Search Results (2,028)

Breast cancer is the leading cause of cancer-related mortality among women world-wide, and early screening is critical for improving patient survival. Medical imaging plays a central role in breast cancer screening, diagnosis, and treatment monitoring. However, conventional imaging modalities—including mammography, ultrasound, and magnetic resonance imaging—face limitations such as low diagnostic specificity, relatively slow imaging speed, ionizing radiation exposure, and dependence on exogenous contrast agents. Photoacoustic imaging (PAI), a novel hybrid imaging technique that combines optical contrast with ultrasonic spatial resolution, has shown great promise in addressing these challenges. By revealing anatomical, functional, and molecular features of the breast tumor microenvironment, PAI offers high spatial resolution, rapid imaging, and minimal operator dependence. This review outlines the fundamental principles of PAI and systematically examines recent advances in its application to breast cancer screening, diagnosis, and therapeutic evaluation. Furthermore, we discuss the translational potential of PAI as an emerging breast imaging modality, complementing existing clinical techniques. Full article

Background/Objectives: Trochanteric femoral fractures pose significant surgical challenges, particularly in elderly patients. Intramedullary nailing (IMN) and plate fixation (PF) are the primary operative strategies, yet their comparative efficacy and safety remain debated. This meta-analysis synthesizes randomized controlled trials (RCTs) to evaluate clinical, functional, perioperative, and biomechanical outcomes of IMN versus PF specifically in trochanteric fractures. Methods: A systematic search of six databases was conducted up to 20 May 2024, to identify RCTs comparing IMN and PF in adult patients with trochanteric femoral fractures. Data extraction followed PRISMA guidelines, and outcomes were pooled using random-effects models. Subgroup analyses examined the influence of fracture stability, implant type, and patient age. Risk of bias was assessed using the Cochrane RoB 2.0 tool. Results: Fourteen RCTs (n = 4603 patients) were included. No significant differences were found in reoperation rates, union time, implant cut-out, or mortality. IMN was associated with significantly reduced operative time (MD = −5.18 min), fluoroscopy time (MD = −32.92 s), and perioperative blood loss (MD = −111.68 mL). It also had a lower risk of deep infection. Functional outcomes and anatomical results were comparable. Subgroup analyses revealed fracture stability and nail type significantly modified operative time, and compression screws were associated with higher reoperation rates than IMN. Conclusions: For trochanteric femoral fractures, IMN and PF yield comparable results for most clinical outcomes, with IMN offering some advantages in surgical efficiency and perioperative morbidity, though functional outcomes were comparable. Implant selection and fracture stability influence outcomes, supporting individualized surgical decision making. Full article

Background/Objectives: Colorectal anastomotic leak (CAL) is one of the most severe postoperative complications in colorectal surgery, impacting patient morbidity and mortality. Current risk assessment methods rely on clinical and intraoperative factors, but no real-time predictive tool exists. This study aimed to develop an artificial intelligence model based on intraoperative laparoscopic recording of the anastomosis for CAL prediction. Methods: A convolutional neural network (CNN) was trained with annotated frames from colorectal surgery videos across three international high-volume centers (Instituto Português de Oncologia de Lisboa, Hospital das Clínicas de Ribeirão Preto, and Royal Liverpool University Hospital). The dataset included a total of 5356 frames from 26 patients, 2007 with CAL and 3349 showing normal anastomosis. Four CNN architectures (EfficientNetB0, EfficientNetB7, ResNet50, and MobileNetV2) were tested. The models’ performance was evaluated using their sensitivity, specificity, accuracy, and area under the receiver operating characteristic (AUROC) curve. Heatmaps were generated to identify key image regions influencing predictions. Results: The best-performing model achieved an accuracy of 99.6%, AUROC of 99.6%, sensitivity of 99.2%, specificity of 100.0%, PPV of 100.0%, and NPV of 98.9%. The model reliably identified CAL-positive frames and provided visual explanations through heatmaps. Conclusions: To our knowledge, this is the first AI model developed to predict CAL using intraoperative video analysis. Its accuracy suggests the potential to redefine surgical decision-making by providing real-time risk assessment. Further refinement with a larger dataset and diverse surgical techniques could enable intraoperative interventions to prevent CAL before it occurs, marking a paradigm shift in colorectal surgery. Full article

