Search Results (37)

Prostate cancer remains the most frequently diagnosed malignancy in men and a leading cause of cancer-related mortality. Multiparametric MRI (mpMRI) has become the gold standard for non-invasive diagnosis, staging, and follow-up. Yet, its widespread adoption is hampered by long acquisition times, inter-reader variability, and interpretative complexity. Though most papers focus on specific applications without offering a cohesive therapeutic perspective, artificial intelligence (AI) has recently attracted attention as a potential solution to these shortcomings. For instance, deep learning models can help optimize imaging protocols for biparametric and multiparametric MRI, and AI-based reconstruction techniques have shown promise for reducing acquisition times without sacrificing diagnostic performance. Several systems have produced outcomes in the diagnostic phase that are comparable to those of skilled radiologists, as demonstrated in multicenter settings such as PI-CAI. Radiomics and radiogenomics provide more detailed insights into the biology of the disease by extracting quantitative features associated with tumor aggressiveness, extracapsular expansion, and treatment response, in addition to detection. Despite these developments, methodological variability, a lack of multicenter validation, proprietary algorithms, and unresolved standardization and governance difficulties continue to restrict clinical translation. Our work emphasizes the maturity of existing technologies, ongoing gaps, and the progressive integration necessary for successful clinical adoption by presenting AI applications aligned with the patient pathway. In this context, this review aims to outline how AI can support the entire patient journey—from acquisition and protocol selection to detection, quantitative analysis, treatment assessment, and follow-up—while maintaining a clinically centered perspective that emphasizes practical relevance over theoretical discussion, potentially enabling more reliable, effective, and customized patient care in the field of prostate cancer. Full article

Background: Cervical cancer remains a major cause of cancer-related morbidity and mortality worldwide, with limited therapeutic options for advanced disease. As we better understand the fine mechanisms involved in the interaction between tumor cells and the tumor microenvironment, new paths and opportunities will emerge. Recent evidence highlights the prognostic and predictive roles of immune checkpoint markers and tumor-infiltrating lymphocytes (TILs), especially CD8⁺ TILs, in shaping treatment outcomes. Objectives: This study investigated the immunohistochemical expression of PD-L1 and CD8⁺ TILs in 48 newly diagnosed, treatment-naive cervical cancer cases and analyzed their associations with clinicopathological features and survival outcomes. Results: In our cohort, we observed that PD-L1 positivity was identified in 68.8% of cases, most frequently in advanced FIGO stages and in tumors with lympho-vascular invasion or with a high proliferation rate evaluated by the Ki-67 index. High levels of intra-tumoral CD8⁺ TILs were observed in 52.1% of cases and correlated positively with stromal TILs, lower proliferation rates, and absence of vascular invasion. A significant inverse relationship was found between PD-L1 expression and the density of CD8⁺ TILs (p = 0.047). Survival analysis showed that patients exhibiting a “cold” immunophenotype with low levels of CD8⁺ TILs and PD-L1-positive tumors had worse outcomes, while high levels of CD8⁺ TILs played a protective role. Conclusions: Our study highlights the importance of the immunohistochemical assessment of PD-L1 and CD8⁺ TILs biomarkers, which have a complementary inter-relationship and have a significant prognostic impact on cervical squamous cell carcinoma. PD-L1 positivity marks aggressive disease features, while higher intra-tumoral CD8⁺ TIL density is protective. Their combined evaluation may improve patient stratification and inform immunotherapy strategies. Full article

