Beta Thalassemia Carriers detection empowered federated Learning

Thalassemia is a group of inherited blood disorders that happen when hemoglobin, the protein in red blood cells that carries oxygen, is not made enough. It is found all over the body and is needed for survival. If both parents have thalassemia, a child's chance of getting it increases. Genetic counselling and early diagnosis are essential for treating thalassemia and stopping it from being passed on to future generations. It may be hard for healthcare professionals to differentiate between people with thalassemia carriers and those without. The current blood tests for beta thalassemia carriers are too expensive, take too long, and require too much screening equipment. The World Health Organization says there is a high death rate for people with thalassemia. Therefore, it is essential to find thalassemia carriers to act quickly. High-performance liquid chromatography (HPLC), the standard test method, has problems such as cost, time, and equipment needs. So, there must be a quick and cheap way to find people carrying the thalassemia gene. Using federated learning (FL) techniques, this study shows a new way to find people with the beta-thalassemia gene. FL allows data to be collected and processed on-site while following privacy rules, making it an excellent choice for sensitive health data. Researchers used FL to train a model for beta-thalassemia carriers by looking at the complete blood count results and red blood cell indices. The model was 92.38 % accurate at telling the difference between beta-thalassemia carriers and people who did not have the disease. The proposed FL model is better than other published methods in terms of how well it works, how reliable it is, and how private it is. This research shows a promising, quick, accurate, and low-cost way to find thalassemia carriers and opens the door for screening them on a large scale.Comment: pages 17, figures

STUDI KEPATUHAN KONSUMSI TABLET TAMBAH DARAH (TTD) DAN ASUPAN ZAT GIZI TERKAIT ANEMIA PADA SISWA PEREMPUAN DI SEKOLAH MENENGAH KEJURUAN (SMK) KOTA BEKASI, INDONESIA

Background. Anemia is a global nutrition problem, especially in developing countries. Several studied found that around 30 percent of the adolescent was anemia, especially in girls. Compliance with iron-folic acid (IFA) tablet consumption is the most influential factor in the successful improvement of iron status and combat anemia. Objective. This study aimed to know the relationship between compliance of IFA tablet consumption and anemi-related nutrient intake with the anemia among girl-students in Vocational High School in Bekasi City, Indonesia. Method. This study was a cross-sectional study conducted in five vocational high schools, with 345 samples. Samples were collected with a purposive sampling technique. Inclusive criteria for the sample are age between 12 and 18, healthy and able to be a subject, and excluded girls that were fasting and or menstruation. Hemoglobin (Hb) measured with the cyanmethemoglobin method, Hb under 12 g/dl was categorized as anemia. The compliance was assessed with a validated questionnaire and a validated food frequency questionnaire (FFQ) to measure and determine the nutrients intake. Data analyzed by univariate and bivariate. Results. The study found that 30.7 percent of girls were anemia and 48.1 percent as mild anemia (Hb 11–11.9 g/dl). From 74 percent who have not a compliant consumed IFA tablet, 34.3 percent was anemia. Most students have enough intake of protein and other macronutrients. In contrast, the study found more than 60 percent of students have less iron, vitamin C, and vitamin B12. There was no significant relationship among compliance IFA, energy intake, protein intake, fat intake, carbohydrates intake, iron intake, vitamin A intake, vitamin B12 intake, and vitamin C intake with the anemia (p>0.05). Conclusion. There was no relationship between compliance and nutrient intake with anemia. Nevertheless, nutrient intake can be related to anemia as well as compliance with iron tablet consumption. Research suggested that nutrition programs at school should be established, well monitoring, and evaluated

Biomarkers of Nutrition for Development (BOND)—Iron Review

This is the fifth in the series of reviews developed as part of the Biomarkers of Nutrition for Development (BOND) program. The BOND Iron Expert Panel (I-EP) reviewed the extant knowledge regarding iron biology, public health implications, and the relative usefulness of currently available biomarkers of iron status from deficiency to overload. Approaches to assessing intake, including bioavailability, are also covered. The report also covers technical and laboratory considerations for the use of available biomarkers of iron status, and concludes with a description of research priorities along with a brief discussion of new biomarkers with potential for use across the spectrum of activities related to the study of iron in human health. The I-EP concluded that current iron biomarkers are reliable for accurately assessing many aspects of iron nutrition. However, a clear distinction is made between the relative strengths of biomarkers to assess hematological consequences of iron deficiency versus other putative functional outcomes, particularly the relationship between maternal and fetal iron status during pregnancy, birth outcomes, and infant cognitive, motor and emotional development. The I-EP also highlighted the importance of considering the confounding effects of inflammation and infection on the interpretation of iron biomarker results, as well as the impact of life stage. Finally, alternative approaches to the evaluation of the risk for nutritional iron overload at the population level are presented, because the currently designated upper limits for the biomarker generally employed (serum ferritin) may not differentiate between true iron overload and the effects of subclinical inflammation

