Exosomal circPVT1 promotes angiogenesis in laryngeal cancer by activating the Rap1b-VEGFR2 signaling pathway.

Laryngeal cancer (LC) is the second most common head and neck cancer and has a decreasing 5-year survival rate worldwide. Circular RNAs regulate cancer development in diverse ways based on their distinct biogenesis mechanisms and expansive regulatory roles. However, currently, there is little research on how exosomal circular RNAs are involved in the development of laryngeal cancer. Here, we demonstrated that circPVT1, a circular RNA derived from the well-studied long noncoding RNA PVT1, is correlated with disease progression in LC and promotes angiogenesis both in vivo and in vitro. Mechanistically, circPVT1 is loaded into LC cell-secreted exosomes and taken up by vascular epithelium cells. By sponging miR-30c-5p, exosomal circPVT1 promotes Rap1b expression, which dramatically enhances VEGFR2 and PI3K/AKT pathway activation, ultimately resulting in the induction of angiogenesis. Furthermore, our xenograft models demonstrated that the combination of shRNA-circPVT1 and cetuximab showed high efficacy in inhibiting tumor growth and angiogenesis. Collectively, these findings uncover a novel mechanism of exosomal circular RNA-mediated angiogenesis modulation and provide a preclinical rationale for testing this analogous combination in patients with LC.

Upfront surgery versus definitive radiotherapy: competing risk analyses for cancer-specific and noncancer mortality in oropharyngeal cancer.

PURPOSE: The optimal treatment strategy for oropharyngeal cancer (OPC) is undetermined. We aim to compare the survival outcomes of OPC patients treated with upfront surgery versus definitive radiotherapy (RT). METHODS: A total of 8057 cases were retrieved from the Surveillance, Epidemiology, and End Results database. Primary endpoints were cancer-specific and noncancer mortalities, which were estimated using cumulative incidence function and compared by Gray's test. Univariate and multivariate Fine-Gray subdistribution hazard models were used to estimate the effects of treatment modality on mortality. Subgroup analyses were performed in propensity-score-matched cohorts. All the analyses were conducted separately in human papillomavirus (HPV)-negative and HPV-positive cohorts. RESULTS: In the HPV-negative cohort, definitive RT was independently associated with increased risk of cancer-specific mortality (adjusted subdistribution hazard ratio [SHR], 1.31; 95% confidence interval [CI], 1.05-1.64; P = 0.017) and noncancer mortality (adjusted SHR, 1.59; 95% CI 1.13-2.25; P = 0.008). In the HPV-positive cohort, definitive RT was independently associated with increased risk of cancer-specific mortality (adjusted SHR, 1.51; 95% CI 1.23-1.85; P < 0.001) and noncancer mortality (adjusted SHR, 1.53; 95% CI 1.11-2.12; P = 0.009). CONCLUSION: Upfront surgery is a superior treatment modality compared with definitive RT in terms of lowering cancer-specific and noncancer mortality in OPC patients, regardless of HPV status. Further prospective clinical trials are needed to confirm our findings.

Type 17 mucosal-associated invariant T cells contribute to neutrophilic inflammation in patients with nasal polyps.

BACKGROUND: Immune regulation in chronic rhinosinusitis with nasal polyps (CRSwNP) with a neutrophilic endotype remains unclear. Mucosal-associated invariant T (MAIT) cells are tissue-resident innate T lymphocytes that respond quickly to pathogens and promote chronic mucosal inflammation. OBJECTIVE: We aimed to investigate the roles of MAIT cells in neutrophilic CRSwNP. METHODS: Nasal tissues were obtained from 113 patients with CRSwNP and 29 control subjects. Peripheral and tissue MAIT cells and their subsets were analyzed by flow cytometry. Polyp-derived MAIT cells were analyzed by RNA sequencing to study their effects on neutrophils. RESULTS: Endotypes of CRSwNP were classified as paucigranulocytic (n = 21), eosinophilic (n = 29), neutrophilic (n = 39), and mixed granulocytic (n = 24). Frequencies of MAIT cells were significantly higher in neutrophilic (3.62%) and mixed granulocytic (3.60%) polyps than in control mucosa (1.78%). MAIT cell percentages positively correlated with local neutrophil counts. MAIT cells were more enriched in tissues than in matched PBMCs. The frequencies of MAIT1 subset or IFN-γ+ MAIT cells were comparable among control tissues and CRSwNP subtypes. The proportions of MAIT17 subset or IL-17A+ MAIT cells were significantly increased in neutrophilic or mixed granulocytic polyps compared with controls. RNA sequencing revealed type 17 and pro-neutrophil profiles in neutrophilic polyp-derived MAIT cells. In patients with neutrophilic CRSwNP, the proportions of MAIT and MAIT17 cells were positively correlated with local proinflammatory cytokines and symptom severity. In vitro experiments demonstrated that neutrophilic polyp-derived MAIT cells promoted neutrophil migration, survival, and activation. CONCLUSIONS: MAIT cells from neutrophilic CRSwNP demonstrate type 17 functional properties and promote neutrophil infiltration in nasal mucosa.

