Use of Artificial Intelligence in Research and Clinical Decision Making for Combating Mycobacterial Diseases
01 Jan 2021-pp 183-215
TL;DR: In this article, the authors proposed a method to understand the full genetic diversity and pathogenicity of leprosy and tuberculosis using the conventional genomic and proteomic approaches, which can assist the clinicians in making a judgment.
Abstract: Tuberculosis (TB) and leprosy (caused by mycobacterial pathogens) are two age-old infections, which we are facing even today. India is a major contributor to the global burden of leprosy and tuberculosis, which adversely affects the diverse communities as well as having a prevalence in different parts of the country. Timely diagnostics and effective treatment are very challenging, and the emergence of drug resistance has further complicated the management of these mycobacterial diseases. Various lineages of these mycobacterial pathogens show varying phenotypes in terms of clinical presentations and treatment outcomes. Altogether these factors make it further difficult to understand the full genetic diversity and pathogenicity of these pathogens using the conventional genomic and proteomic approaches. However, thanks to the recent technological advances in the genomics and proteomics field, many of these constraints have been suitably addressed. While it is relatively simpler to produce the omics data in a high-throughput manner, the bottleneck now is the pace to assimilate this large data into some useful information to reach a relevant, meaningful conclusion in a timely manner to assist the clinician in making a judgment.
01 Jan 2012
TL;DR: In this article, a feature selection based on Linguistic Hedges Neural-Fuzzy classifier is presented for the diagnosis of erythemato-squamous diseases, and the performance evaluation of this system is estimated by using four training-test partition models: 50-50, 60-40, 70-30, and 80-20%.
Abstract: The differential diagnosis of erythemato-squamous diseases is a real challenge in dermatology. In diagnosing of these diseases, a biopsy is vital. However, unfortunately these diseases share many histopathological features, as well. Another difficulty for the differential diagnosis is that one disease may show the features of another disease at the beginning stage and may have the characteristic features at the following stages. In this paper, a new Feature Selection based on Linguistic Hedges Neural-Fuzzy classifier is presented for the diagnosis of erythemato-squamous diseases. The performance evaluation of this system is estimated by using four training-test partition models: 50–50%, 60–40%, 70–30% and 80–20%. The highest classification accuracy of 95.7746% was achieved for 80–20% training-test partition using 3 clusters and 18 fuzzy rules, 93.820% for 50–50% training-test partition using 3 clusters and 18 fuzzy rules, 92.5234% for 70–30% training-test partition using 5 clusters and 30 fuzzy rules, and 91.6084% for 60–40% training-test partition using 6 clusters and 36 fuzzy rules. Therefore, 80–20% training-test partition using 3 clusters and 18 fuzzy rules are the best classification accuracy with RMSE of 6.5139e-013. This research demonstrated that the proposed method can be used for reducing the dimension of feature space and can be used to obtain fast automatic diagnostic systems for other diseases.
01 Jan 2019
TL;DR: Machine Learning in Medicine In as discussed by the authors, a view of the future of medicine, patient-provider interactions are informed and supported by massive amounts of data from interactions with similar patients.
Abstract: Machine Learning in Medicine In this view of the future of medicine, patient–provider interactions are informed and supported by massive amounts of data from interactions with similar patients. The...
TL;DR: A nontargeted metabolomics approach based on ultra high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry was used to find the differential composition between ZNG and NZNG and showed that two sets of combinative biomarkers to distinguish ZNG from NZNG with good sensitivity and specificity.
Abstract: Daodi medicinal material (DMM), which is traditional Chinese herbal medicine that has been used for long periods and have gained credibility in clinical practice, is part of the Chinese culture. However, Zhongning Goji berries (ZNG), a DMM, are illegally adulterated in the market by adding non Zhongning goji berries (NZNG). Consequently, the development of biomarker(s) is necessary for proper identification of ZNG and NZNG. In this study, a nontargeted metabolomics approach based on ultra high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) was used to find the differential composition between ZNG and NZNG. Using a combination of single-factor and multivariate statistical analyses, seven compounds with significant differences were discovered and identified, one of which was an unreported compound (a glycoside of pyrrolidine alkaloid). These compounds could be used as single biomarkers for receiver operating characteristic (ROC) analysis. In particular, the binary logistic regression result showed that two sets of combinative biomarkers to distinguish ZNG from NZNG with good sensitivity and specificity. Moreover, there was a significant positive correlation between the two combinative biomarkers and the glycoside of pyrrolidine alkaloid. The results of this study provide new ideas on the developments of ZNG identification, authenticity control and against adulteration in the Chinese circulation market.
TL;DR: Several big data healthcare systems, such as Hadoop and MapReduce, may be utilized in the biomedical area to synthesis massive volumes of data and extract meaningful insights based on patterns.
Abstract: Data from imaging, pathology, genetics, and electrophysiology may all be utilized to obtain a better understanding of illnesses. Biomedical researchers may use data to discover patterns and enhance healthcare decisions. Several big data healthcare systems, such as Hadoop (Armoogum and Li, 2019) and MapReduce (Lee et al., 2020), may be utilized in the biomedical area to synthesis massive volumes of data and extract meaningful insights based on patterns.
