The FIGO ovulatory disorders classification system.

peer reviewedOvulatory disorders are common causes of amenorrhea, abnormal uterine bleeding, and infertility, and are frequent manifestations of polycystic ovary syndrome (PCOS). There are many potential causes and contributors to ovulatory dysfunction that challenge clinicians, trainees, educators, and those who perform basic, translational, clinical, and epidemiological research. Similarly, therapeutic approaches to ovulatory dysfunction potentially involve a spectrum of lifestyle, psychological, medical, and procedural interventions. Collaborative research, effective education, and consistent clinical care remain challenged by the absence of a consensus comprehensive system for classification of these disorders. The existing and complex system, attributed to WHO, was developed more than three decades ago and did not consider more than 30 years of research into these disorders in addition to technical advances in imaging and endocrinology. This manuscript describes the development of a new classification of ovulatory disorders performed under the aegis of the International Federation of Gynecology and Obstetrics (FIGO) and conducted using a rigorously applied Delphi process. The stakeholder organizations and individuals who participated in this process comprised specialty journals, experts at large, national, specialty obstetrical and gynecological societies, and informed lay representatives. After two face-to-face meetings and five Delphi rounds, the result is a three-level multi-tiered system. The system is applied after a preliminary assessment identifies the presence of an ovulatory disorder. The primary level of the system is based on an anatomic model (Hypothalamus, Pituitary, Ovary) that is completed with a separate category for PCOS. This core component of the system is easily remembered using the acronym HyPO-P. Each anatomic category is stratified in the second layer of the system to provide granularity for investigators, clinicians, and trainees using the "GAIN-FIT-PIE" mnemonic (Genetic, Autoimmune, Iatrogenic, Neoplasm; Functional, Infectious and Inflammatory, Trauma and Vascular; Physiological, Idiopathic, Endocrine). The tertiary level allows for specific diagnostic entities. It is anticipated that, if widely adopted, this system will facilitate education, clinical care, and the design and interpretation of research in a fashion that better informs progress in this field. Integral to the deployment of this system is a periodic process of reevaluation and appropriate revision, reflecting an improved understanding of this collection of disorders

Improving smartphone based transport mode recognition using generative adversarial networks

Wearable devices such as smartphones and smartwatches are widely used and record a significant amount of data. Labelling this data for human activity recognition is a time-consuming task, therefore methods which reduce the amount of labelled data required to train accurate classifiers are important. Generative Adversarial Networks (GANs) can be used to model the implicit distribution of a dataset. Traditional GANs, which only consist of a generator and a discriminator, result in networks able to generate synthetic data and distinguish real from fake samples. This adversarial game can be extended to include a classifier, which allows the training of the classification network to be enhanced with synthetic and unlabelled data. The network architecture presented in this paper is inspired by SenseGAN [1], but instead of generating and classifying sensor-recorded time-series data, our approach operates with extracted features, which drastically reduces the amount of stored and processed data and enables deployment on less powerful and potentially wearable devices. We show that this technique can be used to improve the classification performance of a classifier trained to recognise locomotion modes based on recorded acceleration data and that it reduces the amount of labelled training data necessary to achieve a similar performance compared to a baseline classifier. Specifically, our approach reached the same accuracy as the baseline classifier up to 50% faster and was able to achieve a 10% higher accuracy in the same number of epochs

EEuGene: employing electroencephalograph signals in the rating strategy of a hardware-based interactive genetic algorithm

We describe a novel interface and development platform for an interactive Genetic Algorithm (iGA) that uses Electroencephalograph (EEG) signals as an indication of fitness for selection for successive generations. A gaming headset was used to generate EEG readings corresponding to attention and meditation states from a single electrode. These were communicated via Bluetooth to an embedded iGA implemented on the Arduino platform. The readings were taken to measure subjects’ responses to predetermined short sequences of synthesised sound, although the technique could be applied any appropriate problem domain. The prototype provided sufficient evidence to indicate that use of the technology in this context is viable. However, the approach taken was limited by the technical characteristics of the equipment used and only provides proof of concept at this stage. We discuss some of the limitations of using biofeedback systems and suggest possible improvements that might be made with more sophisticated EEG sensors and other biofeedback mechanisms

Can the Non-pneumatic Anti-Shock Garment (NASG) reduce adverse maternal outcomes from postpartum hemorrhage? Evidence from Egypt and Nigeria

