Comparison of the efficacy and safety of tolterodine 2 mg and 4 mg combined with an a-blocker in men with lower urinary tract symptoms (LUTS) and overactive bladder: a randomized controlled trial ObjectiveTo evaluate the efficacy and safety of low-dose (2 mg) tolterodine extended release (ER) with an a-blocker compared with standard-dose (4 mg) tolterodine ER with an a-blocker for the treatment of men with residual storage symptoms after a-blocker monotherapy. Patients and MethodsThe study was a 12-week, single-blind, randomized, parallelgroup, non-inferiority trial that included men with residual storage symptoms despite receiving at least 4 weeks of ablocker treatment. Inclusion criteria were total International Prostate Symptom Score (IPSS) ≥12, IPSS quality-of-life item score ≥3, and ≥8 micturitions and ≥2 urgency episodes per 24 h. The primary outcome was change in the total IPSS score from baseline. Bladder diary variables, patient-reported outcomes and safety were also assessed. ResultsPatients were randomly assigned to addition of either 2 mg tolterodine ER (n = 47) or 4 mg tolterodine ER (n = 48) to a-blocker therapy for 12 weeks. Patients in both treatment groups had a significant improvement in total IPSS score (À5.5 and À6.3, respectively), micturition per 24 h (À1.3 and À1.7, respectively) and nocturia per night (À0.4 and À0.4, respectively). Changes in IPSS, bladder diary variables, and patient-reported outcomes were not significantly different between the treatment groups. All interventions were well tolerated by patients. ConclusionsThese results suggest that 12 weeks of low-dose tolterodine ER add-on therapy is similar to standard-dose tolterodine ER add-on therapy in terms of efficacy and safety for patients experiencing residual storage symptoms after receiving ablocker monotherapy.

Altered cerebral perfusion has been reported in obstructive sleep apnea (OSA). Using dynamic susceptibility contrast MRI, we compared cerebral perfusion between male OSA patients and male healthy reference subjects and assessed correlations of perfusion abnormalities of OSA patients with sleep parameters and neuropsychological deficits at 3 T MRI, polysomnography and neuropsychological tests in 68 patients with OSA and 21 reference subjects. We found lower global and regional cerebral blood flow and cerebral blood volume, localized mainly in bilateral parietal and prefrontal cortices, as well as multiple focal cortical and deep structures related to the default mode network and attention network. In the correlation analysis between regional hypoperfusion and parameters of polysomnography, different patterns of regional hypoperfusion were distinctively associated with parameters of intermittent hypoxia and sleep fragmentation, which involved mainly parietal and orbitofrontal cortices, respectively. There was no association between brain perfusion and cognition in OSA patients in areas where significant association was observed in reference subjects, largely overlapping with nodes of the default mode network and attention network. Our results suggest that impaired cerebral perfusion in important areas of functional networks could be an important pathomechanism of neurocognitive deficits in OSA.

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Introduction Using deep learning algorithms, we investigated univariate and multivariate effects of four polysomnography features including heart rate (HR), electrocardiogram (ECG), oxygen saturation (SpO2) and nasal air flow (NAF) on the identification of sleep apnea and hypopnea events. This explanatory analysis that may clarify the sensitivity and specificity of those features to SAs and SHs have not been probed. Methods We studied 804 polysomonography samples from 704 patients with obstructive sleep apnea and 100 controls. The input data were converted into scalograms as 4-channel 2D images to train Xception networks. For training, 77,638 patches were sampled from the original 6-hour sleep data with 30-second time width. A 10% of these patches were segregated as the test-set. With each feature sets, we tested the following classifications: 1) normal vs apnea vs hypopnea; 2) normal vs. apnea+hypopnea; 3) normal vs. apnea; and 4) normal vs. hypopnea. Results SpO2 classified normal vs. apnea most accurately (98%), followed by NAF (85%), ECG (77%), and HR (63%). SpO2 also showed the highest accuracy in classifying normal vs. hypopnea (87%), and normal vs. apnea+hypopnea (96%) and three groups (82%). When the combination of four features were used, the classification accuracies were generally improved compared to use of SpO2 only (normal vs. apnea 99%; vs. hypopnea 89%; vs. apnea+hypopnea: 94%; three groups: 86%). Conclusion Deep learning with SpO2 or NAF feature most accurately classified apneas from normal sleep events, suggesting these features’ characterization of sleep apnea events. Oxygen desaturation, which is a typical pattern of hypopnea, was only the feature showing reliable accuracy in classifying hypopnea vs. normal. Nevertheless, combination of four polysomnography features could improve the identification of sleep apnea and hypopnea. Furthermore, classifying normal vs. apnea+hypopnea was more accurate than separately classifying three groups, suggesting deep learning approaches as the primary screen tool. Since the classification accuracy of using SpO2 was higher than any other features, developing a portable equipment measuring SpO2 and running deep learning algorithms has the potential for inexpensive, accurate diagnostics of obstructive sleep apnea syndrome. Support (if any) This study was supported by USC STEVENS CENTER FOR INNOVATION TECHNOLOGY ADVANCEMENT GRANTS (TAG), BrightFocus Foundation Award (A2019052S).

Tactile threshold of low-intensity focused ultrasound (LIFU) haptic devices has been defined as the minimum pressure required for tactile sensation. However, in contact-type LIFU haptic devices using an elastomer as a conductive medium, the tactile threshold is affected by the mechanical properties of the elastomer. Therefore, the tactile threshold needs to be redefined as a parameter that does not change with the mechanical properties of the elastomer. In this study, we used the LIFU haptic device to investigate the displacement of the elastomer surface at the tactile threshold while controlling the pulse duration, pulse repetition frequency, and pressure. We analyzed the displacement magnitude and rate to determine their relationship to the pressure. The displacement magnitude is the spatiotemporal peak of the displacement, and the displacement rate is the initial slope of the displacement at the starting point of LIFU pulse. The tactile threshold measured by the applied pressure showed the U-shaped graph, and the minimum pressure of 475 kPa at 2 ms and 407 kPa at 300 Hz was measured. The tactile threshold measured by the displacement show that the tactile sensation can be evoked at the small displacement magnitude (<3 μm) when the high displacement rate is present (>1.56 mm/s). Furthermore, the large displacement magnitude is required to induce the tactile sensation when the displacement rate is low. This result shows that the tactile threshold of a contact-type LIFU haptic device is affected by both the displacement magnitude and rate of the conductive medium. Our findings can be used as a guideline for developing a contact-type LIFU haptic device regardless of the elastomer used.

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Sleep architecture and microstructures alter with aging and sleep disorder-led accelerated aging. We proposed a sleep electroencephalogram (EEG) based brain age prediction model using convolutional neural networks. We then associated the estimated brain age index (BAI) with brain structural aging features, sleep disorders and various sleep parameters. Our model also showed a higher BAI (predicted brain age minus chronological age) is associated with cortical thinning in various functional areas. We found a higher BAI for sleep disorder groups compared to healthy sleepers, as well as significant differences in the spectral pattern of EEG among different sleep disorders (lower power in slow and θ waves for sleep apnea vs. higher power in β and σ for insomnia), suggesting sleep disorder-dependent pathomechanisms of aging. Our results demonstrate that the new EEG-BAI can be a biomarker reflecting brain health in normal and various sleep disorder subjects, and may be used to assess treatment efficacy.

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