Abstract: Antioxidant activities of ethanolic extracts of Ardisia
japonica Blume., Ageartum conyzoides Linn., and Cocculus hirsutus
Linn Diels. leaves was determined qualitatively and quantitatively in
this research. 1, 1-diphenyl-2-picrylhydrazyl (DPPH) free radical
solution was used to investigate free radical scavenging activity of
these leaves extracts. Ascorbic acid (Vitamin C) was used as the
standard. In the present investigation, it is found that all of these
extracts have remarkable antioxidant activities. The EC50 values of
these ethanolic extracts were 12.72 μg/ml for A. japonica, 15.19
μg/ml for A. conyzoides, 10.68 μg/ml for C. hirsutus respectively.
Among these Myanmar medicinal plants, C. hirsutus showed higher
antioxidant activities as well as free radical scavenging activity than
black tea (Camellia sinensis), the famous antioxidant, and A.
japonica and A. conyzoides showed a rather lower antioxidant
activity than tea extracts. According to results from bioassay with
carrot discs infected with Agrobacterium tumefaciens, all extracts
showed anti-tumor activity after 3 weeks of incubation. No gall was
detected in carrot disks treated with C. hirsutus and A. japonica
extracts in the dose of 100ppm and in carrot discs treated with A.
conyzoides extract in the dose of 1000 ppm. Therefore, the research
clearly indicates that these weedy plants of dry farm land are
exceptionally advantageous for human health.
Abstract: The need to increase the efficiency of health care
systems is becoming an obligation, and one of area of improvement
is the discharge process. The objective of this work is to minimize
the patients discharge time (for insured patients) to be less than 50
minutes by using six sigma approach, this improvement will also:
lead to an increase in customer satisfaction, increase the number of
admissions and turnover on the rooms, increase hospital
profitability.Three different departments were considered in this
study: Female, Male, and Paediatrics. Six Sigma approach coupled
with simulation has been applied to reduce the patients discharge
time for pediatrics, female, and male departments at hospital. Upon
applying these recommendations at hospital: 60%, 80%, and 22% of
insured female, male, and pediatrics patients respectively will have
discharge time less than the upper specification time i.e. 50 min.
Abstract: Five vegetables (spinach, lettuce, cabbage, tomato, and onion) were freshly harvested from the Alau Dam and Gongulong agricultural areas for the determination of some organochlorine pesticide residues (o, p-DDE, p,p’-DDD, o,p’-DDD, p,p’-DDT, α-BHC, γ-BHC, metoxichlor, lindane, endosulfan dieldrin, and aldrin.) Soil samples were also collected at different depths for the determination of the above pesticides. Samples collection and preparation were conducted using standard procedures. The concentrations of all the pesticides in the soil and vegetable samples were determined using GC/MS SHIMADZU (GC-17A) equipped with electron capture detector (ECD). The highest concentration was that of p,p’-DDD (132.4±13.45µg/g) which was observed in the leaf of cabbage, while the lowest concentration was that of p,p’-DDT (2.34µg/g) was observed in the root of spinach. Similar trends were observed at the Gongulong agricultural area, with p,p’-DDD having the highest concentration of 153.23µg/g in the leaf of cabbage, while the lowest concentration was that of p,p’-DDT (12.45µg/g) which was observed in the root of spinach. α-BHC, γ-BHC, Methoxychlor, and lindane were detected in all the vegetable samples studied. The concentrations of all the pesticides in the soil samples were observed to be higher at a depth of 21-30cm, while the lowest concentrations were observed at a depth of 0-10cm. The concentrations of all the pesticides in the vegetables and soil samples from the two agricultural sites were observed to be at alarming levels, much higher than the maximum residue limits (MRLs) and acceptable daily intake values (ADIs) .The levels of the pesticides observed in the vegetables and soil samples investigated, are of such a magnitude that calls for special attention and laws to regulate the use and circulation of such chemicals. Routine monitoring of pesticide residues in these study areas is necessary for the prevention, control and reduction of environmental pollution, so as to minimize health risks.
Abstract: This paper proposes the use of Bayesian belief
networks (BBN) as a higher level of health risk assessment for a
dumping site of lead battery smelter factory. On the basis of the
epidemiological studies, the actual hospital attendance records and
expert experiences, the BBN is capable of capturing the probabilistic
relationships between the hazardous substances and their adverse
health effects, and accordingly inferring the morbidity of the adverse
health effects. The provision of the morbidity rates of the related
diseases is more informative and can alleviate the drawbacks of
conventional methods.
