Search Results (1,358)

Background/Objectives: The fetal-type posterior cerebral artery (fetal PCA) is an anatomical variant that alters hemodynamics and may influence posterior communicating artery (PCoA) aneurysm rupture risk. Aneurysm shape and size irregularity are key rupture predictors. This study investigates the impact of fetal PCA on PCoA aneurysm morphology and rupture risk using a radiomics-based approach. Methods: We retrospectively analyzed 87 patients with PCoA aneurysms (39 ruptured, 48 unruptured) treated at a tertiary center (January 2017–December 2022). Seventeen morphological parameters and 18 radiomic features were extracted per aneurysm. Patients were grouped by fetal PCA presence. Logistic regression and receiver operating characteristic (ROC) analyses identified rupture predictors. Results: Of 87 aneurysms, 38 had fetal PCA (24 ruptured, 14 unruptured), and 49 did not (15 ruptured, 34 unruptured). Fetal PCA was significantly associated with rupture (odds ratio [OR]: 3.28, p = 0.018). A higher non-sphericity index (NSI) correlated with rupture risk (OR: 3.35, p = 0.016). In non-fetal PCA aneurysms, size-related parameters such as height (6.83 ± 3.54 vs. 4.88 ± 2.57 mm, p = 0.034) and area (190.84 ± 167.08 vs. 107.94 ± 103.10 mm², p = 0.046) were key rupture predictors. In fetal PCA aneurysms, flow-related parameters like vessel angle (55.78 ± 31.39 vs. 38.51 ± 24.71, p = 0.035) were more influential. ROC analysis showed good discriminatory power, with an area under the curve: 0.726 for fetal PCA and 0.706 for NSI. Conclusions: Fetal PCA influences PCoA aneurysm rupture risk and morphology. NSI is a reliable rupture marker. Integrating morphological and anatomical data may improve rupture risk assessment and clinical decision-making. Full article

Introduction: Evidence for the diagnostic yield of noninvasive diagnostic assessment for the diagnosis of peripheral arterial disease (PAD) in diabetic foot syndrome (DFS) is poor. Pulse volume recordings (PVRs) at the forefoot level could be a valuable diagnostic tool in the presence of medial arterial calcification. Patients and methods: Patients with DFS who underwent invasive angiography between 01/2020 and 11/2024 and had corresponding PVRs performed within 30 days prior to the procedure were included. DFS was classified according to the Wagner–Armstrong classification. Clinical characteristics and hemodynamic parameters, including systolic ankle pressures and ankle–brachial index were recorded. PVRs were analyzed semiquantitatively by investigators blinded to the clinical information and quantitatively with determination of upstroke time (UST), upstroke ratio (USR), and maximum systolic amplitude (MSA). Angiographic PAD severity was graded according to the GLASS classification. Statistical analysis included univariate significance tests, 2 × 2 contingency tables, receiver–operator characteristic (ROC) analysis and determination of interobserver agreement. Results: In this study, 90 extremities of 70 patients were analyzed, 47 of whom had an ABI ≥ 1.3. Critical limb-threatening ischemia with non-pulsatile PVRs was evident in 6.7%. An abnormal PVR curve morphology (mildly or severely abnormal) yielded a sensitivity and specificity of 63.3% and 85.7% for detection of severe PAD (GLASS stages 2 and 3). Interobserver agreement of semiquantitative PVR rating was substantial (Cohen’s kappa 0.8) in 51 evaluated cases. For detection of any PAD (GLASS ≥ 1) or severe PAD (GLASS ≥ 2), we found the highest diagnostic accuracy for MSA (area under the curve [AUC] 0.89 and 0.82). With a cut-off value of 0.58 mmHg, MSA had a sensitivity of 91.4% and a specificity of 80.8% for detection of any PAD (GLASS ≥ 1). MSA with a cut-off of 0.27 mmHg had a sensitivity of 72.2% and a specificity of 77.1% for detection of severe PAD, whereas the sensitivity and specificity for detection of inframalleolar disease were 62.9% and 69.4%, respectively. Results were consistent in subgroup analyses. Conclusions: PVRs with extraction of quantitative features offer promising diagnostic yield for detection of PAD in the setting of DFS. MSA outperformed UST and USR but showed limited capability of detecting impaired inframalleolar outflow. Full article

