Background: Exercise and the consumption of sugars result in a dysfunction of the intestinal barrier (IB). Here, we determined the effect of sugar in a natural matrix on the intestinal barrier after moderate (A) and intensive endurance exercise (B). Method: The IB function was determined before (pre) and after running (post), and 120 and 180 min after consuming the drink by measuring serum endotoxin concentrations (lipopolysaccharides—LPS), IL-6, CD14, and i-FABP. In study A, nonspecifically trained participants (n = 24, males and females, age 26 ± 4) ran for one hour at 80% of their individual anaerobic threshold (IAT). After finishing, the runners consumed, in a crossover setup, either 500 mL of water, diluted cloudy apple juice (test drink), or an identical drink (placebo) without the fruit juice matrix (FJM). In study B, the participants (n = 30, males and females, age 50 ± 9) completed an ultra-marathon run, were divided into groups, and consumed one of the above-mentioned drinks. Results: Study A: Exercise resulted in a significant increase in serum LPS, i-FABP, and IL-6, which decreased fast after finishing. No impact of the different drinks on LPS i-FABP, or IL-6 could be observed, but there was an impact on CD14. Study B: The ultra-marathon resulted in a strong increase in serum LPS, which decreased fast after finishing in the water and test drink groups, but not in the placebo group. Conclusions: The consumed drinks did not affect the kinetics of IB regeneration after moderate exercise, but impacted CD14 serum concentrations, indicating possible beneficial effects of the FJM on the immune system. After an ultra-marathon, IB function regenerates very fast. The intake of sugar (placebo) seems to have had a negative impact on IB regeneration, which was diminished by the presence of the FJM. Full article

This paper contextualizes Okudzhava’s song “And We to the Doorman” (AWD), initially marginal in the Soviet poetic mainstream. It explores its shifts in tone, irregular rhythms, colloquial language, and semi-criminal undertones. AWD’s structure, with uneven stanzas and no clear refrain, reveals underlying symmetry and recurring themes. The meter is predominantly iambic but varies. Unconventional verse endings and various rhyme schemes, including distant chains, characterize its prosody. The narrative touches on social cohesion and class conflict. The style reflects a challenging attitude toward privilege, employing rhetorical devices and indirect threats. The melody aligns with thematic elements, featuring repetitive patterns and a spoken quality. Semantically, AWD presents an ambiguous message on class struggle and moral issues. In sum, this analysis uncovers Okudzhava’s song’s formal complexities, thematic nuances, and stylistic innovations. Full article

Motion object detection (MOD) with freely moving cameras is a challenging task in computer vision. To extract moving objects, most studies have focused on the difference in motion features between foreground and background, which works well for dynamic scenes with relatively regular movements and variations. However, abrupt illumination changes and occlusions often occur in real-world scenes, and the camera may also pan, tilt, rotate, and jitter, etc., resulting in local irregular variations and global discontinuities in motion features. Such complex and changing scenes bring great difficulty in detecting moving objects. To solve this problem, this paper proposes a new MOD method that effectively leverages local and global visual information for foreground/background segmentation. Specifically, on the global side, to support a wider range of camera motion, the relative inter-frame transformations are optimized to absolute transformations referenced to intermediate frames in a global form after enriching the inter-frame matching pairs. The global transformation is fine-tuned using the spatial transformer network (STN). On the local side, to address the problem of dynamic background scenes, foreground object detection is optimized by utilizing the pixel differences between the current frame and the local background model, as well as the consistency of local spatial variations. Then, the spatial information is combined using optical flow segmentation methods, enhancing the precision of the object information. The experimental results show that our method achieves a detection accuracy improvement of over 1.5% compared with the state-of-the-art methods on the datasets of CDNET2014, FBMS-59, and CBD. It demonstrates significant effectiveness in challenging scenarios such as shadows, abrupt changes in illumination, camera jitter, occlusion, and moving backgrounds. Full article

