Improving the quality of mental health services using patient outcome data: Making the most of HoNOS

Efforts to assess and improve the quality of mental health services are often hampered by a lack of information on patient outcomes. Most mental health services in England have been routinely collecting Health of the Nation Outcome Scales (HoNOS) data for some time. In this article we illustrate how clinical teams have used HoNOS data to identify areas where performance could be improved. HoNOS data have the potential to give clinical teams the information they need to assess the quality of care they deliver, as well as develop and test initiatives aimed at improving the services they provide

Teacher Effectiveness and Student Achievement in the Smart School Hyderabad

The study aimed to identify the role of effective teacher on student’s achievement in school, how teachers teaching can be effective and what are the qualities of effective teachers and how it can affect students learning. This study was conducted in Virtual university of Pakistan in Hyderabad campus, total of 266 respondents were selected to collect data from the school “ the smart school” Hyderabad from three different campuses of the school randomly from class 7, 8, 9 and 10 The main research of the study was on the teacher’s effective teaching and its effect of student’s achievements, the researcher used 5 points grading questionnaire paper in which 1: strongly disagree, 2: disagree, 3: neutral, 4: agree and 5: strongly agree was used in order to know the relationship between the variables. This research highlights the main theories and facts of effective teachers teaching, what teachers should adopt, how teachers can influence and keep students motivated towards learning and what are the effect of effective teachers on students' achievement, in their learning and grades

An In Depth Study into Using EMI Signatures for Appliance Identification

Energy conservation is a key factor towards long term energy sustainability. Real-time end user energy feedback, using disaggregated electric load composition, can play a pivotal role in motivating consumers towards energy conservation. Recent works have explored using high frequency conducted electromagnetic interference (EMI) on power lines as a single point sensing parameter for monitoring common home appliances. However, key questions regarding the reliability and feasibility of using EMI signatures for non-intrusive load monitoring over multiple appliances across different sensing paradigms remain unanswered. This work presents some of the key challenges towards using EMI as a unique and time invariant feature for load disaggregation. In-depth empirical evaluations of a large number of appliances in different sensing configurations are carried out, in both laboratory and real world settings. Insights into the effects of external parameters such as line impedance, background noise and appliance coupling on the EMI behavior of an appliance are realized through simulations and measurements. A generic approach for simulating the EMI behavior of an appliance that can then be used to do a detailed analysis of real world phenomenology is presented. The simulation approach is validated with EMI data from a router. Our EMI dataset - High Frequency EMI Dataset (HFED) is also released

Skin Conductance as Proxy for the Identification of Hydration Level in Human Body

The skin dehydration level can be used to infer serious health conditions in patients since diseases like cardiovascular abnormality, diabetes and cancer symptoms do exhibit correlation with skin disorders. Therefore a systematic analysis of human skin hydration levels is critical for multiple health care applications. Motivated by this, in this study we proposed a unique approach of measuring body hydration levels against different body postures using skin conductance electrical activity. In this paper, we report the collection, processing and analysis techniques used in the analysis of skin conductance data. Subsequently in order to predict body hydration levels we employed state-of-the-art machine learning models using the skin conductance data and achieved 81.82% and 73.91% recognition accuracy for the data of standing and sitting postures,respectively using KNN model

Pericardiocentesis Followed by Thoracotomy and Repair of Penetrating Cardiac Injury Caused by Nail Gun Injury to the Heart

INTRODUCTION: Work site injuries involving high projectile tools such as nail guns can lead to catastrophic injuries. Generally, penetrating cardiac injuries are associated with a high mortality rate. PRESENTATION OF CASE: A construction worker was brought to the emergency room having sustained a nail gun injury to the chest. The patient was hypotensive, tachycardic with prominent jugular venous distention, and had a profound lactic acidosis. Bedside ultrasound confirmed the presence of pericardial fluid. Pericardiocentesis was performed twice using a central venous catheter inserted into the pericardial space, resulting in improvement in the patient\u27s hemodynamics. Thereafter he underwent left anterolateral thoracotomy and repair of a right atrial laceration. He recovered uneventfully. DISCUSSION: Penetrating cardiac injuries caused by nail guns, although rare, have been previously described. However, pericardiocentesis, while retaining a role in the management of medical causes of cardiac tamponade, has been reported only sporadically in the setting of trauma. We report a rare case of penetrating nail gun injury to the heart where pericardiocentesis was used as a temporizing measure to stabilize the patient in preparation for definitive but timely operative intervention. CONCLUSION: We propose awareness that percardiocentesis can serve as a temporary life saving measure in the setting of trauma, particularly as a bridge to definitive therapy. To our knowledge, this represents the first reported case of catheter pericardiocentesis used to stabilize a patient until definitive repair of a penetrating cardiac injury caused by a nail gun