Background: Proteins with different functions, such as Hepatocyte growth factor activator inhibitor type 1 (HAI-1), Stathmin 1 (STMN-1), and Tenascin C (TN-C), whose activity has been observed in various types of cancers, inspired our study in bladder cancer (BC) patients. The aim of the study was to evaluate selected parameters and their combinations in the diagnosis of BC. The study took into account the degree of invasiveness and malignancy of BC. Based on the analysis of the Receiver Operating Characteristic Curve (ROC), the diagnostic value of single parameters and their combinations as potential indicators of BC was assessed. Patients and Methods: The research material consisted of urine samples from patients with BC, and urine samples from a control group without urological diseases. The concentrations of the examined parameters were measured using an immunoenzymatic method. Results: Statistically significant higher concentrations of HAI-1 (p ≤ 0.001), STMN-1 (≤0.001) and TN-C (0.002) were found in the patients with BC compared to the control group. Strong relationships were shown between these parameters. ROC analyses showed that the best single parameter for detecting BC is STMN-1, and in the combination of HAI-1+STMN-1. The highest diagnostic value was obtained for the combination of HAI-1+STMN-1 in the patients with high malignancy (sensitivity 82%, specificity 91%). Conclusions: Preliminary studies of parameters have shown their utility as potential markers in BC, especially of STMN-1 and combinations HAI-1+STMN-1. However, to learn more about the contribution of these parameters to the progression of bladder cancer, it would be appropriate to continue the studies. Full article

Objective: Falciparum malaria is a major global health concern, affecting more than half of the world’s population and causing over half a million deaths annually. Red cell invasion is a crucial step in the parasite’s life cycle, where the parasite invade human erythrocytes to sustain infection and ensure survival. Two parasite proteins, Apical Membrane Antigen 1 (AMA-1) and Rhoptry Neck Protein 2 (RON2), are involved in tight junction formation, which is an essential step in parasite invasion of the red blood cell. Targeting the AMA-1 and RON2 interaction with inhibitors halts the formation of the tight junction, thereby preventing parasite invasion, which is detrimental to parasite survival. This study leverages machine learning (ML) to predict potential small molecule inhibitors of the AMA-1–RON2 interaction, providing putative antimalaria compounds for further chemotherapeutic exploration. Method: Data was retrieved from the PubChem database (AID 720542), comprising 364,447 inhibitors and non-inhibitors of the AMA-1–RON2 interaction. The data was processed by computing Morgan fingerprints and divided into training and testing with an 80:20 ratio, and the classes in the training data were balanced using the Synthetic Minority Oversampling Technique. Five ML models developed comprised Random Forest (RF), Gradient Boost Machines (GBMs), CatBoost (CB), AdaBoost (AB) and Support Vector Machine (SVM). The performances of the models were evaluated using accuracy, F1 score, and receiver operating characteristic—area under the curve (ROC-AUC) and validated using held-out data and a y-randomization test. An applicability domain analysis was carried out using the Tanimoto distance with a threshold set at 0.04 to ascertain the sample space where the models predict with confidence. Results: The GBMs model emerged as the best, achieving 89% accuracy and a ROC-AUC of 92%. CB and RF had accuracies of 88% and 87%, and ROC-AUC scores of 93% and 91%, respectively. Conclusions: Experimentally validated inhibitors of the AMA-1–RON2 interaction could serve as starting blocks for the next-generation antimalarial drugs. The models were deployed as a web-based application, known as PLASMOpred. Full article

Background: Early detection of severe dengue (SD) is crucial in preventing life-threatening complications. Despite its importance, comprehensive knowledge about these early indicators is still limited. This study aimed to identify predictors of SD in a hyperendemic region of Colombia. Methods: A cross-sectional analysis was conducted using data from 2018 to 2022, encompassing 233 patients. By utilizing the 2009 World Health Organization dengue classifications, cases were differentiated between severe dengue (SD) and non-severe dengue (non-SD). Among these, 47 were confirmed as SD. Associations between clinical, demographic, and laboratory data and disease severity were examined using Fisher’s exact tests or the Mann–Whitney U test (p < 0.05). Profiles for SD and non-SD cases were established through multiple correspondence analysis, and a logistic regression-based predictive model was validated using training and test sets. The model’s performance was evaluated using the area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, F1-score, and precision. Results: Differences in place of residence, comorbidities, type of infection, and signs and symptoms were observed between the severe dengue (SD) and non-severe dengue (non-SD) groups. Median levels of platelets, white blood cells (WBC), aspartate aminotransferase (AST), and alanine aminotransferase (ALT) were found to be higher in the SD group compared to the non-SD group. Neutrophils, leukocytes, platelets, AST, and primary infection were significant predictors of SD. The model demonstrated an area under the receiver operating characteristic curve (AUC) of 0.91 (95% CI, 0.85–0.96). Conclusions: The developed predictive model provides significant assistance to clinicians in assessing SD risk and optimizing triage, which is particularly crucial during dengue outbreaks. Full article