The surfactant cetyltrimethylammonium bromide (CTAB) induces the aggregation of gold nanoclusters (GNCs), leading to the development of a proposed fluorometric technique for detecting thiocyanate (SCN⁻) ions based on an anti-aggregation mechanism. This approach is straightforward to execute, highly sensitive, and selective. A significant quenching effect occurs in fluorescence upon using the aggregation agent CTAB in GNCs synthesis, resulting in a transition from intense red fluorescence to dim red. The decrease in fluorescence intensity of GNCs in the presence of CTAB is caused by the mechanism of fluorescence quenching mediated by aggregation. As the levels of SCN⁻ rise, the fluorescence of CTAB-GNCs increases; this may be detected using spectrofluorometry or by visually inspecting under UV irradiation. The recovery of red fluorescence of CTAB-GNCs in the presence of SCN⁻ enables the precise and discerning identification of SCN⁻ within the concentration range of 2.86–140 nM. The minimum detectable concentration of the SCN⁻ ions was 1 nM. The selectivity of CTAB-GNCs towards SCN⁻ ions was investigated compared to other ions, and it was demonstrated that CTAB-GNCs exhibit exceptional selectivity. Furthermore, we believe that CTAB-GNCs have novel possibilities as favorable sensor candidates for various industrial applications. Our detection technique was validated by analyzing SCN⁻ ions in milk samples, which yielded promising results. Full article

The use of 3D laparoscopic partial nephrectomy has emerged as a cornerstone in the surgical arsenal for addressing renal tumors, particularly in managing challenging cases characterized by deeply seated tumors embedded within the renal parenchyma. In these intricate scenarios, the utilization of intraoperative ultrasound (IOUS) acquires paramount importance, serving as an indispensable tool for guiding and meticulously monitoring the surgical process in real time. To further explore the efficacy of IOUS-guided techniques, we conducted a retrospective study comparing outcomes in patients who underwent partial nephrectomy with IOUS guidance (n = 60) between 2020 and 2022 with a cohort from 2018 to 2019 without IOUS guidance (n = 25). Our comprehensive analysis encompassed various post-operative parameters, including the duration until food resumption, analgesia requirements, and length of the hospital stay. While these parameters exhibited comparable outcomes between the two groups, notable distinctions emerged in the intraoperative metrics. The IOUS-guided cohort demonstrated significantly reduced blood loss, a shorter median operative duration, and diminished ischemia time (p = 0.001). These compelling findings underscore the undeniable benefits of IOUS-guided techniques in not only facilitating the attainment of negative surgical margins but also in enhancing procedural safety and precision, thereby contributing to improved patient outcomes in the management of renal tumors. Full article

This study reports five types of metal-doped (Co, Cu, Sn, V, and Zr) NASICON-type Li_1.3Al_0.3Ti_1.7(PO₄)₃ (LATP)/polymer composite solid electrolytes (CSEs) enabling Li₄Ti₅O₁₂ (LTO) anodes to have high rate capability and excellent cycling performance. The high Li⁺-conductivity LATP samples are successfully synthesized through a modified sol–gel method followed by thermal calcination. We find that the cation dopants clearly influence the substitution of Al for Ti, with the type of dopant serving as a crucial factor in determining the ionic conductivity and interfacial resistance of the solid electrolyte. The CSE containing poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP), lithium bis(trifluoromethanesulfonyl)imide (LiTFSI), and Sn-LATP shows an ionic conductivity of 1.88 × 10⁻⁴ S cm⁻¹ at ambient temperature. The optimum conductivity can be attributed to alterations in the lattice parameters and Li⁺ transport pathways owing to Sn doping. The solid-state cell equipped with the LTO-supported CSE containing Sn-LATP fillers demonstrates both excellent high rate capability at 5 C (with a capacity retention of 86% compared to the value measured at 0.2 C) and superior cycling stability, maintaining high Coulombic efficiency (>99.0%) over 510 cycles. These findings indicate that the proposed CSE is highly promising for use in solid-state lithium batteries with desirable charge–discharge properties and high durability. Full article

