Three-dimensional cell culture spheroids are commonly used for drug evaluation studies because they can produce large quantities of homogeneous cell aggregates. As the spheroids grow, nutrients supplied from outer spheroid regions render the inner spheroid areas hypoxic and hyponutrient, which makes them unobservable through confocal microscopy. In this study, we fabricated a cancer cell aggregate culture device that facilitates the observation of nutrient and oxygen gradients. An alginate gel fiber was created in the cell culture chamber to ensure a flow path for supplying the culture medium. A gradient of nutrients and oxygen was generated by positioning the flow channel close to the edge of the chamber. We devised a fabrication method that uses calcium carbonate as a source of Ca²⁺ for the gelation of sodium alginate, which has a slow reaction rate. We then cultured a spheroid of HCT116 cells, which were derived from human colorectal carcinoma using a fluorescent ubiquitination-based cell cycle indicator. Fluorescence observation suggested the formation of a hypoxic and hyponutrient region within an area approximately 500 µm away from the alginate gel fiber. This indicates the development of a cancer cell aggregate culture device that enables the observation of different nutrition and oxygen states. Full article

With the development of deep learning, several graph neural network (GNN)-based approaches have been utilized for text classification. However, GNNs encounter challenges when capturing contextual text information within a document sequence. To address this, a novel text classification model, RB-GAT, is proposed by combining RoBERTa-BiGRU embedding and a multi-head Graph ATtention Network (GAT). First, the pre-trained RoBERTa model is exploited to learn word and text embeddings in different contexts. Second, the Bidirectional Gated Recurrent Unit (BiGRU) is employed to capture long-term dependencies and bidirectional sentence information from the text context. Next, the multi-head graph attention network is applied to analyze this information, which serves as a node feature for the document. Finally, the classification results are generated through a Softmax layer. Experimental results on five benchmark datasets demonstrate that our method can achieve an accuracy of 71.48%, 98.45%, 80.32%, 90.84%, and 95.67% on Ohsumed, R8, MR, 20NG and R52, respectively, which is superior to the existing nine text classification approaches. Full article

This paper provides a novel application of a multi-criterion decision-making (MCDM) method to the multi-objective optimization problem of designing a two-stage helical gearbox. This study’s goal is to identify the ideal primary design elements that increase gearbox efficiency while reducing the gearbox cross-section area. In this work, three primary design parameters were selected for investigation: the gear ratio of the first stage and the coefficients of wheel face width (CWFW) of the first and second stages. The multi-objective optimization problem was further split into two phases: phase 1 solved the single-objective optimization problem of minimizing the gap between the variable levels, and phase 2 solved the multi-objective optimization issue of identifying the ideal key design factors. Moreover, the multi-objective optimization problem was handled by the SAW method as an MCDM approach, and the weight criteria were computed using the entropy approach. This study’s significant characteristics are as follows: First, a multi-objective optimization problem was successfully solved using the MCDM approach (SAW technique) for the first time. Second, the power losses in idle motion were investigated in this work in order to determine the efficiency of a two-stage helical gearbox. From this study’s findings, the ideal values for three major design parameters can be determined for the design of a two-stage helical gearbox. Full article

Background: Increasing evidence supporting the clinical effectiveness of cooled radiofrequency ablation (RFA) therapy for genicular nerves in patients with chronic knee osteoarthritis (OA) exists. However, no study has been conducted to eliminate the potential influence of a placebo effect associated with this procedure. Therefore, we evaluated the efficacy of cooled RFA compared with a sham procedure in patients with painful knees due to OA. Methods: In this double-blind, randomized, controlled study, participants were randomly assigned to receive cooled RFA of the knee (cooled RFA group, n = 20) or a sham procedure (sham group, n = 20). The primary outcome was the proportion of successful responders at the three-month follow-up. The secondary outcomes were successful responders at one and six months; pain intensity of the knee; functional status; medication; and satisfaction at one, three, and six months after the procedures. Results: For the primary outcome, the successful responder rate was significantly higher in the cooled RFA group (76.5%) than in the sham group (33.3%) (p = 0.018). For the secondary outcome, more successful responders were observed in the cooled RFA group than in the sham group at one and six months after the procedure (p = 0.041 and 0.007, respectively). The decreased knee pain intensity was maintained throughout the six-month follow-up period in the cooled RFA group. No differences were observed in functional status, medication change, or satisfaction in both groups. Conclusions: The cooled RFA of genicular nerves offers significant pain relief and surpasses the effects attributable to a placebo. Full article

