RESUMEN

Parkinson's disease (PD) is the second most common neurodegenerative disorder, globally affecting men and women at an exponentially growing rate, with currently no cure. Disease progression starts when dopaminergic neurons begin to die. In PD, the loss of neurotransmitter, dopamine is responsible for the overall communication of neural cells throughout the body. Clinical symptoms of PD are slowness of movement, involuntary muscular contractions, speech & writing changes, lessened automatic movement, and chronic tremors in the body. PD occurs in both familial and sporadic forms and modifiable and non-modifiable risk factors and socioeconomic conditions cause PD. Early detectable diagnostics and treatments have been developed in the last several decades. However, we still do not have precise early detectable biomarkers and therapeutic agents/drugs that prevent and/or delay the disease process. Recently, artificial intelligence (AI) science and machine learning tools have been promising in identifying early detectable markers with a greater rate of accuracy compared to past forms of treatment and diagnostic processes. Artificial intelligence refers to the intelligence exhibited by machines or software, distinct from the intelligence observed in humans that is based on neural networks in a form and can be used to diagnose the longevity and disease severity of disease. The term Machine Learning or Neural Networks is a blanket term used to identify an emerging technology that is created to work in the way of a "human brain" using many intertwined neurons to achieve the same level of raw intelligence as that of a brain. These processes have been used for neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease, to assess the severity of the patient's condition. In the current article, we discuss the prevalence and incidence of PD, and currently available diagnostic biomarkers and therapeutic strategies. We also highlighted currently available artificial intelligence science and machine learning tools and their applications to detect disease and develop therapeutic interventions.

Asunto(s)

RESUMEN

Polycystic Ovary Syndrome (PCOS) is one of the most widespread endocrine and metabolic disorders affecting women of reproductive age. Major symptoms include hyperandrogenism, polycystic ovary, irregular menstruation cycle, excessive hair growth, etc., which sometimes may lead to more severe complications like infertility, pregnancy complications and other co-morbidities such as diabetes, hypertension, sleep apnea, etc. Early detection and effective management of PCOS are essential to enhance patients' quality of life and reduce the chances of associated health complications. Artificial intelligence (AI) techniques have recently emerged as a popular methodology in the healthcare industry for diagnosing and managing complex diseases such as PCOS. AI utilizes machine learning algorithms to analyze ultrasound images and anthropometric and biochemical test result data to diagnose PCOS quickly and accurately. AI can assist in integrating different data sources, such as patient histories, lab findings, and medical records, to present a clear and complete picture of an individual's health. This information can help the physician make more informed and efficient diagnostic decisions. This review article provides a comprehensive analysis of the evolving role of AI in various aspects of the management of PCOS, with a major focus on AI-based diagnosis tools.

RESUMEN

Recently, the rampant administration of antibiotics and their synthetic organic constitutes have exacerbated adverse effects on ecosystems, affecting the health of animals, plants, and humans by promoting the emergence of extreme multidrug-resistant bacteria (XDR), antibiotic resistance bacterial variants (ARB), and genes (ARGs). The constraints, such as high costs, by-product formation, etc., associated with the physico-chemical treatment process limit their efficacy in achieving efficient wastewater remediation. Biodegradation is a cost-effective, energy-saving, sustainable alternative for removing emerging organic pollutants from environmental matrices. In view of the same, the current study aims to explore the biodegradation of ciprofloxacin using microbial consortia via metabolic pathways. The optimal parameters for biodegradation were assessed by employing machine learning tools, viz. Artificial Neural Network (ANN) and statistical optimization tool (Response Surface Methodology, RSM) using the Box-Behnken design (BBD). Under optimal culture conditions, the designed bacterial consortia degraded ciprofloxacin with 95.5% efficiency, aligning with model prediction results, i.e., 95.20% (RSM) and 94.53% (ANN), respectively. Thus, befitting amendments to the biodegradation process can augment efficiency and lead to a greener solution for antibiotic degradation from aqueous media.

