'Doctor, Doctor...' entrepreneurial diagnosis and market making

Doctor-patient jokes are universally popular because of the information asymmetries within the diagnostic relationship. We contend that entrepreneurial diagnosis is present in markets where consumers are unable to diagnose their own problems and, instead, may rely on the entrepreneur to diagnose them. Entrepreneurial diagnosis is a cognitive skill possessed by the entrepreneur. It is an identifiable subset of entrepreneurial judgment and can be modeled – which we attempt to do. In order to overcome the information asymmetries and exploit opportunities, we suggest that entrepreneurs must invest in market making innovations (as distinct from product innovations) such as trustworthy reputations. The diagnostic entrepreneur described in this paper represents a creative response to difficult diagnostic problems and helps to explain the success of many firms whose products are not particularly innovative but which are perceived as offering high standards of service. These firms are trusted not only for their truthfulness about the quality of their product, but for their honesty, confidentiality and understanding in helping customers identify the most appropriate product to their needs.

Towards the computer-aided diagnosis of dementia based on the geometric and network connectivity of structural MRI data

We present an intuitive geometric approach for analysing the structure and fragility of T1-weighted structural MRI scans of human brains. Apart from computing characteristics like the surface area and volume of regions of the brain that consist of highly active voxels, we also employ Network Theory in order to test how close these regions are to breaking apart. This analysis is used in an attempt to automatically classify subjects into three categories: Alzheimer’s disease, mild cognitive impairment and healthy controls, for the CADDementia Challenge.

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n = 30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org.

Towards pain-free diagnosis of skin diseases through multiplexed microneedles: biomarker extraction and detection using a highly sensitive blotting method

Immunodiagnostic microneedles provide a novel way to extract protein biomarkers from the skin in a minimally invasive manner for analysis in vitro. The technology could overcome challenges in biomarker analysis specifically in solid tissue, which currently often involves invasive biopsies. This study describes the development of a multiplex immunodiagnostic device incorporating mechanisms to detect multiple antigens simultaneously, as well as internal assay controls for result validation. A novel detection method is also proposed. It enables signal detection specifically at microneedle tips and therefore may aid the construction of depth profiles of skin biomarkers. The detection method can be coupled with computerised densitometry for signal quantitation. The antigen specificity, sensitivity and functional stability of the device were assessed against a number of model biomarkers. Detection and analysis of endogenous antigens (interleukins 1α and 6) from the skin using the device was demonstrated. The results were verified using conventional enzyme-linked immunosorbent assays. The detection limit of the microneedle device, at ≤10 pg/mL, was at least comparable to conventional plate-based solid-phase enzyme immunoassays.

The case of pre-natal diagnosis

This chapter reconsiders critiques of pre-natal diagnosis in Disability Studies. Underlying assumptions about reproductive technologies are analysed to demonstrate that while many critiques of pre-natal diagnosis by Disability activists and theorists are concerned about children being the product of 'choice' through the selective effects of pre-natal diagnosis, the issue that reproductive technologies (such as IVF) themselves necessarily always already rely on 'choice' -- namely the choice for a 'biological' or 'own' child (different terms are used) -- is nowhere considered. The chapter considers several consequences of thinking through this issue and its implications.

Effects of prenatal and postnatal depression, and maternal stroking, at the glucocorticoid receptor gene

In animal models, prenatal and postnatal stress is associated with elevated hypothalamic–pituitary axis (HPA) reactivity mediated via altered glucocorticoid receptor (GR) gene expression. Postnatal tactile stimulation is associated with reduced HPA reactivity mediated via increased GR gene expression. In this first study in humans to examine the joint effects of prenatal and postnatal environmental exposures, we report that GR gene (NR3C1) 1-F promoter methylation in infants is elevated in the presence of increased maternal postnatal depression following low prenatal depression, and that this effect is reversed by self-reported stroking of the infants by their mothers over the first weeks of life.

Sex differences in the programming effects of prenatal stress on psychopathology and stress responses: an evolutionary perspective

There is strong evidence from animal studies that prenatal stress has different effects on male and female offspring. In general, although not always, prenatal stress increases anxiety, depression and stress responses, both hypothalamic–pituitary–adrenal and cardiovascular, in female offspring rather than in male. Males are more likely to show learning and memory deficits. There have been few studies so far in humans which differentiate effects of prenatal stress on male and female psychopathology. Some studies support the animal models, but the evidence is inconsistent. The mediating mechanisms for any sex specific effects are little understood, but there is evidence that placental function can differ depending on the sex of the fetus. We suggest that there may be an evolutionary reason for any sex differences in the long term effects of prenatal stress. In a stressful environment it may be adaptive for females, who are more likely to stay in one place and look after children, to be more vigilant, alert to danger and thus show more stress responsiveness. This can give rise to a more anxious or depressed phenotype. With males it may be more adaptive to go out and explore new environments, compete with other males, and be more aggressive. For this it may help to be less responsive to external stressors. More research is needed into sex differences in the effects of prenatal stress in humans, to test these ideas.