Accurate forecasting of fluctuations in groundwater table is crucial for the effective management of regional water resources. This study explores the potential of utilizing remotely sensed satellite data to predict and forecast water table variations. Specifically, two Artificial Neural Network (ANN) models were developed to simulate water table fluctuations at two distinct well sites, namely BA Ea 18 and FR Df 35 in Maryland. One model leveraged the relationship between variations in brightness temperature and water table depth, while the other model was founded on the association between changes in soil moisture and water table depth. These models were trained and validated using recorded water table depths from the aforementioned wells, brightness temperature data acquired from the Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E), and soil moisture information generated using the Land Data Assimilation System (LDAS). All models exhibited strong performance in predicting and forecasting water table fluctuations, with root mean square errors ranging from 0.043 m to 0.047 m for a 12-month forecasting horizon. Sensitivity tests revealed that the models displayed greater sensitivity to uncertainties in water table depth compared to uncertainties in both brightness temperature and soil moisture content. This underscores the feasibility of constructing an ANN-based water table prediction model, even in cases where high-resolution remotely sensed data is unavailable. In such situations, the model’s efficacy is contingent on the compatibility of the time series trends in data, such as brightness temperature or soil moisture, with those observed at the study site. Full article

The purpose of this study is to calculate the carbon footprint of the 2023 EuroLeague Men’s Final Four competition and identify the sources of the carbon footprint in sports. This study also aims to predict possible solutions to reduce this footprint. Mixed research methods were used in this study. Firstly, carbon footprint calculations related to the teams and fan travel were carried out. In the second step, interviews were conducted with the managers of the teams that won the basketball Final Four. The interviews were conducted using a semi-structured interview. The total carbon footprint of the four teams participating in the competitions was calculated as 4560.25 kg, and the average of the total carbon footprint of the teams per person was calculated as 189.74 kg. The total carbon footprint of the fans of the four teams was calculated as 4065.686 kg, and the average of the total carbon footprint per capita of the fans was calculated as 3.003 kg. According to the interview findings, the carbon footprint sources in sports were identified as traveling, energy consumption, and waste generation. Turning to renewable energy sources, waste management and sustainable transport alternatives were identified as effective solutions for reducing the carbon footprint. Analysis of the research results showed that the interest and participation in the Final Four and other international organizations are increasing. Due to increasing carbon footprint, these events and competitions should be planned and conducted in accordance with the goals of environmental sustainability. For this purpose, managers and policy makers should turn to clean energy sources, including waste management and recycling strategies, the preparation of sustainable transportation projects, and integrating all these processes into organization activities. Full article

We argue that an epistemology of martial arts is at least as complex as advanced epistemological positions available to the philosophy of science. Part of the complexity is a product of the epistemic relation between the knower and known, or the scientist and the object of inquiry. In science, we measure things without changing them and, sometimes, complex systems can change as we measure them; but, in the epistemology of sport that we are interested in, each measurer is also an object of inquiry. As such, each martial arts practitioner has to use various epistemic tools to measure a responsive system. We proceed in three steps. First, we discuss three epistemological frameworks in the philosophy of science—perspectivism, productivism, and distributed cognition. Second, we develop an epistemology of martial arts that features components from each of those epistemic frameworks. Third, we close the paper with a brief discussion about the unique complexity available to the martial artist, focusing on the responsive measurements that occur between two systems. Full article

Ensuring the stable operation of proton exchange membrane fuel cells is conducive to their real-world application. A promising direction for stabilizing electrodes is the stabilization of the ionomer via the formation of surface compounds with graphene. A comprehensive study of the (electrochemical, chemical, and thermal) stability of composites for fuel cell electrodes containing a modifying additive of few-layer graphene was carried out. Electrochemical stability was studied by cycling the potential on a disk electrode for 5000 cycles. Chemical stability was assessed via the resistance of the composites to H₂O₂ treatment using ion-selective potentiometry. Thermal stability was studied using differential thermal analysis. Composites were characterized by UV-Vis spectroscopy, Raman spectroscopy, EDX, and SEM. It was shown that graphene inhibits Nafion degradation when exposed to heat. Contrariwise, Nafion is corrosive to graphene. During electrochemical and chemical exposure, the determining change for carbon-rich composites is the carbon loss (oxidation) of the carbon material. In the case of carbon-poor composites, the removal of fluorine and sulfur from the Nafion polymer with their partial replacement by oxygen prevails. In all cases, the F/S ratio is stable. The dispersity of Nafion in a sample affects its chemical stability more than the G/Nafion ratio does. Full article