Background and Objectives: Breast cancer (BC) is a global health concern for women; the disease contributes to significant morbidity and mortality. A key element in the diagnosis of BC involves the histopathological diagnosis, which determines patient management and therapy. However, BC is a multifaceted disease, limiting access to early diagnosis and, therefore, treatment. Artificial intelligence (AI) is transforming diagnostics in the medical field, especially in the detection of BC. Due to the increased availability of digital slides, it has facilitated the effective integration of AI in breast cancer diagnosis. Diagnosis poses a great challenge, even for experienced pathologists, due to the heterogeneity of this malignancy. Analysing microscopic slides by pathologists requires a considerable amount of time. Implementation of AI into routine workflows holds potential to improve diagnostic sensitivity and inter-observer concordance, and to increase efficiency by reducing the review time, thereby helping to alleviate the burden of diagnosing BC. Previous studies mainly address imaging modalities or oncology broadly, while a few specifically concentrates on the histopathological aspect of breast cancer. This review aims to explore the novel synthesis of AI advancements in digital pathology, including tumour classification, grading, lymph node staging, and biomarker evaluation, and discuss their potential incorporation into clinical workflows. We will also discuss the current barriers and prospects for future advancements. Materials and Methods: A literature search was conducted in PubMed and Google Scholar using the mentioned keywords. Articles published in English until July 2025 were reviewed and synthesised narratively. Results: Recent studies demonstrate that AI models such as convolutional neural networks (CNNs), YOLO, and RetinaNet achieve high accuracy in tumour detection, histological grading, lymph node metastasis localisation, and biomarker analysis. The reported performance values range from 75% to over 95% accuracy across various tasks, with gains in diagnostic sensitivity and inter-observer concordance, and reduced review time in assisted workflows. However, certain limitations, such as data variability, external validation in clinical practice, and ethical concerns, restrict the growth and optimal performance of AI and its clinical applicability. Conclusions: The future for AI looks promising, as it is rapidly evolving. By analysing evidence across multiple domains, this review evaluates both opportunities and persisting barriers, offering practical overviews for future clinical transition. AI cannot replace pathologists; however, it has the capabilities to enhance diagnostic precision, efficiency, and ultimately patient outcomes. It is only a matter of time before AI is adopted into healthcare. Full article

Soil salinity poses a significant challenge for global agriculture and ecosystems, severely impacting plant growth and land-use efficiency. Leymus chinensis (L. chinensis) is a perennial grass with a high potential for saline soil restoration, yet little is known about whether its salt tolerance during germination aligns with that during seedling development, which are considered the most salt-sensitive stages of its life cycle. Therefore, to investigate whether there is a correlation between salt tolerance during germination and the seedling stage, we evaluated the germination, growth, and survival of 10 genotypes of (G1–G10) L. chinensis under 0, 50, 100, and 150 mM NaCl stress over 12 weeks. Key indicators, including germination traits (germination percentage, radicle length, and shoot length), plant height, and survival rate, were integrated into stage-specific Comprehensive Evaluation Values (D value) to quantify salt tolerance. Salt stress significantly suppressed germination and seedling performance, with inter-genotypic variation. For example, G3 showed only an 18.0% reduction in germination percentage and 62.5% survival rate at 150 mM NaCl, while other genotypes had a 42.0–88.0% germination loss and over 90.0% mortality. However, a correlation analysis showed a positive yet non-significant correlation between D-Germination and D-Survival. Notably, D-Plant Height was negatively correlated with both D-Germination and D-Survival, with G3 and G8 displaying contrasting stress adaptation strategies. Collectively, these results indicate that salt tolerance in L. chinensis is both stage-specific and genotype-specific and that performance at germination does not reliably predict later survival. The findings of this study provide valuable germplasm resources and a theoretical basis for forage breeding and grassland restoration. The identified genotypes, G3 and G8, can serve as important materials for research on salt tolerance mechanisms and breeding programs. Full article

