Electrically Conductive Collagen-PEDOT:PSS Hydrogel Prevents Post-Infarct Cardiac Arrhythmia and Supports hiPSC-Cardiomyocyte Function.

Myocardial infarction (MI) causes cell death, disrupts electrical activity, triggers arrhythmia, and results in heart failure, whereby 50-60% of MI-associated deaths manifest as sudden cardiac deaths (SCD). The most effective therapy for SCD prevention is implantable cardioverter defibrillators (ICDs). However, ICDs contribute to adverse remodeling and disease progression and do not prevent arrhythmia. This work develops an injectable collagen-PEDOT:PSS (poly(3,4-ethylenedioxythiophene) polystyrene sulfonate) hydrogel that protects infarcted hearts against ventricular tachycardia (VT) and can be combined with human induced pluripotent stem cell (hiPSC)-cardiomyocytes to promote partial cardiac remuscularization. PEDOT:PSS improves collagen gel formation, micromorphology, and conductivity. hiPSC-cardiomyocytes in collagen-PEDOT:PSS hydrogels exhibit near-adult sarcomeric length, improved contractility, enhanced calcium handling, and conduction velocity. RNA-sequencing data indicate enhanced maturation and improved cell-matrix interactions. Injecting collagen-PEDOT:PSS hydrogels in infarcted mouse hearts decreases VT to the levels of healthy hearts. Collectively, collagen-PEDOT:PSS hydrogels offer a versatile platform for treating cardiac injuries.

A deep-learning workflow to predict upper tract urothelial carcinoma protein-based subtypes from H&E slides supporting the prioritization of patients for molecular testing.

Upper tract urothelial carcinoma (UTUC) is a rare and aggressive, yet understudied, urothelial carcinoma (UC). The more frequent UC of the bladder comprises several molecular subtypes, associated with different targeted therapies and overlapping with protein-based subtypes. However, if and how these findings extend to UTUC remains unclear. Artificial intelligence-based approaches could help elucidate UTUC's biology and extend access to targeted treatments to a wider patient audience. Here, UTUC protein-based subtypes were identified, and a deep-learning (DL) workflow was developed to predict them directly from routine histopathological H&E slides. Protein-based subtypes in a retrospective cohort of 163 invasive tumors were assigned by hierarchical clustering of the immunohistochemical expression of three luminal (FOXA1, GATA3, and CK20) and three basal (CD44, CK5, and CK14) markers. Cluster analysis identified distinctive luminal (N = 80) and basal (N = 42) subtypes. The luminal subtype mostly included pushing, papillary tumors, whereas the basal subtype diffusely infiltrating, non-papillary tumors. DL model building relied on a transfer-learning approach by fine-tuning a pre-trained ResNet50. Classification performance was measured via three-fold repeated cross-validation. A mean area under the receiver operating characteristic curve of 0.83 (95% CI: 0.67-0.99), 0.8 (95% CI: 0.62-0.99), and 0.81 (95% CI: 0.65-0.96) was reached in the three repetitions. High-confidence DL-based predicted subtypes showed significant associations (p < 0.001) with morphological features, i.e. tumor type, histological subtypes, and infiltration type. Furthermore, a significant association was found with programmed cell death ligand 1 (PD-L1) combined positive score (p < 0.001) and FGFR3 mutational status (p = 0.002), with high-confidence basal predictions containing a higher proportion of PD-L1 positive samples and high-confidence luminal predictions a higher proportion of FGFR3-mutated samples. Testing of the DL model on an independent cohort highlighted the importance to accommodate histological subtypes. Taken together, our DL workflow can predict protein-based UTUC subtypes, associated with the presence of targetable alterations, directly from H&E slides.

Machine Learning-Supported Diagnosis of Small Blue Round Cell Sarcomas Using Targeted RNA Sequencing.

Small blue round cell sarcomas (SBRCSs) are a heterogeneous group of tumors with overlapping morphologic features but markedly varying prognosis. They are characterized by distinct chromosomal alterations, particularly rearrangements leading to gene fusions, whose detection currently represents the most reliable diagnostic marker. Ewing sarcomas are the most common SBRCSs, defined by gene fusions involving EWSR1 and transcription factors of the ETS family, and the most frequent non-EWSR1-rearranged SBRCSs harbor a CIC rearrangement. Unfortunately, currently the identification of CIC::DUX4 translocation events, the most common CIC rearrangement, is challenging. Here, we present a machine-learning approach to support SBRCS diagnosis that relies on gene expression profiles measured via targeted sequencing. The analyses on a curated cohort of 69 soft-tissue tumors showed markedly distinct expression patterns for SBRCS subgroups. A random forest classifier trained on Ewing sarcoma and CIC-rearranged cases predicted probabilities of being CIC-rearranged >0.9 for CIC-rearranged-like sarcomas and <0.6 for other SBRCSs. Testing on a retrospective cohort of 1335 routine diagnostic cases identified 15 candidate CIC-rearranged tumors with a probability >0.75, all of which were supported by expert histopathologic reassessment. Furthermore, the multigene random forest classifier appeared advantageous over using high ETV4 expression alone, previously proposed as a surrogate to identify CIC rearrangement. Taken together, the expression-based classifier can offer valuable support for SBRCS pathologic diagnosis.

