bluuebunny/crossref_metadata_2025 huggingface dataset:
The Influence of Coffee Husk Ash as a Filler on the Performance of Bituminous Concrete Mix
Authors: Tessema Amare Tilahun , Alene Getachew Asefa , Wolelaw Natnael Melsew
Published: December 2022

Utilization of agricultural waste by-products in road construction has become a common practice in developing countries. The present study investigates the effect of coffee husk ash compared to basaltic stone dust on the performance of bituminous concrete mixes for road construction. Based on the Marshall mix design, a total of thirty (30) asphalt concrete specimens were prepared with basaltic stone dust and coffee husk ash as a filler with bitumen content between 4.0% and 6.0% at varying increments of 0.5%, and the Marshall stability test was performed on full mixes to determine the optimum asphalt content of the mixtures. From the test results, it is seen that specimens made with coffee husk ash and basaltic stone dust are found to have satisfactory Marshall properties, and moisture susceptibility shows that mixes using CHA filler provide better resistance to moisture effects than mixtures using BSD filler. When compared to BSD, the CHA shows an 8.43% increase in OBC. From the laboratory results, it has been shown that specimens made with coffee husk ash and basaltic stone dust are found to satisfy Marshall parameters and indirect tensile strength of bituminous concrete mixes when the coffee husk ash exhibits better performance than basaltic stone dust, which indicates that the CHA shows an increase of 35.80% compared to basaltic stone dust. The research findings indicated that the TSR of the paving mixes using basaltic stone dust was 94.58% and that using coffee husk ash was 105.03%, where both fillers satisfy the minimum limiting value of 80%. Overall, using coffee husk ash as a filler in hot mix asphalt concrete helps to solve a significant agricultural waste disposal issue while also being environmentally friendly.
Association between Cardio-Ankle Vascular Index and Masked Uncontrolled Hypertension in Hypertensive Patients: A Cross-Sectional Study
Authors: Wang Ning , Guo Ying , Li Xueting , Dong Ying , Liu Qi , Wang Guohong , Liang Lirong , Qin Mingzhao , Liu Qian , Zhong Jiuchang
Published: December 2022

Detection of masked uncontrolled hypertension (MUCH) that was defined for treated hypertensive individuals who had normal office blood pressure (BP) but elevated ambulatory BP remains largely challenging. Arterial stiffness is one of the leading risk markers for hypertension and can be clinically assessed by the cardio-ankle vascular index (CAVI). This study aimed to evaluate the association between CAVI and MUCH. A total of 155 hypertensive patients were included with their office BP levels and ambulatory BP monitoring measurements, which were divided into controlled hypertension (CH), MUCH, and sustained uncontrolled hypertension (SUCH) groups, respectively. There were 48 patients with CH, 56 patients with MUCH, and 51 patients with SUCH. Both MUCH and SUCH groups had a significantly higher CAVI than the CH group (9.05 (8.20–9.91) vs. 8.33 (7.75–9.15),

p
=
0.017

, and 9.75 (8.35–10.50) vs. 8.33 (7.75–9.15),

p
=
0.002

, respectively). There was no significant difference in CAVI values between the MUCH and SUCH groups. Multinomial logistic regression analysis exhibited that compared with the CH group, increased CAVI levels were positively associated with the presence of MUCH and SUCH (OR 2.046, 95% CI (1.239–3.381),

p
=
0.005

; OR 2.215, 95% CI (1.310–3.747),

p
=
0.003

) after adjusting for confounders. However, there was a similar trend of the CAVI in the MUCH and SUCH groups (OR 0.924, 95% CI (0.629–1.356),

p
=
0.686

). In summary, our findings support, for the first time, the novel notion that CAVI as an arterial stiffness parameter is an independent risk factor for MUCH, being equally important to MUCH and SUCH. When the assessed CAVI is high in hypertensive patients with normotensive office BP levels, it is necessary to further investigate with a 24 h ambulatory BP monitoring to estimate the longstanding BP control. CAVI may be used as a noninvasive indicator to identify patients with MUCH earlier.
The Twofold Role of Osteogenic Small Molecules in Parkinson’s Disease Therapeutics: Crosstalk of Osteogenesis and Neurogenesis
Authors: Tavakol Shima
Published: December 2022

Deemed one of the most problematic neurodegenerative diseases in the elderly population, Parkinson’s disease remains incurable to date. Ongoing diagnostic studies, however, have revealed that a large number of small molecule drugs that trigger the BMP2‐Smad signaling pathway with an osteogenic nature may be effective in Parkinson’s disease treatment. Although BMP2 and Smad1, 3, and 5 biomolecules promote neurite outgrowth and neuroprotection in dopaminergic cells as well, small molecules are quicker at crossing the BBB and reaching the damaged dopaminergic neurons located in the substantia nigra due to a molecular weight less than 500 Da. It is worth noting that osteogenic small molecules that inhibit Smurf1 phosphorylation do not offer therapeutic opportunities for Parkinson’s disease; whereas, osteogenic small molecules that trigger Smad1, 3, and 5 phosphorylation may have strong therapeutic implications in Parkinson’s disease by increasing the survival rate of dopaminergic cells and neuritogenesis. Notably, from a different perspective, it might be said that osteogenic small molecules can possibly put forth therapeutic options for Parkinson’s disease by improving neuritogenesis and cell survival.
The Parallel Mediation Effects of Depression, Well-Being, and Social Activity on Physical Performance and Frailty in Community-Dwelling Middle-Aged and Older People
Authors: Tallutondok Eva Berthy , Hsieh Chia-Jung , Li Pei-Shan
Published: December 2022

