Frequency specificity of contralateral, ipsilateral and bilateral medial olivocochlear acoustic reflexes in humans

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references.A variety of evidence indicates that the brain controls the gain of mechanical amplification in the cochlea in a frequency specific manner through the medial olivocochlear (MOC) efferent pathway, but the degree of MOC frequency specificity in humans is poorly understood. This thesis investigates the tuning properties of the human MOC acoustic reflex at different cochlear frequency regions and with different MOC-elicitor lateralities and frequency contents. Effects produced by the MOC reflex were quantified by the magnitude of the induced changes in stimulus frequency otoacoustic emissions (deltaSFOAEs) at probe frequencies of 0.5, 1 and 4 kHz. With MOC activity elicited by a mid-level (60 dB SPL) tone or half-octave-band of noise, significant MOC-induced deltaSFOAEs were seen over a wide range of elicitor frequencies, e.g. for elicitor frequencies at least 11/2 octaves away from each probe frequency. deltaSFOAE-versus-elicitor-frequency patterns were sometimes skewed so that elicitors at frequencies above (0.5 kHz probe) or below (1 kHz probe) the probe frequency were most effective. In contrast to the wide frequency range of MOC effects from mid-level elicitors, for 1 kHz probes MOC-effect tuning curves (TCs) were narrow with Ql0s of -2, sharper than the MOC-fiber TCs with best frequencies near I kHz in cats and guinea pigs. When MOC effects were looked at as the MOC-inhibited SFOAE relative to the original SFOAE, the SFOAE magnitude decreases and phase changes appeared to be separate functions of elicitor frequency: SFOAE magnitude inhibition was largest for on-frequency elicitors (elicitor frequencies near the probe frequency) while MOC-induced SFOAE phase leads were largest for off-frequency elicitors.(cont) One hypothesis to account for this is that on-frequency elicitors predominantly inhibit the traveling wave from the probe-tone, whereas off-frequency elicitors shift it along the frequency axis by selectively inhibiting apical or basal parts of the traveling-wave. These results are consistent with an anti-masking role of MOC efferents and suggest that MOC efferents do more than just provide feedback to a narrow frequency region around the elicitor frequency.by Watjana Lilaonitkul.Ph.D

Clinical deployment environments: Five pillars of translational machine learning for health

Machine Learning for Health (ML4H) has demonstrated efficacy in computer imaging and other self-contained digital workflows, but has failed to substantially impact routine clinical care. This is no longer because of poor adoption of Electronic Health Records Systems (EHRS), but because ML4H needs an infrastructure for development, deployment and evaluation within the healthcare institution. In this paper, we propose a design pattern called a Clinical Deployment Environment (CDE). We sketch the five pillars of the CDE: (1) real world development supported by live data where ML4H teams can iteratively build and test at the bedside (2) an ML-Ops platform that brings the rigour and standards of continuous deployment to ML4H (3) design and supervision by those with expertise in AI safety (4) the methods of implementation science that enable the algorithmic insights to influence the behaviour of clinicians and patients and (5) continuous evaluation that uses randomisation to avoid bias but in an agile manner. The CDE is intended to answer the same requirements that bio-medicine articulated in establishing the translational medicine domain. It envisions a transition from "real-world" data to "real-world" development

Evaluation of practice change following SAFE obstetric courses in Tanzania: : a prospective cohort study

Funding Information: The study was funded by the Laerdal Foundation. ML and AZ are joint first authors. We would like to thank the World Federation of Societies of Anaesthesiologists and the Association of Anaesthetists, UK for operational and administrative support. We would also like to express our deepest gratitude to the faculty and research assistants: B. Asnake, A. Chamwanzi, A. Cheng, T. Kasole, K. Khalid, L. Frostan Komba, C. L. S. Kwan, A. F. Lwiza, P. Massawe, B. McKenna, S. S. Mohamed, C. Msadabwe, P. Murambi, A. Musgrave, M. C. Mutagwaba, G. Mwakisambwe, A. S. Ndebeya, S. G. Ndezi, H. Phiri, P. Ponsian, R. Samwel, E. Shang'a and R. Swai. This paper is dedicated to the memory of our dear friend and colleague, Soloman Gerald Ndezi (1984–2022), who was a dedicated teacher and compassionate doctor. No competing interests declared. Publisher Copyright: © 2023 The Authors. Anaesthesia published by John Wiley & Sons Ltd on behalf of Association of Anaesthetists.Peer reviewedPublisher PD