Exercise physiology is the science that studies the processes of physical activity and their impact on the body. It usually requires a multidisciplinary approach with a team of experts because of the multiple physiological systems involved. At the cellular level, exercise can trigger a cascade of events that involve metabolic pathways, muscle recruitment and conditioning, neuromuscular adaptations, and optimization of energy production. On a systematic level, this has involved the cardiovascular system, the respiratory system, the musculoskeletal system, the peripheral and central nervous systems, and even the endocrine system to improve adaptation to meet the demands of exercise, improving strength and endurance with the goal of maximizing performance. This field, which began largely with a focus on the kinetics of oxygen consumption and the relationship between aerobic and anaerobic metabolism, has expanded with technological advancements such as wearable devices, advanced imaging techniques, and genomics, which has allowed for a greater appreciation of the physiologic responses of exercise at the level of molecular and cellular interactions. Recently, this science has evolved into the study of overall health promotion and disease prevention to meet individual fitness needs and goals. This philosophy, which is captured by the motto “Exercise is Medicine”, has been adopted as a Global Health Initiative by the World Health Organization (WHO). Despite the advancements in technology and impactful, large-scale studies, a variety of questions, such as how physical activity interacts with aging to affect physical and cognitive function, how inactivity influences acute and chronic disease states, and how we maximize human performance in extreme conditions like microgravity environments, remain. The purpose of this review is to present and discuss established concepts, controversial topics, and unanswered questions within exercise physiology that have allowed this field to become a science that embraces the overall well-being of individuals. Full article

Fairness Artificial Intelligence (AI) aims to identify and mitigate bias throughout the AI development process, spanning data collection, modeling, assessment, and deployment—a critical facet of establishing trustworthy AI systems. Tackling data bias through techniques like reweighting samples proves effective for promoting fairness. This paper undertakes a systematic exploration of reweighting samples for conventional Machine-Learning (ML) models, utilizing five models for binary classification on datasets such as Adult Income and COMPAS, incorporating various protected attributes. In particular, AI Fairness 360 (AIF360) from IBM, a versatile open-source library aimed at identifying and mitigating bias in machine-learning models throughout the entire AI application lifecycle, is employed as the foundation for conducting this systematic exploration. The evaluation of prediction outcomes employs five fairness metrics from AIF360, elucidating the nuanced and model-specific efficacy of reweighting samples in fostering fairness within traditional ML frameworks. Experimental results illustrate that reweighting samples effectively reduces bias in traditional ML methods for classification tasks. For instance, after reweighting samples, the balanced accuracy of Decision Tree (DT) improves to 100%, and its bias, as measured by fairness metrics such as Average Odds Difference (AOD), Equal Opportunity Difference (EOD), and Theil Index (TI), is mitigated to 0. However, reweighting samples does not effectively enhance the fairness performance of K Nearest Neighbor (KNN). This sheds light on the intricate dynamics of bias, underscoring the complexity involved in achieving fairness across different models and scenarios. Full article

