Search Results (81)

The transjugular intrahepatic portosystemic shunt (TIPS) is a cornerstone intervention for complications of portal hypertension, including variceal bleeding and refractory ascites. As the population with cirrhosis ages, clinicians increasingly face the question of whether and how to perform TIPS safely in older adults. We reviewed observational cohorts, registry analyses, and systematic reviews/meta-analyses. Existing evidence does not support chronological age as an absolute contraindication; however, multiple studies suggest that advanced age is associated with higher rates of post-TIPS hepatic encephalopathy (HE), early mortality, and readmissions. These findings underscore the need to shift from a binary “eligible vs. ineligible” paradigm to a structured, actionable framework that addresses modifiable risks and anticipates age-related vulnerabilities. Recent clinical practice guidance emphasizes comprehensive pre-TIPS assessment and vigilant post-procedure care, with specific attention to HE risk factors (e.g., prior HE, hyponatremia, renal dysfunction, sarcopenia) and cardiopulmonary reserve. In this narrative review, we propose an elderly-focused clinical pathway built around a four-domain assessment (Liver–Brain–Body–Heart/Kidney) and a traffic-light risk tiering system to guide patient selection, procedural strategy, follow-up scheduling, and triggered management of HE, cardiac decompensation, and renal dysfunction. This pathway aims to preserve the benefits of portal decompression while reducing preventable complications and improving outcomes that are meaningful to older patients, including functional status and quality of life. This narrative review emphasizes that outcomes after TIPS in older adults are determined not by chronological age alone but by multidomain physiological reserve. The proposed pathway informs patient selection, procedural planning, and early post-discharge monitoring in older adults. Full article

Background: As life expectancy increases, a growing number of elderly patients are considered for curative hepatectomy for hepatocellular carcinoma (HCC). However, perioperative outcomes in elderly patients in the contemporary era of minimally invasive liver surgery remain incompletely defined. Methods: We retrospectively reviewed 277 consecutive patients who underwent elective curative hepatectomy for HCC between 2019 and 2023. Outcomes were compared using age thresholds of ≥75 and ≥80 years. The primary endpoints were 90-day mortality and major postoperative complications (Clavien–Dindo grade ≥ III). Multivariable logistic regression identified predictors of major complications. Results: Elderly patients had more comorbidities, whereas liver function, tumor characteristics, and extent of resection were comparable across age groups. Laparoscopic hepatectomy was performed more frequently in patients aged ≥80 years. Major complication rates and 90-day mortality were similar regardless of age, with no deaths among patients aged ≥75 or ≥80 years. Age ≥75 years, higher ALBI score, major comorbidities, and longer Pringle maneuver time were independently associated with major postoperative complications. Conclusions: Hepatectomy for hepatocellular carcinoma may be performed with acceptable short-term outcomes in carefully selected elderly patients, including octogenarians. Chronological age alone should not be considered an absolute contraindication to surgery, although findings should be interpreted with caution. Full article

Modern, post-Scaliger expectations for constructing an absolute chronology out of ancient biblical narratives introduce a fallacy of assumed time that distorts the reading of these narratives. While absolute chronology undergirds historical-critical interpretation from Spinoza and Reimarus to twentieth-century scholarship, the more recent “temporal turn” in philosophy, historiography, and literary theory aligns with a renewed attention to narrative time and ancient temporal consciousness. Focusing on 2 Maccabees and the Gospel of John as historiographical narratives reveals how both texts configure events through relative temporal devices—such as temporal markers and temporal process verbs—rather than through absolute calendrical dating, even when coordinates appear in 2 Maccabees’ embedded letters. Building on this comparison allows for a dimensional model of time that respects these configurational strategies and avoids obscuring how these texts construct theological and historical meaning within their own narrative worlds. Full article

With the increasing prevalence of neurodegenerative diseases driven by population aging, imaging-based biomarkers are needed to quantify brain aging at an early stage. Brain age, which estimates structural brain aging relative to chronological age, has emerged as a useful indicator. Prior work has mainly used T1-weighted MRI with deep learning models such as convolutional neural networks (CNNs) or transformers; however, many approaches insufficiently capture three-dimensional structural continuity and localized anatomical patterns, and tissue-specific aging in gray matter (GM) and white matter (WM) is often treated as auxiliary. To address these limitations, we propose a 3D Global–Patch Transformer framework for brain age prediction that directly processes volumetric data while jointly learning global brain structure and local anatomical features. Our model runs global and patch pathways in parallel and explicitly incorporates GM and WM structural maps alongside T1-weighted MRI to encode tissue-specific aging signals. Experiments on multiple public datasets, including IXI and OASIS, show that the proposed method reduces mean absolute error (MAE) by approximately 10–15% compared with CNN-based and single-input transformer baselines, with notably improved performance in older populations, highlighting the value of tissue-level structural information for brain age estimation. Full article

