Cumulative summation test for learning curve (LC-CUSUM) in outpatient hysteroscopy

Objectives: Outpatient hysteroscopy has become an integral part of postgraduate training in gynecology. It is an operator-dependent procedure, however there are no recommendations regarding total number of performed procedures to reach proficiency. Material and methods: This study aimed to assess the learning curve (LC) using cumulative summation test for learning curve (LC-CUSUM). Results: A success rate of 97% a failure rate ≥ 10% were established to denote an adequate and an inadequate performance. A third-year trainee needed 56 procedures to reach the predefined level of performance. Conclusions: As the length of the LC for outpatient hysteroscopy seems highly variable, it is reasonable to provide tailored monitoring while training

Metabolic and hormonal effects of a combined Myo-inositol and d-chiro-inositol therapy on patients with polycystic ovary syndrome (PCOS)

Objectives: To evaluate the effects of a combined Myo-inositol (MI) and D-chiro-inositol (DCI) therapy on the hormonal and metabolic parameters of women with PCOS. Prospective clinical study. Clinical Study registration number — EUPAS25705 Material and methods: Seventy women diagnosed with PCOS according to the Rotterdam criteria were enrolled in this study. Patients received a combined therapy of one tablet that contained 550 mg of inositol (myo-inositol (MI) and D-chiro-inositol (DCI) in a ratio of 10:1) twice a day for 6 months. At each of 3 visits, the body weight, height and BMI were all recorded; and serum levels of free testosterone (fT), sex hormone-binding globulin (SHBG), luteinizing hormone (LH), follicle-stimulating hormone (FSH) and glucose with insulin during standard OGTT (75 g) were measured. Also at each visit, transvaginal ultrasonography and skin condition assessments were performed. Results: Significant body weight reduction and decreases in fT, FSH, LH and insulin levels, as well as significant increase of serum SHBG concentrations were observed. Serum glucose levels during OGTT decreased after 6 months of treatment. Also, skin conditions improved after only three months of treatment. Conclusions: Combination of MI and DCI in a ratio 10:1 seems to be efficient in improving both metabolic and hormonal parameters in patients with PCOS.

Assessment of the birth status of children born by elective caesarean section before and after 39 weeks of gestation following in vitro fertilization

The collected material presents 512 mothers with children whose pregnancies were ended by caesarean section at the Department of Obstetrics, Women's Diseases and Oncological Gynecology Central Clinical Hospital of the Ministry of Internal Affairs in Warsaw in the years 2004–2016. The study group consisted of 362 mothers in pregnancies following in vitro fertilization and 150 mothers in spontaneous pregnancy, without the use of assisted reproductive technology. For the purposes of the project, only single pregnancies ending within weeks 37 to 41 of pregnancy were selected. Planned delivery by elective cesarean section (ECS) currently takes place after the 39th week of pregnancy, in line with current common recommendations. This is related to studies finding an overall better birth condition of newborns in the general population, and especially regarding the maturation of the lungs. Currently, there are no specific recommendations regarding cesarean section and the timing of delivery in pregnancies resulting from in vitro fertilization. The aim of this study was to assess the optimal time of an elective cesarean section at full term in an IVF pregnancy. Consistent with findings in the general population and prevailing recommendations, the expected result would be the better condition of the baby born by ECS following the 39th week of gestation. However, our statistical analysis of the collected material shows that the group delivered by ECS prior to the end of 39 weeks of pregnancy may have fewer respiratory system interventions and higher Apgar scores. Nevertheless, results lack statistical significance. In conclusion these findings may indicate a need for a bigger database

Predictors of COVID-19 severity among pregnant patients

Coronavirus disease 2019 (COVID-19) was declared a pandemic and has spread around the globe, unsparingly affecting vulnerable populations. Effective prevention measures for pregnant women, who are particularly affected, include early identification of those patients at risk of developing in-hospital complications, and the continuous improvement of maternal-fetal treatment strategies to ensure the efficient use of health resources. The objective of our retrospective study was to determine which patient biomarkers on hospital admission correlate with disease severity as measured by disease course classification, the need for oxygen supplementation and higher demand for oxygen, the need for mechanical ventilation, intensive care unit admission, and length of hospital stay. Analysis of 52 PCR SARS-CoV-2 positive pregnant women revealed that the median date of hospital admission was the 30th gestational week, with dyspnoea, cough, and fever as the leading symptoms. The presence of diabetes and hypertension predisposed pregnant women to the severe course of illness. Lung involvement shown by CT scans on admission correlated with the greater clinical severity. The main laboratory predictors of disease progression were lymphocytopenia, hypocalcemia, low total cholesterol, low total protein levels, and high serum levels of C-reactive protein, ferritin, interleukin-6, glucose, lactate dehydrogenase, procalcitonin, and troponin I. Further research with a larger cohort of pregnant women is needed to determine the utility of these results for everyday practice