Background. Frailty refers to a decline in an elderly person’s physical, psychological, and social functioning, making them sensitive to stressors. Because frailty is caused by a variety of factors, including certain demographic characteristics, understanding the mediating factors that affect frailty in the elderly is critical. Purpose. To provide evidence about the relationship between depression, well-being, social activity, physical performance, and frailty among older adults. Materials and Methods. The study used secondary data from Taiwan’s Long-term Study of Aging (n = 7,622), excluding people with severe dementia. The chi-square test and Spearmen’s coefficient correlation were used to assess the relationship between the demographic variables and frailty. Nonparametric bootstrapping analysis was used to test whether depression, well-being, and social activity are parallel mediators of the relationship between physical performance and frailty. This study was approved by Fu Jen Catholic University (FJU-IRB No. C110040). Results. The overall frailty prevalence was 13.9%. We calculated a mean score and standard deviation for each measurement in this study. The correlation found low-to-moderate positive and negative statistically significant correlations between the variables. A significant, moderately negative relationship was found between physical performance and frailty that correlated with three potential mediating factors. The path indicated that lower physical performance scores and higher depression scores are more likely to be associated with frailty. Conclusion. Older adults who are depressed are more likely to become frail. Adults who are more socially active and report greater well-being are less likely to become frail. Therefore, further research should design and test a comprehensive intervention for older adults in community settings that addresses all three factors, aimed at increasing well-being and social activity while also treating depression.
The Charactex Protocol for Blood-Derived Cytological Preparation of Nonhematological Cancer
Authors: Malara Natalia , Donato Giuseppe , Ferrazzo Francesca , Garo Nastassia Carmelina , Fulciniti Franco
Published: December 2022

Introduction: Liquid biopsy, especially when performed by the isolation, expansion, and examination of circulating tumor cells (CTCs) from peripheral blood, has become an innovative and transforming diagnostic tool in Clinical Oncology. The CTCs have already entered the clinical practice as an alternative method to invasive tumor biopsy for detecting postsurgical and/or posttreatment minimal residual disease, to predict cancer recurrence and real-time treatment response. In this context, the retrospective observational project, known as CHARACTEX, has permitted to state that it is possible to exploit blood-based cytologic samples through short-term culture and in vitro CTC expansion. Methods: This method is based initially on a gradient-sedimentation technique, which impoverishes without completely depriving the obtained sample from the hematological cells, followed by short-term (14 days) in vitro culture and expansion and cytomorphological and flow cytometric analysis to investigate whether the expanded cell population possesses proliferative advantage and fits with criteria, which are consistent to the known primary tumor. Results: The originality of this method is that, apart from the above exposed criteria, there is no selection bias for the isolation of the cells from peripheral blood (like immunomagnetic bead treatment or preliminary immunocytochemistry), which can potentially introduce some limitation to the cell population under evaluation. Conclusion: The examination of the expanded cell population obtained by this method is very rewarding for both the pathologist – who can assess multiple tumor-related variables (like immunocytochemistry, flow cytometry of several parameters, and molecular pathology on cell suspensions and cell blocks obtained from them) – and the clinician.
Artificial intelligence: the major role it played in the management of healthcare during COVID-19 pandemic
Authors: Zaman Tabrez Uz , Alharbi Elaf Khalid , Bawazeer Aeshah Salem , Algethami Ghala Abdullah , Almehmadi Leen Abdullah , Alshareef Taif Muhammed , Alotaibi Yasmin Awwadh , Osman Karar Hosham Mohammed
Published: December 2022

The sudden arrival of COVID-19 called for new technologies to manage the healthcare system and to reduce the burden of patients in the hospitals. Artificial intelligence (AI) which involved using computers to model intelligent behavior became an important choice. Various AI applications helped a lot in the management of healthcare and delivering quick medical consultations and various services to a wide variety of patients. These new technological developments had significant roles in detecting the COVID-19 cases, monitoring them, and forecasting for the future. Artificial intelligence is applied to mimic the functional system of human intelligence. AI techniques and applications are also applied in proper examinations, prediction, analyzing, and tracking of the whereabouts of patients and the projected results. It also played a significant role in recognizing and proposing the generation of vaccines to prevent COVID-19. This study is therefore an attempt to understand the major role and use of AI in healthcare institutions by providing urgent decision-making techniques that greatly helped to manage and control the spread of the COVID-19 disease.
A review of factors that impact the design of a glove based wearable devices
Authors: Biju Soly Mathew , Al Khatib Obada , Sheikh Hashir Zahid
Published: December 2022