Effective incident detection is essential for emergency response and transportation management. Traditional methods relying on stationary technologies are often costly and provide limited coverage, prompting the exploration of crowdsourced data such as Waze. While Waze offers extensive coverage, its data can be unverified and unreliable. This study aims to identify factors affecting the reliability of Waze alerts and develop a predictive model to distinguish true incidents from false alerts using real-time Waze data, thereby improving emergency response times. Real crash data from the New Jersey Department of Transportation (NJDOT) and crowdsourced data from Waze were matched using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to differentiate true and false alerts. A binary logit model was constructed to reveal significant predictors such as time categories around peak hours, road type, report ratings, and crash type. Findings indicate that the likelihood of accurate Waze alerts increases during peak hours, on streets, and with higher report ratings and major crashes. Additionally, multiple machine learning-based predictive models were developed and evaluated to forecast in real time whether Waze alerts correspond to actual incidents. Among those models, the Random Forest model achieved the highest overall accuracy (82.5%) and F1-score (82.8%), and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.90, demonstrating its robustness and reliability for real-time incident detection. Gradient Boosting, with an AUC-ROC of 0.90 and Area Under the Precision–Recall Curve (AUC-PR) of 0.90, also performed strongly, particularly excelling at predicting true alerts. The analysis further emphasized the importance of key predictors such as time of day, report ratings, and road type. These findings provide actionable insights for enhancing the accuracy of incident detection and improving the reliability of crowdsourced traffic alerts, supporting more effective traffic management and emergency response systems. Full article

Background/Objectives: In the context of acute heart failure, proenkephalin A (penKid) has emerged as a prognostic marker for acute kidney injury (AKI), whereas bioactive adrenomedullin (bio-ADM) has been identified as a significant biomarker linked to shock and organ dysfunction. This raises the question of whether they can serve as predictors of postoperative complications in patients receiving left ventricular assist devices (LVADs). Methods: This observational study prospectively enrolled patients who had received LVAD implantation. Routine laboratory values as well as plasma levels of penKid and bio-ADM were assessed at four time intervals, spanning from preinduction of anesthesia to 48 h post surgery. Clinical data, the HeartMate 3-risk-score (HM3RS), HeartMateII-risk-score (HMRS), Michigan-right-heart-failure risk score (MRHFS), Euromacs-RHFS (EURORHFS), and kidney failure risk score (KFR) were calculated. Multivariate logistic regression and receiver operating characteristic (ROC) analysis were performed. We entered the biomarkers with the established risk scores into the models. Results: In 20 patients who had undergone LVAD implantation, preoperative penKid level was a predictor of postoperative AKI (OR: 1.05, 95%-CI: 1.0–1.09; p = 0.049) and 30-day mortality (OR: 1.01, 95%-CI: 1.0–1.02; p = 0.033). Bio-ADM was the only predictor of postoperative right heart failure (RHF) (OR: 1.11, 95%-CI: 1.01–1.23; p = 0.034) and rehospitalization (OR: 1.06, 95%-CI: 1.0–1.13; p = 0.047). In the ROC analysis, bio-ADM, as a predictor of post-LVAD RHF, had an area under the curve (AUC) of 0.88. When bio-ADM was added to the accepted clinical scores for post-LVAD RHF prediction (CRITT-score, MRHFS, and EURORHFS), the AUC reached 0.98. The AUC for preoperative penKid, as a predictor of postoperative AKI, was 0.95, and after adding its predictive value to the KFR score, the AUC reached 0.97. Conclusions: In the present study, the biomarkers penKid and bio-ADM predicted clinically significant patient outcomes after LVAD implantation such as AKI, RHF, and 30-day mortality. Adding biomarkers to well-established risk scores improved the AUC for prediction of postoperative complications. Full article