Background: A high incidence rate of nasopharyngeal carcinoma (NPC) has been observed in Southeast Asia compared to other parts of the world. Radiomics is a computational tool to predict outcomes and may be used as a prognostic biomarker for advanced NPC treated with concurrent chemoradiotherapy. Recently, radiomic analysis of the peripheral tumor microenvironment (TME), which is the region surrounding the gross tumor volume (GTV), has shown prognostic usefulness. In this study, not only was gross tumor volume (GTVt) analyzed but also tumor peripheral regions (GTVp) were explored in terms of the TME concept. Both radiomic features and delta radiomic features were analyzed using CT images acquired in a routine radiotherapy process. Methods: A total of 50 patients with NPC stages III, IVA, and IVB were enrolled between September 2004 and February 2014. Survival models were built using Cox regression with clinical factors (i.e., gender, age, overall stage, T stage, N stage, and treatment dose) and radiomic features. Radiomic features were extracted from GTVt and GTVp. GTVp was created surrounding GTVt for TME consideration. Furthermore, delta radiomics, which is the longitudinal change in quantitative radiomic features, was utilized for analysis. Finally, C-index values were computed using leave-one-out cross-validation (LOOCV) to evaluate the performances of all prognosis models. Results: Models were built for three different clinical outcomes, including overall survival (OS), local recurrence-free survival (LRFS), and progression-free survival (PFS). The range of the C-index in clinical factor models was (0.622, 0.729). All radiomics models, including delta radiomics models, were in the range of (0.718, 0.872). Among delta radiomics models, GTVt and GTVp were in the range of (0.833, 0.872) and (0.799, 0.834), respectively. Conclusions: Radiomic analysis on the proximal region surrounding the gross tumor volume of advanced NPC patients for survival outcome evaluation was investigated, and preliminary positive results were obtained. Radiomic models and delta radiomic models demonstrated performance that was either superior to or comparable with that of conventional clinical models. Full article

Mechanical insufflation-exsufflation (MI-E) is essential for secretion clearance, especially in neuromuscular disorders. For the best outcomes, initiation of MI-E should be started at the correct time with regular evaluation to the response to treatment. Typically, cough peak flow has been used to evaluate cough effectiveness with and without MI-E. This review highlights the limitations of this and discussed other tools to evaluate MI-E efficacy in this rapidly developing field. Such tools include the interpretation of parameters (like pressure, flow and volumes) that derive from the MI-E device and external methods to evaluate upper airway closure. In this review we pinpoint the differences between different devices in the market and discuss new tools to better titrate MI-E and detect pathological responses of the upper airway. We discuss the importance of point of care ultrasound (POCUS), transnasal fiberoptic laryngoscopy and wave form analysis in this setting. To improve clinical practice newer generation MI-E devices should allow real-time evaluation of waveforms and standardize some of the derived parameters. Full article

Multiple myeloma is an incurable plasma cell malignancy. Most patients end up relapsing and developing resistance to antineoplastic drugs, like bortezomib. Antibiotic tigecycline has activity against myeloma. This study analyzed tigecycline and bortezomib combination on cell lines and plasma cells from myeloma patients. Apoptosis, autophagic vesicles, mitochondrial mass, mitochondrial superoxide, cell cycle, and hydrogen peroxide were studied by flow cytometry. In addition, mitochondrial antioxidants and electron transport chain complexes were quantified by reverse transcription real-time PCR (RT-qPCR) or western blot. Cell metabolism and mitochondrial activity were characterized by Seahorse and RT-qPCR. We found that the addition of tigecycline to bortezomib reduces apoptosis in proportion to tigecycline concentration. Supporting this, the combination of both drugs counteracts bortezomib in vitro individual effects on the cell cycle, reduces autophagy and mitophagy markers, and reverts bortezomib-induced increase in mitochondrial superoxide. Changes in mitochondrial homeostasis and MYC upregulation may account for some of these findings. These data not only advise to avoid considering tigecycline and bortezomib combination for treating myeloma, but caution on the potential adverse impact of treating infections with this antibiotic in myeloma patients under bortezomib treatment. Full article

The impact of climate change has made weather events more extreme, unpredictable and frequent. In the last 4 years, Mozambique has been devastated by 8 major cyclones, resulting in material and human damage and affecting the functioning of basic local services, such as in the water and health sectors. In this study, we explored the environmental components of the climate–water quality–disease relationship that could drive the dynamics of waterborne diseases. Statistical models and geospatial information technologies (GITs) were used to analyse water quality and the relationship with waterborne diseases between 2016 and 2023. Results indicate that water quality is the main element, between precipitation natural disasters and waterborne diseases, on which a relevant public health intervention can act to ameliorate the future negative impacts of climate change and disease incidence. The results of this study also showed that the quality of water affected mainly by heavy rain events varies in different regions and in different seasons. Full article