Accurate electricity load forecasting has become increasingly challenging in Türkiye due to rapid structural changes in the power system driven by renewable energy expansion. Between 2016 and 2022, solar capacity increased by 130% and wind generation by 83%, resulting in renewable-induced variability exceeding 160%. To assess how different forecasting approaches respond to this evolving environment, Facebook Prophet, XGBoost, and Deep Neural Networks (DNNs) were evaluated using more than 55,000 hourly load observations under a strictly chronological out-of-sample validation framework. The comparative analysis reveals substantial differences in model performance. XGBoost achieved the highest forecasting accuracy, with a Mean Absolute Error of 981.48 MWh, a Root Mean Squared Error of 1344.15 MWh, and a Mean Absolute Percentage Error of 2.72%, while effectively capturing rapid intraday variations and maintaining peak deviations within ±1100 MWh. DNN models delivered competitive overall accuracy (MAE: 997.82 MWh; MAPE: 2.77%) but exhibited a tendency to smooth temporal variations, leading to an underestimation of extreme winter peaks by up to 4100 MWh. In contrast, Prophet showed limited adaptability to the observed structural volatility, producing errors nearly seven times higher than XGBoost (MAE: 7041.79 MWh; RMSE: 8718.14 MWh). Based on these findings, a layered forecasting framework is proposed, employing XGBoost for short-term operational dispatch and reserving statistical models for long-term planning and policy analysis. Full article

Background: Convenience stores face volatile demand and a direct trade-off between stock-outs and overstocking, both of which affect service levels and profitability. This study aims to optimize inventory management through a reproducible forecasting-and-optimization workflow, assessing its impact on return on investment (ROI) and operational metrics, such as fill rate and stockouts. Methods: The workflow integrates daily, store-level transactions with external covariates, constructs temporal and lag features, and trains a Random Forest (RF) model using chronological splitting and time-series validation. Daily forecasts are then aggregated to the monthly level and used as inputs to an inventory simulation and an ROI-based economic model. Building on this simulation, a Genetic Algorithm (GA) optimizes the parameters of a monthly replenishment policy, incorporating minimum-coverage constraints. Results: In testing, the forecasting model achieved a mean absolute percentage error (MAPE) below 13%, and the RF+GA scheme outperformed the 28-day moving average baseline (MA28) in ROI across all five stores, with an average improvement of 4.52 percentage points; statistical significance was confirmed using the Wilcoxon test. Conclusions: Overall, the RF+GA approach serves as a decision-support tool that generates monthly order quantities consistent with demand and operational constraints, delivering verifiable improvements in both economic and service metrics. Full article

Objectives: This study evaluated the accuracy of an artificial intelligence (AI)-based cephalometric software (WebCeph version 2.0.0.) compared with manual tracing and determined whether stratifying patients by chronological age or dentition stage provides a more clinically relevant assessment of AI accuracy. Methods: Three hundred lateral cephalometric radiographs of Vietnamese patients were traced manually by an orthodontist (reference standard) and analyzed automatically by WebCeph. Intra-observer reliability was validated using ICC and Dahlberg’s error. We analyzed the data using three stratification strategies: (1) Overall; (2) Chronological age (<18, 18–25, >25 years); and (3) Dentition stage (<9 primary-early mixed, 9–12 late mixed, >12 permanent). The primary outcome was the absolute measurement difference (∣Δ∣), analyzed using the Kruskal–Wallis test and effect size (${\eta }^2$). Results: Overall, WebCeph showed high concordance with manual tracing (ICC > 0.80 for most parameters). Chronological age stratification showed weak associations with measurement error; differences between groups were largely non-significant ($p>0.05$) with a small effect size (${\eta }^2\approx 0.015$). In contrast, the dentition stage revealed significant performance disparities ($p<0.05$). Notably, accuracy for the Mandibular Arc (ICC = 0.349) and Mandibular Plane Angle ($p=0.048$) degraded significantly in the primary-early mixed group, a vulnerability obscured by chronological age-based stratification. Conclusions: Dentition stage is a more sensitive and biologically relevant predictor of AI accuracy than chronological age. While WebCeph is reliable for permanent dentition, accuracy degrades significantly in the primary-early mixed phase. Clinicians should prioritize manual verification of mandibular and incisor landmarks in mixed-dentition children. Full article