An automated approach to identify scientific publications reporting pharmacokinetic parameters [version 1; peer review: awaiting peer review]

Pharmacokinetic (PK) predictions of new chemical entities are aided by prior knowledge from other compounds. The development of robust algorithms that improve preclinical and clinical phases of drug development remains constrained by the need to search, curate and standardise PK information across the constantly-growing scientific literature. The lack of centralised, up-to-date and comprehensive repositories of PK data represents a significant limitation in the drug development pipeline.In this work, we propose a machine learning approach to automatically identify and characterise scientific publications reporting PK parameters from in vivo data, providing a centralised repository of PK literature. A dataset of 4,792 PubMed publications was labelled by field experts depending on whether in vivo PK parameters were estimated in the study. Different classification pipelines were compared using a bootstrap approach and the best-performing architecture was used to develop a comprehensive and automatically-updated repository of PK publications. The best-performing architecture encoded documents using unigram features and mean pooling of BioBERT embeddings obtaining an F1 score of 83.8% on the test set. The pipeline retrieved over 121K PubMed publications in which in vivo PK parameters were estimated and it was scheduled to perform weekly updates on newly published articles. All the relevant documents were released through a publicly available web interface (https://app.pkpdai.com) and characterised by the drugs, species and conditions mentioned in the abstract, to facilitate the subsequent search of relevant PK data. This automated, open-access repository can be used to accelerate the search and comparison of PK results, curate ADME datasets, and facilitate subsequent text mining tasks in the PK domain.</ns4:p

Delineating COVID-19 subgroups using routine clinical data identifies distinct in-hospital outcomes

The COVID-19 pandemic has been a great challenge to healthcare systems worldwide. It highlighted the need for robust predictive models which can be readily deployed to uncover heterogeneities in disease course, aid decision-making and prioritise treatment. We adapted an unsupervised data-driven model-SuStaIn, to be utilised for short-term infectious disease like COVID-19, based on 11 commonly recorded clinical measures. We used 1344 patients from the National COVID-19 Chest Imaging Database (NCCID), hospitalised for RT-PCR confirmed COVID-19 disease, splitting them equally into a training and an independent validation cohort. We discovered three COVID-19 subtypes (General Haemodynamic, Renal and Immunological) and introduced disease severity stages, both of which were predictive of distinct risks of in-hospital mortality or escalation of treatment, when analysed using Cox Proportional Hazards models. A low-risk Normal-appearing subtype was also discovered. The model and our full pipeline are available online and can be adapted for future outbreaks of COVID-19 or other infectious disease

Phenotyping of ABCA4 Retinopathy by Machine Learning Analysis of Full-Field Electroretinography

PURPOSE: Biallelic pathogenic variants in ABCA4 are the commonest cause of monogenic retinal disease. The full-field electroretinogram (ERG) quantifies severity of retinal dysfunction. We explored application of machine learning in ERG interpretation and in genotype–phenotype correlations. METHODS: International standard ERGs in 597 cases of ABCA4 retinopathy were classified into three functional phenotypes by human experts: macular dysfunction alone (group 1), or with additional generalized cone dysfunction (group 2), or both cone and rod dysfunction (group 3). Algorithms were developed for automatic selection and measurement of ERG components and for classification of ERG phenotype. Elastic-net regression was used to quantify severity of specific ABCA4 variants based on effect on retinal function. RESULTS: Of the cohort, 57.6%, 7.4%, and 35.0% fell into groups 1, 2, and 3 respectively. Compared with human experts, automated classification showed overall accuracy of 91.8% (SE, 0.169), and 96.7%, 39.3%, and 93.8% for groups 1, 2, and 3. When groups 2 and 3 were combined, the average holdout group accuracy was 93.6% (SE, 0.142). A regression model yielded phenotypic severity scores for the 47 commonest ABCA4 variants. CONCLUSIONS: This study quantifies prevalence of phenotypic groups based on retinal function in a uniquely large single-center cohort of patients with electrophysiologically characterized ABCA4 retinopathy and shows applicability of machine learning. Novel regression-based analyses of ABCA4 variant severity could identify individuals predisposed to severe disease. Translational Relevance: Machine learning can yield meaningful classifications of ERG data, and data-driven scoring of genetic variants can identify patients likely to benefit most from future therapies

Anaesthetic research in low- and middle-income countries.