Blockchain technology offers unique features, such as transparency, the immutability of data, and the capacity to establish trust without a central authority. Such characteristics can be leveraged to support the collaboration among several different software systems operating within the healthcare ecosystem, while ensuring data integrity and make electronic health records (EHRs) more easily accessible. To provide a solution based on blockchain technology, this paper has evaluated the main issues that arise when large amounts of data are expected, i.e., mainly cost and performance. A balanced approach that maximizes the benefits and mitigates the constraints of the blockchain has been designed. The proposed decentralized application (dApp) architecture employs a hybrid storage strategy that involves storing medical records locally, on users’ devices, while utilizing blockchain to manage an index of these data. The dApp clients facilitate interactions among participants, leveraging a smart contract to enable patients to set authorization policies, thereby ensuring that only designated healthcare providers and authorized entities have access to specific medical records. The blockchain data-immutability property is used to validate data stored externally. This solution significantly reduces the costs related to the utilization of the blockchain, while retaining its advantages, and improves performance, since the majority of data are available off-chain. Full article

The nonlinear characteristics of avalanche photodiodes (APDs) inhibit their performance in high-speed communication systems, thereby limiting their widespread application as optical detectors. Existing theoretical models have not fully elucidated complex phenomena encountered in actual device structures. In this study, actual APD structures exhibiting lower linearity than their ideal counterparts were revealed. Simulation analysis and physical inference based on GaN APDs reveal that electrode size is a noteworthy factor influencing response linearity. This discovery expands the nonlinear theory of APDs, suggesting that APD linearity can be enhanced by suppressing the electrode size effect. A physical model was developed to explain this phenomenon, which is attributed to charge accumulation at the edge of the contact layer. Therefore, we proposed an improved APD design that incorporates an additional gap layer and a buffer layer to stabilize the internal gain under high-current-density conditions, thereby enhancing linearity. Our improved APD design increases the linear threshold for optical input power by 4.46 times. This study not only refines the theoretical model for APD linearity but also opens new pathways for improving the linearity of high-speed optoelectronic detectors. Full article

We study cold strange quark stars employing an enhanced version of the quark-mass density-dependent model, which incorporates excluded volume effects to address non-perturbative QCD repulsive interactions. We provide a comparative analysis of our mass formula parametrization with previous models from the literature. We identify the regions within the parameter space where three-flavor quark matter is more stable than the most tightly bound atomic nucleus (stability window). Specifically, we show that excluded volume effects do not change the Gibbs free energy per baryon at zero pressure, rendering the stability window unaffected. The curves of pressure versus energy density exhibit various shapes—convex upward, concave downward, or nearly linear—depending on the mass parametrization. This behavior results in different patterns of increase, decrease, or constancy in the speed of sound as a function of baryon number density. We analyze the mass–radius relationship of strange quark stars, revealing a significant increase in maximum gravitational mass and a shift in the curves toward larger radii as the excluded volume effect intensifies. Excluded volume effects render our models compatible with all modern astrophysical constraints, including the properties of the recently observed low-mass compact object HESSJ1731. Full article

Silver nanoparticles (AgNPs) possess unique characteristics ideal for enhancing device sensitivity, primarily due to their high surface-to-volume ratio facilitating heightened interaction with analytes. Integrating AgNPs into polymers or carbon-based materials results in nanocomposites with synergistic properties, enabling the detection of minute changes in the environment across various applications. In this study, we investigate the adsorption kinetics of AgNPs within multilayered layer-by-layer (LbL) structures, specifically examining the impact of AgNPs concentration in the LbL film formation that is further explored as sensing units in an impedimetric microfluidic e-tongue. Although absorption kinetic studies are infrequent, they are crucial to optimize the AgNPs adsorption and distribution within LbL structures, significantly influencing upcoming applications. Through systematic variation of AgNPs concentration within identical LbL architectures, we applied the films as sensing units in a microfluidic e-tongue capable of distinguishing food enhancers sharing the umami taste profile. Across all tested scenarios, our approach consistently achieves robust sample separation, evidenced by silhouette coefficient, principal component analyses, and long-term stability. This work contributes to exploring controlled nanomaterial-based developments, emphasizing the importance of precise parameter control for enhanced sensor performance across diverse analytical applications. Full article

