Background and Objectives: Mean platelet volume (MPV) is a routinely available blood marker that measures platelet size and activation, and it has been evaluated as a potential marker for thyroid malignancies. Platelets participate in tumor genesis through angiogenesis, immune evasion, and metastasis, making them plausible adjuncts for cancer risk evaluation. The objective is to systematically evaluate the role of MPV in thyroid cancers, with the main focus on diagnostic accuracy, prognostic value, and limitations, focusing on papillary thyroid carcinoma (PTC). Materials and Methods: A systematic search of PubMed was conducted from January 2015 to September 2025. Only free full-text studies on human subjects were included. Eligible studies included case–control, cohort, or observational designs reporting MPV or platelet indices in thyroid cancer compared with benign nodules or healthy controls. Data on diagnostic performance, associations with tumor stage, lymph node involvement, and recurrence were extracted and synthesized narratively. No formal risk-of-bias or study quality assessment tool was applied. The literature search was restricted to studies with freely available full-text articles, which may have introduced access-based selection bias. Results: Eleven studies met the inclusion criteria. Most of them reported high MPV values in papillary thyroid carcinoma (PTC), with limited evidence regarding other thyroid cancer subtypes. High values of MPC were reported in the majority of studies in PTC compared to benign nodules or healthy controls. The diagnostic performance of MPV alone was poor, but integration with inflammatory ratios such as the neutrophil-to-lymphocyte ratio (NLR) or platelet-to-lymphocyte ratio (PLR), and with ultrasound systems (TI-RADS), improved accuracy. Regarding prognostic utility, some studies linked higher MPV with lymph node involvement or recurrence risk, while others did not find significant data. Thyroid function, autoimmune thyroid disease, and methodological variability in MPV measurement limited comparability across studies. Conclusions: MPV is a low-cost adjunct biomarker, especially when combined with other hematologic and imaging markers. However, MPV should not be used as a stand-alone diagnostic or prognostic tool. Larger, prospective studies are mandatory to clarify its clinical role.

Introduction: Severe acute pancreatitis (SAP) is a critical condition that affects 20–30% of people with acute pancreatitis (AP). Prompt detection and accurate classification are crucial to direct prompt interventions, increase resource allocation, and improve patient outcomes. Current scoring systems, while beneficial, frequently face challenges related to speed, complexity, and early predictive accuracy. Method: We developed and validated an effective six-parameter risk assessment scale for AP, incorporating pancreatic-specific biomarkers (trypsinogen-activating peptide [TAP], trypsin-2), systemic inflammation markers (C-reactive protein), pancreatic enzyme concentrations, blood glucose, and patient age. The study cohort included 104 patient samples. Reliability was assessed using Cronbach’s alpha and Spearman–Brown coefficients, factorial validity was determined by principal component analysis, and predictive validity was analyzed using logistic regression and receiver operating characteristic (ROC) analysis. Biotemporal changes at 24 and 48 h were assessed to classify risk scoring. Results: The scale demonstrated satisfactory internal consistency (Cronbach’s alpha = 0.72) and a distinct structure with two factors representing local pancreatic damage and systemic inflammation, explaining 65% of the variability. Logistic regression established predictive validity for serious outcomes, with TAP and trypsin-2 showing significant correlations. ROC analysis demonstrated remarkable discriminative capacity (AUC = 0.85), showing a sensitivity of 82.4% and a specificity of 76.8%. Assessment of temporal biomarkers showed a reduction in TAP, signifying resolution of the initial enzymatic activation, while trypsin-2 levels continued to increase, indicating persistent damage to the pancreatic tissue. Patients were classified into low-, moderate- and high-risk groups, facilitating practical clinical decision-making. Discussion and Conclusions: This six-parameter risk score provides a rapid, biologically based, and clinically useful method for early detection of patients at risk for SAP. Combining indicators of local pancreatic involvement with systemic inflammation allows for prompt triage, improves the allocation of intensive therapy, and supports informed prognostic conversations.

