Abstract: This article focuses on upper-extremity musculoskeletal disorders risk assessment model at workplace. In this model are used risk factors that are responsible for musculoskeletal system damage. Based on statistic calculations the model is able to define what risk of MSD threatens workers who are under risk factors. The model is also able to say how MSD risk would decrease if these risk factors are eliminated.
Abstract: Image segmentation plays an important role in
medical imaging applications. Therefore, accurate methods are
needed for the successful segmentation of medical images for
diagnosis and detection of various diseases. In this paper, we have
used maximum entropy to achieve image segmentation. Maximum
entropy has been calculated using Shannon, Renyi and Tsallis
entropies. This work has novelty based on the detection of skin lesion
caused by the bite of a parasite called Sand Fly causing the disease is
called Cutaneous Leishmaniasis.
Abstract: In medical investigations, uncertainty is a major
challenging problem in making decision for doctors/experts to
identify the diseases with a common set of symptoms and also has
been extensively increasing in medical diagnosis problems. The
theory of cross entropy for intuitionistic fuzzy sets (IFS) is an
effective approach in coping uncertainty in decision making for
medical diagnosis problem. The main focus of this paper is to
propose a new intuitionistic fuzzy cross entropy measure (IFCEM),
which aid in reducing the uncertainty and doctors/experts will take
their decision easily in context of patient’s disease. It is shown that
the proposed measure has some elegant properties, which
demonstrates its potency. Further, it is also exemplified in detail the
efficiency and utility of the proposed measure by using a real life
case study of diagnosis the disease in medical science.
Abstract: The effects of hypertension are often lethal thus its
early detection and prevention is very important for everybody. In
this paper, a neural network (NN) model was developed and trained
based on a dataset of hypertension causative parameters in order to
forecast the likelihood of occurrence of hypertension in patients. Our
research goal was to analyze the potential of the presented NN to
predict, for a period of time, the risk of hypertension or the risk of
developing this disease for patients that are or not currently
hypertensive. The results of the analysis for a given patient can
support doctors in taking pro-active measures for averting the
occurrence of hypertension such as recommendations regarding the
patient behavior in order to lower his hypertension risk. Moreover,
the paper envisages a set of three example scenarios in order to
determine the age when the patient becomes hypertensive, i.e.
determine the threshold for hypertensive age, to analyze what
happens if the threshold hypertensive age is set to a certain age and
the weight of the patient if being varied, and, to set the ideal weight
for the patient and analyze what happens with the threshold of
hypertensive age.
Abstract: Pulmonary Function Tests are important non-invasive
diagnostic tests to assess respiratory impairments and provides
quantifiable measures of lung function. Spirometry is the most
frequently used measure of lung function and plays an essential role
in the diagnosis and management of pulmonary diseases. However,
the test requires considerable patient effort and cooperation,
markedly related to the age of patients resulting in incomplete data
sets. This paper presents, a nonlinear model built using Multivariate
adaptive regression splines and Random forest regression model to
predict the missing spirometric features. Random forest based feature
selection is used to enhance both the generalization capability and the
model interpretability. In the present study, flow-volume data are
recorded for N= 198 subjects. The ranked order of feature importance
index calculated by the random forests model shows that the
spirometric features FVC, FEF25, PEF, FEF25-75, FEF50 and the
demographic parameter height are the important descriptors. A
comparison of performance assessment of both models prove that, the
prediction ability of MARS with the `top two ranked features namely
the FVC and FEF25 is higher, yielding a model fit of R2= 0.96 and
R2= 0.99 for normal and abnormal subjects. The Root Mean Square
Error analysis of the RF model and the MARS model also shows that
the latter is capable of predicting the missing values of FEV1 with a
notably lower error value of 0.0191 (normal subjects) and 0.0106
(abnormal subjects) with the aforementioned input features. It is
concluded that combining feature selection with a prediction model
provides a minimum subset of predominant features to train the
model, as well as yielding better prediction performance. This
analysis can assist clinicians with a intelligence support system in the
medical diagnosis and improvement of clinical care.