Addressing the constraints inherent in traditional primary Air Quality Index (AQI) forecasting models and the shortcomings in the exploitation of meteorological data, this research introduces a novel air quality prediction methodology leveraging machine learning and the enhanced modeling of secondary data. The dataset employed encompasses forecast data on primary pollutant concentrations and primary meteorological conditions, alongside actual meteorological observations and pollutant concentration measurements, spanning from 23 July 2020 to 13 July 2021, sourced from long-term air quality projections at various monitoring stations within Jinan, China. Initially, through a rigorous correlation analysis, ten meteorological factors were selected, comprising both measured and forecasted data across five categories each. Subsequently, the significance of these ten factors was assessed and ranked based on their impact on different pollutant concentrations, utilizing a combination of univariate and multivariate significance analyses alongside a random forest approach. Seasonal characteristic analysis highlighted the distinct seasonal impacts of temperature, humidity, air pressure, and general atmospheric conditions on the concentrations of six key air pollutants. The performance evaluation of various machine learning-based classification prediction models revealed the Light Gradient Boosting Machine (LightGBM) classifier as the most effective, achieving an accuracy rate of 97.5% and an F₁ score of 93.3%. Furthermore, experimental results for AQI prediction indicated the Long Short-Term Memory (LSTM) model as superior, demonstrating a goodness-of-fit of 91.37% for AQI predictions, 90.46% for O₃ predictions, and a perfect fit for the primary pollutant test set. Collectively, these findings affirm the reliability and efficacy of the employed machine learning models in air quality forecasting. Full article

Influenza pandemics pose a serious risk to the global population, with the potential for high morbidity and mortality. An adjuvanted H5N1 vaccine (aH5N1) has been approved for prophylaxis against the avian influenza virus H5N1, which is a likely cause of future pandemics. In this phase-III, stratified, randomized, controlled, observer-blind, multicenter study, we evaluated the safety and immunogenicity of aH5N1 in four separate groups of adults: adults 18–60 years of age who were healthy or had high-risk medical conditions and older adults ≥61 years of age who were healthy or had high-risk medical conditions. Subjects were randomly assigned to aH5N1 or the comparator, adjuvanted trivalent seasonal influenza vaccine (aTIV). Antibody responses to aH5N1 were increased in all four subgroups and, within each age stratum, largely consistent between healthy subjects and those with medical conditions. Injection-site pain was reported by 66–73% of younger and 36–42% of older–aH5N1 recipients, and fatigue and myalgia were reported by 22–41% of subjects across age and health subgroups. No serious adverse events or deaths were considered related to the study vaccine. In conclusion, aH5N1 increased antibody responses regardless of age or health status and demonstrated a clinically acceptable safety and tolerability profile. Full article

With a growing need for long-term care facilities in general, and for specialized dementia units in particular, it is important to ensure that the architectural layouts of such facilities support the well-being of both the residents and the unit caregivers. This study aimed to investigate correlations between the support provided by the architectural layout of long-term care units for enhancing residents’ well-being and for decreasing unit caregivers’ burnout and increasing their resilience—as layouts may impact each party differently. The Psycho Spatial Evaluation Tool was utilized to assess the support provided by the layouts of seventeen long-term care units (ten regular nursing units and seven specialized dementia units) for the residents’ physical and social well-being (five dimensions); a questionnaire was used to measure the unit caregivers’ burnout and resilience. When analyzing layouts’ support for residents’ physical and social well-being, inconsistencies emerged regarding correlations with caregivers’ burnout and resilience across the two types of long-term care units. Supporting residents’ physical well-being was correlated with increased caregiver resilience in dementia units, and with increased burnout and decreased resilience in regular nursing units. Layouts supporting social well-being showed inconsistent correlations with caregivers’ resilience indexes in dementia units, and with burnout and resilience indexes in regular nursing units. The findings underscore the role of the architectural layout of long-term care units in enhancing residents’ well-being; the results also highlight the possible unintentional yet negative impact of the layout on the caregivers’ burnout and resilience. This study emphasizes the need to identify and rectify design shortcomings as a means of enhancing residents’ well-being, while increasing the unit caregivers’ resilience and decreasing their burnout. These insights should be addressed when developing strategies and interventions for ensuring optimal care environments for all parties involved. Full article

