Search Results (2,745)

Colovesical fistulas (CVFs) are abnormal connections between the colon and bladder, most commonly associated with complicated diverticular disease. The standard treatment involves bowel resection to remove the fistulous tract and reduce the risk of recurrence. This study aimed to evaluate the feasibility and safety of laparoscopic and robotic conservative surgery for CVFs, avoiding bowel resection. Between 2012 and 2019, 12 consecutive patients underwent conservative treatment: 5 with a laparoscopic approach and 7 with a robotic approach. Perioperative outcomes showed shorter operative time and lower blood loss in the robotic group (101 min vs. 144 min, p = 0.02; 47 mL vs. 176 mL, p = 0.02). No surgical conversions were required, and the complication rate was low. One recurrence occurred in the laparoscopic group due to extensive diverticular disease. Short- and long-term outcomes demonstrated favorable functional results, with a reduced risk of complications compared to traditional bowel resection. The robotic technique provided advantages in operative time and blood loss. A conservative approach is an option for selected patients, particularly those with non-extensive diverticular disease. Full article

Hepatocellular carcinoma (HCC) is a leading cause of cancer death, with liver transplantation (LT) offering a curative option for early-stage patients who cannot undergo resection. Although LT provides good long-term outcomes within standard criteria, recurrence occurs in approximately 8–20% of recipients and often leads to poor survival. Traditionally, LT eligibility relied on strict criteria like the Milan criteria, which are effective in selecting patients with low recurrence but may exclude patients who could benefit from transplantation. In response, new expanded criteria and models using tumor biology have been developed for better risk stratification, allowing more personalized selection and management. Despite these advances, recurrence remains a major clinical challenge, with no consensus on optimal imaging timing or frequency post-LT. Treatment depends on the recurrence’s extent and location, including surgical resection and locoregional therapies. Systemic treatments are promising, especially for unresectable or extrahepatic recurrence, though most evidence comes from small retrospective studies, limiting the development of standardized protocols. Future research should focus on addressing these gaps and guiding evidence-based post-transplant care. This is a narrative review summarizing recent advances in HCC recurrence. Full article

Objective: This study aimed to identify risk factors associated with high-grade cervical intraepithelial neoplasia (CIN3+) in young adults with abnormal Pap smears. Methods: We performed a retrospective chart review of women ≤30 years who underwent loop electrosurgical excision procedure (LEEP) for abnormal Pap results (atypical squamous cells of undetermined significance [ASCUS] or higher), between 2012 and 2022 at Dongtan Sacred Heart Hospital. Clinical characteristics, including age, HPV infection, prior gynecologic surgery, pelvic inflammatory disease (PID), complete blood count, and Pap smear screening history were collected. Women with CIN3+ based on punch biopsy or LEEP were designated as CIN3+. Results: A total of 158 women underwent LEEP. Of these, 61.4% were diagnosed with CIN3+ and 8.2% with invasive cervical cancer. Independent predictors of CIN3+ included age >28 years, smoking, lack of regular Pap screening, and high-risk HPV infection. Subgroup analysis suggested age ≥28 years and neutrophil-to-lymphocyte ratio >2.12 were risk factors for invasive cervical cancer. Conclusions: Young Korean women with abnormal Pap smears and risk factors such as older age, smoking, high-risk HPV infection, and irregular screening histories are at increased risk for CIN3+. These findings highlight the importance of timely intervention; however, because our cohort included only women who underwent LEEP, it may represent a higher-risk subset and thus introduce selection bias. Validation in larger multicenter, prospective studies incorporating fertility and recurrence outcomes are needed before definitive recommendations can be made. Full article

