OBJECTIVES: We sought to define the clinical features that distinguish DOCK8 deficiency from other forms of HIES and CIDs, study the mutational spectrum of DOCK8 deficiency, and report on the frequency of specific clinical findings.
METHODS: Eighty-two patients from 60 families with CID and the phenotype of AR-HIES with (64 patients) and without (18 patients) DOCK8 mutations were studied. Support vector machines were used to compare clinical data from 35 patients with DOCK8 deficiency with those from 10 patients with AR-HIES without a DOCK8 mutation and 64 patients with signal transducer and activator of transcription 3 (STAT3) mutations.
RESULTS: DOCK8-deficient patients had median IgE levels of 5201 IU, high eosinophil levels of usually at least 800/μL (92% of patients), and low IgM levels (62%). About 20% of patients were lymphopenic, mainly because of low CD4(+) and CD8(+) T-cell counts. Fewer than half of the patients tested produced normal specific antibody responses to recall antigens. Bacterial (84%), viral (78%), and fungal (70%) infections were frequently observed. Skin abscesses (60%) and allergies (73%) were common clinical problems. In contrast to STAT3 deficiency, there were few pneumatoceles, bone fractures, and teething problems. Mortality was high (34%). A combination of 5 clinical features was helpful in distinguishing patients with DOCK8 mutations from those with STAT3 mutations.
CONCLUSIONS: DOCK8 deficiency is likely in patients with severe viral infections, allergies, and/or low IgM levels who have a diagnosis of HIES plus hypereosinophilia and upper respiratory tract infections in the absence of parenchymal lung abnormalities, retained primary teeth, and minimal trauma fractures.
MATERIALS AND METHODS: Contrast enhanced computed tomography (CT) images of 194 multi-racial NSCLC patients (79 EGFR mutants and 115 wildtypes) were collected from three different countries using 5 manufacturers' scanners with a variety of scanning parameters. Ninety-nine cases obtained from the University of Malaya Medical Centre (UMMC) in Malaysia were used for training and validation procedures. Forty-one cases collected from the Kyushu University Hospital (KUH) in Japan and fifty-four cases obtained from The Cancer Imaging Archive (TCIA) in America were used for a test procedure. Radiomic features were obtained from BN maps, which represent topologically invariant heterogeneous characteristics of lung cancer on CT images, by applying histogram- and texture-based feature computations. A BN-based signature was determined using support vector machine (SVM) models with the best combination of features that maximized a robustness index (RI) which defined a higher total area under receiver operating characteristics curves (AUCs) and lower difference of AUCs between the training and the validation. The SVM model was built using the signature and optimized in a five-fold cross validation. The BN-based model was compared to conventional original image (OI)- and wavelet-decomposition (WD)-based models with respect to the RI between the validation and the test.
RESULTS: The BN-based model showed a higher RI of 1.51 compared with the models based on the OI (RI: 1.33) and the WD (RI: 1.29).
CONCLUSION: The proposed model showed higher robustness than the conventional models in the identification of EGFR mutations among NSCLC patients. The results suggested the robustness of the BN-based approach against variations in image scanner/scanning parameters.
MATERIALS AND METHODS: The EEG signal was used as a brain response signal, which was evoked by two auditory stimuli (Tones and Consonant Vowels stimulus). The study was carried out on Malaysians (Malay and Chinese) with normal hearing and with hearing loss. A ranking process for the subjects' EEG data and the nonlinear features was used to obtain the maximum classification accuracy.
RESULTS: The study formulated the classification of Normal Hearing Ethnicity Index and Sensorineural Hearing Loss Ethnicity Index. These indices classified the human ethnicity according to brain auditory responses by using numerical values of response signal features. Three classification algorithms were used to verify the human ethnicity. Support Vector Machine (SVM) classified the human ethnicity with an accuracy of 90% in the cases of normal hearing and sensorineural hearing loss (SNHL); the SVM classified with an accuracy of 84%.
CONCLUSION: The classification indices categorized or separated the human ethnicity in both hearing cases of normal hearing and SNHL with high accuracy. The SVM classifier provided a good accuracy in the classification of the auditory brain responses. The proposed indices might constitute valuable tools for the classification of the brain responses according to the human ethnicity.