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Background: In India, less than 5% of women get routine screening for breast cancer due to lack of awareness and the absence of a coordinated national breast cancer screening programme. A community health initiative was launched by Niramai in collaboration with City Health officials in Bangalore as a pilot to increase awareness and make breast health screening available to all. Free breast cancer screening using AI powered Thermalytix test is being offered to all the underprivileged women walking into Bruhat Bengaluru Mahanagara Palike (BBMP) government hospitals from November 14, 2017 till today (after a break for 15 months during COVID). Material(s) and Method(s): This observational study was conducted in 22 BBMP-affiliated primary health centers where outpatient women over the age of 18 years and not pregnant were enrolled. The procedure included a briefing on camp procedures, taking patient consent, identification of eligible candidates, general health education, and conducting the Thermalytix test by a healthcare worker who was trained to use the Thermalytix software tool. Women were triaged using the output generated by Thermalytix 180. Those triaged as red were referred for further detailed imaging investigation in a district hospital using mammography, ultrasound and FNAC/biopsy. Result(s): A total of 6935 women underwent Thermalytix screening in 22 BBMP hospitals during Nov 2017 to July 2022. A total of 1687 participants were excluded from the analysis as they did not meet the eligibility criteria. The median age of the 5248 eligible participants was 42 years (range 18-86). Among them, 90 women (1.71%) had previously noticed a lump in their breast, 431 women (8.12%) had breast pain, 16 women had complained of nipple discharge, and 5 women had noticed skin discoloration. When screened, 62 (1.2%) women were detected with abnormalities and triaged positive by Thermalytix. Among them 11 women have so far gone through diagnostic investigations, of which 8 were radiologically positive and were recommended for histopathology correlation. The overall test positivity rate of Thermalytix in this cohort was 1.2% and positive predictive value with radiological positivity as reference was found to be 9/11 = 81.81%. Furhter histological analysis reported 1 DCIS and 8 benign fibroadenoma. The tests were conducted in screening camps and the average cost of conducting the test in the field came to around 6.5 USD per person. Conclusion(s): Thermalytix could be a potential automated screening tool for population-level screening in resource constrained settings. The portable equipment enabled easy movement across different PHCs. Since it is a privacy-aware test, there was less refusal to participate in the test. Community mobilization with the help of the local government health officials was crucial to ensure walk-ins. Conflict of interest: Ownership: yes Board of Directors: yes Corporate-sponsored Research: yesCopyright © 2022 Elsevier Ltd. All rights reserved
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Featured ApplicationThe same tool could be used repeatedly to track the changes in CMJ performance. Average jump heights should be analyzed. Practitioners and sports professionals without extensive knowledge of assessment could self-administer CMJ tests using these devices.Mobile applications and portable assessments make remote self-assessment of the countermovement jump (CMJ) test possible. This study aimed to investigate the concurrent validity and test–retest reliability of three portable measurement systems for CMJ. Thirty physically active college students visited the laboratory twice, with two days in between, and performed three jumps each day. All jumps were recorded by My Jump 2, HomeCourt, and the Takei Vertical Jump Meter (TVJM) simultaneously. Results indicated significant differences among the three systems (p < 0.01). HomeCourt tended to present the highest jump height mean value (46.10 ± 7.57 cm) compared with TVJM (42.02 ± 8.11 cm) and My Jump 2 (40.85 ± 7.86 cm). High concurrent validities among assessments were found (r = 0.85–0.93). Good to excellent reliability of jump assessments was demonstrated (ICC3,1 = 0.80–0.96). Reliable coefficients of variation were shown in all measurements (2.58–5.92%). Significant differences were revealed among the three apparatuses while they demonstrated high intra-device test–retest reliability. TVJM was the most reliable, and average jump heights were recommended for analysis.
