In response to the global call for strategic information to comprehend prostate cancer, this study evaluated the health communication on behavioral practice of prostate cancer in Kwara state, Nigeria. Existing studies in Nigeria on prostate cancer have mostly focused on health practitioners and their patients, ignoring specific empirical data on semi-urban and urban context. This study looks at health communication channels as predictors of knowledge, attitude, and behavioral practices, with a focus on Ilorin, Nigeria’s Kwara state, which has the highest prostate cancer prevalence rate. A total of 336 respondents from Kwara State, Nigeria, were randomly selected using the multistage sample procedure for the survey. The findings show Knowledge of prostate cancer was highest amongst study participants who used the radio (4.00 ± 1.06) and television (3.64 ± 0.51) while it was low amongst those who relied on the internet (3.48 ± 0.50) and health professionals (3.16 ± 0.66) as their primary source of information. Contrastingly, practice was highest amongst persons who used the internet (3.60 ± 0.20) as their primary information source and lowest amongst those who used the television (2.50 ± 1.52) and Health Professionals (2.44 ± 0.65). Demographically, respondents in the 46-55 age group scored the highest (3.93 ± 0.71) as compared to those in the 26-35 (3.43 ± 0.68) who scored the lowest on the knowledge scale.The study concludes that health communication outlets such as television, the Internet, radio, newspapers, and health workers have a good impact on the people of Ilorin, Kwara State, Nigeria. The study suggests creating a nationwide prostate cancer communication system to improve the knowledge, attitude and practice of people, towards the attainment of Sustainable Development Goal 3.
In the current machine vision technology, accurate detection and classification of the crop dis-eases can protect against spoilage. Different diseases of tomato leaf have similar features or traits, making image disease detection confusing and challenging. Farmers cannot recognize whether a crop is infected or not just by looking at its leaves, because the healthy and infected crops resemble the same at first. Deep learning models can be used to overcome this prob-lem within less computational time. As a result, a new framework is implemented in this work through fine tuning the Deep Convolutional Neural Networks (DCNN) model using hyper parameters like learning rate, batch size, and epochs by applying transfer learning techniques for detecting tomato leaf disease. The data in this work is collected from the Plant Vil-lage database, which includes 20,639 images. The pro-posed model is implemented on three pre trained DCNN models-Alex Net, ResNet50 and VGG16. The proposed framework attains highest classification ac-curacy of 99.26% for fine tuning DCNN. The simula-tion results demonstrates that the fine-tuning Res-Net50 performs better classification of crop diseases when compared to the other DCNN models.
The purpose of this study was to determine the levels and components of essential oils between the rhizome and tuber parts of the white turmeric (Kaempferi rotunda) plant. Sampling of white turmeric was done purposively. The plant parts analyzed were the rhizome and tuber of white turmeric. The study was conducted in August 2021. Sampling of white turmeric was carried out in Hampatung Village, Kapuas Hilir District, Kapuas Regency. Laboratory studies were carried out in 3 places, namely the Laboratory of Chemical Technology for Forest Products, Department of Forestry, University of Palangka Raya, BPOM Laboratory of Palangka Raya City and the Test Laboratory of the Academy of Analytical Chemistry, Bogor Polytechnic. From the results of the analysis of white turmeric essential oil content in the rhizome (0.2969%). The results of GC-MS analysis of essential oils obtained from the rhizome showed 33 components and there were 4 main component compounds, namely Bornyl acetate (64.81%), Champhene (35.07%), Pentadecane (47.53%) and ethyl cinnamate (48.57%).
Brain Tumor (BT) categorization is an indispensable task for evaluating Tumors and making an appropriate treatment. Magnetic Resonance Imaging (MRI) modality is commonly used for such an errand due to its unparalleled nature of the imaging and the actuality that it doesn’t rely upon ionizing radiations. The pertinence of Deep Learning (DL) in the space of imaging has cleared the way for exceptional advancements in identifying and classifying complex medical conditions, similar to a BT. Here in the presented paper, the classification of BT through DL techniques is put forward for the characterizing BTs using open dataset which categorize them into benign and malignant. The proposed framework achieves a striking precision of 96.65.
