Human and animal interests are often in conflict. In many situations, however, it is unclear how to evaluate and weigh competing human and animal interests, as the satisfaction of the interests of one group often inevitably occurs at the expense of those of the other group. Human-animal conflicts of this kind give rise to ethical questions. If animals count morally for their own sake, then we must ask in which cases the satisfaction or frustration of the interests of humans and (...) animals in conflict situations is justified or unjustified from an ethical perspective. In this article, we argue that limited aggregation accounts represent a promising means for resolving interspecies conflicts. The reason for this is that they can appropriately consider the qualitative relevance of interests, their relative importance to each other, and the number of individuals affected. For our argument, we start from the premise that animals count morally for their own sake, albeit to a lesser extent than humans. That is, we accept the view that animals may be used, for example, as a source of food or in animal research. However, as we will show, many basic interests of animals are sufficiently similar to human interests and can thus be compared to them. Hence, they ought to be aggregated in cases of conflict with human interests. We illustrate our account and its practical implications with the real-world example of a human-animal conflict during the outbreak of a zoonotic disease among farmed animals. We conclude that, in many cases, animal interests ought to be given more importance than they currently receive, which includes distributing the burdens and risks of farming practices more fairly. (shrink)

The genetic modification of pigs as a source of transplantable organs is one of several possible solutions to the chronic organ shortage. This paper describes existing ethical tensions in xenotransplantation (XTx) that argue against pursuing it. Recommendations for lifelong infectious disease surveillance and notification of close contacts of recipients are in tension with the rights of human research subjects. Parental/guardian consent for pediatric xenograft recipients is in tension with a child’s right to an open future. Individual consent to transplant (...) is in tension with public health threats that include zoonotic diseases. XTx amplifies concerns about justice in organ transplantation and could exacerbate existing inequities. The prevention of infectious disease in source animals is in tension with the best practices of animal care and animal welfare, requiring isolation, ethologically inappropriate housing, and invasive reproductive procedures that would severely impact the well-being of intelligent social creatures like pigs. (shrink)

Over 60% of all epidemics have a zoonotic origin, that is, they result from the transmission of infectious diseases from animals to humans. The spill-over of diseases often happens because humans exploit and use animals. In this article, I outline the four most common interfaces that favour the emergence and spread of zoonotic infectious diseases: wildlife hunting, small-scale farming, industrialised farming practices and live animal markets. I analyse which practices serve human food security – and thus have a (...) non-trivial purpose – and which predominantly have an economic purpose or serve as a symbol of wealth. I conclude that many practices that increase the risk for zoonotic infectious disease outbreaks actually do not contribute to the survival and food security of humans. I make two arguments in turn. First, I argue that in cases where the consumption and use of animal products does not contribute to the food security of a population, then this population has a duty to abstain from them, since they impose a grave and avoidable risk to themselves as well as to innocent third-parties. However, some communities must sometimes rely on practices that increase the risk of emergence and transmission of zoonotic infectious diseases, because they have no healthy alternatives. That is, the food security of the local population depends on the consumption and use of risky animal products. The second argument I advance is that, in such scenarios, the international community has a duty to provide the communities concerned with alternative food options, as well as economic and educational opportunities and technologies, in order to reduce the spill-over risk of infectious diseases. Given that abstention from such practices contributes signifcantly to the public good and benefits the international community, the latter has a corresponding duty to provide local communities which abandon such practices with alternatives. (shrink)

