study conditions. It is well known that motion patterns change with age and the onset of illnesses, and that the risk of falling increases with age. Yet, studies including older persons are rare. Gender distribution was not even provided in several studies, and others included only, or a majority of, men. Another shortcoming is that none of the studies were conducted in real-life conditions. Hence, there is still important work to be done in order to increase the usefulness of wearable sensors in these areas. The article highlights flaws in how studies based on previously collected datasets report on study samples and the data collected, which makes the validity and generalizability of those studies low. Exceptions exist, such as the promising recently reported open dataset FallAllD, wherein a longitudinal study with older adults is ongoing.

Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images.

Automatic melanoma detection from dermoscopic skin samples is a very challenging task. However, using a deep learning approach as a machine vision tool can overcome some challenges. This research proposes an automated melanoma classifier based on a deep convolutional neural network (DCNN) to accurately classify malignant vs. benign melanoma. The structure of the DCNN is carefully designed by organizing many layers that are responsible for extracting low to high-level features of the skin images in a unique fashion. Other vital criteria in the design of DCNN are the selection of multiple filters and their sizes, employing proper deep learning layers, choosing the depth of the network, and optimizing hyperparameters. The primary objective is to propose a lightweight and less complex DCNN than other state-of-the-art methods to classify melanoma skin cancer with high efficiency. For this study, dermoscopic images containing different cancer samples were obtained from the International Skin Imaging Collaboration datastores (ISIC 2016, ISIC2017, and ISIC 2020). We evaluated the model based on accuracy, precision, recall, specificity, and F1-score. The proposed DCNN classifier achieved accuracies of 81.41%, 88.23%, and 90.42% on the ISIC 2016, 2017, and 2020 datasets, respectively, demonstrating high performance compared with the other state-of-the-art networks. Therefore, this proposed approach could provide a less complex and advanced framework for automating the melanoma diagnostic process and expediting the identification process to save a life.

Oral leukoplakia-epidemiological survey and histochemical analysis of 107 cases in Brazil.

OBJECTIVE: To perform an epidemiological survey comparing the cell proliferative activity of 107 cases of oral leukoplakia with their clinical and histopathological characteristics. MATERIALS AND METHODS: A cross-sectional, observational, and histological-histochemical study. The cases came from the Histopathological Diagnostic Service of UPF/RS and the School of Dentistry of Araçatuba FOA/UNESP/SP (1986-2016). The histopathological sections were stained using the silver staining (AgNOR) technique and the nuclei of 100 epithelial cells selected randomly were recorded to count the number of nucleolar organizer regions (NORs). The mean NORs per lesion were correlated to clinical and histological characteristics using ANOVA, at 5% significance. RESULTS: Most of the cases included men (62.62%), white (64.49%), and with an average age of 53.94 years. The most probable etiological factors were smoking (44.7%) and alcohol consumption (9.85%). The evolution time of most lesions was fast (33.65%), manifesting mainly in the form of plaques (70.37%) and without symptoms (58.88%). They were located mainly in the cheek mucosa (26.62%) and presented white color (66.35%), well-defined edges (59.81%), firm consistency (47.5%), and keratinized surface (49.53%). Etiological factor (p = 0.003), evolution time (p = 0.006), symptoms (p = 0.029), location (p = 0.020), consistency (p = 0.047), histopathological characteristics (p = 0.004), and superficial keratinization (p = 0.001) were statistically significant regarding the mean NORs of the leukoplakias studied. CLINICAL RELEVANCE: Oral leukoplakias caused by alcohol consumption and/or tobacco use, considering an evolution time of fewer than 12 months, asymptomatic, located in the lower lip or tongue, and with a firm consistency and increased superficial keratinization should be treated more aggressively by the clinician to avoid cancerization.

Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements.

Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200-300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other classifiers, with an average classification accuracy of above 97%. On the other hand, the real-time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively.

Quality of life of patients following orthognathic surgery.

