Although an essential step, cell functional annotation often proves particularly challenging from single-cell transcriptional data. Several methods have been developed to accomplish this task. However, in most cases, these rely on techniques initially developed for bulk RNA sequencing or simply make use of marker genes identified from cell clustering followed by supervised annotation. To overcome these limitations and automatize the process, we have developed two novel methods, the single-cell gene set enrichment analysis (scGSEA) and the single-cell mapper (scMAP). scGSEA combines latent data representations and gene set enrichment scores to detect coordinated gene activity at single-cell resolution. scMAP uses transfer learning techniques to re-purpose and contextualize new cells into a reference cell atlas. Using both simulated and real datasets, we show that scGSEA effectively recapitulates recurrent patterns of pathways’ activity shared by cells from different experimental conditions. At the same time, we show that scMAP can reliably map and contextualize new single-cell profiles on a breast cancer atlas we recently released. Both tools are provided in an effective and straightforward workflow providing a framework to determine cell function and significantly improve annotation and interpretation of scRNA-seq data.
Although many primary triple negative breast cancers (TNBCs) have enhanced expression of epidermal growth factor receptor (EGFR), EGFR-targeted therapies have shown only variable and unpredictable clinical responses in TNBCs. Here we integrate cellular barcoding and single-cell transcriptomics methods to comprehensively characterize the subclonal dynamics of adaptation of TNBC cells in response to afatinib, tyrosine kinase inhibitor (TKI) that irreversibly inhibits EGFR. Integrated lineage tracing analysis on these cells uncovered a pre-existing subpopulation of cells composed of 192 clones (out of the initial 2,336) with distinct biological features, such as elevated IGFBP2 (Insulin-Like Growth Factor Binding Protein 2) expression levels. We demonstrate that IGFBP2 overexpression is sufficient to make TNBC cells tolerant to afatinib treatment by activating the compensatory IGF1-R signalling pathway. Finally, by using deep learning techniques, we devise an algorithm to predict the afatinib sensitivity of TNBC cells from the transcriptional status of identified marker genes of the afatinib response. Our findings provide a new understanding of EGFR-dependent hierarchy in TNBC.
Demand for automated processes in the manufacturing industry is now shifting toward flexible, human-centered systems that combine productivity and high product quality, thus combining the advantages of automated and robotic systems with the high-value-added skills of operators and craftsmen. This trend is even more crucial for small and medium-sized enterprises operating in the “Made in Italy” fashion industry. The paper presents the study, simulation, and preliminary testing of a collaborative robotic system for shoe polishing that can reduce manual labor by limiting it to the finishing stage of the process, where the aesthetic result is fully achieved, with a benefit also in terms of ergonomics for the operator. The influence of process parameters and design solutions are discussed by presenting preliminary test results and providing hints for future developments.
BACKGROUND AND AIMS Chronic kidney disease (CKD) represents the major postoperative long-term complication in patients who undergo radical nephrectomy (RN) for the presence of a renal mass. The development of a mild to severe grade of CKD can dramatically change the lifespan of this category of patients who can experience not only an augmented rate of cardiovascular diseases but also cancer recurrence and oncological therapies based on nephrotoxic agents. Therefore, preventive strategies for reducing the risk of CKD are strictly required. Among them, one of the most important tools is represented by the correct assessment of renal function. In fact, especially in the oncological scenario, there are various causes of factitious elevation or reduction of serum creatinine due to the frequent modification of body composition, measurement interference with the assay and altered tubular secretion. Nevertheless, the use of estimated GFR based on serum creatinine levels remains nowadays the most common method to define renal function in RN patients, both in the oncological and in the urological universe. The aim of this study was to determine the extent of the error of eGFR compared to the mGFR in a consecutive prospective cohort of RN patients who remained with a solitary kidney. METHOD A total consecutive cohort of 115 RN patients enrolled in a single tertiary institution between 2018 and 2021 was collected in order to compare the most common eGFR formulas used by physicians (Cockroft-Gault, MDRD, CKD-EPI based on serum creatinine and/or serum cystatin and the new eGFR equation based on creatinine and cystatin without race adjustment) with the most widespread mGFR method (Iohexol Plasma Clearance). The mGFR technique together with the serum creatinine/cystatin measurement was performed after 12 months from the operation in order to consider a steady state for the chronic renal function. All clinical variables were reported for each pt. CKD classification was defined accorded to K-DIGO 2012 guidelines. The agreement between eGFR and mGFR was evaluated using bias (as median of difference), precision (as interquartile range of difference-IQR), accuracy (as P30) and total deviation index (TDI). The differences between cohorts were evaluated with Fisher's exact test and Chi-squared test for ordinal characteristics and Wilcoxon rank sum test for continuous variables. Data analysis was performed using programming language R and Python. RESULTS Clinical data were as follows: median age 66 (IQR 27.66), M/F ratio 4.48, median BMI 24.6 (IQR: 0.003, 24.6). 51.3% of patients had hypertension, 10.4% were diabetics. The median creatinine level in the overall population was 1.49 mg/dL (IQR: 0.8, 1.49), the median cystatin level was 1.33 mg/dL (IQR: 0.55, 1.33). Based on iohexol plasma clearance, 0.87% patients were classified in CKD in stage 1, 17.39% in stage 2, 39.13% in stage 3a, 25.22% in stage 3b, 16.52% in stage 4 and 0.87% in stage 5. Surprisingly, a non-negligible error was reported in each CKD class with a huge discrepancy between the eGFR formulas and the gold standard method (Figures 1 and 2), suggesting the pivotal role of mGFR in the clinical decision-making algorithm for RN patients. CONCLUSION In daily clinical practice, an appropriate nephrological tailored follow-up based on mGFR is mandatory for RN patients with a solitary kidney. In fact, a simple renal evaluation using eGFR can only increase the risk of clinical errors, sometimes underestimating and sometimes overestimating the real renal function with important medical consequences.
