The radiological diagnosis of pneumonia in children

Despite the importance of paediatric pneumonia as a cause of short and long-term morbidity and mortality worldwide, a reliable gold standard for its diagnosis remains elusive. The utility of clinical, microbiological and radiological diagnostic approaches varies widely within and between populations and is heavily dependent on the expertise and resources available in various settings. Here we review the role of radiology in the diagnosis of paediatric pneumonia. Chest radiographs (CXRs) are the most widely employed test, however, they are not indicated in ambulatory settings, cannot distinguish between viral and bacterial infections and have a limited role in the ongoing management of disease. A standardised definition of alveolar pneumonia on a CXR exists for epidemiological studies targeting bacterial pneumonias but it should not be extrapolated to clinical settings. Radiography, computed tomography and to a lesser extent ultrasonography and magnetic resonance imaging play an important role in complicated pneumonias but there are limitations that preclude their use as routine diagnostic tools. Large population-based studies are needed in different populations to address many of the knowledge gaps in the radiological diagnosis of pneumonia in children, however, the feasibility of such studies is an important barrier.

Echolocation calls, wing shape, diet and phylogenetic diagnosis of the endemic Chinese bat Myotis pequinius

We describe the echolocation calls, flight morphology and diet of the endemic Chinese bat Myotis pequinius Thomas, 1908. Orientation calls are broadband, and reach low terminal frequencies. Diet comprised 80% beetles by volume. Wing shape and call design suggest that the bats fly in cluttered habitats, and the possession of moderately long ears and the dietary composition imply they forage at least sometimes by gleaning. Myotis pequinius resembles a larger Oriental version of the western Palaearctic species M. nattereri. Phylogenetic analysis based on sequences of the cytochrome b gene of mitochondrial DNA (1,140 base pairs) from a range of Palaearctic Myotis species confirmed that M. pequinius is close to the nattereri group, and is a sister-species to the eastern Palaearctic M. bombinus. One bat sequenced from China could not be identified from available species descriptions. It was smaller than M. pequinius, and also differed from it in sequence divergence by 6.7%, suggesting the existence of additional, cryptic taxonomic diversity in this group. Our phylogenetic analysis also supports the recognition of M. schaubi as a species distinct from M. nattereri in Transcaucasia and south-western Asia. Myotis nattereri tschuliensis is more closely related to M. schaubi than to M. nattereri, and is best considered either as a subspecies of M. schaubi, or possibly as a distinct species.

Wheeze, asthma diagnosis and medication use : a national adult survey in a developing country

Background As relatively little is known about adult wheeze and asthma in developing countries, this study aimed to determine the predictors of wheeze, asthma diagnosis, and current treatment in a national survey of South African adults. Methods A stratified national probability sample of households was drawn and all adults (>14 years) in the selected households were interviewed. Outcomes of interest were recent wheeze, asthma diagnosis, and current use of asthma medication. Predictors of interest were sex, age, household asset index, education, racial group, urban residence, medical insurance, domestic exposure to smoky fuels, occupational exposure, smoking, body mass index, and past tuberculosis. Results A total of 5671 men and 8155 women were studied. Although recent wheeze was reported by 14.4% of men and 17.6% of women and asthma diagnosis by 3.7% of men and 3.8% of women, women were less likely than men to be on current treatment (OR 0.6; 95% confidence interval (CI) 0.5 to 0.8). A history of tuberculosis was an independent predictor of both recent wheeze (OR 3.4; 95% CI 2.5 to 4.7) and asthma diagnosis (OR 2.2; 95% CI 1.5 to 3.2), as was occupational exposure (wheeze: OR 1.8; 95% CI 1.5 to 2.0; asthma diagnosis: OR 1.9; 95% CI 1.4 to 2.4). Smoking was associated with wheeze but not asthma diagnosis. Obesity showed an association with wheeze only in younger women. Both wheeze and asthma diagnosis were more prevalent in those with less education but had no association with the asset index. Independently, having medical insurance was associated with a higher prevalence of diagnosis. Conclusions Some of the findings may be to due to reporting bias and heterogeneity of the categories wheeze and asthma diagnosis, which may overlap with post tuberculous airways obstruction and chronic obstructive pulmonary disease due to smoking and occupational exposures. The results underline the importance of controlling tuberculosis and occupational exposures as well as smoking in reducing chronic respiratory morbidity. Validation of the asthma questionnaire in this setting and research into the pathophysiology of post tuberculous airways obstruction are also needed.

