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Heart sound detection technology plays an important role in the prediction of cardiovascular disease, but the most significant heart sounds are fleeting and may be imperceptible. Hence, obtaining heart sound information in an efficient and accurate manner will be helpful for the prediction and diagnosis of heart disease. To obtain heart sound information, we designed an audio data analysis tool to segment the heart sounds from single heart cycle, and validated the heart rate using a finger oxygen meter. The results from our validated technique could be used to realize heart sound segmentation. Our robust algorithmic platform was able to segment the heart sounds, which could then be compared in terms of their difference from the background. A combination of an electronic stethoscope and artificial intelligence technology was used for the digital collection of heart sounds and the intelligent identification of the first (S1) and second (S2) heart sounds. Our approach can provide an objective basis for the auscultation of heart sounds and visual display of heart sounds and murmurs.

ObjectiveThis study aimed to compare the sensitivity and specificity of diagnosis between the third heart sound (S3) and left ventricular ejection fraction (LVEF) in heart failure (HF).MethodsRelevant studies were searched in PubMed, SinoMed, China National Knowledge Infrastructure, and the Cochrane Trial Register until February 20, 2022. The sensitivity, specificity, likelihood ratio (LR), and diagnostic odds ratio (DOR) were pooled. The symmetric receiver operator characteristic curve (SROC) and Fagan’s nomogram were drawn. The source of heterogeneity was explored by meta-regression and subgroup analysis.ResultsA total of 19 studies, involving 5,614 participants, were included. The combined sensitivity of S3 was 0.23 [95% confidence interval (CI) (0.15–0.33), specificity was 0.94 [95% CI (0.82–0.98)], area under the SROC curve was 0.49, and the DOR was 4.55; while the sensitivity of LVEF was 0.70 [95% CI (0.53–0.83)], specificity was 0.79 [95% CI (0.75–0.82)], area under the SROC curve was 0.79, and the DOR was 8.64. No publication bias was detected in Deeks’ funnel plot. The prospective design, partial verification bias, and blind contributed to the heterogeneity in specificity, while adequate description of study participants contributed to the heterogeneity in sensitivity. In Fagan’s nomogram, the post-test probability was 48% when the pre-test probability was set as 20%, while in LVEF, the post-test probability was 45% when the pre-test probability was set as 20%.ConclusionThe use of S3 alone presented lower sensitivity in diagnosing HF compared with LVEF, whereas it was useful in early pathological assessment.

Acute sleep deprivation can reduce the cognitive ability and change the emotional state in humans. However, little is known about how brain EEGs and facial expressions change during acute sleep deprivation (SD). Herein, we employed 34 healthy adult male subjects to undergo acute SD for 36 h, during which, their emotional states and brain EEG power were measured. The subjects were divided randomly into electronic stimulation and control groups. We performed TDCS on the left dorsolateral prefrontal cortex for 2 mA and 30 min in the TDCS group. These results indicated that the proportion of disgusted expressions in the electrical stimulation group was significantly less than the controls after 36 h post-acute SD, while the proportion of neutral expressions was increased post-restorative sleep. Furthermore, the electrical stimulation group presented a more significant impact on slow wave power (theta and delta) than the controls. These findings indicated that emotional changes occurred in the subjects after 36 h post-acute SD, while electrical stimulation could effectively regulate the cortical excitability and excitation inhibition balance after acute SD.

Background. Speech changes occur in the early stages of Parkinson’s disease (PD) and cause communication difficulties, leading to social isolation. Lee Silverman voice treatment (LSVT) is a speech therapy approach designed to improve patients’ language and voice capabilities. Objective. The effectiveness of the LSVT was compared with that of other speech interventions or no treatment to evaluate PD patients with dysarthria. Design. Systematic review with meta-analysis of randomized trials. Data Sources: PubMed, Embase, Cochrane Library, CNKI, and SinoMed library were searched from inception to December 2021 related to PD and LSVT. Method. Abstracts were screened and reviewed against the eligibility criteria (intervention group participants were PD assessed based on LSVT (LSVT Loud) and randomized control). Result. Ten randomized controlled trials were identified on speech symptoms in patients with PD. Compared with the respiratory therapy (RET) exercise, or no training group, a significant improvement was detected in the sound press level (SPL) after immediate treatment during the reading of vowel and rainbow passages and an increase in semitone standard deviation (STSD). Furthermore, the LSVT training significantly increased the participants’ scores on unified Parkinson’s disease rating scale (UPDRS-III) and speech intelligibility. Conclusion. This meta-analysis demonstrated the efficacy of LSVT in increasing vocal loudness and functional communication among individuals with PD. However, most studies included participants with mild-moderate PD. Thus, additional randomized controlled trials (RCTs) with large sample sizes are needed to validate the efficacy of LSVT in patients with different progressions of PD, including severe PD.

In this paper, a screening machine was designed to remove the impurities in the tuber harvest, which integrates the functions of vibration screening, air separation, and flexible polishing. Discrete element simulation analysis was carried out to investigate the movement of tuber harvest and soil in the machine and the effect of polishing and removing impurities, the rationality of the structure, and the size were verified. Orthogonal tests were designed and carried out, with the rate of impurity, loss, and crushing as indicators and crank speed, impeller speed, and polishing roller speed as factors. The optimum working parameters were obtained: crank speed 280.12 r/min, impeller speed 1056.27 r/min, polishing roller speed 405.02 r/min, the impurity content was 0.29%, the loss rate was 1.01%, and the breakage rate was 0.11%. Through experimental verification, the actual value and theoretical value are basically the same, which verifies the rationality. [ABSTRACT FROM AUTHOR]