Background/Objectives: Red blood cell (RBC) parameters are routinely used to screen for α- and β-thalassemia traits as part of prenatal diagnosis for severe fetal thalassemia in countries with a high prevalence of the disease. In clinical practice, the same cut-off values for these parameters are applied to both females and males. However, given that the normal reference ranges for some RBC parameters differ significantly between sexes, sex-specific cut-off values may be more appropriate, especially in combination. To date, the effectiveness of RBC indices in males for predicting α- and β-thalassemia traits has not been evaluated. The objectives of this study are to assess the diagnostic performance of individual and combined RBC parameters in detecting α⁰-thalassemia traits among non-anemic males. Methods: This diagnostic study is a secondary analysis of prospectively collected data from our project on prenatal control of severe thalassemia. The study population comprised male partners of pregnant women who underwent thalassemia screening during their first antenatal visit. RBC parameters, including hemoglobin (Hb), hematocrit (Hct), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), red cell distribution width (RDW), and RBC count, were measured for each participant. Carrier status for the α0-thalassemia Southeast Asian (SEA) genotype was confirmed by using a polymerase chain reaction (PCR)-based method. The diagnostic performance of each RBC parameter and their combinations, based on predictive models generated using logistic regression, was evaluated and compared using receiver operating characteristic (ROC) curves. Results: A total of 486 Thai males were recruited for the study, including 137 individuals with the α⁰-thalassemia trait and 349 with a normal α-thalassemia genotype (control group). All RBC parameters, except for Hct, differed significantly between the two groups. Among the individual indices, MCH exhibited the highest diagnostic accuracy, followed by MCV, with areas under the curve (AUCs) of 0.981 and 0.973, respectively. An MCH cut-off value of 26 pg and an MCV cut-off value of 80 fL provided a sensitivity of 100% for both indices, with specificities of 88.5% and 86.8%, respectively. The combination predictive model provided the best diagnostic performance, achieving an AUC of 0.987, which was slightly but significantly higher than that of any individual parameter. This model yielded a sensitivity of 100% and a significantly higher specificity of 90.8% at a cut-off probability of 7.0%. Conclusions: MCH and MCV demonstrated excellent screening performance for identifying α0-thalassemia carriers in males. However, the combination model exhibited even greater accuracy while reducing the false-positive rate. Implementing this model could minimize the need for unnecessary PCR testing, leading to substantial cost savings. Full article

Background/Objectives: Hip fractures in elderly patients are associated with high morbidity and mortality, requiring early risk stratification to optimize management. Systemic inflammatory markers such as the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) have emerged as potential prognostic tools. This study aimed to evaluate the predictive value of the NLR and PLR measured at admission for adverse outcomes following hip fracture surgery in elderly patients. Methods: This retrospective, single-center cohort study included patients aged 65 years or older admitted for hip fractures between January 2019 and December 2023. Baseline demographic, clinical, surgical, and laboratory data were collected. Primary outcomes were 30-day, 90-day, and 1-year mortality; secondary outcomes included postoperative ICU admission and prolonged hospitalization (>15 days). Univariable and multivariable Cox regression analyses were performed. Receiver operating characteristic (ROC) curve analysis determined optimal cut-offs for the NLR and PLR. Results: Among 395 included patients (mean age 84 years, 56.4% female), the 30-day, 90-day, and 1-year mortality rates were 4.8%, 10.5%, and 13.9%, respectively. ROC analysis identified cut-offs of 7.2 for the NLR (AUC 0.78, sensitivity 69.7%, specificity 85.4%) and 189.4 for the PLR (AUC 0.73, sensitivity 65.1%, specificity 76.1%). Elevated NLR and PLR were independently associated with increased risk of mortality, ICU admission, and prolonged hospitalization. Conclusions: Elevated NLR and PLR at admission are independent, strong predictors of adverse outcomes in elderly patients with hip fractures. These inexpensive, readily available biomarkers could enhance early risk stratification and inform perioperative management strategies. Full article