Feed supplements of oil and selenium have been studied for their effect on absolute weight growth and a descriptive picture of the nutritional content of protein, fat, cholesterol in tilapia baby fish. Feed experiments using Complete Randomized Design (6x3), R1 (basal/protein ration 28%); R2 addition of a mixture of coconut oil and hazelnut oil without Se and R3 (oil mixture 4%+Se 0.15 mg/kg); R4 (4% coconut oil + Se) and R5 (4% hazelnut oil + Se) and Rs (standard ration of protein 32%). Coconut is dominated by saturated fatty acids (lauric acid 42.67%), while hazelnut is dominated by linoleic unsaturated fatty acids (34.4%) and oleic acid (48.99%). Basal ration with the addition of a mixture of vegetable oils + Se resulted in an absolute growth of 27.33 g and a daily growth rate (DGR) of 0.43 g/day, and matched the Ration with high protein (32%). The addition of vegetable fats and selenium provides fish meat protein content 54.62%-58.54% and meat protein conversion (protein productive value) 27.68-32.03%. The fat content of meat and cholesterol ranges from 7.15%-10.20% and 75.43-103.97 mg/dL, respectively, and Se in tilapia meat ranges from 0.502-0.753 mg/kg).
Respiratory diseases become burden to affect health of the people and five lung related diseases namely COPD, Asthma, Tuberculosis, Lower respiratory tract infection and Lung cancer are leading causes of death worldwide. X-ray or CT scan images of lungs of patients are analysed for prediction of any lung related respiratory diseases clinically. Respiratory sounds also can be analysed to diagnose the respiratory illness prevailing among humans. Sound based respiratory disease classification against healthy subjects is done by extracting spectrogram from the respiratory sound signal and Convolutional neural network (CNN) templates are created by applying the extracted features on the layered CNN architecture. Test sound is classified to be associated with respiratory disease or healthy subjects by applying the testing procedure on the test feature frames of spectrogram. Evaluation of the respiratory disease binary classification is performed by considering 80% and 20% of the extracted spectrogram features for training and testing. An automated system is developed to classify the respiratory diseases namely upper respiratory tract infection (URTI), pneumonia, bronchitis, bronchiectasis, and coronary obstructive pulmonary disease (COPD) against healthy subjects from breathing & wheezing sounds. Decision level fusion of spectrogram, Melspectrogram and Gammatone gram features with CNN for modelling & classification is done and the system has deliberated the accuracy of 98%. Combination of Gammatone gram and CNN has provided very good results for binary classification of pulmonary diseases against healthy subjects. This system is realized in real time by using Raspberry Pi hardware and this system provides the validation error of 14%. This automated system would be useful for COVID testing using breathing sounds if respiratory sound database with breathing sound recordings from COVID patients would be available.
Species diversity characterises an area or a targeted habitat and provides information on the species assemblages, which is a primary reflection of habitat diversity. It incorporates both species richness and abundance and may rely on factors, such as host-plant availability and food resources. Kubah National Park is a lowland mixed dipterocarp forest, and uniquely offers various vegetation types, which includes heath and submontane forests. In the present study, four contrasting forest habitats were selected along forest trails, namely, primary forests, secondary forests, heath forests and forest edges. Forty baited traps were used within a six-months of sampling period. The highest abundance was recorded at the heath forest while the least was at the secondary forest. However, there was no significant difference in terms of species abundance between forest habitats for subfamilies, as well as for the 15 most abundant nymphalid species sampled, except for Mycalesis mnasicles. This satyrine was regarded as being able to differentiate habitat, apart from sensitive to canopy openings. Secondary forest provides a heterogeneous habitat for nymphalids, and thus highest diversity was observed here. This study implies that habitat association of the family is crucial, as it will provide information for both species inventory as well as the fluctuation patterns of the nymphalids diversity. Similar study is suggested to be carried out in the future, which incorporates more than one habitat types and in a more extensive period.