Abstract: In this paper, we used data mining to extract
biomedical knowledge. In general, complex biomedical data
collected in studies of populations are treated by statistical methods,
although they are robust, they are not sufficient in themselves to
harness the potential wealth of data. For that you used in step two
learning algorithms: the Decision Trees and Support Vector Machine
(SVM). These supervised classification methods are used to make the
diagnosis of thyroid disease. In this context, we propose to promote
the study and use of symbolic data mining techniques.
Abstract: Diminished antioxidant defense or increased
production of reactive oxygen species in the biological system can
result in oxidative stress which may lead to various
neurodegenerative diseases including Alzheimer’s disease (AD).
Microglial activation also contributes to the progression of AD by
producing several proinflammatory cytokines, nitric oxide (NO) and
prostaglandin E2 (PGE2). Oxidative stress and inflammation have
been reported to be possible pathophysiological mechanisms
underlying AD. In addition, the cholinergic hypothesis postulates that
memory impairment in patient with AD is also associated with the
deficit of cholinergic function in the brain. Although a number of
drugs have been approved for the treatment of AD, most of these
synthetic drugs have diverse side effects and yield relatively modest
benefits. Marine algae have great potential in pharmaceutical and
biomedical applications as they are valuable sources of bioactive
properties such as anticoagulation, antimicrobial, antioxidative,
anticancer and anti-inflammatory. Hence, this study aimed to provide
an overview of the properties of Malaysian seaweeds (Padina
australis, Sargassum polycystum and Caulerpa racemosa) in
inhibiting oxidative stress, neuroinflammation and cholinesterase
enzymes. These seaweeds significantly exhibited potent DPPH and
moderate superoxide anion radical scavenging ability (P
Abstract: Anxiety is a common psychological problem and also
implicated as a contributor to many chronic diseases which decreased
quality of life even with pharmacological treatment. At the present
time several yogic practices- meditation, pranayama, and mantra,
etcetera are playing important role in treating physiological and
psychological problems. Hence, the present investigation is aimed to
see the effect of Trataka on the level of anxiety among adolescents.
For the present study, a sample of 30 adolescents belonging to the
age range 20-30 years was selected from Devsanskriti Vishwa
Vidyalaya Haridwar through random sampling. In this investigation,
Sinha’s Comprehensive anxiety test has been used to measure the
level of anxiety. Statistical analysis has been done by using t-test.
Findings of this study reveal that Trataka significantly decreases the
level of anxiety among adolescents.
Abstract: This paper proposes a rotational invariant texture
feature based on the roughness property of the image for psoriasis
image analysis. In this work, we have applied this feature for image
classification and segmentation. The fuzzy concept is employed to
overcome the imprecision of roughness. Since the psoriasis lesion is
modeled by a rough surface, the feature is extended for calculating
the Psoriasis Area Severity Index value. For classification and
segmentation, the Nearest Neighbor algorithm is applied. We have
obtained promising results for identifying affected lesions by using
the roughness index and severity level estimation.
Abstract: The ultrasound imaging is very popular to diagnosis
the disease because of its non-invasive nature. The ultrasound
imaging slowly produces low quality images due to the presence of
spackle noise and wave interferences. There are several algorithms to
be proposed for the segmentation of ultrasound carotid artery images
but it requires a certain limit of user interaction. The pixel in an
image is highly correlated so the spatial information of surrounding
pixels may be considered in the process of image segmentation which
improves the results further. When data is highly correlated, one pixel
may belong to more than one cluster with different degree of
membership. There is an important step to computerize the evaluation
of arterial disease severity using segmentation of carotid artery lumen
in 2D and 3D ultrasonography and in finding vulnerable
atherosclerotic plaques susceptible to rupture which can cause stroke.
Abstract: Although there had been a many studies that shows
the impact of air pollution on physical health, comparatively less was
known of human behavioral responses and annoyance impacts.
