METHODS: The most used methods for the quantification of Hb A2 are based on automated high performance liquid chromatography (HPLC) or capillary electrophoresis (CE). In particular Hb analyses were performed by HPLC on three dedicated devices. DNA analyses were performed according to local standard protocols.
RESULTS: Here, we described eight new δ-globin gene variants discovered and characterized in some laboratories in Northern Italy in recent years. These new variants were added to the many already known Hb A2 variants that were found with an estimated frequency of about 1-2% during the screening tests in our laboratories.
CONCLUSIONS: The knowledge recognition of the delta variant on Hb analysis and accurate molecular characterization is crucial to provide an accurate definitive thalassemia diagnosis, particularly in young subjects who would like to ask for a prenatal diagnosis or preimplantation genetic diagnosis.
METHODS: Based on the initial screening, a total of 100 participants (n = 50 euthymic, n = 50 depressive) underwent 32-channel EEG acquisition. Simple logistic regression and C-statistic were used to explore if EEG power could be used to discriminate between the groups. The strongest EEG predictors of mood using multivariate logistic regression models.
RESULTS: Simple logistic regression analysis with subsequent C-statistics revealed that only high-alpha and beta power originating from the left central cortex (C3) have a reliable discriminative value (ROC curve >0.7 (70%)) for differentiating the depressive group from the euthymic group. Multivariate regression analysis showed that the single most significant predictor of group (depressive vs. euthymic) is the high-alpha power over C3 (p = 0.03).
CONCLUSION: The present findings suggest that EEG is a useful tool in the identification of neurophysiological correlates of depressive symptoms in young adults with no previous psychiatric history.
SIGNIFICANCE: Our results could guide future studies investigating the early neurophysiological changes and surrogate outcomes in depression.
METHODS: Children aged <18-years scheduled for FB and MDCT were recruited. FB and MDCT were undertaken within 30-min to 7-days of each other. Tracheobronchomalacia (mild, moderate, severe, very severe) diagnosed on FB were independently scored by two pediatric pulmonologists; VB was independently scored by two pairs (each pair = pediatric pulmonologist and radiologist), in a blinded manner.
RESULTS: In 53 children (median age = 2.5 years, range 0.8-14.3) evaluated for airway abnormalities, tracheomalacia was detected in 37 (70%) children at FB. Of these, VB detected tracheomalacia in 20 children, with a sensitivity of 54.1% (95%CI 37.1-70.2), specificity = 87.5% (95%CI 60.4-97.8), and positive predictive value = 90.9% (95%CI 69.4-98.4). The agreement between pediatric pulmonologists for diagnosing tracheomalacia by FB was excellent, weighted κ = 0.8 (95%CI 0.64-0.97); but only fair between the pairs of pediatric pulmonologists/radiologists for VB, weighted κ = 0.47 (95%CI 0.23-0.71). There were 42 cases of bronchomalacia detected on FB. VB had a sensitivity = 45.2% (95%CI 30.2-61.2), specificity = 95.5% (95%CI 94.2-96.5), and positive predictive value = 23.2 (95%CI 14.9-34.0) compared to FB in detecting bronchomalacia.
CONCLUSION: VB cannot replace FB as the gold standard for detecting tracheobronchomalacia in children. However, VB could be considered as an alternative diagnostic modality in children with symptoms suggestive of tracheobronchomalacia where FB is unavailable. Pediatr Pulmonol. 2017;52:480-486. © 2016 Wiley Periodicals, Inc.
OBJECTIVES: This study aimed to segment the breath cycles from pulmonary acoustic signals using the newly developed adaptive neuro-fuzzy inference system (ANFIS) based on breath phase detection and to subsequently evaluate the performance of the system.
METHODS: The normalised averaged power spectral density for each segment was fuzzified, and a set of fuzzy rules was formulated. The ANFIS was developed to detect the breath phases and subsequently perform breath cycle segmentation. To evaluate the performance of the proposed method, the root mean square error (RMSE) and correlation coefficient values were calculated and analysed, and the proposed method was then validated using data collected at KIMS Hospital and the RALE standard dataset.
RESULTS: The analysis of the correlation coefficient of the neuro-fuzzy model, which was performed to evaluate its performance, revealed a correlation strength of r = 0.9925, and the RMSE for the neuro-fuzzy model was found to equal 0.0069.
