Interstitial lung disease is not rare in immune-mediated necrotizing myopathy with anti-signal recognition particle antibodies

Objectives The purpose was to clarify the characteristics of interstitial lung disease (ILD) in immune-mediated necrotizing myopathy (IMNM) patients with anti-signal recognition particle (SRP) antibodies. Methods Medical records of IMNM patients with anti-SRP antibodies were reviewed retrospectively. Results A total of 60 patients were identified. Twenty-seven (45.0%) patients were diagnosed with ILD based on lung imaging: nonspecific interstitial pneumonia (NSIP) in 17 patients (63.0%) and organizing pneumonia in 9 patients (33.3%). Reticulation pattern was identified in 17 patients (63.0%) whereas 10 cases (37.0%) showed ground glass opacity and patchy shadows by high-resolution computed tomography (HRCT). Pulmonary function tests (PFTs) were available in 18 patients, 6 (33.3%) and 10 (55.6%) patients were included in the mild and moderate group, respectively. The average age at the time of ILD onset was significantly older than those without ILD (48.6 ± 14.4 years vs. 41.2 ± 15.4 years, p < 0.05), and the frequency of dysphagia in the ILD group was higher than the group without ILD (p < 0.05). Long-term follow-up was available on 9 patients. PFTs were stable in 8 (88.9%), and the HRCT remained stable in 6 (66.7%) patients. Conclusions ILD is not rare in IMNM patients with anti-SRP antibodies, most being characterized as mild to moderate in severity. NSIP is the principal radiologic pattern, and ILD typically remains stable following treatment.

Research on multi-view clustering algorithm on epileptic EEG signal

Epilepsy is caused by the abnormal discharge of the patient's brain. Smart medical uses advanced technologies such as signal recognition and machine learning to identify and analyze the biological signals fed back from the subjects’ brain electrical signals and provide diagnostic results. In the past, doctors used their own experience and theoretical knowledge to judge whether there are characteristic signals by observing the subject’s EEG signal to realize the judgment of the condition. This method of diagnosis through observation often infuses the doctor's own subjective judgment, leading to misdiagnosis of the condition and low diagnosis and treatment efficiency. With the continuous development of advanced technologies such as artificial intelligence and signal recognition, this provides new ideas for the realization of EEG signal recognition and processing technology and opens up new development paths. This article is based on epilepsy EEG signal data, realizes EEG signal processing and uses machine learning methods to realize EEG signal identification and diagnosis.

Research on multi-view clustering algorithm on epileptic EEG signal

Epilepsy is caused by the abnormal discharge of the patient's brain. Smart medical uses advanced technologies such as signal recognition and machine learning to identify and analyze the biological signals fed back from the subjects’ brain electrical signals and provide diagnostic results. In the past, doctors used their own experience and theoretical knowledge to judge whether there are characteristic signals by observing the subject’s EEG signal to realize the judgment of the condition. This method of diagnosis through observation often infuses the doctor's own subjective judgment, leading to misdiagnosis of the condition and low diagnosis and treatment efficiency. With the continuous development of advanced technologies such as artificial intelligence and signal recognition, this provides new ideas for the realization of EEG signal recognition and processing technology and opens up new development paths. This article is based on epilepsy EEG signal data, realizes EEG signal processing and uses machine learning methods to realize EEG signal identification and diagnosis.

Increase in Low-Frequency Oscillations in fNIRS as Cerebral Response to Auditory Stimulation with Familiar Music

Recognition of typical patterns of brain response to external stimuli using near-infrared spectroscopy (fNIRS) may become a gateway to detecting covert consciousness in clinically unresponsive patients. This is the first fNIRS study on the cortical hemodynamic response to favorite music using a frequency domain approach. The aim of this study was to identify a possible marker of cognitive response in healthy subjects by investigating variations in the oscillatory signal of fNIRS in the spectral regions of low-frequency (LFO) and very-low-frequency oscillations (VLFO). The experiment consisted of two periods of exposure to preferred music, preceded and followed by a resting phase. Spectral power in the LFO region increased in all the subjects after the first exposure to music and decreased again in the subsequent resting phase. After the second music exposure, the increase in LFO spectral power was less distinct. Changes in LFO spectral power were more proGfirst music exposure and the repetition-related habituation effect strongly suggest a cerebral origin of the fNIRS signal. Recognition of typical patterns of brain response to specific environmental stimulation is a required step for the concrete validation of a fNIRS-based diagnostic tool.

Fidelity of Cotranslational Protein Targeting to the Endoplasmic Reticulum

Fidelity of protein targeting is essential for the proper biogenesis and functioning of organelles. Unlike replication, transcription and translation processes, in which multiple mechanisms to recognize and reject noncognate substrates are established in energetic and molecular detail, the mechanisms by which cells achieve a high fidelity in protein localization remain incompletely understood. Signal recognition particle (SRP), a conserved pathway to mediate the localization of membrane and secretory proteins to the appropriate cellular membrane, provides a paradigm to understand the molecular basis of protein localization in the cell. In this chapter, we review recent progress in deciphering the molecular mechanisms and substrate selection of the mammalian SRP pathway, with an emphasis on the key role of the cotranslational chaperone NAC in preventing protein mistargeting to the ER and in ensuring the organelle specificity of protein localization.

Regional Signal Recognition of Body Sounds

One of the most important signals in the field of biomedicine is audio signals. Sound signals obtained from the body give us information about the general condition of the body. However, the detection of different sounds when recording audio signals belonging to the body or listening to them by doctors makes it difficult to diagnose the disease from these signals. In addition to isolating these sounds from the external environment, it is also necessary to separate their sounds from different parts of the body during the analysis. Separation of heart, lung and abdominal sounds will facilitate digital analysis, in particular. In this study, a dataset was created from the lungs, heart and abdominal sounds. MFCC (Mel Frekans Cepstrum Coefficient) coefficient data were obtained. The obtained coefficients were trained in the CNN (Convolution Neural Network) model. The purpose of this study is to classify audio signals. With this classification, a control system can be created. In this way, erroneous recordings that may occur when recording physicians' body voices will be prevented. When looking at the results, the educational success is about 98% and the test success is about 85%.