Zygotic genome activation (ZGA) represents one of the most vulnerable periods to environmental perturbations. The objective of this study was to investigate the formation of stress granules in mouse embryos in response to temperature reduction during ZGA, preimplantation embryo mortality, and long-term phenotypic outcomes. These outcomes included the evaluation of expression noise in bilateral right/left limbs of offspring as an indicator of developmental instability, behavioral deviation, hippocampal volume, and metabolomics profiling in adult offspring. Exposure to hypothermia during ZGA was associated with an increased number and inter-blastomere variability of stress granules, extended duration of the second embryonic division, and elevated embryonic mortality during the second and third cleavage stages. The embryonic response to hypothermic stress correlated with phenotypic traits indicative of increased pathology risk. Expression noise, serving as an indicator of developmental instability, was reduced in adult offspring derived from two-cell embryos incubated at 35 °C compared to those at 37 °C, while showing no significant difference relative to the control group. These results suggest that embryos surviving hypothermic exposure (35 °C) possess enhanced resilience to the adverse effects commonly associated with embryo transfer procedures. Furthermore, increased hippocampal volume and augmented auditory startle reflex observed in offspring that endured hypothermia during ZGA imply reduced risks of cognitive-related pathologies and reduced risks of pathologies associated with cognitive functions. Full article

Cardiometabolic diseases represent an escalating global health crisis, slowing or even reversing earlier declines in cardiovascular disease (CVD) mortality. Traditionally, conditions such as obesity, type 2 diabetes mellitus (T2DM), atherosclerotic CVD, heart failure (HF), chronic kidney disease (CKD), and metabolic dysfunction-associated steatotic liver disease (MASLD) were managed in isolation. However, emerging evidence reveals that these disorders share overlapping pathophysiological mechanisms and treatment strategies. In 2023, the American Heart Association proposed the Cardiovascular-Kidney-Metabolic (CKM) syndrome, recognizing the interconnected roles of the heart, kidneys, and metabolic system. Yet, this model omits the liver—a critical organ impacted by metabolic dysfunction. MASLD, which can progress to metabolic dysfunction-associated steatohepatitis (MASH), is closely tied to insulin resistance and obesity, contributing directly to cardiovascular and renal impairment. Notably, MASLD is bidirectionally associated with the development and progression of CKM syndrome. As a result, we introduce an expanded framework—the Cardiovascular-Renal-Hepatic-Metabolic (CRHM) syndrome—to more comprehensively capture the broader inter-organ dynamics. We provide guidance for an integrated diagnostic approach aimed at halting progression to advanced stages and preventing further organ damage. In addition, we highlight advances in medical management that target shared pathophysiological pathways, offering benefits across multiple organ systems. Viewing these conditions as an integrated whole, rather than as discrete entities, and incorporating the liver into this framework fosters a more holistic management strategy and offers a promising path to addressing the cardiometabolic pandemic. Full article

Background: Endometrial cancer (EC) incidence and mortality have been steadily rising globally over recent decades. The introduction of advanced molecular technologies, such as next-generation sequencing (NGS) alongside the FIGO 2023 classification, presents opportunities for refined diagnostics and risk stratification. This study aimed to analyze differences in EC classification among oncology centers in southeastern Poland. Methods: Data were collected from 461 consecutive patients newly diagnosed with EC between 2022 and 2024 at four major oncology centers in southeastern Poland. Molecular and immunohistochemical (IHC) analyses were conducted on formalin-fixed paraffin-embedded (FFPE) tissues to identify key markers, including POLE mutations, MSI-H, and p53 status. Results: The application of the FIGO 2023 staging system revealed statistically significant inter-center differences, with Centers 1 and 4 diagnosing a higher proportion of early-stage cases. The most prevalent subtype was NSMP, observed in 51% of cases. MSI-H occurred in 13–36% of patients, depending on the center. p53 mutations ranged from 9% to 26%. POLE mutations were identified in 4% of patients overall. Significant variations in the molecular subtype distribution across centers highlight potential differences in diagnostic access or tumor biology. Conclusions: The findings demonstrate regional differences in EC staging and molecular profiles in Poland, potentially reflecting disparities in diagnostic resources, methodologies, or tumor characteristics. Addressing these variations through standardized diagnostic protocols and equitable access to molecular tools is critical for optimizing patient outcomes. Future research should focus on evaluating the impact of molecular markers on therapy response and prognosis to guide personalized treatment strategies. Full article