The aim of this study was to evaluate how patients with dentofacial deficiency who have undergone orthognathic surgery perceive their quality of life (QoL) with respect to functional, esthetic, and psychosocial issues. In an observational, cross-sectional, descriptive, and quantitative study, 10 patients who had undergone orthognathic surgery answered questionnaires used internationally for assessing QoL: the Short Form Health Survey (SF-36), Oral Health Impact Profile 14 (OHIP-14), and Orthognathic Quality of Life Questionnaire (OQLQ). In addition, the patients completed the Self-Perception Questionnaire of Esteem, Appearance, and Interpersonal Relationships (ASR-26), which explored the differences between their current self-esteem, appearance satisfaction, and interpersonal relationships and their memories of their presurgical feelings about those topics. The data were submitted to descriptive and multivariable statistical analyses. There was a statistically significant difference between the preoperative and postoperative periods regarding self-esteem, appearance satisfaction, and professional relationships (P < 0.05). The data collected with the SF-36, OHIP-14, and OQLQ questionnaires showed high internal consistency (Cronbach α coefficient). The index (mean) scores for the SF-36 (81.5), OHIP-14 (0.6), and OQLQ (5.0) were close to the conditions of high QoL. Principal component analysis revealed 3 distinct groups of patients, and 70% of patients composed a group with high QoL scores, showing no complaints of physical pain, functional limitation, psychological discomfort, social disability, or excessive concern about their oral condition. In this small sample of patients, orthognathic surgery resulted in improved health-related QoL with variations among patients regarding physical pain, psychological discomfort, oral function, facial esthetics, physical function, social function, and self-awareness of facial deformity. The results of this study indicate the importance of applying a questionnaire in individuals who have undergone orthognathic surgery to investigate their personal motivations for treatment and which physical, social, and psychological problems are limiting their QoL.

Evaluation of the Genotoxicity of Tobacco and Alcohol in Oral Mucosa Cells: A Pilot Study.

AIM AND OBJECTIVE: To define the genotoxic potential of tobacco and alcohol in the oral mucosa through a micronuclei (MN) test. MATERIALS AND METHODS: Samples of exfoliative cells from oral mucosa were collected using superficial scraping of the right- and left-cheek mucosa of 83 patients divided into four groups, namely: (G1) 24 individuals abstaining from tobacco and alcoholic beverages; (G2) 23 individuals who smoke and abstain from alcoholic beverages; (G3) 24 smokers and alcoholics; and (G4) 12 individuals who consume alcohol and abstain from tobacco. The samples were stained with Giemsa-Wright, and the frequencies of MN, binucleated cells, and metanuclear changes were recorded in the samples of each group (1,000 cells per patient). RESULTS: Analysis of variance (ANOVA) showed a difference between groups for changes concerning karyorrhexis (p = 0), pycnosis (p = 0.002), karyolysis (p = 0.003), and binucleated cells (p = 0.046). As for the total number of changes, G3, G2, and G4, respectively, were significantly higher than G1. CONCLUSION: It is suggested that the influence of smoking and drinking on exfoliating cells of oral mucosa may cause metanuclear changes due to genetic changes that these products cause, and the MN test is effective in detecting and monitoring such changes. CLINICAL SIGNIFICANCE: MN test may work for constantly monitoring the oral mucosa of smokers and/or alcoholic patients, so that early cell changes may be diagnosed, preventing the genesis of oral cancer.

Evaluation of a Series of Overdentures Made in a Higher Education Dental Institution in Southern Brazil.

AIM AND OBJECTIVE: This study aimed to evaluate the epidemiological profile, oral health self-perception index, and level of satisfaction of users of complete implant-supported overdentures that had been used for at least 1 year and were made at the School of Dentistry of the University of Passo Fundo, Rio Grande do Sul, Brazil (FO/UPF), between 2014 and 2019. MATERIALS AND METHODS: The sample consisted of 30 patients with overdentures, who were selected from the dental records filed at the institution. Data on general health and the dental implants involved (brand, type of prosthetic connection, number of implants, and additional overdenture retention system) were collected from the medical records. The 30 patients were invited to answer the Oral Health Impact Profile-14 (OHIP-14) and visual analog scale (VAS) questionnaires, and due to the coronavirus disease 2019 pandemic, it was possible to contact 15 patients. RESULTS: Most of the prostheses studied were mandibular overdentures, and 66.66% of the cases were retained by the O-ring system. As for the oral health self-perception of the individuals, it was concluded that male patients had a lower mean overall score (p = 0.047) and functional domain (p = 0.042) in the OHIP-14. The number of implants and the installation arch interfered with functional domain and psychological capacity (p <0.05). The VAS showed that women have greater esthetic satisfaction with prostheses (p = 0.048) and that the bar-clip retention system is more satisfactory than the O-ring (p = 0.017). CONCLUSION: Despite the limitations of overdentures, it was noted that, when properly indicated, they are a viable option for oral rehabilitation on implants. CLINICAL SIGNIFICANCE: Oral rehabilitation well-indicated with overdentures, especially those retained by the bar-clip system, results in an improvement in the patients' quality of life.

A Systematic Review on the Use of Wearable Body Sensors for Health Monitoring: A Qualitative Synthesis.

