Organs Of Human Body Research Articles

Evaluation of hematological indices among insecticides factory workers

Insecticides are commonly used pesticides in the world. Chronic exposure to insecticides has adverse effects on various human body organs. In this study, hematological findings were assessed in workers in an insecticide manufacturing plant. Hematological parameters and clinical symptoms were recorded in 99 workers exposed to insecticides and 107 workers not exposed to them in a cross-sectional study. Assessment of the hematological results showed a higher prevalence of thrombocytosis in the exposed group than the non-exposed group (P < 0.05). Mean white blood cell (WBC) and platelet counts, anisocytosis of red blood cells (RBCs), and neutrophils to lymphocytes ratio (NLR) were significantly higher in the exposed group compared to the non-exposed group (P < 0.05). Prevalence rates of headache, itchy skin, cough, and sleep disorders were higher in the exposed group than in the non-exposed group (P < 0.05). Chronic exposure to insecticides can alter hematological parameters in the normal range. Occupational exposure to insecticides may increase WBCs, platelet count, NLR, and red cell distribution width (RDW). It can also cause thrombocytosis. Complete blood count (CBC), as an inexpensive and accessible tool, can help monitor workers' health status exposed to insecticides properly.

Legal Protection of Human Rights of Elderly Persons

Old age is a process which occurs naturally in the human life cycle. It is the decline in the capacity of the performance of the organs of human body. However, they comprise a reservoir of human resource, gifted with knowledge, deep insights and various experiences in many walks of life. They are the senior participants of the family system who aware of better the faith, family history, values and related customary practices. They own better information of the family values and society. The population of the aged has been increasing over the years. With the growing exposure of human towards industrialization in 20th century, the consequential rush towards consumerism has deeply ruptured the intactness of social fabric. Therefore, the elderly persons in the society are made subject to various discriminations, torture and negligence.

Human’s Plexus Systems and “Nikola Tesla’s 369 Theory” for Forming Universe and God

All activities which are taking place in the Cosmos also exist in a human body in subtle micro-scale. Plexuses centers in a human body are the most mysterious kinds of energies. The six-center plexus system is the path of the Kundalini shakti, the primordial cosmic energy of a person. Each plexus has its own propensities (vibrating words/dimensions/vritti) and an acoustic root. These plexuses control some cluster of words of sounds and corresponding physical organs in human body. The 50 main propensities of the human’s unit mind are expressed through the vibration-expression of these plexuses. These vibrations cause hormones to secrete from the corresponding glands of the human’s body. These plexuses can play very important roles in curing the diseases. According to the Theory of Nikola Tesla’s 369, there are all together 1 to 9 digital root numbers exist. All other higher or lower numbers are the combination of those digital root numbers. This statement seems quite true when we compare it with Theory of Absolutivity. If 3, 6, 9 numbers are arranged together it makes a letter Ohm (AUM) which represents actually a Sagun Bramh/Nikola Tesla’s 369 triangle which is a part of NirgunBramh having higher dimensions. Number zero is the NirgunBramh (having all the qualities but in dormant form) which does not possess any quality in action. Number 3, 6, 9 forming as an equilateral triangle of static, mutative and sentient forces representing the Black-hole which is a part of Sagun Bramh projecting Himself as a Visible World through Big-bang along with forming five basic elements such as Ethereal, Areal, Fire, Liquid and Solid. According to the biological science, life emerged in this planet from matter about four billion years ago in the form of DNA that carried the software for the development of billion of species on the way to its final destination in humans. These facts also fully support the above hypothesis of universe formations.