CONCLUSION: The proposed neuro-fuzzy model performs better than the fuzzy inference system (FIS) in detecting the breath phases and segmenting the breath cycles and requires less rules than FIS.
BACKGROUND AND OBJECTIVE: Interstitial fibrosis in renal biopsy samples is a scarring tissue structure that may be visually quantified by pathologists as an indicator to the presence and extent of chronic kidney disease. The standard method of quantification by visual evaluation presents reproducibility issues in the diagnoses due to the uncertainties in human judgement.
METHODS: An automated quantification system for accurately measuring the amount of interstitial fibrosis in renal biopsy images is presented as a consistent basis of comparison among pathologists. The system identifies the renal tissue structures through knowledge-based rules employing colour space transformations and structural features extraction from the images. In particular, the renal glomerulus identification is based on a multiscale textural feature analysis and a support vector machine. The regions in the biopsy representing interstitial fibrosis are deduced through the elimination of non-interstitial fibrosis structures from the biopsy area. The experiments conducted evaluate the system in terms of quantification accuracy, intra- and inter-observer variability in visual quantification by pathologists, and the effect introduced by the automated quantification system on the pathologists' diagnosis.
RESULTS: A 40-image ground truth dataset has been manually prepared by consulting an experienced pathologist for the validation of the segmentation algorithms. The results from experiments involving experienced pathologists have demonstrated an average error of 9 percentage points in quantification result between the automated system and the pathologists' visual evaluation. Experiments investigating the variability in pathologists involving samples from 70 kidney patients also proved the automated quantification error rate to be on par with the average intra-observer variability in pathologists' quantification.
CONCLUSIONS: The accuracy of the proposed quantification system has been validated with the ground truth dataset and compared against the pathologists' quantification results. It has been shown that the correlation between different pathologists' estimation of interstitial fibrosis area has significantly improved, demonstrating the effectiveness of the quantification system as a diagnostic aide.
OBJECTIVE: This study aimed to review and analyse literature related to the detection and classification of acute leukaemia. The factors that were considered to improve understanding on the field's various contextual aspects in published studies and characteristics were motivation, open challenges that confronted researchers and recommendations presented to researchers to enhance this vital research area.
METHODS: We systematically searched all articles about the classification and detection of acute leukaemia, as well as their evaluation and benchmarking, in three main databases: ScienceDirect, Web of Science and IEEE Xplore from 2007 to 2017. These indices were considered to be sufficiently extensive to encompass our field of literature.
RESULTS: Based on our inclusion and exclusion criteria, 89 articles were selected. Most studies (58/89) focused on the methods or algorithms of acute leukaemia classification, a number of papers (22/89) covered the developed systems for the detection or diagnosis of acute leukaemia and few papers (5/89) presented evaluation and comparative studies. The smallest portion (4/89) of articles comprised reviews and surveys.
DISCUSSION: Acute leukaemia diagnosis, which is a field requiring automated solutions, tools and methods, entails the ability to facilitate early detection or even prediction. Many studies have been performed on the automatic detection and classification of acute leukaemia and their subtypes to promote accurate diagnosis.
CONCLUSIONS: Research areas on medical-image classification vary, but they are all equally vital. We expect this systematic review to help emphasise current research opportunities and thus extend and create additional research fields.
OBJECTIVE: This review aims to evaluate the 13C-UBT diagnostic accuracy studies conducted among Asian population and validate its use for the Asian population.
METHODS: Original articles were systematically searched in PubMed, Scopus, and Google Scholar using the PICOS strategy by applying relevant keywords. Only studies published in English and conducted in Asia were included. Our search returned 276 articles. After assessment, 11 articles which answered our research question and met the criteria set for systematic review and meta-analysis were accepted. A total of 15 study protocols were extracted from the 11 accepted articles.
FINDINGS: Majority of the studies were conducted in Hong Kong (six), followed by Taiwan (five), Japan (two), and one each in Singapore and Israel. All studies had used histology as part of its gold standard of reference. All but one study was performed on adult populations. The summary estimate for sensitivity was 97% (95% CI: 96, 98%), and specificity was 96% (95% CI: 95, 97%), with significant heterogeneity between studies. Adjusting for the dose (50 mg) and breath sample collection time (20 minutes) had improved both accuracy estimates and significantly reduced heterogeneity.