This study explores how elevated carbon dioxide (CO₂) levels affects the growth and defense mechanisms of plants. We focused on Aristolochia contorta Bunge (Aristolochiaceae), a wild plant that exhibits growth reduction under elevated CO₂ in the previous study. The plant has Sericinus montela Gray (Papilionidae) as a specialist herbivore. By analyzing primary metabolites, understanding both the growth and defense response of plants to herbivory under elevated CO₂ conditions is possible. The experiment was conducted across four groups, combining two CO₂ concentration conditions (ambient CO₂ and elevated CO₂) with two herbivory conditions (herbivory treated and untreated). Although many plants exhibit increased growth under elevated CO₂ levels, A. contorta exhibited reduced growth with lower height, dry weight, and total leaf area. Under herbivory, A. contorta triggered both localized and systemic responses. More primary metabolites exhibited significant differences due to herbivory treatment in systemic tissue than local leaves that herbivory was directly treated. Herbivory under elevated CO₂ level triggered more significant responses in primary metabolites (17 metabolites) than herbivory under ambient CO₂ conditions (five metabolites). Several defense-related metabolites exhibited higher concentrations in the roots and lower concentrations in the leaves in response to the herbivory treatment in the elevated CO₂ group. This suggests a potential intensification of defensive responses in the underground parts of the plant under elevated CO₂ levels. Our findings underscore the importance of considering both abiotic and biotic factors in understanding plant responses to environmental changes. The adaptive strategies of A. contorta suggest a complex response mechanism to elevated CO₂ and herbivory pressures. Full article

The online detection of surface defects in irregularly shaped products such as rawhide sticks, a kind of pet food, is still a challenge for the food industry. Developing deep learning-based detection algorithms requires a diverse defect database, which is crucial for artificial intelligence applications. Acquiring a sufficient amount of realistic defect data is challenging, especially during the beginning of product production, due to the occasional nature of defects and the associated costs. Herein, we present a novel image data augmentation method, which is used to generate a sufficient number of defect images. A Deep Convolution Generation Adversarial Network (DCGAN) model based on a Residual Block (ResB) and Hybrid Attention Mechanism (HAM) is proposed to generate massive defect images for the training of deep learning models. Based on a DCGAN, a ResB and a HAM are utilized as the generator and discriminator in a deep learning model. The Wasserstein distance with a gradient penalty is used to calculate the loss function so as to update the model training parameters and improve the quality of the generated image and the stability of the model by extracting deep image features and strengthening the important feature information. The approach is validated by generating enhanced defect image data and conducting a comparison with other methods, such as a DCGAN and WGAN-GP, on a rawhide stick experimental dataset. Full article

Akt is an important kinase in metabolism. Akt also phosphorylates and activates endothelial and neuronal nitric oxide (NO) synthases (eNOS and nNOS, respectively) expressed in M0 (unpolarized) macrophages. We showed that e/nNOS NO production downstream of bitter taste receptors enhances macrophage phagocytosis. In airway epithelial cells, we also showed that the activation of Akt by a small molecule (SC79) enhances NO production and increases levels of nuclear Nrf2, which reduces IL-8 transcription during concomitant stimulation with Toll-like receptor (TLR) 5 agonist flagellin. We hypothesized that SC79’s production of NO in macrophages might likewise enhance phagocytosis and reduce the transcription of some pro-inflammatory cytokines. Using live cell imaging of fluorescent biosensors and indicator dyes, we found that SC79 induces Akt activation, NO production, and downstream cGMP production in primary human M0 macrophages. This was accompanied by a reduction in IL-6, IL-8, and IL-12 production during concomitant stimulation with bacterial lipopolysaccharide, an agonist of pattern recognition receptors including TLR4. Pharmacological inhibitors suggested that this effect was dependent on Akt and Nrf2. Together, these data suggest that several macrophage immune pathways are regulated by SC79 via Akt. A small-molecule Akt activator may be useful in some infection settings, warranting future in vivo studies. Full article

A polydopamine polyelectrolyte hydrogel was developed by ionic crosslinking dextran sulfate with a copolymer of polyethyleneimine and polydopamine. Gelation was promoted by the slow hydrolysis of glucono-δ-lactone. Within this hydrogel, silver nanoparticles were generated in situ, ranging from 25 nm to 200 nm in size. The antibacterial activity of the hydrogel was proportional to the quantity of silver nanoparticles produced, increasing as the nanoparticle count rose. The hydrogels demonstrated broad-spectrum antibacterial efficacy at concentrations up to 10⁸ cells/mL for P. aeruginosa, K. pneumoniae, E. coli and S. aureus, the four most prevalent bacterial pathogens in chronic septic wounds. In ex vivo studies on human skin, biocompatibility was enhanced by the presence of polydopamine. Dextran sulfate is a known irritant, but formulations with polydopamine showed improved cell viability and reduced levels of the inflammatory biomarkers IL-8 and IL-1α. Silver nanoparticles can inhibit cell migration, but an ex vivo human skin study showed significant re-epithelialization in wounds treated with hydrogels containing silver nanoparticles. Full article