Objectives: To investigate the diagnostic value of various lymphatic imaging techniques for thoracic duct (TD) outlet obstruction in patients with chylous leakage. Methods: A retrospective analysis was conducted on 23 patients with chylous leakage who were radiologically diagnosed with a TD outlet obstruction and underwent a TD exploration and reconstruction between January 2022 and February 2025. Non-enhanced magnetic resonance lymphangiography (MRL), ⁹⁹Tc^m-DX lymphoscintigraphy, and intranodal lymphangiography were employed to detect abnormalities in the central lymphatic vessels. The Receiver Operating Characteristic (ROC) curve was utilized to analyze the diagnostic performance of these imaging methods for TD outlet obstruction in lymphatic disorders. Results: Twenty-three patients (fifteen males and eight females) with chylous leakage were included in this study, with an average age of 59.78 ± 13.08 years. Non-enhanced MRL, ⁹⁹Tc^m-DX lymphoscintigraphy, and intranodal lymphangiography revealed TD outlet obstructions in 13, 17, and 18 patients, respectively. Twenty patients exhibited findings consistent with preoperative imaging during TD explorations; the intraoperative microscopic visualization demonstrated the difficulty of white chyle entering the bloodstream for these patients. The ROC curve analysis indicated that “at least two imaging modalities were positive” and had the highest Area Under the Curve (AUC) value (0.90); “intranodal lymphangiography” and “non-enhanced magnetic resonance lymphangiography” followed closely with respective AUC values of 0.76 and 0.73, and ⁹⁹Tc^m-DX lymphoscintigraphy exhibited a lower AUC value 0.63. Conclusions: The combined utilization of multimodal lymphatic imaging techniques demonstrated a high diagnostic accuracy in identifying TD outlet obstruction in patients with chylous leakage. Full article

Urban green spaces, closely tied to local climates, significantly affect human comfort levels, yet existing assessment methods vary in applicability across different contexts and regions. Here, we determined the applicability of two commonly used indices to evaluate human comfort in urban green space types in Hohhot City in China, which is in an arid and semi-arid area. We established sites in four different urban green space types (S1–S4) and a control area (CK) through field-based assessment, and collected meteorological data over 10 days in each season from 2020 to 2021. Specifically, air temperature, relative humidity, and average wind speed were observed from 7:00 to 19:00. Air temperature was highest in summer and lowest in winter. Throughout the day, air temperature first increased and then decreased, with the maximum temperature occurring later in winter than in other seasons. Relative humidity showed an opposite diurnal trend to temperature, and there were no significant differences between urban green space types and CK. The average wind speed of CK was significantly higher than that of the urban green space types. HCI_Lu classifies thermal comfort levels across urban green space types and seasons into four distinct categories as uncomfortable, comfortable to less comfortable, less comfortable, and extremely uncomfortable. HCI_CMA further stratifies thermal conditions at urban green space types by season into cool and refreshing, most comfortable, most comfortable to slightly cool, cold, and uncomfortable. The HCI_Lu ranged from 2.3 to 25.1, and tended to first decrease and then increase on a daily basis. Conversely, HCI_CMA fluctuated throughout the day and ranged from 18.6 to 78.0. According to HCI_Lu, the urban green space types were comfortable for 45% of the observation time, and were comfortable for a greater proportion of time compared to if the comfort was calculated using HCI_CMA. HCI_CMA was strongly correlated with air temperature and average wind speed. According to receiver operating characteristic (ROC) curve analysis, the area under the curve (AUC) for HCI_CMA was 0.59–0.91, and was higher than that of HCI_Lu in each season, indicating greater suitability for the study site. Full article

