A machine learning-based quantitative model (LogBB_Pred) to predict the blood-brain barrier permeability (logBB value) of drug compounds.

MOTIVATION: Efficient assessment of the blood-brain barrier (BBB) penetration ability of a drug compound is one of the major hurdles in central nervous system drug discovery since experimental methods are costly and time-consuming. To advance and elevate the success rate of neurotherapeutic drug discovery, it is essential to develop an accurate computational quantitative model to determine the absolute logBB value (a logarithmic ratio of the concentration of a drug in the brain to its concentration in the blood) of a drug candidate. RESULTS: Here, we developed a quantitative model (LogBB_Pred) capable of predicting a logBB value of a query compound. The model achieved an R2 of 0.61 on an independent test dataset and outperformed other publicly available quantitative models. When compared with the available qualitative (classification) models that only classified whether a compound is BBB-permeable or not, our model achieved the same accuracy (0.85) with the best qualitative model and far-outperformed other qualitative models (accuracies between 0.64 and 0.70). For further evaluation, our model, quantitative models, and the qualitative models were evaluated on a real-world central nervous system drug screening library. Our model showed an accuracy of 0.97 while the other models showed an accuracy in the range of 0.29-0.83. Consequently, our model can accurately classify BBB-permeable compounds as well as predict the absolute logBB values of drug candidates. AVAILABILITY AND IMPLEMENTATION: Web server is freely available on the web at http://ssbio.cau.ac.kr/software/logbb_pred/. The data used in this study are available to download at http://ssbio.cau.ac.kr/software/logbb_pred/dataset.zip.

A multi-layered network model identifies Akt1 as a common modulator of neurodegeneration.

The accumulation of misfolded and aggregated proteins is a hallmark of neurodegenerative proteinopathies. Although multiple genetic loci have been associated with specific neurodegenerative diseases (NDs), molecular mechanisms that may have a broader relevance for most or all proteinopathies remain poorly resolved. In this study, we developed a multi-layered network expansion (MLnet) model to predict protein modifiers that are common to a group of diseases and, therefore, may have broader pathophysiological relevance for that group. When applied to the four NDs Alzheimer's disease (AD), Huntington's disease, and spinocerebellar ataxia types 1 and 3, we predicted multiple members of the insulin pathway, including PDK1, Akt1, InR, and sgg (GSK-3ß), as common modifiers. We validated these modifiers with the help of four Drosophila ND models. Further evaluation of Akt1 in human cell-based ND models revealed that activation of Akt1 signaling by the small molecule SC79 increased cell viability in all models. Moreover, treatment of AD model mice with SC79 enhanced their long-term memory and ameliorated dysregulated anxiety levels, which are commonly affected in AD patients. These findings validate MLnet as a valuable tool to uncover molecular pathways and proteins involved in the pathophysiology of entire disease groups and identify potential therapeutic targets that have relevance across disease boundaries. MLnet can be used for any group of diseases and is available as a web tool at http://ssbio.cau.ac.kr/software/mlnet.

A computational clinical decision-supporting system to suggest effective anti-epileptic drugs for pediatric epilepsy patients based on deep learning models using patient's medical history.

BACKGROUND: Epilepsy, a chronic brain disorder characterized by abnormal brain activity that causes seizures and other symptoms, is typically treated using anti-epileptic drugs (AEDs) as the first-line therapy. However, due to the variations in their modes of action, identification of effective AEDs often relies on ad hoc trials, which is particularly challenging for pediatric patients. Thus, there is significant value in computational methods capable of assisting in the selection of AEDs, aiming to minimize unnecessary medication and improve treatment efficacy. RESULTS: In this study, we collected 7,507 medical records from 1,000 pediatric epilepsy patients and developed a computational clinical decision-supporting system for AED selection. This system leverages three multi-channel convolutional neural network (CNN) models tailored to three specific AEDs (vigabatrin, prednisolone, and clobazam). Each CNN model predicts whether a respective AED is effective on a given patient or not. The CNN models showed AUROCs of 0.90, 0.80, and 0.92 in 10-fold cross-validation, respectively. Evaluation on a hold-out test dataset further revealed positive predictive values (PPVs) of 0.92, 0.97, and 0.91 for the three respective CNN models, representing that suggested AEDs by our models would be effective in controlling epilepsy with a high accuracy and thereby reducing unnecessary medications for pediatric patients. CONCLUSION: Our CNN models in the system demonstrated high PPVs for the three AEDs, which signifies the potential of our approach to support the clinical decision-making by assisting doctors in recommending effective AEDs within the three AEDs for patients based on their medical history. This would result in a reduction in the number of unnecessary ad hoc attempts to find an effective AED for pediatric epilepsy patients.