This study aimed to isolate and purify resveratrol and oxyresveratrol from the heartwoods of Maclura cochinchinensis, and to evaluate their inhibitory effects on melanogenesis in B16F10 murine melanoma cells. A methanol maceration process yielded a crude extract comprising 24.86% of the initial mass, which was subsequently analyzed through HPTLC, HPLC, and LC-MS/MS. These analyses revealed the presence of resveratrol and oxyresveratrol at concentrations of 4.32 mg/g and 33.6 mg/g in the extract, respectively. Initial purification employing food-grade silica gel column chromatography separated the extract into two fractions: FA, exhibiting potent inhibition of both tyrosinase activity and melanogenesis, and FM, showing no such inhibitory activity. Further purification processes led to the isolation of fractions Y11 and Gn12 with enhanced concentrations of resveratrol (94.9 and 110.21 mg/g, respectively) and fractions Gn15 and Gn16 with elevated levels of oxyresveratrol (321.93 and 274.59 mg/g, respectively), all of which significantly reduced melanin synthesis. These outcomes affirm the substantial presence of resveratrol and oxyresveratrol in the heartwood of M. cochinchinensis, indicating their promising role as natural agents for skin lightening. Full article

Perfluorooctanoic acid (PFOA) contamination in water sources poses significant environmental and health concerns. The kinetic, equilibrium, and thermodynamic features of PFOA adsorption in the existence of natural organic matter (NOM) were thoroughly investigated in this work using granular activated carbon (GAC) modified by dielectric barrier discharge (DBD) plasma. The impacts of DBD plasma parameters on the adsorption process were systematically examined. The results demonstrated that GAC modified by DBD plasma exhibited enhanced adsorption performance for PFOA, even in the presence of NOM. The optimal condition for plasma-treated GAC was achieved with 20 min of plasma treatment time and 100 W of plasma power, resulting in 92% PFOA removal efficiency in deionized water (DIW) and 97% removal efficiency in Chao Phraya River water (CPRW). A kinetic investigation using the pseudo-first-order model (PFOM), the pseudo-second-order model (PSOM), and the Elovich model (EM) indicated that plasma treatment time and NOM presence influenced the adsorption capacity and rate constants of PFOA with the PSOM having emerged as the most fitting kinetic model. The Langmuir isotherm model indicates monolayer adsorption of PFOA on plasma-treated GAC, with higher maximum adsorption capacity while NOM is present. The Redlich–Peterson and Sips isotherm models indicated varying adsorption capacity and heterogeneity in the adsorption system. The Sips model was determined as the most fitting isotherm model. Furthermore, the favorable and spontaneous character of PFOA adsorption onto plasma-treated GAC was validated by thermodynamic analysis, with endothermic heat absorption during the process. Overall, this comprehensive investigation provides valuable insights into the adsorption characteristics of PFOA in the existence of NOM using GAC modified by DBD plasma. Full article

Astrocytes, the most abundant glial cell type in the brain, play crucial roles in maintaining homeostasis within the central nervous system (CNS). Impairment or abnormalities of typical astrocyte functions in the CNS serve as a causative or contributing factor in numerous neurodevelopmental, neurodegenerative, and neuropsychiatric disorders. Currently, disease-modeling and drug-screening approaches, primarily focused on human astrocytes, rely on human pluripotent stem cell (hPSC)-derived astrocytes. However, it is important to acknowledge that these hPSC-derived astrocytes exhibit notable differences across studies and when compared to their in vivo counterparts. These differences may potentially compromise translational outcomes if not carefully accounted for. This review aims to explore state-of-the-art in vitro models of human astrocyte development, focusing on the developmental processes, functional maturity, and technical aspects of various hPSC-derived astrocyte differentiation protocols. Additionally, it summarizes their successful application in modeling neurological disorders. The discussion extends to recent advancements in the large-scale production of human astrocytes and their application in developing high-throughput assays conducive to therapeutic drug discovery. Full article