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An introduction to articles published within the issue is presented on topics including the effects of physiotherapy on hemodynamics, gas exchange and cerebral physiology in ventilated subjects, an evaluation of four mechanical insufflation-exsufflation (MI-E) devices, and a session of intermittent intrapulmonary deflation technique and positive expiratory pressure therapy in chronic obstructive pulmonary disease (COPD) patients.
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During the COVID-19 pandemic, physical distancing (PD) is highly recommended to stop the transmission of the virus. PD practices are challenging due to humans' nature as social creatures and the difficulty in estimating the distance from other people. Therefore, some technological aspects are required to monitor PD practices, where one of them is computer vision-based approach. Hence, deep learning-based computer vision is utilized to automatically detect human objects in the video surveillance. In this work, we focus on the performance study of deep learning-based object detector with Tensor RT optimization for the application of physical distancing monitoring system. Deep learning-based object detection is employed to discover people in the crowd. Once the objects have been detected, then the distances between objects can be calculated to determine whether those objects violate physical distancing or not. This work presents the physical distancing monitoring system using a deep neural network. The optimization process is based on TensorRT executed on Graphical Processing Unit (GPU) and Computer Unified Device Architecture (CUDA) platform. This research evaluates the inferencing speed of the well-known object detection model You-Only-Look-Once (YOLO) run on two different Artificial Intelligence (AI) machines. Two different systems-based on Jetson platform are developed as portable devices functioning as PD monitoring stations. The results show that the inferencing speed in regard to Frame-Per-Second (FPS) increases up to 9 times of the non-optimized ones, while maintaining the detection accuracies.
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This paper presents a portable system designed to monitor physical and chemical quantities inside the dead space of face masks that are commonly used to prevent the diffusion of SARS-CoV-2 infection. The system is a 'side stream' analyzer that continuously samples the gas inside the gap between the mask and the user mouth and provides, in real time, the temporal evolution of CO2 concentration, differential pressure and temperature. These quantities are related to the mask behaviour in term of breath resistance, CO2 accumulation and overheating that, in turn, can cause physiological side effects and a feeling of discomfort. These effects become more relevant when the masks are employed during physical activities, such as team sports, and for this reason the system has been designed to have a reduced weight and dimensions as well as a reduced invasiveness of the sampling line. The paper also proposes a measurement procedure for the evaluation of the CO2 inspired volume in real conditions during the physical activity. At the current stage, the proposed procedure is only able to provide qualitative measurements, but it can work in real conditions and it does not require bulky and expensive facilities as the ones required by the actual international standards for the certification of respiratory protective devices. © 2022 IEEE.
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Power consumption was considered one of the major expenses in households and small offices in the COVID pandemic situation. The proposed method employs virtual augmented technology and the Internet of Things (IoT) to provide a visual solution for monitoring the power consumption of electrical equipment. The IoT sensors are linked via mobile phones or portable communication devices. For the proposed work, the energy sensor devices are installed on the 4 single-phase electrical devices in the office room equipment. That equipment’s electrical consumption was saved, processed through the ESP8266 board, and then transmitted through WiFi to collect data on the cloud server. The saved electrical energy data in the dashboard can be displayed as a visual comparison with AR technology by using the mobile phone camera to scan the marker of each electrical device. The experimental results show that the value of electrical energy is accurate, and the data values can be used to manage the power consumption of electrical equipment.
