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Waste-to-energy (WtE) technologies play a crucial role in sustainable waste management and energy production. Decision-making in this context requires a comprehensive and structured approach to balance the environmental, socio-economic, and technical dimensions. This systematic literature review aims to provide an overview of the applications of Decision Support Systems (DSS) in the field of WtE, identify key trends, and outline future research directions. The study utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach in analyzing and synthesizing a diverse range of studies to identify key observations, trends, and a pathway for future research. The study has addressed its primary objectives through a rigorous analysis of existing scholarship, identification of essential decision-making criteria, assessment of DSS methodologies' efficacy, and identification of knowledge gaps and future research directions. The review reveals that DSS methodologies, particularly Multi-Criteria Decision Analysis (MCDA), Geographic Information Systems (GIS), and Life Cycle Assessment (LCA), have significantly contributed to enhancing decision-making processes in the WtE sector globally. These tools have demonstrated their effectiveness in handling the complex, multi-faceted nature of WtE technology selection and implementation, addressing issues ranging from site selection to technology ranking based on multiple criteria. The research identifies key criteria for WtE technology selection in Ghana, including technical feasibility, environmental impact, economic viability, social acceptability, and energy efficiency. The study also highlights significant gaps in the current application of DSS to WtE technologies, particularly in the context of developing nations like Ghana. Many existing DSS approaches fail to adequately account for local socio-economic factors, rely on static data, and do not fully incorporate diverse stakeholder perspectives. Adapting global DSS applications to the Ghanaian context requires careful consideration of local priorities, data availability, and the role of the informal waste sector. The review proposes a holistic approach to DSS development, emphasizing the need for context-specific, dynamic, and inclusive methodologies. Future research directions identified by this study include the development of Ghana-specific DSS models, integration of real-time data collection methodologies, creation of user-friendly interfaces for local decision-makers, and exploration of emerging technologies such as blockchain and IoT or Machine learning (ML) for enhancing DSS in WtE management.

Regarding the role of oxidative damage to various tissues in various diseases, using antioxidant compounds that protect tissues from damage is proposed as an important strategy against these diseases. Liver homogenates are frequently employed as in vitro models for investigating oxidative stress owing to the liver's high metabolic activity and susceptibility to oxidative damage. In this study, we assessed the protective effects of two phenolic compounds on Fe²⁺-ascorbate-induced oxidative stress in liver homogenates by analyzing various markers such as lipid peroxidation, protein carbonyl oxidation (PCO), reduced glutathione (GSH), and ROS levels. Catechin was used as a reference antioxidant to compare with the results. The DPPH radical scavenging activity of the compounds was also evaluated. Our findings demonstrated that co-incubation of liver homogenates with the Fe²⁺-ascorbate system and the compounds at various concentrations (10, 25, 50, and 100 μg/mL) led to a dose-dependent reduction in lipid peroxidation, PCO levels, ROS production, and GSH depletion. Furthermore, the IC₅₀ values for DPPH free radicals for pyrogallol and caffeic acid were determined to be 76.26 μg/mL and 106.31 μg/mL, respectively. Notably, pyrogallol and caffeic acid exhibited higher antioxidant activity in all assays than catechin. In most assays (DPPH, Lipid peroxidation, and PCO) and at high concentrations (50 and 100 μg/mL), the antioxidative stress activity of pyrogallol was higher than that of caffeic acid. In conclusion, this study's results suggest that these compounds can potentially ameliorate diseases associated with oxidative stress.

This study aimed to measure the licit and illicit drug concentrations in wastewater treatment plants (WWTPs), their removal efficiency, and their consumption rate by wastewater-based epidemiology (WBE) approach. A 2-season monitoring program was undertaken for the first time in Iran. Residues of licit and illicit drugs (amphetamine, 3,4-methylenedioxyamphetamine, methamphetamine, codeine, 3,4-methylenedioxymethamphetamine, heroin, cocaine, and methadone) and major metabolites (benzoylecgonine, 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine and 6-monoacetylmorphine) were analyzed by LC-MS/MS in composite sampling after solid phase extraction method. These analytes were also measured in rivers vicinity to selected cities. The detected concentration in raw wastewater was converted into the quantity of illicit drugs per capita using back-calculation. There was no detection of cocaine, its metabolite benzoylecgonine, or 3,4-methylenedioxymethamphetamine in any of the samples. Among the analytes measured, morphine and codeine exhibited the most prevalent occurrence and highest concentration levels in all the samples. Heroin was the main illicit drugs used in the studied population with a mean consumption rate of 6108 and 158 mg/day/1000 inh. in cold and warm seasons, respectively. The removal efficiencies of drugs during wastewater treatment ranged from 3.5 % (3,4-methylenedioxyamphetamine) to >99 % (amphetamine, methamphetamine, and codeine); however, 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine demonstrated a negative removal within WWTPs. This study provides crucial insights into the consumption of illicit drugs in Iran, as well as the release of illicit drugs and their metabolites through WWTPs.