Background: Hemorrhoids and anal fissure are among the most common benign anorectal conditions. The incidence of synchronous symptomatic hemorrhoids and chronic anal fissure is unknown. In this study we evaluated the outcomes of our experience with concomitant surgical treatment for both these conditions. Methods: In this retrospective study we included consecutive patients who underwent surgical treatment for symptomatic hemorrhoids combined with lateral internal sphincterotomy for chronic anal fissure, during a time period of over 5 years. Eligible patients were contacted by phone and were asked to answer a questionnaire to evaluate recurrent symptoms, fecal incontinence, satisfaction, and improvement in quality-of-life. Results: A total of 56 patients were included, and 29 (51.8%) were female; the mean age was 46.9 ± 13.7 years, and the median follow-up time was 45.4 months. The median self-assessed improvement in quality-of-life on a scale of 0–10 was 10 [IQR 8, 10]. No significant differences were observed in satisfaction or self-assessed improvement in quality-of-life between genders or across different surgical procedures for hemorrhoids. Conclusions: Patients who underwent concomitant surgical treatment for hemorrhoids and chronic anal fissure were satisfied. This study supports our approach for synchronous treatment for different anorectal pathologies given the right patient selection, being safe and feasible. Full article

Background: Atrial fibrillation (AF) is the most common sustained arrhythmia. AF catheter ablation (CA) is superior to antiarrhythmic drugs (AAD) therapy in maintaining sinus rhythm. However, not much is known regarding the optimal timing of the ablation. Methods: A comprehensive literature search was conducted using PubMed, Embase, and Scopus, focusing on studies published from 2013 until 2022 and including both observational studies and randomized controlled trials (RCTs) with patients undergoing ablation for symptomatic paroxysmal or persistent AF using radiofrequency, cryoablation, or both approaches, studies that reported diagnosis-to-ablation time (DAT), a follow-up period, AF recurrence, or AF burden. Studies that included a surgical ablation, a hybrid ablation approach, or an ablation for arrhythmias other than AF were excluded. Left atrial diameter and ejection fraction (EF) were assessed. Results: Ten studies were selected out of 1387 identified records. After a follow-up period of one year, the early ablation subgroup had a lower mean AF recurrence rate (29.8%) compared to that of the delayed ablation subgroup (39.5%). The median AF recurrence rate was in the radiofrequency ablation group (44.5%), in the cryoablation group (27.3%). In studies that included paroxysmal AF patients exclusively, the AF recurrence rate was directly proportional to the DAT. Conclusions: Our results suggest that DAT correlates with a recurrence rate at one year following AF CA, and that the shorter the DAT the better the outcome, particularly in paroxysmal AF population. Full article

Background: Cervical cancer remains a major global health concern, with high recurrence rates in advanced stages. [¹⁸F]FDG PET/CT provides prognostic biomarkers such as SUV, MTV, and TLG, though these are not routinely integrated into clinical protocols. Radiomics offers quantitative analysis of tumor heterogeneity, supporting risk stratification. Purpose: To evaluate the prognostic value of clinical and radiomic features for disease-free survival (DFS) in locoregionally advanced cervical cancer using machine learning (ML). Methods: Sixty-three patients (mean age 47.9 ± 14.5 years) were diagnosed between 2015 and 2020. Radiomic features were extracted from pre-treatment PET/CT (IBSI-compliant PyRadiomics). Clinical variables included age, T-stage, Dmax, lymph node involvement, SUVmax, and TMTV. Forty-two models were built by combining six feature-selection techniques (UCI, MD, MI, VH, VH.VIMP, IBMA) with seven ML algorithms (CoxPH, CB, GLMN, GLMB, RSF, ST, EV) using nested 3-fold cross-validation with bootstrap resampling. External validation was performed on 95 patients (mean age 50.6 years, FIGO IIB–IIIB) from an independent cohort with different preprocessing protocols. Results: Recurrence occurred in 31.7% (n = 20). SUVmax of lymph nodes, lymph node involvement, and TMTV were the most predictive individual features (C-index ≤ 0.77). The highest performance was achieved by UCI + EV/GLMB on combined clinical + radiomic features (C-index = 0.80, p < 0.05). For single feature sets, IBMA + RSF performed best for clinical (C-index = 0.72), and VH.VIMP + GLMN for radiomics (C-index = 0.71). External validation confirmed moderate generalizability (best C-index = 0.64). Conclusions: UCI-based feature selection with GLMB or EV yielded the best predictive accuracy, while VH.VIMP + GLMN offered superior external generalizability for radiomics-only models. These findings support the feasibility of integrating radiomics and ML for individualized DFS risk stratification in cervical cancer. Full article

