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Background: Deep learning systems for Gleason grading are developed almost entirely on high-end clinical scanners and on cohorts from a small number of Western institutions, yet deployment increasingly involves other devices and other populations. These two distribution shifts, device and population, are rarely tested together on the same physical slides. The PAR dataset, from Erbil, Iraq, digitizes each biopsy on three scanners and provides three distinct pathologist grades, so it permits both tests at once on a Middle Eastern cohort. A concurrent study by the dataset originators validated a task-specific model and two foundation models on PAR; we complement it by testing an independently developed detect-then-grade pipeline and by separating scanner effects on detection from scanner effects on grading. Methods: We applied one fixed model developed on North American and European material to all 1017 whole-slide images (339 slides from 185 patients, three scanners; 49.6% clinically significant cancer) with no scanner-specific or population-specific tuning. We measured cancer detection (area under the ROC curve of the predicted cancer tissue fraction), all-slide ISUP agreement of the deployed detect-then-grade pipeline (quadratic-weighted kappa, QWK), and grading agreement on pathologist-confirmed cancers, at the slide level and, because a case carries up to two slides, at the patient level. The reference reader was S.A.; thresholds and operating points were cross-validated leave-one-out; scanners were compared by paired within-biopsy bootstrap and confidence intervals confirmed by patient-cluster bootstrap. Results: Detection was statistically equivalent across scanners (AUC 0.987 to 0.991; paired differences at most 0.003) and transferred to this non-Western cohort with no per-population tuning. At a 95% sensitivity operating point, the deployed pipeline reached cross-validated all-slide QWK of 0.86, 0.81, and 0.86 (Grundium, Hamamatsu, Leica), matching the inter-pathologist ceiling of 0.81, against 0.23 to 0.62 for the ungated model. Grading of confirmed cancers was scanner-dependent: the compact Grundium (0.63) did not differ from the clinical Leica (0.67; paired difference 0.04, 95% CI −0.03 to 0.11), while both exceeded Hamamatsu (0.44). Results held at the patient level, with grading somewhat lower for two scanners; the two slides of a case disagreed in grade in 43% of cases, and patient clustering did not widen the intervals. Conclusions: Cancer tissue fraction is a triage signal robust across scanner and transferable to an underrepresented population for detection, while grading is the scanner-sensitive step. Prostate grading models should be deployed as a detect-then-grade pipeline, with grading validated per device and confirmed on the local population.

Glycosylation is the most common post-translational modification in the human proteome, with over half of all human proteins bearing covalently attached glycans. These glycan structures direct protein folding through ER quality control machinery, shield polypeptides from proteolytic degradation, regulate circulatory half-life via the asialoglycoprotein receptor, and serve as molecular signals for immune recognition and intracellular trafficking. For biopharmaceuticals, which constitute a rapidly growing share of approved drugs, glycan profiles are critical quality attributes that directly determine clinical efficacy and safety. Yet achieving the correct glycosylation on a therapeutic protein remains one of the field’s central challenges, as glycan biosynthesis is non-template-driven and highly sensitive to expression system and manufacturing conditions. This review connects the biological functions of glycosylation to the practical strategies of glycoengineering, examining how sequence design, expression system selection, and downstream enzymatic remodeling are used to optimize therapeutic glycoproteins. Clinical case studies spanning monoclonal antibodies, cytokines, and enzyme replacement therapies illustrate how glycan engineering translates into improved patient outcomes. We conclude by surveying emerging technologies poised to make precisely glycosylated therapeutics more accessible.

Despite significant advances in artificial intelligence (AI) algorithms for prostate cancer detection from whole slide images, the clinical applicability of these models remains limited. Variability in inter- and intrapathological grading, low generalizability of training datasets, and insufficient annotation precision restrict the performance of downstream models. This article introduces a novel Bayesian framework that addresses these challenges by generating pixel-wise posterior distributions, thereby providing a probabilistic output that enables the simulation of a panel consensus and enabling seamless integration of new data and models as they become available. The framework is demonstrated by integrating a Bayesian prior with a trained AI model to produce a per-pixel distribution of Gleason patterns. It is shown that using this distribution of Gleason patterns rather than a ground-truth label can improve model applicability, mitigate errors, and highlight areas of interest for pathologists. Furthermore, we present a high-quality, hand-curated dataset of prostate histopathological images annotated at the gland level by trained premedical students and verified by an expert pathologist. We highlight the potential of this adaptive and uncertainty-aware framework for developing clinically deployable AI tools that can support pathologists in accurate prostate cancer grading, improve diagnostic accuracy, and create positive patient outcomes. This work is presented as an early-stage, proof-of-concept study; the framework has not been validated for clinical use and is not intended for diagnostic deployment in its current form.