Recognizing Different Foot Deformities Using FSR Sensors by Static Classification of Neural Networks

تُعَدُّ أنظمة النعال الحسّاسة للحركة تقنية واعدة للعديد من التطبيقات في الرعاية الصحية والرياضة. حيث يمكن أن توفّر هذه الأنظمة معلومات قيّمة حول توزيع الضغط على القدم وأنماط المشي لأفراد مختلفين. ومع ذلك، فإن تصميم وتنفيذ مثل هذه الأنظمة يواجه العديد من التحديات، مثل اختيار الحسّاسات والمعايرة ومعالجة البيانات والتفسير. في هذه الدراسة، نقترح نظام نعل حساس باستخدام مقاومات استشعار القوى  لقياس الضغط المطبّق من القدم على مناطق مختلفة من النعل. يقوم هذا النظام بتصنيف أربعة أنواع من تشوهات القدم: طبيعي، مسطح، انحراف القدم إلى الداخل، وزيادة انحراف القدم إلى الخارج. تستخدم مرحلة التصنيف قيم الضغط الفرقية على نقاط الضغط كمدخلات لنموذج التغذية الأمامية للشبكات العصبية. تم جمع البيانات من 60 فرداً تم تشخيصهم بالحالات المدروسة. حقق تنفيذ التغذية الأمامية للشبكات العصبية دقة بنسبة 96.6٪ باستخدام 50٪ من المجموعة البيانية كبيانات تدريبية و 92.8٪ باستخدام 30٪ من البيانات التدريبية فقط. ويوضح المقارنة مع الأعمال ذات الصلة الأثر الإيجابي لاستخدام القيم الفرق لنقاط الضغط كمدخلات للشبكات العصبية مقارنة بالبيانات الأولية.Sensing insole systems are a promising technology for various applications in healthcare and sports. They can provide valuable information about the foot pressure distribution and gait patterns of different individuals. However, designing and implementing such systems poses several challenges, such as sensor selection, calibration, data processing, and interpretation. This paper proposes a sensing insole system that uses force-sensitive resistors (FSRs) to measure the pressure exerted by the foot on different regions of the insole. This system classifies four types of foot deformities: normal, flat, over-pronation, and excessive supination. The classification stage uses the differential values of pressure points as input for a feedforward neural network (FNN) model. Data acquisition involved 60 subjects diagnosed with the studied cases. The implementation of FNN achieved an accuracy of 96.6% using 50% of the dataset as training data and 92.8% using only 30% training data. The comparison with related work shows good impact of using the differential values of pressure points as input for neural networks compared with raw data

A novel method of sampling gingival crevicular fluid from a mouse model of periodontitis

Using a mouse model of silk ligature-induced periodontal disease (PD), we report a novel method of sampling mouse gingival crevicular fluid (GCF) to evaluate the time-dependent secretion patterns of bone resorption-related cytokines. GCF is a serum transudate containing host-derived biomarkers which can represent cellular response in the periodontium. As such, human clinical evaluations of PD status rely on sampling this critical secretion. At the same time, a method of sampling GCF from mice is absent, hindering the translational value of mouse models of PD. Therefore, we herein report a novel method of sampling GCF from a mouse model of periodontitis, involving a series of easy steps. First, the original ligature used for induction of PD was removed, and a fresh ligature for sampling GCF was placed in the gingival crevice for ten minutes. Immediately afterwards, the volume of GCF collected in the sampling ligature was measured using a high precision weighing balance. The sampling ligature containing GCF was then immersed in a solution of PBS-Tween 20 and subjected to ELISA. This enabled us to monitor the volume of GCF and detect time-dependent changes in the expression of such cytokines as IL-1b, TNF-α, IL-6, RANKL, and OPG associated with the levels of alveolar bone loss, as reflected in GCF collected from a mouse model of PD. Therefore, this novel GCF sampling method can be used to measure various cytokines in GCF relative to the dynamic changes in periodontal bone loss induced in a mouse model of PD. Correspondence: Toshihisa Kawai, DDS, PhD, Department of Immunology and Infectious diseases, The Forsyth Institute, 245 First Street, Cambridge, MA 02142, Tel: 617-892-8317, Fax: 617-892-8437, [email protected]. # Contributed equally to this work HHS Public Access Author manuscript J Immunol Methods. Author manuscript; available in PMC 2017 November 01. Published in final edited form as: J Immunol Methods. 2016 November ; 438: 21–25. doi:10.1016/j.jim.2016.08.008. Author Manuscript Author Manuscript Author Manuscript Autho

