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Background and Objectives Vocal fold (VF) scar is known to be the most common cause of dysphonia after laryngeal microsurgery (LMS). Steroids reduce postoperative scar formation by inhibiting inflammation and collagen deposition. However, the clinical evidence of whether steroids are helpful in reducing VF scar formation after LMS is still lacking. The purpose of this study is to determine whether intralesional VF steroid injection after LMS helps to reduce postoperative scar formation and voice quality.Materials and Method This study was conducted on 80 patients who underwent LMS for VF polyp, Reinke’s edema, and leukoplakia. Among them, 40 patients who underwent VF steroid injection after LMS were set as the injection group, and patients who had similar sex, age, and lesion size and who underwent LMS alone were set as the control group. In each group, stroboscopy, multi-dimensional voice program, Aerophone II, and voice handicap index (VHI) were performed before and 1 month after surgery, and the results were statistically analyzed.Results There were no statistically significant differences in the distribution of sex, age, symptom duration, occupation and smoking status between each group. Both groups consisted of VF polyp (n=21), Reinke’s edema (n=11), and leukoplakia (n=9). On stroboscopy, the lesion disappeared after surgery, and the amplitude and mucosal wave were symmetrical on both sides of the VFs in all patients. Acoustic parameters and VHI significantly improved after surgery in all patients. However, there was no significant difference between the injection and control group in most of the results.Conclusion There was no significant difference in the results of stroboscopy, acoustic, aerodynamic, and subjective evaluation before and after surgery in the injection group and the control group.

Systemic lupus erythematosus(SLE) is a multisystemic disorder of autoimmune etiology. SLE can occur commonly in young women, and the early symptoms include fever, myalgia, arthralgia, weight loss, lymphadenopathy and these nonspecific symptoms develop into skin rash, splenomegaly, serositis and encephalopathy. Diagnosis of SLE requires clinical and serologic criteria, and treatment choices are hydroxyquinolone and NSAIDs for mild disease, corticosteroids and immunosuppressant for severe disease.In lupus patient, the prevalence of lymphadenopathy is 12~59%. Although lymphadenopathy is common finding in SLE, it is hard to distinguish in early phase of SLE.A 38-year-old woman visited our hospital for cervical lymphadenopathy with polyarthritis and malaise. Multiple cervical lymph nodes enlargement was found on Neck CT, and serologic laboratory test including ANA, antiphospholipid antibody, and anti-dsDNA was positive. For excluding lymphoma, PET-CT and excisional biopsy were performed. The patient finally diagnosed with SLE, and got regular follow-up without complication.

A tree model with low time complexity can support the application of artificial intelligence to industrial systems. Variable selection based tree learning algorithms are more time efficient than existing Classification and Regression Tree (CART) algorithms. To our best knowledge, there is no attempt to deal with categorical input variable in variable selection based multi-output tree learning. Also, in the case of multi-output regression tree, a conventional variable selection based algorithm is not suitable to large datasets. We propose a mutual information-based multi-output tree learning algorithm that consists of variable selection and split optimization. The proposed method discretizes each variable based on k-means into 2–4 clusters and selects the variable for splitting based on the discretized variables using mutual information. This variable selection component has relatively low time complexity and can be applied regardless of output dimension and types. The proposed split optimization component is more efficient than an exhaustive search. The performance of the proposed tree learning algorithm is similar to or better than that of a multi-output version of CART algorithm on a specific dataset. In addition, with a large dataset, the time complexity of the proposed algorithm is significantly reduced compared to a CART algorithm.