The controlled manufacturing of semiconductor photocatalysts is crucial to their development for drinking water treatment. In this study, TiO₂-coated meltblown nonwoven fabrics prepared via Atomic Layer Deposition (ALD) are applied for the inactivation of Escherichia coli (E. coli). It is observed that in the presence of an ultraviolet light-emitting diode (UV-LED) light source (255 nm), 1.35 log E. coli inactivation is achieved. However, exposure to catalyst-coated fabrics in addition to the light source resulted in >4 log E. coli inactivation, suggesting a much higher rate of hydroxyl radical formation on the surface, leading to cell death. Full article

Outcomes for glioblastoma (GBM) remain poor despite standard-of-care treatments including surgical resection, radiation, and chemotherapy. Intratumoral heterogeneity contributes to treatment resistance and poor prognosis, thus demanding novel therapeutic approaches. Drug repositioning studies on antiretroviral therapy (ART) have shown promising potent antineoplastic effects in multiple cancers; however, its efficacy in GBM remains unclear. To better understand the pleiotropic anticancer effects of ART on GBM, we conducted a comprehensive drug repurposing analysis of ART in GBM to highlight its utility in translational neuro-oncology. To uncover the anticancer role of ART in GBM, we conducted a comprehensive bioinformatic and in vitro screen of antiretrovirals against glioblastoma. Using the DepMap repository and reversal of gene expression score, we conducted an unbiased screen of 16 antiretrovirals in 40 glioma cell lines to identify promising candidates for GBM drug repositioning. We utilized patient-derived neurospheres and glioma cell lines to assess neurosphere viability, proliferation, and stemness. Our in silico screen revealed that several ART drugs including reverse transcriptase inhibitors (RTIs) and protease inhibitors (PIs) demonstrated marked anti-glioma activity with the capability of reversing the GBM disease signature. RTIs effectively decreased cell viability, GBM stem cell markers, and proliferation. Our study provides mechanistic and functional insight into the utility of ART repurposing for malignant gliomas, which supports the current literature. Given their safety profile, preclinical efficacy, and neuropenetrance, ARTs may be a promising adjuvant treatment for GBM. Full article

The structural, elastic, piezoelectric, and electronic properties of Li-doped K_0.5Na_0.5NbO₃ (K_0.5−xNa_0.5−yLi_x+yNbO₃, KNN-L) are calculated. The properties of KNN-L are related to the Li-doping content and the replaced K or Na atoms. The bulk modulus, the shear modulus, and Young’s modulus of KNN-L are mostly higher than those of KNN, and the hardness value increases. The Poisson ratio of KNN-L is lower than that of most KNN, and the ductility is reduced. All doped structures are direct band gap semiconductors. K_0.5Na_0.375Li_0.125NbO₃ has the largest piezoelectric charge constant, d₃₃ = 44.72 pC/N, in the respective structures, which is 1.5 fold that of K_0.5Na_0.5NbO₃ (29.15 pC/N). The excellent piezoelectric performance of Li-doping KNN-L was analyzed from the insights of elastic and electronic properties. Full article