DNA methylation (DNAm) age estimation is one of the hottest topics in forensic contexts. However, there is growing evidence that DNAm can be affected by several factors, including many clinical conditions. In this study, we analyzed the methylation levels within the FHL2 gene in Portuguese women using the droplet digital PCR (ddPCR) methodology to develop age prediction models (APMs). We hypothesized that obesity could affect the accuracy of APMs and would be associated with the advancement in epigenetic aging. We collected blood samples from 62 women (aged 21–58 years old) with overweight and obesity. DNA extracts were subjected to bisulfite conversion followed by ddPCR using dual-labeled probes targeting the methylated and unmethylated FHL2 CpG site cg06639320. The developed APM yielded a mean absolute deviation (MAD) of 4.72 years between predicted and chronological ages in the total sample. When applying the developed APM to women classified as overweight, the MAD was 3.64 years, while, for those with obesity class 1, it was 3.93 years, and, for those with obesity class 2, 6.29 years. The same pattern of accuracy was observed when we developed APMs specifically for the groups categorized by overweight and obesity, obtaining MAD values of 3.75 years (overweight), 3.69 years (obesity class 1) and 6.24 years (obesity class 2). Our study indicates that severe obesity may impact the accuracy of DNA methylation-based age estimators. We did not find evidence of an association between BMI and accelerated epigenetic aging. However, we found signals of epigenetic age acceleration in younger subjects and epigenetic age deceleration in the older participants. Full article

The increasing demand for the energy-efficient and occupant-centred operation of educational buildings requires accurate and interpretable models capable of predicting indoor environmental conditions under real operating constraints. This study proposes a residual learning-enhanced grey-box modelling framework for predicting indoor air temperature and assessing indoor environmental quality indicators in a KNX-enabled educational building operating under simple thermostatic heating control. The approach combines a reduced-order discrete-time RC thermal model with a data-driven machine learning component trained to model the next-step residual between measured and simulated indoor temperatures. High-resolution KNX monitoring data were recorded at a 5 min sampling interval over three consecutive months (October–December) during the heating season. Using a chronological 70/30 train–test split, the identified RC grey-box model achieved a pooled test RMSE of 0.269 °C, an MAE of 0.126 °C, and an R² of 0.987. The proposed hybrid formulation achieved RMSE = 0.343 °C, MAE = 0.106 °C, and R² = 0.978 across 62,456 test samples. While the pooled RMSE remains influenced by occasional larger deviations in a small number of rooms, the hybrid model yields a consistent reduction in absolute error (≈16% MAE reduction) and reduced inter-room variability compared to the physics-based baseline. These results indicate that residual learning can enhance predictive robustness under decentralized thermostatic operation and limited sensing, while preserving physical interpretability. The proposed framework provides a practical and scalable solution for indoor temperature prediction and IEQ assessment in educational buildings using existing KNX automation data. Full article

This study investigates univariate multi-horizon forecasting of national electricity demand as a controlled benchmark for settings where exogenous drivers (e.g., weather and calendar variables) are unavailable or uncertain, through a comparative evaluation of representative deep learning architectures. The examined models include the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, a Temporal Convolutional Network (TCN), and the feed-forward Neural Basis Expansion Analysis for Time Series (N-BEATS) framework. All models are trained and evaluated within a unified experimental setup based on a univariate daily time series of Finnish national electricity demand covering the period from 2016 up to 2021, enabling a controlled assessment of architectural capabilities when relying solely on historical demand. Using a common preprocessing pipeline and a chronological train–validation–test split, forecasts are generated for short-, medium-, and long-term intervals (30, 90, and 365 days), and predictive performance is assessed using the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The experimental results show that N-BEATS achieves the lowest RMSE across all considered horizons in the test set, while the GRU architecture attains the smallest MAE at the longest horizon and exhibits consistently strong performance overall. These findings highlight the complementary strengths of recurrent and feed-forward deep learning paradigms for modelling nonlinear structure and long-range dynamics in electricity demand time series, and provide quantitative evidence to support horizon-aware architecture selection in national electricity demand forecasting and related applied modelling contexts. Full article

