Comparison of New Metrics for Assessment of Risks of Occupational Noise

Noise induced hearing loss (NIHL) is one of the most common occupational related health problems worldwide. Exposure to excessive noise is the major avoidable cause of permanent hearing loss. The conventional metrics for noise evaluation cannot accurately assess the exposure risks to high-level complex noise, which commonly occurs in many industrial and military fields. Recently, we have developed two advanced models, an adaptive weighting (F-weighting) and a complex velocity level (CVL) auditory fatigue model, to evaluate the risks of occupational noise. In this study, we compared performances of five noise assessment metrics, including F-weighted sound pressure level (SPL) LFeq, CVL model based SPL LCVL, equivalent SPL Leq, A-weighted SPL LAeq, and C-weighted SPL LCeq, using animal experimental data. The animal data includes 22 groups of chinchillas exposed to different types of noise (e.g., Gaussian and non-Gaussian noises). Linear regression analysis is applied to evaluate the correlations between the five noise metrics and the chinchillas’ NIHL data. The results show that both developed F-weighting and CVL models have high corrections with animal hearing loss data compared with the conventional noise metrics (i.e., Leq, LAeq and LCeq). It indicates that both developed models could provide accurate assessment of risks of high-level occupational noise in military and industrial applications. The results also suggest that the CVL model is more accurate than the F-weighting model on assessment of occupational noise

Spontaneous activity in default-mode network predicts ascriptions of self-relatedness to stimuli

Spontaneous activity levels prior to stimulus presentation can determine how that stimulus will be perceived. It has also been proposed that such spontaneous activity, particularly in the default-mode network (DMN), is involved in self-related processing. We therefore hypothesised that pre-stimulus activity levels in the DMN predict whether a stimulus is judged as self-related or not. Method: Participants were presented in the MRI scanner with a white noise stimulus that they were instructed contained their name or another. They then had to respond with which name they thought they heard. Regions where there was an activity level difference between self and other response trials two seconds prior to the stimulus being presented were identified. Results: Pre-stimulus activity levels were higher in the right temporoparietal junction (RTPJ), the right temporal pole (RTP), and the left superior temporal gyrus in trials where the participant responded that they heard their own name than trials where they responded that they heard another. Conclusion: Pre-stimulus spontaneous activity levels in particular brain regions, largely overlapping with the DMN, predict the subsequent judgement of stimuli as self-related. This extends our current knowledge of self-related processing and its apparent relationship with intrinsic brain activity in what can be termed a rest-self overlap

Investigations of Auditory Filters Based Excitation Patterns for Assessment of Noise Induced Hearing Loss

Noise induced hearing loss (NIHL) as one of major avoidable occupational related health issues has been studied for decades. To assess NIHL, the excitation pattern (EP) has been considered as one of mechanisms to estimate movements of basilar membrane (BM) in cochlea. In this study, two auditory filters, dual resonance nonlinear (DRNL) filter and rounded-exponential (ROEX) filter, have been applied to create two EPs, referring as the velocity EP and the loudness EP, respectively. Two noise hazard metrics are also proposed based on the developed EPs to evaluate hazardous levels caused by different types of noise. Moreover, Gaussian noise and pure-tone noise have been simulated to evaluate performances of the developed EPs and noise metrics. The results show that both developed EPs can reflect the responses of BM to different types of noise. For Gaussian noise, there is a frequency shift between the velocity EP and the loudness EP. For pure-tone noise, both EPs can reflect the frequencies of input noise accurately. The results suggest that both EPs can be potentially used for assessment of NIHL

GABAA Receptor Deficits Predict Recovery in Patients With Disorders of Consciousness: A Preliminary Multimodal [11C]Flumazenil PET and fMRI Study

Disorders of consciousness (DoC)—that is, unresponsive wakefulness syndrome/vegetative state and minimally conscious state—are debilitating conditions for which no reliable markers of consciousness recovery have yet been identified. Evidence points to the GABAergic system being altered in DoC, making it a potential target as such a marker

Investigations of Auditory Filters Based Excitation Patterns for Assessment of Noise Induced Hearing Loss

Noise induced hearing loss (NIHL) as one of major avoidable occupational related health issues has been studied for decades. To assess NIHL, the excitation pattern (EP) has been considered as one of mechanisms to estimate movements of basilar membrane (BM) in cochlea. In this study, two auditory filters, dual resonance nonlinear (DRNL) filter and rounded-exponential (ROEX) filter, have been applied to create two EPs, referring as the velocity EP and the loudness EP, respectively. Two noise hazard metrics are also proposed based on the developed EPs to evaluate hazardous levels caused by different types of noise. Moreover, Gaussian noise and pure-tone noise have been simulated to evaluate performances of the developed EPs and noise metrics. The results show that both developed EPs can reflect the responses of BM to different types of noise. For Gaussian noise, there is a frequency shift between the velocity EP and the loudness EP. For pure-tone noise, both EPs can reflect the frequencies of input noise accurately. The results suggest that both EPs can be potentially used for assessment of NIHL

Machine learning derivation of four computable 24-h pediatric sepsis phenotypes to facilitate enrollment in early personalized anti-inflammatory clinical trials

BACKGROUND: Thrombotic microangiopathy-induced thrombocytopenia-associated multiple organ failure and hyperinflammatory macrophage activation syndrome are important causes of late pediatric sepsis mortality that are often missed or have delayed diagnosis. The National Institutes of General Medical Science sepsis research working group recommendations call for application of new research approaches in extant clinical data sets to improve efficiency of early trials of new sepsis therapies. Our objective is to apply machine learning approaches to derive computable 24-h sepsis phenotypes to facilitate personalized enrollment in early anti-inflammatory trials targeting these conditions. METHODS: We applied consensus, k-means clustering analysis to our extant PHENOtyping sepsis-induced Multiple organ failure Study (PHENOMS) dataset of 404 children. 24-hour computable phenotypes are derived using 25 available bedside variables including C-reactive protein and ferritin. RESULTS: Four computable phenotypes (PedSep-A, B, C, and D) are derived. Compared to all other phenotypes, PedSep-A patients (n = 135; 2% mortality) were younger and previously healthy, with the lowest C-reactive protein and ferritin levels, the highest lymphocyte and platelet counts, highest heart rate, and lowest creatinine (p \u3c 0.05); PedSep-B patients (n = 102; 12% mortality) were most likely to be intubated and had the lowest Glasgow Coma Scale Score (p \u3c 0.05); PedSep-C patients (n = 110; mortality 10%) had the highest temperature and Glasgow Coma Scale Score, least pulmonary failure, and lowest lymphocyte counts (p \u3c 0.05); and PedSep-D patients (n = 56, 34% mortality) had the highest creatinine and number of organ failures, including renal, hepatic, and hematologic organ failure, with the lowest platelet counts (p \u3c 0.05). PedSep-D had the highest likelihood of developing thrombocytopenia-associated multiple organ failure (Adj OR 47.51 95% CI [18.83-136.83], p \u3c 0.0001) and macrophage activation syndrome (Adj OR 38.63 95% CI [13.26-137.75], p \u3c 0.0001). CONCLUSIONS: Four computable phenotypes are derived, with PedSep-D being optimal for enrollment in early personalized anti-inflammatory trials targeting thrombocytopenia-associated multiple organ failure and macrophage activation syndrome in pediatric sepsis. A computer tool for identification of individual patient membership ( www.pedsepsis.pitt.edu ) is provided. Reproducibility will be assessed at completion of two ongoing pediatric sepsis studies