Search Results (1,442)

Background/Objectives: This study aimed to characterize the expression patterns of B7 homolog 3 (B7-H3) and cluster of differentiation 155 (CD155), two immune-related transmembrane glycoproteins, in resectable gastric adenocarcinoma and to elucidate their clinicopathological, prognostic, and molecular implications. Methods: The study included 112 patients who underwent gastrectomy for gastric adenocarcinoma between 2020 and 2025, along with 30 samples of normal gastric tissue obtained from sleeve gastrectomy specimens. Histological subtype, grade of differentiation, TNM stage, and invasion parameters were re-evaluated. Immunohistochemical expression of B7-H3 and CD155 was quantified for membranous, stromal and membranous/cytoplasmic staining patterns. Quantitative reverse transcription polymerase chain reaction (RT-PCR) was performed on 29 tumor and 25 normal samples to confirm mRNA expression levels, with fold change ≥2 considered biologically significant upregulation and ≤0.5 considered downregulation. Machine learning models were developed to predict metastasis and mortality based on clinical and immunohistochemical features. Results: 78.5% of tumors were at an advanced stage (T3–T4), and metastasis was present in 22.3% of patients. Perineural invasion (PNI) and lymphovascular invasion (LVI) were observed in 67.9% and 88.4% of cases, respectively. Increased B7-H3 and CD155 expression were significantly associated with advanced tumor stage, metastasis, and the presence of PNI and LVI (all p < 0.05). In metastatic tumors, median membranous B7-H3, stromal B7-H3, and CD155 scores were 60, 130, and 190, respectively, compared with 20, 90, and 120 in non-metastatic tumors. A significant positive correlation was found between stromal B7-H3 and CD155 expression (r = 0.384, p < 0.001), indicating parallel upregulation. Quantitative RT-PCR confirmed significant overexpression of both genes in tumor tissues relative to normal controls. B7-H3 was upregulated in 75.9% and CD155 in 58.6% of samples, with co-upregulation in 55.2%. Fold-change levels were markedly higher in metastatic versus non-metastatic cases (B7-H3: 7.69-fold vs. 3.04-fold; CD155: 7.44-fold vs. 1.79-fold). ML analysis using the XGBoost model achieved 91.1% accuracy for metastasis prediction (F1-score 0.800). Key variables included pathological T4b stage, perineural invasion, N3b status, T4a stage, and CD155 score. The mortality model yielded 86.7% accuracy (F1-score 0.864), with metastasis, differentiation status, nodal involvement, age, lymph node ratio, and perineural invasion emerging as principal predictors. Conclusions: Combined evaluation of B7-H3 and CD155, supported by immunohistochemical staining and RT-PCR quantification of B7-H3 and CD155 mRNA expression levels, provides meaningful prognostic insights and supports their potential as dual molecular biomarkers for aggressive gastric adenocarcinoma phenotypes. Full article

Background/Objectives: Crohn’s disease (CD) requires precise, noninvasive monitoring to guide therapy and support treat-to-target management. Magnetic resonance enterography (MRE), particularly diffusion-weighted imaging (DWI), is the preferred cross-sectional technique for assessing small-bowel inflammation. Indices such as the Magnetic Resonance Index of Activity (MaRIA) and its diffusion-weighted variant (DWI MaRIA) are widely used for grading disease activity. This study evaluated whether unsupervised clustering of MRI-derived features can complement these indices by providing more coherent and biologically grounded stratification of disease activity. Materials and Methods: Fifty patients with histologically confirmed CD underwent 1.5 T MRE. Of 349 bowel segments, 84 were pathological and classified using literature-based thresholds (MaRIA, DWI MaRIA) and unsupervised clustering. Differences between inactive, active, and severe disease were analyzed using multivariate analysis of variance (MANOVA), analysis of variance (ANOVA), and t-tests. Mahalanobis distances were calculated to quantify and compare separation between categories. Results: Using MaRIA thresholds, 5, 16, and 63 segments were classified as inactive, active, and severe (Mahalanobis distances 2.60, 4.95, 4.12). Clustering redistributed them into 22, 37, and 25 (9.26, 24.22, 15.27). For DWI MaRIA, 21, 14, and 49 segments were identified under thresholds (3.59, 5.72, 2.85) versus 21, 37, and 26 with clustering (7.40, 16.35, 9.41). Wall thickness dominated cluster-derived separation, supported by diffusion metrics and the apparent diffusion coefficient (ADC). Conclusions: Cluster-derived classification yielded clearer and more biologically consistent separation of disease-activity groups than fixed thresholds, emphasizing its potential to refine boundary definition, enhance MRI-based assessment, and inform future AI-driven diagnostic modeling. Full article