“Effect of Hydroxyapatite Nanoparticles On Compressive strength and Flexural strength of Dental Composites Using Taguchi Method”

Abstract: Several composite materials are being used in biomedical and dental field with their immense applications to repair and transform various organs in human body. Recent advances suggest that Hydroxyapatite is one of the most reliable and widely used inorganic composite in dentistry. Desirable applications of Hydroxyapatite are achieved by utilizing variety of hydroxyapatite and their composites. This study was conducted to evaluate the compressive &amp; flexural strength. Cylindrical specimens (n=9) for compressive strength &amp; rectangular shaped specimens (n=9) for flexural strength were made according to manufacturer’s recommendations. Dental composite is using quartz, silica, and alumina glass as filler for a long time. Taguchi optimization technique keeps the experimentation within limit giving valid product in the calculating of compressive and flexural strength optimization. The goal of the work is to detect the best combination of composite materials. Keywords: Hydroxyapatite, Compressive Strength, Flexural Strength, Taguchi’s optimization method.

The discovAIR project: a roadmap towards the Human Lung Cell Atlas

The Human Cell Atlas (HCA) consortium aims to establish an atlas of all organs in the healthy human body at single-cell resolution to increase our understanding of basic biological processes that govern development, physiology and anatomy, and to accelerate diagnosis and treatment of disease. The Lung Biological Network of the HCA aims to generate the Human Lung Cell Atlas as a reference for the cellular repertoire, molecular cell states and phenotypes, and cell–cell interactions that characterise normal lung homeostasis in healthy lung tissue. Such a reference atlas of the healthy human lung will facilitate mapping the changes in the cellular landscape in disease. The discovAIR project is one of six pilot actions for the HCA funded by the European Commission in the context of the H2020 framework programme. discovAIR aims to establish the first draft of an integrated Human Lung Cell Atlas, combining single-cell transcriptional and epigenetic profiling with spatially resolving techniques on matched tissue samples, as well as including a number of chronic and infectious diseases of the lung. The integrated Human Lung Cell Atlas will be available as a resource for the wider respiratory community, including basic and translational scientists, clinical medicine, and the private sector, as well as for patients with lung disease and the interested lay public. We anticipate that the Human Lung Cell Atlas will be the founding stone for a more detailed understanding of the pathogenesis of lung diseases, guiding the design of novel diagnostics and preventive or curative interventions.

Management of Twak vikruti: a case report

In today’s era skin disease is very commonly found. As there is no specific and permanent treatment available in modern science therefore people are attracting towards Ayurvedic treatments for skin related issues and diseases. Twak (skin) is the largest organ of human body which protects and covers whole body. It is one of five gyanendriya that is Sparsh gyanendriya (sense organ). Nowadays people use so many chemical products internally as well as externally for looking good which causes the imbalance of dosh, dhatu and mala (fecal product). These are the fundaments of human body and skin is the outer most protective organ. So, whenever anything uncommon happened with body, skin start showing some symptoms like redness, swelling, due to dushti of doshas (imbalance/toxicity of dosha). A male patient of 36 years old came in our hospital with the complains of Twak kandu (skin itching), Krushna varnata (hyper pigmentation), Krushna varni pitikotpatti (rashes), Vali (wrinkles) and Rukshta (dryness) which was diagnosed as Twak vikruti (skin disease) and treated with complete ayurvedic treatment Shaman chikitsa (internal medicine) along with Shodhan chikitsa (body purification treatment).

DenseRes-Unet: Segmentation of overlapped/clustered nuclei from multi organ histopathology images

Cancer is the second deadliest disease globally that can affect any human body organ. Early detection of cancer can increase the chances of survival in humans. Morphometric appearances of histopathology images make it difficult to segment nuclei effectively. We proposed a model to segment overlapped nuclei from H&E stained images. U-Net model achieved state-of-the-art performance in many medical image segmentation tasks; however, we modified the U-Net to learn a distinct set of consistent features. In this paper, we proposed the DenseRes-Unet model by integrating dense blocks in the last layers of the encoder block of U-Net, focused on relevant features from previous layers of the model. Moreover, we take advantage of residual connections with Atrous blocks instead of conventional skip connections, which helps to reduce the semantic gap between encoder and decoder paths. The distance map and binary threshold techniques intensify the nuclei interior and contour information in the images, respectively. The distance map is used to detect the center point of nuclei; moreover, it differentiates among nuclei interior boundary and core area. The distance map lacks a contour problem, which is resolved by using a binary threshold. Binary threshold helps to enhance the pixels around nuclei. Afterward, we fed images into the proposed DenseRes-Unet model, a deep, fully convolutional network to segment nuclei in the images. We have evaluated our model on four publicly available datasets for Nuclei segmentation to validate the model's performance. Our proposed model achieves 89.77% accuracy 90.36% F1-score, and 78.61% Aggregated Jaccard Index (AJI) on Multi organ Nucleus Segmentation (MoNuSeg).