Loss of the capability to talk or hear applies psychological and social effects on the affected individuals due to the absence of appropriate interaction. Sign Language is used by such individuals to assist them in communicating with each other. The paper aims to report details of various aspects of wearable healthcare technologies designed in recent years based on the aim of the study, the types of technologies being used, accuracy of the system designed, data collection and storage methods, technology used to accomplish the task, limitations and future research suggested for the study. The aim of the study is to compare the differences between the papers. There is also comparison of technology used to determine which wearable device is better, which is also done with the help of accuracy. The limitations and future research help in determining how the wearable devices can be improved. A systematic review was performed based on a search of the literature. A total of 23 articles were retrieved. The articles are study and design of various wearable devices, mainly the glove-based device, to help you learn the sign language.
Stability of classification performance on an adaptive neuro fuzzy inference system for disease complication prediction
Authors: Kusumadewi Sri , Rosita Linda , Wahyuni Elyza Gustri
Published: December 2022

It is crucial to detect disease complications caused by metabolic syndromes early. High cholesterol, high glucose, and high blood pressure are indicators of metabolic syndrome. The aim of this study is to use adaptive neuro fuzzy inference system (ANFIS) to predict potential complications and compare its performance to other classifiers, namely random forest (RF), C4.5, and naïve Bayesian classification (NBC) algorithms. Fuzzy subtractive clustering is used to construct membership functions and fuzzy rules throughout the clustering process. This study analyzed 148 different data sets. Cholesterol, random glucose, systolic, and diastolic blood pressure are all included in the data collection. This learning process was conducted using a hybrid algorithm. The consequent parameters are adjusted forward using the leastsquare approach, while the premise parameters are adjusted backward using the gradient-descent process. The performance of a system is determined by the following indicators: accuracy, sensitivity, specification, precision, area under the curve (AUC), and root mean squared error (RMSE). The results of the training prove that ANFIS is an "excellent classification" classifier. ANFIS has proven to have very good stability across the six performance parameters. The adaptive properties used in ANFIS training and the implementation of fuzzy subtractive clustering strongly support this stability.
Neural network-based pH and coagulation adjustment system in water treatment
Authors: Vargas Mora Oscar Ivan , Parrado Nieto Daiam Camilo , Cuero Ortega Jairo David , Martinez Baquero Javier Eduardo , Jimenez Moreno Robinson
Published: December 2022

This document presents a machine learning model development as a tool to improve chemical dosing procedure in ariari regional aqueduct (ARA). The supervised learning model has been addressed starting from the knowledge of data color, turbidity and pH at the water inlet to the aqueduct and the dosing results of type A aluminum sulfate and calcium oxide (lime) obtained through jar tests. The construction of the automatic learning model had a comprehensive implementation and improvement field through continuous system training, which allowed an optimal dosage of Aluminum Sulfate and Lime to generate an outlet pH less than 7.5 and outlet turbidity less than 8 nephelometric turbidity unit (NTU). Those outlet water parameters meet the ministry of social protection criteria in Colombia. Also, a virtual jar test was created to reduce the time required to obtain chemical dosing values to less than a minute. In contrast, a laboratory test takes approximately a half-hour to displays results.
An application of Vietnamese handwriting text recognition for information extraction from high school admission form
Authors: Bao Pham The , Anh Dang Le Tran , Tam Nguyen Duy , Truong Nguyen Nhat , Le Thien Vu Pham Cung , Dat Trinh Tan
Published: December 2022

This paper presents an effective Vietnamese handwritten text recognition model by applying an improved convolutional recurrent neural networks (CRNNs) model to high school enrollment forms in Tay Ninh province, Vietnam. First, the proposed model extracts data areas containing text characters from forms. Then, we connect text boxes on the same row and divide the fields that containing text into three specific regions. Finally, we detect areas containing text characters for handwritten text recognition. We use word error rate (WER) to evaluate the recognition process and obtain a result of 0.3602. This result is one of the best solutions to the Vietnamese handwritten text recognition problem.
An image-based convolutional neural network system for road defects detection
Authors: Benallal Mohamed Anis , Tayeb Mustapha Si
Published: December 2022

An application of convolutional neural network (CNN) technique for road surface defects detection is presented in this paper. You only look ones (YOLO) algorithm showed its capabilities as an effective object detection technique in many previous works for different problems. Road damages detection and classification is one of the most challenging problems faced by public and private road management agencies. We present here results for a first attempt on applying YOLO to detect cracks and potholes, the most common defects encountered in surface roadways. Image database of the Brazilian highways were used to prepare input data, train the model and test it. Despite considering different types of cracks in one class and a less amount of potholes images, results show that the YOLO algorithm performs well with a global rate of 91% of defect detection. Output results analysis induce us to work on providing a local database for Algerian roadways with a large number of defect images/videos, as well as producing an automatic road-dedicated defects detector device.
Deep learning speech recognition for residential assistant robot
Authors: Jiménez-Moreno Robinson , Castillo Ricardo A.
Published: December 2022

This work presents the design and validation of a voice assistant to command robotic tasks in a residential environment, as a support for people who require isolation or support due to body motor problems. The preprocessing of a database of 3600 audios of 8 different categories of words like “paper”, “glass” or “robot”, that allow to conform commands such as "carry paper" or "bring medicine", obtaining a matrix array of Mel frequencies and its derivatives, as inputs to a convolutional neural network that presents an accuracy of 96.9% in the discrimination of the categories. The command recognition tests involve recognizing groups of three words starting with "robot", for example, "robot bring glass", and allow identifying 8 different actions per voice command, with an accuracy of 88.75%.
Information system based on multi-value classification of fully connected neural network for construction management
Authors: Honcharenko Tetyana , Akselrod Roman , Shpakov Andrii , Khomenko Oleksandr
Published: December 2022

