This study introduces Structured Light Imaging Mesoscopy (SLIM), a novel non-contact optical method for detecting subsurface morphological tissue alterations. By leveraging the inherent sensitivity of spatial frequenc...
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The increasing success of application of machine and deep learning in many areas of medicine, in particular in imaging diagnostics (Rajpurkar et al., 2020), is pushing towards the implementation of AI-based approaches...
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The increasing success of application of machine and deep learning in many areas of medicine, in particular in imaging diagnostics (Rajpurkar et al., 2020), is pushing towards the implementation of AI-based approaches to extract knowledge from health records data (EHR) (Li et al., 2020). The potential of sophisticated strategies to derive regularities from very large collection of textual data, such as language models, is also generating strong expectations about the capability of extracting information unstructured textual notes as well as in generating biomedical texts (Segura-Bedmar et al., 2022;Luo et al., 2022). The COVID-19 pandemics, being one of the most relevant healthcare challenges synchronously happened worldwide, has represented a strong push towards the timely use of EHR data to characterize the clinical course of the COVID-19 disease. Successful examples are represented by cooperative international efforts, such as the Consortium for Clinical Characterization of COVID-19 by EHR (4CE) initiative (Brat et al., 2020). However, EHR data are particularly complex, due to their multifaceted nature and inherent relationship with the health care organizations generating the data. In a recent paper, Kohane and colleagues summarizing the experience carried on in leading 4CE have identified six main challenges that have proven to be crucial for running EHR-based projects (Kohane et al., 2021): i) data completeness, ii) data collection and handling, iii) data type, iv) robustness of methods against EHR variability (within and across institutions, countries, and time), v) transparency of data and analytic code, and vi) the need of multidisciplinary approach. Those topics, in the context of structured EHR data, have been recently further systematized by a consensus paper by the European Society of Cardiology and the BigData(S?Heart consortium that has defined the CODE-EHR best-practice framework for the use of structured electronic health-care records in clinical re
Stroke is a neurological syndrome that may cause severe cognitive and motor impairments for survival. Alternative rehabilitation techniques have been developed to recover lower-limb movements and gait of post-stroke p...
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This paper proposes the development of an RFID envelope coated with an antimicrobial material that can stop microorganisms' growth while the card is still regularly used. Copper is chosen as the antimicrobial mate...
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Age-related macular degeneration (AMD) is a leading cause of vision loss in older adults, making timely and accurate diagnosis crucial for effective treatment. This study suggests a new framework for AMD diagnosis tha...
Coronary artery stenosis is characterized by the obstruction or narrowing of coronary arteries, impairing blood flow to the cardiac muscle. Cardiovascular diseases rank among the leading global causes of death. Unders...
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Nanofibrous acoustic energy harvesters(NAEHs)have emerged as promising wearable platforms for efficient noise-to-electricity conversion in distributed power energy systems and wearable sound amplifiers for assistive l...
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Nanofibrous acoustic energy harvesters(NAEHs)have emerged as promising wearable platforms for efficient noise-to-electricity conversion in distributed power energy systems and wearable sound amplifiers for assistive listening ***,their reallife efficacy is hampered by low power output,particularly in the low-frequency range(<1 kHz).This study introduces a novel approach to enhance the performance of NAEHs by applying machine learning(ML)techniques to guide the synthesis of electrospun polyvinylidene fluoride(PVDF)/polyurethane(PU)nanofibers,optimizing their application in wearable *** use a feed-forward neural network along with solving an optimization problem to find the optimal input values of the electrospinning(applied voltage,nozzle-collector distance,electrospinning time,and drum rotation speed)to generate maximum output performance(acoustic-to-electricity conversion efficiency).We first prepared a dataset to train the network to predict the output power given the input variables with high *** introducing the neural network,we fix the network and then solve an optimization problem using a genetic algorithm to search for the input values that lead to the maximum energy harvesting *** ML-guided wearable PVDF/PU NAEH platform can deliver a maximal acoustoelectric power density output of 829μW/cm^(3) within the surrounding noise *** addition,our system can function stably in a broad frequency(0.1-2 kHz)with a high energy conversion efficiency of 66%.Sound recognition analysis reveals a robust correlation exceeding 0.85 among lexically akin terms with varying sound intensities,contrasting with a diminished correlation below 0.27 for words with disparate semantic ***,this work provides a previously unexplored route to utilize ML in advancing wearable NAEHs with excellent practicability.
Electroencephalogram (EEG) has been widely used in studies using rodent models to understand brain functions and neurological disorders. However, conventional EEG setups have limits as recording devices are bulky and ...
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Antimicrobial peptides (AMPs) play a vital role in the immune defence systems of various organisms and have garnered significant attention for their potential applications in biotechnology and medicine. There are seve...
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In this brief, two current generator circuits are used to design a self-biasing transconductance amplifier. The current generators are configured using two n-channel and p-channel cascode current mirrors by which a hi...
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