Abstract: Snoring is prevalent and is the most significant feature
of sleep-disordered breathing (SDB). Ignore the therapies of SDB will
lead to serious problems in health. Based on the research of
mechanisms, diagnosis, and treatments of snoring, oral appliances are
ensured in therapeutic effect and compliance, especially the
mandibular advancement devices (MADs). Market survey includes
commercial product reviews and patent analyses. Due to pay more
attention to the sleep medicine, the oral appliances are considered as a
standard treatment of snoring that promoted by American Academy of
Sleep Medicine (AASM). There are more and more adjustable MADs
developed since 1995. According to the patent analyses, there are
many drawbacks existed in the present design, such as uncomfortable,
high cost, bulky volume, and complex adjustment. In this study,
several new designs of the MAD are proposed.
Abstract: Acute kidney injury (AKI) is a new worldwide public
health problem. A diagnosis of this disease using creatinine is still a
problem in clinical practice. Therefore, a measurement of biomarkers
responsible for AKI has received much attention in the past couple
years. Cytokine interleukin-18 (IL-18) was reported as one of the
early biomarkers for AKI. The most commonly used method to
detect this biomarker is an immunoassay. This study used a planar
platform to perform an immunoassay using fluorescence for
detection. In this study, anti-IL-18 antibody was immobilized onto a
microscope slide using a covalent binding method. Make-up samples
were diluted at the concentration between 10 to 1000 pg/ml to create
a calibration curve. The precision of the system was determined
using a coefficient of variability (CV), which was found to be less
than 10%. The performance of this immunoassay system was
compared with the measurement from ELISA.
Abstract: Heart-s electric field can be measured anywhere on
the surface of the body (ECG). When individuals touch, one person-s
ECG signal can be registered in other person-s EEG and elsewhere
on his body. Now, the aim of this study was to test the hypothesis
that physical contact (hand-holding) of two persons changes their
heart rate variability. Subjects were sixteen healthy female (age: 20-
26) which divided into eight sets. In each sets, we had two friends
that they passed intimacy test of J.sternberg. ECG of two subjects
(each set) acquired for 5 minutes before hand-holding (as control
group) and 5 minutes during they held their hands (as experimental
group). Then heart rate variability signals were extracted from
subjects' ECG and analyzed in linear feature space (time and
frequency domain) and nonlinear feature space. Considering the
results, we conclude that physical contact (hand-holding of two
friends) increases parasympathetic activity, as indicate by increase
SD1, SD1/SD2, HF and MF power (p
Abstract: The purpose of this study was to study postpartum breastfeeding mothers to determine the impact their psychosocial and spiritual dimensions play in promoting full-term (6 month duration) breastfeeding of their infants. Purposive and snowball sampling methods were used to identify and recruit the study's participants. A total of 23 postpartum mothers, who were breastfeeding within 6 weeks after giving birth, participated in this study. In-depth interviews combined with observations, participant focus groups, and ethnographic records were used for data collection. The Data were then analyzed using content analysis and typology. The results of this study illustrated that postpartum mothers experienced fear and worry that they would lack support from their spouse, family and peers, and that their infant would not get enough milk It was found that the main barrier mothers faced in breastfeeding to full-term was the difficulty of continuing to breastfeed when returning to work. 81.82% of the primiparous mothers and 91.67% of the non-primiparous mothers were able to breastfeed for the desired full-term of 6 months. Factors found to be related to breastfeeding for six months included 1) belief and faith in breastfeeding, 2) support from spouse and family members, 3) counseling from public health nurses and friends. The sample also provided evidence that religious principles such as tolerance, effort, love, and compassion to their infant, and positive thinking, were used in solving their physical, mental and spiritual problems.
Abstract: PARADIGMA (PARticipative Approach to DIsease
Global Management) is a pilot project which aims to develop and
demonstrate an Internet based reference framework to share scientific
resources and findings in the treatment of major diseases.
PARADIGMA defines and disseminates a common methodology and
optimised protocols (Clinical Pathways) to support service functions
directed to patients and individuals on matters like prevention, posthospitalisation
support and awareness. PARADIGMA will provide a
platform of information services - user oriented and optimised
against social, cultural and technological constraints - supporting the
Health Care Global System of the Euro-Mediterranean Community
in a continuous improvement process.
Abstract: Cyber physical system (CPS) for target tracking, military surveillance, human health monitoring, and vehicle detection all require maximizing the utility and saving the energy. Sensor selection is one of the most important parts of CPS. Sensor selection problem (SSP) is concentrating to balance the tradeoff between the number of sensors which we used and the utility which we will get. In this paper, we propose a performance constrained slide windows (PCSW) based algorithm for SSP in CPS. we present results of extensive simulations that we have carried out to test and validate the PCSW algorithms when we track a target, Experiment shows that the PCSW based algorithm improved the performance including selecting time and communication times for selecting.