Objectives: Cardiovascular diseases (CVD) remain a leading cause of premature mortality globally, despite the proven efficacy of physical activity in reducing risks. This research aims to identify risk characteristics and characterise pathologies related to the onset of CVD in relation to physical activity levels. The study tests the hypothesis that adequate physical activity is associated with CVD-related events, while sedentary behaviour is a factor related to increased risk factors. Methods: A cross-sectional, observational, descriptive, and analytical study was conducted with 116 participants of both sexes (aged 16 to 77 years) in El Espinal, Tolima. Clinical, anthropometric, and biochemical assessments were performed, including blood pressure, Body Mass Index (BMI), visceral fat, and lipid profiles. Physical activity was self-reported and categorised as weekly, monthly, and occasional exercise. Descriptive and bivariate statistical analyses were performed. Quantitative variables were expressed as means and standard deviations. Qualitative variables were presented as absolute frequencies. Statistical interaction graphs were used to analyse the effects of age and exercise frequency on pulse pressure. Results: Weekly exercise was identified as a key modulator of hemodynamic stability; while BMI and visceral fat increased with age, pulse pressure remained stable (44.17–46.55 mmHg). In contrast, occasional exercise was linked to high cardiovascular vulnerability, with pulse pressure spiking to a critical 75.00 mmHg in elderly participants (77 years) and BMI reaching obesity levels (38.15 kg/m²). Monthly exercise showed high variability and progressive lipid profile deterioration, with total cholesterol reaching 282.00 mg/dL in late maturity. Conclusions: Regular weekly physical activity acts as a physiological buffer that dissociates chronological ageing from vascular damage. While weekly exercise maintains optimal hemodynamic and metabolic ranges, occasional or inconsistent activity fails to prevent critical increases in pulse pressure and arterial stiffness during senescence. These findings underscore the necessity of regular, rather than sporadic, exercise as a vital “medicine” for maintaining arterial integrity across the lifespan. Full article

Machine learning (ML) has emerged as a practical approach to forecasting crop yields in climate-vulnerable, rainfed agricultural systems where production uncertainty is strongly influenced by monsoon variability. In India’s semi-arid and sub-humid regions, reliable yield forecasts are critical for agribusiness planning and managing climate risks. This study presents a standardized evaluation of three widely used ML forecasting models—Linear Regression (LR), Random Forest (RF), and Support Vector Regression (SVR)—for rainfed cereal yields in eight Indian administrative divisions from 2000 to 2025. The study applied a unified methodological framework that included data cleaning, z-score normalization, domain-informed feature selection, strict chronological train–test splitting, and five-fold cross-validation. The dataset integrates agroclimatic and soil variables, including temperature, precipitation, relative humidity, wind speed, and soil pH, comprising approximately 1250 division-year observations. Model performance was assessed on an independent, temporally held-out test set using root mean square error (RMSE), mean absolute error (MAE), and R². The results show that RF provides the most robust predictive performance under realistic forecasting conditions. It achieved the lowest RMSE (0.268 t/ha) and the highest R² (0.271), outperforming LR and SVR. Although the explained variance is modest, it reflects strict temporal validation and the inherent uncertainty of rainfed systems. Feature importance analysis highlights temperature and precipitation as dominant yield drivers. Overall, this study establishes a conservative and reproducible baseline for operational machine learning (ML)-based yield forecasting in precision agribusiness. Full article

