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BackgroundSmoking cigarettes is a cause of serious diseases in smokers, including cardiovascular disease. Through a pathway of endothelial dysfunction, lipid infiltration, macrophage recruitment and vascular remodeling, atherosclerosis is fundamental in the development of most cardiovascular diseases. There is an increasing number of next-generation products (NGP) which provide potentially reduced harm forms of nicotine delivery to adult smokers. This study aimed to optimise an in vitro cardiovascular model to assess such products. Human Coronary Artery Endothelial Cells (HCAECs) were cultured on an OrganoPlate®2-lane chip (Mimetas BV) combined with THP-1 monocytes under flow conditions.MethodsAn aqueous aerosol extract from the 1R6F reference cigarette was compared with two categories of NGP, (a heated tobacco product (HTP) and an electronic nicotine delivery system (ENDS)), to assess relative effects on select atherogenic endpoints (oxidative stress, monocyte adhesion, ICAM-1 expression, and inflammatory markers). Following exposure of THP-1 monocytes with the aqueous extracts, the resulting conditioned medium was then added to the HCAEC vessels.Results1R6F was consistently the most potent test article, eliciting observed responses at 4x lower concentrations than applied for both the HTP and ENDS. The HTP was more potent than the ENDS product across all endpoints, however, all test articles increased monocyte adhesion. ICAM-1 did not appear to be a main driver for monocyte adhesion, however, this could be due to replicate variability. Upon comparison to an extract-only control exposure, THP-1-medium pre-conditioning was an important mediator of the responses observed.ConclusionIn conclusion, the data suggests that the NGP extracts, containing primary aerosol chemical constituents exhibit a marked reduction in biological activity in the early key events associated with atherogenesis when compared to a cigarette, adding to the weight of evidence for the tobacco harm reduction potential of such products.

With the use of in vitro new approach methodologies (NAMs) for the assessment of non-combustible next-generation nicotine delivery products, new extrapolation methods will also be required to interpret and contextualize the physiological relevance of these results. Quantitative in vitro to in vivo extrapolation (QIVIVE) can translate in vitro concentrations into in-life exposures with physiologically-based pharmacokinetic (PBPK) modelling and provide estimates of the likelihood of harmful effects from expected exposures. A major challenge for evaluating inhalation toxicology is an accurate assessment of the delivered dose to the surface of the cells and the internalized dose. To estimate this, we ran the multiple-path particle dosimetry (MPPD) model to characterize particle deposition in the respiratory tract and developed a PBPK model for nicotine that was validated with human clinical trial data for cigarettes. Finally, we estimated a Human Equivalent Concentration (HEC) and predicted plasma concentrations based on the minimum effective concentration (MEC) derived after acute exposure of BEAS-2B cells to cigarette smoke (1R6F), or heated tobacco product (HTP) aerosol at the air liquid interface (ALI). The MPPD-PBPK model predicted the in vivo data from clinical studies within a factor of two, indicating good agreement as noted by WHO International Programme on Chemical Safety (2010) guidance. We then used QIVIVE to derive the exposure concentration (HEC) that matched the estimated in vitro deposition point of departure (POD) (MEC cigarette = 0.38 puffs or 11.6 µg nicotine, HTP = 22.9 puffs or 125.6 µg nicotine) and subsequently derived the equivalent human plasma concentrations. Results indicate that for the 1R6F cigarette, inhaling 1/6th of a stick would be required to induce the same effects observed in vitro, in vivo. Whereas, for HTP it would be necessary to consume 3 sticks simultaneously to induce in vivo the effects observed in vitro. This data further demonstrates the reduced physiological potency potential of HTP aerosol compared to cigarette smoke. The QIVIVE approach demonstrates great promise in assisting human health risk assessments, however, further optimization and standardization are required for the substantiation of a meaningful contribution to tobacco harm reduction by alternative nicotine delivery products.

Recent years have seen a rapid increase in digital medicine research in an attempt to transform traditional healthcare systems to their modern, intelligent, and versatile equivalents that are adequately equipped to tackle contemporary challenges. This has led to a wave of applications that utilise AI technologies; first and foremost in the fields of medical imaging, but also in the use of wearables and other intelligent sensors. In comparison, computer audition can be seen to be lagging behind, at least in terms of commercial interest. Yet, audition has long been a staple assistant for medical practitioners, with the stethoscope being the quintessential sign of doctors around the world. Transforming this traditional technology with the use of AI entails a set of unique challenges. We categorise the advances needed in four key pillars: Hear, corresponding to the cornerstone technologies needed to analyse auditory signals in real-life conditions; Earlier, for the advances needed in computational and data efficiency; Attentively, for accounting to individual differences and handling the longitudinal nature of medical data; and, finally, Responsibly, for ensuring compliance to the ethical standards accorded to the field of medicine. Thus, we provide an overview and perspective of HEAR4Health: the sketch of a modern, ubiquitous sensing system that can bring computer audition on par with other AI technologies in the strive for improved healthcare systems.

The COVID-19 pandemic has caused massive humanitarian and economic damage. Teams of scientists from a broad range of disciplines have searched for methods to help governments and communities combat the disease. One avenue from the machine learning field which has been explored is the prospect of a digital mass test which can detect COVID-19 from infected individuals’ respiratory sounds. We present a summary of the results from the INTERSPEECH 2021 Computational Paralinguistics Challenges: COVID-19 Cough, (CCS) and COVID-19 Speech, (CSS).

