Combined analytical approach empowers precise spectroscopic interpretation of subcellular components of pancreatic cancer cells

The lack of specific and sensitive early diagnostic options for pancreatic cancer (PC) results in patients being largely diagnosed with late-stage disease, thus inoperable and burdened with high mortality. Molecular spectroscopic methodologies, such as Raman or infrared spectroscopies, show promise in becoming a leader in screening for early-stage cancer diseases, including PC. However, should such technology be introduced, the identification of differentiating spectral features between various cancer types is required. This would not be possible without the precise extraction of spectra without the contamination by necrosis, inflammation, desmoplasia, or extracellular fluids such as mucous that surround tumor cells. Moreover, an efficient methodology for their interpretation has not been well defined. In this study, we compared different methods of spectral analysis to find the best for investigating the biomolecular composition of PC cells cytoplasm and nuclei separately. Sixteen PC tissue samples of main PC subtypes (ductal adenocarcinoma, intraductal papillary mucinous carcinoma, and ampulla of Vater carcinoma) were collected with Raman hyperspectral mapping, resulting in 191,355 Raman spectra and analyzed with comparative methodologies, specifically, hierarchical cluster analysis, non-negative matrix factorization, T-distributed stochastic neighbor embedding, principal components analysis (PCA), and convolutional neural networks (CNN). As a result, we propose an innovative approach to spectra classification by CNN, combined with PCA for molecular characterization. The CNN-based spectra classification achieved over 98% successful validation rate. Subsequent analyses of spectral features revealed differences among PC subtypes and between the cytoplasm and nuclei of their cells. Our study establishes an optimal methodology for cancer tissue spectral data classification and interpretation that allows precise and cognitive studies of cancer cells and their subcellular components, without mixing the results with cancer-surrounding tissue. As a proof of concept, we describe findings that add to the spectroscopic understanding of PC

Variabilities in global DNA methylation and β\beta-sheet richness establish spectroscopic landscapes among subtypes of pancreatic cancer

Purpose: Knowledge about pancreatic cancer (PC) biology has been growing rapidly in recent decades. Nevertheless, the survival of PC patients has not greatly improved. The development of a novel methodology suitable for deep investigation of the nature of PC tumors is of great importance. Molecular imaging techniques, such as Fourier transform infrared (FTIR) spectroscopy and Raman hyperspectral mapping (RHM) combined with advanced multivariate data analysis, were useful in studying the biochemical composition of PC tissue. Methods: Here, we evaluated the potential of molecular imaging in differentiating three groups of PC tumors, which originate from different precursor lesions. Specifically, we comprehensively investigated adenocarcinomas (ACs): conventional ductal AC, intraductal papillary mucinous carcinoma, and ampulla of Vater AC. FTIR microspectroscopy and RHM maps of 24 PC tissue slides were obtained, and comprehensive advanced statistical analyses, such as hierarchical clustering and nonnegative matrix factorization, were performed on a total of 211,355 Raman spectra. Additionally, we employed deep learning technology for the same task of PC subtyping to enable automation. The so-called convolutional neural network (CNN) was trained to recognize spectra specific to each PC group and then employed to generate CNN-prediction-based tissue maps. To identify the DNA methylation spectral markers, we used differently methylated, isolated DNA and compared the observed spectral differences with the results obtained from cellular nuclei regions of PC tissues. Results: The results showed significant differences among cancer tissues of the studied PC groups. The main findings are the varying content of β-sheet-rich proteins within the PC cells and alterations in the relative DNA methylation level. Our CNN model efficiently differentiated PC groups with 94% accuracy. The usage of CNN in the classification task did not require Raman spectral data preprocessing and eliminated the need for extensive knowledge of statistical methodologies. Conclusions: Molecular spectroscopy combined with CNN technology is a powerful tool for PC detection and subtyping. The molecular fingerprint of DNA methylation and β-sheet cytoplasmic proteins established by our results is different for the main PC groups and allowed the subtyping of pancreatic tumors, which can improve patient management and increase their survival. Our observations are of key importance in understanding the variability of PC and allow translation of the methodology into clinical practice by utilizing liquid biopsy testing

The analysis of goosefoot pollen count in selected Polish cities in 2009

W pracy przedstawiono przebieg sezonu pylenia komosy w 2009 roku. Badania prowadzono w Białymstoku, Bydgoszczy, Krakowie, Lublinie, Łodzi, Olsztynie, Sosnowcu, Szczecinie, Warszawie i we Wrocławiu, z zastosowaniem metody wolumetrycznej, przy użyciu aparatów typu Burkard i Lanzoni. Najwyższe stężenia pyłku komosy zanotowano w Łodzi 27 sierpnia (68 z/m3) oraz w Olsztynie w dniu 9 sierpnia (16 ziaren/m3).This paper presents the course of goosefoot pollen season in selected cites of Poland in 2009. The measurements were performed in Białystok, Bydgoszcz, Kraków, Lublin, Łódź, Olsztyn, Sosnowiec, Szczecin, Warszawa and Wrocław, use of volumetric method with Burkard and Lanzoni Spore Trap. The highest daily pollen count, that reached the level of 68 goosefoot pollen grains/m3, was recorded in Łódź on the 27 of August and the level of 16 goosefoot pollen grains/m3, was recorded in Olsztyn on the 09 of August