This study investigates calcined eggshells (CES) as an effective adsorbent for the remediation of As(V). Characterization of CES was performed using zeta potential analysis, FTIR, XRD and SEM-EDX. Batch studies were conducted to examine the effects of pH, adsorption kinetics, and adsorption isotherms to assess efficacy. The adsorption of As(V) followed the Langmuir isotherm and pseudo-second-order kinetics, with a maximum capacity of 91.05 mg g⁻¹ at pH 6.0 and 298 K. The presence of additional anions such as chloride, sulfate, or nitrate had no significant impact on the biosorption of arsenate. However, the introduction of phosphate ions notably decreased the rate of arsenic adsorption. CES was easily regenerated with an alkaline solution and showed excellent reusability over four cycles. Thermodynamic studies confirmed the spontaneity and feasibility of the biosorption process. This study highlights that CES is a promising adsorbent for As(V) removal from contaminated water.

The utilization of functional magnetic resonance imaging (fMRI) is critical in the preoperative planning phase of brain tumor surgery because it allows for a delicate balance between maximizing tumor resection and maintaining brain function. A decade of fMRI development was examined in this study, with a particular emphasis on its use in diagnosing and assessing the efficacy of brain cancer treatments. We examined the foundational principles, practical implementations, and verification of fMRI via direct brain stimulation, with particular emphasis on its capacity to detect cerebral regions affected by tumors that are eloquent in nature. Recently, fMRI has undergone significant progress, allowing for its integration into clinical workflows to facilitate precise mapping of brain functions. This extensive analysis encompasses the scrutiny of resting-state fMRI (Rs-fMRI) as a method of capturing functional connectivity, thereby providing significant insights into the management of patients with brain tumors. Methodological advancements, clinical applicability, and future orientations of fMRI are highlighted in this review, which emphasizes the substantial influence of the technique on neurosurgical planning and patient outcomes.

The epidemiology of Erectile Dysfunction (ED) continues to exhibit an increasing trend annually. The use of synthetic drugs in treating ED often leads to undesirable side effects and has limited efficacy. In Indonesia, several indigenous plants have been empirically utilized for ED remediation. This study aims to identify the latest scientific evidence on the potential of native Indonesian medicinal plants for ED treatment and elucidate the underlying molecular mechanisms using a systematic review and Pharmacological Network approach. There are 12 potential plants most commonly used by ethnic groups in Indonesia to treat erectile dysfunction (ED) as reviewed in this study. A systematic review search was conducted across three databases (PubMed, Scopus, and Springer) without limiting the publication years. Article screening was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart. Determination of compound target genes was carried out using GeneCards, while disease target genes were analyzed using DisGeNET. Network topology was explored with Cytoscape 3.10, and the construction of Protein-Protein Interaction Networks was realized using STRING version 12.0. GO and KEGG analyses were subsequently conducted with SRplot. The systematic review findings indicated that 12 articles met the predefined inclusion criteria. The pharmacology network analysis demonstrated that the compounds present in Eurycoma longifolia, specifically stigmasterol, eurycomanone, and eurycomalactone, target 13 genes associated with erectile dysfunction (ED), which include BCL2, AKT1, SOAT1, PCSK9, ACHE, BDNF-AS, TMX2-CTNND1, GSK3B, LINCO1672, TP53, H19, HIF1A, and IL1B. These target genes are connected to the biological mechanisms underlying steroid hormone biosynthesis, which is essential for the formation of testosterone. Therefore, Eurycoma longifolia demonstrates significant potential for development as a promising phytopharmaceutical candidate in the treatment of sexual dysfunction.

Cystatin A (CSTA) functions as a cysteine protease inhibitor by forming tight complexes with the cathepsins. Pathogenic mutations in the CSTA gene can disrupt this interaction, potentially leading to physiological ailments. In this study, eight bioinformatics tools (SIFT, PolyPhen-2, PROVEAN, P-Mut, MutPred2, SNAP2, SNPs & GO, and PHD-SNP) were implemented to analyze non-synonymous SNPs from the dbSNP database. Five mutations (Y43C, Y43N, V48F, Y53H, and E94K) located in the conserved region were found to be highly deleterious and less stabilizing. The protein-protein interaction network found that Cathepsin B (CTSB) interacts highly with CSTA. Mutated CSTAs were created by homology modeling, and their altered binding with CTSB was examined through molecular docking and dynamics simulations. Among these, the Y53H (rs1448459675) and E94K (rs200394711) mutants were recognized as weaker inhibitors because they had 2.5 % and an 8 % lower binding affinity, respectively. Moreover, the E94K-CTSB complex, with a root mean square deviation (RMSD) above 5 Å, was found to be highly unstable during molecular dynamics. The root mean square fluctuation (RMSF) of the E94K mutant showed insufficient flexibility, indicating a reduced capacity to suppress CTSB. These findings suggest that the E94K mutation could affect the protein structure and cathepsin B interaction, potentially leading to pathological consequences as evidenced by colorectal adenocarcinoma patients in the COSMIC (Catalogue of Somatic Mutations in Cancer) database.

