Comparison Between an Artificial Neural Network and Logistic Regression in Predicting Long Term Kidney Transplantation Outcome

Predicting clinical outcome following a specific treatment is a challenge that sees physicians and researchers alike sharing the dream of a crystal ball to read into the future. In Medicine, several tools have been developed for the prediction of outcomes following drug treatment and other medical interventions. The standard approach for a binary outcome is to use logistic regression (LR) [1,2] but over the past few years artificial neural networks (ANNs) have become an increasingly popular alternative to LR analysis for prognostic and diagnostic classification in clinical medicine [3]. The growing interest in ANNs has mainly been triggered by their ability to mimic the learning processes of the human brain. The network operates in a feed-forward mode from the input layer through the hidden layers to the output layer. Exactly what interactions are modeled in the hidden layers is still under study. Each layer within the network is made up of computing nodes with remarkable data processing abilities. Each node is connected to other nodes of a previous layer through adaptable inter-neuron connection strengths known as synaptic weights. ANNs are trained for specific applications through a learning process and knowledge is usually retained as a set of connection weights [4]. The backpropagation algorithm and its variants are learning algorithms that are widely used in neural networks. With backpropagation, the input data is repeatedly presented to the network. Each time, the output is compared to the desired output and an error is computed. The error is then fed back through the network and used to adjust the weights in such a way that with each iteration it gradually declines until the neural model produces the desired outpu

What unrelated hematopoietic stem cell transplantation in thalassemia taught us about transplant immunogenetics

Although the past few decades have shown an improvement in the survival and complication-free survival rates in patients with beta-thalassemia major and gene therapy is already at an advanced stage of experimentation, hematopoietic stem cell transplantation (HSCT) continues to be the only effective and realistic approach to the cure of this chronic nonmalignant disease. Historically, human leukocyte antigen (HLA)-matched siblings have been the preferred source of donor cells owing to superior outcomes compared with HSCT from other sources. Nowadays, the availability of an international network of voluntary stem cell donor registries and cord blood banks has significantly increased the odds of finding a suitable HLA matched donor. Stringent immunogenetic criteria for donor selection have made it possible to achieve overall survival (OS) and thalassemia-free survival (TFS) rates comparable to those of sibling transplants. However, acute and chronic graft-versus-host disease (GVHD) remains the most important complication in unrelated HSCT in thalassemia, leading to significant rates of morbidity and mortality for a chronic non-malignant disease. A careful immunogenetic assessment of donors and recipients makes it possible to individualize appropriate strategies for its prevention and management. This review provides an overview of recent insights about immunogenetic factors involved in GVHD, which seem to have a potential role in the outcome of transplantation for thalassemia

Decision trees to evaluate the risk of developing multiple sclerosis

Introduction: Multiple sclerosis (MS) is a persistent neurological condition impacting the central nervous system (CNS). The precise cause of multiple sclerosis is still uncertain; however, it is thought to arise from a blend of genetic and environmental factors. MS diagnosis includes assessing medical history, conducting neurological exams, performing magnetic resonance imaging (MRI) scans, and analyzing cerebrospinal fluid. While there is currently no cure for MS, numerous treatments exist to address symptoms, decelerate disease progression, and enhance the quality of life for individuals with MS. Methods: This paper introduces a novel machine learning (ML) algorithm utilizing decision trees to address a key objective: creating a predictive tool for assessing the likelihood of MS development. It achieves this by combining prevalent demographic risk factors, specifically gender, with crucial immunogenetic risk markers, such as the alleles responsible for human leukocyte antigen (HLA) class I molecules and the killer immunoglobulin-like receptors (KIR) genes responsible for natural killer lymphocyte receptors. Results: The study included 619 healthy controls and 299 patients affected by MS, all of whom originated from Sardinia. The gender feature has been disregarded due to its substantial bias in influencing the classification outcomes. By solely considering immunogenetic risk markers, the algorithm demonstrates an ability to accurately identify 73.24% of MS patients and 66.07% of individuals without the disease. Discussion: Given its notable performance, this system has the potential to support clinicians in monitoring the relatives of MS patients and identifying individuals who are at an increased risk of developing the disease

Prophylactic and Preemptive Therapy with Dasatinib after Hematopoietic Stem Cell Transplantation for Philadelphia Chromosome-Positive Acute Lymphoblastic Leukemia