Asunto(s)

RESUMEN

BACKGROUND: In the global effort to discover biomarkers for cancer prognosis, prediction tools have become essential resources. TCR (T cell receptor) repertoires contain important features that differentiate healthy controls from cancer patients or differentiate outcomes for patients being treated with different drugs. Considering, tools that can easily and quickly generate and identify important features out of TCR repertoire data and build accurate classifiers to predict future outcomes are essential. RESULTS: This paper introduces GENTLE (GENerator of T cell receptor repertoire features for machine LEarning): an open-source, user-friendly web-application tool that allows TCR repertoire researchers to discover important features; to create classifier models and evaluate them with metrics; and to quickly generate visualizations for data interpretations. We performed a case study with repertoires of TRegs (regulatory T cells) and TConvs (conventional T cells) from healthy controls versus patients with breast cancer. We showed that diversity features were able to distinguish between the groups. Moreover, the classifiers built with these features could correctly classify samples ('Healthy' or 'Breast Cancer')from the TRegs repertoire when trained with the TConvs repertoire, and from the TConvs repertoire when trained with the TRegs repertoire. CONCLUSION: The paper walks through installing and using GENTLE and presents a case study and results to demonstrate the application's utility. GENTLE is geared towards any researcher working with TCR repertoire data and aims to discover predictive features from these data and build accurate classifiers. GENTLE is available on https://github.com/dhiego22/gentle and https://share.streamlit.io/dhiego22/gentle/main/gentle.py .

Asunto(s)

RESUMEN

This article presents, for the first time, the results of applying the rheological technique to measure the molecular weights (Mw) and their distributions (MwD) of highly hierarchical biomolecules, such as non-hydrolyzed collagen gels. Due to the high viscosity of the studied gels, the effect of the concentrations on the rheological tests was investigated. In addition, because these materials are highly sensitive to denaturation and degradation under mechanical stress and temperatures close to 40 °C, when frequency sweeps were applied, a mathematical adjustment of the data by machine learning techniques (artificial intelligence tools) was designed and implemented. Using the proposed method, collagen fibers of Mw close to 600 kDa were identified. To validate the proposed method, lower Mw species were obtained and characterized by both the proposed rheological method and traditional measurement techniques, such as chromatography and electrophoresis. The results of the tests confirmed the validity of the proposed method. It is a simple technique for obtaining more microstructural information on these biomolecules and, in turn, facilitating the design of new structural biomaterials with greater added value.

RESUMEN

The gut microbiota plays an important role in the regulation of the immune system and the metabolism of the host. The aim of the present study was to characterize the gut microbiota of patients with type 2 diabetes mellitus (T2DM). A total of 118 participants with newly diagnosed T2DM and 89 control subjects were recruited in the present study; six clinical parameters were collected and the quantity of 10 different types of bacteria was assessed in the fecal samples using quantitative PCR. Taking into consideration the six clinical variables and the quantity of the 10 different bacteria, 3 predictive models were established in the training set and test set, and evaluated using a confusion matrix, area under the receiver operating characteristic curve (AUC) values, sensitivity (recall), specificity, accuracy, positive predictive value and negative predictive value (npv). The abundance of Bacteroides, Eubacterium rectale and Roseburia inulinivorans was significantly lower in the T2DM group compared with the control group. However, the abundance of Enterococcus was significantly higher in the T2DM group compared with the control group. In addition, Faecalibacterium prausnitzii, Enterococcus and Roseburia inulinivorans were significantly associated with sex status while Bacteroides, Bifidobacterium, Enterococcus and Roseburia inulinivorans were significantly associated with older age. In the training set, among the three models, support vector machine (SVM) and XGboost models obtained AUC values of 0.72 and 0.70, respectively. In the test set, only SVM obtained an AUC value of 0.77, and the precision and specificity were both above 0.77, whereas the accuracy, recall and npv were above 0.60. Furthermore, Bifidobacterium, age and Roseburia inulinivorans played pivotal roles in the model. In conclusion, the SVM model exhibited the highest overall predictive power, thus the combined use of machine learning tools with gut microbiome profiling may be a promising approach for improving early prediction of T2DM in the near feature.

RESUMEN

OBJECTIVE: Parkinson's disease is a pervasive neuro disorder that affects people's quality of life throughout the world. The unsatisfactory results of clinical rating scales open the door for more research. PD treatment using current biomarkers seems a difficult task. So automatic evaluation at an early stage may enhance the quality and time period of life. METHODS: Grading of Recommendations Assessment, Development, and Evaluation (GRADE) and population, Intervention, Comparison, and Outcome (PICO) search methodology schemes are followed to search the data and eligible studies for this survey. Approximate 1500 articles were extracted using related search strings. After the stepwise mapping and elimination of studies, 94 papers are found suitable for the present review. RESULTS: After the quality assessment of extracted studies, nine inhibitors are identified to analyze people's gait with Parkinson's disease, where four are critical. This review also differentiates the various machine learning classification techniques with their PD analysis characteristics in previous studies. The extracted research gaps are described as future perspectives. Results can help practitioners understand the PD gait as a valuable biomarker for detection, quantification, and classification. CONCLUSION: Due to less cost and easy recording of gait, gait-based techniques are becoming popular in PD detection. By encapsulating the gait-based studies, it gives an in-depth knowledge of PD, different measures that affect gait detection and classification.