LightBBB: computational prediction model of blood-brain-barrier penetration based on LightGBM.

MOTIVATION: Identification of blood-brain barrier (BBB) permeability of a compound is a major challenge in neurotherapeutic drug discovery. Conventional approaches for BBB permeability measurement are expensive, time-consuming and labor-intensive. BBB permeability is associated with diverse chemical properties of compounds. However, BBB permeability prediction models have been developed using small datasets and limited features, which are usually not practical due to their low coverage of chemical diversity of compounds. Aim of this study is to develop a BBB permeability prediction model using a large dataset for practical applications. This model can be used for facilitated compound screening in the early stage of brain drug discovery. RESULTS: A dataset of 7162 compounds with BBB permeability (5453 BBB+ and 1709 BBB-) was compiled from the literature, where BBB+ and BBB- denote BBB-permeable and non-permeable compounds, respectively. We trained a machine learning model based on Light Gradient Boosting Machine (LightGBM) algorithm and achieved an overall accuracy of 89%, an area under the curve (AUC) of 0.93, specificity of 0.77 and sensitivity of 0.93, when 10-fold cross-validation was performed. The model was further evaluated using 74 central nerve system compounds (39 BBB+ and 35 BBB-) obtained from the literature and showed an accuracy of 90%, sensitivity of 0.85 and specificity of 0.94. Our model outperforms over existing BBB permeability prediction models. AVAILABILITYAND IMPLEMENTATION: The prediction server is available at http://ssbio.cau.ac.kr/software/bbb.

2-D chemical structure image-based in silico model to predict agonist activity for androgen receptor.

BACKGROUND: Abnormal activation of human nuclear hormone receptors disrupts endocrine systems and thereby affects human health. There have been machine learning-based models to predict androgen receptor agonist activity. However, the models were constructed based on limited numerical features such as molecular descriptors and fingerprints. RESULT: In this study, instead of the numerical features, 2-D chemical structure images of compounds were used to build an androgen receptor toxicity prediction model. The images may provide unknown features that were not represented by conventional numerical features. As a result, the new strategy resulted in a construction of highly accurate prediction model: Mathews correlation coefficient (MCC) of 0.688, positive predictive value (PPV) of 0.933, sensitivity of 0.519, specificity of 0.998, and overall accuracy of 0.981 in 10-fold cross-validation. Validation on a test dataset showed MCC of 0.370, sensitivity of 0.211, specificity of 0.991, PPV of 0.882, and overall accuracy of 0.801. Our chemical image-based prediction model outperforms conventional models based on numerical features. CONCLUSION: Our constructed prediction model successfully classified molecular images into androgen receptor agonists or inactive compounds. The result indicates that 2-D molecular mimetic diagram would be used as another feature to construct molecular activity prediction models.

Computational determination of hERG-related cardiotoxicity of drug candidates.

