Multi-task learning for classification, segmentation, reconstruction, and detection on chest CT scans

Lung cancer and covid-19 have one of the highest morbidity and mortality rates in the world. For physicians, the identification of lesions is difficult in the early stages of the disease and time-consuming. Therefore, multi-task learning is an approach to extracting important features, such as lesions, from small amounts of medical data because it learns to generalize better. We propose a novel multi-task framework for classification, segmentation, reconstruction, and detection. To the best of our knowledge, we are the first ones who added detection to the multi-task solution. Additionally, we checked the possibility of using two different backbones and different loss functions in the segmentation task.Comment: presented at the Polish Conference on Artificial Intelligence (PP-RAI), 202

Data distribution analysis – a preliminary approach to quantitative data in biomedical research

Statistical analysis is an integral part of medical research. It helps transform raw data into meaningful insights, supports hypothesis testing, optimises study design, assesses risk and prognosis, and facilitates evidence-based decision-making. The statistical analysis increases research findings' reliability, validity and generalisability, ultimately advancing medical knowledge and improving patient care. Without it, meaningful analysis of the data collected would be impossible. The conclusions drawn would be unsubstantiated and misleading. Many health professionals are unfamiliar with statistical analysis and its basic concepts. The analysis of clinical data is an integral part of medical research. Identifying the data type (continuous, quasi-continuous or discrete) and detecting outliers are the first and most important steps. When analysing the data distribution for normality, graphical and numerical methods are recommended. Depending on the type of data distribution, appropriate non-parametric or parametric tests can be used for further analysis. Data that are not normally distributed can be normalised using various mathematical methods (e.g., square root or logarithm) and analysed using parametric tests in the next steps. This review provides essential explanations of these concepts without using complex mathematical or statistical equations but with several graphical examples of various statistical terms

Zmiany w składzie tkankowym obserwowane w trakcie terapii ciężkich zaburzeń funkcji tarczycy

  Introduction: Hyper- and hypothyroidism are accompanied by altered metabolic rate, thermogenesis, and body weight. The aim of this study was to estimate the relation between treatment-induced changes in thyroid function, and the accompanying body composition in patients with either severe hypo- or hyperthyroidism. Material and methods: Body composition analysis and hormonal assessment were measured at the initial diagnosis of thyroid disorder, after three-month treatment, and finally after complete recovery from hyperthyroidism (n = 18) or hypothyroidism (n = 27). Nonparametric Spearman correlation was used to analyse the relation between thyroid hormones and body composition as well as their respective changes. Results: In hypothyroid patients applied treatment significantly reduced (p < 0.05) total body weight, mainly due to a decrease in fat mass, whereas in hyperthyroid patients it caused a weight gain, with a simultaneous increase in muscle, water and fat mass. Total body weight and fat mass were significantly correlated with thyroid hormones’ concentrations in all patients. Changes of fat, water, or muscle mass were strongly correlated with the changes in the patients’ hormonal status. Conclusions: Body composition is related to the concentration of thyroid hormones in thyroid dysfunction. Treatment-induced changes in thyroid hormones concentrations are correlated with the magnitude of the change of body weight, including muscle, water, and fat amount. (Endokrynol Pol 2016; 67 (4): 359–366)    Wstęp: Zarówno nadczynność, jak i niedoczynność tarczycy charakteryzują się zaburzeniami podstawowej przemiany materii, termogenezy oraz masy ciała. Celem pracy była ocena związku między zmianami funkcji tarczycy oraz zmianami składu tkankowego ciała u pacjentów w trakcie terapii ciężkich zaburzeń funkcji tarczycy. Materiał i metody: Badanie składu ciała oraz badanie biochemicznych wykładników funkcji tarczycy przeprowadzono u 18 chorych z pełnoobjawową nadczynnością tarczycy oraz u 27 chorych z niedoczynnością tarczycy, w okresie rozpoznania choroby, po około trzech miesiącach leczenia oraz po całkowitym wyrównaniu funkcji tarczycy, w okresie eutyreozy. Ponadto przeprowadzono analizę związku między zmianami funkcji tarczycy oraz zmianami w składzie ciała przeprowadzono nieparametryczną analizę Spearmana. Wyniki: W grupie chorych leczonych z powodu niedoczynności tarczycy zaobserwowano statystycznie istotny spadek masy, głównie kosztem masy tkanki tłuszczowej (p< 0,05), podczas gdy w grupie chorych z pierwotnie rozpoznaną nadczynnością tarczycy stwierdzono istotny wzrost masy tkanki tłuszczowej, mięśniowej oraz wody całkowitej (p < 0,05). W obu grupach zaobserwowano ponadto istotne korelacje między stężeniem hormonów tarczycy a masą tkanki tłuszczowej na wszystkich etapach leczenia (p < 0,05). Jednocześnie zmiany wszystkich parametrów składu tkankowego ciała (Δ) silnie korelowały ze zmianami biochemicznych wykładników funkcji tarczycy(Δ) (p < 0,05). Wnioski: W zaburzeniach funkcji tarczycy obserwuje się silne zależności między składem tkankowym ciała a stężeniem hormonów tarczycy. Zmiany w składzie tkankowym ciała są jednocześnie silnie skorelowane ze zmianami stężenia hormonów tarczycy obserwowanymi w trakcie terapii. (Endokrynol Pol 2016; 67 (4): 359–366)

Pulmonary embolism in patients with the Coronavirus Disease 2019

Coronaviruses are RNA viruses causing infectious diseases. They had been responsible for 15% cases of a common cold before December 2019. With the new strain of coronavirus SARS CoV2 which causes COVID-19 disease, the ongoing pandemic surprised with the severity of symptoms and its course compared to the previously known mild respiratory tract infections. In the end of December 2021, over 274 million people were diagnosed with COVID-19 disease, and the total mortality amounted to nearly 5.4 million deaths in more than 200 countries. One of the potentially fatal complications of COVID-19 is pulmonary embolism (PE). It appears that PE has been associated with several coagulation abnormalities as well as with frequent significantly elevated concentration of D‑dimer's. A higher D‑dimer concentration in blood serum, in turn, has been associated with an increased risk of premature death. Moreover, inflammation, typical in the course of COVID-19, is considered a prothrombotic condition; higher interleukin 6 (Il-6) and C‑reactive protein concentrations have been found in patients with more severe forms of COVID-19. So far, none specific for COVID-19 studies have been available with regard to the diagnosis and treatment of PE. Therefore, the practical approach is based on the experience of other groups of patients. Prevention of thrombotic events seems reasonable, at least in COVID-19 patients with the risk factors of developing venous thromboembolism. Low‑molecular‑weight heparins are most commonly prescribed (e.g. enoxaparin, dalteparin). Following the confirmed definite PE diagnosis, proper anticoagulation or, if necessary, thrombolytic treatment must be introduced

Structural basis for small molecule targeting of the programmed death ligand 1 (PD-L1)

Targeting the PD-1/PD-L1 immunologic checkpoint with monoclonal antibodies has provided unprecedented results in cancer treatment in the recent years. Development of chemical inhibitors for this pathway lags the antibody development because of insufficient structural information. The first nonpeptidic chemical inhibitors that target the PD-1/PD-L1 interaction have only been recently disclosed by Bristol-Myers Squibb. Here, we show that these small-molecule compounds bind directly to PD-L1 and that they potently block PD-1 binding. Structural studies reveal a dimeric protein complex with a single small molecule which stabilizes the dimer thus occluding the PD-1 interaction surface of PD-L1s. The small-molecule interaction "hot spots" on PD-L1 surfaces suggest approaches for the PD-1/PD-L1 antagonist drug discovery