Optimization an Off-Road Vehicle Seat Suspension by Metaheuristic Method

Passenger seat is main part of vehicle which has direct effect on transmitted vibration to the body. Seat suspension can remove unwanted and harmful vibration if right parameters were selected. Each human body organs has specific natural frequency. When vehicle vibration reaches to this frequency range, resonance will occur, and this phenomenon is harmful in long term. Usually lumped models were used to predict human body response to vibration. In this paper, via Qassem biodynamic model, the relative displacement of lumbar spine seat to spine vibration transmissibility was minimized by artificial neural network method. By this technique, optimal spring constant and damping coefficient were found and verified.

Management of Skin Disorders in Ayurveda : A Review

Skin is the largest organ of human body. Its size and external location makes it susceptible to various disorders. Skin diseases are mostly caused by infections like bacteria, fungi etc. All the skin diseases in Ayurveda have been classified under the broad heading of Kushtawhich are further classified into Mahakushtaand Kshudrakushta. Ayurveda has its own unique principles of diagnosis and treatment of various skin diseases under the heading of Kuṣṭha. A review was conducted from the relevant texts of Ayurvedic and contemporary science to collect information about the skin diseases and their treatment through Ayurveda. The review concluded that Ayurveda management through Shodhanaand Shamana Chikitsa(with Ayurvedic herbs) is beneficial in skin diseases.

Multi-View Auxiliary Diagnosis Algorithm for Lung Nodules

Lung is an important organ of human body. More and more people are suffering from lung diseases due to air pollution. These diseases are usually highly infectious. Such as lung tuberculosis, novel coronavirus COVID-19, etc. Lung nodule is a kind of high-density globular lesion in the lung. Physicians need to spend a lot of time and energy to observe the computed tomography image sequences to make a diagnosis, which is inefficient. For this reason, the use of computer-assisted diagnosis of lung nodules has become the current main trend. In the process of computer-aided diagnosis, how to reduce the false positive rate while ensuring a low missed detection rate is a difficulty and focus of current research. To solve this problem, we propose a three-dimensional optimization model to achieve the extraction of suspected regions, improve the traditional deep belief network, and to modify the dispersion matrix between classes. We construct a multi-view model, fuse local three-dimensional information into two-dimensional images, and thereby to reduce the complexity of the algorithm. And alleviate the problem of unbalanced training caused by only a small number of positive samples. Experiments show that the false positive rate of the algorithm proposed in this paper is as low as 12%, which is in line with clinical application standards.

Food choices and dietary habits of college going girls with special emphasis on breakfast consumption practices

Choosing nutritious foods, eating mindfully and planning appropriate meals while eating; are the important components which make a significant impact on one's well-being. The choice of whether and what to eat and drink first thing in the morning has been demonstrated to have a significant impact on our health, well-being and cognitive abilities. Breakfast is widely regarded as the most vital meal of the day. A nutritious breakfast ensures the best day possible in the long run of day-to-day life. Human body organs burn around 400 to 500 calories during the night during sleep for seven to eight hours. Breakfast skipping has become common practice in recent years for a variety of reasons. Sadly, this practise gives rise to a variety of diseases and conditions in long term, including weight gain, heart disease, bone thinning, irritability, mood swings, low energy, poor memory, mental stress, high blood pressure, and an unhealthy mix of blood cholesterol. Several studies have looked at the relationship between lifestyle choices (such as skipping breakfast) and health outcomes. College students, especially young girls were shown to be more likely to skip breakfast. This not only increases negative health impact, but also lowers the quality of one's food, sleeping pattern and degree of physical activity. The objective of the study was to assess the socio-demographic profile, dietary and lifestyle pattern of the college going girls and to identify the probable causes of faulty dietary habits, if any, with special emphasis on their breakfast consumption. 850 subjects were selected randomly from the two colleges of the Ghaziabad. Results indicated that 63% (n=536) were found to be skipping breakfast. Factors significantly associated with breakfast skipping were age, average family income (monthly), parental education, time spent in commutation to college, contribution to house hold work. The most common reason cited for skipping breakfast were lack of time, not liking to eat so early, having no appetite. The prevalence of breakfast skipping among the college going girls was moderately high. Lower nutrition knowledge could be associated with the above findings. Thus, nutritional interventions to impart education at college level can be an effective tool, as adolescents/young adults spend most of the time here. It is strongly recommended that this age group especially college going girls should be educated and made aware of healthy breakfast eating practices and good nutrition patterns for healthy lifestyle and thus contribute towards healthy nation.

