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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., (© 2024 The Authors. Advanced Materials published by Wiley‐VCH GmbH.)

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., Competing Interests: Competing interests: AC, FM and FFraggetta report ad hoc advisory board membership with Roche Diagnostics Italia unrelated to the current work. FFraggetta is one of the inventors of 'Sample imaging and imagery archiving for imagery comparison Merlo, P.T. et al. US patent 16/688/613 2020'. The remaining authors have no conflicts of interest to declare., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

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., (© 2024 The Authors. Histopathology published by John Wiley & Sons Ltd.)

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., (© 2024 The Authors. The Journal of Pathology: Clinical Research published by The Pathological Society of Great Britain and Ireland and John Wiley & Sons Ltd.)