The use of wearable body sensors for health monitoring is a quickly growing field with the potential of offering a reliable means for clinical and remote health management. This includes both real-time monitoring and health trend monitoring with the aim to detect/predict health deterioration and also to act as a prevention tool. The aim of this systematic review was to provide a qualitative synthesis of studies using wearable body sensors for health monitoring. The synthesis and analysis have pointed out a number of shortcomings in prior research. Major shortcomings are demonstrated by the majority of the studies adopting an observational research design, too small sample sizes, poorly presented, and/or non-representative participant demographics (i.e., age, gender, patient/healthy). These aspects need to be considered in future research work.

A Comparative Analysis of Hybrid Deep Learning Models for Human Activity Recognition.

Recent advances in artificial intelligence and machine learning (ML) led to effective methods and tools for analyzing the human behavior. Human Activity Recognition (HAR) is one of the fields that has seen an explosive research interest among the ML community due to its wide range of applications. HAR is one of the most helpful technology tools to support the elderly's daily life and to help people suffering from cognitive disorders, Parkinson's disease, dementia, etc. It is also very useful in areas such as transportation, robotics and sports. Deep learning (DL) is a branch of ML based on complex Artificial Neural Networks (ANNs) that has demonstrated a high level of accuracy and performance in HAR. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two types of DL models widely used in the recent years to address the HAR problem. The purpose of this paper is to investigate the effectiveness of their integration in recognizing daily activities, e.g., walking. We analyze four hybrid models that integrate CNNs with four powerful RNNs, i.e., LSTMs, BiLSTMs, GRUs and BiGRUs. The outcomes of our experiments on the PAMAP2 dataset indicate that our proposed hybrid models achieve an outstanding level of performance with respect to several indicative measures, e.g., F-score, accuracy, sensitivity, and specificity.

Open-Source Data Collection and Data Sets for Activity Recognition in Smart Homes.

As research in smart homes and activity recognition is increasing, it is of ever increasing importance to have benchmarks systems and data upon which researchers can compare methods. While synthetic data can be useful for certain method developments, real data sets that are open and shared are equally as important. This paper presents the E-care@home system, its installation in a real home setting, and a series of data sets that were collected using the E-care@home system. Our first contribution, the E-care@home system, is a collection of software modules for data collection, labeling, and various reasoning tasks such as activity recognition, person counting, and configuration planning. It supports a heterogeneous set of sensors that can be extended easily and connects collected sensor data to higher-level Artificial Intelligence (AI) reasoning modules. Our second contribution is a series of open data sets which can be used to recognize activities of daily living. In addition to these data sets, we describe the technical infrastructure that we have developed to collect the data and the physical environment. Each data set is annotated with ground-truth information, making it relevant for researchers interested in benchmarking different algorithms for activity recognition.

Histopathological Classifications of Oral Leukoplakia and its Relation to Cell Proliferative Activity: A Case Series.

AIM: This study relates the average number of nucleolar organizing regions (NORs) obtained in a series of cases of oral leukoplakia, with three methods of histopathological classification of such lesion. MATERIALS AND METHODS: This is a histopathological-histochemical laboratory cross-sectional study. The 18 cases of leukoplakia analyzed were filed at Pathology Service of the Biological Sciences Institute of the University of Passo Fundo, Rio Grande do Sul, Brazil (SDH/ICB/UPF) (2017 and 2018), from which epidemiological data were extracted. New histological sections were performed for impregnation by the argyrophilic nucleolar organizer regions (AgNOR) technique. The histopathological slides were analyzed by photon microscopy (1,000×), and the nuclei of 100 epithelial cells were photographed to count the number of NORs. Three methods were used for the lesions' histopathological classification [World Health Organization (WHO), Brothwell, and binary system]. The means of NORs were compared with the three histopathological classifications by means of the t or analysis of variance (ANOVA) statistical tests, at a significance level of 5%. RESULTS: According to the WHO classification method, most cases (11-61.1%) had a moderate classification. Evaluations by the Brothwell method showed moderate and mild classification in 50 and 38.9% of cases, respectively. According to the binary system, most cases (10-55.6%) had low risk. The average NORs found in 100 nuclei of each of the 18 lesions ranged from 2 to 4. When crossing the average NORs with the histopathological classification methods of the lesions by means of the t test or ANOVA, no significant relationship was noted. CONCLUSION: The average of NORs is not associated with the histological classifications of leukoplakias. Thus, the AgNOR method should be used with caution when differentiating different histological grades of leukoplakias. CLINICAL SIGNIFICANCE: The AgNOR method should be used with caution to determine the clinical treatment of oral leukoplakias, since no agreement was observed between this method and the histopathological classifications available for such lesion.