Real-time detection of laryngopharyngeal cancer using an artificial intelligence-assisted system with multimodal data.

BACKGROUND: Laryngopharyngeal cancer (LPC) includes laryngeal and hypopharyngeal cancer, whose early diagnosis can significantly improve the prognosis and quality of life of patients. Pathological biopsy of suspicious cancerous tissue under the guidance of laryngoscopy is the gold standard for diagnosing LPC. However, this subjective examination largely depends on the skills and experience of laryngologists, which increases the possibility of missed diagnoses and repeated unnecessary biopsies. We aimed to develop and validate a deep convolutional neural network-based Laryngopharyngeal Artificial Intelligence Diagnostic System (LPAIDS) for real-time automatically identifying LPC in both laryngoscopy white-light imaging (WLI) and narrow-band imaging (NBI) images to improve the diagnostic accuracy of LPC by reducing diagnostic variation among on-expert laryngologists. METHODS: All 31,543 laryngoscopic images from 2382 patients were categorised into training, verification, and test sets to develop, validate, and internal test LPAIDS. Another 25,063 images from five other hospitals were used as external tests. Overall, 551 videos were used to evaluate the real-time performance of the system, and 200 randomly selected videos were used to compare the diagnostic performance of the LPAIDS with that of laryngologists. Two deep-learning models using either WLI (model W) or NBI (model N) images were constructed to compare with LPAIDS. RESULTS: LPAIDS had a higher diagnostic performance than models W and N, with accuracies of 0·956 and 0·949 in the internal image and video tests, respectively. The robustness and stability of LPAIDS were validated in external sets with the area under the receiver operating characteristic curve values of 0·965-0·987. In the laryngologist-machine competition, LPAIDS achieved an accuracy of 0·940, which was comparable to expert laryngologists and outperformed other laryngologists with varying qualifications. CONCLUSIONS: LPAIDS provided high accuracy and stability in detecting LPC in real-time, which showed great potential for using LPAIDS to improve the diagnostic accuracy of LPC by reducing diagnostic variation among on-expert laryngologists.

Single-cell RNA sequencing reveals pro-invasive cancer-associated fibroblasts in hypopharyngeal squamous cell carcinoma.

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. Video Abstract.

Low expression of lncRNA SBF2-AS1 regulates the miR-302b-3p/TGFBR2 axis, promoting metastasis in laryngeal cancer.