Prolonged inundations are altering coastal forest ecosystems of the southeastern US, causing extensive tree die-offs and the development of ghost forests. This hydrological stressor also alters carbon fluxes, threatening the stability of coastal carbon sinks. This study was conducted to investigate the interactions between hydrological drivers and ecosystem responses by analyzing daily eddy covariance flux data from a wetland forest in North Carolina, USA, spanning 2009–2019. We analyzed temporal patterns of net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (RE) under both flooded and non-flooded conditions and evaluated their relationships with observed tree mortality. Generalized Additive Modeling (GAM) revealed that groundwater table depth (GWT), leaf area index (LAI), NEE, and net radiation (Rn) were key predictors of mortality transitions (R² = 0.98). Elevated GWT induces root anoxia; declining LAI reduces productivity; elevated NEE signals physiological breakdown; and higher Rn may amplify evapotranspiration stress. Receiver Operating Characteristic (ROC) analysis revealed critical early warning thresholds for tree mortality: GWT = 2.23 cm, LAI = 2.99, NEE = 1.27 g C m⁻² d⁻¹, and Rn = 167.54 W m⁻². These values offer a basis for forecasting forest mortality risk and guiding early warning systems. Our findings highlight the dominant role of hydrological variability in ecosystem degradation and offer a threshold-based framework for early detection of mortality risks. This approach provides insights into managing coastal forest resilience amid accelerating sea level rise. Full article

Insecticide resistance monitoring is essential for effective mosquito control. This study compared CDC Bottle Bioassays (BBAs) using technical and formulated insecticides (deltamethrin/Deltagard and malathion/Fyfanon EW) against the Culex pipiens complex (Fogg Rd) and Culex tarsalis Coquillett (Vic Fazio). BBAs indicated resistance to deltamethrin and emerging resistance to malathion in Fogg Rd, as well as resistance to both in Vic Fazio. Field trials, however, showed high efficacy: Deltagard caused 97.7% mortality in Fogg Rd and 99.4% in Vic Fazio. Fyfanon EW produced 100% mortality in Fogg Rd but only 47% in Vic Fazio. Extended BBA endpoints at 120 and 180 min aligned better with field outcomes. Deltagard achieved 100% mortality at 120 min in both populations; technical deltamethrin reached 85.7% (Fogg Rd) and 83.5% (Vic Fazio) at 180 min. Fyfanon EW and malathion showed similar performance: 100% mortality was achieved in Fogg Rd by 120 min but was lower in Vic Fazio; malathion reached 55%; and Fyfanon EW reached 58.6% by 180 min. Statistical analysis confirmed that BBAs using formulated products better reflected field performance, particularly when proprietary ingredients were involved. These findings support the use of formulated products and extended observation times in BBAs to improve operational relevance and resistance interpretation in addition to detecting levels of insecticide resistance. Full article

Background and Objectives: Malnutrition is one of the most common complications in patients undergoing hemodialysis (HD) and is closely linked to increased morbidity and mortality. This study aimed to investigate the nutritional status of HD patients and the clinical relevance of bioelectrical impedance analysis (BIA) parameters such as the percent body fat (PBF), skeletal muscle mass index (SMI), extracellular water-to-total body water ratio (ECW/TBW), and phase angle (PhA) in assessing malnutrition in Vietnamese HD patients. Materials and Methods: This cross-sectional study was conducted among 184 patients undergoing hemodialysis in Hanoi, Vietnam. The BIA parameters were measured by the InBody S10 body composition analyzer, while malnutrition was assessed by the geriatric nutritional risk index (GNRI), with a GNRI <92 classified as a high risk of malnutrition. The independent BIA variables for predicting malnutrition and its cut-off values were explored using logistic regression models and a receiver operating characteristic (ROC) curve analysis, respectively. Results: Among the study population, 42.9% (79/184) of patients were identified as being at a high risk of malnutrition. The multivariate logistic regression analysis revealed that a higher ECW/TBW was independently associated with an increased risk of malnutrition, while the PBF, SMI, and PhA expressed significant and inverse associations with the malnutrition risk after adjusting for multiple confounders. The cut-off values for predicting the high risk of malnutrition in overall HD patients were determined to be 20.45%, 7.75 kg/m², 5.45°, and 38.03% for the PBF, the SMI, the PhA, and the ECW/TBW ratio, respectively. Conclusions: BIA parameters, including the PBF, SMI, PhA, and ECW/TBW ratio, could serve as indicators of malnutrition in general Vietnamese patients with HD. Full article