Macrophage subpopulations in pediatric patients with lupus nephritis and other inflammatory diseases affecting the kidney.

BACKGROUND: Macrophages play an important role in the pathogenesis of lupus nephritis (LN), but less is known about macrophage subtypes in pediatric LN. Here we compared renal inflammation in LN with other inflammatory pediatric kidney diseases and assessed whether inflammation correlates with clinical parameters. METHODS: Using immunofluorescence microscopy, we analyzed renal biopsies from 20 pediatric patients with lupus nephritis (ISN/RPS classes II-V) and pediatric controls with other inflammatory kidney diseases for infiltration with M1-like (CD68 + /CD206 - , CD68 + /CD163 -), M2a-like (CD206 + /CD68 +), and M2c-like macrophages (CD163 + /CD68 +) as well as CD3 + T-cells, CD20 + B-cells, and MPO + neutrophilic granulocytes. In addition, the correlation of macrophage infiltration with clinical parameters at the time of renal biopsy, e.g., eGFR and serum urea, was investigated. Macrophage subpopulations were compared with data from a former study of adult LN patients. RESULTS: The frequency of different macrophage subtypes in biopsies of pediatric LN was dependent on ISN/RPS class and showed the most pronounced M1-like macrophage infiltration in patients with LN class IV, whereas M2c-like macrophages were most abundant in class III and IV. Interestingly, on average, only half as many macrophages were found in renal biopsies of pediatric LN compared to adult patients with LN. The distribution of frequencies of macrophage subpopulations, however, was different for CD68 + CD206 + (M2a-like) but comparable for CD68 + CD163 - (M1-like) CD68 + CD163 + (M2c-like) cells in pediatric and adult patients. Compared to other inflammatory kidney diseases in children, fewer macrophages and other inflammatory cells were found in kidney biopsies of LN. Depending on the disease, the frequency of individual immune cell types varied, but we were unable to confirm disease-specific inflammatory signatures in our study due to the small number of pediatric cases. Worsened renal function, measured as elevated serum urea and decreased eGFR, correlated particularly strongly with the number of CD68 + /CD163 - M1-like macrophages and CD20 + B cells in pediatric inflammatory kidney disease. CONCLUSION: Although M1-like macrophages play a greater role in pediatric LN patients than in adult LN patients, M2-like macrophages appear to be key players and are more abundant in other pediatric inflammatory kidney diseases compared to LN.

Associations of TACSTD2/TROP2 and NECTIN-4/NECTIN-4 with molecular subtypes, PD-L1 expression, and FGFR3 mutational status in two advanced urothelial bladder cancer cohorts.

AIMS: Treatment options for advanced urothelial carcinoma (aUC) rapidly evolved: besides immunomodulative therapeutic options and inhibitors targeting Fibroblast growth factor receptor (FGFR) alterations, two new antibody-drug conjugates (ADC), sacituzumab govitecan (SG) and enfortumab vedotin (EV), have been approved. However, little is known about the associations of specific aUC properties and the surface target expression of TROP2 and NECTIN-4. Our aim was to characterize associations of TACSTD2/TROP2 and NECTIN-4/NECTIN-4 protein and gene expression with morphomolecular and clinicopathological characteristics of aUC in two large independent cohorts. METHODS AND RESULTS: The TCGA BLCA (n = 405) and the CCC-EMN (n = 247) cohorts were retrospectively analysed. TROP2/TACSTD2 and NECTIN-4/NECTIN-4 are highly expressed at the protein and transcript level in aUC, and their expression status did not correlate with patient survival in both cohorts. NECTIN-4/NECTIN-4 expression was higher in luminal tumours and reduced in squamous aUCs. NECTIN-4 was negative in 10.6% of samples, and 18.4% of samples had low expression (H-score <15). The TROP2 negativity rate amounted to 6.5%. TACSTD2 and NECTIN-4 expression was reduced in neuroendocrine-like and/or protein-based double-negative tumours. TROP2- and NECTIN-4-negative tumours included one sarcomatoid and four neuroendocrine aUC. FGFR3 alterations and PD-L1 expression on tumour and immune cells did not associate with TROP2 or NECTIN-4 expression. CONCLUSIONS: TACSTD2/TROP2 and NECTIN-4/NECTIN-4 are widely expressed in aUC, independent of FGFR3 alterations or PD-L1 expression, thus representing a suitable target for ADC treatment in the majority of aUC. The expression loss was associated with aggressive morphomolecular aUC subtypes, i.e. neuroendocrine(-like) and sarcomatoid aUC.

Digital odyssey: lessons learnt from a reverse transition from a digital to a manual pathology workflow.

In the fully digital Caltagirone pathology laboratory, a reverse shift from a digital to a manual workflow occurred due to a server outage in September 2023. Here, insights gained from this unplanned transition are explored. Surveying the affected pathologists and technicians revealed unanimous preferences for the time-saving and error-reducing capabilities of the digital methodology. Conversely, the return to manual methods highlighted increased dissatisfaction and reduced efficiency, emphasising the superiority of digital workflows. This case study underscores that transition challenges are not inherent to digital workflows but to transitioning itself, advocating for the adoption of digital technologies in all pathology practices.