The Application of the Modified Prim’s Algorithm to Restore the Power System Using Renewable Energy Sources

The recent trends in the development of power systems are focused on the Self-Healing Grid technology fusing renewable energy sources. In the event of a failure of the power system, automated distribution grids should continue to supply energy to consumers. Unfortunately, there are currently a limited number of algorithms for rebuilding a power system with renewable energy sources. This problem is possible to solve by implementing restoration algorithms based on graph theory. This article presents the new modification of Prim’s algorithm, which has been adapted to operate on a power grid containing several power sources, including renewable energy sources. This solution is unique because Prim’s algorithm is ultimately dedicated to single-source graph topologies, while the proposed solution is adapted to multi-source topologies. In the algorithm, the power system is modeled by the adjacency matrices. The adjacency matrixes for the considered undirected graphs are symmetric. The novel logic is based on the original method of determining weights depending on active power, reactive power and active power losses. The developed solution was verified by performing a simulation on a test model of the distribution grid powered by a renewable energy source. The control logic concept was compared with the reference algorithms, which were chosen from the ideas representing available approaches based on graph theory present in the scientific publications. The conducted research confirmed the effectiveness and validity of the novel restoration strategy. The presented algorithm may be applied as a restoration logic dedicated to power distribution systems

The Application of the Modified Prim’s Algorithm to Restore the Power System Using Renewable Energy Sources

The recent trends in the development of power systems are focused on the Self-Healing Grid technology fusing renewable energy sources. In the event of a failure of the power system, automated distribution grids should continue to supply energy to consumers. Unfortunately, there are currently a limited number of algorithms for rebuilding a power system with renewable energy sources. This problem is possible to solve by implementing restoration algorithms based on graph theory. This article presents the new modification of Prim’s algorithm, which has been adapted to operate on a power grid containing several power sources, including renewable energy sources. This solution is unique because Prim’s algorithm is ultimately dedicated to single-source graph topologies, while the proposed solution is adapted to multi-source topologies. In the algorithm, the power system is modeled by the adjacency matrices. The adjacency matrixes for the considered undirected graphs are symmetric. The novel logic is based on the original method of determining weights depending on active power, reactive power and active power losses. The developed solution was verified by performing a simulation on a test model of the distribution grid powered by a renewable energy source. The control logic concept was compared with the reference algorithms, which were chosen from the ideas representing available approaches based on graph theory present in the scientific publications. The conducted research confirmed the effectiveness and validity of the novel restoration strategy. The presented algorithm may be applied as a restoration logic dedicated to power distribution systems

REJESTRACJA ZAKŁÓCEŃ ELEKTROENERGETYCZNYCH W SYSTEMACH ROZPROSZONYCH Z WYKORZYSTANIEM WYMIANY DANYCH W SIECIACH ETHERNET

score: 0collation: 1-

A Fuzzy OLTC Controller: Applicability in the Transition Stage of the Energy System Transformation

This paper introduces a Fuzzy Logic Controller designed for an on-load tap changer within medium voltage distribution systems with bulk penetration of Distributed Energy Resources. As the on-load tap changer remains one of the most essential forms of voltage regulation in medium voltage distribution networks, improving its operation is a cost-effective response to the emerging voltage violations caused by intermittent generation during the early stages of the energy system transformation. Software-in-the-loop simulations were conducted to validate the effectiveness of the proposed algorithm compared to the conventional methods. A modified CIGRE Medium Voltage Distribution Network Benchmark in European Configuration was modelled while the controller code developed in Python 3.12 was running on a PC, both coupled in a real-time closed-loop environment. The analyses showed that the proposed algorithm managed to reduce overvoltage from 7.02% to 4.85% in the benchmark network, thus demonstrating that the algorithm is efficient and ready for on-field implementation