Cardiogenic shock (CS) in the setting of severe aortic stenosis (AS) presents a critical and high-risk scenario with limited therapeutic options and poor prognosis. Transcatheter aortic valve implantation (TAVI), initially reserved for inoperable or high-risk surgical candidates, is increasingly being considered in patients with CS due to improvements in device technology, operator experience, and supportive care. This review synthesizes current evidence from large registries, observational studies, and meta-analyses that support the feasibility, safety, and potential survival benefit of urgent or emergent TAVI in selected CS patients. Procedural success is high, and early intervention appears to confer improved short-term and mid-term outcomes compared to balloon aortic valvuloplasty or medical therapy alone. Critical factors influencing prognosis include lactate levels, left ventricular ejection fraction, renal function, and timing of intervention. The absence of formal guidelines, logistical constraints, and ethical concerns complicate decision-making in this unstable population. A multidisciplinary Heart Team/Shock Team approach is essential to identify appropriate candidates, manage procedural risk, and guide post-intervention care. Further studies and the development of TAVI-specific risk models in CS are anticipated to refine patient selection and therapeutic strategies. TAVI may represent a transformative option for stabilizing hemodynamics and improving outcomes in this otherwise high-mortality group. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

Medical imaging is vital for diagnosing and treating colorectal cancer (CRC), a leading cause of mortality. Classifying colorectal polyps and CRC precursors remains challenging due to operator variability and expertise dependence. Deep learning (DL) models show promise in polyp classification but face adoption barriers due to their ‘black box’ nature, limiting interpretability. This study presents an example-based explainable artificial intehlligence (XAI) approach using Pix2Pix to generate synthetic polyp images with controlled size variations and LIME to explain classifier predictions visually. EfficientNet and Vision Transformer (ViT) were trained on datasets of real and synthetic images, achieving strong baseline accuracies of 94% and 96%, respectively. Image quality was assessed using PSNR (18.04), SSIM (0.64), and FID (123.32), while classifier robustness was evaluated across polyp sizes. Results show that Pix2Pix effectively controls image attributes like polyp size despite limitations in visual fidelity. LIME integration revealed classifier vulnerabilities, underscoring the value of complementary XAI techniques. This enhances DL model interpretability and deepens understanding of their behaviour. The findings contribute to developing explainable AI tools for polyp classification and CRC diagnosis. Future work will improve synthetic image quality and refine XAI methodologies for broader clinical use. Full article

Background/Objectives: Total thymectomy is currently the gold standard operation for treating thymoma. However, recent studies have suggested the potential health consequences of thymus removal in adults, including possible increased autoimmune disease and all-cause mortality. In this context, we assess oncologic outcomes following total vs. partial thymectomy for early-stage thymoma. Methods: We identified patients diagnosed with WHO types A–B3 thymoma between 2010–2021 from a national hospital-based dataset. We excluded patients with stage II or higher disease, open resections and perioperative chemo-/radiation therapy. We stratified patients into total and partial thymectomy cohorts. We used propensity score matching to minimize confounding, Kaplan–Meier analysis to estimate survival, and Cox proportional hazards to identify associations. Results: Of 1598 patients with early-stage thymoma, 495 (31.0%) underwent partial and 1103 (69.0%) total thymectomy. Patients undergoing partial thymectomy were similar in sex (female 53.7% vs. 53.4%; p = 0.914), race (white 74.5% vs. 74.0%; p = 0.921), comorbidities (0 in 77.0% vs. 75.5%; p = 0.742), and tumor size (48.7 mm vs. 50.4 mm; p = 0.455) compared to total thymectomy. There were no differences in 30-day (0.8% vs. 0.6%, p = 0.747) or 90-day mortality (0.8% vs. 0.8%, p > 0.999), which persisted after matching. Moreover, 10-year survival was similar in both unmatched (p = 0.471) and matched cohorts (p = 0.828). Partial thymectomy was not independently associated with survival (aHR = 1.00, p = 0.976). Conclusions: In patients with early-stage thymoma, partial and total thymectomy were associated with similar short- and long-term outcomes. In light of recent attention to the role of the thymus gland, the results add important insights to shared decision-making discussions. Full article

