Diagnostic performance of emergency medical technician for ST-segment elevation myocardial infarction

This study was conducted to determine whether level-1 emergency medical technicians (EMTs) can adequately recognize ST-segment elevation myocardial infarction (STEMI) in the emergency department (ED) and whether their ability to do so differs from that of emergency medicine physicians (EMP). From December 2022 to November 2023, patients aged 20 years or older visiting the ED with chief complaints suggesting acute coronary syndrome (ACS) were enrolled. As soon as the patient arrived at the ED, a level-1 EMT conducted a 12-lead electrocardiogram (ECG) to assess STEMI; an EMP subsequently assessed whether to activate the percutaneous coronary intervention team. Demographic characteristics, test results, and final diagnoses were collected from the medical records. Among the 723 patients with case report forms, 720 were included in the analysis. These were categorized as follows: 117 (16.3%) with STEMI, 159 (22.1%) with non-ST-segment elevation ACS, and 444 (61.7%) with other conditions. STEMI was correctly recognized in 100 patients (91.7%) by level-1 EMTs and in 104 patients (95.4%) by EMPs (kappa=0.646). EMTs with less than 1 year of ED work experience correctly recognized 60 out of 67 STEMI patients (89.6%), which was comparable with the EMPs who recognized 65 out of 67 STEMI patients (97.0%, kappa=0.614). EMTs with more than 1 year of ED work correctly recognized 40 out of 42 STEMI patients (95.2%), and therefore performed better than EMPs, who recognized 39 out of 42 STEMI patients (92.9%, kappa=0.727). The level-1 EMTs adequately recognized STEMI using a 12-lead ECG and were in substantial agreement with the evaluations of the EMPs

Towards Neural Decoding of Imagined Speech based on Spoken Speech

Decoding imagined speech from human brain signals is a challenging and important issue that may enable human communication via brain signals. While imagined speech can be the paradigm for silent communication via brain signals, it is always hard to collect enough stable data to train the decoding model. Meanwhile, spoken speech data is relatively easy and to obtain, implying the significance of utilizing spoken speech brain signals to decode imagined speech. In this paper, we performed a preliminary analysis to find out whether if it would be possible to utilize spoken speech electroencephalography data to decode imagined speech, by simply applying the pre-trained model trained with spoken speech brain signals to decode imagined speech. While the classification performance of imagined speech data solely used to train and validation was 30.5 %, the transferred performance of spoken speech based classifier to imagined speech data displayed average accuracy of 26.8 % which did not have statistically significant difference compared to the imagined speech based classifier (p = 0.0983, chi-square = 4.64). For more comprehensive analysis, we compared the result with the visual imagery dataset, which would naturally be less related to spoken speech compared to the imagined speech. As a result, visual imagery have shown solely trained performance of 31.8 % and transferred performance of 26.3 % which had shown statistically significant difference between each other (p = 0.022, chi-square = 7.64). Our results imply the potential of applying spoken speech to decode imagined speech, as well as their underlying common features.Comment: 4 pages, 2 figure

Development of software for computing forming information using a component based approach

ABSTRACTIn shipbuilding industry, the manufacturing technology has advanced at an unprecedented pace for the last decade. As a result, many automatic systems for cutting, welding, etc. have been developed and employed in the manufacturing process and accordingly the productivity has been increased drastically. Despite such improvement in the manufacturing technology, however, development of an automatic system for fabricating a curved hull plate remains at the beginning stage since hardware and software for the automation of the curved hull fabrication process should be developed differently depending on the dimensions of plates, forming methods and manufacturing processes of each shipyard. To deal with this problem, it is necessary to create a “plug-in” framework, which can adopt various kinds of hardware and software to construct a full automatic fabrication system. In this paper, a framework for automatic fabrication of curved hull plates is proposed, which consists of four components and related software. In particular the software module for computing fabrication information is developed by using the ooCBD development methodology, which can interface with other hardware and software with minimum effort. Examples of the proposed framework applied to medium and large shipyards are presented

Методы локализации выноса песка на водозаборных скважинах Ванкорского нефтегазового месторождения

В данной бакалаврской работе рассмотрены общие сведения о Ванкорском газонефтяном месторождении, описан технологический процесс системы поддержания пластового давления. В технологической части работы проанализированы осложнения при эксплуатации глубинного оборудования, теоретически обоснована необходимость борьбы с пескопроявлением, изучены основные методы, применяемые в мировой практике. В исследовательской части проанализированы показатели эффективности применяемых на месторождении фильтров для защитного оборудования

