Abstract: In developing countries, most roads in rural areas are dirt road. They require frequent maintenance since they are affected by erosive events, such as rain or wind, and the transit of heavy-weight trucks and machinery. Early detection of damages on the road condition is a key aspect, since it allows to reduce the maintenance time and cost, and also the limitations for other vehicles to travel through. Most proposals that help address this problem require the explicit participation of drivers, a permanent internet connection, or important instrumentation in vehicles or roads. These constraints limit the suitability of these proposals when applied into developing regions, like Latin America. This paper proposes an alternative method, based on unattended crowdsensing, to determine the quality of dirt roads in rural areas. This method involves the use of a mobile application that complements the road condition surveys carried out by organizations in charge of the road network maintenance, giving them early warnings about road areas that could be requiring maintenance. Drivers can also take advantage of the early warnings while they move through these roads. The method was evaluated using information from a public dataset. Although they are preliminary, the results indicate the proposal is potentially suitable to provide awareness about dirt roads condition to drivers, transportation authority and road maintenance companies.
Abstract: Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.
Abstract: Bone metastases are observed in a wide range of cancers leading to intolerable pain. While early detection can help the physicians in the decision of the type of treatment, various radiopharmaceuticals using phosphonates like 68Ga-EDTMP have been developed. In this work, due to the importance of absorbed dose, human absorbed dose of this new agent was calculated for the first time based on biodistribution data in Wild-type rats. 68Ga was obtained from 68Ge/68Ga generator with radionuclidic purity and radiochemical purity of higher than 99%. The radiolabeled complex was prepared in the optimized conditions. Radiochemical purity of the radiolabeled complex was checked by instant thin layer chromatography (ITLC) method using Whatman No. 2 paper and saline. The results indicated the radiochemical purity of higher than 99%. The radiolabelled complex was injected into the Wild-type rats and its biodistribution was studied up to 120 min. As expected, major accumulation was observed in the bone. Absorbed dose of each human organ was calculated based on biodistribution in the rats using RADAR method. Bone surface and bone marrow with 0.112 and 0.053 mSv/MBq, respectively, received the highest absorbed dose. According to these results, the radiolabeled complex is a suitable and safe option for PET bone imaging.
Abstract: Although breast cancer prevalence continues to increase, mortality has been decreasing as a result of early detection and improvement in adjuvant systemic therapy. Nevertheless, this disease required further efforts to understand and identify the associated potential risk factors that could play a role in the prevalence of this malignancy among Iraqi women. The objective of this study was to assess the perception of certain predictive risk factors on the prevalence of breast cancer types among a sample of Iraqi women diagnosed with breast cancer. This was a retrospective observational study carried out at National Cancer Research Center in College of Medicine, Baghdad University from November 2017 to January 2018. Data of 100 patients with breast cancer whose biopsies examined in the National Cancer Research Center were included in this study. Data were collected to structure a detailed assessment regarding the patients’ demographic, medical and cancer records. The majority of study participants (94%) suffered from ductal breast cancer with mean age 49.57 years. Among those women, 48.9% were obese with body mass index (BMI) 35 kg/m2. 68.1% of them had positive family history of breast cancer and 66% had low parity. 40.4% had stage II ductal breast cancer followed by 25.5% with stage III. It was found that 59.6% and 68.1% had positive oestrogen receptor sensitivity and positive human epidermal growth factor (HER2/neu) receptor sensitivity respectively. In regard to the impact of prediction of certain variables on the incidence of ductal breast cancer, positive family history of breast cancer (P < 0.0001), low parity (P< 0.0001), stage I and II breast cancer (P = 0.02) and positive HER2/neu status (P < 0.0001) were significant predictive factors among the study participants. The results from this study provide relevant evidence for a significant positive and potential association between certain risk factors and the prevalence of breast cancer among Iraqi women.
Abstract: Early detection of cancer could save human life and quality in insidious cases by advanced biomedical imaging techniques. Designing targeted detection system is necessary in order to protect of healthy cells. Electrospun nanofibers are efficient and targetable nanocarriers which have important properties such as nanometric diameter, mechanical properties, elasticity, porosity and surface area to volume ratio. In the present study, indocyanine green (ICG) organic dye was stabilized and encapsulated in polymer matrix which polyethylene oxide (PEO) and chitosan (CHI) multilayer nanofibers via co-axial electrospinning method at one step. The co-axial electrospun nanofibers were characterized as morphological (SEM), molecular (FT-IR), and entrapment efficiency of Indocyanine Green (ICG) (confocal imaging). Controlled release profile of PEO/CHI/ICG nanofiber was also evaluated up to 40 hours.
