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The ongoing pursuit for laser device emitting in the near‐infrared spectral region on GaAs substrates has led to various material systems and device concepts. Alloys containing dilute amounts of Bismuth are promising candidates due to the already substantial band gap shift when incorporating low molar fractions of Bi in the GaAs host lattice. However, devices emitting at technologically essential wavelengths of 1.3 and 1.55 μm have yet to be demonstrated using this material system. Especially the non‐equilibrium nature of the growth conditions required to grow the metastable material makes epitaxial growth with high molar fractions of Bi challenging. An alternate approach to reach the desired wavelengths exploits a type‐II band alignment between two materials to push the emission wavelength further into the telecom bands. Here, room‐temperature laser operation of the first type‐II structure employing Ga(As,Bi) as hole confining layer and (Ga,In)As as electron confining layer is demonstrated. Sample growth is conducted by low‐pressure metalorganic vapour phase epitaxy. Broad area laser devices are processed and characterized by electroluminescence measurements. A threshold current density of 3.86 kA/cm2 and emission wavelength of 1037 nm are observed, showing this device concept's potential for future lasers in the telecom bands.

Background Transforming Growth Factor (TGF) and Bone Morphogenetic Protein (BMP) signalling critically influence pancreatic ductal adenocarcinoma (PDAC) progression, with TGF-B paradoxically exerting both tumour-promoting and -suppressive effects. Parallel to this observation, the specific context-dependent, spatial dynamics of these pathways and their interaction with the tumour microenvironment (TME) remain poorly understood. Methods We performed a spatially resolved analysis of PDAC on a multi-region tissue microarray cohort of 117 curatively resected PDAC specimens consisting of tumour centre (TC), tumour front (TF), and stromal(-predominant) tissue cores each. Protein (ID1, pSMAD2) and mRNA (TGF-A, TGF-B1/2, BMP4, GREM1) expression were assessed in each tissue compartment by immunohistochemistry and in situ hybridization, respectively, quantified by digital image analysis, and correlated with clinicopathologic features. Results ID1 was significantly overexpressed in PDAC cells compared to associated stroma ( p  < 0.01), while pSMAD2 was largely absent in PDAC cells, but preserved among associated stroma compartments, particularly in TF cores ( p  = 0.04). Higher stromal GREM1 signal correlated with reduced overall tumoural ID1 protein expression ( p  = 0.02), and TGF-B2 high /TGF-A low stroma was significantly associated with worse survival ( p  < 0.01). Intratumoural TGF-B2 was inversely correlated with stromal pSMAD2 expression ( p  = 0.03) and was associated with lymph node involvement ( p  = 0.02). FOXP3 + regulatory T-cells were significantly reduced in TGF-B2 high tumours ( p  = 0.04), while higher tumoural TGF-B1 exhibited a trend towards increased FOXP3 + cells ( p  = 0.08). Conclusions Our spatial analysis reveals intratumoural heterogeneity of TGF/BMP signalling and its significance for PDAC progression. Notably, stromal TGF-B2 emerges as a prognostic biomarker, while TGF-B1 and ID1 are implicated in adverse clinical and pathologic features. These findings highlight the importance of TGF/BMP signalling niches in the TME with implications for PDAC biology and can inform the development of future therapeutic strategies.

Considering the profound transformation affecting pathology practice, we aimed to develop a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide images (WSI). For this, we propose a deep learning (DL) system that learns from weak labels, a sampling strategy that reduces the number of training samples by a factor of six without compromising performance, an approach to leverage a small subset of fully annotated samples, and a prototype with explainable predictions, active learning features and parallelisation. Noting some problems in the literature, this study is conducted with one of the largest WSI colorectal samples dataset with approximately 10,500 WSIs. Of these samples, 900 are testing samples. Furthermore, the robustness of the proposed method is assessed with two additional external datasets (TCGA and PAIP) and a dataset of samples collected directly from the proposed prototype. Our proposed method predicts, for the patch-based tiles, a class based on the severity of the dysplasia and uses that information to classify the whole slide. It is trained with an interpretable mixed-supervision scheme to leverage the domain knowledge introduced by pathologists through spatial annotations. The mixed-supervision scheme allowed for an intelligent sampling strategy effectively evaluated in several different scenarios without compromising the performance. On the internal dataset, the method shows an accuracy of 93.44% and a sensitivity between positive (low-grade and high-grade dysplasia) and non-neoplastic samples of 0.996. On the external test samples varied with TCGA being the most challenging dataset with an overall accuracy of 84.91% and a sensitivity of 0.996.