Reliable Fault Diagnosis for Low-Speed Bearings Using Individually Trained Support Vector Machines With Kernel Discriminative Feature Analysis

This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The proposed approach first extracts wavelet-based fault features that represent diverse symptoms of multiple low-speed bearing defects. The most useful fault features for diagnosis are then selected by utilizing a genetic algorithm (GA)-based kernel discriminative feature analysis cooperating with one-against-all multicategory support vector machines (OAA MCSVMs). Finally, each support vector machine is individually trained with its own feature vector that includes the most discriminative fault features, offering the highest classification performance. In this study, the effectiveness of the proposed GA-based kernel discriminative feature analysis and the classification ability of individually trained OAA MCSVMs are addressed in terms of average classification accuracy. In addition, the proposedGA- based kernel discriminative feature analysis is compared with four other state-of-the-art feature analysis approaches. Experimental results indicate that the proposed approach is superior to other feature analysis methodologies, yielding an average classification accuracy of 98.06% and 94.49% under rotational speeds of 50 revolutions-per-minute (RPM) and 80 RPM, respectively. Furthermore, the individually trained MCSVMs with their own optimal fault features based on the proposed GA-based kernel discriminative feature analysis outperform the standard OAA MCSVMs, showing an average accuracy of 98.66% and 95.01% for bearings under rotational speeds of 50 RPM and 80 RPM, respectively.

Automated melanoma diagnosis system

An automated melanoma diagnosis system, the so-called Skin Polar-probe, was developed to improve the chances of early detection of skin cancers and help save the lives of melanoma victims. The system will offer unique benefits to aid early detection of melanoma - the key to reducing deaths caused by this cancer.

The effect of the ecstasy 'come-down' on the diagnosis of ecstasy dependence

Background: The existence of an ecstasy dependence syndrome is controversial. We examined whether the acute after-effects of ecstasy use (i.e., the “come-down”) falsely lead to the identification of ecstasy withdrawal and the subsequent diagnosis of ecstasy dependence. Methods: The Structured Clinical Interview for DSM-IV-TR Disorders: Research Version (SCID-RV) was administered to 214 Australian ecstasy users. Ecstasy withdrawal was operationalized in three contrasting ways: (i) as per DSM-IV criteria; (ii) as the expected after effects of ecstasy (a regular come-down); or (iii) as a substantially greater or longer come-down than on first use (intense come-down). These definitions were validated against frequency of ecstasy use, readiness to change and ability to resist the urge to use ecstasy. Confirmatory factor analyses were used to see how they aligned with the overall dependence syndrome. Results: Come-down symptoms increased the prevalence of withdrawal from 1% (DSM-IV criterion) to 11% (intense come-downs) and 75% (regular come-downs). Past year ecstasy dependence remained at 31% when including the DSM-IV withdrawal criteria and was 32% with intense come-downs, but increased to 45% with regular come-downs. Intense come-downs were associated with lower ability to resist ecstasy use and loaded positively on the dependence syndrome. Regular come-downs did not load positively on the ecstasy dependence syndrome and were not related to other indices of dependence. Conclusion: The acute after-effects of ecstasy should be excluded when assessing ecstasy withdrawal as they can lead to a false diagnosis of ecstasy dependence. Worsening of the ecstasy come-down may be a marker for dependence.

Diagnose this if you can: On the effectiveness of search engines in finding medical self-diagnosis information

An increasing amount of people seek health advice on the web using search engines; this poses challenging problems for current search technologies. In this paper we report an initial study of the effectiveness of current search engines in retrieving relevant information for diagnostic medical circumlocutory queries, i.e., queries that are issued by people seeking information about their health condition using a description of the symptoms they observes (e.g. hives all over body) rather than the medical term (e.g. urticaria). This type of queries frequently happens when people are unfamiliar with a domain or language and they are common among health information seekers attempting to self-diagnose or self-treat themselves. Our analysis reveals that current search engines are not equipped to effectively satisfy such information needs; this can have potential harmful outcomes on people’s health. Our results advocate for more research in developing information retrieval methods to support such complex information needs.

Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm

In this paper, we propose a highly reliable fault diagnosis scheme for incipient low-speed rolling element bearing failures. The scheme consists of fault feature calculation, discriminative fault feature analysis, and fault classification. The proposed approach first computes wavelet-based fault features, including the respective relative wavelet packet node energy and entropy, by applying a wavelet packet transform to an incoming acoustic emission signal. The most discriminative fault features are then filtered from the originally produced feature vector by using discriminative fault feature analysis based on a binary bat algorithm (BBA). Finally, the proposed approach employs one-against-all multiclass support vector machines to identify multiple low-speed rolling element bearing defects. This study compares the proposed BBA-based dimensionality reduction scheme with four other dimensionality reduction methodologies in terms of classification performance. Experimental results show that the proposed methodology is superior to other dimensionality reduction approaches, yielding an average classification accuracy of 94.9%, 95.8%, and 98.4% under bearing rotational speeds at 20 revolutions-per-minute (RPM), 80 RPM, and 140 RPM, respectively.