This study investigated how process parameters of laser cladding affect the microstructure and mechanical properties of WC-12Co composite coating for use as a protective layer of continuous caster rolls. WC-Co powders, WC-Ni powders, and Ni-Cr alloy powder with various wear resistance characteristics were evaluated in order to determine their applicability for use as cladding materials for continuous caster roll coating. The cladding process was conducted with various parameters, including laser powers, cladding speeds, and powder feeding rates, then the phases, microstructure, and micro-hardness of the cladding layer were analyzed in each specimen. Results indicate that, to increase the hardness of the cladding layer in WC-Co composite coating, the dilution of the cladding layer by dissolution of Fe from the substrate should be minimized, and the formation of the Fe-Co alloy phase should be prevented. The mechanical properties and wear resistance of each powder with the same process parameters were compared and analyzed. The microstructure and mechanical properties of the laser cladding layer depend not only on the process parameters, but also on the powder characteristics, such as WC particle size and the type of binder material. Additionally, depending on the degree of thermal decomposition of WC particles and evolution of W distribution within the cladding layer, the hardness of each powder can differ significantly, and the wear mechanism can change. Full article

Herein, we investigate the temperature compensation for a dual-mass MEMS gyroscope. After introducing and simulating the dual-mass MEMS gyroscope’s working modes, we propose a hybrid algorithm for temperature compensation relying on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sample entropy, time–frequency peak filtering, non-dominated sorting genetic algorithm-II (NSGA II) and extreme learning machine. Firstly, we use ICEEMDAN to decompose the gyroscope’s output signal, and then we use sample entropy to classify the decomposed signals. For noise segments and mixed segments with different levels of noise, we use time–frequency peak filtering with different window lengths to achieve a trade-off between noise removal and signal retention. For the feature segment with temperature drift, we build a compensation model using extreme learning machine. To improve the compensation accuracy, NSGA II is used to optimize extreme learning machine, with the prediction error and the 2-norm of the output-layer connection weight as the optimization objectives. Enormous simulation experiments prove the excellent performance of our proposed scheme, which can achieve trade-offs in signal decomposition, classification, denoising and compensation. The improvement in the compensated gyroscope’s output signal is analyzed based on Allen variance; its angle random walk is decreased from 0.531076°/h/√Hz to 6.65894 × 10⁻³°/h/√Hz and its bias stability is decreased from 32.7364°/h to 0.259247°/h. Full article

The aim of this study is to purify industrial textile effluents by treating two types of commonly encountered dyes: blue maxilon (BM), of cationic nature, and black eriochrome (NE), of anionic nature. We intend to employ an innovative approach based on the adsorption of these dyes onto a novel vegetal biomaterial derived from Aleppo pine fibers (FPAs). A kinetic and thermodynamic study was conducted. The effect of some physicochemical parameters on both dye adsorption and FPAs was also evaluated. The modeling of the adsorption results was performed using Langmuir, Freundlich, Temkin, and Dubinin Radushkevich (D-R) isotherms. The results indicate that the equilibrium time strongly depends on the initial concentration of the two dyes, being 60 min with pseudo-second-order adsorption kinetics for both dyes. Adsorption isotherms under the optimal conditions of adsorbent mass, temperature, medium pH, and dye concentration were used to determine the maximum adsorption efficiency, which was close to 93% and 98% for BM and NE, respectively. The results also show that the adsorption of both dyes on FPAs fits well with Langmuir’s model. The thermodynamic study indicates that the adsorption of both dyes on FPAs is spontaneous and exothermic in nature for BM and endothermic for NE. Full article

In-host models have been essential for understanding the dynamics of virus infection inside an infected individual. When used together with biological data, they provide insight into viral life cycle, intracellular and cellular virus–host interactions, and the role, efficacy, and mode of action of therapeutics. In this review, we present the standard model of virus dynamics and highlight situations where added model complexity accounting for intracellular processes is needed. We present several examples from acute and chronic viral infections where such inclusion in explicit and implicit manner has led to improvement in parameter estimates, unification of conclusions, guidance for targeted therapeutics, and crossover among model systems. We also discuss trade-offs between model realism and predictive power and highlight the need of increased data collection at finer scale of resolution to better validate complex models. Full article