In Brazil, the social demand for veterinary expertise is growing. However, there is still a shortage of professionals trained in this area to apply specific knowledge to each case. Studies and research into forensic veterinary medicine are necessary for veterinary experts to assist in investigations and legal proceedings. Veterinary medicine has subjects on its curriculum that cover the knowledge needed to apply in the fields of animal health, public health and the environment. The interaction between human and veterinary medicine, as (...) well as other areas of health, is of great importance in the prevention and control of various diseases, both in animals and humans. Some cases of infectious bovine abortion have a major impact not only on cattle farming and animal health, but also on public and environmental health. The concept of "One Health" integrates human medicine and health professionals. In this context, knowledge of infectious abortive diseases that affect animal, human and environmental health is fundamental. Cattle are affected by specific infectious abortive diseases, caused by pathogens with different forms of presentation and transmission mechanisms. These include brucellosis, leptospirosis and listeriosis, which are potentially zoonotic diseases. Controlling these diseases can contribute to effective public health actions and reduce global health risks. In order to establish the diagnosis of these diseases, it is essential to properly collect specimens from live or dead animals under biosafety and biosecurity conditions, following the chain of custody for each case. These procedures allow for greater success in diagnosing the suspected agent and facilitate the investigation of other causes. Animal health standards for the prophylaxis and control of these diseases are established by specific legislation, including those of the Ministry of Agriculture, Livestock and Supply (MALS). This literature review was based on current knowledge on the Pubmed platform, Google Scholar, articles and textbooks. The aim of this article is to identify, understand and discuss the main infectious abortive diseases of cattle in the legal and forensic context, which have an impact on cattle farming, animal health, food safety, public and environmental health. -/- Referências bibliográficas Abdullah, N. M., Mohammad, W. M. Z. W., Shafei, M. N., Sukeri, S., Idris, Z., Arifin, W. N., Nozmi, N., Saudi, S. N. S., Samsudin, S., & Zainudin, A.-W. (2019). Leptospirosis and its prevention: knowledge, attitude and practice of urban community in Selangor, Malaysia. BMC Public Health, 19(1), 1–8. Agrimani, D. S. R., Rui, B. R., Cruz, I. V, Romano, R. M., & Lopes, H. C. (2011). 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Revista Ciencia y Tecnologia, Escuela de Postgrado, 25, 328–335. Sá, M. I., & Ferreira, C. (2007). Importância das zoonoses na segurança alimentar. Infoqualidade, 11, 14–17. Sakate, R. I., Aragon, L. C., Raghiante, F., Landgraf, M., Franco, B. D. G. M., & Destro, M. T. (2003). Quantificação de Listeria monocytogenes em salames fatiados embalados a vácuo. Archivos Latinoamericanos de Nutrición, 53, 1–5. Salmanov, A. G., Kotsyumbas, I. Y. А., & Trokhymchuk, V. V. (2018). One World — One Health. International Journal of Antibiotics and Probiotics, 2(1), 8–17. Santana, L., Novelli, M., Vidal, A., Pereira, S., Gasparinni, L., Alvarenga, A. M. O., & Motta, O. J. R. (2020). Brucelose: atualização para a prática clínica. Saúde Dinâmica, 5(3), 1–14. Santos-Filho, A. M. P., & Mayrink, R. R. (2017). Medicina Veterinária Forense. In J. A. Velho, G. C. Geiser, & A. Espíndula. (Eds.), Ciências Forenses, uma introdução às principais áreas da criminalística moderna. Millennium. Schmitt, C. I., Krug, F. D. M., Cereser, N. D., & Pinto, F. R. (2017). Brucelose: uma questão de saúde pública. REDVET. Revista Electrónica de Veterinária, 18(9), 1–17. Silva, H. R., Gianoglou, F. M., Campos, M. F., Graciano, E. M. A., & Toledo, R. C. C. (2016). Listeriose: Uma doença de origem alimentar pouco conhecida no Brasil. Higiene Alimentar, 30, 17– 20. Simões, L. S., Sasahara, T. H. C., Favaron, P. O., & Miglino, M. A. (2016). Leptospirose–Revisão. PUBVET, 10, 111–189. Slowinski, K., Tremori, T. M., Massad, M. R. R., Tasaka, A. C., & Rocha, N. S. (2016). Responsabilidade ética e civil do médico-veterinário no ambiente hospitalar. Revista de Educação Continuada em Medicina Veterinária e Zootecnia Do CRMV-SP, 14(2), 30–37. Snak, A., & Osaki, S. C. (2019). Uma revisão sobre três importantes agentes causadores de aborto em bovinos: Neospora caninum, Leptospira sp. e Trypanosoma vivax. Revista de Ciência Veterinária e Saúde Pública, 6(1), 160–195. Sobrinho, O. P. L., Véras, G. M. C., Silva, L. F. B., Oliveira, L. S., Xavier, R. S., Alvim, J. F., Costa, D. A. S., Pereira, Á. I. S., Carlos, M. A. S., Cantanhede, E. K. P., & Santos, M. L. C. (2018). Brucelose bovina: Situação epidemiológica em rebanhos no município de Codó-MA. PUBVET, 12(12), 1–4. Souza, A. O. (2010). Listeriose na gravidez. Revista Médica de Minas Gerais, 20, 433–434. Suárez, Á. C. A., & Parra, C. A. B. (2017). Actualización de la leptospirosis bovina en Colombia. Conexión Agropecuaria JDC, 7(1), 57–77. Tonin, A. A., Azevedo, M. I., Escobar, T. P., Casassola, I., Santos, L. G., Cocco, M., Martins, J. L. R., Schaefe, P. C., & Badke, M. R. T. (2010). Leptospirose bovina: aumento na incidência da Leptospira interrogans sorovar butembo no rebanho do Estado de Santa Catarina, Brasil. Acta Veterinaria Brasilica, 4(4), 294–297. Topazio, J., Tonin, A. A., Machado, G., Noll, J. C. G., Ribeiro, A., Moura, A. B., Carmo, G. M., Grosskopf, H. M., Martins, J. L. R., & Badke, M. R. T. (2015). Antibodies to Leptospira interrogans in goats and risk factors of the disease in Santa Catarina (West side), Brazil. Research in Veterinary Science, 99, 53–57. Tremori, T. M., & Rocha, N. S. (2013). Exame do corpo de delito na Perícia Veterinária (ensaio). Revista de Educação Continuada em Medicina Veterinária e Zootecnia do CRMV-SP, 11(3), 30–35. Urzêda, M., Ribeiro, P. G. M., Nascente, E. P., Espíndola, W. R., Teodoro, J. V. S., Gonçalves, G. B., Carneiro, Y. F., & Souza, W. J. (2020). Soroprevalência de leptospirose em fêmeas bovinas na microrregião do Vale Do Rio Dos Bois, Goiás, Brasil. Brazilian Journal of Development, 6(9), 69614–69622. Vallim, D. C., Barroso Hofer, C., Lisbôa, R. de C., Barbosa, A. V., Rusak, L. A., Reis, C. M. F., & Hofer, E. (2015). Twenty years of Listeria in Brazil: Occurrence of Listeria species and Listeria monocytogenes serovars in food samples in Brazil between 1990 and 2012. BioMed Research International, 2015. Vasconcelos, C. H., Fonseca, F. R., Lise, M. L. Z., & Arsky, M. (2012). Fatores ambientais e socioeconômicos relacionados à distribuição de casos de leptospirose no Estado de Pernambuco, Brasil, 2001-2009. Caderno de Saúde Coletiva, 20(1), 49–56. Viana, K. F., & Zanini, M. S. (2009). Perfil de produtores frente à vacinação contra doenças infecciosas abortivas em rebanhos bovinos do município de Alegre/ES. Archives of Veterinary Science, 14(2), 103–108. Vitas, A. I., & Garcia-Jalon, V. A. E. I. (2004). Occurrence of Listeria monocytogenes in fresh and processed foods in Navarra (Spain). International Journal of Food Microbiology, 90(3), 349–356. (shrink)