BACKGROUND: Drug candidates often cause an unwanted blockage of the potassium ion channel of the human ether-a-go-go-related gene (hERG). The blockage leads to long QT syndrome (LQTS), which is a severe life-threatening cardiac side effect. Therefore, a virtual screening method to predict drug-induced hERG-related cardiotoxicity could facilitate drug discovery by filtering out toxic drug candidates. RESULT: In this study, we generated a reliable hERG-related cardiotoxicity dataset composed of 2130 compounds, which were carried out under constant conditions. Based on our dataset, we developed a computational hERG-related cardiotoxicity prediction model. The neural network model achieved an area under the receiver operating characteristic curve (AUC) of 0.764, with an accuracy of 90.1%, a Matthews correlation coefficient (MCC) of 0.368, a sensitivity of 0.321, and a specificity of 0.967, when ten-fold cross-validation was performed. The model was further evaluated using ten drug compounds tested on guinea pigs and showed an accuracy of 80.0%, an MCC of 0.655, a sensitivity of 0.600, and a specificity of 1.000, which were better than the performances of existing hERG-toxicity prediction models. CONCLUSION: The neural network model can predict hERG-related cardiotoxicity of chemical compounds with a high accuracy. Therefore, the model can be applied to virtual high-throughput screening for drug candidates that do not cause cardiotoxicity. The prediction tool is available as a web-tool at http://ssbio.cau.ac.kr/CardPred .

Resection of Septal Adenoid Cystic Carcinoma and Primary Reconstruction of the Surgical Defect via Open Rhinoplasty.

Adenoid cystic carcinoma is one of the most common minor salivary gland malignancies of the head and neck region. However, adenoid cystic carcinoma of the nasal septum is extremely rare. The authors herein report a case of a septal adenoid cystic carcinoma in a 68-year-old man who complained of nasal bleeding and nasal obstruction for several months. Diagnostic nasal endoscopy revealed a protruding mass arising from the anterosuperior part of the nasal septum. The tumor was removed with a safety margin using the open rhinoplasty approach and primary reconstruction of the surgical defect was performed using septal cartilage. Histopathology indicated an adenoid cystic carcinoma with cribriform pattern. Two years postoperatively, there was no evidence of recurrence, the functional and cosmetic results were good, and the patient was satisfied with the treatment outcome.

In silico prediction of potential chemical reactions mediated by human enzymes.

BACKGROUND: Administered drugs are often converted into an ineffective or activated form by enzymes in our body. Conventional in silico prediction approaches focused on therapeutically important enzymes such as CYP450. However, there are more than thousands of different cellular enzymes that potentially convert administered drug into other forms. RESULT: We developed an in silico model to predict which of human enzymes including metabolic enzymes as well as CYP450 family can catalyze a given chemical compound. The prediction is based on the chemical and physical similarity between known enzyme substrates and a query chemical compound. Our in silico model was developed using multiple linear regression and the model showed high performance (AUC = 0.896) despite of the large number of enzymes. When evaluated on a test dataset, it also showed significantly high performance (AUC = 0.746). Interestingly, evaluation with literature data showed that our model can be used to predict not only enzymatic reactions but also drug conversion and enzyme inhibition. CONCLUSION: Our model was able to predict enzymatic reactions of a query molecule with a high accuracy. This may foster to discover new metabolic routes and to accelerate the computational development of drug candidates by enabling the prediction of the potential conversion of administered drugs into active or inactive forms.

Aerosolized Fibrin Sealant Is Effective for Postoperative Edema and Ecchymosis in Open Rhinoplasty Without Osteotomy.