Frequency of infant stroking reported by mothers moderates the effect of prenatal depression on infant behavioural and physiological outcomes

Animal studies find that prenatal stress is associated with increased physiological and emotional reactivity later in life, mediated via fetal programming of the HPA axis through decreased glucocorticoid receptor (GR) gene expression. Post-natal behaviours, notably licking and grooming in rats, cause decreased behavioural indices of fear and reduced HPA axis reactivity mediated via increased GR gene expression. Post-natal maternal behaviours may therefore be expected to modify prenatal effects, but this has not previously been examined in humans. We examined whether, according to self-report, maternal stroking over the first weeks of life modified associations between prenatal depression and physiological and behavioral outcomes in infancy, hence mimicking effects of rodent licking and grooming. From a general population sample of 1233 first time mothers recruited at 20 weeks gestation we drew a stratified random sample of 316 for assessment at 32 weeks based on reported inter-partner psychological abuse, a risk to child development. Of these 271 provided data at 5, 9 and 29 weeks post delivery. Mothers reported how often they stroked their babies at 5 and 9 weeks. At 29 weeks vagal withdrawal to a stressor, a measure of physiological adaptability, and maternal reported negative emotionality were assessed. There was a significant interaction between prenatal depression and maternal stroking in the prediction of vagal reactivity to a stressor (p = .01), and maternal reports of infant anger proneness (p = .007) and fear (p = .043). Increasing maternal depression was associated with decreasing physiological adaptability, and with increasing negative emotionality, only in the presence of low maternal stroking. These initial findings in humans indicate that maternal stroking in infancy, as reported by mothers, has effects strongly resembling the effects of observed maternal behaviours in animals, pointing to future studies of the epigenetic, physiological and behavioral effects of maternal stroking.

Predictors of onset of depression and anxiety in the year after diagnosis of breast cancer

Background Depression and anxiety are common after diagnosis of breast cancer. We examined to what extent these are recurrences of previous disorder and, controlling for this, whether shame, self-blame and low social support after diagnosis predicted onset of depression and anxiety subsequently. Method Women with primary breast cancer who had been treated surgically self-reported shame, self-blame, social support and emotional distress post-operatively. Psychiatric interview 12 months later identified those with adult lifetime episodes of major depression (MD) or generalized anxiety disorder (GAD) before diagnosis and onset over the subsequent year. Statistical analysis examined predictors of each disorder in that year. Results Of the patients, two-thirds with episodes of MD and 40% with episodes of GAD during the year after diagnosis were experiencing recurrence of previous disorder. Although low social support, self-blame and shame were each associated with both MD and GAD after diagnosis, they did not mediate the relationship of disorder after diagnosis with previous disorder. Low social support, but not shame or self-blame, predicted recurrence after controlling for previous disorder. Conclusions Anxiety and depression during the first year after diagnosis of breast cancer are often the recurrence of previous disorder. In predicting disorder following diagnosis, self-blame and shame are merely markers of previous disorder. Low social support is an independent predictor and therefore may have a causal role.

The perception of support received from breast care nurses by depressed patients following a diagnosis of breast cancer

INTRODUCTION Due to their specialist training, breast care nurses (BCNs) should be able to detect emotional distress and offer support to breast cancer patients. However, patients who are most distressed after diagnosis generally experience least support from care staff. To test whether BCNs overcome this potential barrier, we compared the support experienced by depressed and non-depressed patients from their BCNs and the other main professionals involved in their care: surgeons and ward nurses. PATIENTS AND METHODS Women with primary breast cancer (n = 355) 2–4 days after mastectomy or wide local excision, self-reported perceived professional support and current depression. Analysis of variance compared support ratings of depressed and non-depressed patients across staff types. RESULTS There was evidence of depression in 31 (9%) patients. Depressed patients recorded less surgeon and ward nurse support than those who were not depressed but the support received by patients from the BCN was high, whether or not patients were depressed. CONCLUSIONS BCNs were able to provide as much support to depressed patients as to non-depressed patients, whereas depressed patients felt less supported by surgeons and ward nurses than did non-depressed patients. Future research should examine the basis of BCNs' ability to overcome barriers to support in depressed patients. Our findings confirm the importance of maintaining the special role of the BCN.

An LDA and probability-based classifier for the diagnosis of Alzheimer's Disease from structural MRI

In this paper a custom classification algorithm based on linear discriminant analysis and probability-based weights is implemented and applied to the hippocampus measurements of structural magnetic resonance images from healthy subjects and Alzheimer’s Disease sufferers; and then attempts to diagnose them as accurately as possible. The classifier works by classifying each measurement of a hippocampal volume as healthy controlsized or Alzheimer’s Disease-sized, these new features are then weighted and used to classify the subject as a healthy control or suffering from Alzheimer’s Disease. The preliminary results obtained reach an accuracy of 85.8% and this is a similar accuracy to state-of-the-art methods such as a Naive Bayes classifier and a Support Vector Machine. An advantage of the method proposed in this paper over the aforementioned state of the art classifiers is the descriptive ability of the classifications it produces. The descriptive model can be of great help to aid a doctor in the diagnosis of Alzheimer’s Disease, or even further the understand of how Alzheimer’s Disease affects the hippocampus.

Mobile sink based fault diagnosis scheme for wireless sensor networks

Network diagnosis in Wireless Sensor Networks (WSNs) is a difficult task due to their improvisational nature, invisibility of internal running status, and particularly since the network structure can frequently change due to link failure. To solve this problem, we propose a Mobile Sink (MS) based distributed fault diagnosis algorithm for WSNs. An MS, or mobile fault detector is usually a mobile robot or vehicle equipped with a wireless transceiver that performs the task of a mobile base station while also diagnosing the hardware and software status of deployed network sensors. Our MS mobile fault detector moves through the network area polling each static sensor node to diagnose the hardware and software status of nearby sensor nodes using only single hop communication. Therefore, the fault detection accuracy and functionality of the network is significantly increased. In order to maintain an excellent Quality of Service (QoS), we employ an optimal fault diagnosis tour planning algorithm. In addition to saving energy and time, the tour planning algorithm excludes faulty sensor nodes from the next diagnosis tour. We demonstrate the effectiveness of the proposed algorithms through simulation and real life experimental results.