Predicting complex events for pro-active IoT applications

The widespread use of IoT devices has opened the possibilities for many innovative applications. Almost all of these applications involve analyzing complex data streams with low latency requirements. In this regard, pattern recognition methods based on CEP have the potential to provide solutions for analyzing and correlating these complex data streams in order to detect complex events. Most of these solutions are reactive in nature as CEP acts on real-time data and does not exploit historical data. In our work, we have explored a proactive approach by exploiting historical data using machine learning methods for prediction with CEP. We propose an adaptive prediction algorithm called Adaptive Moving Window Regression (AMWR) for dynamic IoT data and evaluated it using a realworld use case. Our proposed architecture is generic and can be used across different fields for predicting complex events

A Review on the Role of Nano-Communication in Future Healthcare Systems: A Big Data Analytics Perspective

This paper presents a first-time review of the open literature focused on the significance of big data generated within nano-sensors and nano-communication networks intended for future healthcare and biomedical applications. It is aimed towards the development of modern smart healthcare systems enabled with P4, i.e. predictive, preventive, personalized and participatory capabilities to perform diagnostics, monitoring, and treatment. The analytical capabilities that can be produced from the substantial amount of data gathered in such networks will aid in exploiting the practical intelligence and learning capabilities that could be further integrated with conventional medical and health data leading to more efficient decision making. We have also proposed a big data analytics framework for gathering intelligence, form the healthcare big data, required by futuristic smart healthcare to address relevant problems and exploit possible opportunities in future applications. Finally, the open challenges, future directions for researchers in the evolving healthcare domain, are presented

Bioinformatic Prioritization and Functional Annotation of GWAS-Based Candidate Genes for Primary Open-Angle Glaucoma

BACKGROUND: Primary open-angle glaucoma (POAG) is the most prevalent glaucoma subtype, but its exact etiology is still unknown. In this study, we aimed to prioritize the most likely 'causal' genes and identify functional characteristics and underlying biological pathways of POAG candidate genes. METHODS: We used the results of a large POAG genome-wide association analysis study from GERA and UK Biobank cohorts. First, we performed systematic gene-prioritization analyses based on: (i) nearest genes; (ii) nonsynonymous single-nucleotide polymorphisms; (iii) co-regulation analysis; (iv) transcriptome-wide association studies; and (v) epigenomic data. Next, we performed functional enrichment analyses to find overrepresented functional pathways and tissues. RESULTS: We identified 142 prioritized genes, of which 64 were novel for POAG. BICC1, AFAP1, and ABCA1 were the most highly prioritized genes based on four or more lines of evidence. The most significant pathways were related to extracellular matrix turnover, transforming growth factor-β, blood vessel development, and retinoic acid receptor signaling. Ocular tissues such as sclera and trabecular meshwork showed enrichment in prioritized gene expression (>1.5 fold). We found pleiotropy of POAG with intraocular pressure and optic-disc parameters, as well as genetic correlation with hypertension and diabetes-related eye disease. CONCLUSIONS: Our findings contribute to a better understanding of the molecular mechanisms underlying glaucoma pathogenesis and have prioritized many novel candidate genes for functional follow-up studies

A study on ocular morbidities in children aged between 7 -18 years attending ophthalmology OPD

Background: Ocular morbidities in children involve a spectrum of diseases that critically impact the development, education and quality of life hence require prompt attention. This study was conducted with an objective to assess the prevalence of ocular morbidity. Method: A hospital based cross- sectional study was carried out from November 2020 to April 2021 among children in age group between 7 to 18 years. Data was collected using a semi-structured questionnaire. Detailed eye examination was done under Slit lamp and visual acuity assessment was done using Snellen’s chart. Children with vision less than 6/6p were subjected to refraction. Posterior segment evaluation was done under slit lamp with 90D and IDO. Results: A total of 120 children (7 to 18 years) were examined in this study. Majority of cases were between 14 to 18 years. 58.3% male and 41.6% female. Common presenting complaints were blurring of vision (28.3%) and headache (11.6%).The common ocular morbidity reported were refractive error (35.83%) followed by the allergic conjunctivitis (19.16%) and infections of the eye and adnexa (15.83%). Prevalence of refractive error was more in children aged between 14 to 18 years. Myopia was the most common refractive error. Conclusion: Most of the ocular morbidities are preventable or treatable. Ocular disorder can be easily identified with a regular eye screening. Moreover, health education for the prevention of ocular morbidity and early presentation to ophthalmology OPD for treatment is essential