Human-wildlife interactions frequently lead to conflicts – about the fair use of natural resources, for example. Various principled accounts have been proposed to resolve such interspecies conflicts. However, the existing frameworks are often inadequate to the complexities of real-life scenarios. In particular, they frequently fail because they do not adequately take account of the qualitative importance of individual interests, their relative importance, and the number of individuals affected. This article presents a limited aggregation account designed to overcome these shortcomings and (...) thus to facilitate decision-making in real-world human-wildlife conflicts. (shrink)

The devastating impact of the COVID‐19 (coronavirus disease 2019) pandemic is prompting renewed scrutiny of practices that heighten the risk of infectious disease. One such practice is refusing available vaccines known to be effective at preventing dangerous communicable diseases. For reasons of preventing individual harm, avoiding complicity in collective harm, and fairness, there is a growing consensus among ethicists that individuals have a duty to get vaccinated. I argue that these same grounds establish an analogous duty to avoid (...) buying and eating most meat sold today, based solely on a concern for human welfare. Meat consumption is a leading driver of infectious disease. Wildlife sales at wet markets, bushmeat hunting, and concentrated animal feeding operations (CAFOs) are all exceptionally risky activities that facilitate disease spread and impose immense harms on human populations. If there is a moral duty to vaccinate, we also should recognize a moral duty to avoid most meat. The paper concludes by considering the implications of this duty for policy. (shrink)

Rare diseases pose a particular priority setting problem. The UK gives rare diseases special priority in healthcare priority setting. Effectively, the National Health Service is willing to pay much more to gain a quality-adjusted life-year related to a very rare disease than one related to a more common condition. But should rare diseases receive priority in the allocation of scarce healthcare resources? This article develops and evaluates four arguments in favour of such a priority. These pertain to public values, (...) luck egalitarian distributive justice the epistemic difficulties of obtaining knowledge about rare diseases and the incentives created by a higher willingness to pay. The first is at odds with our knowledge regarding popular opinion. The three other arguments may provide a reason to fund rare diseases generously. However, they are either overinclusive because they would also justify funding for many non-rare diseases or underinclusive in the sense of justifying priority for only some rare diseases. The arguments thus fail to provide a justification that tracks rareness as such. There are no data in this work. (shrink)

The Neurological Disease Ontology (ND) is being developed to provide a comprehensive framework for the representation of neurological diseases (Diehl et al., 2013). ND utilizes the model established by the Ontology for General Medical Science (OGMS) for the representation of entities in medicine and disease (Scheuermann et al., 2009). The goal of ND is to include information for each disease concerning its molecular, genetic, and environmental origins, the processes involved in its etiology and realization, as well as (...) its clinical presentation including signs and symptoms. (shrink)

Cardiovascular diseases, including heart disease, pose a significant global health challenge, contributing to a substantial burden on healthcare systems and individuals. Early detection and accurate prediction of heart disease are crucial for timely intervention and improved patient outcomes. This research explores the potential of neural networks in predicting heart disease using a dataset collected from Kaggle, consisting of 1025 samples with 14 distinct features. The study's primary objective is to develop an effective neural network model for binary (...) classification, identifying the presence or absence of heart disease. The neural network architecture includes an input layer, a hidden layer, and an output layer, designed to capture intricate relationships within the dataset. Rigorous training and validation processes, accompanied by data preprocessing steps, ensure the model's robustness and generalization capabilities. The results demonstrate promising performance, with an accuracy of 92% and an average error of 0.062. Moreover, an analysis of feature importance highlights key predictors, including "oldpeak," "thalach," "trestbps," "ca," "thal," "cp," "chol," "sex," "restecg," "age," "slope," "fbs," and "exang." This research contributes to the field of predictive healthcare by leveraging neural networks to enhance heart disease prediction. The developed model offers the potential for early identification of individuals at risk, facilitating timely medical interventions and ultimately improving public health. Further exploration of machine learning techniques in healthcare promises to reshape disease prediction and prevention strategies. (shrink)

Heart disease remains one of the leading causes of mortality worldwide. Early prediction and diagnosis are critical in preventing severe outcomes and improving the quality of life for patients. This project focuses on developing a robust heart disease prediction system using machine learning techniques. By analyzing a comprehensive dataset consisting of various patient attributes such as age, sex, blood pressure, cholesterol levels, and other medical parameters, the system aims to predict the likelihood of a patient having heart (...) class='Hi'>disease. The project employs various machine learning algorithms such as Logistic Regression, Decision Trees, Support Vector Machines (SVM), and Random Forests to classify the data and provide an accurate prediction. The system's performance is evaluated using metrics like accuracy, precision, recall, and F1-score, ensuring that it can offer reliable results in real-world applications. Furthermore, feature selection techniques are applied to identify the most significant factors contributing to heart disease, thus improving the model's interpretability. The proposed solution is intended to aid healthcare professionals by providing early alerts and recommendations, ultimately facilitating timely interventions. (shrink)

Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and (...) information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain. (shrink)