n/

Decision trees to evaluate the risk of developing multiple sclerosis

IntroductionMultiple sclerosis (MS) is a persistent neurological condition impacting the central nervous system (CNS). The precise cause of multiple sclerosis is still uncertain; however, it is thought to arise from a blend of genetic and environmental factors. MS diagnosis includes assessing medical history, conducting neurological exams, performing magnetic resonance imaging (MRI) scans, and analyzing cerebrospinal fluid. While there is currently no cure for MS, numerous treatments exist to address symptoms, decelerate disease progression, and enhance the quality of life for individuals with MS.MethodsThis paper introduces a novel machine learning (ML) algorithm utilizing decision trees to address a key objective: creating a predictive tool for assessing the likelihood of MS development. It achieves this by combining prevalent demographic risk factors, specifically gender, with crucial immunogenetic risk markers, such as the alleles responsible for human leukocyte antigen (HLA) class I molecules and the killer immunoglobulin-like receptors (KIR) genes responsible for natural killer lymphocyte receptors.ResultsThe study included 619 healthy controls and 299 patients affected by MS, all of whom originated from Sardinia. The gender feature has been disregarded due to its substantial bias in influencing the classification outcomes. By solely considering immunogenetic risk markers, the algorithm demonstrates an ability to accurately identify 73.24% of MS patients and 66.07% of individuals without the disease.DiscussionGiven its notable performance, this system has the potential to support clinicians in monitoring the relatives of MS patients and identifying individuals who are at an increased risk of developing the disease

Prospective Assessment of Health-Related Quality of Life in Pediatric Patients with Beta-Thalassemia following Hematopoietic Stem Cell Transplantation

Although hematopoietic stem cell transplantation (HSCT) has been widely used to treat pediatric patients with beta-thalassemia major, evidence showing whether this treatment improves health-related quality of life (HRQoL) is lacking. We used child-self and parent-proxy reports to prospectively evaluate HRQoL in 28 children with beta-thalassemia from Middle Eastern countries who underwent allogeneic HSCT in Italy. The PedsQL 4.0 Generic Core Scales were administered to patients and their parents 1 month before and 3, 6, and 18 months after transplantation. Two-year overall survival, thalassemia-free survival, mortality, and rejection were 89.3%, 78.6%, 10.9% and 14.3%, respectively. The cumulative incidence of acute and chronic graft-versus-host disease (GVHD) was 36% and 18%, respectively. Physical functioning declined significantly from baseline to 3 months after HSCT (median PedsQL score, 81.3 vs 62.5; P = .02), but then increased significantly up to 18 months after HSCT (median score, 93.7; P = .04). Agreement between child-self and parent-proxy ratings was high. Chronic GVHD was the most significant factor associated with lower HRQoL scores over time ( P = .02). The child-self and parent-proxy reports showed improved HRQoL in the children with beta-thalassemia after HSCT. Overall, our study provides preliminary evidence-based data to further support clinical decision making in this area

Is the Inversion in the Trend of the Lethality of the COVID-19 in the Two Hemispheres due to the Difference in Seasons and Weather?

The climate has an influence on the COVID-19 virus lethality. The aim of this study is to verify if the summer weather coincided with the decrease of the Case Fatality Ratio (CFR) in Europe and if, on the contrary, an inverse trend was observed in Australia and New Zealand. To verify our hypothesis, we considered the largest European countries (Germany, UK, France, Italy, and Spain), plus Belgium and the Netherlands. Furthermore, we compared these countries with Australia and New Zealand. For each country considered, we have calculated the CFR from the beginning of the pandemic to May 6th and from May 6th to September 21st (late summer in Europe, late winter in the southern hemisphere). The CFRs were calculated from the John Hopkins University database. According to the results, in all European countries, a progressive decrease in CFR is observed. A diametrically opposite result is found in Australia where, on the contrary, the CFR is much higher at the end of September (at the end of winter) than on May 6th (mid-autumn), and the risk of dying if we count the infection is higher in September. In New Zealand, there are no statistically significant differences between the two surveys. The present study was based on public access macro data

The COVID-19 incidence in Italian regions correlates with low temperature, mobility and PM10 pollution but lethality only with low temperature

Background: The aim was to verify whether the density of particulate matter (PM10), the climate, and the mobility of people can influence the pandemic in the 19 regions and in the two autonomous Italian provinces as incidence rate and lethality. Design and Methods: The incidence rates per 100,000 inhabitants and the case fatality ratio (CFR) (dependent variables) in all Italian regions were calculated in January 2021 at John Hopkins University Coronavirus Center. The independent variables were: Minimum average temperatures in the same month (January) of 2020; average pollution of PM10 in the air in each region in the last year available reported on a 0-10 scale to 0 = total absence of PM10 to 10 maximum pollutions; number of places in hotels occupied per inhabitants in 2020. Linear regression and Multiple Regression Analysis were carried out. Results: The spread of the COVID-19 in the Italian regions seems to be related to pollution of PM10, the number of beds occupied in hotels (as an index of mobility and temperature (indirect correlation). On the contrary, the CFR correlates inversely with temperature but not with pollution. Measuring the concomitant effect of two independent variables by means of Multiple Regression Analysis, temperature and pollution show a synergistic effect on COVID-19 incidence. Conclusions: The study seems to confirm the literature on the influence of temperature on the lethality of COVID-19 but adds the new results of an inverse relationship between the spread of the virus and low temperature in regions between the Mediterranean area (which includes southern Italy and Sicily and Sardinia islands) and the cold European temperate zone which includes the northern regions under the Alps. A new date also con-Non-comme cerns the summation effect of the risk between cold weather and PM10 air pollution was found. Due to several methodic weakness the study has an exploratory than conclusive relevance