Current practice in nutrition diagnosis and intervention for the management of Parkinson's disease in Australia and Canada

Aim To document current practice by dietitians in Australia and Canada in the nutrition management of Parkinson's disease. This will help identify priority areas for review and development of practice guidelines and direct future research. Methods Current practice in the phases of the Nutrition Care Plan was captured using an online survey distributed to Dietitians Association of Australia members and Practice-Based Evidence in Nutrition subscribers through their email newsletters. The results of the diagnosis, intervention and monitoring phases are presented here. Results Eighty-four dietitians responded. There was consistency in practice for nutrition issues that are encountered in other populations, such as malnutrition and constipation. There was more variation in practice in the nutrition issues that are more specific to Parkinson's disease, such as nutrition and meal interactions with medication. A lack of awareness of emerging treatments, such as deep brain stimulation surgery, appears to exist in the responding dietitians. Conclusions The variation in practice that was present for the nutrition issues specific to Parkinson's disease may reflect the lack of quality evidence and subsequently evidence-based guidelines in these areas. Work to provide background information about treatment options and to translate current evidence for the nutrition issues that are specific to Parkinson's disease into practice recommendations should be completed.

Teaching children who have a diagnosis of Autism Spectrum Disorder

Best practice dictates that the Autism Spectrum Disorder (ASD) diagnostic process is informed by experienced professionals from at least two disciplines, for example psychology or speech pathology, with the diagnosis ultimately provided by a specialist medical practitioner e.g. child psychiatrist, neurologist or paediatrician. Irrespective of a child’s age, diagnosis relies upon information about their early development. Current information and observations on a child’s behaviour, communication and socialisation are considered by the specialist medical practitioner against the signs and symptoms detailed in one of several diagnostic systems. Two recently used classification systems in Australia have been the fourth edition of the Diagnostic Statistical Manual of Mental Disorders (DSM-IV) published by the American Psychiatric Association (1994) and the tenth edition of the International Classification of Disease (ICD-10), published by the World Health Organisation (2003).

Kits and methods for diagnosis, screening, treatment and disease monitoring [WO 2015048852 A1]

Disclosed are methods for detecting the presence of a carcinoma or an increased likelihood that a carcinoma is present in a subject. More particularly, the present invention discloses methods for diagnosis, screening, treatment and monitoring of carcinomas associated with aberrant DNA methylation of the MED15 promoter region

Current trends in the etiology and diagnosis of HPV-related head and neck cancers

Human papilloma virus (HPV) infection is a major risk factor for a distinct subset of head and neck squamous cell carcinoma (HNSCC). The current review summarizes the epidemiology of HNSCC and the disease burden, the infectious cycle of HPV, the roles of viral oncoproteins, E6 and E7, and the downstream cellular events that lead to malignant transformation. Current techniques for the clinical diagnosis of HPV-associated HNSCC will also be discussed, that is, the detection of HPV DNA, RNA, and the HPV surrogate marker, p16 in tumor tissues, as well as HPV-specific antibodies in serum. Such methods do not allow for the early detection of HPV-associated HNSCC and most cases are at an advanced stage upon diagnosis. Novel noninvasive approaches using oral fluid, a clinically relevant biological fluid, allow for the detection of HPV and cellular alterations in infected cells, which may aid in the early detection and HPV-typing of HNSCC tumors. Noninvasive diagnostic methods will enable early detection and intervention, leading to a significant reduction in mortality and morbidity associated with HNSCC.

A data mining approach for fault diagnosis: An application of anomaly detection algorithm

Rolling-element bearing failures are the most frequent problems in rotating machinery, which can be catastrophic and cause major downtime. Hence, providing advance failure warning and precise fault detection in such components are pivotal and cost-effective. The vast majority of past research has focused on signal processing and spectral analysis for fault diagnostics in rotating components. In this study, a data mining approach using a machine learning technique called anomaly detection (AD) is presented. This method employs classification techniques to discriminate between defect examples. Two features, kurtosis and Non-Gaussianity Score (NGS), are extracted to develop anomaly detection algorithms. The performance of the developed algorithms was examined through real data from a test to failure bearing. Finally, the application of anomaly detection is compared with one of the popular methods called Support Vector Machine (SVM) to investigate the sensitivity and accuracy of this approach and its ability to detect the anomalies in early stages.

Plasma-enabled carbon nanostructures for early diagnosis of neurodegenerative diseases

Carbon nanostructures (CNs) are amongst the most promising biorecognition nanomaterials due to their unprecedented optical, electrical and structural properties. As such, CNs may be harnessed to tackle the detrimental public health and socio-economic adversities associated with neurodegenerative diseases (NDs). In particular, CNs may be tailored for a specific determination of biomarkers indicative of NDs. However, the realization of such a biosensor represents a significant technological challenge in the uniform fabrication of CNs with outstanding qualities in order to facilitate a highly-sensitive detection of biomarkers suspended in complex biological environments. Notably, the versatility of plasma-based techniques for the synthesis and surface modification of CNs may be embraced to optimize the biorecognition performance and capabilities. This review surveys the recent advances in CN-based biosensors, and highlights the benefits of plasma-processing techniques to enable, enhance, and tailor the performance and optimize the fabrication of CNs, towards the construction of biosensors with unparalleled performance for the early diagnosis of NDs, via a plethora of energy-efficient, environmentally-benign, and inexpensive approaches.