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.

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.

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.

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.

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 Dual-Scale Convolutional Neural Network for Sleep Apnea Detection with Time-Delayed SpO2 Signals.

Sleep apnea (SA) is a common breathing disease, with clinical manifestations of sleep snoring at night with apnea and daytime sleepiness. It could lead to ischemic heart disease, stroke, or even sudden death. SpO2 signal is highly related to SA, and many automatic SA detection methods have been proposed. However, extant work focuses on small datasets with relatively few subjects (less than 100) and is unaware of SA syndromes occurring about 5 seconds prior to the SpO2 change. This study proposes an automatic SA detector called DSCNN using a single-lead SpO2 signal with a dual-scale convolutional neural network. To solve the time-delayed problem of SpO2 changes, we enlarge the target SpO2 segment information by combining its subsequent segment information. To utilize neighbouring segments information and further facilitate the SA detection performance, a dual-scale neural network with the fusing information of the prolonged target segment and its two surrounding segments is proposed. Three datasets from multiple centres are employed to verify the generic performance of DSCNN. Here, we must point out that we use two datasets as external datasets, and one of them is collected from the First Affiliated Hospital of Sun Yat-sen University with a large sample size (450 subjects). Extensive experiment results show that DSCNN can achieve promising results which are superior to the existing state-of-the-art methods.

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.

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.

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.

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.

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.

Salvage transoral laser microsurgery for early local recurrence of glottic squamous cell cancer.

BACKGROUND: For recurrent laryngeal cancer, the feasibility of salvage transoral laser microsurgery (TLM) remains controversial. This study compared the efficacy of TLM and open partial laryngectomy (OPL) for treatment of early local recurrence of glottic squamous cell cancer (GSCC) and confirm the effectiveness of salvage TLM as a treatment option. METHODS: This retrospective study involved 55 patients with early local recurrent GSCC treated with TLM, and the oncologic outcomes, functional outcomes, hospitalization time and complications were compared with a group of 40 recurrent GSCC patients matched for clinical variables of TLM group, treated by OPL by the same team of surgeons. RESULTS: The 5-year overall survival and disease-specific survival rates were 65.8% and 91.5%, respectively, for 55 patients with rTis-rT2 stage treated by TLM and 77.1% and 94.7%, respectively, for 40 patients with rTis-rT2 stage treated by OPL (OPL group). In the TLM and OPL groups, the local control rates after 5 years were 77.5% and 79.3%, respectively, and the laryngeal preservation rates were 94.4% and 83.6%, respectively (p > 0.05). Compared with the OPL group, the complication rate (1.82%) and hospitalization duration (5.42 ± 2.26 days) were significantly lower in the TLM group (p < 0.05). Compared with the OPL group, postsurgical health-related quality of life and quality of voice were significantly better in the TLM group (p < 0.001). CONCLUSION: Salvage TLM can be used as an effective treatment option for suitable patients after a full, comprehensive, and careful assessment of the characteristics of early locally recurrent glottic carcinoma.

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.

Validity study of a multiscaled fusion network using single-lead electrocardiogram signals for obstructive sleep apnea diagnosis.

STUDY OBJECTIVES: We evaluated the validity of a squeeze-and-excitation and multiscaled fusion network (SE-MSCNN) using single-lead electrocardiogram (ECG) signals for obstructive sleep apnea detection and classification. METHODS: Overnight polysomnographic data from 436 participants at the Sleep Center of the First Affiliated Hospital of Sun Yat-sen University were used to generate a new FAH-ECG dataset comprising 260, 88, and 88 single-lead ECG signal recordings for training, validation, and testing, respectively. The SE-MSCNN was employed for detection of apnea-hypopnea events from the acquired ECG segments. Sensitivity, specificity, accuracy, and F1 scores were assigned to assess algorithm performance. We also used the SE-MSCNN to estimate the apnea-hypopnea index, classify obstructive sleep apnea severity, and compare the agreement between 2 sleep technicians. RESULTS: The SE-MSCNN's accuracy, sensitivity, specificity, and F1 score on the FAH-ECG dataset were 86.6%, 83.3%, 89.1%, and 0.843, respectively. Although slightly inferior to previously reported results using public datasets, it is superior to state-of-the-art open-source models. Furthermore, the SE-MSCNN had good agreement with manual scoring, such that the Spearman's correlations for the apnea-hypopnea index between the SE-MSCNN and 2 technicians were 0.93 and 0.94, respectively. Cohen's kappa scores in classifying the SE-MSCNN and the 2 sleep technicians were 0.72 and 0.78, respectively. CONCLUSIONS: In this study, we validated the use of the SE-MSCNN in a clinical environment, and despite some limitations the network appeared to meet the performance standards for generalizability. Therefore, updating algorithms based on single-lead ECG signals can facilitate the development of novel wearable devices for efficient obstructive sleep apnea screening. CITATION: Yue H, Li P, Li Y, et al. Validity study of a multiscaled fusion network using single-lead electrocardiogram signals for obstructive sleep apnea diagnosis. J Clin Sleep Med. 2023;19(6):1017-1025.