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Before the shutdown due to COVID-19, all courses and laboratories from the Electrical and Computer Engineering Department (ECE) at The University of Texas at El Paso (UTEP) were conducted in person at the university facilities. Many of the laboratories required students to work in groups due to the limited equipment availability. Most universities were forced to adopt distance learning as a primary teaching modality when the pandemic started. Previously, the Learning Management Systems (LMS) were used mainly for support course functions, where students could review the content and grades in their own time, submit assignments, or download materials. During the pandemic time, students attended virtual sessions via video conference, reviewed materials independently, or had restricted interactions. This modality limited the ability to conduct lab experiments. The adopted lab methodologies were to offer online circuits laboratories implemented via portable equipment, designed for work at home, and acquired for the students;or by providing remote access to some university equipment. The ECE department had additional challenges because most of our students live in the border region between USA and México, and many had limited technological resources to access virtual or remote laboratories. UTEP started resuming face-to-face courses and events on campus after the pandemic acute phase period. For the fall 2021 semester, the school initiated activities under enhanced safety precautions for in-person classes. Currently, the circuits laboratory returned to face-to-face delivery mode using bench industrial-grade equipment with higher resolution and accuracy than personal devices, offering students a more comprehensive range of experiments to improve their abilities and knowledge in the technical field. However, some characteristics of the virtual model were kept, such as working at home in the preliminary laboratory phase using portable equipment and then allowing students to work individually at the university workstation, using time more efficiently, and keeping the improved LMS content. This paper compares online and in-person circuits laboratory sessions, exploring the differences, limitations, benefits, and challenges for the students and the response due to geographic restrictions. © American Society for Engineering Education, 2022
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Speed read WHO compendium of innovations aims to help low-resource settings respond to COVID-19 Collection of tools doesn’t require specialists or electricity, says WHO Grassroots capacity building crucial for scale-up — expert Easy-to-use equipment including portable respiratory monitoring systems and ventilators with extended battery life are among a collection of new health innovations identified by the World Health Organization (WHO) to help manage COVID-19 in low-resource settings. Jeremiah Owiti, executive director, Centre for Independent Research, Nairobi Adriana Velazquez Berumen, WHO senior advisor for medical devices, said: “WHO has been collecting innovative technologies that can be impactful at places where there is unstable electricity and a lack of specialised health workforce.” According to Anuraj Shankar, lead researcher at the Eijkman-Oxford Clinical Research Unit in Jakarta, Indonesia, enabling anyone to asses blood pressure accurately with an already available device such as a smartphone opens the door to personal monitoring of many acute and chronic medical conditions.
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In the recent COVID-19 outbreak, chest X-rays were the main tool for diagnosing and monitoring the pathology. To prevent further spread of this disease, special circuits had to be implemented in the healthcare services. For this reason, these chest X-rays were captured with portable X-ray devices that compensate its lower quality and limitations with more deployment flexibility. However, most of the proposed computer-aided diagnosis methodologies were designed to work with traditional fixed X-ray machines and their performance is diminished when faced with these portable images. Additionally, given that the equipment needed to properly treat the disease (such as for life support and monitoring of vital signs) most of these systems learnt to identify these artifacts in the images instead of real clinically-significant variables. In this work, we present the first methodology forced to extract features exclusively from the pulmonary region of interest that is specially designed to work with these difficult portable images. Additionally, we generate a class activation map so the methodology also provides explainability to the results returned to the clinician. To ensure the robustness of our proposal, we tested the methodology with chest radiographs from patients diagnosed with COVID-19, pathologies similar to COVID-19 (such as other types of viral pneumonias) and healthy patients in different combinations with three convolutional networks from the state of the art (for a total of 9 studied scenarios). The experimentation confirms that our proposal is able to separate COVID-19 cases, reaching a 94.7% ± 1.34% of accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Evaluations of analytical performance through interlaboratory comparisons and proficiency tests are underway globally for biomolecular-based methods [e.g., reverse-transcription quantitative polymerase chain reaction (RT-qPCR)] used in the surveillance of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater. These evaluations often rely on sharing a common reference wastewater sample that is split among participating laboratories. A known quantity of recovery surrogates can be introduced to the wastewater matrix by the coordinating laboratory as an exogenous control in a spike-and-recovery approach;however, split-sample comparisons are increasingly performed to evaluate in situ quantities of SARS-CoV-2 genetic signal native to the sample due to the lack of a universally accepted recovery surrogate of SARS-CoV-2. A reproducible procedure that minimizes the variability of SARS-CoV-2 genetic signal among split wastewater aliquots is therefore necessary to facilitate the method comparisons, especially when a large number of aliquots are required. Emerging literature has suggested that SARS-CoV-2 genetic signal in wastewater is linked to the solids fraction. Accordingly, a protocol that allows for equal distribution of solids content evenly among wastewater aliquots was also likely to facilitate even distribution of the SARS-CoV-2 genetic signal. Based on this premise, we reviewed existing sample splitting apparatus and approaches used for solids-based parameters in environmental samples. A portable batch reactor was designed, comprised of readily accessible materials and equipment. This design was validated through splitting of real wastewater samples collected from a municipal wastewater treatment facility serving a population with reported cases of COVID-19. This work applies well-established solid-liquid mixing theory and concepts that are likely unfamiliar to molecular microbiologists and laboratory analysts, providing (1) a prototype adaptable for a range of sample quantities, aliquot sizes, microbial targets, and water matrices;and (2) a pragmatic demonstration of critical considerations for design and validation of a reproducible and effective sample splitting protocol.