The 5-year survival rate of laryngeal cancer continues to decline, and the laryngeal particularity of the anatomy adversely affects the patient's quality of life. Emerging evidence suggests that long noncoding RNAs (lncRNAs) are closely correlated to key steps in the malignant progression of cancer cells. In this study, we report the role of lncRNA SBF2-AS1/miR-302b-3p/TGFBR2 interactions in the metastasis of laryngeal squamous cell carcinoma (LSCC). We verified that SBF2-AS1 was significantly downregulated in LSCC tissues and cell lines using qRT-PCR analysis. Its low expression was correlated to lymph node metastasis and an advanced clinical stage. More importantly, LSCC patients with low expression of SBF2-AS1 tended to have a poor prognosis. Based on this, we performed gain-of-function and loss-of-function experiments in LSCC cell lines. The results confirmed that knocking down SBF2-AS1 can promote the metastasis of LSCC cells and enhance epithelial-mesenchymal transition phenotype, while the upregulation of SBF2-AS1 expression resulted in the opposite. Our in vivo model verified that SBF2-AS1 overexpression could inhibit LSCC cell metastasis. Subsequent mechanistic studies revealed that SBF2-AS1 acted as a competing endogenous RNA that upregulated the expression of TGFBR2 by endogenous sponging for miR-302b-3p in LSCC cell lines. Moreover, miR-302b-3p overexpression reversed the inhibitory effects on LSCC metastasis induced by upregulation of SBF2-AS1 expression, and inhibition of TGFBR2 expression reversed the effect of SBF2-AS1 on metastasis. Our study proposes SBF2-AS1 as a biomarker to predict the prognosis of LSCC patients and a novel potential therapeutic target.

Mast cell marker gene signature in head and neck squamous cell carcinoma.

BACKGROUND: Mast cells can reshape the tumour immune microenvironment and greatly affect tumour occurrence and development. However, mast cell gene prognostic and predictive value in head and neck squamous cell carcinoma (HNSCC) remains unclear. This study was conducted to identify and establish a prognostic mast cell gene signature (MCS) for assessing the prognosis and immunotherapy response of patients with HNSCC. METHODS: Mast cell marker genes in HNSCC were identified using single-cell RNA sequencing analysis. A dataset from The Cancer Genome Atlas was divided into a training cohort to construct the MCS model and a testing cohort to validate the model. Fluorescence in-situ hybridisation was used to evaluate the MCS model gene expression in tissue sections from patients with HNSCC who had been treated with programmed cell death-1 inhibitors and further validate the MCS. RESULTS: A prognostic MCS comprising nine genes (KIT, RAB32, CATSPER1, SMYD3, LINC00996, SOCS1, AP2M1, LAT, and HSP90B1) was generated by comprehensively analysing clinical features and 47 mast cell-related genes. The MCS effectively distinguished survival outcomes across the training, testing, and entire cohorts as an independent prognostic factor. Furthermore, we identified patients with favourable immune cell infiltration status and immunotherapy responses. Fluorescence in-situ hybridisation supported the MCS immunotherapy response of patients with HNSCC prediction, showing increased high-risk gene expression and reduced low-risk gene expression in immunotherapy-insensitive patients. CONCLUSIONS: Our MCS provides insight into the roles of mast cells in HNSCC prognosis and may have applications as an immunotherapy response predictive indicator in patients with HNSCC and a reference for immunotherapy decision-making.

Bacteriological analysis based on disease severity and clinical characteristics in patients with deep neck space abscess.

BACKGROUND: Deep neck space abscess (DNSA) is a serious infection in the head and neck. Antibiotic therapy is an important treatment in patients with DNSA. However, the results of bacterial culture need at least 48 h, and the positive rate is only 30-50%, indicating that the use of empiric antibiotic treatment for most patients with DNSA should at least 48 h or even throughout the whole course of treatment. Thus, how to use empiric antibiotics has always been a problem for clinicians. This study analyzed the distribution of bacteria based on disease severity and clinical characteristics of DNSA patients, and provides bacteriological guidance for the empiric use of antibiotics. METHODS: We analyzed 433 patients with DNSA who were diagnosed and treated at nine medical centers in Guangdong Province between January 1, 2015, and December 31, 2020. A nomogram for disease severity (mild/severe) was constructed using least absolute shrinkage and selection operator-logistic regression analysis. Clinical characteristics for the Gram reaction of the strain were identified using multivariate analyses. RESULTS: 92 (21.2%) patients developed life-threatening complications. The nomogram for disease severity comprised of seven predictors. The area under the receiver operating characteristic curves of the nomogram in the training and validation cohorts were 0.951 and 0.931, respectively. In the mild cases, 43.2% (101/234) had positive culture results (49% for Gram-positive and 51% for Gram-negative strains). The positive rate of cultures in the patients with severe disease was 63% (58/92, 37.9% for Gram-positive, and 62.1% for Gram-negative strains). Diabetes mellitus was an independent predictor of Gram-negative strains in the mild disease group, whereas gas formation and trismus were independent predictors of Gram-positive strains in the severe disease group. The positivity rate of multidrug-resistant strains was higher in the severe disease group (12.1%) than in the mild disease group (1.0%) (P < 0.001). Metagenomic sequencing was helpful for the bacteriological diagnosis of DNSA by identifying anaerobic strains (83.3%). CONCLUSION: We established a DNSA clinical severity prediction model and found some predictors for the type of Gram-staining strains in different disease severity cases. These results can help clinicians in effectively choosing an empiric antibiotic treatment.

