Oral malodor in Special Care Patients: current knowledge

Epidemiological studies report that about 50% of the population may have oral malodor with a strong social and psychological impact in their daily life. When intra-oral causes are excluded, referral to an appropriate medical specialist is paramount for management and treatment of extra-oral causes. The intra-oral causes of halitosis are highly common, and the dentist is the central clinician to diagnose and treat them. Pseudohalitosis or halitophobia may occur and an early identification of these conditions by the dentist is important in order to avoid unnecessary dental treatments for patients who need psychological or psychiatric therapy. The organoleptic technique is still considered the most reliable examination method to diagnose genuine halitosis. Special needs patients are more prone than others to have oral malodor because of concurrent systemic or metabolic diseases, and medications. The present report reviews halitosis, its implications, and the management in special care dentistry

Breathing Signature as Vitality Score Index Created by Exercises of Qigong: Implications of Artificial Intelligence Tools Used in Traditional Chinese Medicine.

Rising concerns about the short- and long-term detrimental consequences of administration of conventional pharmacopeia are fueling the search for alternative, complementary, personalized, and comprehensive approaches to human healthcare. Qigong, a form of Traditional Chinese Medicine, represents a viable alternative approach. Here, we started with the practical, philosophical, and psychological background of Ki (in Japanese) or Qi (in Chinese) and their relationship to Qigong theory and clinical application. Noting the drawbacks of the current state of Qigong clinic, herein we propose that to manage the unique aspects of the Eastern 'non-linearity' and 'holistic' approach, it needs to be integrated with the Western "linearity" "one-direction" approach. This is done through developing the concepts of "Qigong breathing signatures," which can define our life breathing patterns associated with diseases using machine learning technology. We predict that this can be achieved by establishing an artificial intelligence (AI)-Medicine training camp of databases, which will integrate Qigong-like breathing patterns with different pathologies unique to individuals. Such an integrated connection will allow the AI-Medicine algorithm to identify breathing patterns and guide medical intervention. This unique view of potentially connecting Eastern Medicine and Western Technology can further add a novel insight to our current understanding of both Western and Eastern medicine, thereby establishing a vitality score index (VSI) that can predict the outcomes of lifestyle behaviors and medical conditions

Applications of Proton Transfer Reaction and Selected Ion Flow Tube Mass Spectrometry in Health Monitoring

This thesis investigates the use of Volatile Organic Compounds (VOCs) in disease diagnosis and monitoring. VOCs may be found in the human body, in exhaled breath, faecal matter, urine, and skin. Analysis of the volatile profile produced in the human body can provide an indicator of metabolic status, allowing the screening and monitoring of different diseases and conditions, non-invasively and painlessly. In this thesis a range of highly sensitive analytical techniques have been adopted to measure such VOCs and demonstrate that such monitoring may be used as a disease diagnostic. For example breath samples may be analysed and calibrated against gas-phase standards prepared under physiologically representative concentrations as a tool for non-invasive disease monitoring, e.g. type 2 diabetes. Detailed faecal headspace analyses of two different mouse models of type 2 diabetes (Cushing´s mice and Afmid) were made. The mouse model of Cushing’s syndrome develop excessive circulating glucocorticoid concentrations, which are associated with obesity, hyperglycaemia and insulin resistance. The Afmid knockout mice suffer inactivation of Afmid genes, which in part regulates many functions including pancreatic secretion. These mice show impaired glucose tolerance. The gut microbiota of diabetic mice appear to have a different composition when compared to wild-type littermates, i.e. significantly increased levels of short-chain fatty acids (SCFAs), ketones, alcohols and aldehydes were found in the faecal headspace of diabetic mice, and a possible link between gut microbiota and type 2 diabetes is demonstrated. The use of VOCs as a screening tool of colorectal cancer was also explored. The current screening tools show lack of sensitivity and specificity for the screening of the disease. The volatile faecal profile of patients with colorectal cancer was investigated, and sulphide compounds, including hydrogen sulphide (H2S) are shown to have potential as biomarkers for screening of colorectal cancer

Non-Invasive Method of Human Exhaled Breath Analysis for Diabetes Detection Using Bidirectional Long-Short-Term Memory Algorithm