Background/Objectives: In a series of 33 patients with advanced penile squamous cell carcinoma (PSCC), we evaluated tissue factor (TF), TROP2, and nectin-4 protein expression as potential therapeutic targets. Expression levels of these proteins were also correlated to clinicopathological characteristics, including high-risk human papillomavirus (HPV), CDKN2A (p16) status, and aberrant p53 expression. Methods: A tissue microarray (TMA) was constructed with three cores per patient tumor (99 total cores). Anti-TF antibody staining was performed by immunohistochemistry, and H-scores for membrane and cytoplasm staining were assessed (range 0–300). The percentage of cores and patient tumors staining positive for TF (≥10% of tumor cells with at least 1+ intensity in cytoplasm and/or membrane) and H-scores were described and compared with HPV and p16 status. The association of TF expression with tumor grade, presence of metastatic disease, lymphovascular invasion (LVI), perineural invasion (PNI), aberrant p53 expression, recurrence-free survival (RFS), and cancer-specific survival (CSS) was assessed. Nectin-4 and TROP2 staining and their association with clinical/pathological data were determined in a similar manner. Results: TF staining was evident in 26 (81.3%) of the cohort and was more prominent in HPV-negative tumors in both the membrane (H-score 69.6 vs. 18.8; p = 0.003) and cytoplasm (H-score 59.2 vs. 17.7, p = 0.007). Cytoplasmic (H-score 61.7 vs. 11.7, p < 0.001) and membrane TF staining (H-score 71.7 vs. 15.0, p < 0.001) favored p16-negative tumors. The p53 status was more likely to be aberrant in the higher TF staining samples (cytoplasm H-score 61.7 vs. 18.3, p = 0.012; membrane H-score 67.5 vs. 20.3, p = 0.006). We observed an association with TROP2 staining and positive p16 status (membrane H-score 120.3 vs. 85, p = 0.052; cytoplasmic H-score 135 vs. 107.5, p = 0.041). We observed an association of TROP2 staining with positive LVI (membrane H-score 136.7 vs. 66.7, p = 0.014; cytoplasmic H-score 110 vs. 93.3, p = 0.04). We found no association between TF, TROP2, or nectin-4 staining with CSS or RFS; however, we suspect that this was due to our small sample size. Conclusions: Our results indicate that TF was expressed in the majority of advanced PSCC with enhanced expression among HPV-independent, p53-aberrant tumors and may represent a novel therapy target in advanced PSCC. Full article

Background/Objectives: Accurate diagnosis, prognosis, and prediction of treatment response are essential in managing gynecologic cancers and maintaining patient quality of life. Computational pathology, powered by artificial intelligence (AI), offers a transformative opportunity for objective histopathological assessment. This review provides a comprehensive, user-oriented overview of existing AI tools for the characterization of gynecological cancers, critically evaluating their clinical applicability and identifying key challenges for future development. Methods: A systematic literature search was conducted in PubMed and Web of Science for studies published up to 2025. The search focused on AI tools developed for the diagnosis, prognosis, or treatment prediction of gynecologic cancers based on histopathological images. After applying selection criteria, 36 studies were included for in-depth analysis, covering ovarian, uterine, cervical, and other gynecological cancers. Studies on cytopathology and pure tumor detection were excluded. Results: Our analysis identified AI tools addressing critical clinical tasks, including histopathologic subtyping, grading, staging, molecular subtyping, and prediction of therapy response (e.g., to platinum-based chemotherapy or PARP inhibitors). The performance of these tools varied significantly. While some demonstrated high accuracy and promising results in internal validation, many were limited by a lack of external validation, potential biases from training data, and performance that is not yet sufficient for routine clinical use. Direct comparison between studies was often hindered by the use of non-standardized evaluation metrics and evolving disease classifications over the past decade. Conclusions: AI tools for gynecologic cancers represent a promising field with the potential to significantly support pathological practice. However, their current development is heterogeneous, and many tools lack the robustness and validation required for clinical integration. There is a pressing need to invest in the creation of clinically driven, interpretable, and accurate AI tools that are rigorously validated on large, multicenter cohorts. Future efforts should focus on standardizing evaluation metrics and addressing unmet diagnostic needs, such as the molecular subtyping of rare tumors, to ensure these technologies can reliably benefit patient care. Full article