Background: C3 glomerulopathies (C3G), including dense deposit disease (DDD) and C3 glomerulonephritis (C3GN), are rare kidney disorders driven by dysregulation of the alternative complement pathway. Eculizumab, a terminal complement inhibitor targeting C5, has emerged as a potential therapeutic option in these conditions. This systematic review evaluated the efficacy and safety of eculizumab in patients with C3G or DDD. Methods: Literature searches in PubMed and Cochrane databases identified case reports and case series reporting eculizumab use. Results: Only eight studies involving ten patients met the inclusion criteria. Eculizumab stabilized renal function and reduced proteinuria in most cases, especially when C5b-9 deposition was present. Histopathological improvements were variable, and recurrence after discontinuation occurred in some patients. Responses were limited in cases with alternative mechanisms of C5 activation. Conclusions: Eculizumab offers clinical benefit in select C3G and DDD patients but does not address the underlying cause of complement dysregulation. The need for long-term therapy, incomplete histologic resolution, and risk of relapse underscore the necessity of larger trials and the development of personalized treatment strategies. Full article

Microgrids are becoming an innovative piece of modern energy systems as they provide locally sourced and resilient energy opportunities and enable efficient energy sourcing. However, microgrid operations can be greatly affected by sudden environmental changes, deviating demand, and unexpected outages. In particular, extreme climatic events expose the vulnerability of microgrid infrastructure and resilience, often leading to increased risk of system-wide outages. Thus, successful microgrid operation relies on timely and accurate outage predictions. This research proposes a data-driven machine learning framework for the optimized operation of a microgrid and predictive outage detection using a Recurrent Neural Network–Long Short-Term Memory (RNN-LSTM) architecture that reflects inherent temporal modeling methods. A time-aware embedding and masking strategy is employed to handle categorical and sparse temporal features, while mutual information-based feature selection ensures only the most relevant and interpretable inputs are retained for prediction. Moreover, the model addresses the challenges of experiencing rapid power fluctuations by looking at long-term learning dependency aspects within historical and real-time data observation streams. Two datasets are utilized: a locally developed real-time dataset collected from a 5 MW microgrid of Maple Cement Factory in Mianwali and a 15-year national power outage dataset obtained from Kaggle. Both datasets went through intensive preprocessing, normalization, and tokenization to transform raw readings into machine-readable sequences. The suggested approach attained an accuracy of 86.52% on the real-time dataset and 84.19% on the Kaggle dataset, outperforming conventional models in detecting sequential outage patterns. It also achieved a precision of 86%, a recall of 86.20%, and an F1-score of 86.12%, surpassing the performance of other models such as CNN, XGBoost, SVM, and various static classifiers. In contrast to these traditional approaches, the RNN-LSTM’s ability to leverage temporal context makes it a more effective and intelligent choice for real-time outage prediction and microgrid optimization. Full article

The prediction of stock prices is challenging due to their volatility, irregular patterns, and complex time-series structure. Reliably forecasting stock market data plays a crucial role in minimizing financial risk and optimizing investment strategies. However, traditional models often struggle to capture temporal dependencies and extract relevant features from noisy inputs, which limits their predictive performance. To improve this, we developed an enhanced recursive feature elimination (RFE) method that blends the importance of impurity-based features from random forest and gradient boosting models with Kendall tau correlation analysis, and we applied SHapley Additive exPlanations (SHAP) analysis to externally validate the reliability of the selected features. This approach leads to more consistent and reliable feature selection for short-term stock prediction over 1-, 3-, and 7-day intervals. The proposed deep learning (DL) architecture integrates a temporal convolutional network (TCN) for long-term pattern recognition, a gated recurrent unit (GRU) for sequence capture, and multi-head attention (MHA) for focusing on critical information, thereby achieving superior predictive performance. We evaluate the proposed approach using daily stock price data from three leading companies—HDFC Bank, Tata Consultancy Services (TCS), and Tesla—and two major stock indices: Nifty 50 and S&P 500. The performance of our model is compared against five benchmark models: temporal convolutional network (TCN), long short-term memory (LSTM), GRU, Bidirectional GRU, and a hybrid TCN–GRU model. Our method consistently shows lower error rates and higher predictive accuracy across all datasets, as measured by four commonly used performance metrics. Full article