Background and Objectives: Acute pancreatitis (AP) has a wide range of clinical severity, and early prediction of disease progression is still challenging. Trypsinogen-activating peptide (TAP) and trypsin-2 serve as direct biomarkers for intrapancreatic proteolytic activation and may provide earlier pathophysiological information compared with traditional markers. Materials and Methods: In this retrospective cohort analysis involving 54 AP patients, we evaluated 24 h serum and urinary TAP and trypsin-2 concentrations by Bayesian correlation, mediation analysis, unsupervised K-means clustering, and supervised machine learning (Elastic Net and Random Forest). The analyses investigated the relationships of biomarkers with inflammation (CRP), enzymatic activities (amylase, lipase), and clinical factors, as well as inflammation severity (CRP levels). Results: Bayesian correlations indicated moderate evidence for a relationship between serum TAP and CRP (BF₁₀ = 8.42), as well as strong evidence linking age to serum TAP (BF₁₀ = 12.75). Serum trypsin-2 showed no correlation with CRP, while urinary trypsin-2 had a correlation with amylase (BF₁₀ = 6.89). Mediation analysis indicated that TAP and trypsin-2 accounted for 42–44% of the impact of CRP on pancreatic enzyme elevation. Clustering revealed three phenotypic subgroups (“Mild Activation”, “Moderate System”, and “Severe Pancreatic-Renal”), the latter showing the highest levels of CRP and biomarkers. Machine learning models highlighted urinary trypsin-2 and age as the most significant predictors of inflammation, with Random Forest achieving the highest performance (R² = 0.53). Conclusions: Early urinary trypsin-2 outperforms serum markers as a predictor of systemic inflammatory intensity, indicating total proteolytic impairment and renal clearance. This integrative analysis reveals unique biological phenotypes and highlights the potential of these biomarkers for early assessment of the inflammatory burden. Their role in predicting clinical disease progression requires prospective validation. Integrative biomarker analysis reveals unique biological phenotypes and improves assessment of inflammatory burden in PA. Larger cohorts are required for prospective validation to incorporate these biomarkers into precision-based diagnostic frameworks.

Background: Early identification of patients at increased risk of severe acute pancreatitis (AP) remains challenging because clinically relevant disease heterogeneity may not be fully apparent during the initial evaluation. Premature activation of pancreatic digestive zymogens within acinar cells is a central event in the pathophysiology of AP, and biomarkers directly related to this proteolytic process may provide information that is biologically distinct from nonspecific inflammatory markers. This study investigated the prognostic relevance of early serum and urinary trypsinogen-2 and trypsin activation peptide (TAP) in patients with AP, using complementary statistical, Bayesian, and machine-learning approaches. Methods: This retrospective observational cohort included 73 adults with AP who underwent serum and urinary proteolytic biomarker assessment within the first 24 h after admission. Imaging severity was evaluated using the modified Computed Tomography Severity Index (mCTSI) and was available for 66 patients. Associations between biomarkers and clinical or imaging variables were assessed using Spearman correlation and Bayesian inference. Receiver operating characteristic (ROC) analysis evaluated urinary trypsinogen-2 for mCTSI ≥ 4 and mCTSI ≥ 8. A multiple mediation model assessed indirect pathways linking C-reactive protein (CRP) with mCTSI. Unsupervised K-means clustering was used to identify proteolytic profiles, while Elastic Net and Random Forest models were developed to predict higher imaging severity (mCTSI ≥ 6) and log-transformed CRP. No imputation was performed; each analysis used the available valid observations. Results: Urinary trypsinogen-2 concentrations increased significantly across mild, moderate, and severe imaging categories (medians 5149.8, 11,952.8, and 27,008.6 pg/mL, respectively; Kruskal–Wallis p = 0.0073). For mCTSI ≥ 8, urinary trypsinogen-2 yielded an AUC of 0.791 (95% CI, 0.638–0.944), with 77.8% sensitivity and 75.4% specificity at a threshold > 14,707 pg/mL, and a negative predictive value of 95.6%. For mCTSI ≥ 4, the AUC was 0.683 (95% CI, 0.550–0.817). Urinary trypsinogen-2 correlated positively with mCTSI (rho = 0.412, p < 0.001), serum trypsinogen-2 (rho = 0.512, p < 0.001), and urinary TAP (rho = 0.447, p < 0.001), but not with CRP, amylase, or lipase. Bayesian analysis