This study is devoted to solving the problem to determine the professional adaptive capabilities of construction management staff using artificial intelligence systems. It is proposed fully connected feed-forward neural network (FCF-FNN) architecture and performed empirical modeling to create a data set. Model of artificial intelligence system allows evaluating the processes in an FCF-FNN during the execution of multi-value classification of professional areas. A method has been developed for the training process of a machine learning model, which reflects the internal connections between the components of an artificial intelligence system that allow it to “learn” from training data. To train the neural network, a data set of 35 input parameters and 29 output parameters was used; the amount of data in the set is 936 data lines. Neural network training occurred in the proportion of 10% and 90%, respectively. Results of this study research can be used to further improve the knowledge and skills necessary for successful professional realization.
A hybrid approach for face recognition using a convolutional neural network combined with feature extraction techniques
Authors: Benradi Hicham , Chater Ahmed , Lasfar Abdelali
Published: December 2022

Facial recognition technology has been used in many fields such as security, biometric identification, robotics, video surveillance, health, and commerce due to its ease of implementation and minimal data processing time. However, this technology is influenced by the presence of variations such as pose, lighting, or occlusion. In this paper, we propose a new approach to improve the accuracy rate of face recognition in the presence of variation or occlusion, by combining feature extraction with a histogram of oriented gradient (HOG), scale invariant feature transform (SIFT), Gabor, and the Canny contour detector techniques, as well as a convolutional neural network (CNN) architecture, tested with several combinations of the activation function used (Softmax and Segmoïd) and the optimization algorithm used during training (adam, Adamax, RMSprop, and stochastic gradient descent (SGD)). For this, a preprocessing was performed on two databases of our database of faces (ORL) and Sheffield faces used, then we perform a feature extraction operation with the mentioned techniques and then pass them to our used CNN architecture. The results of our simulations show a high performance of the SIFT+CNN combination, in the case of the presence of variations with an accuracy rate up to 100%.
Location-aware hybrid microscopic routing scheme for mobile opportunistic network
Authors: Bharamagoudar Shobha R. , Saboji Shivakumar V.
Published: December 2022

Mobile opportunistic networks (MON) has been used for provisioning delay-tolerant applications. In MON the device communicates with each other with no assured end-to-end paths from source and destination because of frequent topology changes, node mobility, low density, and intermittent connectivity. In MON the device battery drains very fast for performing activities such as scanning, transceiver, and other computational processes, impacting the overall performance thus, designing energy-efficient routing is a challenging task. The routing employs a store-carry-and-forward mechanism for packet communication, where the packet is composed of time-to-live (TTL) and is kept in buffer till the opportunity arises. In improving delivery ratio message replication has been adopted; however, induces high network congestion. Here we present a location-aware hybrid microscopic routing (LAHMR) scheme for MON. The LAHMR provides an effective packet transmission scheme with location awareness and high reliability by limiting unnecessary packets being circulated in the network. Experiment outcome shows the LAHMR scheme achieves a much better delivery ratio with less delay, and also reduces the number of a forwarder for transmitting a packet, aiding in the reduction of network overhead concerning recent routing method namely the social-aware reliable forwarding (SCARF) technique.
Cuckoo search algorithm for construction site layout planning
Authors: Nur Maghfiroh Meilinda Fitriani , Perwira Redi Anak Agung Ngurah , Ong Janice , Fikri Muhamad Rausyan
Published: December 2022

A novel metaheuristic optimization algorithm based on cuckoo search algorithm (CSA) is presented to solve the construction site layout planning problem (CSLP). CSLP is a complex optimization problem with various applications, such as plant layout, construction site layout, and computer chip layout. Many researchers have investigated the CSLP by applying many algorithms in an exact or heuristic approach. Although both methods yield a promising result, technically, nature-inspired algorithms demonstrate high achievement in successful percentage. In the last two decades, researchers have been developing a new nature-inspired algorithm for solving different types of optimization problems. The CSA has gained popularity in resolving large and complex issues with promising results compared with other nature-inspired algorithms. However, for solving CSLP, the algorithm based on CSA is still minor. Thus, this study proposed CSA with additional modification in the algorithm mechanism, where the algorithm shows a promising result and can solve CSLP cases.
Architecting a machine learning pipeline for online traffic classification in software defined networking using spark
Authors: Samaan Sama Salam , Jeiad Hassan Awheed
Published: December 2022

Precise traffic classification is essential to numerous network functionalities such as routing, network management, and resource allocation. Traditional classification techniques became insufficient due to the massive growth of network traffic that requires high computational costs. The arising model of software defined networking (SDN) has adjusted the network architecture to get a centralized controller that preserves a global view over the entire network. This paper proposes a model for SDN traffic classification based on machine learning (ML) using the Spark framework. The proposed model consists of two phases; learning and deployment. A ML pipeline is constructed in the learning phase, consisting of a set of stages combined as a single entity. Three ML models are built and evaluated; decision tree, random forest, and logistic regression, for classifying a well-known 75 applications, including Google and YouTube, accurately and in a short time scale. A dataset consisting of 3,577,296 flows with 87 features is used for training and testing the models. The decision tree model is elected for deployment according to the performance results, which indicate that it has the best accuracy with 0.98. The performance of the proposed model is compared with the state-of-the-art works, and better accuracy result is reported.
Insights on assessing image processing approaches towards health status of plant leaf using machine learning
Authors: Raju Harsha , Narasimhaiah Veena Kalludi
Published: December 2022