Asunto(s)

RESUMEN

The increasingly sophisticated investigations of complex systems require more robust estimates of the correlations between the measured quantities. The traditional Pearson correlation coefficient is easy to calculate but sensitive only to linear correlations. The total influence between quantities is, therefore, often expressed in terms of the mutual information, which also takes into account the nonlinear effects but is not normalized. To compare data from different experiments, the information quality ratio is, therefore, in many cases, of easier interpretation. On the other hand, both mutual information and information quality ratio are always positive and, therefore, cannot provide information about the sign of the influence between quantities. Moreover, they require an accurate determination of the probability distribution functions of the variables involved. As the quality and amount of data available are not always sufficient to grant an accurate estimation of the probability distribution functions, it has been investigated whether neural computational tools can help and complement the aforementioned indicators. Specific encoders and autoencoders have been developed for the task of determining the total correlation between quantities related by a functional dependence, including information about the sign of their mutual influence. Both their accuracy and computational efficiencies have been addressed in detail, with extensive numerical tests using synthetic data. A careful analysis of the robustness against noise has also been performed. The neural computational tools typically outperform the traditional indicators in practically every respect.

RESUMEN

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a major public health problem and cause of mortality worldwide. However, COPD in the early stage is usually not recognized and diagnosed. It is necessary to establish a risk model to predict COPD development. METHODS: A total of 441 COPD patients and 192 control subjects were recruited, and 101 single-nucleotide polymorphisms (SNPs) were determined using the MassArray assay. With 5 clinical features as well as SNPs, 6 predictive models were established and evaluated in the training set and test set by the confusion matrix AU-ROC, AU-PRC, sensitivity (recall), specificity, accuracy, F1 score, MCC, PPV (precision) and NPV. The selected features were ranked. RESULTS: Nine SNPs were significantly associated with COPD. Among them, 6 SNPs (rs1007052, OR = 1.671, P = 0.010; rs2910164, OR = 1.416, P < 0.037; rs473892, OR = 1.473, P < 0.044; rs161976, OR = 1.594, P < 0.044; rs159497, OR = 1.445, P < 0.045; and rs9296092, OR = 1.832, P < 0.045) were risk factors for COPD, while 3 SNPs (rs8192288, OR = 0.593, P < 0.015; rs20541, OR = 0.669, P < 0.018; and rs12922394, OR = 0.651, P < 0.022) were protective factors for COPD development. In the training set, KNN, LR, SVM, DT and XGboost obtained AU-ROC values above 0.82 and AU-PRC values above 0.92. Among these models, XGboost obtained the highest AU-ROC (0.94), AU-PRC (0.97), accuracy (0.91), precision (0.95), F1 score (0.94), MCC (0.77) and specificity (0.85), while MLP obtained the highest sensitivity (recall) (0.99) and NPV (0.87). In the validation set, KNN, LR and XGboost obtained AU-ROC and AU-PRC values above 0.80 and 0.85, respectively. KNN had the highest precision (0.82), both KNN and LR obtained the same highest accuracy (0.81), and KNN and LR had the same highest F1 score (0.86). Both DT and MLP obtained sensitivity (recall) and NPV values above 0.94 and 0.84, respectively. In the feature importance analyses, we identified that AQCI, age, and BMI had the greatest impact on the predictive abilities of the models, while SNPs, sex and smoking were less important. CONCLUSIONS: The KNN, LR and XGboost models showed excellent overall predictive power, and the use of machine learning tools combining both clinical and SNP features was suitable for predicting the risk of COPD development.