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Aptamers represent the next frontier as biorecognition elements in biosensors thanks to a smaller size and lower molecular weight with respect to antibodies, more structural flexibility with the possibility to be regenerated, reduced batch-to-batch variation, and a potentially lower cost. Their high specificity and small size are particularly interesting for their application in optical biosensors since the perturbation of the evanescent field are low. Apart from the conventional plasmonic optical sensors, platforms based on silica and plastic optical fibers represent an interesting class of devices for point-of-care testing (POCT) in different applications. The first example of the coupling between aptamers and silica optical fibers was reported by Pollet in 2009 for the detection of IgE molecules. Six years later, the first example was published using a plastic optical fiber (POF) for the detection of Vascular Endothelial Growth Factor (VEGF). The excellent flexibility, great numerical aperture, and the large diameter make POFs extremely promising to be coupled to aptamers for the development of a sensitive platform easily integrable in portable, small-size, and simple devices. Starting from silica fiber-based surface plasmon resonance devices, here, a focus on significant biological applications based on aptamers, combined with plasmonic-POF probes, is reported.
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This project describes the evolving role of robotics in healthcare and allied are as with special concerns relating to the management and control of the spread of the novel coronavirus disease 2019 (COVID-19). The prime utilization of such robots is to minimize person-to-person contact and to ensure cleaning, sterilization and support in hospitals and similar facilities such as quarantine. This will result in minimizing the life threat to medical staffs and doctors taking an active role in the management of the COVID-19 pandemic. The intention of the present research is to highlight the importance of medical robotics in general and then to connect its utilization with the perspective of COVID-19 management so that the hospital management can direct themselves to maximize the use of medical robots for various medical procedures. This is despite the popularity of telemedicine, which is also effective in similar situations. In essence, the recent achievement of the Korean and Chinese health sectors in obtaining active control of the COVID-19 pandemic was not possible without the use of state of the art medical technology.To overcome the problem developed automatic portable sanitizing equipment for spraying sanitization solution. The portable sanitizer unit is attached to the top of the mobile robot. The system integrates a sprinkler mechanism and is used to distribute air and disinfectant fluid mixture. The mobile robot main components consist of a DC motor, Bluetooth module, Arduino, Motor driver, Submergible pump, Sprinkler, Battery, DC Converters are used. The system is capable of sanitizing the floors of hospitals.