Debates about the genuine disease status of controversial diseases rely on intuitions about a range of factors. Adopting tools from experimental philosophy, this paper explores some of the factors that influence judgments about whether low sexual desire should be considered a disease and whether it should be medically treated. Drawing in part on some assumptions underpinning a divide in the literature between viewing low sexual desire as a genuine disease and seeing it as improperly medicalized, we investigate (...) whether health and disease judgments are affected by factors such as an individual’s gender, the cause of the low desire, whether the desire is high or low, and both personal and societal valuations of the condition. Our main findings indicate that (a) the cause of a condition influences whether it is judged a disorder, (b) how the individual values the condition influences whether the condition is seen as a proper target of medical intervention, and (c) perceived dysfunction influences judgments regarding health, disorder classification, medicalization, and medical intervention. Our findings help further illuminate the intricate interplay of factors that influence judgments about health and disease in controversial conditions. (shrink)

Abstract: In recent years, the outbreak of various poultry diseases has posed a significant threat to the global poultry industry. Therefore, the accurate and timely detection of chicken diseases is critical to reduce economic losses and prevent the spread of diseases. In this study, we propose a method for classifying chicken diseases using a convolutional neural network (CNN). The proposed method involves preprocessing the chicken images, building and training a CNN model, and evaluating the performance of the model. The dataset (...) used in this study comprises chicken images of three different diseases: Newcastle disease, avian influenza, and infectious bursal disease. The proposed method achieved an overall F1-score of 99.04% in classifying the chicken diseases. The results demonstrate the effectiveness of using CNN in classifying chicken diseases and provide a promising approach for early detection and diagnosis of poultry diseases. (shrink)

Abstract: Alzheimer's disease (AD) is one of the most common types of dementia. Symptoms appear gradually and end with severe brain damage. People with Alzheimer's disease lose the abilities of knowledge, memory, language and learning. Recently, the classification and diagnosis of diseases using deep learning has emerged as an active topic covering a wide range of applications. This paper proposes examining abnormalities in brain structures and detecting cases of Alzheimer's disease especially in the early stages, using features (...) derived from medical images. The entire brain image was passed on through the transmission of Xception learning architectures. The Convolutional Neural Network (CNN) was constructed with the help of separable convolution layers that It can automatically learn general features from imaging data for classification. (shrink)

Artificial Intelligence (AI) technologies are now widely used in a variety of fields to aid with knowledge acquisition and decision-making. Health information systems, in particular, can gain the most from AI advantages. Recently, symptoms-based illness prediction research and manufacturing have grown in popularity in the healthcare business. Several scholars and organisations have expressed an interest in applying contemporary computational tools to analyse and create novel approaches for rapidly and accurately predicting illnesses. In this study, we present a paradigm for assessing (...) the efficacy of combining Machine Learning (ML) and Natural Language Processing (NLP) technologies in a disease prediction system. We scraped a disease-symptom dataset with NLP characteristics from one of the UK's most trusted National Health Service (NHS) websites as an example. In addition, we will thoroughly examine our data using symptom frequency, similarity, and clustering analysis. As a consequence, we can observe that the forecast has a high efficiency rate, but there are still some challenges to work out. (shrink)

In the contemporary biomedical literature, every disease is considered genetic. This extension of the concept of genetic disease is usually interpreted either in a trivial or genocentrist sense, but it is never taken seriously as the expression of a genetic theory of disease. However, a group of French researchers defend the idea of a genetic theory of infectious diseases. By identifying four common genetic mechanisms (Mendelian predisposition to multiple infections, Mendelian predisposition to one infection, and major gene (...) and polygenic predispositions), they attempt to unify infectious diseases from a genetic point of view. In this article, I analyze this explicit example of a genetic theory, which relies on mechanisms and is applied only to a specific category of diseases, what we call “a regional genetic theory.” I have three aims: to prove that a genetic theory of disease can be devoid of genocentrism, to consider the possibility of a genetic theory applied to every disease, and to introduce two hypotheses about the form that such a genetic theory could take by distinguishing between a genetic theory of diseases and a genetic theory of Disease. Finally, I suggest that network medicine could be an interesting framework for a genetic theory of Disease. (shrink)

There are many eye diseases but the most two common retinal diseases are Age-Related Macular Degeneration (AMD), which the sharp, central vision and a leading cause of vision loss among people age 50 and older, there are two types of AMD are wet AMD and DRUSEN. Diabetic Macular Edema (DME), which is a complication of diabetes caused by fluid accumulation in the macula that can affect the fovea. If it is left untreated it may cause vision loss. Therefore, early detection (...) of diseases is a critical importance. Our main goal is to help doctors detect these diseases quickly before reaching a late stage of the disease. In ophthalmology, optical coherence tomography (OCT) is critical for evaluating retinal conditions. OCT is an imaging technique used to capture high-resolution cross-sections of the retinas of patient. In this thesis, we review ways and techniques to use deep learning classification of the optical coherence tomography images of diseases from which a Retinal is suffering. The models used to improve patient care are (VGG-16, MobileNet, ResNet-50, Inception V3, and Xception) to reduce costs and allow fast and reliable analysis in large studies. The obtained results are encouraging, since the best model ResNet-50 reaching 96.21% of testing accuracy, which is very useful for doctors, to diagnose retinal diseases. (shrink)

In this paper, we identify some key features of what makes something a disease, and consider whether these apply to pregnancy. We argue that there are some compelling grounds for regarding pregnancy as a disease. Like a disease, pregnancy affects the health of the pregnant person, causing a range of symptoms from discomfort to death. Like a disease, pregnancy can be treated medically. Like a disease, pregnancy is caused by a pathogen, an external organism invading (...) the host’s body. Like a disease, the risk of getting pregnant can be reduced by using prophylactic measures. We address the question of whether the ‘normality’ of pregnancy, its current necessity for human survival, or the value often attached to it are reasons to reject the view that pregnancy is a disease. We point out that applying theories of disease to the case of pregnancy, can in many cases illuminate inconsistencies and problems within these theories. Finally, we show that it is difficult to find one theory of disease that captures all paradigm cases of diseases, while convincingly excluding pregnancy. We conclude that there are both normative and pragmatic reasons to consider pregnancy a disease. (shrink)