Atrial fibrillation (AF) and ventricular arrhythmias (VAs) are common pathological arrhythmias of dogs and are both associated with a poor prognosis in those with cardiac disease. This study aimed to assess the ability of 2 to 5 min electrocardiography (routine ECG) to detect the presence and severity of concomitant VAs in dogs with secondary AF. Continuous 24 h ECG monitoring (Holter) was used as the reference standard to identify VAs, quantify the number of premature ventricular ectopic complexes (VPCs) and evaluate the degree of their organization using a modified Lown–Wolf classification scale. In light of the Holter findings, VAs were classified as severe based on two criteria: the presence of more than 100 VPCs and a Lown–Wolf grade ≥ 4. Thirty-five dogs with secondary AF were included, where all exhibited VAs on Holter monitoring. Most dogs had severe VAs, according to both the VPC count (69%) and Lown–Wolf classification (77%). However, only 13 dogs (37%) had VAs detectable on routine ECG. A significant positive correlation was found between the presence of VAs on routine ECG and the severity of VAs identified via Holter. Nevertheless, the diagnostic accuracy of routine ECG in predicting severe VAs was only moderate (68.6% based on VPC count and 60% based on Lown–Wolf grade). Overall, a 2 to 5 min ECG appears to be a highly specific but relatively insensitive tool for detecting VAs in dogs with secondary AF. Full article

High-grade serous carcinoma (HGSC) of the ovary is characterized by two major histological patterns: a classic papillary/micropapillary architecture and a solid pseudo-endometrioid transitional (SET) variant. We investigated whether the distinct morphologic subtypes are underpinned by transcriptomic differences in the tumor microenvironment (TME). We profiled 21 HGSC tumors (7 SET, 14 classic) using a 770-gene NanoString PanCancer Progression panel. Differential expression analysis revealed ~20 genes with significantly different expression (>4-fold, adjusted p < 0.01) between SET and classic tumors. Unsupervised clustering partially separated SET and classic tumors, suggesting that global gene expression patterns correlate with histologic subtype. SET tumors exhibited upregulation of cell-cycle and epithelial genes (e.g., PTTG1, TRAIL, HER3) and downregulation of genes involved in epithelial–mesenchymal transition (EMT), extracellular matrix (ECM) organization, and angiogenesis (e.g., TWIST2, FGF2, decorin) relative to classic tumors. Notably, PTTG1 and TRAIL were upregulated ~6–9-fold in SET tumors, whereas TWIST2 was ~7-fold downregulated, consistent with reduced EMT in SET tumors. Pathway analysis indicated that SET tumors appear to have an immune-active, stroma-poor microenvironment, in line with an “immunoreactive” phenotype, whereas classic tumors showed a mesenchymal, stroma-rich profile. These molecular distinctions could have diagnostic utility and may inform therapeutic stratification, with key dysregulated genes (e.g., HER3, TRAIL, FGF2) representing potential prognostic or predictive biomarkers. For example, high HER3 expression in SET tumors might predict sensitivity to ERBB3/PI3K inhibitors, whereas stromal factors (e.g., FGF2) enriched in classic HGSC could be targeted with microenvironment-modulating therapies. These preliminary findings require validation before translation into pathology practice via immunohistochemical (IHC) assays (e.g., for HER3 or TRAIL), potentially enabling improved classification and personalized treatment of HGSC. We report effect sizes as log₂ fold change with 95% confidence intervals and emphasize FDR-adjusted q-values. Given the small sample size and the absence of outcome data (OS/PFS/PFI), results are preliminary and hypothesis-generating. Orthogonal protein-level validation and replication in larger, independent cohorts are required before any translational inference. Full article

