N6-methyladenosine (m6A) modifications play an essential role in tumorigenesis. These modifications modulate RNAs, including mRNAs and lncRNAs. However, the prognostic role of m6A-related lncRNAs in head and neck squamous cell carcinoma (HNSCC) is poorly understood. Based on LASSO Cox regression, enrichment analysis, univariate and multivariate Cox regression analysis, a prognostic risk model, and consensus clustering analysis, we analyzed 12 m6A-related lncRNAs in HNSCC sample data from The Cancer Genome Atlas (TCGA) database. We found 12 m6A-related lncRNAs in the training cohort and validated them in all cohorts by Kaplan–Meier and Cox regression analyses, revealing their independent prognostic value in HNSCC. Moreover, ROC analysis was conducted, confirming the strong predictive ability of this signature for HNSCC survival. GSEA and detailed immune infiltration analyses revealed specific pathways associated with m6A-related lncRNAs. In this study, a novel risk model including twelve genes (SAP30L-AS1, AC022098.1, LINC01475, AC090587.2, AC008115.3, AC015911.3, AL122035.2, AC010226.1, AL513190.1, ZNF32-AS1, AL035587.1 and AL031716.1) was built. It could accurately predict HNSCC outcomes and could provide new therapeutic targets for HNSCC patients.

Allergic rhinitis (AR) is a common and increasingly prevalent chronic inflammatory disorder of the nasal mucosa that severely impacts patients’ quality of life, causing symptoms like nasal congestion, sneezing, and itching. AR is primarily mediated by immunoglobulin E (IgE) when allergens are present, making it challenging to manage despite available therapies like pharmacotherapy, immunotherapy, and surgery. Recently, research has focused on biologics as an emerging therapeutic option for AR. Biologics target specific immune pathways in type 2 inflammation, which underlies many allergic diseases including AR. Biologics offer a targeted and potentially more effective alternative to traditional therapies, addressing the underlying immune mechanisms rather than simply alleviating symptoms. Based on key clinical trial evidence, this paper tentatively proposes a multidimensional strategy for selecting biologics in AR, integrating serum IgE levels, disease phenotypes (seasonal/persistent), and comorbid characteristics to guide individualized treatment. However, the long-term cost-effectiveness, optimal dosing regimens, and patient adherence to biologics require further validation through real-world data. Despite these challenges, recent advancements in biologics represent a promising step forward in AR management. With ongoing research and clinical trials, biologics may soon provide more effective and lasting relief for patients suffering from allergic rhinitis.

Fibroblast growth factor receptor 1 (FGFR1) has recently been identified as a promising novel therapeutic target and prognostic marker in different types of cancer. In the present study, a meta-analysis was performed to clarify the correlation between FGFR1 and the survival outcomes of head and neck squamous cell carcinoma (HNSCC) patients. PubMed, Embase, and Web of Science were systematically searched for relevant studies in order to explore the prognostic significance of FGFR1 in HNSCC. Hazards ratios (HR) and 95% confidence intervals (CI) were collected to estimate the correlation between overexpression and amplification of FGFR1 and survival outcomes of HNSCC patients. Nine studies including 2708 patients with HNSCC were finally selected for the meta-analysis. The results indicated that FGFR1 predicted poor overall survival (OS) (HR, 1.97; 95% CI, 1.49–2.61, P<0.001) in HNSCC patients. Futhermore, FGFR1 was related to poor OS in human papillomavirus (HPV) negative HNSCC not in HPV positive HNSCC patients. Subgroup analysis stratified by molecular abnormalities, such as overexpression or amplification showed the similar results. The present study demonstrated that HNSCC patients with FGFR1 overexpression and amplification were more likely to exhibit poorer survival.