A safe and effective surgical method for complex pyriform sinus fistula.

Objective: This article aims to propose a new surgical method for the treatment of pyriform fistula, especially for the complex pyriform fistula. Methods: A total of 36 patients with pyriform fistula underwent the procedure between August 2017 to October 2020. Surgery was performed by the senior authors using the same technique at the same clinical center for all patients. The median follow-up time was 33 months. Meantime, we collected information on patients with pyriform fistula using traditional surgical methods in our hospital from April 2015 to November 2018 for comparison. Results: The surgery was successfully completed in 36 patients. In all, 32 patients had a history of multiple incisions and drainage, 16 patients had a history of surgical resections, and two patients had a history of cauterization of the internal fistula. Compared with traditional surgical methods, our new surgical method greatly shortens the length of the surgical incision (4.3 vs. 5.5, p < 0.0001), reduces the operation time (8.1 vs. 27.1, p < 0.0001), and reduces the blood loss (103.2 vs. 196.8, p < 0.0001). None of the 36 patients in this study had complications such as pharyngeal fistula, recurrent laryngeal nerve paralysis, or hypothyroidism. The mean follow-up duration after the excision of the lesion was 34.1 months. To date, no patients have relapsed. Conclusion: Our experience showed that this surgical technique could be used to completely remove the fistula, and it was easier to perform than the conventional strategies. These treatment options result in less trauma and reliable results, especially for complex pyriform fistulas. Level of evidence: IV.

CD69 and SBK1 as potential predictors of responses to PD-1/PD-L1 blockade cancer immunotherapy in lung cancer and melanoma.

Background: PD-1/PD-L1 blockade is a promising immunotherapeutic strategy with the potential to improve the outcomes of various cancers. However, there is a critically unmet need for effective biomarkers of response to PD-1/PD-L1 blockade. Materials and methods: Potential biomarkers of response to PD-1/PD-L1 blockade were obtained from the Cancer Treatment Response gene signature Database (CTR-DB). A comprehensive pan-cancer analysis was done on The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) datasets. Correlations between gene expression and infiltration by immune cells were assessed using TIMER, EPIC, MCPcounter, xCell, CIBERSORT, and quanTIseq. Immunophenoscore (IPS) was used to assess the potential application of the biomarkers to all TCGA tumors. Results: Analysis of CTR-DB data identified CD69 and SBK1 as potential biomarkers of response to PD-1/PD-L1 blockade. Correlation analysis revealed that in various TCGA cancer datasets, CD69 expression level correlated positively with most immune checkpoints and tumor-infiltrating immune cells, while SBK1 expression level correlated negatively with infiltrating immune cells. IPS analysis demonstrated the ability of CD69 and SBK1 to predict PD-1/PD-L1 blockade responses in various cancers. Conclusion: CD69 and SBK1 are potential predictors of response to cancer immunotherapy using PD-1/PD-L1 blockade. These biomarkers may guide treatment decisions, leading to precise treatment and minimizing the waste of medical resources.

Effect of Bilateral Vocal Fold Microsuturing on Voice Quality Improvement in Patients With Anterior Glottic Webs: A Retrospective Observational Study.