<p>Abstract</p> <p>Background</p> <p>Postpartum hemorrhage (PPH) is the leading cause of maternal mortality and severe maternal morbidity. The Non-pneumatic Anti-Shock Garment (NASG), a first-aid lower-body compression device, may decrease adverse outcomes from obstetric hemorrhage. This article is the first to report the effect of the NASG for PPH.</p> <p>Methods</p> <p>This pre-intervention/NASG study of 854 women was conducted in four referral facilities in Nigeria and two in Egypt between 2004-2008. Entry criteria were women with PPH due to uterine atony, retained placenta, ruptured uterus, vaginal or cervical lacerations or placenta accreta with estimated blood loss of ≥ 750 mL and one clinical sign of shock. Differences in demographics, conditions on study entry, treatment and outcomes were examined. The Wilcoxon rank-sum test and relative risks with 95% confidence intervals were calculated for primary outcomes - measured blood loss, emergency hysterectomy, mortality, morbidity (each individually), and a combined variable, "adverse outcomes", defined as severe morbidity and mortality. A multiple logistic regression model was fitted to test the independent association between the NASG and the combined severe morbidity and mortality outcome.</p> <p>Results</p> <p>Measured blood loss decreased by 50% between phases; women experienced 400 mL of median blood loss after study entry in the pre-intervention and 200 mL in the NASG phase (p < 0.0001). As individual outcomes, mortality decreased from 9% pre-intervention to 3.1% in the NASG phase (RR 0.35, 95% CI 0.19-0.62); severe morbidity decreased from 4.2% to 1%, in the NASG phase (RR 0.24, 95% CI 0.09-0.67). As a combination, "adverse outcomes," decreased from 12.8% to 4.1% in the NASG phase (RR 0.32, 95% CI 0.19-0.53). In a multiple logistic regression model, the NASG was associated with the combined outcome of severe maternal morbidity and mortality (OR 0.42, 95% CI 0.18-0.99).</p> <p>Conclusion</p> <p>In this non-randomized study, in which bias is inherent, the NASG showed promise for reducing blood loss, emergency hysterectomy, morbidity and mortality associated with PPH in referral facilities in Egypt and Nigeria.</p

New paradigm old thinking: the case for emergency obstetric care in the prevention of maternal mortality in Nigeria

<p>Abstract</p> <p>Background</p> <p>The continuing burden of maternal mortality, especially in developing countries has prompted a shift in paradigm from the traditional risk assessment approach to the provision of access to emergency obstetric care services for all women who are pregnant. This study assessed the knowledge of maternity unit operatives at the primary and secondary levels of care about the concept of emergency obstetric care (EmOC) and investigated the contents of antenatal care (ANC) counseling services they delivered to clients. It also described the operatives' preferred strategies and practices for promoting safe motherhood and averting maternal mortality in South-west Nigeria.</p> <p>Methods</p> <p>The study population included all the 152 health workers (doctors, midwives, nurses and community health extension workers) employed in the maternity units of all the public health facilities (n = 22) offering maternity care in five cities of 2 states. Data were collected with the aid of a self-administered, semi-structured questionnaire and non-participant observation checklist. Results were presented using descriptive statistics.</p> <p>Results</p> <p>Ninety one percent of the maternity unit staff had poor knowledge concerning the concept of EmOC, with no difference in knowledge of respondents across age groups. While consistently more than 60% of staff reported the inclusion of specific client-centered messages such as birth preparedness and warning/danger signs of pregnancy and delivery in the (ANC) delivered to clients, structured observations revealed that less than a quarter of staff actually did this. Furthermore, only 40% of staff reported counseling clients on complication readiness, but structured observations revealed that no staff did. Only 9% of staff had ever been trained in lifesaving skills (LSS). Concerning strategies for averting maternal deaths, 70% of respondents still preferred the strengthening of routine ANC services in the health facilities to the provision of access to EmOC services for all pregnant women who need it.</p> <p>Conclusion</p> <p>We concluded that maternity unit operatives at the primary and secondary care levels in South-west Nigeria were poorly knowledgeable about the concept of emergency obstetric care services and they still prioritized the strengthening of routine antenatal care services based on the risk approach over other interventions for promoting safe motherhood despite a global current shift in paradigm. There is an urgent need to reorientate/retrain the staff in line with global best practices.</p

Recognition of Crowd Behavior from Mobile Sensors with Pattern Analysis and Graph Clustering Methods

Mobile on-body sensing has distinct advantages for the analysis and understanding of crowd dynamics: sensing is not geographically restricted to a specific instrumented area, mobile phones offer on-body sensing and they are already deployed on a large scale, and the rich sets of sensors they contain allows one to characterize the behavior of users through pattern recognition techniques. In this paper we present a methodological framework for the machine recognition of crowd behavior from on-body sensors, such as those in mobile phones. The recognition of crowd behaviors opens the way to the acquisition of large-scale datasets for the analysis and understanding of crowd dynamics. It has also practical safety applications by providing improved crowd situational awareness in cases of emergency. The framework comprises: behavioral recognition with the user's mobile device, pairwise analyses of the activity relatedness of two users, and graph clustering in order to uncover globally, which users participate in a given crowd behavior. We illustrate this framework for the identification of groups of persons walking, using empirically collected data. We discuss the challenges and research avenues for theoretical and applied mathematics arising from the mobile sensing of crowd behaviors