Using RNA sequencing to identify a putative lncRNA-associated ceRNA network in laryngeal squamous cell carcinoma.

Accumulating evidence indicates that lncRNAs can interact with miRNAs to regulate target mRNAs through competitive interactions. However, this mechanism remains largely unexplored in laryngeal squamous cell carcinoma (LSCC). In this study, transcriptome-wide RNA sequencing was performed on 3 pairs of LSCC tissues and adjacent normal tissues to investigate the expression profiles of lncRNAs, miRNAs and mRNAs, with differential expression of 171 lncRNAs, 36 miRNAs and 1709 mRNAs detected. Seven lncRNAs, eight mRNAs and three miRNAs were identified to be dysregulated in patients' tissues by using qRT-PCR. GO and KEGG pathway enrichment analyses were performed to elucidate the potential functions of these differentially expressed genes in LSCC. Subsequently, a ceRNA (lncRNA-miRNA-mRNA) network including 4631 ceRNA pairs was constructed based on predicted miRNAs shared by lncRNAs and mRNAs. Cis- and transregulatory lncRNAs were analysed by bioinformatics-based methods. Importantly, mRNA-related ceRNA networks (mRCNs) were further obtained based on potential cancer-related coding genes. Coexpression between lncRNAs and downstream mRNAs was used as a criterion for the validation of mRCNs, with the ZNF561-AS1-miR217-WNT5A and SATB1-AS1-miR1299-SAV1/CCNG2/SH3 KBP1/JADE1/HIPK2 ceRNA regulatory interactions determined, followed by experimental validation after siRNA transfection. Moreover, ceRNA activity analysis revealed that different activities of ceRNA modules existing in specific pathological environments may contribute to the tumorigenesis of LSCC. Consistently, both downregulated SATB1-AS1 and ZNF561-AS1 significantly promoted laryngeal cancer cell migration and invasion, indicating their important roles in LSCC via a ceRNA regulatory mechanism. Taken together, the results of this investigation uncovered and systemically characterized a lncRNA-related ceRNA regulatory network that may be valuable for the diagnosis and treatment of LSCC.

A positive-feedback loop between tumour infiltrating activated Treg cells and type 2-skewed macrophages is essential for progression of laryngeal squamous cell carcinoma.

BACKGROUND: Foxp3+ regulatory T (Treg) cells and M2 macrophages are associated with increased tumour progression. However, the interaction between Treg cells and M2 macrophages remains unclear. METHODS: The expression of FoxP3 and CD163 was detected by immunohistochemistry in 65 cases of laryngeal squamous cell carcinoma (LSCC). In vitro, the generation of activated Treg (aTreg) cells and M2 macrophages by interactions with their precursor cells were analysed by flow cytometry and ELISA. In vivo, the antitumour effects were assessed by combined targeting aTreg cells and M2 macrophages, and intratumoural immunocytes were analysed by flow cytometry. RESULTS: In LSCC tissue, accumulation of aTreg cells and M2 macrophages predicted a poor prognosis and were positively associated with each other. In vitro, aTreg cells were induced from CD4+CD25- T cells by cancer cell-activated M2-like macrophages. Consequently, these aTreg cells skewed the differentiation of monocytes towards an M2-like phenotype, thereby forming a positive-feedback loop. Combined targeting aTreg cells and M2 macrophages led to potent antitumour immunity in vivo. CONCLUSIONS: The positive-feedback loop between aTreg cells and M2 macrophages is essential to maintain or promote immunosuppression in the tumour microenvironment and may be a potential therapeutic target to inhibit tumour progression.