Some ‘naturalist’ accounts of disease employ a biostatistical account of dysfunction, whilst others use a ‘selected effect’ account. Several recent authors have argued that the biostatistical account offers the best hope for a naturalist account of disease. We show that the selected effect account survives the criticisms levelled by these authors relatively unscathed, and has significant advantages over the BST. Moreover, unlike the BST, it has a strong theoretical rationale and can provide substantive reasons to decide difficult cases. (...) This is illustrated by showing how life-history theory clarifies the status of so-called diseases of old age. The selected effect account of function deserves a more prominent place in the philosophy of medicine than it currently occupies. _1_ Introduction _2_ Biostatistical and Selected Effect Accounts of Function _3_ Objections to the Selected Effect Account _3.1_ Boorse _3.2_ Kingma _3.3_ Hausman _3.4_ Murphy and Woolfolk _4_ Problems for the Biostatistical Account _4.1_ Schwartz _5_ Analysis versus Explication _6_ Explicating Dysfunction: Life History Theory and Senescence _7_ Conclusion. (shrink)

Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and (...) information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain. (shrink)

There is no doubt that custard apple diseases are among the important reasons that destroy the Custard Apple plant and its agricultural crops. This leads to obvious damage to these plants and they become inedible. Discovering these diseases is a good step to provide the appropriate and correct treatment. Determining the treatment with high accuracy depends on the method used to correctly diagnose the disease, expert systems can greatly help in avoiding damage to these plants. The expert system correctly (...) diagnoses Custard Apple disease to make it easier for farmers to find the right treatment based on the appropriate diagnosis. Objectives: A specialized syllable language system was established for the diagnosis of Custard Apple plant disease. (shrink)

Our goal in this paper is to articulate a precise concept of at least a certain kind of disease-mongering, showing how pharmaceutical marketing can commercially exploit certain diseases when their best definition is given through the success of a treatment in a clinical trial. We distinguish two types of disease-mongering according to the way they exploit the definition of the trial population for marketing purposes. We argue that behind these two forms of disease-mongering there are two well-known (...) problems in the statistical methodology of clinical trials (the reference class problem and the distinction between statistical and clinical significance). Overcoming them is far from simple. (shrink)

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed (...) with a heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

Parkinson's Disease (PD) is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. The symptoms generally come on slowly over time. Early in the disease, the most obvious are shaking, rigidity, slowness of movement, and difficulty with walking. Doctors do not know what causes it and finds difficulty in early diagnosing the presence of Parkinson’s disease. An artificial neural network system with back propagation algorithm is presented in this paper for helping (...) doctors in identifying PD. Previous research with regards to predict the presence of the PD has shown accuracy rates up to 93% [1]; however, accuracy of prediction for small classes is reduced. The proposed design of the neural network system causes a significant increase of robustness. It is also has shown that networks recognition rates reached 100%. (shrink)

This paper aims is to present the design and development of an intelligent tutoring system for teaching students about stomach diseases to help and give them a clear idea about stomach ulcer diseases. Furthermore, the researchers designed an intelligent tutoring system with a clear interface including 3D animation with Delphi that show how the ulcer appears in the stomach with more details about the disease, after that there are some questions researchers going to adopt from clinical books about the (...) diseases that show the students how to diagnose the diseases. This system integrated Delphi programming with multimedia elements such as (voice, motion, text, photo, and interactive) to carry out the project. The idea of this paper is considering the intelligent tutoring system for students who are studying medicine to enhance their knowledge and deep understanding about the diseases. (shrink)

Cardiovascular diseases (CVD) represent a significant cause of morbidity and mortality worldwide, necessitating early detection for effective intervention. This research explores the application of machine learning (ML) algorithms in predicting cardiovascular diseases with enhanced accuracy by integrating optimization techniques. By leveraging data-driven approaches, ML models can analyze vast datasets, identifying patterns and risk factors that traditional methods might overlook. This study focuses on implementing various ML algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, optimized through (...) techniques like hyperparameter tuning, cross-validation, and feature selection to improve prediction accuracy. (shrink)

The Infectious Disease Ontology (IDO) is a suite of interoperable ontology modules that aims to provide coverage of all aspects of the infectious disease domain, including biomedical research, clinical care, and public health. IDO Core is designed to be a disease and pathogen neutral ontology, covering just those types of entities and relations that are relevant to infectious diseases generally. IDO Core is then extended by a collection of ontology modules focusing on specific diseases and pathogens. In (...) this paper we present applications of IDO Core within various areas of infectious disease research, together with an overview of all IDO extension ontologies and the methodology on the basis of which they are built. We also survey recent developments involving IDO, including the creation of IDO Virus; the Coronaviruses Infectious Disease Ontology (CIDO); and an extension of CIDO focused on COVID-19 (IDO-CovID-19).We also discuss how these ontologies might assist in information-driven efforts to deal with the ongoing COVID-19 pandemic, to accelerate data discovery in the early stages of future pandemics, and to promote reproducibility of infectious disease research. (shrink)