The FIGO Ovulatory Disorders Classification System.

peer reviewedOvulatory disorders are common causes of amenorrhea, abnormal uterine bleeding, and infertility, and are frequent manifestations of polycystic ovary syndrome (PCOS). There are many potential causes and contributors to ovulatory dysfunction that challenge clinicians, trainees, educators, and those who perform basic, translational, clinical, and epidemiological research. Similarly, therapeutic approaches to ovulatory dysfunction potentially involve a spectrum of lifestyle, psychological, medical, and procedural interventions. Collaborative research, effective education, and consistent clinical care remain challenged by the absence of a consensus comprehensive system for classification of these disorders. The existing and complex system, attributed to WHO, was developed more than three decades ago and did not consider more than 30 years of research into these disorders in addition to technical advances in imaging and endocrinology. This manuscript describes the development of a new classification of ovulatory disorders performed under the aegis of the International Federation of Gynecology and Obstetrics (FIGO) and conducted using a rigorously applied Delphi process. The stakeholder organizations and individuals who participated in this process comprised specialty journals, experts at large, national, specialty obstetrical and gynecological societies, and informed lay representatives. After two face-to-face meetings and five Delphi rounds, the result is a three-level multi-tiered system. The system is applied after a preliminary assessment identifies the presence of an ovulatory disorder. The primary level of the system is based on an anatomic model (Hypothalamus, Pituitary, Ovary) that is completed with a separate category for PCOS. This core component of the system is easily remembered using the acronym HyPO-P. Each anatomic category is stratified in the second layer of the system to provide granularity for investigators, clinicians, and trainees using the "GAIN-FIT-PIE" mnemonic (Genetic, Autoimmune, Iatrogenic, Neoplasm; Functional, Infectious and Inflammatory, Trauma and vascular; Physiological, Idiopathic, Endocrine). The tertiary level allows for specific diagnostic entities. It is anticipated that, if widely adopted, this system will facilitate education, clinical care, and the design and interpretation of research in a fashion that better informs progress in this field. Integral to the deployment of this system is a periodic process of reevaluation and appropriate revision, reflecting an improved understanding of this collection of disorders

Effectiveness of community based safe motherhood promoters in improving the utilization of obstetric care. The case of Mtwara Rural District in Tanzania

In Tanzania, maternal mortality ratio remains unacceptably high at 578/100,000 live births. Despite a high coverage of antenatal care (96%), only 44% of deliveries take place within the formal health services. Still, "Ensure skilled attendant at birth" is acknowledged as one of the most effective interventions to reduce maternal deaths. Exploring the potential of community-based interventions in increasing the utilization of obstetric care, the study aimed at developing, testing and assessing a community-based safe motherhood intervention in Mtwara rural District of Tanzania. This community-based intervention was designed as a pre-post comparison study, covering 4 villages with a total population of 8300. Intervention activities were implemented by 50 trained safe motherhood promoters (SMPs). Their tasks focused on promoting early and complete antenatal care visits and delivery with a skilled attendant. Data on all 512 deliveries taking place from October 2004 to November 2006 were collected by the SMPs and cross-checked with health service records. In addition 242 respondents were interviewed with respect to knowledge on safe motherhood issues and their perception of the SMP's performance. Skilled delivery attendance was our primary outcome; secondary outcomes included antenatal care attendance and knowledge on Safe Motherhood issues. Deliveries with skilled attendant significantly increased from 34.1% to 51.4% (rho < 0.05). Early ANC booking (4 to 16 weeks) rose significantly from 18.7% at baseline to 37.7% in 2005 and 56.9% (rho < 0.001) at final assessment. After two years 44 (88%) of the SMPs were still active, 79% of pregnant women were visited. Further benefits included the enhancement of male involvement in safe motherhood issues. The study has demonstrated the effectiveness of community-based safe motherhood intervention in promoting the utilization of obstetric care and a skilled attendant at delivery. This improvement is attributed to the SMPs' home visits and the close collaboration with existing community structures as well as health services

Mobile Phones and Social Signal Processing for Analysis and Understanding of Dyadic Conversations

Social Signal Processing is the domain aimed at bridging the social intelligence gap between humans and machines via modeling, analysis and synthesis of nonverbal behavior in social interactions. One of the main challenges of the domain is to sense unobtrusively the behavior of social interaction participants, one of the key conditions to preserve the spontaneity and naturalness of the interactions under exam. In this respect, mobile devices offer a major opportunity because they are equipped with a wide array of sensors that, while capturing the behavior of their users with an unprecedented depth, are still invisible. This is particularly important because mobile devices are part of the everyday life of a large number of individuals and, hence, they can be used to investigate and sense natural and spontaneous scenarios