PURPOSE: Fibrin sealant (FS) was approved as a hemostatic agent, sealant, and adhesive by the Food and Drug Administration in 1998. Our study sought to determine whether FS also reduced edema and pain in rhinoplasty without osteotomy. MATERIALS AND METHODS: We conducted a prospective randomized trial involving patients who underwent open rhinoplasty without osteotomy. The patients were randomly assigned to 1 of 2 groups: those treated with aerosolized FS (FS group) and those not treated (control group). The effect of FS on edema of the eyelid, edema of the dorsum and tip of the nose, and periorbital ecchymosis was separately rated postoperatively using a scale of 0 to 4 or 0 to 3. Postoperative pain was evaluated using questionnaires quantified with a visual analog scale. The Mann-Whitney U and Wilcoxon tests were used to compare parameters between the groups. RESULTS: A total of 41 patients were included in this study. The FS group (n = 20) consisted of 13 male and 7 female patients with a mean age of 34.8 ± 5.8 years. The control group (n = 21) consisted of 15 male and 6 female patients with a mean age of 32.4 ± 4.8 years. There were no statistically significant differences between the 2 groups with respect to age, gender, or combined surgical techniques (P > .05). Aerosolized FS significantly reduced not only ecchymosis and swelling in the periorbital area but also edema of the nasal dorsum (P < .05). CONCLUSIONS: Aerosolized FS can be effective for reducing eyelid edema, dorsal edema, and periorbital ecchymosis after open rhinoplasty. FS may serve as an adhesive for minimizing dead space by promoting adherence of the skin flap and as a hemostatic agent in reducing the amount of postoperative bleeding by sealing capillary vessels.

Low-concentration hypochlorous acid nasal irrigation for chronic sinonasal symptoms: a prospective randomized placebo-controlled study.

Low-concentration hypochlorous acid (HOCl) is an endogenous antibacterial and antiviral agent. The purpose of this study was to evaluate the effectiveness of HOCl irrigation in patients with chronic rhinosinusitis (CRS) refractory to medical therapy. Forty-three adult patients (mean age 45.5 years) were enrolled in this study. They were randomly chosen to receive nasal irrigation with either low-concentration HOCl generated by a Salicid device (n = 21), or a placebo (saline; n = 22) for 8 weeks. The outcome measures were scores on the 20-Item SinoNasal Outcome Test (SNOT-20), rhinosinusitis disability index (RSDI), nasal endoscopic score, and bacterial cultures. The SNOT-20 scores were significantly lower in the HOCl group than in the placebo group after 2 weeks of treatment (p < 0.05) and remained lower after 4 weeks of treatment. With respect to the RSDI scores, there was a significant improvement in the HOCl group at 1 week after treatment and in both groups at 2 weeks after treatment (p < 0.05). There were no significant differences in the endoscopic scores between the two groups after the treatment. The bacterial culture rates were lower in the HOCl group than in the placebo group after 4 weeks of treatment, but this was not significant (p > 0.05). Our results showed that low-concentration HOCl irrigation resulted in a greater improvement in CRS symptoms as compared to saline irrigation.

Preoperative computer simulation for Asian rhinoplasty patients: analysis of accuracy and patient preference.

BACKGROUND: Preoperative computer simulation (PCS) is a tool for demonstrating potential rhinoplasty results to patients and determining the patient's preferred external nasal appearance. OBJECTIVES: The authors evaluated the effectiveness of PCS in Asian rhinoplasty patients. METHODS: The records of 224 patients who underwent rhinoplasty were reviewed. Sixty-eight (30.4%) of these patients had received PCS. To evaluate the accuracy of PCS in predicting postoperative results, postoperative photographs and PCS images were graded on a 4-point scale by a panel of 3 otolaryngologists. Postoperative patient satisfaction was compared between the PCS and non-PCS groups. Aesthetic parameters were assessed in the PCS images to determine the patient's preferred external nasal appearance. RESULTS: The mean overall accuracy of PCS was 86.0% according to the otolaryngologist panel's ratings: 41.2% of the surgical results were rated as identical, 44.1% as similar, 13.2% as approximate, and 1.5% as poor. There were no significant differences between the PCS and non-PCS groups in terms of patient satisfaction or revision rates (P > .05). The most favored nasal appearances were straight dorsum (63.2%), straight columella (50.0%), and convergent alar axis (64.7%). The mean (± standard deviation) preferred nasofrontal and nasolabial angles were 137.5° ± 6.9° and 97.3° ± 8.6°, respectively. CONCLUSIONS: Preoperative computer simulation is an accurate tool for assessing preferred external nasal appearance and can be a reliable predictor of postoperative rhinoplasty results in Asian patients. LEVEL OF EVIDENCE: 3.