Similarly to other accounts of disease, Christopher Boorse’s Biostatistical Theory (BST) is generally presented and considered as conceptual analysis, that is, as making claims about the meaning of currently used concepts. But conceptual analysis has been convincingly critiqued as relying on problematic assumptions about the existence, meaning, and use of concepts. Because of these problems, accounts of disease and health should be evaluated not as claims about current meaning, I argue, but instead as proposals about how to define (...) and use these terms in the future, a methodology suggested by Quine and Carnap. I begin this article by describing problems with conceptual analysis and advantages of “philosophical explication,” my favored approach. I then describe two attacks on the BST that also question the entire project of defining “disease.” Finally, I defend the BST as a philosophical explication by showing how it could define useful terms for medical science and ethics. (shrink)

Background: The castor bean is a large grassy or semi-wooden shrub or small tree. Any part of the castor plant parts can suffering from a disease that weakens the ability to grow and eliminates its production. Therefore, in this paper will identify the pests and diseases present in castor culture and detect the symptoms in each disease. Also images is showing the symptom form in this disease. Objectives: The main objective of this expert system is to obtain (...) appropriate diagnosis of the disease. Methods: In this paper, the expert system is designed for the ability of agricultural engineers to detect and diagnose disease of castor like as: seeding blight, alternaria blight, cercospora leaf spot, powdery mildew and wilt. This system presents the disease symptoms, survival and spread, favorable conditions and image for each disease. Clips and Delphi expert system languages are used for designing and implementing the proposed expert system. Results: The expert system in the diagnosis of castor diseases was assessed by farmers and agricultural engineers and they were satisfied and accepted with its quality of performance. Conclusions: The expert system is easy for farmers and people have experience in the plant of castor to detect and diagnosis the symptoms that may face this plant from several disease. (shrink)

Claims about whether or not infertility is a disease are sometimes invoked to defend or criticize the provision of state-funded treatment for infertility. In this paper, I suggest that this strategy is problematic. By exploring infertility through key approaches to disease in the philosophy of medicine, I show that there are deep theoretical disagreements regarding what subtypes of infertility qualify as diseases. Given that infertility's disease status remains unclear, one cannot uncontroversially justify or undermine its claim to (...) medical treatment by claiming that it is or is not a disease. Instead of focusing on disease status, a preferable strategy to approach the debate about state-funded treatment is to explicitly address the specific ethical considerations raised by infertility. I show how this alternative strategy can be supported by a recent theoretical framework in the philosophy of medicine, which avoids the problems associated with the concepts of health and disease. (shrink)

The advancement of machine learning (ML) has revolutionized healthcare by enabling the early detection and diagnosis of multiple diseases. This paper presents a Multiple Disease Prediction System using machine learning algorithms to analyze patient data and predict the likelihood of diseases such as diabetes, heart disease, and kidney disease. The proposed model utilizes various ML classifiers, including Decision Trees, Random Forest, Support Vector Machines (SVM), and Neural Networks, to enhance prediction accuracy. The system aims to provide a (...) costeffective, accurate, and efficient tool for healthcare professionals and patients. (shrink)

Philosophers of medicine have formulated different accounts of the concept of disease. Which concept of disease one assumes has implications for what conditions count as diseases and, by extension, who may be regarded as having a disease and for who may be accorded the social privileges and personal responsibilities associated with being sick. In this article, we consider an ideal diagnostic test for coronavirus disease 2019 infection with respect to four groups of people—positive and asymptomatic; positive (...) and symptomatic; negative; and untested—and show how different concepts of disease impact on the disease and sickness judgements for these groups. The suggestion is that sickness judgements and social measures akin to those experienced during the current COVID-19 outbreak presuppose a concept of disease containing social harm as a component. We indicate the problems that arise when adopting this kind of disease concept beyond a state of emergency. (shrink)

This review was conducted to examine the causes, diagnoses, clinical manifestations, and available treatments for Graves' disease. Keywords like "Graves' disease," "radioactive iodine," "etiology," and "treatment" were used to search for data pertaining to Textbooks on endocrinology and other papers from these sources were also located. The introduction, etiology, risk factors, symptoms, diagnosis, course of treatment, and the contribution of many factors to the beginning of Graves' disease are all covered in this review article.

The Healthcare industry generally clinical diagnosis is done mostly by doctor’s expertise and experience. Computer Aided Decision Support System plays a major role in medical field. Data mining techniques and machine learning algorithms play a very important role in this area. The researchers accelerating their research works to develop a software with the help machine learning algorithm which can help doctors to take decision regarding both prediction and diagnosing of heart disease. The main objective of this research paper is (...) predicting the heart disease of a patient using machine learning algorithms. Initially the dataset is preprocessed and feature selection has been implemented for efficient data selection. Once data selection is done SVM classifier is implemented to detect whether the particular patient is affected by heart disease or not in accurate way. In addition to this the parameters used for heart disease prediction is also analyzed to extract the information if any parameter has high value than threshold level will be notified. Therefore in our proposed system for the heart disease predicted persons a general suggestion will be generated based on their information. Hence our system predicts heart disease in accurate way and providing suggestion to particular patient in efficient way. (shrink)

Response to commentary. We are grateful to Crockett and Craigie for their interesting remarks on our paper. We accept Crockett’s claim that there is a need for caution in drawing inferences about patient groups from work on healthy volunteers in the laboratory. However, we believe that the evidence we cited established a strong presumption that many of the patients who are routinely taking a medication, including many people properly prescribed the medication for a medical condition, have morally significant aspects of (...) their cognition and behavior modified in a way that is unintended and may sometimes be unwelcome. Crockett notes that in some cases the effects of long-term drug use may differ, sometimes markedly, from the effects of short-term use. However, if acute use of a drug affects a neural system involved in mediating moral cognition or behavior, this nevertheless provides some evidence that chronic use of the drug may affect that same system and thus have morally significant effects. It is also plausible, in some cases, that an acute moral effect would give rise to a chronic moral effect via cognitive mechanisms. ... (shrink)