The analysis of hornbeam (Carpinus) pollen count in selected Polish cities in 2007

Praca przedstawia przebieg sezonu pylenia grabu w 2007 r. w Sosnowcu, Białymstoku, Krakowie, Lublinie, Łodzi, Olsztynie, Warszawie, we Wrocławiu i w Szczecinie. Badania prowadzono metodą objętościową przy zastosowaniu aparatów typu Burkard i Lanzoni. Sezon pyłkowy wyznaczono metodą 98%. Najwcześniej pyłek grabu zarejestrowano w Lublinie (24 marca), a we Wrocławiu, w Warszawie, Krakowie i Szczecinie w ciągu kolejnych trzech dni. Najwyższe wartości stężeń średniodobowych zanotowano w Lublinie 19 kwietnia (133 z/m3), najniższe w Łodzi (29 z/m3).The article presents a record of the 2007 hornbeam pollination season in Sosnowiec, Białystok, Kraków, Łódź, Olsztyn, Warszawa, Wrocław and Szczecin. The research was carried out by means of the volumetric method with the use of Burkard and Lanzoni devices. The pollen season was determined by means of the 98 % method. Hornbeam pollen was earliest recorded in Lublin (24 March), it was recorded in Wrocław, Warszawa, Kraków and Szczecin during the next three days and latest in Łódź – on 4 April. The highest concentration values were recorded in Lublin on 19 April (133 grains/m3) and the lowest in Łódź (29 grains/m3)

The analysis of alder pollen count in Poland in 2012

W pracy przeanalizowano przebieg sezonu pyłkowego olszy w Białymstoku, Bydgoszczy, Drawsku Pomorskim, Lublinie, Łodzi, Krakowie, Olsztynie, Piotrkowie Trybunalskim, Sosnowcu, Szczecinie, Warszawie, Wrocławiu i Zielonej Górze w 2012 r. Badania prowadzono metodą objętościową przy wykorzystaniu aparatu typu Burkard i Lanzoni. Początek i koniec sezonu pyłkowego wyznaczono metodą 95% rocznej sumy ziaren pyłku. Początek sezonu pyłkowego olszy w Sosnowcu w 2012 r. nastąpił 26 lutego, w Drawsku Pomorskim, Krakowie, Piotrkowie Trybunalskim, Warszawie, Wrocławiu i Zielonej Górze miał miejsce w pierwszej dekadzie marca, a w Białymstoku, Bydgoszczy, Lublinie i Olsztynie w drugiej dekadzie marca. Najwyższe dobowe stężenie (1344 ziarna w 1 m3 powietrza) stwierdzono 28 marca w Białymstoku. Indeks SPI obliczony jako suma średnich dobowych stężeń ziaren pyłku w danym sezonie był najwyższy Lublinie (4449), Białymstoku (4401) i we Wrocławiu (4150).The paper presents the course of alder pollination season in Bialystok, Bydgoszcz, Drawsko Pomorskie, Krakow, Lublin, Lodz, Olsztyn, Piotrkow Trybunalski, Sosnowiec, Szczecin, Warsaw, Wroclaw and Zielona Gora in 2012. The research was conducted by means of the volumetric method using a Burkard and Lanzoni-type spore trap. The start and end of pollen season was determined by means of the 95% method. Pollen season of alder in Sosnowiec started in 2012 on February 26. The beginning of pollination season of alder took place in the first decade of March in Drawsko Pomorskie, Krakow, Piotrkow Trybunalski, Warsaw, Wroclaw, Zielona Gora and in the second decade of March in Bialystok, Bydgoszcz, Lublin and Olsztyn. The highest daily concentration reaching 132 grains per m2 was recorded on March 21. Seasonal pollen index (SPI), estimated as annual sum of daily average pollen concentration, was the highest in Lublin (4449), Bialystok (4401) and Wroclaw (4150)

The analysis of hazel pollen count in Poland in 2012

W pracy przedstawiono analizę sezonu pylenia leszczyny w 2012 r. w Białymstoku, Bydgoszczy, Drawsku Pomorskim, Krakowie, Lublinie, Łodzi, Olsztynie, Piotrkowie Trybunalskim, Sosnowcu, Szczecinie, Warszawie, Wrocławiu i Zielonej Górze. Sezon pylenia leszczyny w 2012 r. w Bydgoszczy, Drawsku Pomorskim, Piotrkowie Trybunalskim, Warszawie, Wrocławiu i Zielonej Górze rozpoczął się w drugiej i trzeciej dekadzie stycznia, a w Białymstoku, Krakowie, Lublinie i Sosnowcu w pierwszej dekadzie marca. Różnice w poziomie stężenia pyłku leszczyny między poszczególnymi latami spowodowane są przede wszystkim oddziaływaniem czynników pogodowych.The paper presents the course of hazel pollen season in Bialystok, Bydgoszcz, Drawsko Pomorskie, Krakow, Lublin, Lodz, Olsztyn, Piotrkow Trybunalski, Sosnowiec, Szczecin, Warsaw, Wroclaw and Zielona Gora in year 2011. The beginning of pollination season of hazel started at the second and the third decade of January in Bydgoszcz, Drawsko Pomorskie, Piotrkow Trybunalski, Warsaw, Wroclaw, Zielona Gora and in the first decade of March in Bialystok, Krakow, Lublin, Sosnowiec. The differences between level of hazel pollen grains in the air in individual year are caused by meteorological factors

