Evaluation of Confounders in Toxoplasmosis Indirect Fluorescent Antibody Assay

Background: The IFA test is one of the most usual methods for detecting anti-Toxoplasma antibod­ies, although it has not any unique standardization. It seems that the microscopic judg­ment of results is an important confounder in IFA test. Therefore, we conducted the present study to clarify the role of microscopic observer, and other confounders on the test.Methods: Eighty sera were collected from patients suspicious to toxoplasmosis for detection IgG anti-T. gondii by this test. Samples were examined against different series of antigens, IgG anti-human conjugates, and observers.Results: There were no significant differences between the two series of antigens and conjugates. For the observers groups the kappa coefficient of the test results in the experts group (0.97, 0.94-1.00) were significantly higher than the less experienced observers (0.77, 0.68-0.87).Conclusion: We recommend the IFA test to be performed only in reference laboratories and by laboratory technicians that have enough experience for this test. Otherwise, we suggest the substitution of this test with other tests like ELISA for the diagnosis and epidemiological studies

The total torsion element graph of semimodules over commutative semirings

We introduce and investigate the total torsion element graph of semimodules over a commutative semiring with non-zero identity. The main purpose of this paper is to extend the definition and results given in [2] to more general semimodule case

Improved risk prediction of chemotherapy-induced neutropenia-model development and validation with real-world data

Background The existing risk prediction models for chemotherapy-induced febrile neutropenia (FN) do not necessarily apply to real-life patients in different healthcare systems and the external validation of these models are often lacking. Our study evaluates whether a machine learning-based risk prediction model could outperform the previously introduced models, especially when validated against real-world patient data from another institution not used for model training.Methods Using Turku University Hospital electronic medical records, we identified all patients who received chemotherapy for non-hematological cancer between the years 2010 and 2017 (N = 5879). An experimental surrogate endpoint was first-cycle neutropenic infection (NI), defined as grade IV neutropenia with serum C-reactive protein >10 mg/l. For predicting the risk of NI, a penalized regression model (Lasso) was developed. The model was externally validated in an independent dataset (N = 4594) from Tampere University Hospital.Results Lasso model accurately predicted NI risk with good accuracy (AUROC 0.84). In the validation cohort, the Lasso model outperformed two previously introduced, widely approved models, with AUROC 0.75. The variables selected by Lasso included granulocyte colony-stimulating factor (G-CSF) use, cancer type, pre-treatment neutrophil and thrombocyte count, intravenous treatment regimen, and the planned dose intensity. The same model predicted also FN, with AUROC 0.77, supporting the validity of NI as an endpoint.Conclusions Our study demonstrates that real-world NI risk prediction can be improved with machine learning and that every difference in patient or treatment characteristics can have a significant impact on model performance. Here we outline a novel, externally validated approach which may hold potential to facilitate more targeted use of G-CSFs in the future.</p

Investigation of Meconium Aspiration Syndrome in Newborns, after NRP Protocol Changing

Meconium is a thick green-black odorant substance, which is produced in the embryo's gut at the 12th week of gestation, and then stored in the colon throughout the pregnancy. Meconium can lead to pulmonary injury by various mechanisms, which in the presence of respiratory distress and other radiological findings in neonates born with meconium-stained amniotic fluid (MSAF), are defined as meconium aspiration syndrome (MAS). Given the frequent need for newborns to be resuscitated at birth, educated people are needed to resuscitate them. In the United States, the Neonatal Resuscitation Program (NRP) is a training guideline for newborns. The purpose of the NRP is to provide the cognitive, technical, and behavioral skills needed to resuscitate neonates after delivery.Due to the changes of NRP 6 and 7 guidelines in using PPV, Tracheal intubation and suctioning and using both guidelines in Shahid Mostafa Khomeini and Taleghani hospitals of Ilam, during 2015-2019, we decided to compare these two methods in terms of infant mortality and morbidity over the mentioned years. In this study, we aimed to determine the Meconium Aspiration Syndrome in neonates, born between the years 2015 and 2019, in Shahid Mostafa Khomeini and Taleghani Hospitals of Ilam, before and after the NRP protocol change

Comparison of Marital Satisfaction of Nurse Couples and Those Whose Spouse is not a Nurse and Predicting Factors that Determine their Marital Satisfaction