Asunto(s)

RESUMEN

Leishmaniasis and trypanosomiasis occur primarily in undeveloped countries and account for millions of deaths and disability-adjusted life years. Limited therapeutic options, high toxicity of chemotherapeutic drugs and the emergence of drug resistance associated with these diseases demand urgent development of novel therapeutic agents for the treatment of these dreadful diseases. In the last decades, different in silico methods have been successfully implemented for supporting the lengthy and expensive drug discovery process. In the current review, we discuss recent advances pertaining to in silico analyses towards lead identification, lead modification and target identification of antileishmaniasis and anti-trypanosomiasis agents. We describe recent applications of some important in silico approaches, such as 2D-QSAR, 3D-QSAR, pharmacophore mapping, molecular docking, and so forth, with the aim of understanding the utility of these techniques for the design of novel therapeutic anti-parasitic agents. This review focuses on: (a) advanced computational drug design options; (b) diverse methodologies - e.g.: use of machine learning tools, software solutions, and web-platforms; (c) recent applications and advances in the last five years; (d) experimental validations of in silico predictions; (e) virtual screening tools; and (f) rationale or justification for the selection of these in silico methods.

Asunto(s)

RESUMEN

INTRODUCTION: Age-related macular degeneration (AMD) is the main cause of visual impairment and the most important cause of blindness in older people. However, there is currently no effective treatment for this disease, so it is necessary to establish a risk model to predict AMD development. METHODS: This study included a total of 202 subjects, comprising 82 AMD patients and 120 control subjects. Sixty-six single-nucleotide polymorphisms (SNPs) were identified using the MassArray assay. Considering 14 independent clinical variables as well as SNPs, four predictive models were established in the training set and evaluated by the confusion matrix, area under the receiver operating characteristic (ROC) curve (AUROC). The difference distributions of the 14 independent clinical features between the AMD and control groups were tested using the chi-squared test. Age and diabetes were adjusted using logistic regression analysis and the "genomic-control" method was used for multiple testing correction. RESULTS: Three SNPs (rs10490924, OR = 1.686, genomic-control corrected p-value (GC) = 0.030; rs2338104, OR = 1.794, GC = 0.025 and rs1864163, OR = 2.125, GC = 0.038) were significant risk factors for AMD development. In the training set, four models obtained AUROC values above 0.72. CONCLUSIONS: We believe machine learning tools will be useful for the early prediction of AMD and for the development of relevant intervention strategies.

RESUMEN

This study was aimed to construct classification and regression tree (CART) model of glycosaminoglycans (GAGs) for the differential diagnosis of Mucopolysaccharidoses (MPS). Two-dimensional electrophoresis and liquid chromatography-tandem mass spectrometry (LC-MS/MS) were used for the qualitative and quantitative analysis of GAGs. Specific enzyme assays and targeted gene sequencing were performed to confirm the diagnosis. Machine learning tools were used to develop CART model based on GAG profile. Qualitative and quantitative CART models showed 96.3% and 98.3% accuracy, respectively, in the differential diagnosis of MPS. The thresholds of different GAGs diagnostic of specific MPS types were established. In 60 MPS positive cases, 46 different mutations were identified in six specific genes. Among 31 different mutations identified in IDUA, nine were nonsense mutations and two were gross deletions while the remaining were missense mutations. In IDS gene, four missense, two frameshift, and one deletion were identified. In NAGLU gene, c.1693C > T and c.1914_1914insT were the most common mutations. Two ARSB, one case each of SGSH and GALNS mutations were observed. LC-MS/MS-based GAG pattern showed higher accuracy in the differential diagnosis of MPS. The mutation spectrum of MPS, specifically in IDUA and IDS genes, is highly heterogeneous among the cases studied.

Asunto(s)

RESUMEN

microRNAs are evolutionarily conserved, endogenously produced, noncoding RNAs (ncRNAs) of approximately 19-24 nucleotides (nts) in length known to exhibit gene silencing of complementary target sequence. Their deregulated expression is reported in various disease conditions and thus has therapeutic implications. In the last decade, various computational resources are published in this field. In this chapter, we have reviewed bioinformatics resources, i.e., miRNA-centered databases, algorithms, and tools to predict miRNA targets. First section has enlisted more than 75 databases, which mainly covers information regarding miRNA registries, targets, disease associations, differential expression, interactions with other noncoding RNAs, and all-in-one resources. In the algorithms section, we have compiled about 140 algorithms from eight subcategories, viz. for the prediction of precursor (pre-) and mature miRNAs. These algorithms are developed on various sequence, structure, and thermodynamic based features incorporated into different machine learning techniques (MLTs). In addition, computational identification of miRNAs from high-throughput next generation sequencing (NGS) data and their variants, viz. isomiRs, differential expression, miR-SNPs, and functional annotation, are discussed. Prediction and analysis of miRNAs and their associated targets are also evaluated under miR-targets section providing knowledge regarding novel miRNA targets and complex host-pathogen interactions. In conclusion, we have provided comprehensive review of in silico resources published in miRNA research to help scientific community be updated and choose the appropriate tool according to their needs.