TL;DR: The purpose of this review is to improve the understanding of the outcomes of current tests and technologies used in leprosy diagnosis and to emphasize critical aspects concerning the detection of leproSy bacilli.
Abstract: Leprosy is a public health issue, and early detection is critical to avert disability. Despite the global attempt to eradicate this disease as a public health problem, it remains an important cause of global neurological disability. India, Brazil and Indonesia share more than 70% of the cases. The reduction of new cases is a priority in the WHO global strategy 2021-2030 which aims to reduce disease transmission in the community by diagnosing cases and identifying subclinical infection. The clinical manifestations of leprosy range from a few to several lesions. The identification remains difficult due to the limited sensitivity of traditional approaches based on bacillary counts of skin smears and histology. To aid in the diagnosis of this disease, molecular biology, and biotechnological technologies have been applied, each with its own set of benefits and downsides despite providing an essential tool to validate the clinical diagnosis of leprosy. Because of this, it is strongly recognized that specific, inexpensive point of care technologies should be developed, particularly to identify asymptomatic M. leprae infections or leprosy nearer to the suspected cases seeking medical attention. Thus, this review will provide an overview of the advancements in leprosy diagnosis over the world. The purpose of this review is to improve our understanding of the outcomes of current tests and technologies used in leprosy diagnosis and to emphasize critical aspects concerning the detection of leprosy bacilli.
TL;DR: This work demonstrates an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists, trained end-to-end from images directly, using only pixels and disease labels as inputs.
Abstract: Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images-two orders of magnitude larger than previous datasets-consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.
TL;DR: A novel wound model based on application of negative pressure and its effects for epidermal regeneration and immune cell behaviour is presented, which recapitulates the main features of epithelial wound regeneration, and can be applied for testing wound healing therapies and investigating underlying mechanisms.
Abstract: A large body of literature is available on wound healing in humans. Nonetheless, a standardized ex vivo wound model without disruption of the dermal compartment has not been put forward with compelling justification. Here, we present a novel wound model based on application of negative pressure and its effects for epidermal regeneration and immune cell behaviour. Importantly, the basement membrane remained intact after blister roof removal and keratinocytes were absent in the wounded area. Upon six days of culture, the wound was covered with one to three-cell thick K14+Ki67+ keratinocyte layers, indicating that proliferation and migration were involved in wound closure. After eight to twelve days, a multi-layered epidermis was formed expressing epidermal differentiation markers (K10, filaggrin, DSG-1, CDSN). Investigations about immune cell-specific manners revealed more T cells in the blister roof epidermis compared to normal epidermis. We identified several cell populations in blister roof epidermis and suction blister fluid that are absent in normal epidermis which correlated with their decrease in the dermis, indicating a dermal efflux upon negative pressure. Together, our model recapitulates the main features of epithelial wound regeneration, and can be applied for testing wound healing therapies and investigating underlying mechanisms.
TL;DR: An unexpected role for U1 homeostasis (available U1 relative to transcription) in oncogenic and activated cell states is revealed, and U1 is suggested as a potential target for their modulation.
Abstract: Stimulated cells and cancer cells have widespread shortening of mRNA 3'-untranslated regions (3'UTRs) and switches to shorter mRNA isoforms due to usage of more proximal polyadenylation signals (PASs) in introns and last exons. U1 snRNP (U1), vertebrates' most abundant non-coding (spliceosomal) small nuclear RNA, silences proximal PASs and its inhibition with antisense morpholino oligonucleotides (U1 AMO) triggers widespread premature transcription termination and mRNA shortening. Here we show that low U1 AMO doses increase cancer cells' migration and invasion in vitro by up to 500%, whereas U1 over-expression has the opposite effect. In addition to 3'UTR length, numerous transcriptome changes that could contribute to this phenotype are observed, including alternative splicing, and mRNA expression levels of proto-oncogenes and tumor suppressors. These findings reveal an unexpected role for U1 homeostasis (available U1 relative to transcription) in oncogenic and activated cell states, and suggest U1 as a potential target for their modulation.
TL;DR: Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient–doctor relationship or facilitate its erosion remains to be seen.
Abstract: The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.
01 Jan 2006
TL;DR: This first edition provides information on disease control interventions for the most common diseases and injuries in developing countries to help them define essential health service packages and offers preventive and case management guidelines critical to improving the quality of care.
Abstract: This first edition provides information on disease control interventions for the most common diseases and injuries in developing countries to help them define essential health service packages. Life expectancy in developing countries increased from forty to sixty-three years between 1950 and 1990 with a concommitant rise in the incidence of noncommunicable diseases of adults and the elderly. It is still necessary to deal with under nutrition and communicable childhood diseases. Also, new epidemic diseases like AIDS are emerging, and the health of the poor during economic crisis is a growing concern. These health developments intensify the need for better information on the effectiveness and cost of health interventions. The information is intended for health practitioners at every level. Individual chapters offer preventive and case management guidelines critical to improving the quality of care. The need for health sector reform is global. Both developed and developing countries, and centrally planned and market oriented health systems share basic dissatisfaction with the present organization and financing of health care delivery and a conviction that there are better ways to obtain results with the available resources. This book attempts to assist health sector reformers to review existing services and adapt them to provide the most cost effective interventions available.