Background/Objectives: This paper presents the results of predicting chronological age from psychophysiological tests using machine learning regressors. Methods: Subjects completed a series of psychological tests measuring various cognitive functions, including reaction time and cognitive conflict, short-term memory, verbal functions, and color and spatial perception. The sample included 99 subjects, 68 percent of whom were men and 32 percent were women. Based on the test results, 43 features were generated. To determine the optimal feature selection method, several approaches were tested alongside the regression models using MAE, $R^2$, and $CV\text{\_}{R}^2$ metrics. SHAP and Permutation Importance (via Random Forest) delivered the best performance with 10 features. Features selected through Permutation Importance were used in subsequent analyses. To predict participants’ age from psychophysiological test results, we evaluated several regression models, including Random Forest, Extra Trees, Gradient Boosting, SVR, Linear Regression, LassoCV, RidgeCV, ElasticNetCV, AdaBoost, and Bagging. Model performance was compared using the determination coefficient ($R^2$) and mean absolute error (MAE). Cross-validated performance ($CV\text{\_}{R}^2$) was estimated via 5-fold cross-validation. To assess metric stability and uncertainty, bootstrapping (1000 resamples) was applied to the test set, yielding distributions of MAE and RMSE from which mean values and 95% confidence intervals were derived. Results: The study identified RidgeCV with winsorization and standardization as the best model for predicting cognitive age, achieving a mean absolute error of 5.7 years and an $R^2$ of 0.60. Feature importance was evaluated using SHAP values and permutation importance. SHAP analysis showed that stroop_time_color and stroop_var_attempt_time were the strongest predictors, followed by several task-timing features with moderate contributions. Permutation importance confirmed this ranking, with these two features causing the largest performance drop when permuted. Partial dependence plots further indicated clear positive relationships between these key features and predicted age. Correlation analysis stratified by sex revealed that most features were significantly associated with age, with stronger effects generally observed in men. Conclusions: Feature selection revealed Stroop timing measures and task-related metrics from math and campimetry tests as the strongest predictors, reflecting core cognitive processes linked to aging. The results underscore the value of careful outlier handling, feature selection, and interpretable regularized models for analyzing psychophysiological data. Future work should include longitudinal studies and integration with biological markers to further improve clinical relevance. Full article

Background/Objectives: Mild Encephalopathy with Reversible Splenial Lesion (MERS) is a potential complication of certain viral infections, but adult cases involving influenza are rare in the literature. Here, we report a case of a 31-year-old Chinese gentleman with an atypical presentation of Influenza B-associated mild encephalopathy with reversible splenial lesion (MERS). Methods: This is a case report with a detailed chronology followed by a discussion of pathophysiology. Results: The patient presented acutely to the tertiary hospital with a severe headache and a peculiar automatism pattern of behaviour involving intermittent screaming, involuntary jerking movements of the upper limbs, and incoherent speech, which culminated in an episode of tonic–clonic seizure lasting 3 min. Symptoms started on the day that the patient was diagnosed with Influenza B and given the antiviral Baloxavir by his GP. Clinically, there was high anion gap metabolic acidosis with hyperlactatemia, rhabdomyolysis, hepatitis transaminitis and absolute lymphopenia. Nasopharyngeal swab PCR and immunofluorescence was positive for Influenza B. EEG was normal, but an MRI of the brain showed a cytotoxic lesion of the splenium of the corpus callosum. The patient was started on Oseltamivir and made a complete neurological recovery, with a repeat MRI showing resolution of the splenial lesion at 3 months. MERS is a rare clinic-radiological syndrome characterized by a transient encephalopathy and a reversible lesion in the splenium of the corpus callosum, which has been reported mostly in the pediatric population. Conclusions: This case report of an influenza B-triggered MERS in an adult highlights the importance of maintaining MERS as a differential for acute encephalopathy in adults with a viral prodrome. Full article

The Late Holocene flood history of the Cauvery River floodplain in the Poompuhar region was reconstructed using a multiproxy sedimentological approach applied to three trench cores. Lithostratigraphy, loss on ignition (LOI), magnetic susceptibility (MS), sand–silt–clay textural analysis, granulometric statistics (Folk and Ward), Passega CM diagrams, and grain angularity provide complementary evidence to differentiate high-energy flood deposits from background slackwater sediments. Grain-size processing and statistical analyses were carried out in R using the G2Sd package, ensuring reproducible quantification of mean size, sorting, skewness, kurtosis, and transport signatures. We identified 10 discrete high-energy event beds. These layers are characterised by >80% sand content, low LOI (<3.5%), and low frequency-dependent MS (χfd% < 2%), confirming rapid, mineral-dominated deposition. A tentative chronology, projected from the regional aggradation rate, suggests two major flood clusters: a maximum-magnitude event at ~3.2 ka and a synchronous cluster at ~1.6–1.8 ka. These events chronologically align with the documented phases of channel avulsion in the adjacent Palar River Basin, supporting the existence of a synchronised Late Holocene climato-tectonic regime across coastal Tamil Nadu. This hydrological evidence supports the hypothesis that recurrent high-magnitude flooding triggered catastrophic channel avulsion of the Cauvery distributary, leading to the fluvial abandonment and decline of the ancient port city of Poompuhar. Securing an absolute chronology requires advanced K-feldspar post-IR IRSL dating to overcome quartz saturation issues in fluvial deposits. Full article