Annoyance caused by air pollution is a public health problem because
it can be an ambient stressor causing stress and disease and can affect
quality of life. The objective of this work is to evaluate the
annoyance caused by air pollution in two different industrialized
urban areas, Dunkirk (France) and Vitoria (Brazil). The populations
of these cities often report feeling annoyed by dust. Surveys were
conducted, and the collected data were analyzed using statistical
analyses. The results show that sociodemographic variables,
importance of air quality, perceived industrial risk, perceived air
pollution and occurrence of health problems play important roles in
the perceived annoyance. These results show the existence of a
common problem in geographically distant areas and allow
stakeholders to develop prevention strategies.
Abstract: The work studied the effect of germination on
proximate, phenol and flavonoid content and antioxidant activities
(AOA) of African Yam been (AYB). Germination was done in
controlled dark chamber (100% RH, 28oC). The proximate, phenol
and flavonoid content and antioxidant activities before and after
germination were investigated. The crude protein, moisture, and
crude fiber content of germinated AYB were significantly higher
(P
Abstract: With increasingly more mobile health applications
appearing due to the popularity of smartphones, the possibility arises
that these data can be used to improve the medical diagnostic process,
as well as the overall quality of healthcare, while at the same time
lowering costs. However, as of yet there have been no reports of a
successful combination of patient-generated data from smartphones
with data from clinical routine. In this paper we describe how these
two types of data can be combined in a secure way without
modification to hospital information systems, and how they can
together be used in a medical expert system for automatic nutritional
classification and triage.
Abstract: For the treatment of acute and chronic lung diseases it is preferred to deliver medicaments by inhalation. The drug is delivered directly to tracheobronchial tree. This way allows the given medicament to get directly into the place of action and it makes rapid onset of action and maximum efficiency. The transport of aerosol particles in the particular part of the lung is influenced by their size, anatomy of the lungs, breathing pattern and airway resistance. This article deals with calculation of airway resistance in the lung model of Horsfield. It solves the problem of determination of the pressure losses in bifurcation and thus defines the pressure drop at a given location in the bronchial tree. The obtained data will be used as boundary conditions for transport of aerosol particles in a central part of bronchial tree realized by Computational Fluid Dynamics (CFD) approach. The results obtained from CFD simulation will allow us to provide information on the required particle size and optimal inhalation technique for particle transport into particular part of the lung.
Abstract: Analyzing DNA microarray data sets is a great
challenge, which faces the bioinformaticians due to the complication
of using statistical and machine learning techniques. The challenge
will be doubled if the microarray data sets contain missing data,
which happens regularly because these techniques cannot deal with
missing data. One of the most important data analysis process on
the microarray data set is feature selection. This process finds the
most important genes that affect certain disease. In this paper, we
introduce a technique for imputing the missing data in microarray
data sets while performing feature selection.
Abstract: This paper presents an evolutionary algorithm for
solving multi-objective optimization problems-based artificial neural
network (ANN). The multi-objective evolutionary algorithm used in
this study is genetic algorithm while ANN used is radial basis
function network (RBFN). The proposed algorithm named memetic
elitist Pareto non-dominated sorting genetic algorithm-based RBFN
(MEPGAN). The proposed algorithm is implemented on medical
diseases problems. The experimental results indicate that the
proposed algorithm is viable, and provides an effective means to
design multi-objective RBFNs with good generalization capability
and compact network structure. This study shows that MEPGAN
generates RBFNs coming with an appropriate balance between
accuracy and simplicity, comparing to the other algorithms found in
literature.
Abstract: In Brazil, neonatal mortality rate is considered
incompatible with the country development conditions, and has been
a Public Health concern. Reduction in infant mortality rates has also
been part of the Millennium Development Goals, a commitment
made by countries, members of the Organization of United Nations
(OUN), including Brazil. Fetal mortality rate is considered a highly
sensitive indicator of health care quality. Suitable actions, such as
good quality and access to health services may contribute positively
towards reduction in these fetal and neonatal rates. With appropriate
antenatal follow-up and health care during gestation and delivery,
some death causes could be reduced or even prevented by means of
early diagnosis and intervention, as well as changes in risk factors
and interventions. Objectives: To study the quality of maternal and
infant health care based on fetal and neonatal mortality, as well as the
possible actions to prevent those deaths in Botucatu (Brazil).