The analysis of mugwort pollen count in selected Polish cities in 2011

W pracy przedstawiono przebieg sezonu pylenia bylicy w 2011 r. Badania prowadzono w Białymstoku, Bydgoszczy, Krakowie, Sosnowcu, Łodzi, Lublinie, Szczecinie, Warszawie i we Wrocławiu. Zastosowano metodę wolumetryczną z wykorzystaniem aparatów typu Burkard i Lanzoni. Najwyższe średniodobowe stężenie pyłku bylicy, wynoszące 211 ziaren/m3, zanotowano w Szczecinie 4 sierpnia, w Białymstoku zaś odnotowano 143 ziarna/m3 15 sierpnia.This paper presents the course of mugwort pollen season in selected cities of Poland in 2011. The measurements were performed in Bialystok, Bydgoszcz, Krakow, Sosnowiec, Lodz, Lublin, Szczecin, Warszawa and Wroclaw, with the use of volumetric method with Burkard and Lanzoni Spore Trap. The highest daily pollen count, that reached the level of 211 mugwort pollen grains/m3, was recorded in Lodz on the 04 of August, while the level of 143 mugwort pollen grains/m3 was recorded in Bialystok on the 15 of August

The analysis of birch pollen count in selected Polish cities in 2012

Celem pracy była analiza przebiegu sezonu pylenia brzozy w 2012 r. w Białymstoku, Bydgoszczy, Drawsku Pomorskim, Krakowie, Lublinie, Łodzi, Olsztynie, Opolu, Piotrkowie Trybunalskim, Sosnowcu, Szczecinie, Warszawie, Wrocławiu i Zielonej Górze. W badaniach wykorzystano aparaty pomiarowe firmy Burkard i Lanzoni. Długość sezonu pyłkowego wyznaczono metodą 95%. Najwyższe dobowe stężenie ziaren pyłku brzozy zanotowano w Piotrkowie Trybunalskim 28 kwietnia (7986 z/m3), następne w kolejności (7200 z/m3) zanotowano 22 kwietnia w Łodzi. W większości miast zarejestrowano znaczną liczbę dni ze stężeniem przekraczającym stężenie progowe dla brzozy, tj. 75 z/m3 (14–29 dni).In the present study, birch pollen season patterns in Poland in 2012 have been compared. Airborne pollen counts were made in Białystok, Bydgoszcz, Drawsko Pomorskie, Kraków, Lublin, Łódź, Olsztyn, Opole, Piotrków Trybunalski, Sosnowiec, Szczecin, Warsaw, Wrocław and Zielona Góra. The investigations were performed using the volumetric method as well as the Burkard and Lanzoni traps. The highest diurnal birch pollen count was recorded in Warsaw in 28 April (7986 grains/m3) in Piotrkow Trybunalski and in 22 April in Lodz (7200 grains/m3). In all the cities, a large number of days was recorded with a concentration exceeding the threshold concentration for birch (14–29 days)

Teoretyczne i praktyczne aspekty współczesnej edukacji

Praca recenzowana / peer-reviewed pape

On the search for the right definition of heart failure with preserved ejection fraction

The definition of heart failure with preserved ejection fraction (HFpEF) has evolved from a clinically based “diagnosis of exclusion” to definitions focused on objective evidence of diastolic dysfunction and/or elevated left ventricular filling pressures. Despite advances in our understanding of HFpEF pathophysiology and the development of more sophisticated imaging modalities, the diagnosis of HFpEF remains challenging, especially in the chronic setting, given that symptoms are provoked by exertion and diagnostic evaluation is largely conducted at rest. Invasive hemodynamic study, and in particular — invasive exercise testing, is considered the reference method for HFpEF diagnosis. However, its use is limited as opposed to the high number of patients with suspected HFpEF. Thus, diagnostic criteria for HFpEF should be principally based on non-invasive measurements. As no single non-invasive variable can adequately corroborate or refute the diagnosis, different combinations of clinical, echocardiographic, and/or biochemical parameters have been introduced. Recent years have brought an abundance of HFpEF definitions. Here, we present and compare four of them: 1) the 2016 European Society of Cardiology criteria for HFpEF; 2) the 2016 echocardiographic algorithm for diagnosing diastolic dysfunction; 3) the 2018 evidence-based H2FPEF score; and 4) the most recent, 2019 Heart Failure Association HFA-PEFF algorithm. These definitions vary in their approach to diagnosis, as well as sensitivity and specificity. Further studies to validate and compare the diagnostic accuracy of HFpEF definitions are warranted. Nevertheless, it seems that the best HFpEF definition would originate from a randomized clinical trial showing a favorable effect of an intervention on prognosis in HFpEF