Search Results (159)

This work introduces RAMA (Recursive Aesthetic Modular Approximation), a metaheuristic framework that models a restricted form of mathematical intuition inspired by the notebooks of Srinivasa Ramanujan. While Ramanujan often produced deep results without formal proofs, the heuristic processes guiding such discoveries remain poorly understood. RAMA treats large language models (LLMs) as proposal mechanisms within an iterative search that generates, evaluates, and refines candidate conjectures under an explicit energy functional balancing fit, description length, and aesthetic structure. A small set of Ramanujan-inspired heuristics—modular symmetries, integrality cues, aesthetic compression, and near-invariance detection—is formalized as micro-operators acting on symbolic states. We instantiate RAMA in two domains: (i) inverse engineering eta-quotients from partial q-series data and (ii) designing cyclotomic fingerprints with shadow gadgets for quantum circuits. In both settings, RAMA recovers compact structures from limited information and improves separation from classical baselines, illustrating how intuitive heuristic patterns can be rendered as explicit, reproducible computational procedures. Full article

Naringenin (NAR) is a naturally occurring flavanone characteristic of citrus fruits and other foods whose anti-obesity effects have been reported. As a dietary xenobiotic, it is metabolized and excreted mainly by the liver and kidneys, respectively. Since an organism does not normally consume pure phenolic compounds, there are concerns about its safety when administered as such. The present work reports an analysis on the safety of consuming NAR as an anti-obesity agent (100 mg/kg body weight) alongside a Western diet (WD) during an eight-week period, according to various serum biochemical markers of liver and kidney function in Wistar rats. Blood samples were analyzed to determine liver function, including enzyme activity (ALT, AST, GGT, and ALP), bilirubin, and albumin. Biochemical markers of kidney function were urea, blood urea nitrogen (BUN), creatinine, uric acid, and electrolytes. Results show that a 100 mg/kg oral dose of NAR for eight weeks exerted no apparent hepato- or nephrotoxicity, suggesting a suitable safety profile at said dose, since all variables analyzed remained within normal reference limits in NAR-treated animals. Urea, BUN, and ALP showed significant differences between the WD and the control group fed a basal diet (BD), although this was independent of NAR (p < 0.05, WD and WD + NAR vs. BD and BD + NAR), suggesting that diet played a role. The data support the previously reported hepatoprotective effects of NAR and suggest a favorable safety profile. Altogether, the findings indicate that pure NAR may be safe at the dose employed and during the analyzed time period, which further supports the need for clinical studies to validate its application in human consumers. Full article

This study aims to develop an explainable radiomics-based model for the automatic assessment of image quality in breast cancer Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) data. A cohort of 280 images obtained from a public database was annotated by two clinical experts, resulting in 110 high-quality and 110 low-quality images. The proposed methodology involved the extraction of 819 radiomic features and 2 No-Reference image quality metrics per patient, using both the whole image and the background as regions of interest. Feature extraction was performed under two scenarios: (i) from a sample of 12 slices per patient, and (ii) from the middle slice of each patient. Following model training, a range of machine learning classifiers were applied with explainability assessed through SHapley Additive Explanations (SHAP). The best performance was achieved in the second scenario, where combining features from the whole image and background with a support vector machine classifier yielded sensitivity, specificity, accuracy, and AUC values of 85.51%, 80.01%, 82.76%, and 89.37%, respectively. This proposed model demonstrates potential for integration into clinical practice and may also serve as a valuable resource for large-scale repositories and subgroup analyses aimed at ensuring fairness and explainability. Full article