Signal Quality Improvement Algorithms for MEMS Gyroscope-Based Human Motion Analysis Systems: A Systematic Review.

Motion sensors such as MEMS gyroscopes and accelerometers are characterized by a small size, light weight, high sensitivity, and low cost. They are used in an increasing number of applications. However, they are easily influenced by environmental effects such as temperature change, shock, and vibration. Thus, signal processing is essential for minimizing errors and improving signal quality and system stability. The aim of this work is to investigate and present a systematic review of different signal error reduction algorithms that are used for MEMS gyroscope-based motion analysis systems for human motion analysis or have the potential to be used in this area. A systematic search was performed with the search engines/databases of the ACM Digital Library, IEEE Xplore, PubMed, and Scopus. Sixteen papers that focus on MEMS gyroscope-related signal processing and were published in journals or conference proceedings in the past 10 years were found and fully reviewed. Seventeen algorithms were categorized into four main groups: Kalman-filter-based algorithms, adaptive-based algorithms, simple filter algorithms, and compensation-based algorithms. The algorithms were analyzed and presented along with their characteristics such as advantages, disadvantages, and time limitations. A user guide to the most suitable signal processing algorithms within this area is presented.

A Comparison of Torque Stress on Abutment Screw of External Hexagon and Morse Taper Implant.

AIM: This three-dimensional (3D) finite element analysis observed the stress distribution on the prosthetic screws on external hexagon implant and morse taper implant with different tightening loads. MATERIALS AND METHODS: For this, two different 3D models assembly were obtained from the manufacturer and transferred to finite element analysis software: External hex implant model (EHM), and Morse taper implant model (MTM), both compounds by 3.75 × 7 mm implant, abutment and abutment screw. Bolt pretension force was applied on the shaft next to the threads of the prosthetic abutment. Preload was calculated using the torque on the prosthetic screw as recommended by the manufacturer (EHM30 and MTM20) and 10 Ncm torque above the manufacturer recommendations (EHM40 and MTM30). Maximum von mises equivalent stresses were obtained on the screws. RESULTS: Preload values results were 243.18N (EHM30), 229.71N (MTM20), 324.24N (EHM40) and 344.57N (MTM30). In EHM30, EHM40 and MTM20 models the maximum stresses were below the yield strength of the abutment screw material. However, the maximum stress in MTM30 model was higher than the reference value. CONCLUSION: The torque loads above the manufacturer recommendations can cause plastic deformation in the MT abutment screw threads. The screws of morse taper implant can be more sensitive to higher loads than external hexagon implant. CLINICAL SIGNIFICANCE: The adequate torque can result in screw loosening, while fracture may occur if torque is excessive. Abutment screw suffers many screwing cycles in its lifetime in a way that some tightening forces are above manufacturer recommendations.

Oral Lesions found in a Dental School in Southern Brazil.

OBJECTIVE: This study aimed to perform an epidemiological survey of the cases of oral lesions diagnosed in a Brazilian dental school. MATERIALS AND METHODS: An epidemiological survey was carried out at the Faculty of Dentistry of the University of Passo Fundo (FOUPF), Rio Grande do Sul, Brazil, between March 2015 and June 2017, characterizing the patients in terms of gender, age, and ethnicity. A total of 3,200 records were evaluated, from which 716 presented information on oral lesions diagnosed and treated at the clinics of the FOUPF. The data were evaluated by descriptive statistics of frequency. RESULTS: The most frequent change was the coated tongue (13.83%). The majority of the patients were female (56%), white (31.57%), in the sixth decade of life (21.65%). Regarding the classification of lesions, the most frequent group was developmental changes (25%). CONCLUSION: It is considered that the sample studied presented a significant index of oral lesions and normality changes, considering that 716 (22.38%) patients studied showed some type of change in the oral cavity. CLINICAL SIGNIFICANCE: The sample studied presents peculiar characteristics regarding the number of oral lesions/conditions diagnosed. It is also considered that Schools of Dentistry are valuable environments for carrying out epidemiological surveys in stomatology, providing the diagnostic exercise, as long as the students are stimulated for this purpose.

Evaluation of Cellular Proliferative Activity in Patients with Actinic Cheilitis through Silver-stained Nucleolar Organizer Region Method.

