As regards their morphology and biology, tumours consist of heterogeneous cell populations. The cancer stem cell (CSC) hypothesis assumes that a tumour is hierarchically organized and not all of the cells are equally capable of generating descendants, similarly to normal tissue. The only cells being able to self-renew and produce a heterogeneous tumour cell population are cancer stem cells. CSCs probably derive from normal stem cells, although progenitor cells may be taken into consideration as the source of cancer stem cells. CSCs reside in the niche defined as the microenvironment formed by stromal cells, vasculature and extracellular matrix. The CSC assays include FACS sorting, xenotransplantation to immunodeficient mice (SCID), incubation with Hoechst 33342 dye, cell culture in non-adherent conditions, cell culture with bromodeoxyuridine. CSCs have certain properties that make them resistant to anticancer therapy, which suggests they may be the target for potential therapeutic strategies.

The proliferation index (PI) is crucial in histopathologic diagnostics, in particular tumors. It is calculated based on Ki-67 protein expression by immunohistochemistry. PI is routinely evaluated by a visual assessment of the sample by a pathologist. However, this approach is far from ideal due to its poor intra- and interobserver variability and time-consuming. These factors force the community to seek out more precise solutions. Virtual pathology as being increasingly popular in diagnostics, armed with artificial intelligence, may potentially address this issue. The proposed solution calculates the Ki-67 proliferation index by utilizing a deep learning model and fuzzy-set interpretations for hot-spots detection. The obtained region-of-interest is then used to segment relevant cells via classical methods of image processing. The index value is approximated by relating the total surface area occupied by immunopositive cells to the total surface area of relevant cells. The achieved results are compared to the manual calculation of the Ki-67 index made by a domain expert. To increase results reliability, we trained several models in a threefold manner and compared the impact of different hyper-parameters. Our best-proposed method estimates PI with 0.024 mean absolute error, which gives a significant advantage over the current state-of-the-art solution.

The SARS-CoV-2 pandemic is a worldwide challenge. The CRIT-CoV-U pilot study generated a urinary proteomic biomarker consisting of 50 peptides (COV50), which predicted death and disease progression from SARS-CoV-2. After the interim analysis presented for the German Government, here, we aimed to analyse the full dataset to consolidate the findings and propose potential clinical applications of this biomarker. CRIT-CoV-U was a prospective multicentre cohort study. In eight European countries (Austria, France, Germany, Greece, North Macedonia, Poland, Spain, and Sweden), 1012 adults with PCR-confirmed COVID-19 were followed up for death and progression along the 8-point WHO scale. Capillary electrophoresis coupled with mass spectrometry was used for urinary proteomic profiling. Statistical methods included logistic regression and receiver operating characteristic curve analysis with a comparison of the area under curve (AUC) between nested models. Hospitalisation costs were derived from the care facility corresponding with the Markov chain probability of reaching WHO scores ranging from 3 to 8 and flat-rate hospitalisation costs adjusted for the gross per capita domestic product of each country. From June 30 to Nov 19, 2020, 228 participants were recruited, and from April 30, 2020, to April 14, 2021, 784 participants were recruited, resulting in a total of 1012 participants. The entry WHO scores were 1–3 in 445 (44%) participants, 4–5 in 529 (52%) participants, and 6 in 38 (4%) participants; and of all participants, 119 died and 271 had disease progression. The odds ratio (OR) associated with COV50 in all 1012 participants for death was 2·44 (95% CI 2·05–2·92) unadjusted and 1·67 (1·34–2·07) when adjusted for sex, age, BMI, comorbidities, and baseline WHO score; and for disease progression, the OR was 1·79 (1·60–2·01) when unadjusted and 1·63 (1·41–1·91) when adjusted (p<0·0001 for all). The predictive accuracy of the optimised COV50 thresholds was 74·4% (71·6–77·1%) for mortality (threshold 0·47) and 67·4% (64·4–70·3%) for disease progression (threshold 0·04). When adjusted for covariables and the baseline WHO score, these thresholds improved AUCs from 0·835 to 0·853 (p=0·033) for death and from 0·697 to 0·730 (p=0·0008) for progression. Of 196 participants who received ambulatory care, 194 (99%) did not reach the 0·04 threshold. The cost reductions associated with 1 day less hospitalisation per 1000 participants were million Euro (M€) 0·887 (5–95% percentile interval 0·730–1·039) in participants at a low risk (COV50 <0·04) and M€2·098 (1·839-2·365) in participants at a high risk (COV50 ≥0·04). The urinary proteomic COV50 marker might be predictive of adverse COVID-19 outcomes. Even in people with mild-to-moderate PCR-confirmed infections (WHO scores 1–4), the 0·04 COV50 threshold justifies earlier drug treatment, thereby potentially reducing the number of days in hospital and associated costs. German Federal Ministry of Health.