Background/Objectives: Advanced heart failure (AdvHF) characterizes patients with impaired functional capacity, severe systolic or diastolic cardiac function, unplanned visits or hospitalizations, raised natriuretic peptides, and increased mortality. Methods: Ninety-five consecutive AdvHF patients followed in a tertiary academic center in Northwestern Greece (2nd Department of Cardiology, University Hospital of Ioannina) were enrolled over a 30-month period. Three distinctive patterns of management were recognized and assessed: intermittent levosimendan administration to 33 patients, intermittent intravenous furosemide administration to 17 patients, and 45 patients were followed up exclusively on an outpatient basis with frequent visits. MAGGIC, SHFM, and BCN-Bio scores were assessed in all patients and mortality was also assessed. Results: Mean age was 73 (±10) years, and 38% were females, 41% had diabetes mellitus, 41% had chronic obstructive pulmonary disease, 59% had coronary artery disease (CAD), 73% had a history of atrial fibrillation, and 82.1% had a cardiac device implanted. The median duration of follow-up was 24 months (IQ range 14, 30). The 12-month and 30-month mortality rates were 19% and 49%, respectively. Higher rates of 1-year mortality were observed in the levosimendan group (30%). The median 12-month mortality of the three scores was comparable to the actual mortality, but their prognostic value was not satisfactory (AUC < 0.540 and p > 0.05 for all), while they performed better for 30-month mortality (AUC < 0.756 and p > 0.05 for all). In the current study, mortality at 12 months was associated with decreasing diastolic blood pressure (DBP) and sodium levels; the presence of CAD (p < 0.05 for all) and mortality at 30 months was associated with decreasing systolic blood pressure, as well as DBP and left ventricle ejection fraction, but also with the presence of CAD and the use of renin–angiotensin–aldosterone system blockers. Logistic regression-based models incorporating these factors have a greater diagnostic accuracy (AUC = 0.824 and 0.817 for 12 and 30 months, respectively; p < 0.001 for both). Conclusions: AdvHF patients represent a complex population requiring close follow-up and novel strategies to improve survival. Larger studies are needed to refine and update predictive scores in this population. Full article

Background: The change over time of certain inflammatory markers, such as the neutrophil–lymphocyte ratio (NLR) and systemic inflammatory response index (SIRI), is a prognostic factor in many cancers, including breast cancer. This study retrospectively evaluated how a 12-week intensive exercise program might have influenced both the NLR and SIRI in women with breast cancer. Methods: Two institutional review board-approved prospective clinical trials, EXERT-BC (NCT05747209, 2 November 2022) and EXERT-BCN (NCT05978960, 31 July 2023), were retrospectively assessed. Complete blood count (CBC) values performed before and after participation in a 12-week intensive resistance program were analyzed post hoc. Blood tests were ordered as part of routine clinical care and not pre-specified by either study protocol. Participants who had blood work more than four months from study intake or completion were excluded. Additionally, those undergoing active systemic therapy or with underlying inflammatory conditions were also excluded. The NLR and SIRI values were analyzed via the Mann–Whitney test, with pair-wise assessment of pre- and post-intervention values via the Wilcoxon signed-rank test. Results: Out of 84 participants, 21 people met the inclusion criteria. Roughly 70% had either ductal carcinoma in situ (DCIS) or early-stage breast cancer. The average blood draw was taken within two months of study intake and outtake. After the 12-week structured exercise program, there was an associated reduction in both the NLR (2.26 [IQR, 1.70–4.22] to 1.99 [1.44–2.62]; ΔNLR = −0.27, W = 47.0, p = 0.016) and SIRI (1.23 [0.82–1.64] to 0.80 [0.59–1.45]; ΔSIRI = −0.43, W = 48.0, p = 0.018). Of those who saw their inflammatory markers improve, roughly two thirds showed a clinically relevant improvement. Conclusions: Completion of a 12-week intensive resistance exercise program was associated with a statistically improved NLR and SIRI. The small sample size and retrospective nature limit the broader application of these findings. The results, however, provide a genesis for prospective validation examining the potential benefit exercise might have on the NLR and SIRI in women with breast cancer. Full article