Non-Invasive Early Diagnosis of Obstructive Lung Diseases Leveraging Machine Learning Algorithms

Lungs are a vital human body organ, and different Obstructive Lung Diseases (OLD) such as asthma, bronchitis, or lung cancer are caused by shortcomings within the lungs. Therefore, early diagnosis of OLD is crucial for such patients suffering from OLD since, after early diagnosis, breathing exercises and medical precautions can effectively improve their health state. A secure non-invasive early diagnosis of OLD is a primordial need, and in this context, digital image processing supported by Artificial Intelligence (AI) techniques is reliable and widely used in the medical field, especially for improving early disease diagnosis. Hence, this article presents an AI-based non-invasive and secured diagnosis for OLD using physiological and iris features. This research work implements different machine-learning-based techniques which classify various subjects, which are healthy and effective patients. The iris features include gray-level run-length matrix-based features, gray-level co-occurrence matrix, and statistical features. These features are extracted from iris images. Additionally, ten different classifiers and voting techniques, including hard and soft voting, are implemented and tested, and their performances are evaluated using several parameters, which are precision, accuracy, specificity, F-score, and sensitivity. Based on the statistical analysis, it is concluded that the proposed approach offers promising techniques for the non-invasive early diagnosis of OLD with an accuracy of 97.6%.

Mathematical Simulation of Behavior of Female Breast Consisting Malignant Tumor During Hormonal Changes

The thermal models of human organs for normal, as well as malignant tissues, can be used for generating thermal information for detecting and analyzing tumors. Thermography suffers from the issues of accuracy and specificity. This is due to the non-availability of complete thermal information of human body organs due to benign and malignant disorders. The complete study of thermal patterns in human body organs due to benign and malignant disorders will be useful for generating thermal information for improving the specificity and sensitivity in existing thermographic techniques for the detection of cancer. We have proposed a thermal model of a woman&#x2019;s breast involving two benign disorders, namely cysts and hormonal changes due to the menstrual cycle has been proposed. In the proposed model, various critical factors like thermal conductivity and metabolic-heat generation were correlated with blood mass flow while the boundary conditions for acceptance are framed. The model is developed in terms of a boundary-value problem involving the partial equations for a 2-D steady-state case. Thermal patterns within a woman&#x2019;s breast due to cysts during the presence and absence of various phases of the mensuration cycle are obtained and analyzed. Also, the thermal patterns due to the cyst becoming malignant neoplasm in the Female breast are obtained and analyzed. The substantial differences within thermal patterns in the breast due to malignant tumors and benign disorders like cysts and various phases of the cycle are observed. The thermal information generated from such models is often useful to differentiate between benign and malignant disorders by thermography.