Green sea turtles (Chelonia mydas) are using selected Sarawak sandy beaches for nesting. Its small-scale conservation efforts have started since the 1950s. However, the success of the effort is still debatable. Moreover, public participation in the conservation effort is still at an infancy stage. This study involved analysing nesting data of green turtles of Sarawak Turtle Islands (STIs) in terms of numbers of turtle landing, eggs collected, eggs incubated, and eggs hatched for Sarawak’s green turtle population from 1949 to 2016 with addition of estimation of the egg-laying females and their remigration interval. There was a sharp decline in terms of number of turtle landings from 1949 to the 1970s. The exact cause of this pattern could only be confirmed from old articles of historical value due to limited information on the events happening during those 30 years period. From 1980 to 2016, the annual nesting trend of turtles on STIs shows irregular fluctuation pattern and remigration interval of every 3 to 4 years with internesting frequency of 3. Turtle eggs were harvested annually, with a 36-year average of 223,558 eggs per year. Detailed analysis for data 1980 to 2016 shows that the lowest percentage of eggs incubated was in 1981 (20.4 %) and starting from the year 1991, egg incubation at STIs reached between 90 to 100 percent. There is an upward trend of egg hatching rate for the past 36 years, especially from the 1990s to 2000s, with the latest seven years average of 60%. The results imply that conservation of green turtle in Sarawak have been successful at different levels. However, there is still a need for relevant state agencies to modify and step-up efforts related to conservation of green turtles in Sarawak especially in terms of male:female ratio of hatchlings from STI’s hatcheries.
Biological Tomato leaf classification is very important to decide the pesticide, insecticide, and other treatments needed for the plant to yield good crop. The images captured by handheld cameras or using drones are used by various machine learning algorithms to identify the diseases. Such methods need extraction of features from the images before the machine learning methods can be used for disease identification. In this paper, a deep learning framework is proposed that automatically extracts features in a hierarchical manner. The features are classified using neural networks to classify the leaves into three classes, viz. no disease, bacterial spot, and Septoria leaf spot. The performance of the model is tested using accuracy as the performance metric. The obtained performance metric validates the performance of the method. The method is useful for taking corrective measures to disease management of tomato plants.
Regular dosed physical activity in all cases has a tonic and healing effect on a person. Feasible muscle activity helps to improve the function of the heart, lungs, blood vessels and the nervous system. In martial arts, the reactivity of the vestibular apparatus is of great importance, providing the flow of information about the current position of the body in space, the success of movement in it and maintaining the stability of the posture due to the effective redistribution of muscle tone. Purpose of the work: to find out the dependence of the reaction of the cardiovascular system to the arising vestibular irritation in different types of martial arts. Martial arts classes help to increase the degree of vestibular stability. The statokinetic stability is very pronounced and increases in the course of hand-to-hand combat, which has a lot of moments with different severity of angular accelerations. The peculiarities of motor activity in any kind of martial arts inevitably affect the peculiarities of the reaction to the test with a change in the position of the head.
In the current scenario, usage of the smart medical pump is predominant in the medical field. The precise drug dosage, flow accuracy should be maintained to increase the performance of an infusion pump. In this work, an attempt has been made to predict and control the speed of the infusion pump for suitable infusion flowrate using machine learning technique and Linear Quadratic Gaussian (LQG) controller. The data for this study is considered from the publicly available online database, electronic Medicines Compendium (eMC). The speed of the infusion pump has been calculated using the drug dosage and flow rate for two different drugs. The prediction of infusion pump speed is achieved using Linear regression with Principal Component analysis (PCR) and Support Vector Machine Regression (SVR). The performance of the prediction schemes is evaluated using standard metrics. To validate the optimal control of the predicted speed, two different medical graded motors are considered. Further, the optimal control of the pump speed is investigated using Proportional–Integral–Derivative (PID), Linear Quadratic Regulator (LQR), and LQG controllers for its stability criteria. The prediction of the pump speed using regression models PCR, SVR has been verified and then the transient response analysis with rise time, settling time for both the motors have been examined. Results demonstrate that the LQG optimal control strategy achieves fast rise time, settling time of motor1 with 0.653s, 1.15s, and 0.22, 0.392s for motor2 respectively.