Aim: To identify ultrasound (US) predictors of persistence or change in the diagnosis of rheumatoid arthritis (RA) after five years in a cohort of patients with early RA. Patients and Methods: One hundred and twenty patients with early arthritis who met the 2010 ACR/EULAR classification criteria for RA were followed for a period of five years. The US assessment at baseline included a bilateral evaluation of the wrists, second to fifth metacarpophalangeal (MCP) joints, second to fifth proximal interphalangeal (PIP) joints, the IV and VI extensor compartments of the wrists, and the flexor tendons of the second to fifth fingers. This evaluation was conducted using both grayscale ultrasound (GSUS) and power Doppler ultrasound (PDUS). The following scores were recorded for each patient: synovitis score, mini-enthesitis score (including paratenonitis of the finger extensor tendon at the MCP joint, central slip enthesitis at the PIP joint, pseudotenosynovitis, and the A1 pulley of the second finger), finger flexor tenosynovitis score, and tenosynovitis score for the IV and VI wrist extensor compartments. The receiver operating characteristic (ROC) curve was utilized to identify the ultrasound predictors for either maintaining or revising an initial diagnosis of RA. Results: At month 6, 82 (68%) patients were classified as having RA according to 1987 ACR RA criteria, 23 (19.2%) were diagnosed with psoriatic arthritis (PsA), 10 (8.3%) with systemic connective tissue disease (SCTD)–8 (6.7%) patients with Sjogren Syndrome and 2 (1.7%) patients with systemic lupus erythematosus (SLE)–and 5 (4.2%) patients with calcium pyrophosphate deposition disease (CPPD). The most significant predictor of RA in the fifth year was the VI extensor compartment tenosynovitis score, with an AUC of 0.915 and a criterion value > 0, associated with a sensitivity of 82.93% and a specificity of 100% (p < 0.001). The PDUS synovitis score demonstrated the second-best prognostic ability with an AUC of 0.823, a criterion value > 2, a sensitivity of 82.93%, and a specificity of 73.68% (p < 0.001). The mini-enthesitis score showed the best prognostic ability of a PsA diagnosis with an AUC of 0.998, a criterion value > 1, a sensitivity of 95.65%, and a specificity of 100% (p < 0.001). The paratenonitis score, pseudotenosynovitis score, and thickened A1 pulley were also predictive of PsA diagnosis with AUCs of 0.977, 0.955, and 0.919, respectively (p < 0.001 for all). Conclusions: Nearly one-third of the patients who were initially classified as having RA had their diagnosis revised at the end of the fifth year. Ultrasound of joints, tendons, and mini-entheses at baseline may serve as potential imaging predictive biomarkers for persistence or change in diagnosis after 5 years. Full article

Background: Long non-coding RNAs (lncRNAs) are crucial factors affecting the occurrence, progression, and prognosis of gastric carcinoma (GC). The accumulation of disulfide bonds to excessive levels in cells expressing high SLC7A11 triggers disulfidptosis, which functions as a regulated form of cellular death. Research has demonstrated that upregulated SLC7A11 is common in human cancers, but the effect of disulfidptosis on GC remains unclear. Identifying lncRNAs associated with disulfidptosis (drlncRNAs) and establishing a prognostic risk profile holds considerable importance for advancing GC research and treatment. Methods: Clinical records and transcriptomic datasets from individuals with GC were acquired from The Cancer Genome Atlas (TCGA) repository. A three-drlncRNA risk model was built using three common regression analysis methods. Then we used receiver operating characteristic (ROC) curves, independent prognostic analysis, and additional statistical approaches to assess the precision of the model. This investigation additionally encompassed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, immune cell infiltration evaluation, and pharmacological sensitivity predictions. To further investigate immunotherapy response disparities between patient cohorts with elevated- and reduced-risk scores, analyses of tumor mutational burden (TMB), tumor immune dysfunction and exclusion (TIDE), and microsatellite instability (MSI) were implemented. Results: We constructed a unique model composed of three drlncRNAs (AC107021.2, AC016394.2, and AC129507.1). Its independent prognostic capability for GC patients was validated through both single-variable and multivariable Cox regression analyses. GO and KEGG pathway assessments revealed predominant enrichment within the elevated-risk cohort, particularly in pathways involving sulfur compound interactions, traditional Wnt signaling mechanisms, cell-substrate adherens junctions, and cAMP signaling cascades, among others. Tumor microenvironment (TME) evaluation demonstrated elevated ImmuneScores, StromalScores, and ESTIMATEScores within the high-risk patient population. Concurrently, this elevated-risk cohort exhibited enhanced immune cell infiltration patterns, whereas the reduced-risk group displayed superior expression of immune checkpoints (ICPs). Additional investigations revealed that patients categorized into the reduced-risk classification possessed greater tumor mutational burden, increased MSI-high proportions, and diminished tumor immune dysfunction and exclusion scores compared to their high-risk counterparts. Pharmacological sensitivity assessments confirmed the superior efficacy of several therapeutic agents, including gemcitabine and veliparib (ABT.888), in patients with lower risk classifications. Conclusions: Our established risk stratification system demonstrates independent prognostic predictive capacity while offering personalized clinical intervention guidance for individuals diagnosed with GC. Full article