Context: Breast cancer is the most common malignancy in females. It is routinely classified according to the WHO histological typing. However, there is also a molecular classification of breast cancer which is routinely substituted with surrogate subtypes based on expression of estrogen, progesterone, and human epidermal growth factor receptor 2 receptors and proliferation index (PI). PI is defined as the percentage of Ki-67-positive cells among overall cell population. The method commonly applied by pathologists to determine PI is visual scoring of the sample. Strict recommendations for PI assessment do not exist. Thus, the mode of PI evaluation differs significantly between pathologists. Aims: The aim of our study was to evaluate the daily approach to defining the PI. Settings and Design: Four practicing nonscholar pathologists were asked to evaluate PI in cases of invasive breast carcinoma. Subjects and Methods: The study was performed on a group of 98 patients diagnosed with invasive breast carcinoma. Immunohistochemical reaction was performed with monoclonal antibody against human Ki-67 antigen using Ventana BenchMark XT. Statistical Analysis Used: Results were compared using Pearson's and Spearman's rank correlation coefficients and Fleiss and Cohen's kappa values. Results: Statistical analysis showed pairwise Pearson's coefficients ranging between 0.77 and 0.84 (P < 0.001) and Spearman's rank correlation coefficients ranging between 0.68 and 0.83 (P < 0.001). The Fleiss kappa value for the 14% cutoff point was 0.58 whereas for the 20% cutoff point was 0.60. The pairwise Cohen's kappa values ranged from 0.45 to 0.69 for the 14% cutoff point and 0.53 to 0.67 for the 20% cutoff point. Friedman's rank ANOVA test showed significant differences among the four pathologists (P < 0.001). Conclusions: Our study shows a significant difference in results and methods of evaluation of PI between pathologists.

This article proposes a novel concept for a two-step Ki-67/lymphocytes classification cell detection pipeline on Ki-67 stained histopathological slides utilizing commonly available and undedicated, in terms of the medical problem considered, deep learning models. Models used vary in implementation, complexity, and applications, allowing for the use of a dedicated architecture depending on the physician’s needs. Moreover, generic models’ performance was compared with the problem-dedicated one. Experiments highlight that with relatively small training datasets, commonly used architectures for instance segmentation and object detection are competitive with a dedicated model. To ensure generalization and minimize biased sampling, experiments were performed on data derived from two unrelated histopathology laboratories.

The aim of the experiment was to determine if possible changes in connective tissue induced by massage could have a positive effect justifing the use of massage in all post-traumatic connective tissue conditions, e.g. tendon injuries. The investigations were performed in a group of 18 Buffalo rats. The rats were divided into two groups (experimental and control). To standardize the massage procedure, it was performed with an algometer probe of 0.5 cm2 with constant pressure force of 1 kG (9,81 N). To analyse the number and diameter of collagen fibrils, two electron micrographs were performed for each rat of the collected segments of tendons of rat tail lateral extensor muscle. After image digitalization and calibration, the measurements were carried out using iTEM 5.0 software. The number of fibrils, their diameter and area were measured in a cross-sectional area. An increase of the number of collagen fibrils was observed in the tendons of massaged animals compared to the control group. Our study demonstrated that massage may cause a beneficial effect on metabolic activity of tendon’s fibroblasts and, in consequence, may be applied for more effective use of massage for the prevention of tendon injury as well as after the injury has occurred. (Folia Histochemica et Cytobiologica 2013, Vol. 51, No. 1, 103–106) The aim of the experiment was to determine if possible changes in connective tissue induced by massage could have a positive effect justifing the use of massage in all post-traumatic connective tissue conditions, e.g. tendon injuries. The investigations were performed in a group of 18 Buffalo rats. The rats were divided into two groups (experimental and control). To standardize the massage procedure, it was performed with an algometer probe of 0.5 cm2 with constant pressure force of 1 kG (9,81 N). To analyse the number and diameter of collagen fibrils, two electron micrographs were performed for each rat of the collected segments of tendons of rat tail lateral extensor muscle. After image digitalization and calibration, the measurements were carried out using iTEM 5.0 software. The number of fibrils, their diameter and area were measured in a cross-sectional area. An increase of the number of collagen fibrils was observed in the tendons of massaged animals compared to the control group. Our study demonstrated that massage may cause a beneficial effect on metabolic activity of tendon’s fibroblasts and, in consequence, may be applied for more effective use of massage for the prevention of tendon injury as well as after the injury has occurred. (Folia Histochemica et Cytobiologica 2013, Vol. 51, No. 1, 103–106)