Abstract: Music has a great effect on human body and mind; it
can have a positive effect on hormone system. Objective of this study
is to analysis the effect of music (carnatic, hard rock and jazz) on
brain activity during mental work load using electroencephalography
(EEG). Eight healthy subjects without special musical education
participated in the study. EEG signals were acquired at frontal (Fz),
parietal (Pz) and central (Cz) lobes of brain while listening to music
at three experimental condition (rest, music without mental task and
music with mental task). Spectral powers features were extracted at
alpha, theta and beta brain rhythms. While listening to jazz music, the
alpha and theta powers were significantly (p < 0.05) high for rest as
compared to music with and without mental task in Cz. While
listening to Carnatic music, the beta power was significantly (p <
0.05) high for with mental task as compared to rest and music
without mental task at Cz and Fz location. This finding corroborates
that attention based activities are enhanced while listening to jazz and
carnatic as compare to Hard rock during mental task.
Abstract: The healthcare environment is generally perceived as
being information rich yet knowledge poor. However, there is a lack
of effective analysis tools to discover hidden relationships and trends
in data. In fact, valuable knowledge can be discovered from
application of data mining techniques in healthcare system. In this
study, a proficient methodology for the extraction of significant
patterns from the Coronary Heart Disease warehouses for heart
attack prediction, which unfortunately continues to be a leading cause
of mortality in the whole world, has been presented. For this purpose,
we propose to enumerate dynamically the optimal subsets of the
reduced features of high interest by using rough sets technique
associated to dynamic programming. Therefore, we propose to
validate the classification using Random Forest (RF) decision tree to
identify the risky heart disease cases. This work is based on a large
amount of data collected from several clinical institutions based on
the medical profile of patient. Moreover, the experts- knowledge in
this field has been taken into consideration in order to define the
disease, its risk factors, and to establish significant knowledge
relationships among the medical factors. A computer-aided system is
developed for this purpose based on a population of 525 adults. The
performance of the proposed model is analyzed and evaluated based
on set of benchmark techniques applied in this classification problem.
Abstract: In this paper, we present user pattern learning
algorithm based MDSS (Medical Decision support system) under
ubiquitous. Most of researches are focus on hardware system, hospital
management and whole concept of ubiquitous environment even
though it is hard to implement. Our objective of this paper is to design
a MDSS framework. It helps to patient for medical treatment and
prevention of the high risk patient (COPD, heart disease, Diabetes).
This framework consist database, CAD (Computer Aided diagnosis
support system) and CAP (computer aided user vital sign prediction
system). It can be applied to develop user pattern learning algorithm
based MDSS for homecare and silver town service. Especially this
CAD has wise decision making competency. It compares current vital
sign with user-s normal condition pattern data. In addition, the CAP
computes user vital sign prediction using past data of the patient. The
novel approach is using neural network method, wireless vital sign
acquisition devices and personal computer DB system. An intelligent
agent based MDSS will help elder people and high risk patients to
prevent sudden death and disease, the physician to get the online
access to patients- data, the plan of medication service priority (e.g.
emergency case).
Abstract: Chronic conditions carry with them strong emotions
and often lead to charged relationships between patients and their
health providers and, by extension, patients and health researchers.
Persons are both autonomous and relational and a purely cognitive
model of autonomy neglects the social and relational basis of chronic
illness. Ensuring genuine informed consent in research requires a
thorough understanding of how participants perceive a study and
their reasons for participation. Surveys may not capture the
complexities of reasoning that underlies study participation.
Contradictory reasons for participation, for instance an initial claim
of altruism as rationale and a subsequent claim of personal benefit
(therapeutic misconception), affect the quality of informed consent.
Individuals apply principles through the filter of personal values and
lived experience. Authentic autonomy, and hence authentic consent
to research, occurs within the context of patients- unique life
narratives and illness experiences.
Abstract: In this competitive age, one of the key tools of most successful organizations is knowledge management. Today some organizations measure their current knowledge and use it as an indicator for rating the organization on their reports. Noting that the universities and colleges of medical science have a great role in public health of societies, their access to newest scientific research and the establishment of organizational knowledge management systems is very important. In order to explore the Application of Knowledge Management Factors, a national study was undertaken. The main purpose of this study was to find the rate of the application of knowledge management factors and some ways to establish more application of knowledge management system in Esfahan University-s Medical College (EUMC). Esfahan is the second largest city after Tehran, the capital city of Iran, and the EUMC is the biggest medical college in Esfahan. To rate the application of knowledge management, this study uses a quantitative research methodology based on Probst, Raub and Romhardt model of knowledge management. A group of 267 faculty members and staff of the EUMC were asked via questionnaire. Finding showed that the rate of the application of knowledge management factors in EUMC have been lower than average. As a result, an interview with ten faculty members conducted to find the guidelines to establish more applications of knowledge management system in EUMC.