With the advancement of digital image processing in agriculture and crop cultivation, imaging techniques are adopted to acquire real-time health status. Out of all the parts of plants, the leaf is the direct indicator of its health status, and hence applying various image processing approaches could benefit the process of yielding informative cases of plant health. At present, there are various approaches, e.g., feature extraction, segmentation, identification, the classification being evolved up with more dependencies being found in using machine learning; the studies show many contributions towards this challenge. However, it is not yet conclusive to understand the optimal approach. Hence, this paper highlights an explicit strength and weakness associated with the existing approaches existing imaging processing techniques to identify the disease condition from an input of plant leaves' image. The study also contributes to highlighting open-end research problems to have conclusive remarks about effectiveness. 
Evaluation of massive multiple-input multiple-output communication performance under a proposed improved minimum mean squared error precoding
Authors: Kadhim Dheyaa Jasim , Saleh Muna Hadi , Abou-Loukh Sadiq Jassim
Published: December 2022

The fundamental of a downlink massive multiple-input multiple-output (MIMO) energy- issue efficiency strategy is known as minimum mean squared error (MMSE) implementation degrades the performance of a downlink massive MIMO energy-efficiency scheme, so some improvements are adding for this precoding scheme to improve its workthat is called our proposal solution as a proposed improved MMSE precoder (PIMP). The energy efficiency (EE) study has also taken into mind drastically lowering radiated power while maintaining high throughput and minimizing interference issues. We further find the tradeoff between spectral efficiency (SE) and EE although they coincide at the beginning but later their interests become conflicting and divergent then leading EE to decrease so gradually while SE continues increasing logarithmically. The results achieved that for a single-cellular massive MU-MIMO downlink model, our PIMP scheme is the appropriate scenario to achieve higher precoding performance system. Furthermore, both maximum ratio transmission (MRT) and PIMP are suitable for performance improvement in massive MIMO results of EE and SE. So, the main contribution comes with this work that highest EE and SE are belong to use a PIMP which performs better appreciably than MRT at bigger ratio of number of antennas to the number of the users. 
Perfil clínico-hemodinâmico de pacientes com hipertensão arterial pulmonar e hipertensão pulmonar tromboembólica crônica de um hospital terciário de São Paulo
Authors: Santos Pamela Cristina Costa dos , Alves César Henrique Morais , Vitório Patrícia Kittler , Rodrigues Mauri Monteiro
Published: December 2022

Objetivo. Descrever as características clínicas e hemodinâmicas dos pacientes com Hipertensão Arterial Pulmonar (HAP) e Hipertensão Pulmonar Tromboembólica Crônica (HPTEC) atendidos no Hospital do Servidor Público Estadual de São Paulo (HSPE). Métodos. Foram revisados os prontuários dos pacientes em seguimento no ambulatório de Circulação Pulmonar do HSPE em acompanhamento entre 2012 e 2019. Hipertensão pulmonar (HP) foi confirmada por cateterismo cardíaco direito e o diagnóstico final de HAP ou HPTEC foi confirmado após avaliação especializada. Resultados. Foram avaliados 46 casos de HP, sendo 31 de HAP e 15 de HPTEC. A média de idade foi de 67,5 (± 10,5) anos, com uma frequência de participantes com idade maior que 60 anos de 73,9%. Houve predomínio do sexo feminino (69,6%). A mediana do tempo de dispneia até o diagnóstico foi de 2 anos. A principal etiologia da HAP foi a esclerose sistêmica e não houve casos de esquistossomose na população estudada. Havia sinais de disfunção do ventrículo direito no momento do diagnóstico em 73,3% dos casos. Entre os pacientes com HPTEC, 46,7% desconheciam evento tromboembólico prévio. Conclusões. Este estudo abordou uma população predominantemente idosa com diagnóstico de HP, destacando as características de dois subtipos da doença. Há necessidade de novos estudos para melhorar o entendimento sobre HP na população brasileira, em especial sobre os portadores de HPTEC.
Comprometimento clínico de crianças e adolescentes com doença de Charcot-Marie-Tooth e aspectos sociais associados: resultados preliminares
Authors: Cardoso Juliana , Paula Caroline Soares de , Souza Mariana Angélica de , Mattiello-Sverzut Ana Claudia
Published: December 2022

O conhecimento acerca do comprometimento clínico da doença de Charcot-Marie-Tooth (CMT) durante a infância e adolescência é extremamente importante para guiar os profissionais de reabilitação em suas terapias. Mesmo que estudos anteriores já tenham apresentado dados consideráveis, nenhum estudo incluiu a população brasileira.
Efeito da radioterapia profilática para ossificação heterotópica em pacientes com lesão medular traumática
Authors: Nakamura Ahy Natally Sawaki , Tavares Heloísa Duarte de Andrade , Eidam Maria Lígia , Carvalho Heloísa de Andrade , Souza Daniel Rubio de
Published: December 2022

