Background: Nowadays, social media tools such as short message service, Twitter, video, and web-based systems are more and more used in clinical follow-up, making clinical follow-up much more time- and cost-effective than ever before. However, as the most popular social media in China, little is known about the utility of smartphone WeChat application in follow-up. In this study, we aimed to investigate the feasibility and superiority of WeChat application in clinical follow-up. Methods: A total of 108 patients diagnosed with head and neck tumor were randomized to WeChat follow-up (WFU) group or telephone follow-up (TFU) group for 6-month follow-up. The follow-ups were delivered by WeChat or telephone at 2 weeks, 1, 2, 3, and 6 months to the patients after being discharged. The study measurements were time consumption for follow-up delivery, total economic cost, lost-to-follow-up rate, and overall satisfaction for the follow-up method. Results: Time consumption in WFU group for each patient (23.36 ± 6.16 min) was significantly shorter than that in TFU group (42.89 ± 7.15 min) (P < 0.001); total economic cost in WFU group (RMB 90 Yuan) was much lower than that in TFU group (RMB 196 Yuan). Lost-to-follow-up rate in the WFU group was 7.02% (4/57) compared with TFU group, 9.80% (5/51), while no significance was observed (95% confidence interval [CI]: 0.176–2.740; P = 0.732). The overall satisfaction rate in WFU group was 94.34% (50/53) compared with 80.43% (37/46) in TFU group (95% CI: 0.057–0.067; P = 0.034). Conclusions: The smartphone WeChat application was found to be a viable option for follow-up in discharged patients with head and neck tumors. WFU was time-effective, cost-effective, and convenient in communication. This doctor-led follow-up model has the potential to establish a good physician-patient relationship by enhancing dynamic communications and providing individual health instructions. Trial Registration: Chinese Clinical Trial Registry, ChiCTR-IOR-15007498; http://www.chictr.org.cn/ showproj.aspx?proj=12613.

Over the past few decades, researchers have attempted to simplify and accelerate the process of sleep stage classification through various approaches; however, only a few such approaches have gained widespread acceptance. Artificial intelligence technology, particularly deep learning, is promising for earning the trust of the sleep medicine community in automated sleep-staging systems, thus facilitating its application in clinical practice and integration into daily life. We aimed to comprehensively review the latest methods that are applying deep learning for enhancing sleep staging efficiency and accuracy. Starting from the requisite "data" for constructing deep learning algorithms, we elucidated the current landscape of this domain and summarized the fundamental modeling process, encompassing signal selection, data pre-processing, model architecture, classification tasks, and performance metrics. Furthermore, we reviewed the applications of automated sleep staging in scenarios such as sleep-disorder screening, diagnostic procedures, and health monitoring and management. Finally, we conducted an in-depth analysis and discussion of the challenges and future in intelligent sleep staging, particularly focusing on large-scale sleep datasets, interdisciplinary collaborations, and human-computer interactions.

The study aims to present our experience of the clinical course and management of deep neck infection and try to determine if the characteristics of this kind of infection were similar between the children and adults in southern China. Patients diagnosed with deep neck infection in the Division of Otolaryngology in the First Affiliated Hospital of Sun Yat–sen University between January 2002 and December 2011 were screened retrospectively for demographic characteristics, presenting symptoms, antibiotic therapy before admission, the history of antibiotics abuse, leucocyte count, etiology, bacteriology, disease comorbidity, imaging, treatment, complications, and outcomes. One hundred thirty patients were included and 44 (33.8%) were younger than 18 years old (the children group), 86 patients (66.2%) were older than 18 years old (the adults group). Fever, trismus, neck pain, and odynophagia were the most common symptoms in both groups. Forty children (90.9%) and 49 adults (57.0%) had been treated with broad-spectrum antibiotic therapy before admission. Thirty one children (70.5%) and 24 adults (27.9%) had a history of antibiotics abuse. In children group, the site most commonly involved was the parapharyngeal space (18 patients, 40.9%). In adults group, the site most commonly involved was multispace (30 patients, 34.9%). In children group, the most common cause was branchial cleft cyst (5 patients, 11.4%) and the cause remained unknown in 31 patients (70.5%). In adults group, the most common cause was pharyngeal infection (19 patients, 22.2%). All of the 27 patients with associated disease comorbidity were adults and 17 were diabetes mellitus (DM). Streptococcus viridans was the most common pathogen in both children and adults groups. Eighty six (66.2%) underwent surgical drainage and complications were found in 31 patients (4 children, 27 adults). Deep neck infection in adults is easier to have multispace involvement and lead to complications and appears to be more serious than that in children. Understanding the different characteristics between the children and adults with deep neck infection may be helpful in accurate evaluation and proper management.