In this issue of Anaesthesia, Kwikiriza et al. [1] report a randomised controlled trial comparing the analgesic effects of intrathecal morphine with ultrasound-guided transversus abdominis plane block after caesarean section at a Ugandan Regional Referral Hospital. The publication of this study, authored by an international team, represents an important example of the role of academic anaesthesia in global health.NIHR Global Health Research Group on Neurotraum

Frequency tuning of the efferent effect on cochlear gain in humans

Cochlear gain reduction via efferent feedback from the medial olivocochlear bundle is frequency specific (Guinan, Curr Opin Otolaryngol Head Neck Surg 18:447-453, 2010). The present study with humans used the Fixed Duration Masking Curve psychoacoustical method (Yasin et al., J Acoust Soc Am 133:4145-4155, 2013a; Yasin et al., Basic aspects of hearing: physiology and perception, pp 39-46, 2013b; Yasin et al., J Neurosci 34:15319-15326, 2014) to estimate the frequency specificity of the efferent effect at the cochlear level. The combined duration of the masker-plus-signal stimulus was 25 ms, within the efferent onset delay of about 31-43 ms (James et al., Clin Otolaryngol 27:106-112, 2002). Masker level (4.0 or 1.8 kHz) at threshold was obtained for a 4-kHz signal in the absence or presence of an ipsilateral 60 dB SPL, 160-ms precursor (200-Hz bandwidth) centred at frequencies between 2.5 and 5.5 kHz. Efferent-mediated cochlear gain reduction was greatest for precursors with frequencies the same as, or close to that of, the signal (gain was reduced by about 20 dB), and least for precursors with frequencies well removed from that of the signal (gain remained at around 40 dB). The tuning of the efferent effect filter (tuning extending 0.5-0.7 octaves above and below the signal frequency) is within the range obtained in humans using otoacoustic emissions (Lilaonitkul and Guinan, J Assoc Res Otolaryngol 10:459-470, 2009; Zhao and Dhar, J Neurophysiol 108:25-30, 2012). The 10 dB bandwidth of the efferent-effect filter at 4000 Hz was about 1300 Hz (Q10 of 3.1). The FDMC method can be used to provide an unbiased measure of the bandwidth of the efferent effect filter using ipsilateral efferent stimulation

Fast and Slow Effects of Medial Olivocochlear Efferent Activity in Humans

Background: The medial olivocochlear (MOC) pathway modulates basilar membrane motion and auditory nerve activity on both a fast (10–100 ms) and a slow (10–100 s) time scale in guinea pigs. The slow MOC modulation of cochlear activity is postulated to aide in protection against acoustic trauma. However in humans, the existence and functional roles of slow MOC effects remain unexplored. Methodology/Principal Findings: By employing contralateral noise at moderate to high levels (68 and 83 dB SPL) as an MOC reflex elicitor, and spontaneous otoacoustic emissions (SOAEs) as a non-invasive probe of the cochlea, we demonstrated MOC modulation of human cochlear output both on a fast and a slow time scale, analogous to the fast and slow MOC efferent effects observed on basilar membrane vibration and auditory nerve activity in guinea pigs. The magnitude of slow effects was minimal compared with that of fast effects. Consistent with basilar membrane and auditory nerve activity data, SOAE level was reduced by both fast and slow MOC effects, whereas SOAE frequency was elevated by fast and reduced by slow MOC effects. The magnitudes of fast and slow effects on SOAE level were positively correlated. Conclusions/Significance: Contralateral noise up to 83 dB SPL elicited minimal yet significant changes in both SOAE leve