Background: Heart failure (HF) is common and deadly, affecting over 60 million people worldwide, and it remains a leading cause of hospitalization and post-discharge death. One-year mortality after an acute decompensated HF (ADHF) admission often approaches 40%. Prognostic models are critical for stratifying mortality risk in heart failure (HF) patients. This study compared the performance of the MAGGIC and BCN Bio-HF models in predicting 1-year and 3-year all-cause mortality (ACM) in patients discharged after acute decompensated HF (ADHF). Methods: A retrospective analysis was conducted on 229 patients hospitalized for ADHF at the Clinical University Hospital of Zaragoza. The required variables were extracted from medical records, and ACM risks were calculated using web-based tools. Calibration, discrimination (AUC), and Kaplan–Meier survival analysis and calibration curves assessed risk stratification and alignment with observed outcomes. Reclassification metrics (Net Reclassification Index [NRI], Integrated Discrimination Improvement [IDI]) were used to compare the models’ predictive performances. Results: Both of the models demonstrated robust discrimination for 1-year ACM (AUC: MAGGIC = 0.738, BCN Bio-HF = 0.769) but showed lower performance for 3-year predictions. Calibration was poor, with both models exhibiting significant risk underestimation at the individual level. MAGGIC achieved higher sensitivity (1-year: 0.911; 3-year: 0.685), favoring high-risk patient identification, whereas BCN Bio-HF offered superior specificity (1-year: 0.679; 3-year: 0.746) and a positive prediction value, reducing false positives. BCN Bio-HF showed a significant 12.7% reclassification improvement for 1-year mortality prediction. Conclusions: BCN Bio-HF did not outperform MAGGIC in our cohort. MAGGIC is preferable for the initial high-risk patient identification, requiring more intense short-term follow-up, while BCN Bio-HF’s higher specificity is best-suited to avoid overtreatment. Altogether, the clinical utility of both models was limited in our cohort by severe miscalibration, which may render adequate risk stratification difficult. Full article

Little is known about cartilaginous endplate (CEP) mechanobiology or how it changes in a catabolic microenvironment, partly due to difficulties in conducting mechanotransduction in vitro. Recent studies have found blended collagen–agarose hydrogels to offer improved mechanotransduction in chondrocytes compared to agarose alone. It was hypothesized that blended collagen–agarose hydrogels would be sufficient to improve the mechanobiological response in CEP cells relative to that in agarose alone, while maintaining the chondrocyte phenotype and ability to respond to pro-inflammatory stimulation. Thus, human CEP cells were seeded into blended 2% agarose and 2 mg/mL type I collagen hydrogels, followed by culture with dynamic compression up to 7% and stimulation with TNF. Results confirmed CEP cells retained a rounded phenotype and high cell viability during culture in blended collagen–agarose hydrogels. Additionally, TNF induced a catabolic response through downregulation of pericellular marker COL6A1 and anabolic markers ACAN and COL2A1. No significant changes were seen due to dynamic compression, suggesting addition of collagen to agarose was not sufficient to induce mechanotransduction in human CEP cells in this study. However, blended collagen–agarose hydrogels increased stiffness by 4× and gene expression of key cartilage marker SOX9 and physioosmotic mechanosensor TRPV4, offering an improvement on agarose alone. Full article

In most commercial lithium-ion batteries, graphite remains the primary anode material. However, its theoretical specific capacity is only 372 mAh∙g⁻¹, which falls short of meeting the demands of high-performance electronic devices. Silicon anodes, despite boasting an ultra-high theoretical specific capacity of 4200 mAh∙g⁻¹, suffer from significant volume expansion (>300%) during cycling, leading to severe capacity fade and limiting their commercial viability. Currently, silicon-based polymer-derived ceramics have emerged as a highly promising next-generation anode material for lithium-ion batteries, thanks to their unique nano-cluster structure, tunable composition, and low volume expansion characteristics. The maximum capacity of the ceramics can exceed 1000 mAh∙g⁻¹, and their unique synthesis routes enable customization to align with diverse electrochemical application requirements. In this paper, we present the progress of silicon oxycarbide (SiOC), silicon carbonitride (SiCN), silicon boron carbonitride (SiBCN) and silicon oxycarbonitride (SiOCN) in the field of LIBs, including their synthesis, structural characteristics and electrochemical properties, etc. The mechanisms of lithium-ion storage in the Si-based anode materials are summarized as well, including the key role of free carbon in these materials. Full article