Introduction: Actinic cheilitis (AC) is a lesion potentially malignant that affects the lips after prolonged exposure to solar ultraviolet (UV) radiation. The present study aimed to assess and describe the proliferative cell activity, using silver-stained nucleolar organizer region (AgNOR) quantification proteins, and to investigate the potential associations between AgNORs and the clinical aspects of AC lesions. Materials and methods: Cases diagnosed with AC were selected and reviewed from Center of Histopathological Diagnosis of the Institute of Biological Sciences, Passo Fundo University, Brazil. Clinical data including clinical presentation of the patients affected with AC were collected. The AgNOR techniques were performed in all recovered cases. The different microscopic areas of interest were printed with magnification of *1000, and in each case, 200 epithelial cell nuclei were randomly selected. The mean quantity in each nucleus for NORs was recorded. One-way analysis of variance was used for statistical analysis. Results: A total of 22 cases of AC were diagnosed. The patients were aged between 46 and 75 years (mean age: 55 years). Most of the patients affected were males presenting asymptomatic white plaque lesions in the lower lip. The mean value quantified for AgNORs was 2.4 ± 0.63, ranging between 1.49 and 3.82. No statistically significant difference was observed associating the quantity of AgNORs with the clinical aspects collected from the patients (p > 0.05). Conclusion: The present study reports the lack of association between the proliferative cell activity and the clinical aspects observed in patients affected by AC through the quantification of AgNORs. Clinical significance: Knowing the potential relation between the clinical aspects of AC and the proliferative cell activity quantified by AgNORs could play a significant role toward the early diagnosis of malignant lesions in the clinical practice. Keywords: Actinic cheilitis, Proliferative cell activity, Silver-stained nucleolar organizer regions.

A Systematic Review of Wearable Patient Monitoring Systems - Current Challenges and Opportunities for Clinical Adoption.

The aim of this review is to investigate barriers and challenges of wearable patient monitoring (WPM) solutions adopted by clinicians in acute, as well as in community, care settings. Currently, healthcare providers are coping with ever-growing healthcare challenges including an ageing population, chronic diseases, the cost of hospitalization, and the risk of medical errors. WPM systems are a potential solution for addressing some of these challenges by enabling advanced sensors, wearable technology, and secure and effective communication platforms between the clinicians and patients. A total of 791 articles were screened and 20 were selected for this review. The most common publication venue was conference proceedings (13, 54%). This review only considered recent studies published between 2015 and 2017. The identified studies involved chronic conditions (6, 30%), rehabilitation (7, 35%), cardiovascular diseases (4, 20%), falls (2, 10%) and mental health (1, 5%). Most studies focussed on the system aspects of WPM solutions including advanced sensors, wireless data collection, communication platform and clinical usability based on a specific area or disease. The current studies are progressing with localized sensor-software integration to solve a specific use-case/health area using non-scalable and 'silo' solutions. There is further work required regarding interoperability and clinical acceptance challenges. The advancement of wearable technology and possibilities of using machine learning and artificial intelligence in healthcare is a concept that has been investigated by many studies. We believe future patient monitoring and medical treatments will build upon efficient and affordable solutions of wearable technology.

Influence of Age on Factors associated with Peri-implant Bone Loss after Prosthetic Rehabilitation over Osseointegrated Implants.

INTRODUCTION: To verify the influence of age on factors associated with peri-implant bone loss after prosthetic rehabilitation over osseointegrated implants. MATERIALS AND METHODS: This is an analytical, observational, and longitudinal study with initial 23 participants. Patients presenting with osseointegrated implants with their respective prostheses installed were included, and they could be carriers of chronic and degenerative diseases, such as diabetes, osteoporosis, hypothyroidism, cardiovascular disease (CVD), and systemic arterial hypertension. Thus, 18 participants with 57 implants were selected and followed up from 2009 to 2013. For statistical analysis, chi-square or Fisher's exact test was used for the association of systemic conditions and bone loss. Student's t-test was used for mean comparisons of age and number of total upper and lower implants. RESULTS: The average age of the sample studied was 71.05 years (65-80). The average implant per person was 3.2. Smoking had an influence on both mesial and distal bone loss, and the latter was significant (p = 0.0370). The association between bone loss and gender was also significant (p < 0.05). Moreover, male gender and upper implants were factors significantly associated with bone loss. The systemic conditions, when isolated, did not have significant influence on implant survival. CONCLUSION: Age is not a factor that, alone, contraindicates implant-rehabilitating therapy. On the contrary, smoking has a significant influence on dental implant survival. Systemic diseases, such as osteoporosis, hypothyroidism, diabetes, hypertension, and heart diseases, when controlled, are not contraindication factors. CLINICAL SIGNIFICANCE: This study is relevant for assessing peri-implant bone loss in elderly patients, right after implant installation and over time. Therefore, it was possible to verify that age is not a limiting factor for this procedure. Controlled systemic diseases do not contraindicate implant installation, but smoking is a factor that affects implant survival.