BackgroundImmunotherapy has revolutionized the treatment paradigm of advanced non-small cell lung cancer (NSCLC), opening possibilities for long-term survival outcomes with superior tolerability.1 Eligibility for immune checkpoint inhibitors is set by estimating the tumor proportion score (TPS) in programmed cell death ligand 1 (PD-L1) immunohistochemically stained slides.2 However, high interobserver variability in reporting PD-L1 expression may result in suboptimal treatment decisions.3 We developed HALO PD-L1 Lung AI to support pathologists in quantifying PD-L1 expression in NSCLC samples, with the aim of reducing interobserver variability.MethodsHALO PD-L1 Lung AI was trained using 146,984 expert annotations to identify PD-L1 tumor-positive and tumor-negative cells, within segmented tumor regions. The agreement and reliability of the algorithm’s TPS score with scores obtained from three independent pathologists were analyzed by calculatingthe interobserver percent agreement and intraclass correlation coefficient (ICC) in a cohort of 203 whole slide images stained with the SP263 clone.ResultsPairwise pathologist agreement ranged from 74.9% to 77.3%. Agreement of HALO PD-L1 Lung AI TPS scores with the pathologists’ mode (where at least 2/3 pathologists agreed) was 75.4%. Agreement of HALO PD-L1 Lung AI with the pathologists’ mode at the clinically relevant cut-offs <1%, 1–49% and >50% was 0.81 (95% CI 0.75 – 0.88), 0.72 (95% CI 0.64 – 0.79), and 0.70 (95% CI 0.57 – 0.81), respectively. In 15 of the 18 disagreement cases at the 1% cut-off, the algorithm score was within a 1–4% range, showing that although in a categorical scale the cases were in disagreement, the results were close on a continuous scale. In fact, the intraclass correlation coefficient (ICC) between the algorithm and pathologists’ TPS scores was 0.95 (95% CI 0.93 – 0.97) and between the three pathologists was 0.96 (95% CI 0.93 – 0.97).ConclusionsThe percent agreement of HALO PD-L1 Lung AI with the pathologists’ mode is in line with the pairwise agreement between pathologists. On the continuous scale, ICC results show good-to-excellent reliability between the algorithm and pathologists’ TPS scores. Computer-aided diagnostic tools such as HALO PD-L1 Lung AI have the potential to increase consistency in the reported TPS results and ultimately improve treatment decision making.ReferencesJiang M, Liu C, Ding D, Tian H, Yu C. Comparative Efficacy and Safety of Anti-PD-1/PD-L1 for the Treatment of Non-Small Cell Lung Cancer: A Network Meta-Analysis of 13 Randomized Controlled Studies. Front Oncol. 2022;12:827050.Tsao MS, Kerr KM, Kockx M, Beasley MB, Borczuk AC, Botling J, et al. PD-L1 Immunohistochemistry Comparability Study in Real-Life Clinical Samples: Results of Blueprint Phase 2 Project. J Thorac Oncol. 2018 Sep 1;13(9):1302–11.Rimm DL, Han G, Taube JM, Yi ES, Bridge JA, Flieder DB, et al. A Prospective, Multi-institutional, Pathologist-Based Assessment of 4 Immunohistochemistry Assays for PD-L1 Expression in Non-Small Cell Lung Cancer. JAMA Oncol. 2017 Aug 1;3(8):1051–8.

For the first time, room temperature laser emission of an electrically pumped edge emitting laser device based on (Ga,In)(N,As)/Ga(As,Sb)/(Ga,In)(N,As) type‐II W’ quantum well heterostructures is shown. The device was grown using metal organic vapour phase epitaxy utilising the alternative nitrogen precursor di‐tert‐butyl‐amino‐arsane and characterised with electroluminescence spectroscopy. At low current densities, the emission wavelength was 1339 nm. It shifted blue with 4.26 meV/(kA/cm2) until the threshold current density was reached. At that point, the peak emission wavelength reached 1270 nm. With a cavity length of 2012 μm, the investigated device reached lasing threshold at 9.5 kA/cm2.