Heart failure (HF) poses a substantial global burden due to its high morbidity, mortality, and healthcare costs. Accurate prognostication is crucial for optimizing treatment, resource allocation, and patient counseling. Prognostic tools range from simple clinical scores such as ADHERE and MAGGIC to more complex models incorporating biomarkers (e.g., NT-proBNP, sST2), imaging, and artificial intelligence techniques. In acute HF, models like EHMRG and STRATIFY aid early triage, while in chronic HF, tools like SHFM and BCN Bio-HF support long-term management decisions. Despite their utility, most models are limited by poor generalizability, reliance on static inputs, lack of integration into electronic health records, and underuse in clinical practice. Novel approaches involving machine learning, multi-omics profiling, and remote monitoring hold promise for dynamic and individualized risk assessment. However, these innovations face challenges regarding interpretability, validation, and ethical implementation. For prognostic models to transition from theoretical promise to practical impact, they must be continuously updated, externally validated, and seamlessly embedded into clinical workflows. This review emphasizes the potential of prognostic models to transform HF care but cautions against uncritical adoption without robust evidence and practical integration. In the evolving landscape of HF management, prognostic models represent a hopeful avenue, provided their limitations are acknowledged and addressed through interdisciplinary collaboration and patient-centered innovation. Full article

This study aims to explore the walkability of three small areas (basic healthcare areas) of Barcelona city (Catalonia, Spain) for frail older adults. A mixed methods study design was conducted with 132 frail older adults in three primary care centers of Barcelona: Larrard, Barceloneta, and Vila Olímpica. A literature review was conducted to identify urban design indicators related to walkability and the aging population. These were then reflected in the surveys administered to the program participants, capturing information on their preferred routes, usual destinations, and walkability perceptions. Findings reveal significant mobility challenges for older adults, particularly the ones related to safety issues, the adequacy of sidewalk widths, greenery and urban furniture maintenance, and the presence/absence of commercial activities. This research underscores the importance of age-sensitive urban design in healthcare environments and provides a framework for enhancing walkability and accessibility for populations at greater risk of mobility-related health problems, such as frail older adults. Full article

Deep learning approaches for pork freshness grading typically require large datasets, which limits their practical application due to the high costs associated with data collection. To address this challenge, we propose BBSNet, a lightweight few-shot learning model designed for accurate freshness classification with a limited number of images. BBSNet incorporates a batch channel normalization (BCN) layer to enhance feature distinguishability and employs BiFormer for optimized fine-grained feature extraction. Trained on a dataset of 600 pork images graded by microbial cell concentration, BBSNet achieved an average accuracy of 96.36% in a challenging 5-way 80-shot task. This approach significantly reduces data dependency while maintaining high accuracy, presenting a viable solution for cost-effective real-time pork quality monitoring. This work introduces a novel framework that connects laboratory freshness indicators to industrial applications in data-scarce conditions. Future research will investigate its extension to various food types and optimization for deployment on portable devices. Full article

The growing demand for healthy and sensorially pleasing foods is accompanied by increasing sustainability concerns among consumers and industry. Therefore, exploring native and underutilized resources for traditional preparations is important. This study evaluated the incorporation of Moringa oleifera leaves and baru almonds (Dipteryx alata) in pesto sauce, comparing them to the traditional recipe regarding composition, color, total phenolics, volatiles, sensory characteristics, and acceptability. The following four formulations were developed: basil with cashew nuts (B/CN); basil with baru almonds (B/BA); and two versions with 50% basil replaced by moringa, combined with cashew (BM/CN) or baru (BM/BA). BM/BA presented the highest protein content (9.0%), compared to B/CN (7.9%). BM/CN showed a greener color. BM/CN and BM/BA showed total phenolics and antioxidant capacities similar to B/CN. BM/BA showed elevated condensed tannins (113.28 mg CE/100 g). All samples contained 1,8-Cineole and linalool, key to the aroma of basil. Pesto with moringa and/or baru showed good sensory acceptance, rated as “liked moderately”, with no difference from the conventional version (p > 0.05). There were no differences in the basil aroma, nutty flavor, or greasiness. Pesto sauce is a promising matrix for incorporating regional, underused ingredients such as moringa leaves and baru almonds, expanding their potential in new food development. Full article