Predicting drug-target binding affinity via in silico methods is crucial in drug discovery. Traditional machine learning relies on manually engineered features from limited data, leading to suboptimal performance. In contrast, deep learning excels at extracting features from raw sequences but often overlooks essential biological context features, hindering effective binding prediction. Additionally, these models struggle to capture global and local feature distributions efficiently in protein sequences and drug SMILES. Previous state-of-the-art models, like transformers and graph-based approaches, face scalability and resource efficiency challenges. Transformers struggle with scalability, while graph-based methods have difficulty handling large datasets and complex molecular structures. In this paper, we introduce InceptionDTA, a novel drug-target binding affinity prediction model that leverages CharVec, an enhanced variant of Prot2Vec, to incorporate both biological context and categorical features into protein sequence encoding. InceptionDTA utilizes a multi-scale convolutional architecture based on the Inception network to capture features at various spatial resolutions, enabling the extraction of both local and global features from protein sequences and drug SMILES. We evaluate InceptionDTA across a range of benchmark datasets commonly used in drug-target binding affinity prediction. Our results demonstrate that InceptionDTA outperforms various sequence-based, transformer-based, and graph-based deep learning approaches across warm-start, refined, and cold-start splitting settings. In addition to using CharVec, which demonstrates greater accuracy in absolute predictions, InceptionDTA also includes a version that employs simple label encoding and excels in ranking and predicting relative binding affinities. This versatility highlights how InceptionDTA can effectively adapt to various predictive requirements. These results emphasize the promise of our approach in expediting drug repurposing initiatives, enabling the discovery of new drugs, and contributing to advancements in disease treatment.

Impaired glucose regulation is increasingly recognised in amyotrophic lateral sclerosis (ALS), yet the precise mechanisms remain unclear. Here, we investigated energy balance and glucose control in TAR DNA-binding protein 43 (TDP-43)^Q331K mice, a model of ALS, at both the early and late symptomatic stages of disease. Mutant TDP-43^Q331K mice and non-transgenic controls underwent indirect calorimetry, as well as intraperitoneal glucose, insulin, and glucagon tolerance testing. We also examined plasma hormone levels and quantified α- and β-cell areas in pancreatic islets. Throughout disease progression, TDP-43^Q331K mice exhibited elevated metabolic rates, with a transient increase in food intake at the early stages. At the later stages of disease, heightened glucose uptake was observed despite unchanged insulin secretion or tolerance, indicating mechanisms independent of insulin. Notably, TDP-43^Q331K mice maintained fasting blood glucose levels even when circulating glucagon levels were reduced, suggesting that alternative pathways contribute to preserving euglycemia. These findings reveal a distinct metabolic profile in TDP-43^Q331K mice, underscoring the complexity of glucose dyshomeostasis in ALS.

Although self-citation is a common practice among scholars, its impact and significance remain under scrutiny within the academic community. The current study aimed to provide a detailed analysis and ranking of the top 2 % scientists on self-citation, ranked by Stanford University researchers. Data extraction and organisation were performed between January and February 2024. Self-citation percentages were collected for authors belonging to 20 fields and 174 subfields using Excel spreadsheets. Entities were categorised into quartiles based on their self-citation percentages and rankings were assigned accordingly. Comparative analyses were conducted to assess the impact of self-citations on overall rankings. The study focused on data published by a group of researchers from Stanford University regarding the top 2 % of researchers, analysing the career-long records of 204,643 scholars and 210,198 scholars for the most recent year, 2022. Notably, in the single-year analysis, among the top 20 countries, approximately eight (40 %) exhibited self-citation percentages exceeding the average of 25.96 %. The self-citation percentages ranged from 4.47 % in Economics and Business to 20.88 % in Physics and Astronomy. Regarding career-long analysis, the percentage of self-citations ranged from 22.84 % in Poland to 41.31 % in Armenia, with significant drop in rankings among most entities when self-citations were excluded. These findings highlight the dramatic impact of self-citation exclusion on the rankings of the top 2 % of the researchers, underscoring the critical importance of accounting for self-citation in ranking assessments.