This paper proposes the development of automatic sleep stage detection by using physiological signals. We aim to develop an application to assist drivers after drowsiness or fatigue detection by a commercial driver vigilance system. The proposed method used a low-cost surface electromyography (EMG) device for sleep stage detection. We investigate skeletal muscle location and EMG features from sleep stage 2 to provide an EMG-based nap monitoring system. The results showed that using only one channel of a bipolar EMG signal from an upper trapezius muscle with median power frequency can achieve 84% accuracy. We implement a MyoWare muscle sensor into the proposed nap monitoring device. The results showed that the proposed system is feasible for detecting sleep stages and waking up the napper. A combination of EMG and electroencephalogram (EEG) signals might be yield a high system performance for nap monitoring and alarm system. We will prototype a portable device to connect the application to a smartphone and test with a target group, such as truck drivers and physicians.
Bivariate calibration algorithm is compared with the results obtained by the usage of high-dimensional calibration methods such as partial least squares (PLS) and multi-way partial least-squares (N-PLS) by using UV-Vis spectrophotometric data of first and second-order. The algorithms were applied to the determination of a mixture of an analgesic and a stimulant compound and their actual concentrations of them were calculated by using spectroscopic data. The direct reading of absorbance values at 227 nm and 271 nm were employed for quantification of the compounds in the case of the bivariate method. The approaches of first-order and multi-way methods were applied with a previous optimization of the calibration matrix by constructing sets of calibration and validation with 20 and 10 samples (mixtures) respectively according to a central composite design and their UV absorption spectra were recorded at 200-350 nm. All algorithms were satisfactorily applied to the simultaneous determination of these compounds in pharmaceutical formulations with mean percentage recovery of 100.5 ± 3.67, 98.7 ± 3.42, and 100.5 ± 3.74 for bivariate, PLS-1, and N-PLS, respectively. The statistical evaluation of the bivariate method showed that this procedure is comparable with those algorithms that employ high-dimensional structured information. The aim of the work is to compare the methods under study and it can be seen that there are no significant differences, so a simple spectrophotometer can be used up to a very specialized one. However, the advantage of bivariate calibration is its simplicity, due to the minimal experimental manipulation.
Mini percutaneous nephrolithotomy (mPNL) is a standard treatment for kidney stones larger than 1.5 cm, with the placement of a nephrostomy drainage at the end of it, which is considered the standard procedure, but tubeless/ totally tubeless mPNL techniques reduce postoperative discomfort in patients and shorten hospital stays. The aim of article was to compare the efficacy and safety of our proposed modified method of totally tubeless mPNL with control of the parenchymal canal, with existing methods of tubeless/totally tubeless mPNL. Novelty of the study presented by modified method of totally tubeless mPNL. During the period from 2018 to 2020 we performed 486 mPNL were performed in our clinic in total, among which 63 (12.9%) patients underwent tubeless PNL. Patients whose surgeries ended with using tubeless techniques were divided into three groups: Group I – 22 patients who had tubeless mPNL (with ureteral stent), Group II (20 patients) – totally tubeless mPNL with a safety thread (the proposed procedure), Group III (21 patients) – totally tubeless mPNL. In all three groups, the access point was most often made through the lower group of renal calyces: Group I – 12 (54.5%), Group II – 14 (70.0%), Group III – 13 (61.9%); then through the middle calyx: Group I – 8 (36.4%), Group II – 6 (30.0%), Group III – 7 (33.3%); and the upper calyx: Group І – 2 (9.1%), Group ІІ – 0%, Group ІІІ – 1 (4.8%), no differences in the distribution of access points between groups were found (p=0.67). There were no differences in the distribution of tract sizes between the groups (p=0.95) with tract dilatation to 16.5/17.5 Fr was performed most often: Group I – 12 (54.5%), in Group II – 11 (55.0%) and Group III – 11 (52.4%). The mean duration of surgery in Group I was 83.0±22.9 min, in Group II – 74.9±13.6 min, in Group III – 72.6±12.0 min (p=0.47). This study confirms the high effectiveness of totally tubeless mPNL. The proposed modification to perform totally tubeless mPNL allows you to have permanent postoperative control over the parenchymal channel and in case of postoperative bleeding it enables you to immediately insert nephrostomy drainage through the safety thread. Study contributes to practical methods as an intermediate step for surgeons who are considering transition to a totally tubeless PCNL technique.