Abstract: Physical education (PE) is still neglected in schools
despite its academic, social, psychological, and health benefits.
Based on the assumption that Information and Communication
Technologies (ICTs) can contribute to the development of PE in
schools, this study aims to design a model of the factors affecting the
adoption of ICTs for PE in schools. The proposed model is based on
a sound theoretical framework. It was designed following a literature
review of technology adoption theories and of ICT adoption factors
for physical education. The technology adoption model that fitted to
the best all ICT adoption factors was then chosen as the basis for the
proposed model. It was found that the Unified Theory of Acceptance
and Use of Technology (UTAUT) is the most adequate theoretical
framework for the modeling of ICT adoption factors for physical
education.
Abstract: The aim of this study was to investigate the
environmental conservation behavior of the Applied Health Science
students of Suranaree University of Technology, a green and clean
university. The sample group was 184 Applied Health Science
students (medical, nursing, and public health). A questionnaire was
used to collect information.
The result of the study found that the students had more negative
than positive behaviors towards energy, water, and forest
conservation. This result can be used as basic information for
designing long-term behavior modification activities or research
projects on environmental conservation. Thus Applied Health
Science students will be encouraged to be conscious and also be a
good example of environmental conservation behavior.
Abstract: This paper introduces a tool that is being developed for the expression of information security policy controls that govern electronic healthcare records. By reference to published findings, the paper introduces the theory behind the use of knowledge management for automatic and consistent security policy assertion using the formalism called the Secutype; the development of the tool and functionality is discussed; some examples of Secutypes generated by the tool are provided; proposed integration with existing medical record systems is described. The paper is concluded with a section on further work and critique of the work achieved to date.
Abstract: Sickness absence represents a major economic and
social issue. Analysis of sick leave data is a recurrent challenge to analysts because of the complexity of the data structure which is
often time dependent, highly skewed and clumped at zero. Ignoring these features to make statistical inference is likely to be inefficient
and misguided. Traditional approaches do not address these problems. In this study, we discuss model methodologies in terms of statistical techniques for addressing the difficulties with sick leave data. We also introduce and demonstrate a new method by performing a longitudinal assessment of long-term absenteeism using
a large registration dataset as a working example available from the Helsinki Health Study for municipal employees from Finland during the period of 1990-1999. We present a comparative study on model
selection and a critical analysis of the temporal trends, the occurrence
and degree of long-term sickness absences among municipal employees. The strengths of this working example include the large
sample size over a long follow-up period providing strong evidence in supporting of the new model. Our main goal is to propose a way to
select an appropriate model and to introduce a new methodology for analysing sickness absence data as well as to demonstrate model
applicability to complicated longitudinal data.
Abstract: According to the statistics, the prevalence of congenital hearing loss in Taiwan is approximately six thousandths; furthermore, one thousandths of infants have severe hearing impairment. Hearing ability during infancy has significant impact in the development of children-s oral expressions, language maturity, cognitive performance, education ability and social behaviors in the future. Although most children born with hearing impairment have sensorineural hearing loss, almost every child more or less still retains some residual hearing. If provided with a hearing aid or cochlear implant (a bionic ear) timely in addition to hearing speech training, even severely hearing-impaired children can still learn to talk. On the other hand, those who failed to be diagnosed and thus unable to begin hearing and speech rehabilitations on a timely manner might lose an important opportunity to live a complete and healthy life. Eventually, the lack of hearing and speaking ability will affect the development of both mental and physical functions, intelligence, and social adaptability. Not only will this problem result in an irreparable regret to the hearing-impaired child for the life time, but also create a heavy burden for the family and society. Therefore, it is necessary to establish a set of computer-assisted predictive model that can accurately detect and help diagnose newborn hearing loss so that early interventions can be provided timely to eliminate waste of medical resources. This study uses information from the neonatal database of the case hospital as the subjects, adopting two different analysis methods of using support vector machine (SVM) for model predictions and using logistic regression to conduct factor screening prior to model predictions in SVM to examine the results. The results indicate that prediction accuracy is as high as 96.43% when the factors are screened and selected through logistic regression. Hence, the model constructed in this study will have real help in clinical diagnosis for the physicians and actually beneficial to the early interventions of newborn hearing impairment.