Differential sequential patterns supporting insulin therapy of new-onset type 1 diabetes

Background: In spite of numerous research efforts on supporting the therapy of diabetes mellitus, the subject still involves challenges and creates active interest among researchers. In this paper, a decision support tool is presented for setting insulin therapy in new-onset type 1 diabetes. Methods: The concept of differential sequential patterns (DSPs) is introduced with the aim of representing deviations in the patient's blood glucose level (BGL) and the amount of insulin injections administered. The decision support tool is created using data mining algorithms for discovering sequential patterns. Results: By using the DSPs, it is possible to support the physician's decisionmaking concerning changing the treatment (i.e., whether to increase or decrease the insulin dosage). The other contributions of the paper are an algorithm for generating DSPs and a new method for evaluating nocturnal glycaemia. The proposed qualitative evaluation of nocturnal glycaemia improves the generalization capabilities of the DSPs. Conclusions: The usefulness of the proposed approach was evident in the results of experiments in which juvenile diabetic patients actual data were used. It was confirmed that the proposed DSPs can be used to guide the therapy of numerous juvenile patients with type 1 diabetes

Mining clinical pathways for daily insulin therapy of diabetic children

We propose a decision support framework (DSF) assisting insulin therapy of diabetic children. Our DSF relies on a medical treatment graph (MTG), which models and graphically represents clinical pathways. Using the MTG, it is possible to plan and adapt medical decisions dependent upon the current health state of a patient and the progress of the treatment. Our MTG fits well with the requirements of clinical practice. The presented work is a cooperative effort of researchers in computer science and medicine. The MTG model has been thoroughly tested and validated using real-world clinical data. The usefulness of the approach has been confirmed by physicians

Rule-based Medical Treatment Graph for the Modeling of Hypo- and Hyperglycemia at Onset

Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 25th International Conference KES2021; 08-10.09.2021, SzczecinThis paper proposes a rule-based medical treatment graph (RB-MTG), a decision support tool that assists physicians in establishing insulin therapy. The RB-MTG models clinical pathways, i.e. the sequences of blood glucose measurements and insulin injections. It provides visualization of alternative clinical pathways, especially those that lead to dangerous states of the patient’s health. By interpreting the RB-MTG, the physician assesses the patient’s condition and plans their insulin therapy. At each phase of the treatment, the RB-MTG suggests the insulin dosage that leads to normoglycemia - the blood glucose level that is the norm for a healthy person. This way, it is possible to avoid the course of the disease that leads to hypo- or hyperglycemia. Physicians have verified the usefulness of our approach

The impact of gender on in-hospital mortality and long-term mortality in patients undergoing surgical aortic valve replacement: SAVR and SEX Study

Background: Surgical aortic valve replacement (SAVR) is among the most commonly performed valve valvular surgeries. Despite many previous studies conducted in this setting, the impact of gender on outcomes in the patients undergoing SAVR is still unclear. Aims: To define gender differences in short- and long-term mortality in patients undergoing SAVR. Methods: We analyzed retrospectively all the patients undergoing isolated SAVR from January 2006 to March 2020 in the Department of Cardiovascular Surgery and Transplantology in John Paul II Hospital in Cracow. The primary end point was in-hospital and long-term mortality. Secondary end points included the length duration of hospital stay and perioperative complications. Groups of men and women with regard to the prosthesis type were compared. Propensity score matching was performed to adjust for differences in baseline characteristics. Results: A total number of 4 510 patients undergoing isolated surgical SAVR were analyzed. A follow-up median (interquartile range [IQR]) was 2120 (1000–3452) days. Females constituted 41.55% of the cohort and were  older, displayed more non-cardiac comorbidities and faced a higher operative risk. In both genders, bioprostheses were more often applied (55.5% vs. 44.5%; P < 0.0001). In univariable analysis, gender was not associated linked to in-hospital fatality (3.7% vs. 3%; P = 0.15) and late mortality (rates) (23.37% vs. 23.52 %; P = 0.9). Upon adjustment for baseline characteristics (propensity score matching analysis) and considering 5-year survival, a long-term prognosis proved to be better in women with 86.8% comparing to 82.7% in men (P = 0.03). Conclusions: A key finding from this study suggests that the female gender was not associated with a higher in-hospital and late mortality rate compared to men. Further studies are needed to confirm long-term benefits  in women undergoing SAVR

The Usefulness of Genotyping of Celiac Disease-Specific HLA among Children with Type 1 Diabetes in Various Clinical Situations