The continuous growth in global demand for mineral resources and the increasing difficulty of mineral exploration have created bottlenecks for traditional mineral prediction methods in handling complex geological information and large amounts of data. This review aims to explore the latest research progress in machine learning technology in the field of large-scale mineral prediction from 2016 to 2025. By systematically searching the Web of Science core database, we have screened and analyzed 255 high-quality scientific studies. These studies cover key areas such as mineral information extraction, target area selection, mineral regularity modeling, and resource potential evaluation. The applied machine learning technologies include Random Forests, Support Vector Machines, Convolutional Neural Networks, Recurrent Neural Networks, etc., and have been widely used in the exploration and prediction of various mineral deposits such as porphyry copper, sandstone uranium, and tin. The findings indicate a substantial shift within the discipline towards the utilization of deep learning methodologies and the integration of multi-source geological data. There is a notable rise in the deployment of cutting-edge techniques, including automatic feature extraction, transfer learning, and few-shot learning. This review endeavors to synthesize the prevailing state and prospective developmental trajectory of machine learning within the domain of large-scale mineral prediction. It seeks to delineate the field’s progression, spotlight pivotal research dilemmas, and pinpoint innovative breakthroughs. Full article

The Russian empire’s displays of applied and decorative art at the Great Exhibition of 1851 and its immediate successors have long galvanised scholars for their semantic complexity. By contrast, Russia’s first selection of paintings for this fiercely competitive arena, shown at London’s International Exhibition of 1862, failed to ignite the public imagination and has largely evaded the historian’s gaze. While the three-dimensional artworks provided a recurrent source of wonderment for their superlative craftsmanship, stupendous materials, and often hyperbolic proportions, the paintings were apparently flat in every sense of the word: derivative, lacklustre, and incapable of capitalising on the opportunity that international exhibitions offered to present a national school. The dismissive comments they attracted set the tone for many later accounts, embedding the idea that Russian painting prior to the twentieth century was of limited consequence—a perception that would prove convenient to those asserting the originality of the avant-garde. Yet renewed consideration of Russia’s display of paintings in 1862 suggests that their critical reception speaks to concerns that went well beyond the pictures’ supposed obligation to represent a national school. Notably, a small but significant number of history and portrait paintings by academically trained and often well-travelled artists challenged notions of Russians as primitive and parochial. The technically adventurous of these parried the belief that Russian art was insufficiently mature to experiment in painterly effect. Most audacious of all, they broached unspoken national boundaries by daring to suggest that Imperial Russian artists could innovate in areas on which the success of British painting rested. The attitudes towards Russian painting in 1862 thus invite fresh scrutiny, revealing as they do a disruptive arena in which aesthetic rivalries and chauvinist sensibilities came to the fore. Full article

Equine sarcoids are the most common dermatological neoplasm in horses worldwide, associated with bovine papillomavirus (BPV) infection and characterized by high recurrence rates after conventional therapies. Bacillus Calmette–Guérin (BCG) immunotherapy has historically been used for sarcoid treatment, yet its role in contemporary veterinary oncology remains debated. This narrative review critically examines the immunological mechanisms, clinical efficacy, and limitations of BCG in equine sarcoid therapy, while integrating insights from comparative oncology and One Health perspectives. A systematic search following PRISMA-based criteria identified 55 relevant studies published over the past four decades. Evidence indicates that BCG activates innate and adaptive immunity through TLR2/4 signaling, macrophage polarization, and enhanced CD8+ T-cell responses, leading to partial or complete sarcoid regression in select cases. However, therapeutic outcomes are highly variable due to heterogeneity in protocols (dose, strain, adjuvant use) and frequent adverse inflammatory reactions. Comparative analyses highlight that modern alternatives—such as cryotherapy, cisplatin-based protocols, and topical imiquimod—achieve higher efficacy and lower recurrence rates in many clinical settings. Although BCG is now rarely considered a first-line therapy, it remains relevant in resource-limited regions, such as the Amazon Biome, where cost-effectiveness and accessibility are critical. Future directions include randomized controlled trials, standardized protocols, and innovative approaches such as checkpoint inhibition, CRISPR-Cas9 targeting of viral oncogenes, and nanoparticle delivery systems. This review provides a balanced and data-driven synthesis of BCG immunotherapy, clarifying its historical contributions, current limitations, and translational opportunities for advancing equine and comparative oncology. Full article