Diagnostic accuracy of clinical visual assessment using endoscopic images for nasal cavity mass lesions.

Objectives: Discrimination of nasal cavity lesions using nasal endoscopy is challenging because of the differences in clinical manifestations and treatment strategies. We aimed to investigate the diagnostic accuracy of clinical visual assessment (CVA) of nasal cavity masses using endoscopic images and determine whether there is a difference according to pathologic class and the examiners' experience. Methods: We collected pathologically confirmed endoscopic images of normal findings, nasal polyp (NP), benign tumor, and malignant tumor (each class contained 100 images) randomly selected. Eighteen otolaryngologists, including six junior residents, six senior residents, and six board-certified rhinologists classified the test set images into four classes of lesions by CVA. Diagnostic performance according to the pathologic class and the examiner's experience level was evaluated based on overall accuracy, F1-score, confusion matrix, and area under the receiver operating characteristic curve (AUC). Results: Diagnostic performance was significantly different according to the pathological class of nasal cavity mass lesions with the overall accuracy reported high in the order of normal, NP, benign tumor, and malignant tumor (0.926 ± 0.100; 0.819 ± 0.135; 0.580 ± 0.112; 0.478 ± 0.187, respectively), F1 score (0.937 ± 0.076; 0.730 ± 0.093; 0.549 ± 0.080; 0.554 ± 0.146, respectively) and AUC value (0.96 ± 0.06; 0.84 ± 0.07; 0.70 ± 0.05; 0.71 ± 0.08, respectively). The expert rhinologist group achieved higher overall accuracy than the resident group (0.756 ± 0.157 vs. 0.680 ± 0.239, p < .05). Conclusion: CVA for nasal cavity mass was highly dependent on the pathologic class and examiner's experience. The overall accuracy was reliably high for normal findings, but low in classifying benign and malignant tumors. Differential diagnosis of lesions solely based on nasal endoscopic evaluation is challenging. Therefore, clinicians should consider further clinical evaluation for suspicious cases.

Sinonasal Organizing Hematoma: Demographics, Diagnosis, and Treatment Outcomes of 112 patients.

INTRODUCTION: Sinonasal organizing hematoma (OH) is a rare, nonneoplastic lesion that often presents with epistaxis, a reddish mass, and destruction of the involved sinonasal structures. Due to its rarity, the demographics, diagnostic modalities, treatment strategies, and outcomes have not yet been studied in a large, long-term study. MATERIALS AND METHODS: Retrospect cohort of 112 sinonasal OH patients treated between 1997 and 2020 in a tertiary, university hospital were evaluated. Demographics, systemic comorbidities, sinonasal surgery history, serum laboratory tests, radiological findings, and treatment results were collected. The present study aimed to assess the accuracy of preoperative computed tomography (CT), Gadolinum-enhanced magnetic resonance (MR), and punch biopsies in detecting sinonasal OH as the most likely diagnosis. In addition, incidental differences by age and year of diagnosis were calculated using the Poisson log-linear regression model. RESULTS: The median age was 44, and 58% were male. Fewer than 20% of these cases had a chronic systemic comorbidity, bleeding tendency, or sinonasal surgery history. MR had the highest accuracy of (87%) to detect sinonasal OH as the most likely diagnosis, compared with contrast-enhanced-CT (53%), punch biopsy (49%), and non-enhanced-CT (16%) (all <0.05). Sinonasal OH incidence did not vary by age, but the yearly rate significantly increased by 1.05 times over 23 years (p < 0.05). Notably, 84% of 112 patients received surgical removal through the assistance of an endoscope, and none had substantial bleeding without preoperative embolization. CONCLUSION: Sinonasal OH was observed regardless of age, sex, systemic comorbidities, bleeding tendency, prior sinonasal surgery, or trauma. Preoperative MR gives the highest accuracy for detecting this disease. Sinonasal OH may be safely managed with endoscopic-assisted surgery removal without embolization. LEVEL OF EVIDENCE: 4 Laryngoscope, 134:1581-1590, 2024.