Anemia management in end stage renal disease patients undergoing dialysis: a comprehensive approach through machine learning techniques and mathematical modeling

Kidney impairment has global consequences in the organism homeostasis and a disorder like Chronic Kidney Disease (CKD) might eventually exacerbates into End Stage Renal Disease (ESRD) where a complete renal replacement therapy like dialysis is necessary. Dialysis partially reintegrates the blood ltration process; however, even when it is associated to a pharmacological therapy, this is not su fficient to completely replace the renal endocrine role and causes the development of common complications, like CKD secondary anemia (CKD-anemia) The availability of exogenous Erythropoiesis Stimulating Agents (ESA, synthetic molecules with similar structure and same mechanism of action as human erythropoietin) improved the treatment of CKD-anemia although the clinical outcomes are still not completely successful. In particular, for ERSD dialysis patients main di culties in the selection of an optimal therapy dosing derive from the high intra- and inter-individual response variability and the temporal discrepancy between the short ESA permanence in the blood (hours) and the long Red Blood Cells lifespan (months). The aim of this thesis has been to describe the development of the Anemia Control Model (ACM), a tool designed to support physicians in managing anemia for ESRD patines undergoing dialysis. Five main pillars constitute the foundation of this work: - Understanding the medical problem; - Availability of the data needed to derive the models; - Mathematical and Machine Learning modeling; - Development of a product usable at the point of care; - Medical device certi cation and clinical evaluation of the developed product. The understanding of the medical problem is fundamental for two reasons: firstly because the medical problem must be the driver of the product scope and consequently of its design; secondly because a good understanding of the medical problem is of fundamental importance to develop optimized models. In the case of anemia management the drug dosing is an important task where predictive models could support physicians to improve the treatment quality. In particular, considering that hemoglobin is the typical parameter used to measure anemia, our model were tailored to predict hemoglobin response to the two main drugs normally used to correct anemia, that is ESA and Iron. In a mathematical model based on di erential equations, like the one presented in this thesis, the knowledge of the main physiological processes related to anemia is the base to properly design the equations. A machine learning approach in principle can be built with no hypotesis, because it relays in learning from data, nevertheless knowledge of the domain helps to make better use of the available data. The medical problem has been discussed in Chapter 1. The availability of a huge database of very well structured data was basic for the development of models. Quality of the data is another important aspect. Chapter 2 gives the reader an overview of the available data.. The core of the ACM is the capability to predict for each patient the future hemoglobin concentrations as a function of past patient's clinical history and future drug prescription. By means of well performing and personalized predictive model it is possible to simulate how, for each specific c patient, di erent doses would a ffect hemoglobin trends. Mathematical and machine learning models present both advantages and limitations. Chapter 3 describes the mathematical model and analyzes its performances, while Chapter 4 is dedicated to the machine learning models. In our case the machine learning approach resulted more suitable for our scope, because its was well performing on the entire population, more stable and, once trained, very quick in elaborating the prediction. Once the predictive model was obtained, the next step was to wrap it into a service that could be consumed by a third party system (for example an app or a clinical system) where physicians could benefi t from the model prediction capability. To achieve that, firstly an algorithm for the dose selection was developed; secondly, a data structure for the communication with the third party system was defi ned; fi nally, the whole package was wrapped in a web service. These arguments have been discussed in the rst part of Chapter 5. Mistakes in ESA or Iron dosing might have serious consequences on patients' health, for this reason ACM intended use was limited to provide dose suggestions only; physicians must evaluate them and decide whether to accept or reject them. Nevertheless, such a tool could be considered as Medical Device under European Medical Device Directive (MDD); for this reason, to be on the safe side, it was decided to certify the ACM as medical device. A novel approach was developed to perform the risk assessment, the main idea being that ACM might generate risks when a dose suggestion is produced based on a wrong prediction. To assess this risk the model error distribution over the test set was utilized as estimation of the error distribution of the live system. Finally, a clinical evaluation of the ACM in three pilot clinics has been performed before deciding to roll-out the tool in more clinics. These arguments have been discussed in the second part of Chapter 5

The Importance of Identifying Iron Deficiency Anaemia in the Early Detection of Colorectal Cancer.