Objectives: This study aims to evaluate the technical and clinical outcomes of the E-nside stent graft for thoraco-abdominal aortic aneurysm (TAAA) and pararenal abdominal aortic aneurysm (PAAA) endovascular treatment at our University Hospital Center. Methods: We conducted a retrospective analysis of patients electively treated by inner branched EVAR (iBEVAR) between 05/2021 and 03/2023. Demographic, procedural, and clinical data were analyzed. The technical success and clinical outcomes, such as access-site-related complications were reported. The perioperative and early mortality rate, freedom from aortic reintervention, target vessels’ (TVs) patency, and the endoleak rate were evaluated during the follow-up. The technical aspects (external iliac artery diameter, iliac tortuosity, extent of aortic coverage) were retrospectively analyzed. Results: Twenty-two patients were included (age 75.9 ± 5.5; 72.7% male). The aneurism extent was Crawford I = 4 (18.2%), III = 8 (36.4%), IV = 5 (22.7%), V = 1 (4.5%), and PAAA = 4 (18.2%). The mean aortic diameter was 63.5 ± 9.9 mm. The technical success was 95.5% (assisted primary success 100%). The clinical success was 86.4%. The perioperative and early freedom from all-cause mortality rates were 90.9% and 90%, respectively. No case of inter-stage aortic-related mortality was reported, and there was no permanent/temporary spinal cord ischemia (SCI). Seventy-eight out of 81 patent TVs were incorporated through a bridging stent (96.3%). The TV success was 95.1%. The mean external iliac artery (EIA) diameter was 7.5 ± 1.1 mm. Twelve patients (54.5%, including all female patients) were considered outside the instructions for use (IFU) due to narrow iliac arteries. One access-site-related complication was reported. Conclusions: Our experience confirms that E-nside has promising technical and clinical success rates, as well as a low reintervention rate, but it requires a significant compromise of the healthy aortic tissue and adequate iliac arteries that still represents a limitation, especially among women. Mid- to long-term studies and prospective registries are mandatory to evaluate the long-term efficacy and safety, as a comparison between E-nside and other alternative off-the-shelf solutions. Full article

Background: A Norwood procedure performed after 14 days of life is notably burdened by a high mortality. We analysed the real risk and which other factors influence the mortality in late Norwood procedures. Methods: A single-centre, retrospective review of a series of consecutive patients who underwent a surgical Norwood procedure from January 2019 until December 2023. The patients’ characteristics were considered to identify the factors associated with in-hospital and interstage mortality. Results: 35 patients were included and 71% (25) of the patients underwent the Norwood procedure after 14 days of life. The median age was 27 days (6–259 days). The in-hospital mortality was 26% (9/35) with 89% (8) of the deceased being older than 15 days at the time of the surgery. Other factors that negatively affected the outcome were a restrictive interatrial septum defect (ASD) in 66% of all patients (23), the need for mechanical ventilation in 46% (16) and systemic infection prior to surgery in 43% (15). Conclusions: Age at the time of Norwood was not associated with a higher risk of mortality, but other factors such as restrictive ASD, preoperative infection and the need for mechanical ventilation prior to surgery are even more important in predicting the short-term outcome. Full article

Background/Objectives: Hybrid palliation (HP) procedures for hypoplastic left heart syndrome (HLHS) are increasing. Our objective was to compare mortality and morbidity following HP and NP (Norwood palliation) procedures. Methods: Systematic review and meta-analysis of HLHS patients of peer-reviewed literature between 2000 and 2023. Mortality and/or heart transplantation in HP versus NP in the neonatal period, interstage period, and at 1, 3 and 5 years of age, and morbidity including completion of Stage II and Stage III palliation, unexpected interventions, pulmonary artery pressures, right ventricle function, neurodevelopmental outcomes and length of hospital stay were evaluated. Results: Twenty-one (meta-analysis: 16; qualitative synthesis: 5) studies evaluating 1182 HLHS patients included. HP patients had higher interstage mortality (RR = 1.61; 95% CI: 1.10–2.33; p = 0.01) and 1-year mortality (RR = 1.22; 95% CI: 1.03–1.43; p = 0.02) compared to NP patients without differences in 3- and 5-years mortality. HP procedure in high-risk HLHS patients had lower mortality (RR = 0.48; 95% CI: 0.27–0.87; p = 0.01) only in the neonatal period. HP patients underwent fewer Stage II (RR = 0.90; 95% CI: 0.81–1.00; p = 0.05) and Stage III palliation (RR = 0.78; 95% CI: 0.69–0.90; p < 0.01), had more unplanned interventions (RR = 3.38; 95% CI: 2.04–5.59; p < 0.01), and longer hospital stay after Stage I palliation (weighted mean difference = 12.88; 95% CI: 1.15–24.62; p = 0.03) compared to NP patients. Conclusions: Our study reveals that HP, compared to NP for HLHS, is associated with increased morbidity risk without an improved survival rate. Full article

