Prospective validation study of Cernea classification for predicting EMG alterations of the external branch of the superior laryngeal nerve

Purposes

Cernea classification is applied to describe the external branch of the superior laryngeal nerve (EBSLN). Using intraoperative neural monitoring we evaluated whether or not this classification is useful for predicting which EBSLN subtype has an increased risk of injury.

Methods

An analysis of 400 EBSLN. The identification of EBSLN was achieved with both cricothyroid muscle twitch and the glottis evoked electromyography response. We defined S1 initial EBSLN stimulation at identification and S2 final nerve stimulation achieved in the most cranial aspect of the nerve exposed above the area of surgical dissection after superior artery ligation.

Results

The mean S1 amplitude acquired was 259+/67 (180–421), 321 +/79 (192–391), 371 +/38 (200–551) μV, respectively, for type 1, 2A, 2B (p = 0.08). The S1 and S2 amplitudes were similar in type 1 (p = 0.3). The S1 and S2 determinations changed significantly in type 2A and 2B (p = 0.04 and 0.03). EBSLN which demonstrated a >25 % decreased amplitude in S2 increased significantly from Type 1 (4.9 %) to Type 2A (11.2 %) and 2B (18 %) (p = 0.01). None of type 1, 2.8 % type 2A and 3 % type 2B showed a loss of EBSLN conductivity. The latency determinations did not vary significantly for any parameter compared.

Conclusions

The Cernea classification was, therefore, found to predict the risk of EBSLN stress. We identified amplitude differences between S1 and S2 determinations in type 2A and 2B, thus confirming that surgical dissection in these subtypes is, therefore, extremely difficult to perform.

     

Deep Convolutional Neural Network Analysis of Flow Imaging Microscopy Data to Classify Subvisible Particles in Protein Formulations

Flow-imaging microscopy (FIM) is commonly used to characterize subvisible particles in therapeutic protein formulations. Although pharmaceutical companies often collect large repositories of FIM images of protein therapeutic products, current state-of-the-art methods for analyzing these images rely on low-dimensional lists of “morphological features” to characterize particles that ignore much of the information encoded in the existing image databases. Deep convolutional neural networks (sometimes referred to as “CNNs or ConvNets”) have demonstrated the ability to extract predictive information from raw macroscopic image data without requiring the selection or specification of “morphological features” in a variety of tasks. However, the inherent heterogeneity of protein therapeutics and optical phenomena associated with subvisible FIM particle measurements introduces new challenges regarding the application of ConvNets to FIM image analysis. We demonstrate a supervised learning technique leveraging ConvNets to extract information from raw images in order to predict the process conditions or stress states (freeze-thawing, mechanical shaking, etc.) that produced a variety of different protein particles. We demonstrate that our new classifier, in combination with a “data pooling” strategy, can nearly perfectly differentiate between protein formulations in a variety of scenarios of relevance to protein therapeutics quality control and process monitoring using as few as 20 particles imaged via FIM.     

Effectiveness of hypnotherapy with cancer patients' trajectory: emesis, acute pain, and analgesia and anxiolysis in procedures

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