Volatile organic compounds (VOCs) have the potential to be used as biomarkers for pathophysiological and physical abnormalities associated with several disorders. A promising non-invasive metabolic monitoring method is the Analysis of VOCs in exhaled breath. It may also be used to monitor the development of certain diseases and their early detection. Diabetes is a metabolic disease and a complicated syndrome. The relationship between oxidative stress, inflammatory syndrome, hypertension, and diabetes is complicated. This study describes the creation of an Internet of Things (IoT) based breath analyzer to identify and track diseases using exhaled breath. Diabetic breath biomarkers and breath analysis are the main topics of discussion. A group of 25 diabetic patients and 15 non-diabetic individuals were tested using this system. Data is initially gathered using the wired module and the Cool Term software. The system is created for both wired and wireless devices. A deep learning algorithm analyses the disease characteristics after data collection. It clearly distinguishes between samples with diabetes and those without with 84% accuracy. This technology could detect a non-transmissible or transmissible disease early, preventing infection to others

Asthma Identification Using Gas Sensors and Support Vector Machine

The exhaled breath analysis is a procedure of measuring several types of gases that aim to identify various diseases in the human body. The purpose of this study is to analyze the gases contained in the exhaled breath in order to recognize healthy and asthma subjects with varying severity. An electronic nose consisting of seven gas sensors equipped with the Support Vector Machine classification method is used to analyze the gases to determine the patient's condition. Non-linear binary classification is used to identify healthy and asthma subjects, whereas the multiclass classification is applied to recognize the subjects of asthma with different severity. The result of this study showed that the system provided a low accuracy to distinguish the subjects of asthma with varying severity. This system can only differentiate between partially controlled and uncontrolled asthma subjects with good accuracy. However, this system can provide high sensitivity, specificity, and accuracy to distinguish between healthy and asthma subjects. The use of five gas sensors in the electronic nose system has the best accuracy in the classification results of 89.5%. The gases of carbon monoxide, nitric oxide, volatile organic compounds, hydrogen, and carbon dioxide contained in the exhaled breath are the dominant indications as biomarkers of asthma.The performance of electronic nose was highly dependent on the ability of sensor array to analyze gas type in the sample. Therefore, in further study we will employ the sensors having higher sensitivity to detect lower concentration of the marker gases

Application and uses of electronic noses for clinical diagnosis on urine samples: A review

The electronic nose is able to provide useful information through the analysis of the volatile organic compounds in body fluids, such as exhaled breath, urine and blood. This paper focuses on the review of electronic nose studies and applications in the specific field of medical diagnostics based on the analysis of the gaseous headspace of human urine, in order to provide a broad overview of the state of the art and thus enhance future developments in this field. The research in this field is rather recent and still in progress, and there are several aspects that need to be investigated more into depth, not only to develop and improve specific electronic noses for different diseases, but also with the aim to discover and analyse the connections between specific diseases and the body fluids odour. Further research is needed to improve the results obtained up to now; the development of new sensors and data processing methods should lead to greater diagnostic accuracy thus making the electronic nose an effective tool for early detection of different kinds of diseases, ranging from infections to tumours or exposure to toxic agents

Electronic Noses for Biomedical Applications and Environmental Monitoring

This book, titled “Electronic Noses for Biomedical Applications and Environmental Monitoring”, includes original research works and reviews concerning the use of electronic nose technology in two of the more useful and interesting fields related to chemical compounds detection of gases. Authors have explained their latest research work, including different gas sensors and materials based on nanotechnology and novel applications of electronic noses for the detection of diverse diseases. Some reviews related to disease detection through breath analysis, odor monitoring systems standardization, and seawater quality monitoring are also included

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.11Ysciescopu

Novel translational approaches to the search for precision therapies for acute respiratory distress syndrome.

In the 50 years since acute respiratory distress syndrome (ARDS) was first described, substantial progress has been made in identifying the risk factors for and the pathogenic contributors to the syndrome and in characterising the protein expression patterns in plasma and bronchoalveolar lavage fluid from patients with ARDS. Despite this effort, however, pharmacological options for ARDS remain scarce. Frequently cited reasons for this absence of specific drug therapies include the heterogeneity of patients with ARDS, the potential for a differential response to drugs, and the possibility that the wrong targets have been studied. Advances in applied biomolecular technology and bioinformatics have enabled breakthroughs for other complex traits, such as cardiovascular disease or asthma, particularly when a precision medicine paradigm, wherein a biomarker or gene expression pattern indicates a patient's likelihood of responding to a treatment, has been pursued. In this Review, we consider the biological and analytical techniques that could facilitate a precision medicine approach for ARDS