PURPOSE: This study was performed to introduce a modified procedure involving a combination of bilateral vocal fold mucosal flaps and microsurgical sutures for the management of anterior glottic webs and to study its efficacy in decreasing the recurrence rate and improving voice quality. METHODS: We retrospectively reviewed 102 patients with anterior glottic webs who underwent surgical treatment by a carbon dioxide laser incision with or without microsurgical suturing in our hospital from May 2014 to April 2021. We focused on the reoperation rate and the voice outcomes based on the 30-item Voice Handicap Index. RESULTS: This study included 102 patients with anterior glottic webs, which were caused by papilloma excision and endoscopic laryngocarcinoma resection in 97 (95.1%) of the 102 patients; less common causes were infection and traumatic injury. All incisions were performed along the midline with a carbon dioxide laser under microscopy and a self-retaining laryngoscope; 37 (36.3%) patients underwent microsurgical suturing and 65 (63.7%) patients did not. The microsuture group had a lower reoperation rate (χ2= 7.069, P = 0.0078) and higher voice quality (t = 2.054, P = 0.0462) than the non-microsuture group. CONCLUSIONS: We introduced a modified procedure that can both decrease the recurrence rate and improve the voice quality in patients with anterior glottic webs. Hence, this combination therapy involving bilateral vocal fold mucosal flaps and microsurgical sutures is worthy of clinical application and promotion.

ELISA based assays to measure adenosine deaminases concentration in serum and saliva for the diagnosis of ADA2 deficiency and cancer.

Adenosine deaminases (ADAs) are enzymes of purine metabolism converting adenosine to inosine. There are two types of ADAs in humans ADA1 and ADA2. While both ADA1 and ADA2 share the same substrate, they differ in expression, cellular localization, and catalytic properties. The genetic deficiency of ADA1 results in severe combined immunodeficiency (SCID), while lack in ADA2 (DADA2) results in multiple phenotypes ranging from systemic inflammation to vascular pathology. Clinical studies have shown that the levels of ADAs in biological fluids are altered in pathophysiological conditions, suggesting that ADA activity could be a convenient marker for the diagnosis of immune diseases and cancer. Here, we describe sensitive and straightforward ELISA assays to measure ADA1 and ADA2 concentrations in biological fluids. Analysis of the serum and saliva samples from the healthy controls and DADA2 patients revealed that ADA2 enzyme concentration is significantly lower in patients than in healthy controls. In contrast, the concentration of ADA2 increases in the serum of patients with large granular leukocyte leukemia (LGLL) and patients' saliva with head and neck cancer. Thus, this simple, non-invasive method allows for distinguishing healthy controls from the affected patient. It can be implemented in screening and diagnosis of DADA2 and follow up the treatment of LGLL and several types of head and neck cancer.

Type 2 and Type 17 Invariant Natural Killer T Cells Contribute to Local Eosinophilic and Neutrophilic Inflammation and Their Function Is Regulated by Mucosal Microenvironment in Nasal Polyps.

Chronic rhinosinusitis with nasal polyps (CRSwNP) is characterized by heterogeneous inflammatory endotypes of unknown etiology. Invariant natural killer T (iNKT) cells are multifunctional innate T cells that exhibit Th1-, Th2-, and Th17-like characteristics. We investigated functional relationships between iNKT cells and inflammatory subtypes of CRSwNP. Eighty patients with CRSwNP and thirty-two control subjects were recruited in this study. Flow cytometry was used to analyze the frequencies and functions of iNKT cells and their subsets in peripheral blood mononuclear cells (PBMCs) and tissues. Polyp tissue homogenates were used to study the multifunctionality of iNKT cells. iNKT cells were significantly increased in polyps (0.41%) than in control mucosa (0.12%). iNKT cells were determined in the paucigranunlocytic (n=20), eosinophilic (n=22), neutrophilic (n=23), and mixed granulocytic (n=13) phenotypes of CRSwNP. The percentages of iNKT cells and HLA-DR+PD-1+ subsets were lower in eosinophilic or mixed granulocytic polyps than those of other phenotypes. iNKT cells and subsets were enriched in polyp tissues than in matched PBMCs. The evaluation of surface markers, transcription factors, and signature cytokines indicated that the frequencies of iNKT2 and iNKT17 subsets were significantly increased in eosinophilic and neutrophilic polyps, respectively, than in the paucigranulocytic group. Moreover, the production of type 2 (partially dependent on IL-7) and type 17 (partially dependent on IL-23) iNKT cells could be stimulated by eosinophilic and neutrophilic homogenates, respectively. Our study revealed that type 2 and type 17 iNKT cells were involved in eosinophilic and neutrophilic inflammation, respectively, in CRSwNP, while different inflammatory microenvironments could modulate the functions of iNKT cells, suggesting a role of iNKT cells in feedback mechanisms and local inflammation.

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.