Mateusz Maniewski

Optical coherence elastography (OCE) enables label-free imaging of tissue mechanical properties, but conventional scanning implementations have limited volumetric acquisition speed. We present a full-field swept-source optical coherence elastography (FF-SS-OCE) system, based on compressive loading, that provides rapid volumetric imaging of strain at a rate of 2 volumes/s, enabled by an effective OCT A-scan rate of 4.2 MHz, corresponding to an order-of-magnitude increase in imaging speed over previous OCE demonstrations. The system achieves a shot-noise-limited phase sensitivity in volumetric imaging by combining highly parallel signal detection using a full-field configuration, implementation of a common-path interferometer to minimize the impact of environmental noise, a highly stable tunable laser, and active reduction of spatial coherence to reduce cross-talk noise. We demonstrate, for the same signal-to-noise ratio (SNR) of ∼20dB, that FF-SS-OCE provides 41% higher phase sensitivity and 35% higher strain sensitivity compared to a scanning OCT system. We validate the method using structured phantoms and freshly excised human breast tissue, demonstrating rapid visualization of mechanical contrast and showing its potential to discern between tumor and healthy tissue based on their distinct mechanical properties. These results establish FF-SS-OCE as a promising approach for high-speed, high-sensitivity volumetric elastography in biomedical tissue imaging.

Cervical cancer remains a major global health challenge, particularly in low- and middle-income countries, where access to screening, vaccination, and timely treatment may be limited. Cervical cytology has played an important historical role in prevention, but it is labor-intensive, time-consuming, and subject to observer variability and limited sensitivity. In many contemporary screening programs, HPV testing is now used as the primary screening test, while cytology is used mainly for the triage of HPV-positive women. In recent years, artificial intelligence (AI), particularly deep learning (DL), has shown considerable potential in medical image analysis and computer-aided diagnosis. This review summarizes current applications of AI in cervical cytology and related diagnostic workflows, including automated and assisted slide screening, liquid-based cytology, the triage of equivocal or HPV-positive cases, and colposcopy support. Across these settings, AI-assisted systems may improve efficiency, standardization, and diagnostic consistency, and may reduce workload in resource-constrained environments. However, the evidence is heterogeneous, and important challenges remain, including the need for large and diverse datasets, prospective validation, regulatory approval, digital infrastructure, workflow integration, and the resolution of ethical and legal issues. AI should therefore be regarded as a promising adjunct to human expertise rather than a replacement in cervical cytology and related clinical diagnostic pathways.

Artificial intelligence (AI)-based algorithms are increasingly implemented in histopathological cancer diagnostics to enhance reproducibility and efficiency in biomarker assessment. Although AI-driven image analysis shows promise in standardizing immunohistochemical evaluation and reducing inter-observer variability, its clinical reliability as a substitute for expert assessment requires rigorous validation. Minor discrepancies in Programmed Death Ligand 1 (PD-L1) interpretation can alter patient stratification and treatment outcomes, especially in borderline expression ranges. This study evaluated the concordance between PD-L1 expression scores assessed by an expert pathologist and the uPath VENTANA PD-L1 (SP263) Assay Algorithm. The cohort included 333 non-small cell lung cancer (NSCLC) cases stained with the anti-PD-L1 (SP263) antibody. Digital slides were independently assessed by a board-certified pathologist and the uPath algorithm. Analysis used the Tumor Proportion Score (TPS), stratified into three categories: <1%, 1–49%, and ≥50%. The overall concordance of PD-L1 status (cutoff ≥1%) between the uPath algorithm and the pathologist was 95.2% (κ = 0.89), while the concordance for the three-tier categorization was 94.5% (weighted κ = 0.93). The median PD-L1 expression score from uPath was 1.2 percentage points (pp) higher than the pathologist’s assessment. Discrepancies varied by TPS group, with mean differences of 0.2 pp, 4.4 pp, and 1.95 pp for TPS <1%, 1–49%, and ≥50%, respectively. Category changes occurred in 6% (20/333) of cases, potentially altering immunotherapy eligibility. AI-assisted assessment demonstrates high concordance with the expert pathologist, particularly in binary classification. However, due to discrepancies in the intermediate range, the algorithm is currently best positioned as a supportive tool for verifying borderline cases.

Background: SARS-CoV-2 can damage human placentas, leading to pregnancy complications, such as preeclampsia and premature birth. This study investigates the histopathological changes found in COVID-19-affected placentas. Materials and Methods: This study included 23 placentas from patients with active COVID-19 during delivery and 22 samples from patients without COVID-19 infection in their medical history. The samples underwent histopathological examination for pathology, such as trophoblast necrosis, signs of vessel damage, or fetal vascular malperfusion. Results: Newborns from the research group have lower weights and Apgar scores than healthy newborns. In the COVID-19 group, calcifications and collapsed intervillous space were more frequent, and inflammation was more severe than in the healthy group. At the same time, the placenta of SARS-CoV-2-positive patients showed signs of accelerated vascular maturation. Trophoblast necrosis was found only in the placentas of the research group. The expression of CD68+ was elevated in the COVID-19 cohort, suggesting that macrophages constituted a significant part of the inflammatory infiltrate. The increase in lymphocyte B markers was associated with placental infarctions, while high levels of CD3+, specific for cytotoxic T lymphocytes, correlated with vascular injury. Conclusions: SARS-CoV-2 is associated with pathological changes in the placenta, including trophoblast necrosis, calcification, and accelerated villous maturation. Those changes appear to be driven by T cells and macrophages, whose increased expression reflects ongoing histiocytic intervillositis in the placenta.

Anti-programmed cell death ligand 1 (anti-PD-L1) immunotherapy is an increasingly crucial in cancer treatment. To date, the Federal Drug Administration (FDA) has approved four PD-L1 immunohistochemistry (IHC) staining protocols, commercially available in the form of "kits", facilitating testing for PD-L1 expression. These kits comprise four PD-L1 antibodies on two separate IHC platforms, each utilizing distinct, non-interchangeable scoring systems. Several factors, including tumor heterogeneity and the size of the tissue specimens assessed, can lead to PD-L1 status misclassification, potentially hindering the initiation of therapy. Therefore, the development of more accurate predictive biomarkers to distinguish between responders and non-responders prior to anti-PD-1/PD-L1 therapy warrants further research. Achieving this goal necessitates refining sampling criteria, enhancing current methods of PD-L1 detection, and deepening our understanding of the impact of additional biomarkers. In this article, we review potential solutions to improve the predictive accuracy of PD-L1 assessment in order to more precisely anticipate patients' responses to anti-PD-1/PD-L1 therapy, monitor disease progression and predict clinical outcomes.