A incidência da ossificação heterotópica (OH) varia de 10 a 53%. Em pacientes com lesão medular (LM), o principal fator de risco associado ao desenvolvimento de OH é gravidade do comprometimento medular de acordo com a American Spinal Injury Association Impairment Scale (AIS).
Reabilitação em paciente politraumatizado: relato de trabalho em equipe multidisciplinar
Authors: Silva Wagner Thales , Araújo Eliane Machado de
Published: December 2022

O politraumatismo ocorre quando há duas ou mais lesões graves em pelo menos duas áreas do corpo. Constitui uma das principais causas de morbidade e mortalidade em países desenvolvidos e em desenvolvimento, sendo o acidente automobilístico um dos principais fatores.
Uso de dispositivo auxiliar respiratório, cânula de traqueostomia, em pacientes internados para reabilitação
Authors: Ribeiro Thaís Cristine Cardoso , Borges Kissy Rodrigues , Medeiros Rodrigo Parente , Santos Lethicia Silva , Dias Wanderson Alves
Published: December 2022

A traqueostomia deve ser realizada no paciente que não conseguiu realizar desmame do ventilador mecânico, servindo como alternativa para manter via aérea pérvia. Apesar de segura pode causar alterações na deglutição, dificuldade do manejo de saliva, risco de bronco-aspiração e estenose.
Extreme Hydrometeorological Conditions and Changes in the Amu Darya River Basin in Central Asia
Authors: Murodov Amirkhamza , Cuo Lan , Li Ning , Murodov Davlatkhudzha , Hou Mei , Hussain Gulfam
Published: December 2022

The Amu Darya contributed 70% of the flow to the Aral Sea in central Asia before the 1960s, when the Amu Darya streamflow to the Aral Sea started to dwindle. The severe environmental and socioeconomic disaster happened mainly due to intensified water abstraction with the backdrop of climate change. However, knowledge of up to the most recent extreme climate conditions and their changes, as well as their relations to streamflow in the basin, is still lacking. This study aims to understand extreme hydrometeorological conditions and their changes, as well as their relations in the past several decades, especially in the upper Amu Darya basin. The spatial patterns of the means of all extreme temperature indices followed the elevation gradient. The majority of the basin showed an increasing trend in extreme warm events but a decreasing trend in extreme cold events. The north of the upper basin had over 1000 mm annual precipitation, and the east had less than 300 mm annual precipitation. Overall, the upper Amu Darya basin underwent a wetting and warming annual trend. Annual streamflow in the upper subbasins was less than 750 m3 s−1, but together they produced over 1500 m3 s−1 flow in the middle reach and basin outlet. Streamflow change varied among subbasins. Correlations between climatic factors and streamflow at annual time steps were weak but distinct at monthly time steps with lagged effects. In highland subbasins with high coverage of glaciers and snow, temperature minima and maxima impacts were opposite and overwhelmed precipitation, whereas in lowland subbasins, precipitation was more important.
Frozen Soil Advances the Effect of Spring Snow Cover Anomalies on Subsequent Precipitation over the Tibetan Plateau
Authors: Yang Kai , Wang Chenghai
Published: December 2022

Frozen soil distributed over alpine cold regions causes obvious changes in the soil hydrothermal regime and influences the water–heat exchanges between land and atmosphere. In this study, by comparing the effects of snow cover anomalies and frozen soil thawing anomalies on the soil hydrothermal regime, the impact of the frozen soil thawing anomalies in spring on precipitation in early summer over the Tibetan Plateau (TP) was investigated via diagnostic analysis and model simulations. The results show that a delay (advance) in the anomalies of frozen soil thawing in spring can induce distinct cold (warm) anomalies in the soil temperature in the eastern TP. These soil temperature cold (warm) anomalies further weaken (enhance) the surface diabatic heating over the mideastern TP; meanwhile, the anomalies in the western TP are inconspicuous. Compared to the albedo effect of snow cover anomalies, impacts of frozen soil thawing anomalies on soil hydrothermal regime and surface diabatic heating can persist longer from April to June. Corresponding to the anomalous delay (advance) of frozen soil thawing, the monsoon cell is weakened (enhanced) over the southern and northern TP, resulting in less (more) water vapor advection over the eastern TP and more (less) water vapor advection over the southwestern TP. This difference in water vapor advection induces a west–east reversed pattern of precipitation anomalies in June over the TP. The results have potential for improving our understanding of the interactions between the cryosphere and climate in cold regions.