Background/Objective: This study examines the composite influence of frailty, malnutrition, and anemia on postoperative outcomes for patients with adult spinal deformity (ASD). Methods: In this retrospective cohort study using the 2011–2022 NSQIP database, we utilized CPT and ICD codes to identify ASD patients who underwent PSF. Subjects were stratified based on frailty status. Frail patients were then classified according to malnutrition and anemia status. Frailty was determined using the revised risk analysis index (RAI-rev). Our primary outcomes were extended length of stay (LOS), non-routine discharge (NRD), 30-day adverse events (AE), and 30-day mortality. For each outcome, we fitted four nested multivariable logistic regression models (RAI-rev + anemia + malnutrition, RAI-rev + anemia, RAI-rev + malnutrition, and RAI-rev alone) and compared the incremental discrimination of each model using receiver operating characteristic (ROC) analysis. Results: Of 3639 patients, 460 were frail alone, 266 were frail + anemic, 37 were frail + malnourished, 121 were frail + anemic + malnourished, and 2755 were not frail. RAI-rev (aOR: 1.84, 95% CI: 1.45–2.35), anemia (aOR: 1.84, 95% CI: 1.45–2.35), and malnourishment (aOR: 2.34, 95% CI: 1.69–3.24) were independent predictors of extended LOS. RAI-rev (aOR: 1.07, 95% CI: 1.04–1.11) and anemia (aOR: 2.09, 95% CI: 1.66–2.61) were associated with an increased risk of 30-day AEs. RAI-rev and malnutrition were independent predictors of NRD (RAI-rev: aOR: 1.11, 95% CI: 1.06–1.16; Malnutrition: aOR: 1.57, 95% CI: 1.08–2.29) and 30-day mortality (RAI-rev: aOR: 1.10, 95% CI: 1.04–1.17; Malnutrition: aOR: 3.79, 95% CI: 1.24–11.60). Based on ROC analysis, RAI-rev + anemic + malnourished was a superior predictor of LOS and 30-day AEs (both p < 0.001). Compared to RAI-rev, RAI-rev + anemic superiorly predicted LOS and 30-day AEs, and RAI-rev + malnutrition superiorly predicted LOS (all p < 0.001). Conclusions: Our results reveal RAI-rev combined with malnutrition and anemia superiorly predicts 30-day AEs and LOS in postoperative ASD patients. Future studies should investigate the feasibility and efficacy of these models for perioperative risk stratification and optimized recovery planning to improve outcomes for ASD patients. Full article

Heart failure (HF) and left ventricular hypertrophy (LVH) are linked to fibroblast growth factor 23 (FGF23). This study aims to analyze whether FGF23 can predict postoperative outcomes in unselected left ventricular assist device (LVAD) candidates. Methods: We conducted a prospective observational study that included 27 patients (25 HeartMate3 and 2 HeartMateII) with a median follow-up of 30 months. We measured preoperative FGF23 plasma levels and computed the HeartMateII risk score (HMRS), the HeartMate3 risk score (HM3RS) and the EuroSCOREII with respect to postoperative mortality, as well as the Michigan right heart failure risk score (MRHFS), the Euromacs RHF risk score (EURORHFS), the CRITT score with respect to RHF prediction and the kidney failure risk equation (KFRE) with respect to kidney failure. Multivariate logistic regression and receiver operating characteristic (ROC) analyses were performed. Results: In the multivariate logistic regression, preoperative FGF23 level was found to be a predictor of postoperative RHF (OR: 1.37, 95-CI: 0.78–2.38; p = 0.031), mortality (OR: 1.10, 95%-CI: 0.90–1.60; p = 0.025) and the need for postoperative dialysis (OR: 1.09, 95%-CI: 0.91–1.44; p = 0.032). In the ROC analysis, FGF23 as a predictor of post-LVAD RHF had an area under the curve (AUC) of 0.81. Conclusions: FGF23 improves the prediction of clinically significant patient outcomes—such as need for dialysis, RHF and mortality—after HM3 and HMII implantation, as adding FGF23 to established risk scores increased their predictive value. Full article