CONCLUSION: This review supports the test-and-treat strategy for H. pylori infection management. Prevalence and cost-effectiveness studies are mandatory for health authorities to adopt this strategy into national policy.
OBJECTIVE: The aim of this review is to examine studies that focused on the different types of samples which may serve as a good and promising biomarker for early diagnosis of CKD or to detect rapidly declining renal function among CKD patient.
METHOD: The review of international literature was made on paper and electronic databases Nature, PubMed, Springer Link and Science Direct. The Scopus index was used to verify the scientific relevance of the papers. Publications were selected based on the inclusion and exclusion criteria.
RESULT: 63 publications were found to be compatible with the study objectives. Several biomarkers of interest with different sample types were taken for comparison.
CONCLUSION: Biomarkers from urine samples yield more significant outcome as compare to biomarkers from blood samples. But, validation and confirmation with a different type of study designed on a larger population is needed. More comparison studies on different types of samples are needed to further illuminate which biomarker is the better tool for the diagnosis and prognosis of CKD.
METHOD: Targeted sequencing of fourteen genes panel was performed to identify the mutations in 29 OI patients with type I, III, IV and V disease. The mutations were determined using Ion Torrent Suite software version 5 and variant annotation was conducted using ANNOVAR. The identified mutations were confirmed using Sanger sequencing and in silico analysis was performed to evaluate the effects of the candidate mutations at protein level.
RESULTS: Majority of patients had mutations in collagen genes, 48% (n = 14) in COL1A1 and 14% (n = 4) in COL1A2. Type I OI was caused by quantitative mutations in COL1A1 whereas most of type III and IV were due to qualitative mutations in both of the collagen genes. Those with quantitative mutations had milder clinical severity compared to qualitative mutations in terms of dentinogenesis imperfecta (DI), bone deformity and the ability to walk with aid. Furthermore, a few patients (28%, n = 8) had mutations in IFITM5, BMP1, P3H1 and SERPINF1.
CONCLUSION: Majority of our OI patients have mutations in collagen genes, similar to other OI populations worldwide. Genotype-phenotype analysis revealed that qualitative mutations had more severe clinical characteristics compared to quantitative mutations. It is crucial to identify the causative mutations and the clinical severity of OI patients may be predicted based on the types of mutations.
METHODOLOGY: A total of 56 consecutive children aged 6 to 18 years old were recruited from the pediatric obesity and type 2 diabetes mellitus (T2DM) clinic in University Malaya Medical Centre (UMMC) from 2016 to 2019. Data on anthropometric measurements, clinical components of metabolic syndrome and fasting serum insulin were collected. Triglyceride to high-density lipoprotein cholesterol ratio (TG: HDL-C), Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) and Single Point Insulin Sensitivity Estimator (SPISE) were calculated. Transient elastography was performed with hepatic steatosis and liver fibrosis assessed by controlled attenuation parameter (CAP) and liver stiffness measurement (LSM), respectively.
RESULTS: A total of 44 children (78.6%) had liver steatosis and 35.7% had presence of significant liver fibrosis (stage F≥2). Majority (89.3%) are obese and 24 children (42.9%) were diagnosed with metabolic syndrome. Higher number of children with T2DM and significant liver fibrosis were associated with higher tertiles of TG: HDL-C ratio (p<0.05). Top tertile of TG: HDL-C ratio was an independent predictor of liver fibrosis (OR=8.14, 95%CI: 1.24-53.36, p=0.029). ROC analysis showed that the area under the curve (AUC) of HOMA-IR (0.77) and TG: HDL-C ratio (0.71) were greater than that of metabolic syndrome (0.70), T2DM (0.62) and SPISE (0.22). The optimal cut-off values of HOMA-IR and TG: HDL-C ratio for detecting liver fibrosis among children with NAFLD are 5.20 and 1.58, respectively.
CONCLUSION: Children with NAFLD and higher TG: HDL-C ratio are more likely to have liver fibrosis. TG: HDL-C ratio is a promising tool to risk stratify those with NAFLD who are at risk of developing advanced liver disease.