Low Temperature Storage of Southern Rice Black-Streaked Dwarf Virus-Infected Rice Plants Cannot Sustain Virus Transmission by the Vector.

Southern rice black-streaked dwarf virus (SRBSDV) is a novel virus transmitted by white-backed planthopper Sogatella furcifera (Hováth) (Hemiptera: Delphacidae). Due to low virus transmission efficiency by the planthopper, researchers are frequently confronted with shortage of viruliferous vectors or infected rice plants, especially in winter and the following spring. To find new ways to maintain virus-infected materials, viral rice plants were stored at -80°C for 45 or 140 d and evaluated as virus sources in virus transmission by the vector. SRBSDV virions were not degraded during storage at -80°C as indicated by reverse transcription-polymerase chain reaction and reverse transcription real-time PCR detection. The planthopper nymphs fed on the infected thawed plants for 48 h survived at about 40% and showed positive detection of SRBSDV, but they lost the virus after feeding for another 20 d (the circulative transmission period) on noninfected plants. Transmission electron microscope images indicated broken capsid of virions in infected thawed leaves in contrast to integrity capsid of virions in infected fresh leaves. These results show that low temperature storage of SRBSDV-infected rice plants cannot sustain virus transmission by white-backed planthopper.

Interactive Effects of Southern Rice Black-Streaked Dwarf Virus Infection of Host Plant and Vector on Performance of the Vector, Sogatella furcifera (Homoptera: Delphacidae).

Performance of insect vectors can be influenced by the viruses they transmit, either directly by infection of the vectors or indirectly via infection of the host plants. Southern rice black-streaked dwarf virus (SRBSDV) is a propagative virus transmitted by the white-backed planthopper, Sogatella furcifera (Hovath). To elucidate the influence of SRBSDV on the performance of white-backed planthopper, life parameters of viruliferous and nonviruliferous white-backed planthopper fed rice seedlings infected or noninfected with SRBSDV were measured using a factorial design. Regardless of the infection status of the rice plant host, viruliferous white-backed planthopper nymphs took longer to develop from nymph to adult than did nonviruliferous nymphs. Viruliferous white-backed planthopper females deposited fewer eggs than nonviruliferous females and both viruliferous and nonviruliferous white-backed planthopper females laid fewer eggs on infected than on noninfected plants. Longevity of white-backed planthopper females was also affected by the infection status of the rice plant and white-backed planthopper. Nonviruliferous white-backed planthopper females that fed on infected rice plants lived longer than the other three treatment groups. These results indicate that the performance of white-backed planthopper is affected by SRBSDV either directly (by infection of white-backed planthopper) or indirectly (by infection of rice plant). The extended development of viruliferous nymphs and the prolonged life span of nonviruliferous adults on infected plants may increase their likelihood of transmitting virus, which would increase virus spread.

Sleep apnea detection from single-lead electrocardiogram signals using effective deep-shallow fusion network.

Objective.Explore a network architecture that can efficiently perform single-lead electrocardiogram (ECG) sleep apnea (SA) detection by utilizing the beneficial information of extended ECG segments and reducing the impact of their noisy information.Approach.We propose an effective deep-shallow fusion network (EDSFnet). The deeper residual network is used to extract high-level features with stronger semantics and less noise from the original ECG segments. The shallower convolutional neural network is used to extract lower-level features with higher resolution containing more detailed neighborhood information from the extended ECG segments. These two types of features are then fused using Effective Channel Attention, implementing automatic weight assignment to take advantage of their complementary nature.Main results.The performance of EDSFnet is evaluated on the Apnea-ECG dataset and the FAH-ECG dataset. In the Apnea-ECG dataset with 35 subjects as the training set and 35 subjects as the test set, the accuracy of EDSFnet was 92.6% and 100% for per-segment and per-recording test, respectively. In the FAH-ECG dataset with 348 subjects as the training set and 88 subjects as the test set, the accuracy of EDSFnet was 89.0% and 93.2% for per-segment and per-recording test, respectively. EDSFnet has achieved state-of-the-art results in both experiments using the publicly available Apnea-ECG dataset and subject-independent experiments using the FAH-ECG clinical dataset.Significance.The success of EDSFnet in handling SA detection underlines its robustness and adaptability. By achieving superior results across different datasets, EDSFnet offers promise in advancing the cost-effective and efficient detection of SA through single-lead ECG, reducing the burden on patients and healthcare systems alike.