Brain Tumour Detection Using Image Segmentation: A Review

Abstract: Medical Image Processing is one of the most challenging and emerging fields. MRI, CT scan , ultra scan, X-rays etc. are different machines to diagnose the condition of the patient. Human body is made up of several types of cells. Brain is a highly specialized and sensitive organ of human body. Brain tumour is one of the severe problems in the medical science. MRI imaging is often used when treating brain tumour. There are various image segmentation algorithms in order to detect brain tumour using image processing. Firstly quality of scanned MRI image is enhanced and then different image segmentation techniques are applied to detect the tumour in the scanned image. Different segmentation methods reviewed here are thresholding, kmeans, watershed, edge detection, morphological, fuzzy c-means. Here sample 5 MRI images are taken and processed by using MATLAB software. With the help of these techniques, area of the tumour, execution time, number pixel can be determined. Keywords: MATLAB, segmentation, thresholding , kmeans, watershed, edge detection, morphological, fuzzy c-means.

A fuzzy sensitivity analysis approach to estimate brain effective connectivity and its application to epileptic seizure detection.

The brain is considered to be the most complicated organ in human body. Inferring and quantification of effective (causal) connectivity among regions of the brain is an important step in characterization of its complicated functions. The proposed method is comprised of modeling multivariate time series with Adaptive Neurofuzzy Inference System (ANFIS) and carrying out a sensitivity analysis using Fuzzy network parameters as a new approach tointroduce a connectivity measure for detecting causal interactions between interactive input time series. The results of simulations indicate that this method is successful in detecting causal connectivity. After validating the performance of the proposed method on synthetic linear and nonlinear interconnected time series, it is applied to epileptic intracranial Electroencephalography (EEG) signals. The result of applying the proposed method on Freiburg epileptic intracranial EEG data recorded during seizure shows that the proposed method is capable of discriminating between the seizure and non-seizure states of the brain.

Case Report on Cirrhosis of Liver

Liver is the second largest organ in human body, more than 5,000 separate bodily functions .including helping blood to clot, cleansing the blood of toxins to converting food into nutrients to control hormone levels, fighting infections and illness, regenerating back after injury and metabolizing cholesterol, glucose, iron and controlling their levels. A 56- years old patient was admitted in AVBRH on date 9/12/2020 in ICU with the chief complaint of abdominal distension, breathlessness on exertion, pedal edema, fever since 8 days. After admitted in hospital all investigation was done including blood test, ECG, fluid cytology, peripheral smear, ultrasonography, etc. All investigation conducted and then final diagnosis confirmed as cirrhosis of liver. Patient was not having any history of communicable disease or any hereditary disease but he has history of hypertension and type II Diabetes mellitus for 12 years. Patient was COVID-19 negative and admitted in intensive care unit. Patient had been undergone with various investigations like physical examination, blood test, CSF fluid examination, ascitic fluid examination, fluid cytology, peripheral smear, ultrasonography, RT-PCR etc. Patient was treated with tab. farobact ER 300 mg BD, tab. Lasix 40 mg OD, tab. Udilive 300 mg BD, tab. Rifagut 300 mg BD, tab. Metformin 500 mg OD, tab. Amlo 5mg OD, syp. Duphalac 30ml HS. Monitor vital signs, maintain input output, Monitoring and managing potential complications like, bleeding and haemorrhage, hepatic encephalopathy, fluid volume excess, monitor laboratory tests as indicated, Identify and assess for pedal edema.&#x0D; Conclusion: Cirrhosis of the liver is one of the final stages of liver disease. It is a serious condition, causing scarring and permanent damage to the liver. Life expectancy depends on the stage and type of cirrhosis of liver. Cirrhosis progresses, more and more scar tissue forms, making it difficult for the liver to function (decompensated cirrhosis). Advanced cirrhosis is life-threatening. If liver cirrhosis is diagnosed early and the cause is treated, further damage can be limited and, rarely, reversed.