BACKGROUND AND AIMS In daily clinical practice, an accurate assessment of renal function is of paramount importance in several categories of patients where the adequate medical or surgical treatment depends on the values of glomerular filtration rate (GFR). In particular, both oncological and urological patients require a personalized medical approach able to avoid misleading errors which could dramatically shorten their lifespan. Unfortunately, the most used method to measure GFR in these groups of patients is represented by the estimated glomerular filtration rate (eGFR), which harbours a significant error in comparison to gold standards methods (mGFR). The aim of this study was to determine the extent of the error of eGFR compared to the mGFR in a consecutive prospective cohort of oncological patients affected by urological malignancies. METHOD A total consecutive cohort of 352 patients enrolled in a single tertiary institution between 2018 and 2021 was collected in order to compare the most common eGFR formulas used by physicians (Cockroft-Gault, MDRD, CKD-EPI based on serum creatinine and/or serum cystatin and the new eGFR equation based on creatinine and cystatin without race adjustment) with the most widespread mGFR method (Iohexol Plasma Clearance). The study cohort was composed by 188 oncological patients affected by different types of urological malignancies (cases) before surgical operation and/or medical treatment and 164 non-oncological patients (controls) matched for baseline clinical variables and GFR. The agreement between eGFR and mGFR was evaluated using bias (as median of difference), precision (as interquartile range of difference—IQR) accuracy (as P30) and total deviation index (TDI). The differences between cohorts were evaluated with Fisher's exact test and Chi-squared test for ordinal characteristics and Wilcoxon rank sum test for continuous variables. Data analysis was performed using programming language R and Python. RESULTS Clinical data were as follows: median age 68 (IQR: 20.68), M/F ratio 3.19, median BMI 24.9 (IQR: 0.003, 24.938). A total of 61.2% of patients had hypertension, and 14.3% were diabetics. The median creatinine value in the overall population was 1.48 mg/dL (IQR: 0.45, 1.48); the median cystatin value was 1.26 mg/dL (IQR: 0.42, 1.26). Based on iohexol plasma clearance, 3.13% of patients were classified in CKD stage 1, 23.30% in stage 2, 29.55% in stage 3a, 27.27% in stage 3b, 14.77% in stage 4 and 1.99% in stage 5. The two matched cohorts of oncological and non-oncological patients displayed no statistical differences in terms of clinical variables and agreement parameters (TDI, CCC and P30). Surprisingly, both groups harboured a non-negligible error in each CKD class with a huge discrepancy between the eGFR formulas and the gold standard method (Figures 1 and 2), suggesting the great relevance of mGFR in the clinical decision-making algorithm, both in oncological and in non-oncological patients. CONCLUSION We observed that the error in the classification of CKD stages using eGFR formulas was very common, both in oncological and in nephrological patients, with a poor agreement with mGFR in all CKD classes. Therefore, mGFR remains a crucial tool for a correct clinical decision in all onconephrological patients who require surgery or potential nephrotoxic agents.
Although an essential step, the functional annotation of cells often proves particularly challenging in the analysis of single-cell transcriptional data. Several methods have been developed to accomplish this task. However, in most cases, these rely on techniques initially developed for bulk RNA sequencing or simply make use of marker genes identified from cell clustering followed by supervised annotation. To overcome these limitations and automatise the process, we have developed two novel methods, the single-cell gene set enrichment analysis (scGSEA) and the single cell mapper (scMAP). scGSEA combines latent data representations and gene set enrichment scores to detect coordinated gene activity at single-cell resolution. scMAP uses transfer learning techniques to re-purpose and contextualise new cells into a reference cell atlas. Using both simulated and real datasets, we show that scGSEA effectively recapitulates recurrent patterns of pathways' activity shared by cells from different experimental conditions. At the same time, we show that scMAP can reliably map and contextualise new single cell profiles on a breast cancer atlas we recently released. Both tools are provided in an effective and straightforward workflow providing a framework to determine cell function and significantly improve annotation and interpretation of scRNA-seq data.
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