Asunto(s)

RESUMEN

BACKGROUND: Identification of differentially expressed genes, i.e., genes whose transcript abundance level differs across different biological or physiological conditions, was indeed a challenging task. However, the inception of transcriptome sequencing (RNA-seq) technology revolutionized the simultaneous measurement of the transcript abundance levels for thousands of genes. OBJECTIVE: In this paper, such next-generation sequencing (NGS) data is used to identify biomarker signatures for several of the most common cancer types (bladder, colon, kidney, brain, liver, lung, prostate, skin, and thyroid) METHODS: Here, the problem is mapped into the comparison of optimization algorithms for selecting a set of genes that lead to the highest classification accuracy of a two-class classification task between healthy and tumor samples. As the optimization algorithms Artificial Bee Colony (ABC), Ant Colony Optimization, Differential Evolution, and Particle Swarm Optimization are chosen for this experiment. A standard statistical method called DESeq2 is used to select differentially expressed genes before being feed to the optimization algorithms. Classification of healthy and tumor samples is done by support vector machine RESULTS: Cancer-specific validation yields remarkably good results in terms of accuracy. Highest classification accuracy is achieved by the ABC algorithm for Brain lower grade glioma data is 99.10%. This validation is well supported by a statistical test, gene ontology enrichment analysis, and KEGG pathway enrichment analysis for each cancer biomarker signature CONCLUSION: The current study identified robust genes as biomarker signatures and these identified biomarkers might be helpful to accurately identify tumors of unknown origin.

Asunto(s)

RESUMEN

OBJECTIVE: Radiotherapy treatment planning aims at delivering a sufficient radiation dose to cancerous tumour cells while sparing healthy organs in the tumour-surrounding area. It is a time-consuming trial-and-error process that requires the expertise of a group of medical experts including oncologists and medical physicists and can take from 2 to 3h to a few days. Our objective is to improve the performance of our previously built case-based reasoning (CBR) system for brain tumour radiotherapy treatment planning. In this system, a treatment plan for a new patient is retrieved from a case base containing patient cases treated in the past and their treatment plans. However, this system does not perform any adaptation, which is needed to account for any difference between the new and retrieved cases. Generally, the adaptation phase is considered to be intrinsically knowledge-intensive and domain-dependent. Therefore, an adaptation often requires a large amount of domain-specific knowledge, which can be difficult to acquire and often is not readily available. In this study, we investigate approaches to adaptation that do not require much domain knowledge, referred to as knowledge-light adaptation. METHODOLOGY: We developed two adaptation approaches: adaptation based on machine-learning tools and adaptation-guided retrieval. They were used to adapt the beam number and beam angles suggested in the retrieved case. Two machine-learning tools, neural networks and naive Bayes classifier, were used in the adaptation to learn how the difference in attribute values between the retrieved and new cases affects the output of these two cases. The adaptation-guided retrieval takes into consideration not only the similarity between the new and retrieved cases, but also how to adapt the retrieved case. RESULTS: The research was carried out in collaboration with medical physicists at the Nottingham University Hospitals NHS Trust, City Hospital Campus, UK. All experiments were performed using real-world brain cancer patient cases treated with three-dimensional (3D)-conformal radiotherapy. Neural networks-based adaptation improved the success rate of the CBR system with no adaptation by 12%. However, naive Bayes classifier did not improve the current retrieval results as it did not consider the interplay among attributes. The adaptation-guided retrieval of the case for beam number improved the success rate of the CBR system by 29%. However, it did not demonstrate good performance for the beam angle adaptation. Its success rate was 29% versus 39% when no adaptation was performed. CONCLUSIONS: The obtained empirical results demonstrate that the proposed adaptation methods improve the performance of the existing CBR system in recommending the number of beams to use. However, we also conclude that to be effective, the proposed adaptation of beam angles requires a large number of relevant cases in the case base.

Asunto(s)