This review describes the production systems and products pertaining to goats and sheep (GS) in Indonesia. These small ruminant species are found across 38 provinces in Indonesia. Almost 99% of the GS are farmed by smallholders, with the animals either being housed in an enclosure (small shed near the farmer’s house) or in a mix of both grazing and confinement. The livestock are typically fed natural and/or artificial feed. Two sheep types, thin-tailed and fat-tailed are the most popular with smallholder producers, while Bali, Etawah, Boerawa, Jawa Randu Kosta, and Gembrong are the dominant goat breeds. As one of the biggest Muslim countries in the world, Indonesian production and consumption of GS is influenced by culture and religious systems. The Halal slaughter method applied is described, as are the religious/cultural festivals in Indonesia, which have a significant relationship on the GS demand and consumption, such as Eid al-Adha and Aqiqah. The preparation, processing, and consumption of GS meat in Indonesia are strongly influenced by the use of spices, being part of the Moluccas archipelago, also known as the “Spice islands” with over 30,000 species of spices grown. Some traditional meats and meat processing technologies in Indonesia are presented to provide insight into how GS meat is processed. The main challenges in the processing of GS meat in Indonesia and some current research on the development of GS production are presented in this paper. Implications: Goat and sheep belong to the most widely consumed meats in the world after pork, poultry, and beef. There are some reasons why GS meat has become one of the more popular protein choices for many families, of which its low-fat levels are one. GS meat is also considered one of the only meats with minimal religious taboos in the world. For these reasons, GS meat is also favored by Indonesian people in the most populous Muslim-majority country. However, information regarding goat and sheep in Indonesia is still lacking. This review aims to provide information on the distribution of GS in Indonesia over 38 provinces, the breed types that grow there, their production, and Indonesian consumption behavior, highlighting the religious festivals that cause the highest demand for these meats. This review provides information to the stakeholders of goat and sheep production chains. Full article

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the progressive degeneration of upper and lower motor neurons (MNs) in the brain and spinal cord, leading to progressive paralysis and death. Increasing evidence indicates that neuroinflammation plays an important role in ALS’s pathogenesis and disease progression. Neuroinflammatory responses, primarily driven by activated microglia and astrocytes, and followed by infiltrating peripheral immune cells, contribute to exacerbate/accelerate MN death. In particular, the role of the microglia in ALS remains unclear, partly due to the lack of experimental models that can fully recapitulate the complexity of ALS’s pathology. In this study, we developed and characterized a microglial cell line, SIM-A9-expressing human mutant protein Cu⁺/Zn⁺ superoxide dismutase_1 (SIM-A9hSOD1(G93A)), as a suitable model in vitro mimicking the microglia activity in ALS. The expression of hSOD1(G93A) in SIM-A9 cells induced a change in their metabolic activity, causing polarization into a pro-inflammatory phenotype and enhancing reactive oxygen species production, which is known to activate cell death processes and apoptosis. Afterward, we used our microglial model as an experimental set-up to investigate the therapeutic action of extracellular vesicles isolated from adipose mesenchymal stem cells (ASC-EVs). ASC-EVs represent a promising therapeutic treatment for ALS due to their neuroprotective and immunomodulatory properties. Here, we demonstrated that treatment with ASC-EVs is able to modulate activated ALS microglia, reducing their metabolic activity and polarizing their phenotype toward an anti-inflammatory one through a mechanism of reduction of reactive oxygen species. Full article

The global issue of substance abuse demands ongoing initiatives aligned with the United Nations Sustainable Development Goals. With drug use remaining prevalent worldwide, interventions are critical to addressing the associated health challenges and societal implications. Exercise and physical activities have emerged as integral components of substance use disorder (SUD) treatment, offering promising avenues for prevention, intervention, and recovery. Recent research underscores the efficacy of exercise in reducing substance cravings, promoting abstinence, and improving overall well-being. However, integrating exercise into SUD recovery programs presents challenges such as dropout rates and cultural considerations. This paper synthesizes existing literature on exercise integration into SUD recovery, highlighting strategies for enhancing treatment outcomes and addressing barriers to exercise adherence. Drawing on cognitive–behavioral therapy, experiential learning, motivational interviewing, and goal-setting techniques, the holistic approach outlined in this paper aims to empower individuals both mentally and physically, fostering resilience and supporting long-term recovery. In conclusion, new initiatives need to be taken by advocating for inclusive policies, promoting community engagement, and fostering collaborations across sectors. By doing so, stakeholders can optimize the effectiveness of exercise programs and contribute to sustainable rehabilitation efforts for individuals with SUD. Full article