A bimodal feature fusion convolutional neural network for detecting obstructive sleep apnea/hypopnea from nasal airflow and oximetry signals.

The most prevalent sleep-disordered breathing condition is Obstructive Sleep Apnea (OSA), which has been linked to various health consequences, including cardiovascular disease (CVD) and even sudden death. Therefore, early detection of OSA can effectively help patients prevent the diseases induced by it. However, many existing methods have low accuracy in detecting hypopnea events or even ignore them altogether. According to the guidelines provided by the American Academy of Sleep Medicine (AASM), two modal signals, namely nasal pressure airflow and pulse oxygen saturation (SpO2), offer significant advantages in detecting OSA, particularly hypopnea events. Inspired by this notion, we propose a bimodal feature fusion CNN model that primarily comprises of a dual-branch CNN module and a feature fusion module for the classification of 10-second-long segments of nasal pressure airflow and SpO2. Additionally, an Efficient Channel Attention mechanism (ECA) is incorporated into the second module to adaptively weight feature map of each channel for improving classification accuracy. Furthermore, we design an OSA Severity Assessment Framework (OSAF) to aid physicians in effectively diagnosing OSA severity. The performance of both the bimodal feature fusion CNN model and OSAF is demonstrated to be excellent through per-segment and per-patient experimental results, based on the evaluation of our method using two real-world datasets consisting of polysomnography (PSG) recordings from 450 subjects.

Intranasal PAMAM-G3 scavenges cell-free DNA attenuating the allergic airway inflammation.

Allergic airway inflammation (AAI), including allergic rhinitis (AR) and allergic asthma, is driven by epithelial barrier dysfunction and type 2 inflammation. However, the underlying mechanism remains uncertain and available treatments are constrained. Consequently, we aim to explore the role of cell-free DNA (cfDNA) in AAI and assess the potential alleviating effects of cationic polymers (CPs) through cfDNA elimination. Levels of cfDNA were evaluated in AR patients, allergen-stimulated human bronchial epithelium (BEAS-2B cells) and primary human nasal epithelium from both AR and healthy control (HC), and AAI murine model. Polyamidoamine dendrimers-generation 3 (PAMAM-G3), a classic type of cationic polymers, were applied to investigate whether the clearance of cfDNA could ameliorate airway epithelial dysfunction and inhibit AAI. The levels of cfDNA in the plasma and nasal secretion from AR were higher than those from HC (P < 0.05). Additionally, cfDNA levels in the exhaled breath condensate (EBC) were positively correlated with Interleukin (IL)-5 levels in EBC (R = 0.4191, P = 0.0001). Plasma cfDNA levels negatively correlated with the duration of allergen immunotherapy treatment (R = -0.4297, P = 0.006). Allergen stimulated cfDNA secretion in vitro (P < 0.001) and in vivo (P < 0.0001), which could be effectively scavenged with PAMAM-G3. The application of PAMAM-G3 inhibited epithelial barrier dysfunction in vitro and attenuated the development of AAI in vivo. This study elucidates that cfDNA, a promising biomarker for monitoring disease severity, aggravates AAI and the application of intranasal PAMAM-G3 could potentially be a novel therapeutic intervention for AAI. Allergen stimulates the secretion of cell-free DNA (cfDNA) in both human and mouse airway. Intranasal polyamidoamine dendrimers-generation 3 (PAMAM-G3) scavenges cfDNA and alleviates allergic airway inflammation.

Research and application of deep learning-based sleep staging: Data, modeling, validation, and clinical practice.

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.

Intelligent method to predict intensive care unit admission after drainage operation in patients with deep neck space abscess: A multicenter retrospective study.