Thyroglobulin (Tg) is essential for thyroid hormone synthesis and thyroid function. Its levels are regulated by external environmental changes. Zinc is widely involved in cellular processes as a cofactor of enzymes and participates in metabolic processes. Here we investigated whether zinc depletion affected Tg production and secretion through the endoplasmic reticulum (ER) in the PCCL3 thyroid cell line exposed to the zinc chelator N,N,N′,N′-tetrakis(2-pyridylmethyl)ethylenediamine (Tpen). Although zinc depletion did not affect the gene expression of ER chaperones (BiP and PDI), it increased the expression of ER transmembrane signaling proteins (PKR-like ER kinase, inositol requiring enzyme 1, and activating transcription factor 6 (ATF6)). This resulted in the activation of downstream factors as shown by the increase of eIF2-α phosphorylation, X-box binding protein 1 mRNA splicing, and ATF6 fragmentation. Zinc depletion induced an inhibition of Tg expression and secretion and activated apoptosis in PCCL3 cells. Moreover, a reduction of secreted T4 levels and histologically abnormal thyroid follicle structures were found after zinc depletion. Therefore, zinc depletion likely inhibited the biosynthesis and extracellular secretion of Tg through ER stress signaling. These findings provide valuable insight into zinc potential as a treatment of hyperthyroidism.
Recently, image processing has proven itself as a fast and reliable technique in research in medicine and biology. Bacterial colony separation is an important and time-consuming process in studies in the field of microbiology. Bacteria counting is usually carried out by the naked eye or even by Coulter counter machines, which are based on the rather expensive electric field measurement method. In this study, image-based enumeration of Escherichia Coli over the colony morphology in the petri dish was investigated. In the experimental study, 4 different bacteria from the Enterobacteriaceae family were planted on petri dishes containing Eosin-methylene blue agar (Merck, Darmstadt, Germany). Escherichia Coli colony characteristics were determined by digitizing planted bacterial petri images. For the study, counting was done with the interface developed in MATLAB R2013a. After the classification criteria were determined, the method was tested on new petri dishes and successful results were obtained.
Panoramic dental x-ray, a two-dimensional dental x-ray that captures the entire mouth in a single image, is used for the initial screening of various dental anomalies. One such is Jaw bone cyst, which, if not identified earlier, may lead to complications which in turn may lead to disfigurement and loss of function. Hence processing of radiographic images plays a vital role in identifying and locating the cystic region and extracting related features to assist clinical experts in further analysis. Objective: To develop an application of active contour model, known as Geodesic Active Contour, to generate Panoramic Dental X-Ray, a single 2 D X-ray image of the entire mouth highlighting the dental specifications. Methods: The process involves the image conversion from the OPG image into grayscale, Contrast adjustment using intensity level slicing, edge smoothing, segmentation, and cyst segmentation by Morphological Geodesic Active Contour to obtain the results. Hence processing of radiographic images plays a vital role in identifying and locating the cystic region. It is crucial in extracting related features to assist clinical experts in further analysis. Conclusion: When efficient and accurate diagnostic methods exist, the treatment and cure become easy and concrete. Based on the morphological snake and level sets, it aims at identifying the boundary by minimizing the energy. Results: Using the structural similarity index, an accuracy of 97.6% is obtained. Advances in Knowledge: This process is advantageous as it is simpler, faster, and does not suffer from instability problems. Morphological methods improve their functional gradient descent by improving stability and speed. The hysteresis algorithm exhibits better edge detection performance, a significant reduction in computational time and scalability.