Precise quality screening of tea tree seeds is crucial for the development of the tea industry. This study proposes a high-precision quality classification method for tea tree seeds by integrating mid-infrared (MIR) spectroscopy with an improved deep learning model. Four types of tea tree seeds in different states were prepared, and their spectral data were collected and preprocessed using Savitzky–Golay (SG) filtering and wavelet transform. Aiming at the deficiencies of DenseNet121 in one-dimensional spectral processing, such as insufficient generalization ability and weak feature extraction, the ECA-DenseNet model was proposed. Based on DenseNet121, the Batch Channel Normalization (BCN) module was introduced to reduce the dimensionality via 1 × 1 convolution while preserving the feature extraction capabilities, the Attention–Convolution Mix (ACMix) module was integrated to combine convolution and self-attention, and the Efficient Channel Attention (ECA) mechanism was utilized to enhance the feature discriminability. Experiments show that ECA-DenseNet achieves 99% accuracy, recall, and F1-score for classifying the four seed quality types, outperforming the original DenseNet121, machine learning models, and deep learning models. This study provides an efficient solution for tea tree seeds detection and screening, and its modular design can serve as a reference for the spectral classification of other crops. Full article

Background: The MVA-BN vaccine is considered effective for preventing mpox in key populations, based on observational studies, though no randomized trials have yet confirmed its effectiveness. Observational studies published to date rely on retrospective analyses of routine data, often missing information on relevant risk factors for mpox. Methods: Multi-country target trial emulation study with prospective data collection. Between 1 September 2022 and 15 June 2023, we recruited individuals eligible for mpox vaccination based on clinical history and exposure behaviors via healthcare centers and social venues in Spain, Peru, Panama, and Chile. Vaccinated individuals were paired with unvaccinated counterparts matched by mpox risk factors, country, recruitment date, and age. Follow-up continued via periodic surveys until 31 March 2024. The primary endpoint was symptomatic mpox occurrence ≥14 days post-vaccination. Results: The primary analysis included 1028 individuals (514 vaccinated, 514 unvaccinated) with a median follow-up time of 9.3 months (IQR 4.7–13.7). Mpox occurred in eight participants (0.8%): three vaccinated and five unvaccinated (HR 0.6; 95% CI 0.21–1.70). Adverse reactions were reported by 731 (49.6%) participants, predominantly skin reactions (703/1475; 47.7%), while systemic reactions occurred in 107 (7.3%). Long-lasting erythema at the injection site was reported in 450/1058 (42.5%) participants, persisting >6 months in 107 of them (23.8%). Conclusions: The low incidence of mpox during the study period resulted in a limited number of endpoint events, precluding robust conclusions on the efficacy of the MVA-BN vaccine as pre-exposure prevention for mpox. However, our analysis, which accounted for key confounders such as exposure behaviors, yielded results consistent with previous studies suggesting the effectiveness of the vaccine in the mpox setting. Full article

The development of photothermal-assisted photocatalytic systems with broad-spectrum solar utilization and high charge separation efficiency remains a critical challenge for antibiotic degradation. Herein, we report novel black g-C₃N₄ (BCN) materials synthesized via a one-step thermal copolymerization strategy using C/N precursors and tetrachlorofluorescein. After the introduction of tetrachlorofluorescein, the color of the sample changes, which gives BCN enhanced light absorption and a significant photothermal effect for poorly heating-assisted photocatalysis. The synergistic coupling of photothermal and photocatalytic processes enabled the optimal BCN-U sample to achieve exceptional degradation efficiency (89% within 120 min) for a typical antibiotic (e.g., tetracycline) under an LED lamp as the visible light source, outperforming conventional yellow g-C₃N₄ (YCN-U) by a factor of 1.37. Mechanistic studies revealed that the photothermal effect facilitates carrier separation via thermal-driven electron excitation while accelerating reactive oxygen species (•OH and •O₂⁻) generation. The synergistic interplay between photocatalysis and photothermal effects, which improved mass transfer, ensures robust stability, which provides new insights into designing dual-functional carbon nitride-based materials for sustainable environmental remediation. Full article