ABSTRACT This study evaluated the effects of postpartum diseases and body condition score (BCS) on the reproductive performance of dairy herds. Cows were monitored during the postpartum to diagnose diseases and changes in BCS. The cows were divided into those with no disease and those with one or more diseases. The incidence of diseases, pregnancy rate (PR) at the first postpartum service, number of days open, percentage of pregnant cows at 150d, and gestational loss were analyzed. No difference was (...) observed between healthy and diseased cows in the PR at the first service or for days open. Diseased cows showed greater gestational loss and the percentage of pregnant cows at 150d postpartum was higher among healthy cows. Cows with a BCS ≥ 3.0 at prepartum had a lower PR at the first service and shorter days open. In conclusion, dairy herds affected by diseases during postpartum show impaired reproductive efficiency Keywords: dairy cattle, postpartum diseases, transition period, pregnancy rate. (shrink)

On 11 March 2020, the World Health Organization declared a pandemic of the COVID-19 coronavirus disease that was first recognized in China in late 2019. Among the primary effects caused by the pandemic, there was the dissemination of health preventive measures such as physical distancing, travel restrictions, self-isolation, quarantines, and facility closures. This includes the global disruption of socio-economic systems including the postponement or cancellation of various public events (e.g., sporting, cultural, or religious), supply shortages and fears of the (...) same, schools and universities closure, evacuation of foreign citizens, a rise of unemployment, changes in the international aid schemes, misinformation, and incidents of discrimination toward people affected by or suspected of having the COVID-19 disease. The pandemic has brought to the fore unpreparedness in critical areas that require attention, amid prospects and challenges. Moreover, considerable reorganization efforts are required with implications for assets, resources, norms, and value systems. COVID-19 is challenging the concept of globalization and stimulating responses at the levels of local and regional socio-economic systems that lead to the mobilization of assets that have been unrecognized earlier on, such as various forms of economic capital, social capital, cultural capital, human capital, and creative capital. For example, through digital channels, local groups are forming to create schemes of support for physical and mental wellbeing. These emerging exchanges lead to various social and technological innovations by building on skills and assets that are less important in the free-market economy, such as empathy, skills for crafts, making and fixing; locally grown microgreens; and micromanufacturing. Isolation and local living are also making it much harder to ignore the civic responsibilities towards communities, meant as individuals, vulnerable groups, and local businesses. Whilst the pandemic is limiting physical participation, this challenging time is uncovering alternative ways of mutual support, which may create long-term benefits for socio-economic systems, including environmental and biodiversity protection, reduction of the air pollution, and climate action. The pandemic’s threat to public health will hopefully be overcome with implications for disruption for an extended period that we are unable to forecast at this stage. It is key to focus on studies recognizing the activities and interventions leading to the recovery of socio-economic systems after the pandemic. Reflecting and planning on how societies and economies will go back to “business as usual” requires new forms of communication and cooperation, imaginative design thinking, new styles of management, as well as new tools and forms of participation in various public policies. Many questions related to the care of the vulnerable, economic restart, and the risk of future pandemics, to mention but a few, are already occupying the academic, scientific, experts, and activist communities, who have started to imagine the “new normal.”. (shrink)

In this article, I will reconstruct the monocausal model and argue that modern 'multifactorial diseases' are not monocausal by definition. 'Multifactorial diseases' are instead defined according to a constitutive disease model. On closer analysis, infectious diseases are also defined using the constitutive model rather than the monocausal model. As a result, our classification models alone cannot explain why infectious diseases have a universal etiology while chronic and noncommunicable diseases lack one. The explanation is instead provided by the nineteenth-century germ (...) theorists. (shrink)

Whipple disease is a rare, infectious, disease first described from a single case by Whipple in 1907. As well as characterising the clinical and pathological features of the condition, Whipple made two suggestions regarding its aetiology. These were either than the disease was caused by an infectious agent, or that it was of metabolic origin. As the disease is now thought to be caused by infection with the bacterium Tropheryma whipplei, historical reviews of the history of (...) the disease typically mention only the first of these suggestions. In this paper, we therefore revisit Whipple’s other theory. We argue that a diverse and often successful research programme was developed around this mechanism of disease causation which gave rise to many useful findings on the condition. In the later parts of this article, we then turn to discuss the surprising neglect of this period of Whipple disease research in the current literature, and conclude by offering a brief reconstruction of this early history suitable for use in a technical context. (shrink)

Brain-related diseases are among the most difficult diseases due to their sensitivity, the difficulty of performing operations, and their high costs. In contrast, the operation is not necessary to succeed, as the results of the operation may be unsuccessful. One of the most common diseases that affect the brain is Alzheimer’s disease, which affects adults, a disease that leads to memory loss and forgetting information in varying degrees. According to the condition of each patient. For these reasons, it (...) is important to classify memory loss and to know the patient at what level and his assessment of Alzheimer's disease through CT scans of the brain. In this thesis, we review ways and techniques to use deep learning classification to classifying the Alzheimer's Disease The proposed method used to improve patient care, reduce costs, and allow fast and reliable analysis in large studies. The model will be designed using Python language for implementing the system, which is very useful for doctors, classifying the Alzheimer's Disease, was used. The model used 70% from image for training and 30% from image for validation, our trained model achieved an accuracy of 100% on a held-out test set. (shrink)