Associating liver partition and portal vein ligation for staged hepatectomy (ALPPS) is a two-stage procedure that can potentially cure patients with large cholangiocarcinoma. The current study evaluates the impact of modifications on the outcomes of ALPPS in patients with cholangiocarcinoma. In this single-center study, a series of 30 consecutive patients with cholangiocarcinoma (22 extrahepatic and 8 intrahepatic) who underwent ALPPS between 2011 and 2021 was evaluated. The ALPPS procedure in our center was modified in 2016 by minimizing the first stage of the surgical procedure through biliary externalization after the first stage, antibiotic administration during the interstage phase, and performing biliary reconstructions during the second stage. The rate of postoperative major morbidity and 90-day mortality, as well as the one- and three-year disease-free and overall survival rates were calculated and compared between patients operated before and after 2016. The ALPPS risk score before the second stage of the procedure was lower in patients who were operated on after 2016 (before 2016: median 6.4; after 2016: median 4.4; p = 0.010). Major morbidity decreased from 42.9% before 2016 to 31.3% after 2016, and the 90-day mortality rate decreased from 35.7% before 2016 to 12.5% after 2016. The three-year survival rate increased from 40.8% before 2016 to 73.4% after 2016. Our modified ALPPS procedure improved perioperative and postoperative outcomes in patients with extrahepatic and intrahepatic cholangiocarcinoma. Minimizing the first step of the ALPPS procedure was key to these improvements. Full article

Breast cancer is a common cancer that affects women all over the world. Therefore, detection at an early stage is crucial for reducing the mortality rate linked to this disease. Mammography is the primary screening method for breast cancer. However, it has drawbacks, including high rates of false-positive and negative results, inter-observer variability, and limited sensitivity with dense breast tissue. To solve such problems, breast cancer was analyzed and classified using mammography images and deep learning models from the Radiological Society of North America (RSNA) database. This database contains processed and raw images from the RSNA that consist of annotated malignancies and clinical data. Using deep learning models based on convolutional neural network (CNN) models such as visual geometry group (VGG), Googlenet, EfficientNet, and Residual Networks, mammograms were classified into cancer or non-cancer categories. In this study, a novel architecture was proposed by combining CNNs and attention mechanisms, which extracted and highlighted the relevant features. A dataset of 8000 patients with 47,000 photographs was used to train and assess the model via 5-fold cross-validation. The results outperformed prior methods using the same database and reached an average accuracy rate of 95%. The results showed that mammography with deep learning methods considerably improved breast cancer detection and diagnosis. Full article