Corydalis ternata Nakai Alleviates Cognitive Decline in Alzheimer's Disease by Reducing ß-Amyloid and Neuroinflammation.

Recently, natural herbs have gained increasing attention owing to their comparatively low toxicity levels and the abundance of historical medical documentation regarding their use. Nevertheless, owing to a lack of knowledge regarding these herbs and their compounds, attempts to find those that could be beneficial for treating diseases have often been ad hoc; thus, there is now a growing demand for an in silico method to identify beneficial herbs. In this study, we present a computational approach for identifying natural herbs specifically effective in treating cognitive decline in Alzheimer's disease (AD) sufferers, which analyzes the similarities between herbal compounds and known drugs targeting AD-related proteins. Our in silico method suggests that Corydalis ternata can improve cognitive decline in AD sufferers. Behavioral tests with an AD mouse model for the confirmation of the in silico prediction reveals that C. ternata significantly alleviated the cognitive decline (memory and motor functions) caused by neurodegeneration. Further pathology analyses reveal that C. ternata decreases the level of Aß plaques, reduces neuroinflammation, and promotes autophagy flux, and thus C. ternata can be clinically effective for preventing mild cognitive impairment during the early stages of AD. These findings highlight the potential utility of our in silico method and the potential clinical application of the identified natural herb in treating and preventing AD.

Impact of long-term nasal airflow deprivation on sinonasal structures and chronic rhinosinusitis in total laryngectomy patients.

Objective: Total laryngectomy (TL) patients are good models in which to evaluate the effects of nasal airflow cessation on the sinonasal tract. Here, we evaluated changes in sinonasal structures and association with sinus opacification in the computed tomography (CT) images 3 years post-TL. Methods: Patients who underwent TL from 2005 to 2017 in a teaching academic center were reviewed retrospectively. Patients with a final follow-up CT taken less than 3 years after TL, tracheoesophageal puncture, inadequate CT image, or history of sinonasal surgery were excluded. The control group included patients who underwent a partial laryngectomy or hypopharyngectomy without requiring a tracheotomy for more than a month. Altogether, 45 TL patients and 38 controls were selected. The volume of all four paranasal sinuses, inferior turbinate soft tissue volume (ITSTV), maxillary sinus natural ostium (MSNO) mucosal width, and Lund-Mackay scores (LMS) were measured on preoperative and postoperative CT scans. Results: The mean duration between surgery and the final CT scan was 6.3 ± 2.4 and 5.5 ± 2.3 years for the TL and control groups, respectively. Neither group showed significant changes in the four paranasal sinuses' volume or MSNO mucosa width. The ITSTV decreased significantly, from 4.6 ± 1.3 to 2.8 ± 1.1 mL (p < .001), in the TL group, regardless of the presence of nasal septal deviation, showing ITSTV reduction on both concave and convex sides. By contrast, the control group showed no significant changes in ITSTV. Postoperative LMS changes in both groups were insignificant. The number of patients with LMS aggravation or alleviation was the same in both groups, regardless of preoperative sinus opacification. Conclusions: Paranasal sinus structures and sinus opacification are not affected significantly by nasal airflow cessation; however, the inferior turbinate mucosa is affected by long-term discontinuation of nasal airflow. Level of Evidence: 4 (case-control study).

Deep learning algorithm for the automated detection and classification of nasal cavity mass in nasal endoscopic images.

