The technology of robot-assisted prostate seed implantation has developed rapidly. However, during the process, there are some problems to be solved, such as non-intuitive visualization effects and complicated robot control. To improve the intelligence and visualization of the operation process, a voice control technology of prostate seed implantation robot in augmented reality environment was proposed. Initially, the MRI image of the prostate was denoised and segmented. The three-dimensional model of prostate and its surrounding tissues was reconstructed by surface rendering technology. Combined with holographic application program, the augmented reality system of prostate seed implantation was built. An improved singular value decomposition three-dimensional registration algorithm based on iterative closest point was proposed, and the results of three-dimensional registration experiments verified that the algorithm could effectively improve the three-dimensional registration accuracy. A fusion algorithm based on spectral subtraction and BP neural network was proposed. The experimental results showed that the average delay of the fusion algorithm was 1.314 s, and the overall response time of the integrated system was 1.5 s. The fusion algorithm could effectively improve the reliability of the voice control system, and the integrated system could meet the responsiveness requirements of prostate seed implantation.

The early screening of depression is highly beneficial for patients to obtain better diagnosis and treatment. While the effectiveness of utilizing voice data for depression detection has been demonstrated, the issue of insufficient dataset size remains unresolved. Therefore, we propose an artificial intelligence method to effectively identify depression. The wav2vec 2.0 voice-based pre-training model was used as a feature extractor to automatically extract high-quality voice features from raw audio. Additionally, a small fine-tuning network was used as a classification model to output depression classification results. Subsequently, the proposed model was fine-tuned on the DAIC-WOZ dataset and achieved excellent classification results. Notably, the model demonstrated outstanding performance in binary classification, attaining an accuracy of 0.9649 and an RMSE of 0.1875 on the test set. Similarly, impressive results were obtained in multi-classification, with an accuracy of 0.9481 and an RMSE of 0.3810. The wav2vec 2.0 model was first used for depression recognition and showed strong generalization ability. The method is simple, practical, and applicable, which can assist doctors in the early screening of depression.

UNASSIGNED: Parkinson\'s disease (PD) is the second most common neurodegenerative disease and affects millions of people. Accurate diagnosis and subsequent treatment in the early stages can slow down disease progression. However, making an accurate diagnosis of PD at an early stage is challenging. Previous studies have revealed that even for movement disorder specialists, it was difficult to differentiate patients with PD from healthy individuals until the average modified Hoehn-Yahr staging (mH&Y) reached 1.8. Recent researches have shown that dysarthria provides good indicators for computer-assisted diagnosis of patients with PD. However, few studies have focused on diagnosing patients with PD in the early stages, specifically those with mH&Y ≤ 1.5.
UNASSIGNED: We used a machine learning algorithm to analyze voice features and developed diagnostic models for differentiating between healthy controls (HCs) and patients with PD, and for differentiating between HCs and patients with mild PD (mH&Y ≤ 1.5). The models were independently validated using separate datasets.
UNASSIGNED: Our results demonstrate that, a remarkable diagnostic performance of the model in identifying patients with mild PD (mH&Y ≤ 1.5) and HCs, with area under the ROC curve 0.93 (95% CI: 0.851.00), accuracy 0.85, sensitivity 0.95, and specificity 0.75.
UNASSIGNED: The results of our study are helpful for screening PD in the early stages in the community and primary medical institutions where there is a lack of movement disorder specialists and special equipment.