Breast cancer (BC) is the leading cause of cancer mortality among women in Ethiopia. Overall, women of African ancestry have the highest death toll due to BC compared to other racial/ethnic groups. The cause of the disparity in mortality is unclear. Recently, studies conducted in the United States and other high-income countries highlighted the role of microbial dysbiosis in BC initiation, tumor growth, and treatment outcome. However, the extent to which inter-individual differences in the makeup of microbiota are associated with clinical and histopathological outcomes in Ethiopian women has not been studied. The goal of our study was to profile the microbiome in breast tumor and normal adjacent to tumor (NAT) tissues of the same donor and to identify associations between microbial composition and abundance and clinicopathological factors in Ethiopian women with BC. We identified 14 microbiota genera in breast tumor tissues that were distinct from NAT tissues, of which Sphingobium, Anaerococcus, Corynebacterium, Delftia, and Enhydrobacter were most significantly decreased in breast tumors compared to NAT tissues. Several microbial genera significantly differed by clinicopathological factors in Ethiopian women with BC. Specifically, the genus Burkholderia more strongly correlated with aggressive triple negative (TNBC) and basal-like breast tumors. The genera Alkanindiges, Anoxybacillus, Leifsonia, and Exiguobacterium most strongly correlated with HER2-E tumors. Luminal A and luminal B tumors also correlated with Anoxybacillus but not as strongly as HER2−E tumors. A relatively higher abundance of the genus Citrobacter most significantly correlated with advanced-stage breast tumors compared to early-stage tumors. This is the first study to report an association between breast microbial dysbiosis and clinicopathological factors in Ethiopian women. Full article

Selected patients with early-stage melanoma have a “hidden high risk” of poor oncologic outcomes. They might benefit from clinical trials, and ultimately, if warranted by trial results, judicious everyday use of adjuvant therapy. A promising tool to identify these individuals is the immunoprint^® assay. This immunohistochemical 7-biomarker prognostic test was clinically validated in three independent cohorts (N = 762) to classify early-stage patients as high-risk or low-risk regarding melanoma recurrence and mortality. Using College of American Pathologists (CAP) recommendations, we analytically validated this assay in primary melanoma specimens (N = 20 patients). We assessed assay precision by determining consistency of risk classification under repeated identical conditions (repeatability) or across varying conditions (reproducibility), involving separate assay runs, operators (laboratory scientists), and/or observers (e.g., dermatopathologists). Reference classification was followed by five analytical validation phases: intra-run/intra-operator, intra-observer, inter-run, inter-operator, and inter-observer. Concordance of classifications in each phase was assessed via Fleiss’ kappa (primary endpoint) and percent agreement (secondary endpoint). Seven-marker signature classification demonstrated high consistency across validation categories (Fleiss’ kappa 0.864–1.000; overall percent agreement 95–100%), in 9/10 cases, exceeding, and in 1/10 cases, closely approaching, CAP’s recommended 0.9 level. The 7-marker assay has now been verified to provide excellent repeatability, reproducibility, and precision, besides having been clinically validated. Full article

Islands offer exclusive prisms for an experimental investigation of biodiversity x ecosystem function interplay. Given that species in upper trophic layers, e.g., arthropod predators, experience a comparative disadvantage on small, isolated islands, such settings can help to clarify how predation features within biotic resistance equations. Here, we use observational and manipulative studies on a chain of nine Indonesian islands to quantify predator-mediated biotic resistance against the cassava mealybug Phenacoccus manihoti (Homoptera: Pseudococcidae) and the fall armyworm Spodoptera frugiperda (Lepidoptera: Noctuidae). Across island settings, a diverse set of generalist lacewing, spider and ladybeetle predators aggregates on P. manihoti infested plants, attaining max. (field-level) abundance levels of 1.0, 8.0 and 3.2 individuals per plant, respectively. Though biotic resistance—as imperfectly defined by a predator/prey ratio index—exhibits no inter-island differences, P. manihoti population regulation is primarily provided through an introduced monophagous parasitoid. Meanwhile, resident predators, such as soil-dwelling ants, inflict apparent mortality rates up to 100% for various S. frugiperda life stages, which translates into a 13- to 800-fold lower S. frugiperda survivorship on small versus large islands. While biotic resistance against S. frugiperda is ubiquitous along the island chain, its magnitude differs between island contexts, seasons and ecological realms, i.e., plant canopy vs. soil surface. Hence, under our experimental context, generalist predators determine biotic resistance and exert important levels of mortality even in biodiversity-poor settings. Given the rapid pace of biodiversity loss and alien species accumulation globally, their active conservation in farmland settings (e.g., through pesticide phasedown) is pivotal to ensuring the overall resilience of production ecosystems. Full article