BACKGROUNDS: A deep neck space abscess (DNSA) is a critical condition resulting from infection of deep neck fascia and soft issue, leading to high morbidity and mortality. Therefore, intensive care can be very significant for patients with DNSA. This study aimed to develop models to predict the need for postoperative intensive care in patients with DNSA. METHODS: We retrospectively analyzed the records of 332 patients with DNSA who received drainage operation between 2015 and 2020. Multivariate logistic regression analysis and the eXtrem Gradient Boosting (XGBoost) algorithm were used to develop predictive models. RESULTS: We developed two predictive models, the nomogram and the XGBoost model. The area under the curve (AUC) of the nomogram was 0.911 and of the XGBoost model was 0.935. CONCLUSION: We developed two predictive models for guiding clinical decision making for postoperative ICU admission for DNSA patients, which may help improve prognosis and optimize intensive care resource allocation.

Transnasal negative pressure therapy for accelerating healing and improving the prognosis of pharyngocutaneous fistula.

BACKGROUND: Pharyngocutaneous fistula (PCF) is one of the most common complications of total laryngectomy. This study is to investigate the efficacy of a novel platform called transnasal negative pressure therapy (TNPT) in the management of PCF. METHODS: We retrospectively reviewed 47 patients who underwent total laryngectomy between April 2015 and February 2021 and developed PCF in our hospital. We focused on the healing rate, dressing change frequency, and healing time between the TNPT and non-TNPT groups. The 2 years overall survival (OS) was compared through the log-rank test. RESULTS: There were 18 patients in the TNPT group and 29 in the non-TNPT group. There was no significant between-group difference in the healing rate (chi-square test). However, the frequency of dressing changes was significantly lower (p < 0.001) and the healing time was significantly shorter (p = 0.0194) in the TNPT group than in the non-TNPT group. The 2-year OS rate was significantly higher in the TNPT group (p = 0.0473, log-rank test). CONCLUSION: TNPT promoted wound healing after surgery for PCF and improved the 2-year OS rate. This tool is worthy of clinical application and promotion.

RAFNet: Restricted attention fusion network for sleep apnea detection.

Sleep apnea (SA) is a common sleep-related breathing disorder, which would lead to damage of multiple systemic organs or even sudden death. In clinical practice, portable device is an important tool to monitor sleep conditions and detect SA events by using physiological signals. However, SA detection performance is still limited due to physiological signals with time-variability and complexity. In this paper, we focus on SA detection with single lead ECG signals, which can be easily collected by a portable device. Under this context, we propose a restricted attention fusion network called RAFNet for sleep apnea detection. Specifically, RR intervals (RRI) and R-peak amplitudes (Rpeak) are generated from ECG signals and divided into one-minute-long segments. To alleviate the problem of insufficient feature information of the target segment, we combine the target segment with two pre- and post-adjacent segments in sequence, (i.e. a five-minute-long segment), as the input. Meanwhile, by leveraging the target segment as the query vector, we propose a new restricted attention mechanism with cascaded morphological and temporal attentions, which can effectively learn the feature information and depress redundant feature information from the adjacent segments with adaptive assigning weight importance. To further improve the SA detection performance, the target and adjacent segment features are fused together with the channel-wise stacking scheme. Experiment results on the public Apnea-ECG dataset and the real clinical FAH-ECG dataset with sleep apnea annotations show that the RAFNet greatly improves SA detection performance and achieves competitive results, which are superior to those achieved by the state-of-the-art baselines.

Economic analysis of global microalgae biomass energy potential.

With the increasing demand for renewable energy, microalgae, as a renewable biomass energy, can fix carbon dioxide and have broad application prospects in alleviating the energy crisis and improving the environment. In this paper, the potential biomass of global microalgae is calculated based on the mathematical growth model of microalgae proposed by predecessors. Based on this, this study further uses Newton's gravity model as the basic model of economic analysis and calculates the economic potential coefficient of microalgae production in various regions of the world by using the data of the world's top 20 cities in terms of urban population and urban GDP in 2020. The study has obtained the current global unused land with the high economic value of large-scale microalgae production areas, such as western North America, northern Africa, and northwest China, etc., which can provide guidance for the future site selection and development of microalgae biomass energy.