The diagnosis and management of acute coronary syndrome (ACS) have improved significantly over the past few decades; however, the recognition of myocardial ischemia still proves to be a dilemma for ...cardiologists. The aim of this study was to determine the role of hematological and coagulation parameters in the diagnosis and prognosis of patients with ACS.
This prospective study recruited 250 patients with ACS and 250 healthy controls. The diagnostic role of hematological and coagulation parameters was assessed by comparing the patients with ACS with the control group. The relationships between these parameters and mortality were determined by dividing the patients into 2 groups: Group A (discharged) and Group B (patients who died within 30 days of follow-up). Multivariate Cox regression analysis was performed to calculate the hazard ratio (HR).
The mean age of the patients was 55.14±10.71 years, and 65.2% of them were male. Prothrombin time (P<0.001), activated partial thromboplastin time (P<0.001), mean platelet volume (MPV) (P<0.001), white blood cell (WBC) count (P<0.001), and red blood cell distribution width (RDW) (P<0.001) were significantly higher in the case group than in the control group. WBC count (P<0.001), RDW (P<0.001), and MPV (P<0.001) were significantly higher in the controls than in the case group. The Cox regression model showed that RDW above 16.55% (HR=6.8), MPV greater than 11.25 fL (HR=2.6), and WBC higher than 10.55×10
/μL (HR=6.3) were the independent predictors of mortality.
: In addition to being the independent predictors of short-term mortality, RDW, WBC, and MPV when used together with the coagulation profile may aid in the diagnosis of ACS in patients presenting with chest pain.
HEAT RELATED ILLNESSES Mustafvi, Sajid Ali; Yousaf, Nadeem; Amjad, Zainab ...
The professional medical journal,
05/2015, Letnik:
22, Številka:
5
Journal Article
Objective: To study the adaptive strategies from harmful effect of heat waveon an urban, educated, well to do subjects for a period of May to July 2014.Data Source:250 selected young students of ...RIHS. Design of Study: Descriptive Study. Setting: RawalInstitute of Health Sciences, Islamabad. Period: March – July 2014. Method: A questionnairewas circulated among the students of Rawal Institute of Health Sciences Islamabad regardingeffects of heat and measures taken to combat its effects. Results: A total of 250 urban studentswith mean age of 19.77±1.10 years were inducted in the study, having resources to face theextreme heat. A significant number of female non boarder students (p=0.000), wearing cottonclothes (p=0.000) having fair skin (p=0.000) and using air condition at homes (p=0.000) werenot acclimatized to heat waves still have headache and anxiety. A great percent of students didnot complaint of headache, heat exhaustion, heat cramp or syncope, except mild sweating,effect on studies. A great percentage (>65%) of students complained of malaise, nauseavomiting. Male students showed increase thirst than female, while anxiety state was noticedmore in female than male students. Conclusion: The use of cotton clothing, daily bathing,increased water intake and use of air conditioner minimized the severe adverse effects likeheat exhaustion, heat syncope, and heat stroke, although the minor effects like skin tanning,disturbed sleep, anxiety and adverse effects on studies cannot be avoided in heat wave season.
Developing countries like Pakistan are facing a serious shortage of electrical energy due to massive urbanization. Renewable energy resources (RER's) and energy storage system (ESS) can play ...significant role in solving this problem. In this paper, we have studied the impact of rooftop photovoltaic (PV) and ESS installation on the operational cost of electrical energy usage in smart residential homes. Additionally, concept of home to grid energy exchange (H2G) is also incorporated to study its benefits on energy management. Home Energy management (HEM) problem is modeled with the integration of PV and ESS by considering user preferences and other system constraints. Genetic algorithm (GA) is applied to find out optimal scheduling of appliances. Simulation results show that the proposed method can enhance the performance of the home electricity scheduling, decrease the effect of uncertainty on system and reduce the overall energy cost.
Energy conservation has gained much attention among recent demand-side management strategies due to environmental concerns and rising energy costs. Load disaggregation can act as an enabler for ...energy conservation by informing about the individual energy consumption of various loads. Different studies have shown a significant increase in energy savings when consumers are aware of their energy-intensive appliances. This paper presents a Non-Intrusive Load Monitoring (NILM) technique for determining the appliance operating status from the aggregate demand data of a household. The proposed supervised learning model includes a multi-layer and multi-node adaptive-neuro fuzzy interface system for extracting the power consumption features of various appliances. A fine tree classifier learner is then employed for classifying the operating status of different appliances using the extracted features. The proposed technique was tested on publicly available United Kingdom Domestic Appliance Level Energy (UK-DALE) dataset. Different evaluation metrics are used to evaluate the results of the proposed technique and to compare its performance with other recent methods. The comparison shows that the results of the proposed technique are comparable with existing NILM techniques available in the literature.
Decentralization of conventional power system into small scale, smart and efficient micro-grids emphasizes the requirement of short-term load forecasting on the minute level. The postulation of ...short-term load forecasting for household level has evolved with this transition in the power system. However, fluctuations and uncertainty in the load profile of individual customers make it difficult to predict. This paper presents the implementation and comparison of three machine learning based approaches to accurately forecast the meter level aggregate demand using consumption data of a few major appliances. The forecasting techniques include the feed-forward neural network (FNN), long short-term memory (LSTM) network and particle swarm optimization (PSO) based FNN. The results prove that PSO-FNN exhibits better forecasting accuracy while the conventional FNN has better computational efficiency.
Elastic light scattering from a germanium microsphere with a radius of 2000 µm is analyzed numerically in the terahertz region from 297 µm to 303 µm. Germanium has a high refractive index (n = 4) in ...the terahertz range 1, which makes it possible to trap light effectively and achieve high quality factor whispering gallery modes (WGMs) 2. Microsphere WGMs have high quality factors 3,4 and germanium microsphere WGMs 5 can find many applications in the terahertz range, which can be exploited for astronomical 6 and security 7 applications. We studied 90° elastic scattering and 0° transmission spectra from the germanium microsphere using the generalized Lorenz-Mie theory (GLMT) 8,9. The excitation terahertz source can be coupled to the germanium microsphere through single mode terahertz waveguides 10. The numerical simulations are performed for both transverse electric (TE) and transverse magnetic (TM) polarization 11 of the terahertz excitation source. The WGM mode spacing, i.e., the spectral separation of the modes with the same radial mode number and consecutive polar mode orders, can be used for the analysis of the microsphere WGM spectra 12. We observed a mode spacing of 2.4 µm, which agrees well with the theoretically estimated value of the WGM mode spacing. All in all, germanium microspheres with their high quality factor WGMs might find applications in terahertz photonics.
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