Abstract: Alzheimer's prevalence is on the rise, and the disease comes with problems like cessation of treatment, high cost of treatment, and the lack of early detection methods. The pathology of this disease causes the formation of protein deposits in the brain of patients called plaque amyloid. Generally, the diagnosis of this disease is done by performing tests such as a cerebrospinal fluid, CT scan, MRI, and spinal cord fluid testing, or mental testing tests and eye tracing tests. In this paper, we tried to use the Medial Temporal Atrophy (MTA) method and the Leave One Out (LOO) cycle to extract the statistical properties of the three Fz, Pz, and Cz channels of ERP signals for early diagnosis of this disease. In the process of CT scan images, the accuracy of the results is 81% for the healthy person and 88% for the severe patient. After the process of ERP signaling, the accuracy of the results for a healthy person in the delta band in the Cz channel is 81% and in the alpha band the Pz channel is 90%. In the results obtained from the signal processing, the results of the severe patient in the delta band of the Cz channel were 89% and in the alpha band Pz channel 92%.
Abstract: One of the significant and continual public health problems in the world is breast cancer. Early detection is very important to fight the disease, and mammography has been one of the most common and reliable methods to detect the disease in the early stages. However, it is a difficult task, and computer-aided diagnosis (CAD) systems are needed to assist radiologists in providing both accurate and uniform evaluation for mass in mammograms. In this study, a multiresolution statistical method to classify mammograms as normal and abnormal in digitized mammograms is used to construct a CAD system. The mammogram images are represented by wave atom transform, and this representation is made by certain groups of coefficients, independently. The CAD system is designed by calculating some statistical features using each group of coefficients. The classification is performed by using support vector machine (SVM).
Abstract: Wireless sensor networks (WSNs) can facilitate continuous monitoring of patients and increase early detection of emergency conditions and diseases. High density WSNs helps us to accurately monitor a remote environment by intelligently combining the data from the individual nodes. Due to energy capacity limitation of sensors, enhancing the lifetime and the reliability of WSNs are important factors in designing of these networks. The clustering strategies are verified as effective and practical algorithms for reducing energy consumption in WSNs and can tackle WSNs limitations. In this paper, an Energy-efficient weight-based Clustering Protocol (EWCP) is presented. Artificial retina is selected as a case study of WSNs applied in body sensors. Cluster heads’ (CHs) selection is equipped with energy efficient parameters. Moreover, cluster members are selected based on their distance to the selected CHs. Comparing with the other benchmark protocols, the lifetime of EWCP is improved significantly.
Abstract: Background and Objective: In most areas of the world, the incidence of thyroid cancer has been increasing over the last decade, mostly due to a combination of early detection of the neoplasm resulting from sensitive procedures and increased population exposure to radiation and unrecognized carcinogens. Methods: Cases of thyroid cancer have been retrieved from the cancer registry at King Khalid Hospital during the period from August 2012 to April 2016. Age, gender and histopathologic types have been recorded. Results: Thyroid carcinoma ranked as the second most common malignancy in females (25%) after breast cancer (31%). It constituted 20.8% of all newly diagnosed cancer cases. As for males, it ranked the 4th type of malignancy after gastrointestinal cancer, lymphomas and soft tissue sarcomas. Mean age for females and males was 38.7 +/- 13.2 and 60.25 +/- 11.5 years, respectively, and the difference between the two groups was statistically significant (p value = 0.0001). Fifty-five (82%) were papillary carcinomas including 10 follicular variant of papillary (FVPC), and eight papillary micro carcinomas (PMC) and two tall cell/oncocytic variants. Follicular carcinomas constituted two (3.1%), while two (3.1%) were anaplastic, and two (3.1%) were medullary. Conclusion: Thyroid cancer incidence in Hail is ranking as the 2nd most common female malignancy similar to other regions in the Kingdom. However, this high incidence contrasts with much lower rates worldwide.
Abstract: Retinoblastoma is a rare type of childhood genetic cancer that affects children worldwide. The diagnosis is often missed due to lack of education and difficulty in presentation of the tumor. Frequently, the tumor on the retina is noticed by photography when the red-eye flash, commonly seen in normal eyes, is not produced. Instead, a yellow or white colored patch is seen or the child has a noticeable strabismus. Early detection can be life-saving though often results in removal of the affected eye. Remaining functioning in the healthy eye when the child is young has resulted in super-vision and high or above-average intelligence. Technological advancement of cameras has helped in early detection. Brain imaging has also made possible early detection of neurological diseases and, together with the monitoring of cortisol levels and yawning frequency, promises to be the next new early diagnostic tool for the detection of neurological diseases where cortisol insufficiency is particularly salient, such as multiple sclerosis and Cushing’s disease.