Abstract: In this study, we explore the challenge of identifying and preventing diseases in apple trees, which is a popular activity but can be difficult due to the susceptibility of these trees to various diseases. To address this challenge, we propose the use of Convolutional Neural Networks, which have proven effective in automatically detecting plant diseases. To validate our approach, we use images of apple leaves, including Apple Rot Leaves, Leaf Blotch, Healthy Leaves, and Scab Leaves collected from Kaggle which (...) is part from the Plant Village dataset. We generate a comprehensive training dataset using techniques such as image filtering, compression, and generation. Our model achieves impressive accuracy scores for all classes, with an overall accuracy of 99.93% on a dataset of 10,000 labeled images . (shrink)

This paper addresses the use of dispositions in the Infectious Disease Ontology (IDO). IDO is an ontology constructed according to the principles of the Open Biomedical Ontology (OBO) Foundry and uses the Basic Formal Ontology (BFO) as an upper ontology. After providing a brief introduction to disposition types in BFO and IDO, we discuss three general techniques for representing combinations of dispositions under the headings blocking dispositions, complementary dispositions, and collective dispositions. Motivating examples for each combination of dispositions is (...) given along with a specific use case in IDO. Description logic restrictions are used to formalize statements relating to these combinations. (shrink)

We recently suggested that there are both pragmatic and normative reasons to classify pregnancy as a disease. Several scholars argued against our claims. In this response, we defend the disease view of pregnancy against their criticism. We claim that the dysfunctional account of disease that some of our critics rely on has some counterintuitive results. Furthermore, we claim that our critics assume what needs to be argued that the primary function of our sexual organs is to reproduce. (...) Since only a small percentage of sexual intercourse leads to pregnancy, it is far from obvious that reproduction is the primary biological function of our sexual organs. We also claim that while taking pregnancy itself as a reference class could avoid the conclusion that pregnancy is a disease, the strategy is problematic since it renders the Boorsean approach to disease and health circular and effectively deprives it of any utility in determining whether a particular phenomenon is a disease or not. (shrink)

This essay explores the theoretical treatment of leprosy in 13th-century theological discussions on the transmission of the original sin. According to scholastic theologians, both the existence of the original sin and its transmission from parents to progeny were factual truths, whose dynamics could be explained by analogy with inherited diseases, such as leprosy. Different uses of natural philosophy and medicine in discussing the transmission of leprosy will be shown in theological and biblical-exegetical works of William of Auvergne, Roland of Cremona, (...) Bonaventure, Thomas Aquinas, and Henry of Ghent. -/- . (shrink)

The highly contagious 2019 novel coronavirus disease (COVID-19) outbreak has not only impacted health systems, economies, and governments, it has also rapidly grown into a global health crisis, which is now threatening the lives of millions of people globally. While, on one hand, medical institutions are critically attempting to find a cure, on the other hand, governments have introduced striking measures and policies to curtail the rapid spread of the disease. Although COVID-19 has achieved pandemic status and is (...) predominantly viewed as a biomedical issue, it is argued that it should also be treated as a psychological crisis. This paper also reviews the literature to examine and comment on the detrimental effects of isolation, which has been enforced as one of the primary preventative measures to manage the spread of COVID-19. This paper further outlines key recommendations that should be addressed across different levels to buffer against the known adverse effects of isolation, which is especially relevant for the current COVID-19 situation, where a large proportion of the global population is isolated, confined, and/or quarantined. (shrink)

Non-communicable diseases (NCDs) such as cardiovascular diseases and diabetes are reported to have caused significant deaths for more than a decade. Consequently, NCDs have posed as a threat to the socio-economic well-being of individuals and families, contributed to a rise in healthcare costs and largely undermined the attainment of the Sustainable Development Goals (SDGs) especially in developing countries. According to the World Health Organization (WHO), the prevalence of NCDs have compounded the problem of already ill equipped healthcare systems in these (...) countries as they are faced with constraints to deal with the burden of both infectious and non-communicable diseases. Informed largely by the rapid increase in NCDs and their subsequent threat to public health, we aimed to ascertain the various healthcare interventions that Ghana has fashioned out in her bid to prevent and control the incidence of NCDs, how these interventions were rolled out and examined past and present barriers to their implementation since 1990. We culled the data gathered for this paper from both primary and secondary sources to construct a coherent synthesis and to facilitate discussions on Ghana’s NCDs interventions from 1990 to 2018. A systematic analysis of the data gathered, revealed that Ghana’s healthcare system has by far tackled the NCDs burden in two folds; the clinical care aspect and the health promotion aspect. While certain healthcare interventions were purposively directed toward addressing NCDs, others were directed at promoting healthy lifestyles but had a bearing on the prevention and control of NCDs. Present challenges concerning shortfalls in interventions are a reflection of unresolved challenges in the past. We argue that despite the significant strides made for more than two decades, the interventions have addressed the burden of NCDs with limited success given the trends in NCDs mortality and morbidity. (shrink)

This paper advances a theory of disease as domino dysfunction. It is often argued that diseases are biological dysfunctions. However, a theory of disease as biological dysfunction is complicated by some plausible cases of dysfunction, which seem clearly non-pathological. I argue that pathological conditions are not just dysfunctions but domino dysfunctions, and that domino dysfunctions can be distinguished on principled biological grounds from non-pathological dysfunctions. I then show how this theory can make sense of the problem cases; they (...) are not diseases because they are not domino dysfunctions. (shrink)

Deutsche Krankheit: eine Diagnosestellung mit Rückblick und Ausblick auf Krankheitsverlauf.