Significance Statement

Frozen soil and snow are widely distributed over alpine and high-latitude cold regions, and their feedbacks to climate have attracted much attention. The purpose of this study is to investigate the role of frozen soil in effects of snow cover anomalies on surface diabatic heating and its feedback to subsequent precipitation over the Tibetan Plateau. The results highlight that frozen soil modulates the effect of snow cover anomalies on the soil hydrothermal regime from April to June and interseasonal variations of frozen soil thawing anomaly zones result in a thermal contrast between the western and eastern Tibetan Plateau, which further lead to a reversed pattern of early summer precipitation anomalies over the Tibetan Plateau. These findings emphasize the role of frozen soil in land–atmosphere interactions.
Retrieval of Peak Thunderstorm Wind Velocities Using WSR-88D Weather Radars
Authors: Ibrahim Ibrahim , Kopp Gregory A. , Sills David M. L.
Published: December 2022

The current study develops a variant of the VAD method to retrieve thunderstorm peak event velocities using low-elevation WSR-88D radar scans. The main challenge pertains to the localized nature of thunderstorm winds, which complicates single-Doppler retrievals as it dictates the use of a limited spatial scale. Since VAD methods assume constant velocity in the fitted section, it is important that retrieved sections do not contain background flow. Accordingly, the current study proposes an image processing method to partition scans into regions, representing events and the background flows, that can be retrieved independently. The study compares the retrieved peak velocities to retrievals using another VAD method. The proposed technique is found to estimate peak event velocities that are closer to measured ASOS readings, making it more suitable for historical analysis. The study also compares the results of retrievals from over 2600 thunderstorm events from 19 radar–ASOS station combinations that are less than 10 km away from the radar. Comparisons of probability distributions of peak event velocities for ASOS readings and radar retrievals showed good agreement for stations within 4 km from the radar while more distant stations had a higher bias toward retrieved velocities compared to ASOS velocities. The mean absolute error for velocity magnitude increases with height ranging between 1.5 and 4.5 m s−1. A proposed correction based on the exponential trend of mean errors was shown to improve the probability distribution comparisons, especially for higher velocity magnitudes.
Clinical and imaging features of six Han patients with SAPHO syndrome
Authors: Ruan Dan-dan , Wang Ruo-li , Hu Ya-nan , Lin Xing , Luo Jie-wei , Yu Qing-hua , Wu Jia-bin
Published: December 2022

Background

Synovitis, acne, pustulosis, hyperostosis, and osteitis (SAPHO) syndrome is a rare autoimmune disease characterized by skin or osteoarticular damage. SAPHO syndrome is often misdiagnosed or missed diagnosis due to lack of overall understanding of the disease by clinicians.

Purpose

To analyze the clinical symptoms and imaging features of six Han patients with SAPHO syndrome in order to provide reference for doctors to diagnose SAPHO syndrome.

Material and Methods

This study retrospectively analyzed the clinical data of six Han patients with SAPHO syndrome.

Results

All six Han patients with SAPHO syndrome had severe acne or pustulosis of the hands and feet, and all of them had osteoarticular damage, including five cases involving the sternoclavicular joint. Some patients showed a specific and typical “bull's head” sign on 99mTc-labeled methylene diphosphonate bone imaging. Among the six patients recruited, there was one thoracic vertebra, one cervical vertebra, one sacroiliac joint, and one peripheral joint involvement. Two patients had limited activity due to severe osteoarticular damage.

Conclusion

Due to the atypical clinical symptoms of SAPHO syndrome, most patients will experience a tortuous and long diagnostic process, while a correct understanding and timely intervention of SAPHO syndrome are essential to improve the prognosis of patients.
Weathering the storm together: Does unemployment insurance help couples avoid divorce?
Authors: Kessler Dorian , Hevenstone Debra , Vandecasteele Leen , Sepahniya Samin
Published: December 2022

This study examines whether unemployment insurance benefit generosity impacts divorce, drawing on full population administrative data and a Swiss reform that reduced unemployment insurance maximum benefit duration. We assess the effect of the reform by comparing the pre- to the post-reform change in divorce rates among unemployed individuals who were affected by the reform with the change in divorce rates among a statistically balanced group of unemployed individuals who was not affected by the reform. Difference-in-differences estimates suggest that the reform caused a 2.8 percentage point increase in divorce (a 25% increase). Effects were concentrated among low-income couples (+58%) and couples with an unemployed husband (+32%) though gender differences are attributable to men’s breadwinner status. Female main breadwinners were more strongly affected (+78%) than male main breadwinners (+40%). Results confirm the ‘family stress model’ which posits that job search and financial stress cause marital conflict. Policymakers should consider a broad array of impacts, including divorce, when considering reductions in unemployment insurance generosity.
Dapansutrile ameliorated chondrocyte inflammation and osteoarthritis through suppression of MAPK signaling pathway
Authors: Tang L , Sim I , Moqbel SAA , Wu L
Published: December 2022

Introduction

Osteoarthritis (OA) is one of the most common joint diseases in the elderly population. Proinflammatory cytokines, such as Interleukin-1β (IL-1β), play an important role in the development and progression of OA. Dapansutrile is a specific inhibitor of the NOD-like receptor protein 3 (NLRP3) inflammasome and exhibits anti-inflammatory properties.

Methods

In this study, we investigated the protective effect and the underlying mechanism of dapansutrile on cartilage degeneration in vitro and in vivo. In the present study, chondrocytes were isolated from rats and then were treated with dapansutrile. After that, the expression of ( Cox-2, inducible nitric oxide synthase ( iNOS), Mmp-3, Mmp-9, Mmp-13 and IL-10) were evaluated at RNA level, then the expression of (COX-2, MMP-3, MMP-9, MMP-13, SOX-9 and COL2) were evaluated at protein level. Subsequently, the activation of the mitogen-activated protein kinase (MAPK) pathway was tested using western blotting (WB). Additionally, the rat OA model was developed to evaluate the protective effects of dapansutrile in vivo.