Background and Objectives: Metabolic tumor volume (MTV) and inflammation-based indices have recently gained attention as potential prognostic markers of diffuse large B-cell lymphoma (DLBCL). We aimed to evaluate the prognostic significance of metabolic and systemic inflammatory parameters in predicting treatment response, relapse, and overall survival (OS) in patients with DLBCL. Materials and Methods: This retrospective cohort study included 70 patients with DLBCL. Clinical characteristics, laboratory values, and metabolic parameters, including maximum standardized uptake value (SUVmax^liver and SUVmax), heterogeneity indices HI1 and HI2, and MTV were analyzed. Survival outcomes were assessed using Kaplan–Meier and log-rank tests. Receiver operating characteristic analyses helped evaluate the diagnostic performance of the selected biomarkers in predicting relapse and mortality. Univariate and multivariate logistic regression analyses were conducted to identify the independent predictors. Results: The mean OS and mean relapse-free survival (RFS) were 71.6 ± 7.4 and 38.7 ± 2.9 months, respectively. SUVmax^liver ≤ 22 and HI2 > 62.3 were associated with a significantly shorter OS. High lactate dehydrogenase (LDH) levels and HI2 > 87.9 were significantly associated with a reduced RFS. LDH, SUVmax^liver, and HI2 had a significant predictive value for relapse. SUVmax^liver and HI2 levels were also predictive of mortality; SUVmax^liver ≤ 22 and HI2 > 62.3 independently predicted mortality, while HI2 > 87.9 independently predicted relapse. MTV was not significantly associated with survival. Conclusions: Metabolic tumor burden and inflammation-based markers, particularly SUVmax^liver and HI2, are significant prognostic indicators of DLBCL and may enhance risk stratification and aid in identifying patients with an increased risk of relapse or mortality, potentially guiding personalized therapy. Full article

Left ventricular systolic dysfunction (LVSD) is associated with increased mortality and is sometimes reversible when found early. Artificial intelligence (AI)-enabled electrocardiogram (ECG) has emerged as an efficient screening tool for LVSD, but has not been validated in left bundle branch block (LBBB) patients. The clinical significance of developing an AI prediction model for LBBB patients lies in the fact that LBBB can be a cause, consequence, or both of LVSD. This pilot study was designed to develop an AI model for LVSD detection in the LBBB population using a limited dataset. ECG data from 508 patients with sinus rhythm and LBBB were labeled based on an LVSD threshold of 35%. To enhance the performance of a model derived from such a small and skewed dataset, we combined an autoencoder-based anomaly detection model with a convolutional neural network (CNN). We used a lead-wise ensemble technique for the final classification. Experimental results showed an accuracy of 0.81, precision of 0.87, recall of 0.56, and an area under the receiver operating characteristic curve of 0.75 in LVSD prediction among LBBB patients. Despite the limited dataset size, our study findings suggest the potential of deep learning techniques in detecting LVSD in patients with LBBB. Full article

Chronic kidney disease (CKD) is a progressive condition which still leads to significant morbidity and mortality among patients at all ages. Its proper management should be focused on slowing down the disease sequelae, as well as establishing an early diagnosis and treatment of its complications. Eotaxin is a potent, selective eosinophil chemoattractant, which is reported to have an impact on various kidney diseases. Nevertheless, data regarding the potential correlation between eotaxin and CKD in a pediatric population is still scarce. This study aims to assess the concentration of eotaxin in children with CKD and evaluate potential correlations with selected biochemical markers and disease occurrence. Both serum and urine eotaxin concentrations were markedly higher in children with CKD compared to healthy controls. Moreover, Receiver Operating Characteristic (ROC) curves have shown that serum eotaxin and urine eotaxin levels demonstrated high sensitivity and high specificity for the allocation of patients to the study and control groups. The authors advanced a thesis that eotaxin might serve as a marker of CKD occurrence in a pediatric population. Such a research design is innovative, since it has not been analyzed in the literature yet. However, further studies are required. Full article