Tobacco harm reduction (THR) involves providing adult smokers with potentially reduced harm modes of nicotine delivery as alternatives to smoking combustible cigarettes. Heated tobacco products (HTPs) form a category with THR potential due to their ability to deliver nicotine and flavours through heating, not burning, tobacco. By eliminating burning, heated tobacco does not produce smoke but an aerosol which contains fewer and lower levels of harmful chemicals compared to cigarette smoke. In this study we assessed the in vitro toxicological profiles of two prototype HTPs’ aerosols compared to the 1R6F reference cigarette using the 3D human (bronchial) MucilAir™ model. To increase consumer relevance, whole aerosol/smoke exposures were delivered repeatedly across a 28 day period (16, 32, or 48 puffs per exposure). Cytotoxicity (LDH secretion), histology (Alcian Blue/H&E; Muc5AC; FoxJ1 staining), cilia active area and beat frequency and inflammatory marker (IL-6; IL-8; MMP-1; MMP-3; MMP-9; TNFα) levels were assessed. Diluted 1R6F smoke consistently induced greater and earlier effects compared to the prototype HTP aerosols across the endpoints, and in a puff dependent manner. Although some significant changes across the endpoints were induced by exposure to the HTPs, these were substantially less pronounced and less frequently observed, with apparent adaptive responses occurring over the experimental period. Furthermore, these differences between the two product categories were observed at a greater dilution (and generally lower nicotine delivery range) for 1R6F (1R6F smoke diluted 1/14, HTP aerosols diluted 1/2, with air). Overall, the findings demonstrate the THR potential of the prototype HTPs through demonstrated substantial reductions in toxicological outcomes in in vitro 3D human lung models.

Deep neural speech and audio processing systems have a large number of trainable parameters, a relatively complex architecture, and require a vast amount of training data and computational power. These constraints make it more challenging to integrate such systems into embedded devices and utilize them for real-time, real-world applications. We tackle these limitations by introducing DeepSpectrumLite, an open-source, lightweight transfer learning framework for on-device speech and audio recognition using pre-trained image Convolutional Neural Networks (CNNs). The framework creates and augments Mel spectrogram plots on the fly from raw audio signals which are then used to finetune specific pre-trained CNNs for the target classification task. Subsequently, the whole pipeline can be run in real-time with a mean inference lag of 242.0 ms when a DenseNet121 model is used on a consumer-grade Motorola moto e7 plus smartphone. DeepSpectrumLite operates decentralized, eliminating the need for data upload for further processing. We demonstrate the suitability of the proposed transfer learning approach for embedded audio signal processing by obtaining state-of-the-art results on a set of paralinguistic and general audio tasks, including speech and music emotion recognition, social signal processing, COVID-19 cough and COVID-19 speech analysis, and snore sound classification. We provide an extensive command-line interface for users and developers which is comprehensively documented and publicly available at https://github.com/DeepSpectrum/DeepSpectrumLite.

Life in modern societies is fast-paced and full of stress-inducing demands. The development of stress monitoring methods is a growing area of research due to the personal and economic advantages that timely detection provides. Studies have shown that speech-based features can be utilised to robustly predict several physiological markers of stress, including emotional state, continuous heart rate, and the stress hormone, cortisol. In this contribution, we extend previous works by the authors, utilising three German language corpora including more than 100 subjects undergoing a Trier Social Stress Test protocol. We present cross-corpus and transfer learning results which explore the efficacy of the speech signal to predict three physiological markers of stress—sequentially measured saliva-based cortisol, continuous heart rate as beats per minute (BPM), and continuous respiration. For this, we extract several features from audio as well as video and apply various machine learning architectures, including a temporal context-based Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). For the task of predicting cortisol levels from speech, deep learning improves on results obtained by conventional support vector regression—yielding a Spearman correlation coefficient (ρ) of 0.770 and 0.698 for cortisol measurements taken 10 and 20 min after the stress period for the two corpora applicable—showing that audio features alone are sufficient for predicting cortisol, with audiovisual fusion to an extent improving such results. We also obtain a Root Mean Square Error (RMSE) of 38 and 22 BPM for continuous heart rate prediction on the two corpora where this information is available, and a normalised RMSE (NRMSE) of 0.120 for respiration prediction (−10: 10). Both of these continuous physiological signals show to be highly effective markers of stress (based on cortisol grouping analysis), both when available as ground truth and when predicted using speech. This contribution opens up new avenues for future exploration of these signals as proxies for stress in naturalistic settings.

Acute myeloid leukemia with myelodysplasia-related changes (AML-MRC) and treatment-related acute myeloid leukemia (tAML) after chemotherapy or radiation therapy for other neoplasms are associated with poor outcomes. CPX-351, a dual-drug liposomal encapsulation of daunorubicin and cytarabine, has been shown to improve outcomes in AML-MRC and tAML compared with standard 7+3 regimens. Here we report the cases of four consecutive patients with AML-MRC or tAML who received CPX-351 as outpatient induction therapy immediately followed by allogeneic hematopoietic stem cell transplantation (allo-HSCT). Two patients received allo-HSCT in remission (one in complete remission and one in partial remission) and two patients received allo-HSCT in aplasia (one at 11 days and one at 52 days after the start of induction therapy with CPX-351). With a median follow-up of 188 days after allo-HSCT, all but one patient are alive and two are in remission. Further studies will help define and expand the role of CPX-351 in the treatment of AML-MRC and tAML, especially in patients expected to undergo allo-HSCT.