Abnormal cognitive dysfunction in patients with restless legs syndrome: A event-related potential study

Recent study reported that patients with restless legs syndrome (RLS) may have cognitive deficit, particularly prefrontal lobe dysfunction (Pearson et al., 2006). The cognitive dysfunction may be attributed to either secondary to daytime sleepiness and/or attention deficit due to RLS symptoms, or primary to intrinsic brain dysfunction underneath RLS syndrome. Event-related potential (ERP), which offers high temporal resolution, provides information about the precise timing of dynamic neural mechanisms of different cognitive processes. ERP involved in stimulus categorization, probability sequence, attention resource allocation, and memory processing. To identify cognitive dysfunction in patients with RLS, event-related potential (ERP) study was performed. Daytime sleepiness and RLS symptoms were checked to delineate underlying mechanism of cognitive dysfunction in RLS.OAIID:oai:osos.snu.ac.kr:snu2009-01/104/2014017262/9SEQ:9PERF_CD:SNU2009-01EVAL_ITEM_CD:104USER_ID:2014017262ADJUST_YN:NEMP_ID:A079623DEPT_CD:801CITE_RATE:0FILENAME:abnormal cognitive dysfunction in patients with restless legs syndrome.pdfDEPT_NM:의학과CONFIRM:

Cellular stress-induced up-regulation of FMRP promotes cell survival by modulating PI3K-Akt phosphorylation cascades

<p>Abstract</p> <p>Background</p> <p>Fragile X syndrome (FXS), the most commonly inherited mental retardation and single gene cause of autistic spectrum disorder, occurs when the Fmr1 gene is mutated. The product of Fmr1, fragile X linked mental retardation protein (FMRP) is widely expressed in HeLa cells, however the roles of FMRP within HeLa cells were not elucidated, yet. Interacting with a diverse range of mRNAs related to cellular survival regulatory signals, understanding the functions of FMRP in cellular context would provide better insights into the role of this interesting protein in FXS. Using HeLa cells treated with etoposide as a model, we tried to determine whether FMRP could play a role in cell survival.</p> <p>Methods</p> <p>Apoptotic cell death was induced by etoposide treatment on Hela cells. After we transiently modulated FMRP expression (silencing or enhancing) by using molecular biotechnological methods such as small hairpin RNA virus-induced knock down and overexpression using transfection with FMRP expression vectors, cellular viability was measured using propidium iodide staining, TUNEL staining, and FACS analysis along with the level of activation of PI3K-Akt pathway by Western blot. Expression level of FMRP and apoptotic regulator BcL-xL was analyzed by Western blot, RT-PCR and immunocytochemistry.</p> <p>Results</p> <p>An increased FMRP expression was measured in etoposide-treated HeLa cells, which was induced by PI3K-Akt activation. Without FMRP expression, cellular defence mechanism via PI3K-Akt-Bcl-xL was weakened and resulted in an augmented cell death by etoposide. In addition, FMRP over-expression lead to the activation of PI3K-Akt signalling pathway as well as increased FMRP and BcL-xL expression, which culminates with the increased cell survival in etoposide-treated HeLa cells.</p> <p>Conclusions</p> <p>Taken together, these results suggest that FMRP expression is an essential part of cellular survival mechanisms through the modulation of PI3K, Akt, and Bcl-xL signal pathways.</p

The efficacy of memory load on speech-based detection of Alzheimer’s disease

IntroductionThe study aims to test whether an increase in memory load could improve the efficacy in detection of Alzheimer’s disease and prediction of the Mini-Mental State Examination (MMSE) score.MethodsSpeech from 45 mild-to-moderate Alzheimer’s disease patients and 44 healthy older adults were collected using three speech tasks with varying memory loads. We investigated and compared speech characteristics of Alzheimer’s disease across speech tasks to examine the effect of memory load on speech characteristics. Finally, we built Alzheimer’s disease classification models and MMSE prediction models to assess the diagnostic value of speech tasks.ResultsThe speech characteristics of Alzheimer’s disease in pitch, loudness, and speech rate were observed and the high-memory-load task intensified such characteristics. The high-memory-load task outperformed in AD classification with an accuracy of 81.4% and MMSE prediction with a mean absolute error of 4.62.DiscussionThe high-memory-load recall task is an effective method for speech-based Alzheimer’s disease detection