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Introduction Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality. Methods Prospective cohort study in 109 institutions in 41 countries. Inclusion criteria: children <18 years who were newly diagnosed with or undergoing active treatment for acute lymphoblastic leukaemia, non-Hodgkin's lymphoma, Hodgkin lymphoma, retinoblastoma, Wilms tumour, glioma, osteosarcoma, Ewing sarcoma, rhabdomyosarcoma, medulloblastoma and neuroblastoma. Of 2327 cases, 2118 patients were included in the study. The primary outcome measure was all-cause mortality at 30 days, 90 days and 12 months. Results All-cause mortality was 3.4% (n=71/2084) at 30-day follow-up, 5.7% (n=113/1969) at 90-day follow-up and 13.0% (n=206/1581) at 12-month follow-up. The median time from diagnosis to multidisciplinary team (MDT) plan was longest in low-income countries (7 days, IQR 3-11). Multivariable analysis revealed several factors associated with 12-month mortality, including low-income (OR 6.99 (95% CI 2.49 to 19.68); p<0.001), lower middle income (OR 3.32 (95% CI 1.96 to 5.61); p<0.001) and upper middle income (OR 3.49 (95% CI 2.02 to 6.03); p<0.001) country status and chemotherapy (OR 0.55 (95% CI 0.36 to 0.86); p=0.008) and immunotherapy (OR 0.27 (95% CI 0.08 to 0.91); p=0.035) within 30 days from MDT plan. Multivariable analysis revealed laboratory-confirmed SARS-CoV-2 infection (OR 5.33 (95% CI 1.19 to 23.84); p=0.029) was associated with 30-day mortality. Conclusions Children with cancer are more likely to die within 30 days if infected with SARS-CoV-2. However, timely treatment reduced odds of death. This report provides crucial information to balance the benefits of providing anticancer therapy against the risks of SARS-CoV-2 infection in children with cancer

TRAP-positive osteoclast precursors mediate ROS/NOdependent bactericidal activity via TLR4

Osteoclastogenesis was induced by RANKL stimulation in mouse monocytes to examine the possible bactericidal function of osteoclast precursors (OCp) and mature osteoclasts (OCm) relative to their production of NO and ROS. Tartrate-resistant acid phosphatase (TRAP)-positive OCp, but few or no OCm, phagocytized and killed Escherichia coli in association with the production of reactive oxygen species (ROS) and nitric oxide (NO). Phagocytosis of E. coli and production of ROS and NO were significantly lower in TRAP+ OCp derived from Toll-like receptor (TLR)-4 KO mice than that derived from wild-type (WT) or TLR2-KO mice. Interestingly, after phagocytosis, TRAP+ OCp derived from wild-type and TLR2-KO mice did not differentiate into OCm, even with continuous exposure to RANKL. In contrast, E. coliphagocytized TRAP+ OCp from TLR4-KO mice could differentiate into OCm. Importantly, neither NO nor ROS produced by TRAP+ OCp appeared to be engaged in phagocytosis-induced suppression of osteoclastogenesis. These results suggested that TLR4 signaling not only induces ROS and NO production to kill phagocytized bacteria, but also interrupts OCm differentiation. Thus, it can be concluded that TRAP+ OCp, but not OCm, can mediate bactericidal activity via phagocytosis accompanied by the production of ROS and NO via TLR4-associated reprograming toward phagocytic cell type

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality