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1 A method for monitoring the effect of drugs on the genital response to stimulation provided by vibration is described. 2 Using this method, the effect on male sexual response of two oral doses (100 mg and 300 mg) labetalol were studied and compared with placebo in six subjects. 3 Labetalol did not affect the attainment or maintenance of erection. 4 Labetalol delayed ejaculation in a dose-related manner. 5 Labetalol treatment resulted in a significant dose-related delay in detumescence. [ABSTRACT FROM AUTHOR]

The Ear-ECG provides a continuous Lead I like electrocardiogram (ECG) by measuring the potential difference related to heart activity by electrodes which are embedded within earphones. However, the significant increase in wearability and comfort enabled by Ear-ECG is often accompanied by a degradation in signal quality - an obstacle that is shared by the majority of wearable technologies. We aim to resolve this issue by introducing a Deep Matched Filter (Deep-MF) for the highly accurate detection of R-peaks in wearable ECG, thus enhancing the utility of Ear-ECG in real-world scenarios. The Deep-MF consists of an encoder stage, partially initialised with an ECG template, and an R-peak classifier stage. Through its operation as a Matched Filter, the encoder searches for matches with an ECG template in the input signal, prior to filtering these matches with the subsequent convolutional layers and selecting peaks corresponding to the ground-truth ECG. The latent representation of R-peak information is then fed into a R-peak classifier, of which the output provides precise R-peak locations. The proposed Deep Matched Filter is evaluated using leave-one-subject-out cross-validation over 36 subjects with an age range of 18-75, with the Deep-MF outperforming existing algorithms for R-peak detection in noisy ECG. The Deep-MF achieves a median R-peak recall of 94.9% and a median precision of 91.2% across subjects when evaluated with leave-one-subject-out cross validation. Overall, this Deep-Match framework serves as a valuable step forward for the real-world functionality of Ear-ECG and, through its interpretable operation, the acceptance of deep learning models in e-Health.

The rapidly increasing prevalence of debilitating breathing disorders, such as chronic obstructive pulmonary disease (COPD), calls for a meaningful integration of artificial intelligence (AI) into respiratory healthcare. Deep learning techniques are "data hungry" whilst patient-based data is invariably expensive and time consuming to record. To this end, we introduce a novel COPD-simulator, a physical apparatus with an easy to replicate design which enables rapid and effective generation of a wide range of COPD-like data from healthy subjects, for enhanced training of deep learning frameworks. To ensure the faithfulness of our domain-aware COPD surrogates, the generated waveforms are examined through both flow waveforms and photoplethysmography (PPG) waveforms (as a proxy for intrathoracic pressure) in terms of duty cycle, sample entropy, FEV 1 /FVC ratios and flow-volume loops. The proposed simulator operates on healthy subjects and is able to generate FEV 1 /FVC obstruction ratios ranging from greater than 0.8 to less than 0.2, mirroring values that can observed in the full spectrum of real-world COPD. As a final stage of verification, a simple convolutional neural network is trained on surrogate data alone, and is used to accurately detect COPD in real-world patients. When training solely on surrogate data, and testing on real-world data, a comparison of true positive rate against false positive rate yields an area under the curve of 0.75, compared with 0.63 when training solely on real-world data., (© 2024 The Authors.)

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology., (Creative Commons Attribution license.)

The success of deep learning methods has enabled many modern wearable health applications, but has also highlighted the critical caveat of their extremely data hungry nature. While the widely explored wrist and finger photoplethysmography (PPG) sites are less affected, given the large available databases, this issue is prohibitive to exploring the full potential of novel recording locations such as in-ear wearables. To this end, we assess the feasibility of transfer learning from finger PPG to in-ear PPG in the context of deep learning for respiratory monitoring. This is achieved by introducing an encoder-decoder framework which is set up to extract respiratory waveforms from PPG, whereby simultaneously recorded gold standard respiratory waveforms (capnography, impedance pneumography and air flow) are used as a training reference. Next, the data augmentation and training pipeline is examined for both training on finger PPG and the subsequent fine tuning on in-ear PPG. The results indicate that, through training on two large finger PPG data sets (95 subjects) and then retraining on our own small in-ear PPG data set (6 subjects), the model achieves lower and more consistent test error for the prediction of the respiratory waveforms, compared to training on the small in-ear data set alone. This conclusively demonstrates the feasibility of transfer learning from finger PPG to in-ear PPG, leading to better generalisation across a wide range of respiratory rates.

Accurate pulse-oximeter readings are critical for clinical decisions, especially when arterial blood-gas tests - the gold standard for determining oxygen saturation levels - are not available, such as when determining COVID-19 severity. Several studies demonstrate that pulse oxygen saturation estimated from photoplethysmography (PPG) introduces a racial bias due to the more profound scattering of light in subjects with darker skin due to the increased presence of melanin. This leads to an overestimation of blood oxygen saturation in those with darker skin that is increased for low blood oxygen levels and can result in a patient not receiving potentially life-saving supplemental oxygen. This racial bias has been comprehensively studied in conventional finger pulse oximetry but in other less commonly used measurement sites, such as in-ear pulse oximetry, it remains unexplored. Different measurement sites can have thinner epidermis compared with the finger and lower exposure to sunlight (such as is the case with the ear canal), and we hypothesise that this could reduce the bias introduced by skin tone on pulse oximetry. To this end, we compute SpO 2 in different body locations, during rest and breath-holds, and compare with the index finger. The study involves a participant pool covering 6-pigmentation categories from Fitzpatrick's Skin Pigmentation scale. These preliminary results indicate that locations characterized by cartilaginous highly vascularized tissues may be less prone to the influence of melanin and pigmentation in the estimation of SpO 2 , paving the way for the development of non-discriminatory pulse oximetry devices.

An ability to extract detailed spirometry-like breathing waveforms from wearable sensors promises to greatly improve respiratory health monitoring. Photoplethysmography (PPG) has been researched in depth for estimation of respiration rate, given that it varies with respiration through overall intensity, pulse amplitude and pulse interval. We compare and contrast the extraction of these three respiratory modes from both the ear canal and finger and show a marked improvement in the respiratory power for respiration induced intensity variations and pulse amplitude variations when recording from the ear canal. We next employ a data driven multi-scale method, noise assisted multivariate empirical mode decomposition (NA-MEMD), which allows for simultaneous analysis of all three respiratory modes to extract detailed respiratory waveforms from in-ear PPG. For rigour, we considered in-ear PPG recordings from healthy subjects, both older and young, patients with chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) and healthy subjects with artificially obstructed breathing. Specific in-ear PPG waveform changes are observed for COPD, such as a decreased inspiratory duty cycle and an increased inspiratory magnitude, when compared with expiratory magnitude. These differences are used to classify COPD from healthy and IPF waveforms with a sensitivity of 87% and an overall accuracy of 92%. Our findings indicate the promise of in-ear PPG for COPD screening and unobtrusive respiratory monitoring in ambulatory scenarios and in consumer wearables.

The feasibility of using in-ear [Formula: see text] to track cognitive workload induced by gaming is investigated. This is achieved by examining temporal variations in cognitive workload through the game Geometry Dash, with 250 trials across 7 subjects. The relationship between performance and cognitive load in Dark Souls III boss fights is also investigated followed by a comparison of the cognitive workload responses across three different genres of game. A robust decrease in in-ear [Formula: see text] is observed in response to cognitive workload induced by gaming, which is consistent with existing results from memory tasks. The results tentatively suggest that in-ear [Formula: see text] may be able to distinguish cognitive workload alone, whereas heart rate and breathing rate respond similarly to both cognitive workload and stress. This study demonstrates the feasibility of low cost wearable cognitive workload tracking in gaming with in-ear [Formula: see text], with applications to the play testing of games and biofeedback in games of the future.

Passive acoustic mapping (PAM) is an algorithm that reconstructs the location of acoustic sources using an array of receivers. This technique can monitor therapeutic ultrasound procedures to confirm the spatial distribution and amount of microbubble activity induced. Current PAM algorithms have an excellent lateral resolution but have a poor axial resolution, making it difficult to distinguish acoustic sources within the ultrasound beams. With recent studies demonstrating that short-length and low-pressure pulses-acoustic wavelets-have the therapeutic function, we hypothesized that the axial resolution could be improved with a quasi-pulse-echo approach and that the resolution improvement would depend on the wavelet's pulse length. This article describes an algorithm that resolves acoustic sources axially using time of flight and laterally using delay-and-sum beamforming, which we named axial temporal position PAM (ATP-PAM). The algorithm accommodates a rapid short pulse (RaSP) sequence that can safely deliver drugs across the blood-brain barrier. We developed our algorithm with simulations (k-wave) and in vitro experiments for one-, two-, and five-cycle pulses, comparing our resolution against that of two current PAM algorithms. We then tested ATP-PAM in vivo and evaluated whether the reconstructed acoustic sources mapped to drug delivery within the brain. In simulations and in vitro, ATP-PAM had an improved resolution for all pulse lengths tested. In vivo, experiments in mice indicated that ATP-PAM could be used to target and monitor drug delivery into the brain. With acoustic wavelets and time of flight, ATP-PAM can locate acoustic sources with a vastly improved spatial resolution.

The non-invasive estimation of blood oxygen saturation (SpO 2 ) by pulse oximetry is of vital importance clinically, from the detection of sleep apnea to the recent ambulatory monitoring of hypoxemia in the delayed post-infective phase of COVID-19. In this proof of concept study, we set out to establish the feasibility of SpO 2 measurement from the ear canal as a convenient site for long term monitoring, and perform a comprehensive comparison with the right index finger-the conventional clinical measurement site. During resting blood oxygen saturation estimation, we found a root mean square difference of 1.47% between the two measurement sites, with a mean difference of 0.23% higher SpO 2 in the right ear canal. Using breath holds, we observe the known phenomena of time delay between central circulation and peripheral circulation with a mean delay between the ear and finger of 12.4 s across all subjects. Furthermore, we document the lower photoplethysmogram amplitude from the ear canal and suggest ways to mitigate this issue. In conjunction with the well-known robustness to temperature induced vasoconstriction, this makes conclusive evidence for in-ear SpO 2 monitoring being both convenient and superior to conventional finger measurement for continuous non-intrusive monitoring in both clinical and everyday-life settings., Competing Interests: The authors declare no conflict of interest.

Numerous automatic sleep staging approaches have been proposed to provide an eHealth alternative to the current gold-standard - hypnogram scoring by human experts. However, a majority of such studies exploit data of limited scale, which compromises both the validation and the reproducibility and transferability of such automatic sleep staging systems in real clinical settings. In addition, the computational issues and physical meaningfulness of the analysis are typically neglected, yet affordable computation is a key criterion in Big Data analytics. To this end, we establish a comprehensive analysis framework to rigorously evaluate the feasibility of automatic sleep staging from multiple perspectives, including robustness with respect to the number of training subjects, model complexity, and different classifiers. This is achieved for a large collection of publicly accessible polysomnography (PSG) data, recorded over 515 subjects. The trade-off between affordable computation and satisfactory accuracy is shown to be fulfilled by an extreme learning machine (ELM) classifier, which in conjunction with the physically meaningful hidden Markov model (HMM) of the transition between the different sleep stages (smoothing model) is shown to achieve both fast computation and the highest average Cohen's kappa value of κ = 0.73 (Substantial Agreement). Finally, it is shown that for accurate and robust automatic sleep staging, a combination of structural complexity (multi-scale entropy) and frequency-domain (spectral edge frequency) features is both computationally affordable and physically meaningful.

Automatic sleep staging provides a cheaper, faster and more accessible alternative for evaluating sleep patterns and quality compared with manual hypnogram scoring performed by a clinician. Traditionally, classification methods treat sleep stages independently of their temporal order, despite sleep patterns themselves being highly sequential. Such independent sleep stage classification can result in poor sensitivity and precision, in particular when attempting to classify the sleep stage N1, otherwise known as the transition stage of sleep which links periods of wakefulness to periods of deep sleep. To this end, we propose a novel transition sleep classification method which aims to improve classification accuracy. This is achieved by utilising both the temporal information of previous stages and treating the transitions between stages as classes in their own right. Simulations on publicly available polysomnography (PSG) data and a comprehensive performance comparison with standard classifiers demonstrate a marked improvement achieved by the proposed method in both N1 sensitivity and precision across all considered classifiers. This includes an increase in N1 precision from 0.01% to 36.75% in an MLP classifier, and an increase in both accuracy and Cohen's kappa value in two of the three classifiers. Overall best mean performance is obtained by transition classification with a random forest classifier (RF) which achieved a kappa value of κ = 0.75 (substantial agreement), and an N1 stage precision of 58%.

Urbanisation and a changing climate are leading to more frequent and severe flood, heat and air pollution episodes in Britain's cities. Interest in nature-based solutions to these urban problems is growing, with urban forests potentially able to provide a range of regulating ecosystem services such as stormwater attenuation, heat amelioration and air purification. The extent to which these benefits are realized is largely dependent on urban forest management objectives, the availability of funding, and the understanding of ecosystem service concepts within local governments, the primary delivery agents of urban forests. This study aims to establish the extent to which British local authorities actively manage their urban forests for regulating ecosystem services, and identify which resources local authorities most need in order to enhance provision of ecosystem services by Britain's urban forests. Interviews were carried out with staff responsible for tree management decisions in fifteen major local authorities from across Britain, selected on the basis of their urban nature and high population density. Local authorities have a reactive approach to urban forest management, driven by human health and safety concerns and complaints about tree disservices. There is relatively little focus on ensuring provision of regulating ecosystem services, despite awareness by tree officers of the key role that urban forests can play in alleviating chronic air pollution, flood risk and urban heat anomalies. However, this is expected to become a greater focus in future provided that existing constraints - lack of understanding of ecosystem services amongst key stakeholders, limited political support, funding constraints - can be overcome. Our findings suggest that the adoption of a proactive urban forest strategy, underpinned by quantified and valued urban forest-based ecosystem services provision data, and innovative private sector funding mechanisms, can facilitate a change to a proactive, ecosystem services approach to urban forest management., (Copyright © 2017. Published by Elsevier Inc.)

Diode lasers can be tuned by simultaneous rotation and translation of an external grating. This can be achieved by rotating the grating about a displaced pivot point. We derive the tuning range as a function of pivot point position for various extended cavity geometries. In each case, the geometric problem reduces to the solution of a quadratic equation. For near-infrared wavelengths, placement of the pivot is relatively noncritical for tuning ranges of the order of 10 GHz, but requires millimeter accuracy for a tuning range >100 GHz.

Programmed cell death has been demonstrated in several specific neuronal populations as a mechanism for modulating the population size following differentiation, but its applicability to all neuronal types is unclear. Evidence for programmed cell death in some populations such as the numerous spinal interneurons has been lacking. We have studied the incidence of apoptosis in the rat spinal cord with three different methods and found a previously undocumented wave of apoptosis occurring in spinal grey matter shortly after birth. The apoptotic morphology was confirmed ultrastructurally. Dying cells were identified as neurons by immunocytochemical labelling for neuronal markers and had an anatomical distribution which indicated that most of the apoptotic cells were interneurons not motoneurons. This wave of apoptosis has the characteristics of a discrete developmental process and occurs later than that of either ventral horn motoneurons or dorsal root ganglion cells, to which most spinal interneurons are connected. These findings indicate that interneurons do undergo programmed cell death, and we suggest that this occurs in response to the earlier reduction in size of their main synaptic targets.

Salbutamol was administered intravenously to 14 patients in premature labour. Inhibition of uterine contractions occurred on 22 out of 23 occasions. The mean increase in maternal and fetal heart rate and blood pressure were within clinically acceptable limits but there were wide individual variations. The mean delay achieved when the intravenous regime was supplemented with oral salbutamol was greater than that achieved by intravenous salbutamol alone, but the difference was not statistically significant.

We compared the metabolic effects of 8-wk caloric restrictions with 330 or 780 kcal/d in two groups of eight obese hospitalized subjects; six control subjects were also studied. Loss of weight but not of adipose tissue was significantly greater on the 330-kcal/d diet. It is likely that dehydration rather than protein catabolism was responsible for additional loss of fat-free mass in the 330-kcal/d group because the nitrogen deficit was not excessive. The thermic response to food was blunted only in the 330-kcal/d group whereas resting oxygen uptake decreased by equal amounts in both groups. There was a decrease in 24-h urinary noradrenaline in the 330-kcal/d group but plasma fT4 was sustained when compared with the 780-kcal/d group; fT3 decreased significantly more quickly in the 330-kcal/d group. There was no correlation between plasma hormone levels and changes in oxygen uptake. Hunger scores were greater on the 780-kcal/d diet.

Preliminary investigation suggests that some asthmatic patients find it difficult to synchronize the release of a metered dose from a pressurized aerosol with the correct phase of inspiration. An automatic device has been developed to overcome this problem. Information is presented outlining the physical characteristics of the unit and the methods used to establish its performance in patients. It is suggested that the device may have a place in the management of asthma.

Ranitidine, 300 mg daily, was given to 92 patients with duodenal ulcer (DU), 38 with prepyloric ulcer (PPU), and 21 with gastric corporeal ulcer (GCU). The healing rates at 4 weeks differed for the different types of ulcers (P less than 0.01), being 91% for DU, 68% for PPU, and 81% for GCU. After established ulcer healing, maintenance treatment with either ranitidine, 100 mg twice daily or 150 mg at night, or placebo was given for 1 year or until ulcer relapse in a total of 108 patients--71 with DU, 24 with PPU, and 13 with GCU. There were no significant differences in relapse rates between the two groups treated with active drug or between the three ulcer groups. However, the overall relapse rate in the active drug groups was 16%, against 72% in the placebo group (P less than 0.001).

A new indoor nasal allergen challenge system has been developed which allows subjects with hay fever to be exposed to pre-determined concentration of grass pollen grains in the immediate environment. Thirteen subjects were exposed during the winter months to an atmosphere containing approximately 1000 grains/m3 for 30 min. No changes in nasal airway patency or symptoms of nasal congestion were recorded. It was necessary to increase the pollen concentration to approximately 9000 and 35 000 grains/m3 in two subjects and six subjects respectively, before a positive response was recorded. This response was reproducible in all subjects. Administration of intra-nasal sodium cromoglycate before exposure significantly inhibited the decrease in nasal airway patency and when administered 1 min before exposure totally abolished symptoms of nasal congestion. This new challenge system may therefore be used as an in vivo screen to assess the potential therapeutic value of drugs in hay fever under controlled conditions.

In a double-blind controlled trial 14 chronic asthmatic patients with regular nocturnal exacerbations took 16 mg slow-release oral salbutamol (two Ventolin spandets), 450 mg slow-release aminophylline (two Phyllocontin Continus tablets), or placebo at midnight. Mean peak expiratory flow rates on waking were significantly higher on the active drugs than on placebo (p < 0.01 for salbutamol; p < 0.05 for aminophylline) but neither drug abolished the overnight fall in PEFR. Plasma drug levels at 0600 hr were 17.3 ng/ml (+/- 5.3 ng/ml SD) for salbutamol, and 7.1 micrograms/ml (+/- 3.1 micrograms/ml SD) for theophylline. Steady-state derived from plasma levels of salbutamol during intravenous infusion indicated that the morning salbutamol levels were probably in a therapeutic range for asthma. The morning theophylline levels, however, were suboptimal when aminophylline was given only at night.

A standard Vitalograph was used to measure the volume of air inspired through the mouth (M-FIV0.5) and nose (N-FIV0.5) in 0.5 sec. The ratio of these parameters was used as a nasal patency index (NPI) and the characteristics of NPI and N-FIV0.5 investigated in terms of reproducibility and sensitivity. Both indices were significantly reduced by intranasal histamine (100 micrograms and 200 micrograms) and it is concluded that NPI and N-FIV0.5 are reproducible and sensitive indices of nasal patency.