Background: The purpose of this study was to investigate the value of machine learning models for preoperative non-invasive prediction of International Society of Urological Pathology (ISUP) grading in clear cell renal cell carcinoma (ccRCC) based on CT urography (CTU)-related peritumoral area (PAT) radiomics features. Methods: We retrospectively analysed 328 ccRCC patients from our institution, along with an external validation cohort of 175 patients from The Cancer Genome Atlas. A total of 1218 radiomics features were extracted from contrast-enhanced CT images, with LASSO regression used to select the most predictive features. We employed four machine learning models, namely, Logistic Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), for training and evaluation using Receiver Operating Characteristic (ROC) analysis. The model performance was assessed in training, internal validation, and external validation sets. Results: The XGBoost model demonstrated consistently superior discriminative ability across all datasets, achieving AUCs of 0.95 (95% CI: 0.92–0.98) in the training set, 0.93 (95% CI: 0.89–0.96) in the internal validation set, and 0.92 (95% CI: 0.87–0.95) in the external validation set. The model significantly outperformed LR, MLP, and SVM (p < 0.001) and demonstrated prognostic value (Log-rank p = 0.018). Transcriptomic analysis of model-stratified groups revealed distinct biological signatures, with high-grade predictions showing significant enrichment in metabolic pathways (DPEP3/THRSP) and immune-related processes (lymphocyte-mediated immunity, MHC complex activity). These findings suggest that peritumoral imaging characteristics provide valuable biological insights into tumor aggressiveness. Conclusions: The machine learning models based on PAT radiomics features of CTU demonstrated significant value in the non-invasive preoperative prediction of ISUP grading for ccRCC, and the XGBoost modeling had the best predictive ability. This non-invasive approach may enhance preoperative risk stratification and guide clinical decision-making, reducing reliance on invasive biopsy procedures. 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

Background: Neoadjuvant chemotherapy (NACT) is widely used in patients with high-risk HER2-amplified (HER2+) or triple negative early breast cancer (TNBC). Advantages of NACT include allowing less extensive surgery, assessing response to treatment and guiding adjuvant therapy. NACT-related toxicities are common and can result in treatment alterations and hospitalisation, which may adversely impact outcomes. Aim: To assess NACT treatment in Hawke’s Bay (HB), New Zealand, by evaluating pathologic complete response (pCR) rates and toxicities of different regimens. Method: Data were retrospectively obtained from medical records of NACT patients. pCR rates were compared to results from the previous literature. Toxicity was assessed by recording severe (grade 3 or above) toxicities, treatment-limiting toxicities (those leading to dose reductions, dose delays or early cessation) and hospitalisations for different NACT regimens. Results: A total of 71 NACT patients were included. pCR rates for HER2+ disease and TNBC were 19/45 (42%) and 8/24 (33%), respectively. The most common severe toxicities were diarrhoea, anaemia and febrile neutropaenia (all 16%) in FEC-D (5-fluorouracil/epirubicin/cyclophosphamide + docetaxel +/− carboplatin +/− immunotherapy) patients, neutropaenia (50%) in FEC-DH (FEC-D + trastuzumab +/− pertuzumab) patients and diarrhoea (38%) in TCH (docetaxel/carboplatin/trastuzumab +/− pertuzumab) patients. Comparing treatment-limiting toxicity in FEC-DH vs. TCH, 9/16 (56%) vs. 13/21 (62%) had dose reduction, 2/16 (13%) vs. 8/21 (38%) had dose delay, 1/16 (6%) vs. 5/21(24%) had early cessation and 6/16 (38%) vs. 13/21 (62%) were hospitalised, respectively. Conclusions: NACT was associated with high rates of severe and treatment-limiting toxicity. Despite this, pCR rates were consistent with the previous literature. With the caveat of small patient numbers, FEC-DH-based therapy was associated with fewer dosing delays, early cessations and hospitalisations compared with TCH-based therapy. Full article

Frozen shoulder (FS), traditionally regarded as an idiopathic musculoskeletal disorder characterized by pain, stiffness, and capsular fibrosis, is increasingly recognized as the clinical manifestation of systemic endocrine, metabolic, vascular, and immunological dysfunctions. This narrative review reframes FS within a broader neuro–endocrine–immunometabolic model, emphasizing the central role of estrogen deficiency, resistance, and receptor-level disruption, together with their interactions with thyroid dysfunction, endothelial health, and lifestyle-related low-grade inflammation (LGI). Evidence from epidemiological, clinical, and mechanistic studies shows that estrogen signaling failure weakens anti-inflammatory, antifibrotic, and antioxidant defenses, predisposing peri- and postmenopausal women to more severe FS phenotypes. Thyroid dysfunction, particularly hypothyroidism, further contributes to fibrosis and pain sensitization. Endothelial dysfunction—driven by poor diet, advanced glycation end-products (AGEs), and oxidative stress—impairs vascular integrity and promotes local microvascular inflammation. In parallel, lifestyle factors such as sedentarism, circadian misalignment, psychosocial stress, and environmental exposures sustain systemic LGI and hormonal resistance. Together, these interconnected mechanisms suggest that FS is not merely a localized joint pathology but a systemic disorder requiring integrative clinical strategies that combine orthopedic management with endocrine evaluation, metabolic monitoring, dietary interventions, circadian health, and stress regulation. In addition, this review outlines specific clinical implications, highlighting how an integrative, personalized approach that targets hormonal, metabolic, vascular, and lifestyle dimensions may improve pain, function, and long-term prognosis in FS. This paradigm shift underscores the need for future research to focus on stratified patient profiling and interventional trials targeting hormonal, vascular, and lifestyle axes to improve outcomes, particularly in women who remain disproportionately affected by FS. Full article