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Onsite molecular diagnostics can revolutionize fecal pollution source tracking. We aimed to validate a method for onsite qPCR assays with a miniature speaker-sized Q qPCR instrument and other portable equipment items. We showed that marker genes for total bacteria (16S) and E. coli (rodA) in 100 mL of river water measured with this method agreed within ±0.3 log10 units with results obtained when using conventional laboratory equipment items. We then deployed the portable method in a mobile laboratory (‘lab in a van’) and quantified HF183 marker genes for human host associated Bacteroides in river water within 3 h of sampling. We also used the mobile laboratory to investigate urban river water and effluents from two storm drains and a retention pond and collected comprehensive microbial and physicochemical water quality data. We found significantly higher HF183 gene levels in the older storm drain compared to the river water (6.03 ± 0.04 vs. 4.23 ± 0.03 log10 gene copies per 100 mL), and a principal component analysis revealed that storm drain effluent retention in a pond beneficially altered water characteristics, making them more like those of the receiving river. In conclusion, onsite qPCR assays can be performed with portable equipment items to quickly test water.
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The smuggling of illicit drugs urges the development of new tools for rapid on-site identification in cargos. Current methods rely on presumptive color tests and portable spectroscopic techniques. However, these methods sometimes exhibit inaccurate results due to commonly used cutting agents, the colorful nature of the sample or because the drugs are smuggled in common goods. Interestingly, electrochemical sensors can deal with these specific problems. Herein, an electrochemical device is presented that uses affordable screen-printed electrodes for the electrochemical profiling of several illicit drugs by square-wave voltammetry (SWV). The identification of the illicit compound is based on the oxidation potential of the analyte. Hence, a library of electrochemical profiles is built upon the analysis of illicit drugs and common cutting agents. This library allows the design of a tailor-made script that enables the identification of each drug through a user-friendly interface (laptop or mobile phone). Importantly, the electrochemical test is compared by analyzing 48 confiscated samples with other portable devices based on Raman and FTIR spectroscopy as well as a laboratory standard method (i.e., gas chromatography–mass spectrometry). Overall, the electrochemical results, obtained through the analysis of different samples from confiscated cargos at an end-user site, present a promising alternative to current methods, offering low-cost and rapid testing in the field.
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Direct solar vapor generation (SVG) provides a sustainable and eco‐friendly solution to the current global water scarcity challenges. However, existing SVG systems operating under natural sunlight suffer from low water yield and high energy requirement of vaporization. New materials with reduced latent heat of water vaporization are in urgent demand to boost SVG process. Herein, we propose a novel strategy to additively fabricate anisotropic hybrid 3D structure from photocurable thermoresponsive p(NIPAm‐co‐PEGDA) hydrogel on the top of PEGDA foam for SVG. The in‐situ post‐printing synthesis of iron oxide nanoparticles within the p(NIPAm‐co‐PEGDA) hydrogel on the top surface, thus introducing anisotropy, is achieved by adding metallic salt precursor into the printing solution. The as‐fabricated hydrogel composite structure exhibits superior light absorption properties and rapid capillary‐driven water transport through a 3D‐printed microchannel network within the hydrogel. As a result, our SVG device achieves an extraordinary water evaporation rate of 5.12 kg m−2 h−1 under one sun (1 kW/m2). The intrinsic water activation states, in addition to wettability modulation with temperature increase within p(NIPAm‐co‐PEGDA) hydrogel, plays a critical role in reducing the equivalent vaporization enthalpy and shifting the vaporization to relatively lower temperatures. The proposed hybrid SVG device is feasible, portable, and highly efficient, promising great potential for grand water‐energy nexus challenges.
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The global pandemic outbreak of coronavirus disease 2019 (COVID-19) has put the world in a panic situation. It has been evident that the geriatric population is more susceptible to acquire this infection. Thus, due to this ongoing crisis, geriatric individuals cannot visit a dental operatory, and there is the possibility of their dental needs and emergencies to remain unattended. Partial or complete edentulism is very prevalent among the geriatric population, and prosthodontic management of these patients is essential for their well-being. However, the prosthesis can lead to various emergencies of the oral and its supporting structures. There are currently no available recommendations to address these prosthodontic emergencies in geriatric individuals during this pandemic period. Hence, the aims of this minireview were to discuss the common prosthodontic emergencies in the geriatric population and provide recommendations to manage these issues during the COVID-19 pandemic outbreak.