Colorectal cancer (CRC) is common and carries a relatively poor prognosis. The strong relationship between tumour stage at diagnosis and survival is the basis of the English Bowel Cancer Screening Programme (BCSP) and highlights the importance of early diagnosis. Iron deficiency anaemia (IDA) is also common. About 10% of cases in males and post-menopausal females are due to underlying gastro-intestinal (GI) cancer, most commonly CRC - and IDA is often the first manifestation. This thesis examines the detailed relationship between IDA and CRC. Chapters 4 and 5 describe the analysis of four large IDA datasets, confirming the prevalence of GI cancer, and demonstrating that cancer risk can be predicted from four simple, objective clinical indicators. This IDIOM model proved robust on internal and external validation. This research is valuable for patient counselling, targeting the investigation of high-risk individuals and (perhaps) avoiding invasive investigation in ultra-low risk cases. Chapter 6 outlines the analysis of a subset with recurrent IDA, suggesting that the subsequent risk of GI cancer is higher in those who were incompletely investigated the first time around. Chapter 7 describes the development of the IDIOM App. This is a freely available web-tool which allows cancer risk in IDA to be calculated within seconds, lending itself to clinical usage. Chapters 8 and 9 report the analysis of a large CRC database, demonstrating that diagnosis through the IDA pathway (1) generates as many cases as the BCSP; (2) identifies a distinct sub-population with a predominance of right- sided lesions; and (3) like the BCSP, is associated with a favourable tumour stage profile. The findings suggest that identifying iron deficiency anaemia could play an important role in the early diagnosis of CRC

Evaluating the Impact of Defeasible Argumentation as a Modelling Technique for Reasoning under Uncertainty

Limited work exists for the comparison across distinct knowledge-based approaches in Artificial Intelligence (AI) for non-monotonic reasoning, and in particular for the examination of their inferential and explanatory capacity. Non-monotonicity, or defeasibility, allows the retraction of a conclusion in the light of new information. It is a similar pattern to human reasoning, which draws conclusions in the absence of information, but allows them to be corrected once new pieces of evidence arise. Thus, this thesis focuses on a comparison of three approaches in AI for implementation of non-monotonic reasoning models of inference, namely: expert systems, fuzzy reasoning and defeasible argumentation. Three applications from the fields of decision-making in healthcare and knowledge representation and reasoning were selected from real-world contexts for evaluation: human mental workload modelling, computational trust modelling, and mortality occurrence modelling with biomarkers. The link between these applications comes from their presumptively non-monotonic nature. They present incomplete, ambiguous and retractable pieces of evidence. Hence, reasoning applied to them is likely suitable for being modelled by non-monotonic reasoning systems. An experiment was performed by exploiting six deductive knowledge bases produced with the aid of domain experts. These were coded into models built upon the selected reasoning approaches and were subsequently elicited with real-world data. The numerical inferences produced by these models were analysed according to common metrics of evaluation for each field of application. For the examination of explanatory capacity, properties such as understandability, extensibility, and post-hoc interpretability were meticulously described and qualitatively compared. Findings suggest that the variance of the inferences produced by expert systems and fuzzy reasoning models was higher, highlighting poor stability. In contrast, the variance of argument-based models was lower, showing a superior stability of its inferences across different system configurations. In addition, when compared in a context with large amounts of conflicting information, defeasible argumentation exhibited a stronger potential for conflict resolution, while presenting robust inferences. An in-depth discussion of the explanatory capacity showed how defeasible argumentation can lead to the construction of non-monotonic models with appealing properties of explainability, compared to those built with expert systems and fuzzy reasoning. The originality of this research lies in the quantification of the impact of defeasible argumentation. It illustrates the construction of an extensive number of non-monotonic reasoning models through a modular design. In addition, it exemplifies how these models can be exploited for performing non-monotonic reasoning and producing quantitative inferences in real-world applications. It contributes to the field of non-monotonic reasoning by situating defeasible argumentation among similar approaches through a novel empirical comparison

Drug Repositioning for Congenital Disorders of Glycosylation (CDG)

R.F. and acknowledge the funding from the Fundação para a Ciência e Tecnologia (FCT), Portugal. S.B. was supported by CDG & Allies—PAIN funding. M.A. acknowledges PhD program at the DISTABIF, Università degli Studi della Campania “Luigi Vanvitelli”, PhD fellowship POR Campania FSE 2014/2020 “Dottorati di Ricerca Con Caratterizzazione Industriale”.Advances in research have boosted therapy development for congenital disorders of glycosylation (CDG), a group of rare genetic disorders affecting protein and lipid glycosylation and glycosylphosphatidylinositol anchor biosynthesis. The (re)use of known drugs for novel medical purposes, known as drug repositioning, is growing for both common and rare disorders. The latest innovation concerns the rational search for repositioned molecules which also benefits from artificial intelligence (AI). Compared to traditional methods, drug repositioning accelerates the overall drug discovery process while saving costs. This is particularly valuable for rare diseases. AI tools have proven their worth in diagnosis, in disease classification and characterization, and ultimately in therapy discovery in rare diseases. The availability of biomarkers and reliable disease models is critical for research and development of new drugs, especially for rare and heterogeneous diseases such as CDG. This work reviews the literature related to repositioned drugs for CDG, discovered by serendipity or through a systemic approach. Recent advances in biomarkers and disease models are also outlined as well as stakeholders' views on AI for therapy discovery in CDG.publishersversionpublishe