Recognizing talkers\' identity via speech is an important social skill in interpersonal interaction. Behavioral evidence has shown that listeners can identify better the voices of their native language than those of a non-native language, which is known as the language familiarity effect (LFE). However, its underlying neural mechanisms remain unclear. This study therefore investigated how the LFE occurs at the neural level by employing functional near-infrared spectroscopy (fNIRS). Late unbalanced bilinguals were first asked to learn to associate strangers\' voices with their identities and then tested for recognizing the talkers\' identities based on their voices speaking a language either highly familiar (i.e., native language Chinese), or moderately familiar (i.e., second language English), or completely unfamiliar (i.e., Ewe) to participants. Participants identified talkers the most accurately in Chinese and the least accurately in Ewe. Talker identification was quicker in Chinese than in English and Ewe but reaction time did not differ between the two non-native languages. At the neural level, recognizing voices speaking Chinese relative to English/Ewe produced less activity in the inferior frontal gyrus, precentral/postcentral gyrus, supramarginal gyrus, and superior temporal sulcus/gyrus while no difference was found between English and Ewe, indicating facilitation of voice identification by the automatic phonological encoding in the native language. These findings shed new light on the interrelations between language ability and voice recognition, revealing that the brain activation pattern of the LFE depends on the automaticity of language processing.

The genetic influence on human vocal pitch in tonal and non-tonal languages remains largely unknown. In tonal languages, such as Mandarin Chinese, pitch changes differentiate word meanings, whereas in non-tonal languages, such as Icelandic, pitch is used to convey intonation. We addressed this question by searching for genetic associations with interindividual variation in median pitch in a Chinese major depression case-control cohort and compared our results with a genome-wide association study from Iceland. The same genetic variant, rs11046212-T in an intron of the ABCC9 gene, was one of the most strongly associated loci with median pitch in both samples. Our meta-analysis revealed four genome-wide significant hits, including two novel associations. The discovery of genetic variants influencing vocal pitch across both tonal and non-tonal languages suggests the possibility of a common genetic contribution to the human vocal system shared in two distinct populations with languages that differ in tonality (Icelandic and Mandarin).

Since the outbreak of COVID-19, mask-wearing has become a widespread phenomenon. Even after the pandemic, people continue to maintain the habit of wearing masks in their daily lives. While existing research has explored how mask-wearing can influence wearers\' behavior in everyday life, its effects in the workplace have received less attention. Drawing on self-perception theory, this study examined the positive effect of mask-wearing in the workplace on wearers\' voice behavior via psychological safety. An online experiment (N = 291) using a within-subject manipulation of wearing masks supported our hypotheses. This study uncovered the positive psychological and behavioral consequences of mask-wearing beyond its benefits in people\'s health conditions and everyday life.

BACKGROUND: Abusive supervision by the nurse manager significantly influences nurses\' withholding voice about patient safety. The role of impression management motivation and speak up-related climate is crucial in understanding their connection. This study aimed to explore the relationship between abusive supervision, impression management motivation, speak up-related climate, and withholding voice about patient safety.
METHODS: This cross-sectional study employed a convenience sampling method to recruit 419 clinical nurses from Taizhou Hospital, Zhejiang Province, China, between 1 November 2022 and 31 January 2023. The study adhered to the STROBE checklist. Abusive supervision and impression management motivation were assessed using the Chinese versions of the Abusive Supervision Scale and the Impression Management Motivation Scale, respectively. Withholding voice about patient safety and speak up-related climate were identified using the Chinese version of the Speaking Up about Patient Safety Questionnaire.
RESULTS: Nurse leaders\' abusive supervision (β=0.40, p<0.01) and nurses\' impression management motivation (β=0.10, p<0.01) significantly and positively influenced nurses\' withholding voice about patient safety. We introduced impression management motivation as a mediating variable, and the effect of abusive supervision on nurses\' withholding voice decreased (β from 0.40 to 0.38, p< 0.01). Nurses\' speak up-related climate played a moderating role between abusive supervision and impression management motivation (β= 0.24, p<0.05).
CONCLUSIONS: Abusive supervision by nursing leaders can result in nurses withholding voice about patient safety out of self-protective impression management motives. This phenomenon inhibits nurses\' subjective initiative and undermines their proactive involvement in improving patient safety, and hinders the cultivation of a culture encouraging full participation in patient safety, which should warrant significant attention.