Efforts by the World Health Organization (WHO) have clarified the descriptive nomenclature and histologic grading of neuroendocrine neoplasms (NENs) in human medicine. Employing a standardized stratification scheme in conjunction with specific immunohistochemical markers, such as gastrin, enhances prognostic accuracy and guides treatment recommendations. Yet, this classification system has yet to be applied consistently in veterinary pathology. Histopathologic features and gastrin expression were analyzed in a group of canine gallbladder (GB) NENs. Based on the human WHO histologic system, which stratifies grade based on proliferative indices (mitotic count and Ki67%), all gradable GB NENs were classified as neuroendocrine tumors (NETs) rather than neuroendocrine carcinomas (NECs). Only one GB NET was positive for gastrin using immunohistochemical staining. Collectively, our data suggest that canine GB NENs have a lower grade than most human GB NENs and rarely express gastrin. The use of proliferative indices in the histologic characterization of canine GB NENs is likely to improve prognostic information. Given the limited expression of gastrin in these neoplasms in dogs, this marker is unlikely to be widely applicable as a druggable target. Full article

Background/Objectives: Sentinel lymph node biopsy (SLNB) is currently the standard approach for axillary staging in breast cancer. Conventional techniques are radioisotope-based (Technetium-99m, Tc99m) and remain widely used, but novel tracers like Indocyanine Green (ICG) fluorescence provide potential advantages regarding feasibility and logistics. Methods: We conducted a prospective, observational study including 476 female patients diagnosed with primary invasive breast cancer who underwent SLNB at the Institute of Oncology “Prof. Dr. I. Chiricuță”, Cluj-Napoca, Romania, between January 2022 and May 2025. Clinical, surgical, and pathological variables were systematically extracted. SLNB was performed using either Tc99m or ICG, according to institutional protocols. Comparative analyses were performed to evaluate sentinel node characteristics, histopathological parameters, and positive surgical margins predictors. Results: The median age was 60 years (IQR: 52–69). Breast-conserving surgery (BCS) was performed in 77.9% of cases, while mastectomy was performed in 22.1%. Sentinel lymph node positivity was reported in 25.6% of cases, with no significant differences in the number of excised or metastatic nodes between Tc99m and ICG (mean nodes: 3.23 vs. 3.20, p = 0.860; mean positive nodes: 0.35 vs. 0.36, p = 0.897). Histologically, invasive carcinoma NST was predominant (90.1%), and surgical margins were negative in 96.8% of patients, with all margin-positive cases occurring following BCS. No pathological markers (grade, Ki67, TILs, DCIS extent) predicted margin status or nodal involvement. Notably, younger age correlated inversely with the extent of ductal carcinoma in situ (r = −0.21, p < 0.00001). Conclusions: Tc99m and ICG provided comparable diagnostic performance in performing SLNB, with equivalent rates of nodal detection and pathological yield. These findings support that ICG is a safe and effective alternative for routine axillary staging in breast cancer. Full article