Nasal endoscopy is routinely performed to distinguish the pathological types of masses. There is a lack of studies on deep learning algorithms for discriminating a wide range of endoscopic nasal cavity mass lesions. Therefore, we aimed to develop an endoscopic-examination-based deep learning model to detect and classify nasal cavity mass lesions, including nasal polyps (NPs), benign tumors, and malignant tumors. The clinical feasibility of the model was evaluated by comparing the results to those of manual assessment. Biopsy-confirmed nasal endoscopic images were obtained from 17 hospitals in South Korea. Here, 400 images were used for the test set. The training and validation datasets consisted of 149,043 normal nasal cavity, 311,043 NP, 9,271 benign tumor, and 5,323 malignant tumor lesion images. The proposed Xception architecture achieved an overall accuracy of 0.792 with the following class accuracies on the test set: normal = 0.978 ± 0.016, NP = 0.790 ± 0.016, benign = 0.708 ± 0.100, and malignant = 0.698 ± 0.116. With an average area under the receiver operating characteristic curve (AUC) of 0.947, the AUC values and F1 score were highest in the order of normal, NP, malignant tumor, and benign tumor classes. The classification performances of the proposed model were comparable with those of manual assessment in the normal and NP classes. The proposed model outperformed manual assessment in the benign and malignant tumor classes (sensitivities of 0.708 ± 0.100 vs. 0.549 ± 0.172, 0.698 ± 0.116 vs. 0.518 ± 0.153, respectively). In urgent (malignant) versus nonurgent binary predictions, the deep learning model achieved superior diagnostic accuracy. The developed model based on endoscopic images achieved satisfactory performance in classifying four classes of nasal cavity mass lesions, namely normal, NP, benign tumor, and malignant tumor. The developed model can therefore be used to screen nasal cavity lesions accurately and rapidly.

Treatment of obstructive sleep apnea syndrome using radiofrequency-assisted uvulopalatoplasty with tonsillectomy.

Radiofrequency surgery was introduced to minimize thermal damage to the tissue. A radiofrequency electrode can be used to make cuts in the free edge of the soft palate like those done in laser-assisted uvulopalatoplasty [radiofrequency-assisted uvulopalatoplasty (RAUP)]. Tonsillectomy can enlarge the lateral diameter of the pharynx. The aim of our study was to evaluate the efficacy of RAUP with tonsillectomy in treatment of obstructive sleep apnea syndrome (OSAS). Ninety-two patients with obstructive sleep apnea were included in this study. Patients were categorized according to disease severity and Friedman's staging system. Patients were assessed with the preoperative visual analog scale (VAS) for snoring, Epworth Sleepiness Scale (ESS) and apnea-hypopnea index (AHI) at baseline and repeated at 6 months postoperatively. The intensity of postoperative pain, speech deficits and dysphagia were also recorded. There was a significant improvement in the VAS score for snoring, ESS and AHI before and after surgery. Overall, the results of the present study indicated a surgery success rate (a 50 % decrease in AHI and AHI <20) of 66 % (61 of 92 patients). Postoperative pain, speech deficits and dysphagia were reduced at 2 weeks after surgery. The results of this study suggest that RAUP with tonsillectomy is an effective treatment for patients with OSAS.

Preoperative Prediction of Sinonasal Inverted Papilloma-associated Squamous Cell Carcinoma (IP-SCC).

INTRODUCTION: Sinonasal inverted papillomas (IP) can undergo transformation into IP-squamous cell carcinomas (IP-SCC). More aggressive treatment plan should be established when IP-SCC is suspected. Nevertheless, inaccuracy of the preoperative punch biopsy results to detect IP-SCC from IP raises the need for an additional strategy. The present study aimed to investigate significant clinicoradiological remarks associated with IP-SCC than IP. MATERIAL AND METHODS: Postoperative surgical specimens obtained from patients with confirmed IP or IP-SCC at a single tertiary medical center from 1997 to 2018 were retrospectively evaluated. Patients' demographic and clinical characteristics, preoperative in-office punch biopsy results, and preoperative computed tomography (CT) or magnetic resonance images were reviewed. Univariate and multivariate analyses were performed to assess the odds ratio (OR) associated with IP-SCC. The area under the curve (AUC) in the receiver Operating Characteristic (ROC) curve was calculated in the prediction model to discriminate IP-SCC from IP. RESULTS: The study included 44 IP-SCC and 301 patients with IP. The diagnostic sensitivity of in-office punch biopsy to detect IP-SCC was 70.7%. Multivariate analysis showed that factors significantly associated with IP-SCC included tobacco smoking >10PY (adjusted-OR [aOR]: 4.1), epistaxis (aOR: 3.4), facial pain (aOR: 4.2), bony destruction (aOR: 37.6), bony remodeling (aOR: 36.3), and invasion of adjacent structures (aOR: 31.6) (all p < 0.05). Combining all significantly related clinicoradiological features, the ability to discriminate IP-SCC from IP reached an AUC of 0.974. CONCLUSION: IP patients with a history of tobacco smoking, facial pain, epistaxis, and bony destruction, remodeling, or invasion of an adjacent structure on preoperative images may be at higher risk for IP-SCC. LEVEL OF EVIDENCE: 3 Laryngoscope, 133:2502-2510, 2023.