Background: Hypopharyngeal squamous cell carcinoma (HPSCC) has the worst prognosis among all head-and-neck cancers, and treatment options are limited. Tumor microenvironment (TME) analysis can help identify new therapeutic targets and combined treatment strategies. Methods: Six primary HPSCC tissues and two adjacent normal mucosae from six treatment-naïve patients with HPSCC were analyzed using scRNA-seq. Cell types were curated in detail, ecosystemic landscapes were mapped, and cell–cell interactions were inferred. Key results were validated with The Cancer Genome Atlas and cell biology experiments. Results: Malignant HPSCC epithelial cells showed significant intratumor heterogeneity. Different subtypes exhibited distinct histological features, biological behaviors, and spatial localization, all affecting treatment selection and prognosis. Extracellular matrix cancer-associated fibroblasts (mCAFs) expressing fibroblast activation protein were the dominant CAFs in HPSCC tumors. mCAFs, constituting an aggressive CAF subset, promoted tumor cell invasion, activated endothelial cells to trigger angiogenesis, and synergized with SPP1⁺ tumor associated macrophages to induce tumor progression, ultimately decreasing the overall survival of patients with HPSCC. Moreover, the LAMP3⁺ dendritic cell subset was identified in HPSCC and formed an immunosuppressive TME by recruiting Tregs and suppressing CD8+ T cell function. Conclusions: mCAFs, acting as the communication center of the HPSCC TME, enhance the invasion ability of HPSCC cells, mobilizing surrounding cells to construct a tumor-favorable microenvironment. Inhibiting mCAF activation offers a new anti-HPSCC therapeutic strategy. Graphical

MicroRNAs (miRNAs) are small noncoding RNA molecules which are involved in tumorigenesis and development. To investigate their role in primary laryngeal squamous cell carcinoma (LSCC), miRNA GeneChips were used to screen the differentially expressed miRNA, and then validated by real-time quantitative PCR in LSCC samples, we found that miR-375 was frequently downregulated in primary LSCC tissues. The tumor-suppressive effect of miR-375 was determined by in vitro assays; through gain-of-function studies we demonstrated that miR-375 can inhibit LSCC cell (SNU-48 and SNU-899) proliferation, motility, and invasion, and promote their apoptosis. In addition, bioinformatics tools TargetScan, PicTar, and Miranda were used to investigate the potential target of miR-375; bioinformatics analysis and dual-luciferase reporter assay indicated that IGF1R was a novel direct target of miR-375. Ectopic transfection of miR-375 led to a significant reduction in IGF1R and its downstream signaling molecule AKT at both the mRNA and protein levels in LSCC cells. Our results suggested that downregulation of miR-375 is one of the molecular mechanisms for the development and progression of LSCC by directly targeting IGF1R and affecting its downstream AKT signaling pathways. Furthermore, miR-375 and IGF1R may serve as a novel therapeutic target for LSCC.

Purpose: This study evaluated a novel approach for diagnosis and classification of obstructive sleep apnea (OSA), called Obstructive Sleep Apnea Smart System (OSASS), using residual networks and single-channel nasal pressure airflow signals. Methods: Data were collected from the sleep center of the First Affiliated Hospital, Sun Yat-sen University, and the Integrative Department of Guangdong Province Traditional Chinese Medical Hospital. We developed a new model called the multi-resolution residual network (Mr-ResNet) based on a residual network to detect nasal pressure airflow signals recorded by polysomnography (PSG) automatically. The performance of the model was assessed by its sensitivity, specificity, accuracy, and F1-score. We built OSASS based on Mr-ResNet to estimate the apnea‒hypopnea index (AHI) and to classify the severity of OSA, and compared the agreement between OSASS output and the registered polysomnographic technologist (RPSGT) score, assessed by two technologists. Results: In the primary test set, the sensitivity, specificity, accuracy, and F1-score of Mr-ResNet were 90.8%, 90.5%, 91.2%, and 90.5%, respectively. In the independent test set, the Spearman correlation for AHI between OSASS and the RPSGT score determined by two technologists was 0.94 (p < 0.001) and 0.96 (p < 0.001), respectively. Cohen’s Kappa scores for classification between OSASS and the two technologists’ scores were 0.81 and 0.84, respectively. Conclusion: Our results indicated that OSASS can automatically diagnose and classify OSA using signals from a single-channel nasal pressure airflow, which is consistent with polysomnographic technologists’ findings. Thus, OSASS holds promise for clinical application.