Aim. The aim of the study was to determine the usefulness of HLA DQ2/DQ8 genotyping in children with T1D in various clinical situations: as a screening test at the diabetes onset, as a verification of the diagnosis in doubtful situations, and as a test estimating the risk of CD in the future. Materials and methods. Three groups of patients with T1D were included: newly diagnosed (n=92), with CD and villous atrophy (n=30), and with potential CD (n=23). Genetic tests were performed (commercial test, PCR, and REX), and clinical data were collected. Results. The results of genetic tests confirmed the presence of DQ2/DQ8 in 94% of children with diabetes (group I) and in 100% of children with diabetes and CD (groups II and III, respectively). Comparative analysis of the HLA DQ2/DQ8 distribution did not show any differences. Allele DRB1∗04 (linked with HLA DQ8) was significantly less common in children with diabetes and CD (group I versus groups II and III, 56.5% vs. 24.5%; p=0.001). The probability of developing CD in DRB1∗04-positive patients was 4 times lower (OR 0.25; 95% CI 0.118-0.529; p=0.001). DRB1∗04 was significantly less frequent in children with villous atrophy compared to potential CD (13% vs. 39%; p=0.03). Conclusions. Genotyping HLA DQ2/DQ8 as a negative screening has limited use in assessing the risk of CD at the diabetes onset and does not allow to verify the diagnosis of CD in doubtful situations. The presence of the DRB1∗04 allele modulates the risk of CD and significantly reduces it and can predict a potential form

Using Machine Learning for Particle Identification in ALICE

Particle identification (PID) is one of the main strengths of the ALICE experiment at the LHC. It is a crucial ingredient for detailed studies of the strongly interacting matter formed in ultrarelativistic heavy-ion collisions. ALICE provides PID information via various experimental techniques, allowing for the identification of particles over a broad momentum range (from around 100 MeV/c to around 50 GeV/c). The main challenge is how to combine the information from various detectors effectively. Therefore, PID represents a model classification problem, which can be addressed using Machine Learning (ML) solutions. Moreover, the complexity of the detector and richness of the detection techniques make PID an interesting area of research also for the computer science community. In this work, we show the current status of the ML approach to PID in ALICE. We discuss the preliminary work with the Random Forest approach for the LHC Run 2 and a more advanced solution based on Domain Adaptation Neural Networks, including a proposal for its future implementation within the ALICE computing software for the upcoming LHC Run 3.Particle identification (PID) is one of the main strengths of the ALICE experiment at the LHC. It is a crucial ingredient for detailed studies of the strongly interacting matter formed in ultrarelativistic heavy-ion collisions. ALICE provides PID information via various experimental techniques, allowing for the identification of particles over a broad momentum range (from around 100 MeV/$c$ to around 50 GeV/$c$). The main challenge is how to combine the information from various detectors effectively. Therefore, PID represents a model classification problem, which can be addressed using Machine Learning (ML) solutions. Moreover, the complexity of the detector and richness of the detection techniques make PID an interesting area of research also for the computer science community. In this work, we show the current status of the ML approach to PID in ALICE. We discuss the preliminary work with the Random Forest approach for the LHC Run 2 and a more advanced solution based on Domain Adaptation Neural Networks, including a proposal for its future implementation within the ALICE computing software for the upcoming LHC Run 3

Common Variants in Osteopontin and <i>CD44</i> Genes as Predictors of Treatment Outcome in Radiotherapy and Chemoradiotherapy for Non-Small Cell Lung Cancer

Osteopontin (OPN)-CD44 signaling plays an important role in promoting tumor progression and metastasis. In cancer, OPN and CD44 overexpression is a marker of aggressive disease and poor prognosis, and correlates with therapy resistance. In this study, we aimed to evaluate the association of single nucleotide polymorphisms (SNPs) in the OPN and CD44 genes with clinical outcomes in 307 non-small cell lung cancer (NSCLC) patients treated with radiotherapy or chemoradiotherapy. The potential impact of the variants on plasma OPN levels was also investigated. Multivariate analysis showed that OPN rs11730582 CC carriers had a significantly increased risk of death (p = 0.029), while the CD44 rs187116 A allele correlated with a reduced risk of locoregional recurrence (p = 0.016) in the curative treatment subset. The rs11730582/rs187116 combination was associated with an elevated risk of metastasis in these patients (p = 0.016). Furthermore, the OPN rs1126772 G variant alone (p = 0.018) and in combination with rs11730582 CC (p = 7 × 10−5) was associated with poor overall survival (OS) in the squamous cell carcinoma subgroup. The rs11730582 CC, rs187116 GG, and rs1126772 G, as well as their respective combinations, were independent risk factors for unfavorable treatment outcomes. The impact of rs11730582-rs1126772 haplotypes on OS was also observed. These data suggest that OPN and CD44 germline variants may predict treatment effects in NSCLC