A Features Fusion Approach for Neonatal and Pediatrics Brain Tumor Image Analysis Using Genetic and Deep Learning Techniques

&lt;p class="0abstract"&gt;Nowadays, Deep learning (DL) is the growing trend towards creating visual representations of human body organs for clinical analysis, medical interventions as well as to diagnose and treat diseases. This paper propose a method for neonatal and pediatric brain tumors image analysis and prerequisites a T2- weighted MR images only. The pipeline stages of the proposed work as follows: In the first stage, designed a set of specific feature vectors description for high-level classification task using Conventional and deep learning (DL) Feature Extraction methods. The second stage, select a deep features based on proposed convolutional neural network (CNN) method and conventional subset features are from Genetic Algorithm (GA). The third stage, merge the selected features by adapting fusion technique. Finally, predict the brain image is either normal or abnormal. The results demonstrated that the proposed method obtained accurate classification and revealed its robustness to difference in ages and acquisition protocols. The obtained results shows that based on combined deep learning features (DLF) and conventional features have been significantly improves the classification accuracy of the support vector machines (SVM) classifier up to 97.00%.&lt;/p&gt;

An Automatic Detection of Liver Tumor from CT Abdominal Images—A Comparative Approach

The liver is a vital organ in human body. Liver performs an important function including metabolism, digestion, and detoxification. Liver is a significant organ in an abdomen, and is connected to the nearby organ such as spleen, pancreas, gallbladder, abdomen, and gut through blood vessels. Specific approaches such as image gradient and region growing are not quite reliable for the segmentation of the liver tumor. A level-set approach is evaluated in this paper compared with the active contour approach of segmentation of the liver imaging from the image of the CT abdomen and Unified level set method, spatial Fuzzy C-means method for segmenting tumor from segmented liver images is appraised. The proposed approach is implemented by using the 3DIRCADB dataset available to the public as well as non-public datasets taken from Arthi Hospital, Chennai and Tirunelveli scanning centre. For validating the system based on the diverse quantitative measures, including space overlap, coefficient of similarity, Jaccard indices, using ground truth images, which are available in the public data set 3DIRCADB and the expert segmentation results which are manually identified by the clinical partner for nonpublic datasets. The analysis of the algorithm shows the better results for segmenting liver using level set system and spatial segmentation of Fuzzy C means of the tumor segmentation.

Predict Diabetes Mellitus Using Machine Learning Algorithms

Diabetes mellitus has become a very frequent disease that affects totally different organs of human body. Diabetes cause diverge depending on genetic, family history, health and environmental factors. Diabetes mellitus refers to a gaggle of diseases that affect how your body uses blood glucose. The underlying reason behind diabetes varies by type. But, despite what kind of diabetes you’ve got, it will cause excess sugar in your blood. Diabetes will be of two types, they are Type1 Diabetes and Type2 Diabetes. Early prediction will help in society a lot. It will provides the humanlife in safe way. The aim of this analysis is to develop a system that predicts the diabetes with a better accuracy. Parameters used to predict the type of Diabetes Mellitus are Glucose, Pregnancies, skin thickness, Blood pressure, Insulin, BMI, Diabetes pedigree function, age and upshot. In this we are with different machine learning algorithms, namely SVM, ANN, Decision tree, Logistic regression and Farthest first to predict the accuracy. Our experimental results show that farthest first attain superior correctness compare to dissimilar machine learning techniques.

GAME PENGENALAN ORGAN TUBUH MANUSIA BERBASIS MULTIMEDIA MENGGUNAKAN STRUKTUR ALUR MULTIMEDIA HIERARKI

Natural science is the study of nature and all its contents in it, one of which studies the human body organs which are divided into two parts, namely internal organs and human external organs. The delivery of learning materials in schools is still conventional, namely using the media of printed books. The delivery media that is still conventional and many terms on the material of human organs cause students to have difficulty in understanding the material. There are many media that can be applied in learning human organs, one of which is by using learning game media. Game is a medium of delivery that is widely enjoyed by all people. So it is necessary to create an application system or multimedia-based learning game, as a solution to solving the problem of students' difficulties in understanding the material. Multimedia has aspects that are of interest to students because of its appearance in the form of images, animations, audio, and video. In designing this game, the human body uses a hierarchical multimedia flow structure, while the software used to build this game is Adobe Photoshop, Adobe Flash, and CorelDraw. The results of the game design will be implemented into a multimedia-based human organ recognition game using a multimedia hierarchy structure which is expected to be an effective way of delivering material in overcoming various problems that occur in learning human organs