BACKGROUND: Wolfram syndrome (WS) is a rare autosomal recessive multisystem neurodegenerative disease characterized by non-autoimmune insulin-dependent diabetes mellitus, optic atrophy, sensorineural deafness, and diabetes as the main features. Owing to clinical phenotypic heterogeneity, the misdiagnosis rate is high. However, early accurate diagnosis and comprehensive management are key to improving quality of life and prolonging life.
RESULTS: Eleven patients from seven WS pedigrees with 10 mutation sites (c.1314_1317delCTTT, c.C529T, c.C529A, c.G2105A, c.C1885T, c.1859_1860del, c.G2020A, c.C529A, c.G2105A, and c.G1393C) in the WFS1 gene were included. We conducted further expert department analysis to clarify the diagnosis and analyze the correlation between genes and phenotypes.
CONCLUSIONS: The genotypes of these patients were closely associated with their phenotypes. The clinical data of the patients were analyzed to provide a basis for the diagnosis and clinical management of the disease.

To predict diabetes mellitus model data mining (DM) based approaches on the dataset collected from the seven northwestern states of Nigeria. Data were collected from both primary and secondary sources through questionnaires and verbal interviews from patients with diabetic mellitus and other chronic diseases. Some hospital data were also used from the records of patients involved in this work. The dataset comprises 281 instances with 8 attributes. R programming software (version 5.3.1) was used in the experiments. The DM techniques used in this research were binomial logistic regression, classification, confusion matrix and correlation coefficient. The data were partitioned into training and testing sets. Training data were used in building the model while testing data were used to validate the model. The algorithm for the best-fitted model converges with null deviance: 281.951, residual deviance: 16.476 and AIC: 30.476. The significance variables are AGE, GLU, DBP and KDYP with 0.025, 0.01, 0.05 and 0.025 P values, respectively. The predicted model accounted for the accuracy of ∼97.1%. The correlation analysis results revealed that diabetic patients are more likely to be hypertensive than patients with other chronic diseases considered in the research.

Diabetic retinopathy (DR) is one of the leading cause of blindness worldwide and the most common cause of blindness among the working population. Early treatment of the disease is essential to prevent severe visual loss among patients. But there are few therapeutic options available for early stage diabetic retinopathy. We present the case of an early stage diabetic retinopathy patient presented with retinal hemorrhages in the superior temporal area and disc hemorrhages of disc nasal area. The patient was diagnosed with mild NPDR on fundus examinations. After 6 months of taking modified-Goshaiinkigan (mGJG), the characteristic features of mild NPDR disappeared. Throughout three consecutive years of follow-ups, no evident lesions that could be diagnosed as DR were found during fundus examinations. Many components of mGJG have potential efficacy toward diabetic retinopathy. This study suggests that mGJG is a possible medication for early stage DR. Concerning the degenerative characteristics of DR, early management strategies are important in young DM patients and integrative care, such as in this case, are worth investigating further.