Background: While early risk stratification in STEMI is essential, the threat of cardiogenic shock (CS) persists after revascularization due to reperfusion injury and evolving instability. However, risk prediction in later phases—after revascularization—is less explored, despite its importance in guiding intensive care decisions. This study evaluates machine learning (ML) models for dynamic risk assessment in interventional cardiology and cardiac intensive care unit (CICU) phases, where timely detection of deterioration can guide treatment escalation. Methods: We retrospectively analyzed clinical and procedural data from 158 patients diagnosed with STEMI complicated by cardiogenic shock, treated between 2019 and 2022 at the Cardiology Department of the University Emergency Hospital of Bucharest, Romania. Machine learning models—Random Forest (RF), and Quadratic Discriminant Analysis (QDA)—were developed and tested specifically for the interventional cardiology and CICU phases. Model performance was evaluated using area under the receiver operating characteristic curve (ROC-AUC), accuracy (ACC), sensitivity, specificity, and F1-score. Results: In the interventional phase, RF and QDA achieved the highest accuracy, both reaching 87.50%. In the CICU, RF and QDA demonstrate the best performance, reaching ACCs of 0.843. QDA maintained consistent performance across phases. Relevant predictors included reperfusion strategy, TIMI flow before percutaneous coronary intervention (PCI), Killip class, creatinine, and Creatine Kinase Index (CKI)—all parameters routinely assessed in STEMI patients. These models effectively identified patients at risk for post-reperfusion complications and hemodynamic decline, supporting decisions regarding extended monitoring and ICU-level care. Conclusions: Predictive models implemented in advanced STEMI phases can contribute to dynamic, phase-specific risk reassessment and optimize CICU resource allocation. These findings support the integration of ML-based tools into post-PCI workflows, enabling earlier detection of clinical decline and more efficient deployment of intensive care resources. When combined with earlier-stage models, the inclusion of interventional and CICU phases forms a dynamic, end-to-end risk assessment framework. With further refinement, this system could be implemented as a mobile application to support clinical decisions throughout the STEMI care continuum. Full article

Background/Objectives: Acute pancreatitis (AP) remains a pressing clinical challenge, largely due to its potential to lead to life-threatening complications and increased mortality. Over the years, numerous tools have been proposed to evaluate the intensity of AP and estimate likely health outcomes. Despite their usefulness, many of these assessment models are complex and rely on a wide array of clinical inputs, making them less practical in everyday healthcare settings. In contrast, the Aggregate Systemic Inflammation Index (AISI), which is calculated using routine blood count parameters, provides a simpler and more inclusive approach to measuring systemic inflammation. This research focuses on examining how effectively AISI can be used to gauge disease severity and project clinical trajectories in individuals affected by pancreatitis. Methods: This retrospective study reviewed the medical records of 412 individuals diagnosed with acute pancreatitis, all of whom received care at the Internal Medicine Clinic of Ankara Bilkent City Hospital between 1 April 2019 and 1 September 2024. The investigation encompassed a thorough analysis of patients’ demographic characteristics, lab parameters, and clinical findings, with special attention given to inflammatory markers, including the Aggregate Systemic Inflammation Index (AISI), its revised version, the Platelet-to-Lymphocyte Ratio (PLR), the Neutrophil-to-Lymphocyte Ratio (NLR), and the Systemic Inflammatory Response Index (SIRI). Comparative analyses between groups were performed using independent sample t-tests and one-way ANOVA, complemented by Tukey’s post hoc tests where appropriate. Correlations among continuous variables were determined through Pearson’s analysis, and the prognostic accuracy of both AISI and its modified form was assessed using Receiver Operating Characteristic (ROC) curve methodology. Results: The mean age among participants was 63.47 ± 17.92 years, while the average AISI value was calculated as 1183.89 ± 1067.42. Both the original and modified versions of the AISI index showed strong positive correlations with several key clinical measures, including prolonged hospitalization, a Glasgow score of 2 or above, BISAP, Ranson scoring, the revised Atlanta classification, and APACHE II. AISI was also significantly linked to the presence of complications and overall mortality (p < 0.01). Analysis through ROC curves demonstrated that an AISI level above 236.626 effectively predicted hospital stays exceeding 10 days, with a sensitivity of 94.40% and a specificity of 91.00%. Moreover, both AISI and its modified form reliably distinguished patients who had a Ranson score of zero, with high diagnostic accuracy. Conclusions: AISI and its modified version demonstrate a strong association with both the intensity and clinical course of acute pancreatitis. Thanks to their simplicity, low cost, and broad usability in healthcare settings, these indices hold considerable promise as practical and dependable tools for assessing the severity and likely outcomes of this increasingly prevalent disease. Full article