Features of Odontogenic Sinusitis Associated With Dental Implants.

OBJECTIVES: With the increase in dental implants for tooth loss, odontogenic sinusitis following maxillary dental implants is frequently encountered in otorhinolaryngology practice. The authors aimed to reveal the association between implant extrusion into maxillary sinus, along with implant-related complications in patients diagnosed with implant-related odontogenic sinusitis (IR-ODS). STUDY DESIGN: Case-control study. METHODS: This study enrolled 60 patients who received functional endoscopic sinus surgery due to IR-ODS. The preoperative sinus computed tomography was retrospectively reviewed. Among the 120 maxillary sinuses of the 60 patients, 68 sides were diagnosed with IR-ODS sides, whereas 27 sides showed no clinical or radiological evidence of this condition after the implant insertion and were defined as the control sides. Statistical analysis between these two groups was conducted, in addition to odds ratio (OR) calculations for associations with IR-ODS. RESULTS: The mean age of the IR-ODS subjects was 59.5 ± 19.1, with a male to female ratio of 32/28 (53.3%/46.7%). Implants extruding by more than 4 mm into the maxillary sinus, peri-implantitis, bone graft disruption-extrusion were associated with a significantly higher incidence in the IR-ODS (p = 0.035, p = 0.003, p = 0.011, respectively). The IR-ODS sides showed an adjusted-OR (95% confidence interval) of 27.4 (2.7-276.5) for extrusion length >4 mm, 11.8 (3.0-46.5) for peri-implantitis, and 34.1 (3.3-347.8) for bone graft disruption (p = 0.005, p < 0.001, and p = 0.003, respectively). CONCLUSION: Maxillary dental implants extruding more than 4 mm into the maxillary sinus, peri-implantitis, and disrupted-extruded bone grafts show significant association with IR-ODS. LEVEL OF EVIDENCE: 4 Laryngoscope, 133:237-243, 2023.

Feasibility of distraction osteogenesis maxillary expansion in patients with persistent nasal obstruction after septoplasty.

BACKGROUND: Distraction osteogenesis maxillary expansion (DOME) is an emerging surgical concept for obstructive sleep apnea patients with a high-arched and narrow hard palate presenting with nasal obstruction. Herein we demonstrate the feasibility of DOME in the treatment of patients with persistent nasal obstruction after primary septoplasty. METHODS: A total of 32 consecutive patients with persistent nasal obstruction after septoplasty who underwent DOME were enrolled in this study. The efficacy of DOME in treating persistent nasal obstruction in septoplasty failure patients was evaluated using the validated Nasal Obstruction Symptom Evaluation (NOSE) scale. Pre- and postoperative computed tomography (CT) scans were performed to measure the change of nasal floor width after surgery. RESULTS: NOSE score improved significantly from 13.62 ± 4.74 to 5.15 ± 3.50 (p < 0.001) after DOME. The postoperative CT scan showed a significant increase of the nasal floor width from 22.4 ± 4.21 to 28.06 ± 4.52 (p < 0.01). No patient had major complications such as wound infection, oronasal fistula, sinusitis, loss of incisor teeth, or malunion. CONCLUSION: We found that DOME appears to improve persistent nasal obstruction after septoplasty in patients with a narrow and high-arched hard palate.