Context: The nature of the nursing profession is one of the effective factors in the marital satisfaction of nurses. Objectives: This study aimed to examine the level of marital satisfaction in nurse couples in comparison to those whose spouse is not a nurse and predict factors that determine their marital satisfaction. Methods: Following a cross-sectional design, a total of 252 nurses working in educational hospitals in western and northwestern cities of Iran were recruited for this study. Participants were selected using the convenience sampling method. Data were collected using a two-part questionnaire: (1) items related to socio-demographic characteristics; and (2) items related to ENRICH Marital Satisfaction (EMS) Scale. Data were analyzed using SPSS v 21.0. Results: The mean (SD) age of participants was 32.4 (6.39) years. Marital satisfaction was higher among employed nurse couples, those with rotating shifts, those with a lower number of night-work shifts per month, those with personal housing, and those whose spouse was a nurse. Also, a significant association was found between income level and marital satisfaction (P = 0.002, F = 6.67). Conclusions: According to the findings, nurse couples had higher marital satisfaction in comparison to those whose spouse was not a nurse. Nurses reported their marital satisfaction as moderate. Paying attention to the livelihood conditions of nurses, providing more flexibility, and giving nurses the right to choose to set a monthly work schedule can improve their marital satisfaction. © 2022, Author(s)

Safety and efficacy of cryoballoon ablation for the treatment of atrial fibrillation in elderly patients

PubMed ID: 30187178Background: Catheter ablation (CA) is an established therapy for treatment of atrial fibrillation (AF). However, data about AF ablation using the cryoballoon (CB) in the elderly population are sparse. The aim of this single center retrospective study is to evaluate the safety and efficacy of CB ablation in patients ? 75 years compared to patients < 75 years. Methods and results: Fifty-five consecutive patients aged ? 75 years (elderly group) were compared with 183 patients aged < 75 years (control group). All patients underwent pulmonary vein isolation (PVI) using the second-generation CB. The mean age in the elderly group was 78 ± 2.8 years and 60.8 ± 9.5 in the control group (p < 0.001). During 11.8 ± 5.4 months of follow-up, single procedure success rate for the elderly and the control group was 72.8 and 76%, respectively (p = 0.37). During redo ablation (n = 40), low-voltage areas in the LA were more frequently observed in elderly patients compared to the control group [1.0 (IQR 0–2.0) segments vs 2.0 (IQR 2.0–3.0) segments, respectively, p = 0.03]. The most common complication was transient phrenic nerve palsy, which only occurred in patients < 75 years (0 vs 7, p = 0.33). No severe complication such as procedure-related deaths, atrio-esophageal fistula, or cerebrovascular embolic events occurred. Conclusions: Our data strengthen the value of CB ablation for the treatment of AF as an effective and safe procedure in elderly patients, with similar success and complication rates when compared with a younger population. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.Conflict of interest K. Yalin received research and educational grant from Turkish Society of Cardiology. E. Lyan received travel grants and Speaker’s Bureau Honoraria from BiosenseWebster, Medtronic, Boston Scientific. R. Tilz received travel grants from St. Jude Medical, Topera, BiosenseWebster, Daiichi Sankyo, Sentrheart and Speaker’s Bureau Honoraria from BiosenseWebster, Biotronik, Pfizer, Topera, Bristol-Myers Squibb; Bayer, Sanofi Aventis. Christian Heeger received travel grants and research grants by Medtronic, Claret Medical and SentreHeart. All other authors have no disclosures

Improved risk prediction of chemotherapy-induced neutropenia : model development and validation with real-world data

Background: The existing risk prediction models for chemotherapy-induced febrile neutropenia (FN) do not necessarily apply to real-life patients in different healthcare systems and the external validation of these models are often lacking. Our study evaluates whether a machine learning-based risk prediction model could outperform the previously introduced models, especially when validated against real-world patient data from another institution not used for model training. Methods: Using Turku University Hospital electronic medical records, we identified all patients who received chemotherapy for non-hematological cancer between the years 2010 and 2017 (N = 5879). An experimental surrogate endpoint was first-cycle neutropenic infection (NI), defined as grade IV neutropenia with serum C-reactive protein >10 mg/l. For predicting the risk of NI, a penalized regression model (Lasso) was developed. The model was externally validated in an independent dataset (N = 4594) from Tampere University Hospital. Results: Lasso model accurately predicted NI risk with good accuracy (AUROC 0.84). In the validation cohort, the Lasso model outperformed two previously introduced, widely approved models, with AUROC 0.75. The variables selected by Lasso included granulocyte colony-stimulating factor (G-CSF) use, cancer type, pre-treatment neutrophil and thrombocyte count, intravenous treatment regimen, and the planned dose intensity. The same model predicted also FN, with AUROC 0.77, supporting the validity of NI as an endpoint. Conclusions: Our study demonstrates that real-world NI risk prediction can be improved with machine learning and that every difference in patient or treatment characteristics can have a significant impact on model performance. Here we outline a novel, externally validated approach which may hold potential to facilitate more targeted use of G-CSFs in the future.publishedVersionPeer reviewe