In recent years, there has been a notable rise in the number of patients afflicted with laryngeal diseases, including cancer, trauma, and other ailments leading to voice loss. Currently, the market is witnessing a pressing demand for medical and healthcare products designed to assist individuals with voice defects, prompting the invention of the artificial throat (AT). This user-friendly device eliminates the need for complex procedures like phonation reconstruction surgery. Therefore, in this review, we will initially give a careful introduction to the intelligent AT, which can act not only as a sound sensor but also as a thin-film sound emitter. Then, the sensing principle to detect sound will be discussed carefully, including capacitive, piezoelectric, electromagnetic, and piezoresistive components employed in the realm of sound sensing. Following this, the development of thermoacoustic theory and different materials made of sound emitters will also be analyzed. After that, various algorithms utilized by the intelligent AT for speech pattern recognition will be reviewed, including some classical algorithms and neural network algorithms. Finally, the outlook, challenge, and conclusion of the intelligent AT will be stated. The intelligent AT presents clear advantages for patients with voice impairments, demonstrating significant social values.

UNASSIGNED: There is a lack of effective treatment for idiopathic unilateral vocal fold paralysis (IUVFP). A better phonation was reported by patients after laryngeal nerve stimulation during our clinical examination.
UNASSIGNED: This study aims to investigate immediate effect of recurrent laryngeal nerve (RLN) stimulation on phonation in patients with IUVFP.
UNASSIGNED: Sixty-two patients with clinically identified IUVFP underwent RLN stimulation with needle electrodes. Laryngoscopy, acoustic analysis, and voice perception assessment were performed for quantitative comparison of vocal function and voice quality before and after the intervention.
UNASSIGNED: Laryngoscopic images showed a larger motion range of the paralyzed vocal fold (p < .01) and better glottal closure (p < .01) after RLN stimulation. Acoustic analysis revealed that the dysphonia severity index increased significantly (p < .01) while the jitter and shimmer decreased after the intervention (p < .05). According to perceptual evaluation, RLN stimulation significantly increased RBH grades in patients with IUVFP (p < .01). Furthermore, the improvement in voice perception had a moderate positive correlation with the decrease in the glottal closure.
UNASSIGNED: This study shows a short-term improvement of phonation in IUVFP patients after RLN stimulation, which provides proof-of-concept for trialing a controlled delivery of RLN stimulation and assessing durability of any observed responses.

BACKGROUND: Web-based health care has the potential to improve health care access and convenience for patients with limited mobility, but its success depends on active physician participation. The economic returns of internet-based health care initiatives are an important factor that can motivate physicians to continue their participation. Although several studies have examined the communication patterns and influences of web-based health consultations, the correlation between physicians\' communication characteristics and their economic returns remains unexplored.
OBJECTIVE: This study aims to investigate how the linguistic features of 2 modes of physician-patient communication, instrumental and affective, determine the physician\'s economic returns, measured by the honorarium their patients agree to pay per consultation. We also examined the moderating effects of communication media (web-based text messages and voice messages) and the compounding effects of different communication features on economic returns.
METHODS: We collected 40,563 web-based consultations from 528 physicians across 4 disease specialties on a large, web-based health care platform in China. Communication features were extracted using linguistic inquiry and word count, and we used multivariable linear regression and K-means clustering to analyze the data.
RESULTS: We found that the use of cognitive processing language (ie, words related to insight, causation, tentativeness, and certainty) in instrumental communication and positive emotion-related words in affective communication were positively associated with the economic returns of physicians. However, the extensive use of discrepancy-related words could generate adverse effects. We also found that the use of voice messages for service delivery magnified the effects of cognitive processing language but did not moderate the effects of affective processing language. The highest economic returns were associated with consultations in which the physicians used few expressions related to negative emotion; used more terms associated with positive emotions; and later, used instrumental communication language.
CONCLUSIONS: Our study provides empirical evidence about the relationship between physicians\' communication characteristics and their economic returns. It contributes to a better understanding of patient-physician interactions from a professional-client perspective and has practical implications for physicians and web-based health care platform executives.