Abstract: The oral cavity can be the site for early manifestations of mucocutaneous disorders (MD) or the only site for occurrence of these disorders. It can also exhibit oral lesions with simultaneous associated skin lesions. The MD involving the oral mucosa commonly presents with signs such as ulcers, vesicles and bullae. The unique environment of the oral cavity may modify these signs of the disease, thereby making the clinical diagnosis an arduous task. In addition to the unique environment of oral cavity, the overlapping of the signs of various mucocutaneous disorders, also makes the clinical diagnosis more intricate. The aim of this review is to present the oral signs of dermatological disorders having common oral involvement and emphasize their importance in early detection of the systemic disorders. The aim is also to highlight the necessity of oral examination by a dermatologist while examining the skin lesions. Prior to the oral examination, it must be imperative for the dermatologists and the dental clinicians to have the knowledge of oral anatomy. It is also important to know the impact of various diseases on oral mucosa, and the characteristic features of various oral mucocutaneous lesions. An initial clinical oral examination is may help in the early diagnosis of the MD. Failure to identify the oral manifestations may reduce the likelihood of early treatment and lead to more serious problems. This paper reviews the oral manifestations of immune mediated dermatological disorders with common oral manifestations.
Abstract: Wireless Body Area Network (WBAN) is a short-range
wireless communication around human body for various applications
such as wearable devices, entertainment, military, and especially
medical devices. WBAN attracts the attention of continuous health
monitoring system including diagnostic procedure, early detection of
abnormal conditions, and prevention of emergency situations.
Compared to cellular network, WBAN system is more difficult to
control inter- and inner-cell interference due to the limited power,
limited calculation capability, mobility of patient, and
non-cooperation among WBANs.
In this paper, we compare the performance of resource allocation
scheme based on several Pseudo Orthogonal Codewords (POCs) to
mitigate inter-WBAN interference. Previously, the POCs are widely
exploited for a protocol sequence and optical orthogonal code. Each
POCs have different properties of auto- and cross-correlation and
spectral efficiency according to its construction of POCs. To identify
different WBANs, several different pseudo orthogonal patterns based
on POCs exploits for resource allocation of WBANs. By simulating
these pseudo orthogonal resource allocations of WBANs on
MATLAB, we obtain the performance of WBANs according to
different POCs and can analyze and evaluate the suitability of POCs
for the resource allocation in the WBANs system.
Abstract: Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate. This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.
Abstract: Escherichia coli (E. coli) is the most isolated bacteria
from blood circulation of septicemic calves. Given the prevalence of
septicemia in animals and its economic importance in veterinary
practice, better understanding of changes in clinical signs following
disease, may contribute to early detection of disorder. The present
study has been carried out to detect changes of clinical signs in
induced sepsis in calves with E. coli. Colisepticemia has been
induced in 10 twenty-day old healthy Holstein- Frisian calves with
intravenous injection of 1.5 X 109 colony forming units (cfu) of
O111:H8 strain of E. coli. Clinical signs including rectal temperature,
heart rate, respiratory rate, shock, appetite, sucking reflex, feces
consistency, general behavior, dehydration and standing ability were
recorded in experimental calves during 24 hours after induction of
colisepticemia. Blood culture was also carried out from calves four
times during experiment. ANOVA with repeated measure is used to
see changes of calves’ clinical signs to experimental colisepticemia,
and values of P≤ 0.05 was considered statistically significant. Mean
values of rectal temperature and heart rate as well as median values
of respiratory rate, appetite, suckling reflex, standing ability and feces
consistency of experimental calves increased significantly during
study (P 0.05). The
results of present study showed that total score of clinical signs in
calves with experimental colisepticemia increased significantly,
although score of some clinical signs such as shock did not change
significantly.
Abstract: Myocardial infarction is one of the leading causes of
death in the world. Some of these deaths occur even before the
patient reaches the hospital. Myocardial infarction occurs as a result
of impaired blood supply. Because the most of these deaths are due to
coronary artery disease, hence the awareness of the warning signs of
a heart attack is essential. Some heart attacks are sudden and intense,
but most of them start slowly, with mild pain or discomfort, then
early detection and successful treatment of these symptoms is vital to
save them. Therefore, importance and usefulness of a system
designing to assist physicians in early diagnosis of the acute heart
attacks is obvious. The main purpose of this study would be to enable patients to
become better informed about their condition and to encourage them
to seek professional care at an earlier stage in the appropriate
situations. For this purpose, the data were collected on 711 heart
patients in Iran hospitals. 28 attributes of clinical factors can be
reported by patients; were studied. Three logistic regression models
were made on the basis of the 28 features to predict the risk of heart
attacks. The best logistic regression model in terms of performance
had a C-index of 0.955 and with an accuracy of 94.9%. The variables,
severe chest pain, back pain, cold sweats, shortness of breath, nausea
and vomiting, were selected as the main features.