Results

The results showed that dapansutrile had no obvious cytotoxicity on rat chondrocytes at 24 h (0, 1, 2, 5 and 10 μM). Dapansutrile significantly decreased IL-1β-induced upregulation of COX2, iNOS, matrix metalloproteinase 3 (MMP3), 9 (MMP9) and 13 (MMP13), and reversed IL-1β-induced the downregulation of IL-10, SOX9 and COL2. Dapansutrile also inhibited IL-1β-induced upregulation of the MAPK signaling pathway by downregulating the expression levels of phospho-ERK, and phospho-P38 in a concentration dependent manner. In addition, dapansutrile exhibited protective effects in rat OA model with lower Mankin’s score and Osteoarthritis Research Society International (OARSI) score.

Conclusion

Our study suggested that dapansutrile effectively inhibited chondrocyte inflammation by suppressing MAPK signaling pathway in vitro, and ameliorated cartilage degeneration in vivo, indicating an anti-inflammatory effect in OA treatment.
Channeling of Brain Towards Engaging Sensorimotor Tasks for Inducing Hedonic Pleasure to Alleviate Blood Pressure in Hypertension
Authors: Taranikanti Madhuri , Mudunuru Aswin Kumar , Gaur Archana , Kauser Anzala , Taranikanti Sai Shriya , Umesh Madhusudhan , Ganji Vidya , Medala Kalpana , Katta Roja , Sakthivadivel Varatharajan , Dronamraju Akhila , Guntuka Rohith Kumar
Published: December 2022

Background:

Hypertension is a modifiable risk factor for cardiovascular disease and is responsible for major deaths due to stroke and coronary heart disease. Several pharmacological and non-pharmacological interventions for reducing blood pressure have been tried earlier. Modulating brain regions such as prefrontal cortex (PFC) to channelize activities is an effective tool to target blood pressure.

Purpose:

Prefrontal cortex (PFC) exerts inhibitory control over sympathoexcitatory circuits, which was explored using a novel reaction time paradigm.

Methods:

Thirty participants of both genders in the age group 40–70 years with established hypertension were included. A structured reaction time paradigm was designed to include psychomotor and visuomotor elements with integrated sensory attention and motor performance tasks. Blood pressure, Lead II ECG, and EEG from F3 and F4 were recorded. A paired t-test was used to examine the variations in these parameters across tasks.

Results:

A significant reduction in mean arterial pressure by 4.04 mmHg ( p = .0232) during the visuomotor task and a reduction of 3.38 mmHg during the auditory cue task ( p = .0446) were observed. Analysis of the difference in heart rate has shown a profound decrease after passive listening tasks by 3.7 beats ( p < .0001*). Spectral analysis from F3 and F4 shows high power in low-frequency zone of EEG indicating a relaxed state during auditory cues and passive listening.

Conclusion:

The reaction time paradigm, when applied to hypertensives, helped decrease blood pressure and heart rate and improved the high frequency (HF) component of heart rate variability, indicating parasympathetic dominance. Such reward-oriented paradigms may act as biofeedback modules that cause hyperactivity of the PFC to suppress the sympathoexcitatory circuit with increased parasympathetic activity beneficial to hypertensive individuals.
Selective vulnerability of brainstem and cervical spinal cord regions in people with non-progressive multiple sclerosis of Black or African American and European ancestry
Authors: Okuda Darin T , Stanley Thomas , McCreary Morgan , Smith Alexander , Wilson Andrew , Pinho Marco C , Yu Fang F , Billiet Thibo , Van Hecke Wim , Ribbens Annemie , Zeydan Burcu , Kantarci Orhun , Guo Xiaohu , Moog Tatum M
Published: December 2022

Background:

We evaluated imaging features suggestive of neurodegeneration within the brainstem and upper cervical spinal cord (UCSC) in non-progressive multiple sclerosis (MS).

Methods:

Standardized 3-Tesla three-dimensional brain magnetic resonance imaging (MRI) studies were prospectively acquired. Rates of change in volume, surface texture, curvature were quantified at the pons and medulla-UCSC. Whole and regional brain volumes and T2-weighted lesion volumes were also quantified. Independent regression models were constructed to evaluate differences between those of Black or African ancestry (B/AA) and European ancestry (EA) with non-progressive MS.

Results:

209 people with MS (pwMS) having at least two MRI studies, 29% possessing 3–6 timepoints, resulted in 487 scans for analysis. Median follow-up time between MRI timepoints was 1.33 (25th–75th percentile: 0.51–1.98) years. Of 183 non-progressive pwMS, 88 and 95 self-reported being B/AA and EA, respectively. Non-progressive pwMS demonstrated greater rates of decline in pontine volume ( p < 0.0001) in B/AA and in medulla-UCSC volume ( p < 0.0001) for EA pwMS. Longitudinal surface texture and curvature changes suggesting reduced tissue integrity were observed at the ventral medulla-UCSC ( p < 0.001), dorsal pons ( p < 0.0001) and dorsal medulla ( p < 0.0001) but not the ventral pons ( p = 0.92) between groups.

Conclusions:

Selectively vulnerable regions within the brainstem-UCSC may allow for more personalized approaches to disease surveillance and management.