Background/Objectives: Cutaneous angiosarcoma (CAS) is a rare and aggressive endothelial malignancy with a high rate of local recurrence and distant metastasis. In advanced cases, where surgical resection is not feasible, systemic therapy remains the cornerstone of treatment. This review aims to summarize the current landscape of systemic therapies for unresectable or metastatic CAS and discuss emerging strategies, particularly focusing on immune checkpoint inhibitors (ICIs). Methods: A comprehensive review of the literature was conducted, including clinical trials, retrospective studies, and case series focusing on systemic treatments for advanced CAS. Therapeutic approaches covered include cytotoxic chemotherapy, molecular targeted therapies, and ICIs, as well as combination strategies. Special attention was given to biomarker studies and ongoing clinical trials. Results: Taxane-based chemotherapy, particularly paclitaxel, has demonstrated clinical activity and remains a standard option. Molecular targeted agents such as pazopanib have yielded modest efficacy. Recent trials of ICIs, including the SWOG S1609 DART and AngioCheck studies, have shown encouraging results in select subgroups, especially tumors from sun-exposed regions associated with high tumor mutational burden (TMB). Although AngioCheck did not meet its predefined response criteria, a subset of patients achieved disease control. Biomarkers such as TMB, PD-L1 expression, and tumor-infiltrating lymphocytes are under investigation to guide patient selection. Combination therapies with ICIs and tyrosine kinase inhibitors (TKIs) are being actively explored. Conclusions: While systemic therapies for CAS remain limited in efficacy, ICIs—particularly in combination with TKIs—represent a promising avenue. Future trials should emphasize biomarker-driven, CAS-specific strategies to improve clinical outcomes in this challenging malignancy. Full article

Advances in cardiac regenerative medicine increasingly rely on integrating artificial intelligence with spatial multi-omics technologies to decipher intricate cellular dynamics in cardiomyocyte differentiation. This systematic review, synthetising insights from 88 PRISMA selected studies spanning 2015–2025, explores how deep learning architectures, specifically Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs), synergise with multi-modal single-cell datasets, spatially resolved transcriptomics, and epigenomics to advance cardiac biology. Innovations in spatial omics technologies have revolutionised our understanding of the organisation of cardiac tissue, revealing novel cellular communities and metabolic landscapes that underlie cardiovascular health and disease. By synthesising cutting-edge methodologies and technical innovations across these 88 studies, this review establishes the foundation for AI-enabled cardiac regeneration, potentially accelerating the clinical adoption of regenerative treatments through improved therapeutic prediction models and mechanistic understanding. We examine deep learning implementations in spatiotemporal genomics, spatial multi-omics applications in cardiac tissues, cardiomyocyte differentiation challenges, and predictive modelling innovations that collectively advance precision cardiology and next-generation regenerative strategies. Full article

Tyrosine kinase 2 (TYK2) mediates the signaling pathways of proinflammatory cytokines such as interleukin (IL)-12, IL-23, and type I interferons (IFNs) and plays a pivotal role in the pathogenesis of psoriasis and various other immune-mediated diseases. Deucravacitinib, a selective oral TYK2 inhibitor, has been approved for the treatment of psoriasis and demonstrated high efficacy and a favorable safety profile. This review summarizes the potential for expanding deucravacitinib indications based on case reports, clinical trials, and preclinical studies. Diseases in which TYK2 pathway has been demonstrated to be involved and for which clinical benefit of deucravacitinib has been reported include discoid lupus erythematosus, systemic lupus erythematosus, alopecia areata, lichen planus, palmoplantar pustulosis, psoriatic arthritis, systemic sclerosis, interstitial pneumonia, inflammatory bowel disease, and chronic recurrent multifocal osteomyelitis. Furthermore, emerging research suggests potential therapeutic applications in neurodegenerative diseases such as Alzheimer’s disease, and malignancies such as type 1 diabetes, vascular calcification in chronic kidney disease, T-cell acute lymphoblastic leukemia, and multiple sclerosis. Deucravacitinib may exert therapeutic effects by broadly suppressing cytokine signaling in a diverse range of inflammatory disorders. Ongoing clinical trials and mechanistic studies are required to clarify the efficacy and support its future indications. Full article