Intestinal dual-specificity phosphatase 6 regulates the cold-induced gut microbiota remodeling to promote white adipose browning.

Gut microbiota rearrangement induced by cold temperature is crucial for browning in murine white adipose tissue. This study provides evidence that DUSP6, a host factor, plays a critical role in regulating cold-induced gut microbiota rearrangement. When exposed to cold, the downregulation of intestinal DUSP6 increased the capacity of gut microbiota to produce ursodeoxycholic acid (UDCA). The DUSP6-UDCA axis is essential for driving Lachnospiraceae expansion in the cold microbiota. In mice experiencing cold-room temperature (CR) transitions, prolonged DUSP6 inhibition via the DUSP6 inhibitor (E/Z)-BCI maintained increased cecal UDCA levels and cold-like microbiota networks. By analyzing DUSP6-regulated microbiota dynamics in cold-exposed mice, we identified Marvinbryantia as a genus whose abundance increased in response to cold exposure. When inoculated with human-origin Marvinbryantia formatexigens, germ-free recipient mice exhibited significantly enhanced browning phenotypes in white adipose tissue. Moreover, M. formatexigens secreted the methylated amino acid Nε-methyl-L-lysine, an enriched cecal metabolite in Dusp6 knockout mice that reduces adiposity and ameliorates nonalcoholic steatohepatitis in mice. Our work revealed that host-microbiota coadaptation to cold environments is essential for regulating the browning-promoting gut microbiome.

Office-Based Structural Autologous Fat Injection Laryngoplasty for Unilateral Vocal Fold Paralysis.

Unilateral vocal fold paralysis (UVFP) is a common cause of incomplete glottic closure, leading to significant somatic and social disabilities. Office-based autologous fat injection laryngoplasty (AFIL) has been proposed as an effective treatment for glottic insufficiency but has not been well-studied for UVFP. We enrolled 23 patients who underwent office-based structural AFIL due to unilateral vocal paralysis at our institution between February 2021 and January 2022. In the procedure, autologous fat was harvested and injected into the vocal fold under the guidance of flexible digital endoscopy for structural fat grafting. The voice handicap index-10 (VHI-10) score and perceptual voice measurements were collected before the operation, 2 weeks postoperatively, and 3 months postoperatively. Twenty-two patients were followed-up for at least 3 months. The VHI-10 score improved significantly from 29.65 ± 8.52 preoperatively to 11.74 ± 7.42 at 2 weeks (p < 0.0001) and 5.36 ± 6.67 at 3 months (p < 0.0001). Significant improvements in grades of dysphonia (p < 0.0001), breathiness (p < 0.0001), and asthenia (p = 0.004) were also noted at 3 months postoperatively when perceptual measurements were investigated. Office-based structural AFIL is an effective treatment for improving voice-related disability for UVFP patients.

Deep Learning Application for Vocal Fold Disease Prediction Through Voice Recognition: Preliminary Development Study.

BACKGROUND: Dysphonia influences the quality of life by interfering with communication. However, a laryngoscopic examination is expensive and not readily accessible in primary care units. Experienced laryngologists are required to achieve an accurate diagnosis. OBJECTIVE: This study sought to detect various vocal fold diseases through pathological voice recognition using artificial intelligence. METHODS: We collected 189 normal voice samples and 552 samples of individuals with voice disorders, including vocal atrophy (n=224), unilateral vocal paralysis (n=50), organic vocal fold lesions (n=248), and adductor spasmodic dysphonia (n=30). The 741 samples were divided into 2 sets: 593 samples as the training set and 148 samples as the testing set. A convolutional neural network approach was applied to train the model, and findings were compared with those of human specialists. RESULTS: The convolutional neural network model achieved a sensitivity of 0.66, a specificity of 0.91, and an overall accuracy of 66.9% for distinguishing normal voice, vocal atrophy, unilateral vocal paralysis, organic vocal fold lesions, and adductor spasmodic dysphonia. Compared with the accuracy of human specialists, the overall accuracy rates were 60.1% and 56.1% for the 2 laryngologists and 51.4% and 43.2% for the 2 general ear, nose, and throat doctors. CONCLUSIONS: Voice alone could be used for common vocal fold disease recognition through a deep learning approach after training with our Mandarin pathological voice database. This approach involving artificial intelligence could be clinically useful for screening general vocal fold disease using the voice. The approach includes a quick survey and a general health examination. It can be applied during telemedicine in areas with primary care units lacking laryngoscopic abilities. It could support physicians when prescreening cases by allowing for invasive examinations to be performed only for cases involving problems with automatic recognition or listening and for professional analyses of other clinical examination results that reveal doubts about the presence of pathologies.