Ki67 has potential clinical importance in breast cancer but has yet to see broad acceptance due to inter-laboratory variability. Here we tested an open source and calibrated automated digital image analysis (DIA) platform to: (i) investigate the comparability of Ki67 measurement across corresponding core biopsy and resection specimen cases, and (ii) assess section to section differences in Ki67 scoring. Two sets of 60 previously stained slides containing 30 core-cut biopsy and 30 corresponding resection specimens from 30 estrogen receptor-positive breast cancer patients were sent to 17 participating labs for automated assessment of average Ki67 expression. The blocks were centrally cut and immunohistochemically (IHC) stained for Ki67 (MIB-1 antibody). The QuPath platform was used to evaluate tumoral Ki67 expression. Calibration of the DIA method was performed as in published studies. A guideline for building an automated Ki67 scoring algorithm was sent to participating labs. Very high correlation and no systematic error (p = 0.08) was found between consecutive Ki67 IHC sections. Ki67 scores were higher for core biopsy slides compared to paired whole sections from resections (p ≤ 0.001; median difference: 5.31%). The systematic discrepancy between core biopsy and corresponding whole sections was likely due to pre-analytical factors (tissue handling, fixation). Therefore, Ki67 IHC should be tested on core biopsy samples to best reflect the biological status of the tumor.

Background: The novel beta-coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), enters the human body via mucosal surfaces of the upper and/or lower respiratory tract. Viral entry into epithelial cells is mediated via angiotensin-converting enzyme 2 (ACE2) and auxiliary molecules, but the precise anatomic site of infection still remains unclear. Methods: Here, we systematically investigated the main SARS-CoV-2 receptor proteins ACE2 and transmembrane serine protease 2 (TMPRSS2), as well as 2 molecules potentially involved in viral entry, furin and CD147, in formalin-fixed, paraffin-embedded human tissues. Tissue microarrays incorporating a total of 879 tissue cores from conjunctival (n = 84), sinonasal (n = 95), and lung (bronchiolar/alveolar; n = 96) specimens were investigated for protein expression by immunohistochemistry. Results: ACE2 and TMPRSS2 were expressed in ciliated epithelial cells of the conjunctivae and sinonasal tissues, with highest expression levels observed in the apical cilia. In contrast, in the lung, the expression of those molecules in bronchiolar and alveolar epithelial cells was much rarer and only very focal when present. Furin and CD147 were more uniformly expressed in all tissues analyzed, including the lung. Interestingly, alveolar macrophages consistently expressed high levels of all 4 molecules investigated. Conclusions: Our study confirms and extends previous findings and contributes to a better understanding of potential SARS-CoV-2 infection sites along the human respiratory tract.

Abstract Ki67 has potential clinical importance in breast cancer but has yet to see broad acceptance due to inter-laboratory variability. Here we tested an open source and calibrated automated digital image analysis (DIA) platform to: (i) investigate the comparability of Ki67 measurement across corresponding core biopsy and resection specimen cases, and (ii) assess section to section differences in Ki67 scoring. Two sets of 60 previously stained slides containing 30 core-cut biopsy and 30 corresponding resection specimens from 30 estrogen receptor-positive breast cancer patients were sent to 17 participating labs for automated assessment of average Ki67 expression. The blocks were centrally cut and immunohistochemically (IHC) stained for Ki67 (MIB-1 antibody). The QuPath platform was used to evaluate tumoral Ki67 expression. Calibration of the DIA method was performed as in published studies. A guideline for building an automated Ki67 scoring algorithm was sent to participating labs. Very high correlation and no systematic error ( p = 0.08) was found between consecutive Ki67 IHC sections. Ki67 scores were higher for core biopsy slides compared to paired whole sections from resections ( p ≤ 0.001; median difference: 5.31%). The systematic discrepancy between core biopsy and corresponding whole sections was likely due to pre-analytical factors (tissue handling, fixation). Therefore, Ki67 IHC should be tested on core biopsy samples to best reflect the biological status of the tumor.