Polycystic ovary syndrome (PCOS) is a systemic metabolic and endocrine disorder that significantly disrupts reproductive physiology and endometrial function. In this narrative review, we examine the molecular impact of metabolic and hormonal imbalances on the endometrium of women with PCOS. We investigate the specific mechanisms that delineate how hyperinsulinemia and insulin resistance, chronic low-grade inflammation, and estrogen/progesterone/androgen imbalance contribute to altered epigenetic, transcriptomic, metabolomic, and signaling profiles in a wide array of different cell types within endometrial tissues. The synergistic interplay between upregulated inflammatory cytokines (e.g., IL-1,2,6,8,17,18, and TNF-α), along with key changes in critical molecular pathways associated with hyperinsulinemia and insulin resistance (e.g., PI3K/AKT/MAPK, and Wnt/β-catenin), in addition to aberrant sex steroid hormone signaling (e.g., CYP19A1, COX-2, PGE₂, HOXA10, 11βHSD2), promotes deleterious changes within the endometrial microenvironment. These anomalies underpin a spectrum of clinical manifestations observed in women with PCOS at each stage of the life course, including abnormal uterine bleeding in reproductive-age women, impaired decidualization in pregnancy, and altered postmenopausal endometrial physiology. Clinically, these alterations are associated with abnormal uterine bleeding, subfertility, implantation failure, miscarriage, pregnancy complications, and postmenopausal endometrial hyperplasia and cancer. Overall, our review provides novel insights into the molecular mechanisms linking systemic metabolic and endocrine dysfunction with endometrial pathology in PCOS and has broader implications that apply to all women. Full article

Skeletal muscle pathologies, including sarcopenia, inflammatory myopathies, and various muscular dystrophies, are strongly influenced by chronic low-grade inflammation and impaired proteostasis. Immunoproteasomes (IMPs), inducible proteolytic complexes activated by pro-inflammatory cytokines, are emerging as regulators linking immune signaling to protein quality control. Evidence suggests that IMPs have paradoxical, context-dependent roles in skeletal muscle. On one hand, they can support proteostasis and muscle regeneration under stress; on the other, persistent activation may sustain cytokine production, antigen presentation, and maladaptive immune–muscle interactions, promoting chronic inflammation and muscle wasting. Selective IMP inhibitors, such as ONX 0914 and KZR-616, display potent anti-inflammatory effects in preclinical models of autoimmune myositis and muscle atrophy. Yet, their use in skeletal muscle pathologies is controversial; while inhibition may dampen harmful immune activation, it could also impair muscle repair and proteostasis. This review summarizes current findings, highlights key contradictions, and explores unresolved questions about the role of IMPs in skeletal muscle pathologies. We emphasize the need for a deeper understanding of IMP-mediated mechanisms in skeletal muscle pathology and strategies combining selective inhibitors to enhance therapeutic efficacy while minimizing adverse effects. IMPs thus represent both a promising and potentially risky therapeutic target, with outcomes highly dependent on disease context. Full article

Background and Objectives: Minimally invasive surgery is considered the standard of care for early-stage endometrial cancer. However, the oncological safety of intrauterine manipulator (IUM) use during laparoscopic hysterectomy remains controversial. The aim of this study was to evaluate the impact of intrauterine manipulator use during laparoscopic hysterectomy on oncological outcomes in patients with clinically early-stage endometrial cancer. Materials and Methods: In this retrospective cohort study, 612 patients with FIGO 2009 stage I–III endometrial cancer who underwent staging surgery at a tertiary center between January 2010 and May 2025 were included. Clinical and pathological characteristics were compared between laparoscopy (n = 168) and laparotomy (n = 444). To reduce selection bias, propensity score matching (PSM) was performed based on age, histological subtype, and FIGO stage. Kaplan–Meier survival analysis and Cox regression modeling were utilized to evaluate disease-free survival (DFS) and overall survival (OS). Results: After matching, groups were balanced except for higher rates of para-aortic lymphadenectomy and adjuvant therapy in the laparotomy group. IUM use was not associated with increased LVSI or positive peritoneal cytology. Recurrence was more frequent after laparoscopy (10.1% vs. 6.0%, p = 0.028), with inferior 5-year DFS (87.6% vs. 97.4%, HR 5.60, p = 0.0006), while OS was similar (82.0% vs. 87.6%, p = 0.842). In multivariate Cox analysis, independent predictors of worse DFS were non-endometrioid histology (HR 3.57), FIGO stage III (HR 3.06), grade 3 tumors (HR 2.63), and laparoscopic surgery (HR 0.51). For OS, non-endometrioid histology (HR 5.12), stage III disease (HR 2.98), and grade 3 tumors (HR 4.51) were independent adverse factors, whereas surgical approach was not. Conclusions: The use of an intrauterine manipulator in laparoscopic hysterectomy for early-stage endometrial cancer was linked to worse DFS but not OS. These findings suggest caution regarding the routine use of IUMs and highlight the need for prospective randomized trials to clarify their oncological safety. Full article