Introduction: Head and neck squamous cell carcinoma (HNSCC), a highly heterogeneous malignancy is often associated with unfavorable prognosis. Due to its unique anatomical position and the absence of effective early inspection methods, surgical intervention alone is frequently inadequate for achieving complete remission. Therefore, the identification of reliable biomarker is crucial to enhance the accuracy of screening and treatment strategies for HNSCC.Method: To develop and identify a machine learning-derived prognostic model (MLDPM) for HNSCC, ten machine learning algorithms, namely CoxBoost, elastic network (Enet), generalized boosted regression modeling (GBM), Lasso, Ridge, partial least squares regression for Cox (plsRcox), random survival forest (RSF), stepwise Cox, supervised principal components (SuperPC), and survival support vector machine (survival-SVM), along with 81 algorithm combinations were utilized. Time-dependent receiver operating characteristics (ROC) curves and Kaplan-Meier analysis can effectively assess the model’s predictive performance. Validation was performed through a nomogram, calibration curves, univariate and multivariate Cox analysis. Further analyses included immunological profiling and gene set enrichment analyses (GSEA). Additionally, the prediction of 50% inhibitory concentration (IC50) of potential drugs between groups was determined.Results: From analyses in the HNSCC tissues and normal tissues, we found 536 differentially expressed genes (DEGs). Subsequent univariate-cox regression analysis narrowed this list to 18 genes. A robust risk model, outperforming other clinical signatures, was then constructed using machine learning techniques. The MLDPM indicated that high-risk scores showed a greater propensity for immune escape and reduced survival rates. Dasatinib and 7 medicine showed the superior sensitivity to the high-risk NHSCC, which had potential to the clinical.Conclusions: The construction of MLDPM effectively eliminated artificial bias by utilizing 101 algorithm combinations. This model demonstrated high accuracy in predicting HNSCC outcomes and has the potential to identify novel therapeutic targets for HNSCC patients, thus offering significant advancements in personalized treatment strategies.

(1) Background: Vestibular migraine (VM) and Meniere’s disease (MD) share multiple features in terms of clinical presentations and auditory-vestibular dysfunctions, e.g., vertigo, hearing loss, and headache. Therefore, differentiation between VM and MD is of great significance. (2) Methods: We retrospectively analyzed the medical records of 110 patients with VM and 110 patients with MD. We at first established a regression equation by using logistic regression analysis. Furthermore, sensitivity, specificity, accuracy, positive predicted value (PV), and negative PV of screened parameters were assessed and intuitively displayed by receiver operating characteristic curve (ROC curve). Then, two visualization tools, i.e., nomograph and applet, were established for convenience of clinicians. Furthermore, other patients with VM or MD were recruited to validate the power of the equation by ROC curve and the Gruppo Italiano per la Valutazione degli Interventi in Terapia Intensiva (GiViTI) calibration belt. (3) Results: The clinical manifestations and auditory-vestibular functions could help differentiate VM from MD, including attack frequency (X5), phonophobia (X13), electrocochleogram (ECochG) (X18), head-shaking test (HST) (X23), ocular vestibular evoked myogenic potential (o-VEMP) (X27), and horizontal gain of vestibular autorotation test (VAT) (X30). On the basis of statistically significant parameters screened by Chi-square test and multivariable double logistic regression analysis, we established a regression equation: P = 1/[1 + e^{−(−2.269× X5 − 2.395× X13 + 2.141× X18 + 3.949 × X23 + 2.798× X27 − 4.275× X30(1) − 5.811× X30(2) + 0.873)}] (P, predictive value; e, natural logarithm). Nomographs and applets were used to visualize our result. After validation, the prediction model showed good discriminative power and calibrating power. (4) Conclusions: Our study suggested that a diagnostic algorithm based on available clinical features and an auditory-vestibular function regression equation is clinically effective and feasible as a differentiating tool and could improve the differential diagnosis between VM and MD.

Background: Obstructive sleep apnea (OSA) as an independent cardiovascular risk factor has been proposed, but the mechanisms underlying cardiovascular disease is far from being completely elucidated. Leptin, an inflammatory cytokine produced by adipocytes, contributes to the modulation of metabolism, respiratory control, and inflammation, which are factors associated with cardiovascular disease. Serum levels of leptin in children with OSA have shown conflicting results in previous studies. Methods: We performed a meta-analysis to clarify the correlation between leptin expression of the OSA patients following the PRISMA. PubMed, Embase, and Web of Science were systematically searched for relevant studies, and then independently screened by two researchers, and analyzed the data through STATA version 12.0. Results: In a total of 5 articles including 469 participants, the data analysis showed that serum leptin levels were elevated in children with OSA (MD, 6.36; 95% CI, 0.24–12.49, P < .001), compared to the control group. Subgroup analysis were performed based on body mass index. The results of subgroup analysis demonstrated that the serum leptin concentration was correlated with body mass index in children with OSA (MD, 9.70; 95% CI, 0.22–11.18, P < .001). Conclusions: The serum leptin levels were elevated in children with OSA, compared to the control group. It could add to our developing understanding of the pathogenesis and potential treatments for children with OSA, and help us to recognize the relevance of OSA in determining cardiovascular issues among children.