Search Results (120)

Introduction: Maintaining adherence and access to antiretroviral treatment is basic for good management of people with HIV (PWH), while enhancing patient satisfaction. With the aim of shifting from drug-centered into patient-centered care and integrating care interventions into community settings, here we share an outpatient hospital pharmaceutical care implementation model for PWH. This model involves the delivery of medication through a community center, BCN-Checkpoint, using a proprietary app and coordinated with automated locker systems. Methods: During the pre-implementation phase the circuit was defined and seven steps were considered critical for successful implementation: (1) assignation of teams and roles; (2) adaptation of the self-developed app; (3) development of a patient journey map; (4) locker installation and system integration with data from the electronic records; (5) staff training; (6) review of data protection regulations; (7) simulation tests. A two-phase simulation—with fictitious users and with real ones—validated the system. The implementation phase included an initial pilot study, in which 46 patients were included in the project. Results: System uptake was high, with strong adherence to the dispensing pathway; only five discontinuations due to personal preferences or availability of alternative dispensing pathways. Several barriers to implementation emerged, primarily categorized into technical issues, process and operational challenges, coordination, and user-related difficulties. However, a communitarian setting, flexible attention times and protocols, and the strong intersectoral collaboration between specialists are believed to increase patient retention and overall satisfaction. Conclusions: The implementation of an outpatient dispensing hospital medication model using an app and automated locker systems is feasible, considering detail to procedures, timely adaptations, and staff training. Full article

Biochar is widely used in composting to reduce nitrogen loss; however, the application of acid-modified and alkali-modified biochar in composting remains insufficient. We hypothesize that acid-modified maize straw biochar can simultaneously reduce NH₃ and N₂O losses during the composting process. To test this, a composting experiment was conducted with four treatments: a control with pig manure and corn straw only (CK), adding 5% corn straw biochar (BC), adding 5% HNO₃-modified corn straw biochar (BCN), and adding 5% NaOH-modified corn straw biochar (BCNa). The results showed that, compared to CK, NH₃ emissions were not decreased by BC, but significantly reduced (p < 0.05) by 32.6% in BCN and 36.8% in BCNa, respectively. N₂O was significantly decreased (p < 0.05) by 27.6% in BC and 30.9% in BCNa, respectively. However, BCN significantly increased N₂O emission (p < 0.05) by 368.7%, compared to CK. Compared to CK, the global warming potential (GWP) in the BCNa treatment was significantly reduced by 35.2% (p < 0.05), while the GWP in BCN was significantly decreased by 10.3%. Overall, although BCN treatment may increase N₂O emissions, it can still reduce the GWP. In comparison, BCNa treatment achieves the most significant reduction in GWP during pig manure composting. Full article

The sustainable management of sewage sludge remains a key environmental challenge for rapidly urbanizing regions such as Kazakhstan. This study explores the potential of sewage sludge-derived biochar as an efficient, low-cost adsorbent for removing Rhodamine B (RhB) dye and toxic metals from water. Sewage sludge was pyrolyzed at 700 °C (BC) and subsequently activated with hydrochloric acid (BCH) and sodium hydroxide (BCN) to improve its surface functionality and porosity. The morphology, surface area, porosity, and functional groups of the obtained biochars were characterized using SEM-EDS, BET, FTIR, and XRD analyses. Batch adsorption experiments demonstrated that the pseudo-second-order kinetic model (R² = 0.99) best described the data, indicating chemisorption-controlled uptake. Experimental RhB adsorption capacity was 14.53 mg/g for BCH at RhB concentration of 75 mg/L after 120 min. Moreover, BCH exhibited enhanced metal adsorption capacities of 22.85 mg/g (Cu²⁺), 17.55 mg/g (Zn²⁺), 15.08 mg/g (Cd²⁺), 7.97 mg/g (Cr³⁺), and 3.68 mg/g (As³⁺). These results confirm that acid activation significantly improves adsorption efficiency compared with pristine biochar due to increased surface area and the introduction of oxygen-containing functional groups. Overall, sewage sludge-derived biochar shows strong potential as a sustainable adsorbent for dye and heavy metal removal. Full article

Advanced ceramics are known for their lightweight, high-temperature resistance, corrosion resistance, and biocompatibility. They are crucial in energy conversion, environmental protection, and aerospace fields. This review highlights the recent advancements in ceramic matrix composites, high-entropy ceramics, and polymer-derived ceramics, alongside various fabrication techniques such as three-dimensional printing, advanced sintering, and electric-field-assisted joining. Beyond the fabrication process, we emphasize how different processing methods impact microstructure, transport properties, and performance metrics relevant to catalysis. Additive manufacturing routes, such as direct ink writing, digital light processing, and binder jetting, are discussed and normalized based on factors such as relative density, grain size, pore architecture, and shrinkage. Cold and flash sintering methods are also examined, focusing on grain-boundary chemistry, dopant compatibility, and scalability for catalyst supports. Additionally, polymer-derived ceramics (SiOC, SiCN, SiBCN) are reviewed in terms of their catalytic performance in hydrogen evolution reaction, oxygen evolution reaction, oxygen reduction reaction, and CO₂ reduction reaction. CeO₂-ZrO₂ composites are particularly highlighted for their use in environmental catalysis and high-temperature gas sensing. Furthermore, insights on the future industrialization, cross-disciplinary integration, and performance improvements in catalytic applications are provided. 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

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

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

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

Carbon fiber-reinforced SiBCN ceramic matrix composite ($C_f$/SiBCN CMC) is emerging as a promising candidate for advanced thermal protection systems, owing to its superior thermal stability and notable ablation resistance. In this study, the mechanical properties of $C_f$/SiBCN CMC are investigated theoretically and experimentally. Utilizing a multiscale approach, a representative volume element (RVE) is developed to predict mechanical properties based on detailed microstructural characterization. The predictions derived from the RVE demonstrate agreement with the experimental findings. The experimental results show dispersion in the mechanical properties of $C_f$/SiBCN CMC. To investigate whether the dispersion of mechanical properties is associated with defects, this study examines the impact of the location, content, and size of defects on the mechanical properties of the $C_f$/SiBCN CMC. The analysis reveals that the location, content, and size of defect all impact the mechanical properties of the $C_f$/SiBCN CMC, with overall porosity having the most significant effect. When the porosity is constant, variations in defect location and size also contribute to the observed variability in the mechanical performance of the $C_f$/SiBCN CMC. Full article

Borocarbonitrides (BCNs), a new class of ternary materials combining boron, carbon, and nitrogen atoms, have emerged as promising candidates in decarbonization technologies due to their unique physicochemical properties. BCNs offer an adjustable atom composition and electronic structure, thermal stability, and potentially a large specific surface area, which are attractive features for efficient interactions with carbon dioxide. These make BCNs suitable for carbon dioxide capture, storage, and catalytic conversion applications. Furthermore, BCNs have the potential to (electro)catalyze the synthesis of green fuels, such as hydrogen, as well as that of other hydrogen carriers such as ammonia. With this review, we examine the recent advances in BCN synthesis methods, characterization, and functional applications while focusing on their role in the decarbonization technologies mentioned above. We aim to highlight the potential of BCNs to drive innovation in sustainable carbon management. Additionally, in the last section of this paper, we discuss the challenges and prospects of BCNs in decarbonization and beyond. Full article