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Investigation in Frequency Doubling Aftereffect of Thermoacoustic Phone speaker Based on Graphene Motion picture.
Purpose Despite the current legislative indications toward the digitization of patient health records, 80% of health data are unstructured and in a format that cannot be used in electronic archives or in registries of diseases. An innovative automated system is here proposed to efficiently retrieve and digitize clinical information from original unstructured ear, nose, and throat (ENT) medical records, in order to reduce the manual workload in the retrieval and digitization process. Method The system, based on an eHealth technology named cognitive computing, interprets medical reports to transform unstructured clinical data (e.g., narrative text) into a structured digital format. The system has been tailored to handle the reports of aged cochlear implant (CI) patients by digitizing the information typically requested in electronic CI registries and by the current ENT/audiology guidelines. Results were obtained from the reports generated by an outpatient ENT care service from 52 older adult CI patients. Results The system allowed a quick and automated interpretation and retrieval of all the information required, such as the patient's medical history, risk factors, examination outcomes, communicative performances before and after CI implantation, and CI settings. The accuracy of the system in correctly interpreting and retrieving the above information from the original unstructured medical reports was very good (recall = 0.78; precision = 0.95). The system allowed to reduce the time needed to manually digitize the information from 20-30 min/report to only 20 s/report. Conclusion The proposed system is a viable solution for the automated digitization of unstructured health data as recommended by the ENT/audiology clinical best practices.Objectives To develop a Mandarin version of the Hearing in Noise Test for Children (MHINT-C) and examine the maturational effects on sentence recognition.Design Sentences suitable for evaluating children aged 6-18 years were selected from the adult MHINT to form 12 lists of 10 MHINT-C sentences (Study 1). List equivalence, inter-list reliability, response variability, and maturational effects on sentence recognition were examined using the MHINT-C (Study 2).Study sample A total of 246 children aged 6.1-17.11 years were included. Six children participated in Study 1; the rest were included in Study 2. To compare these results with adults, 20 native Mandarin-speaking adults aged 18 or above were included in Study 2.Results MHINT-C list equivalency, inter-list reliability, and response variability were similar to those of the adult MHINT and the Cantonese HINT for children. Sentence recognition in children reached adult-like performance around age 8 in quiet and at ages 15 and 14 in front and side noise conditions, respectively.Conclusions The MHINT-C can reliably measure sentence recognition in quiet and noise in Mandarin-speaking children. Age-specific correction factors were established.Objective The aim of this cross sectional study was to evaluate frequency of neuropathic back pain in ankylosing spondylitis (AS) patients and to determine the relation with disease variables and occurrence of neuropathic pain.Methods Fifty-eight AS patients who were not having any comorbid disease and/or using drugs that would cause neuropathy, were recruited to the study. Demographic properties and clinical characteristics (functional status and disease activity assessed by BASFI and BASDAI respectively, ESR, CRP) and quality of life determined by AS quality of life-QoL questionnaire, were recorded. The neuropathic property of back pain was assessed by both Leeds Assessment of Neuropathic symptoms and signs (LANSS) and Douleur Neuropathique 4 (DN4) scales.Results 58 AS patients (17 female, 41 male) with a mean age of 45 ± 18 years were included. 33 patients (56.9%) and 31 patients (53.4%) were defined as having neuropathic pain depending on the LANSS (scores >12) and DN4 (scores >4) questionnaire scores respectively. The mean score of LANSS scale was correlated with ASQoL, BASFI, BASDAI, and DN4; and the mean score of DN4 scale was correlated with ASQoL, BASFI and LANSS. The mean levels of BASFI and ASQoL scores were significantly higher in patients having neuropathic pain than in patients not having (p  less then  0.05).Conclusion Neuropathic pain is common and determined in more than half of the patients with AS and related with functional status and quality of life. Diagnosis and treatment of neuropathic pain are warranted in order to increase functional ability and quality of life in patients suffering from AS.Aim To assemble, characterize and assess the antifungal effects of a new fluconazole (FLZ)-carrier nanosystem. Materials & methods The nanosystem was prepared by loading FLZ on chitosan (CS)-coated iron oxide nanoparticles (IONPs). Antifungal effects were evaluated on planktonic cells (by minimum inhibitory concentration determination) and on biofilms (by quantification of cultivable cells, total biomass, metabolism and extracellular matrix) of Candida albicans and Candida glabrata. Results Characterization results ratified the formation of a nanosystem ( less then 320 nm) with FLZ successfully embedded. IONPs-CS-FLZ nanosystem reduced minimum inhibitory concentration values and, in general, showed similar antibiofilm effects compared with FLZ alone. selleck compound Conclusion IONPs-CS-FLZ nanosystem was more effective than FLZ mainly in inhibiting Candida planktonic cells. This nanocarrier has potential to fight fungal infections.Aim Rapid identification of bacteria would facilitate timely initiation of therapy and improve cost-effectiveness of treatment. Traditional methods (culture, PCR) require reagents, consumables and hours to days to complete the identification. In this study, we examined whether differential mobility spectrometry could classify most common bacterial species, genera and between Gram status within minutes. Materials & methods Cultured bacterial sample gaseous headspaces were measured with differential mobility spectrometry and data analyzed using k-nearest-neighbor and leave-one-out cross-validation. Results Differential mobility spectrometry achieved a correct classification rate 70.7% for all bacterial species. For bacterial genera, the rate was 77.6% and between Gram status, 89.1%. Conclusion Largest difficulties arose in distinguishing bacteria of the same genus. Future improvement of the sensor characteristics may improve the classification accuracy.
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