provided strong evidence for the association between urinary trypsinogen-2 and mCTSI (BF₁₀ = 42.37). In the mediation model, the indirect pathway through urinary trypsinogen-2 was statistically significant (β = 0.14, p = 0.009). K-means clustering identified three proteolytic profiles that differed significantly in age, CRP, and BISAP score (p = 0.003, p < 0.001, and p < 0.001, respectively). For mCTSI ≥ 6, Random Forest achieved an AUC of 0.86 and Elastic Net an AUC of 0.81, with urinary trypsinogen-2 the most important predictor in both models. In CRP regression, R² was 0.53 for Random Forest and 0.41 for Elastic Net. Conclusions: Early urinary trypsinogen-2 was consistently associated with imaging severity and represented the strongest predictor across multiple analytical frameworks, including frequentist correlation, Bayesian inference, mediation analysis, clustering, and machine learning. These findings support its potential role as a component of multimodal early risk stratification in AP, providing information complementary to conventional clinical and biochemical assessment. However, the identified ROC thresholds and machine-learning models remain exploratory and require prospective, multicenter external validation before clinical implementation.

Background: Prolonged postoperative ileus (PPOI) remains a common complication after colorectal surgery, leading to delayed gastrointestinal recovery, prolonged hospitalization, and increased healthcare costs. Despite minimally invasive surgery and enhanced recovery protocols, postoperative gastrointestinal dysfunction remains frequent. Interpretation of current evidence is limited by substantial heterogeneity in PPOI definitions and outcome reporting. Methods: This systematic review was conducted according to PRISMA 2020 and Cochrane Handbook recommendations, following a protocol prospectively registered for PROSPERO (CRD420261307668). PubMed/MEDLINE, Embase, and CENTRAL were searched for randomized controlled trials published since January 2000 involving adult colorectal surgery patients. Studies evaluating all perioperative factors associated with PPOI were included. Screening, data extraction, and risk-of-bias assessment were independently performed by multiple reviewers. Findings were synthesized narratively according to perioperative phase and intervention type. Results: Of the 36 included RCTs, 7 (19.4%) reported an operational definition of POI/PPOI, of which 5 (13.9% of all included trials) applied a predefined time-based definition corresponding specifically to PPOI. The remaining studies reported POI/PPOI without sufficiently detailed diagnostic criteria or primarily assessed surrogate measures of gastrointestinal recovery. Across the included trials, effects on postoperative gastrointestinal recovery were heterogeneous, although selected analgesia strategies, early oral feeding, gum chewing, and selected pharmacological interventions showed potentially favorable effects on gastrointestinal recovery outcomes. Non-opioid and multimodal analgesic strategies were associated with earlier bowel recovery, with time to first flatus within 1.5–2 days and first bowel movement within 2–3 days. Although leukocyte count and CRP remained elevated postoperatively in the limited number of trials reporting these markers, no significant between-group differences were observed; therefore, these data do not establish an independent association between postoperative inflammation and PPOI. Conclusions: Because clinically defined PPOI was assessed in only a small proportion of included trials, the available evidence relates predominantly to postoperative gastrointestinal recovery rather than to prevention of clinically defined PPOI. PPOI remains a multifactorial complication arising within the complex interplay of surgical, pharmacological, inflammatory, and perioperative factors; however, the independent effects of opioid exposure, postoperative inflammation, and background recovery protocols cannot be established from the included trials unless these factors are directly randomized. Potential signals of benefit were observed with selected analgesic strategies, early feeding, gum chewing, and some pharmacological interventions; however, heterogeneity in PPOI definitions, interventions, and outcome reporting precludes definitive conclusions regarding their efficacy for PPOI prevention. Persistent heterogeneity in PPOI definitions limits direct comparison between studies. The randomized intervention–control comparisons identified potential signals of benefit for selected strategies, although their efficacy for preventing clinically defined PPOI remains uncertain.