Methods: Classification of prevention according to the International
Classification of Diseases and the modified Wigglesworth´s
classification. In order to evaluate adequacy, indicators of quality of
antenatal and delivery care were established by the authors. Results:
Considering fetal deaths, 56.7% of them occurred before delivery,
which reveals possible shortcomings in antenatal care, and 38.2% of
them were a result of intra- labor changes, which could be prevented
or reduced by adequate obstetric management. These findings were
different from those in the group of early neonatal deaths which were
also studied. Adequacy of health services showed that antenatal and
childbirth care was appropriate for 24% and 33.3% of pregnant
women, respectively, which corroborates the results of prevention.
These results revealed that shortcomings in obstetric and antenatal
care could be the causes of deaths in the study. Early and late
neonatal deaths have similar characteristics: 76% could be prevented
or reduced mainly by adequate newborn care (52.9%) and adequate
health care for gestational women (11.7%). When adequacy of care
was evaluated, childbirth and newborn care was adequate in 25.8%
and antenatal care was adequate in 16.1%. In conclusion, direct
relationship was found between adequacy and quality of care
rendered to pregnant women and newborns, and fetal and infant
mortality. Moreover, our findings highlight that deaths could be
prevented by an adequate obstetric and neonatal management.
Abstract: Leukaemia is a blood cancer disease that contributes
to the increment of mortality rate in Malaysia each year. There are
two main categories for leukaemia, which are acute and chronic
leukaemia. The production and development of acute leukaemia cells
occurs rapidly and uncontrollable. Therefore, if the identification of
acute leukaemia cells could be done fast and effectively, proper
treatment and medicine could be delivered. Due to the requirement of
prompt and accurate diagnosis of leukaemia, the current study has
proposed unsupervised pixel segmentation based on clustering
algorithm in order to obtain a fully segmented abnormal white blood
cell (blast) in acute leukaemia image. In order to obtain the
segmented blast, the current study proposed three clustering
algorithms which are k-means, fuzzy c-means and moving k-means
algorithms have been applied on the saturation component image.
Then, median filter and seeded region growing area extraction
algorithms have been applied, to smooth the region of segmented
blast and to remove the large unwanted regions from the image,
respectively. Comparisons among the three clustering algorithms are
made in order to measure the performance of each clustering
algorithm on segmenting the blast area. Based on the good sensitivity
value that has been obtained, the results indicate that moving kmeans
clustering algorithm has successfully produced the fully
segmented blast region in acute leukaemia image. Hence, indicating
that the resultant images could be helpful to haematologists for
further analysis of acute leukaemia.
Abstract: Compositions of different molar ratios of
polymethylmethacrylate-co-methacrylic acid (PMMA-co-MAA)
were synthesized via free-radical polymerization. Polymer coated
surfaces have been produced on silicon wafers. Coated samples were
analyzed by atomic force microscopy (AFM). The results have shown
that the roughness of the surfaces have increased by increasing the
molar ratio of monomer methacrylic acid (MAA). This study reveals
that the gradual increase in surface roughness is due to the fact that
carboxylic functional groups have been generated by MAA segments.
Such surfaces can be desirable platforms for fabrication of the
biosensors for detection of the viruses and diseases.
Abstract: The Roma (Gypsies) is a transnational minority with a
high degree of consanguineous marriages. Similar to other
genetically isolated founder populations, the Roma harbor a number
of unique or rare genetic disorders. This paper discusses about a rare
form of Charcot-Marie-Tooth disease – type 4G (CMT4G), also
called Hereditary Motor and Sensory Neuropathy type Russe, an
autosomal recessive disease caused by mutation private to Roma
characterized by abnormally increased density of non-myelinated
axons. CMT4G was originally found in Bulgarian Roma and in 2009
two putative causative mutations in the HK1 gene were identified.
Since then, several cases were reported in Roma families mainly
from Bulgaria and Spain. Here we present a Slovak Roma family in
which CMT4G was diagnosed on the basis of clinical examination
and genetic testing. This case is a further proof of the role of the HK1
gene in pathogenesis of the disease. It confirms that mutation in the
HK1 gene is a common cause of autosomal recessive CMT disease in
Roma and should be considered as a common part of a diagnostic
procedure.