Abstract: Leishmaniasis is the collective name for a number of
diseases caused by protozoan flagellates of the genus Leishmania,
which is transmitted by Phlebotomine sandfly, the disease has diverse
clinical manifestations and found in many areas of the world,
particularly in Africa, Latin America, South and Central Asia, the
Mediterranean basin and the Middle East. This study was done to
assess primary health care physicians’ knowledge (PHP) and attitude
about leishmaniasis and to assess awareness of local inhabitants
about the disease and its vector in four areas in west Alexandria,
Egypt. It is a cross sectional survey that was conducted in four PHC
units in west Alexandria. All physicians currently working in these
units during the study period were invited to participate in the study;
only 20 PHP completed the questionnaire. 60 local inhabitants were
selected randomly from the four areas of the study, 15 from each
area; Data was collected through two different specially designed
questionnaires. Results showed that 11 (55%) percent of the
physicians had satisfactory knowledge; they answered more than 9
(60%) questions out of a total 14 questions about leishmaniasis and
sandfly. On the other hand when attitude of the primary health care
physicians about leishmaniasis was measured, results showed that 17
(85%) had good attitude and 3 (15%) had poor attitude. The second
questionnaire showed that the awareness of local inhabitants about
leishmaniasis and sandfly as a vector of the disease is poor and needs
to be corrected. (90%) of the interviewed inhabitants had not heard
about leishmaniasis, Only 3 (5%) of them said they know sandfly and
its role in transmission of leishmaniasis. Thus we conclude that
knowledge and attitudes of physicians are acceptable. However, there
is, room for improvement and could be done through formal training
courses and distribution of guidelines. In addition to raising the
awareness of primary health care physicians about the importance of
early detection and notification of cases of leishmaniasis, health
education for raising awareness of the public regarding the vector and
the disease is necessary because related studies have demonstrated
that for inhabitants to take enough protective measures against the
vector, they should perceive that it is responsible for causing a
disease.
Abstract: Cortisol is essential to the regulation of the immune
system and yawning is a pathological symptom of multiple sclerosis
(MS). Electromyography activity (EMG) in the jaw muscles typically
rises when the muscles are moved and with yawning is highly
correlated with cortisol levels in healthy people. Saliva samples from
59 participants were collected at the start and after yawning, or at the
end of the presentation of yawning-provoking stimuli, in the absence
of a yawn, together with EMG data and questionnaire data: Hospital
Anxiety and Depression Scale, Yawning Susceptibility Scale,
General Health Questionnaire, demographic, health details. Exclusion
criteria: chronic fatigue, diabetes, fibromyalgia, heart condition, high
blood pressure, hormone replacement therapy, multiple sclerosis,
stroke. Significant differences were found between the saliva cortisol
samples for the yawners, t (23) = -4.263, p = 0.000, as compared with
the non-yawners between rest and post-stimuli, which was nonsignificant.
Significant evidence was found to support the Thompson
Cortisol Hypothesis suggesting that rises in cortisol levels are
associated with yawning. Further research is exploring the use of
cortisol as an early diagnostic tool for MS. Ethics approval granted
and professional code of conduct, confidentiality, and safety issues
are approved therein.
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: Early detection of breast cancer saves many thousands
of lives each year via application of mammography and genetic
screening and many more lives could be saved if nurses are involved
in breast care screening practices. So, the aim of the study was to
identify nurse's role in early detection of breast cancer through
mammography and genetic screening and its impact on patient's
outcome. In order to achieve this aim, 400 women above 40 years,
asymptomatic were recruited for mammography and genetic
screening. In addition, 50 nurses and 6 technologists were involved in
the study. A descriptive analytical design was used. Five tools were
utilized: sociodemographic, mammographic examination and risk
factors, women's before, during and after mammography, items
relaying to technologists, and items related to nurses were also
obtained. The study finding revealed that 3% of women detected for
malignancy and 7.25% for fibroadenoma. Statistically significant
differences were found between mammography results and age,
family history, genetic screening, exposure to smoke, and using
contraceptive pills. Nurses have insufficient knowledge about
screening tests. Based on these findings the present study
recommended involvement of nurses in breast care which is very
important to in force population about screening practices.
Abstract: Mammography has been one of the most reliable
methods for early detection of breast cancer. There are different
lesions which are breast cancer characteristic such as
microcalcifications, masses, architectural distortions and bilateral
asymmetry. One of the major challenges of analysing digital
mammogram is how to extract efficient features from it for accurate
cancer classification. In this paper we proposed a hybrid feature
extraction method to detect and classify all four signs of breast
cancer. The proposed method is based on multiscale surrounding
region dependence method, Gabor filters, multi fractal analysis,
directional and morphological analysis. The extracted features are
input to self adaptive resource allocation network (SRAN) classifier
for classification. The validity of our approach is extensively
demonstrated using the two benchmark data sets Mammographic
Image Analysis Society (MIAS) and Digital Database for Screening
Mammograph (DDSM) and the results have been proved to be
progressive.