Waterborne parasites, particularly Cryptosporidium and Giardia, are emerging pathogens implicating the safety level of drinking water globally. The aim of this study was to determine the distribution pattern of waterborne parasites in raw and treated water at urban and rural water treatment plants and untreated water from gravity-feed system in Kuching, Sarawak. This study focused on water treatment plants (four urban and two rural) and Bong rural community that utilise gravity-feed system in Kuching, Sarawak. A total of 69 raw and treated water samples were collected and processed before being used in detection of Cryptosporidium and Giardia using Aqua-Glo™ G/C Direct and 4′,6-diamidino-2-phenylindole stains, as well as other parasites that were detected using Lugol’s iodine staining. Parameters which were temperature, pH, turbidity, dissolved oxygen, total dissolved solids, conductivity, faecal coliform of the water as well as rainfall intensity were determined. Correlation of the parameters with distribution of the waterborne parasites was analysed. Out of 69 water samples collected across all localities, 25 samples were contaminated with waterborne parasites with varying waterborne parasite concentration in the water samples. The presence of waterborne parasites in the raw and treated water of water treatment plants in this study signifies public health threats do exist despite being conventionally treated. This study also highlights that the gravity-feed system which is commonly depended by rural communities in Malaysia may facilitate waterborne parasitic infections.
The number of deaths worldwide caused by COVID-19 continues to increase and the variants of the virus whose process we do not yet master are aggravating this situation. To deal with this global pandemic, early diagnosis has become important. New investigation methods are needed to improve diagnostic performance. A very large number of patients with COVID-19 have with cardiac arrhythmias often with ST segment elevation or depression on an electrocardiogram. Can ST-segment changes contribute to automatic diagnosis of COVID-19? In this article, we have tried to answer this question. We propose in this work a method for the automatic identification of COVID patients which exploits in particular the modifications of the ST segment observed on recordings of the ECG signal. Two sources of data allowed the development of the database for this study: 300 ECGs from the "physioNet" database with prior measurement of the ST segments, and 100 paper ECGs of patients from the cardiology department of the hospital X in Tunis registered on (non-covid) topics and covid topics. Four learning algorithms (ANN, CNN-LSTM, Xgboost, Random forest) were then applied on this database. The evaluation results show that CNN-LSTM and Xgboost present better accuracy in terms of classifying covid and non-covid patients with an accuracy rate of 87% and 88.7% respectively.
At present, health disorder is growing day by way of the day due to existence lifestyle, hereditary. Particularly, heart disease has ended up greater frequent these days. Heart disorder prognosis technique is very quintessential and integral trouble for the patient's health. Besides, it will help out to limit disorder to a larger distinctive level. The role of using strategy like machine learning and algorithm such as heart disease diagnosis using Data Mining(DM) techniques is very significant. In the previous system, the Fuzzy Extreme Learning Machine (FELM) was proposed to predict heart disease, ensuring an accurate and timely diagnosis. However, it only achieves 87.14 % of accuracy. To improve the classification accuracy, the proposed system designed an Improved Step Adjustment based Glowworm Swarm Optimization Algorithm with Weighted Feature based Support Vector Machine (ISAGSO-WFSVM) for Heart disease diagnosis. This proposed venture utilizes the dataset of heart disease for input. Using the Improved Step Adjustment based Glowworm Swarm Optimization Algorithm (ISAGSO) to enhance the true positive rate, optimal features are then selected. Finally, with the aid of the Weighted Feature based Support Vector Machine (WFSVM) classifier, classification is carried out relying selected features. In the proposed method, better performance obtained and that is validated through the experimental results in terms of precision, accuracy, recall and f-measures
