Search Results (3,074)

Background and Clinical Significance: Cocaine use disorder (CUD) is characterized by recurrent, cue-triggered and intrusive urges to use cocaine (craving), compulsive drug-seeking despite adverse consequences, and impaired control over intake, often co-occurring with excess weight and hedonic overeating. A dual-target rationale supports the fixed-dose naltrexone–bupropion (NB) combination: μ-opioid receptor (MOR) antagonism may mitigate opioid-facilitated mesolimbic reinforcement, while bupropion’s catecholaminergic effects and POMC activation support satiety and weight loss. Case Presentation: We describe a case study from an Italian outpatient setting of a 35-year-old man with a 10-year history of CUD, multiple failed detoxifications, and class I obesity (body mass index [BMI] 31 kg/m²) who initiated fixed-dose NB and was followed for 12 weeks under routine care. NB was associated with progressive attenuation of cue-reactive cocaine craving and improved appetite control, alongside clinically meaningful weight reduction, without psychiatric destabilization or emergent safety concerns; medication adherence remained stable. The patient maintained abstinence throughout follow-up and reported improved psychosocial functioning. Quantitatively, CCQ-B scores decreased from 7.2 at baseline to 2.1 at Week 12 (≈70% reduction), while BMI decreased from 31.0 to 25.5 kg/m² (≈−17.7%), with clinically meaningful weight loss and stable adherence. Conclusions: This case study supports the mechanistic rationale that dual NB therapy can simultaneously attenuate cocaine craving and facilitate weight control, addressing two clinically relevant targets in CUD. Although evidence for NB in CUD remains limited and mixed across stimulant populations, this observation highlights a plausible, testable therapeutic hypothesis that integrates mesolimbic and hypothalamic pathways and may inform the design of controlled trials in patients with co-occurring CUD and obesity. Full article

Antibody–drug conjugates (ADCs) are a rapidly advancing class of targeted cancer therapeutics that couple the antigen specificity of monoclonal antibodies (mAbs) with the potent cytotoxicity of small-molecule drugs. In their core design, a tumor-targeting antibody is covalently linked to a cytotoxic payload via a chemical linker, enabling the selective delivery of highly potent agents to malignant cells while sparing normal tissues, thereby improving the therapeutic index. Humanized and fully human immunoglobulin G1(IgG1) antibodies are the most common ADC backbones due to their stability in systemic circulation, robust Fcγ receptor engagement for immune effector functions, and reduced immunogenicity. Antibody selection requires balancing tumor specificity, internalization rate, and binding affinity to avoid barriers to tissue penetration, such as the binding-site barrier effect, while emerging designs exploit tumor-specific antigen variants or unique post-translational modifications to further enhance selectivity. Advances in antibody engineering, linker chemistry, and payload innovation have reinforced the clinical success of ADCs, with more than a dozen agents FDA approved for hematologic malignancies and solid tumors and over 200 in active clinical trials. This review critically examines established and emerging conjugation strategies, including lysine- and cysteine-based chemistries, enzymatic tagging, glycan remodeling, non-canonical amino acid incorporation, and affinity peptide-mediated methods, and discusses how conjugation site, drug-to-antibody ratio (DAR) control, and linker stability influence pharmacokinetics, efficacy, and safety. Innovations in site-specific conjugation have improved ADC homogeneity, stability, and clinical predictability, though challenges in large-scale manufacturing and regulatory harmonization remain. Furthermore, novel ADC architectures such as bispecific ADCs, conditionally active (probody) ADCs, immune-stimulating ADCs, protein-degrader ADCs, and dual-payload designs are being developed to address tumor heterogeneity, drug resistance, and off-target toxicity. By integrating mechanistic insights, preclinical and clinical data, and recent technological advances, this work highlights current progress and future directions for next-generation ADCs aimed at achieving superior efficacy, safety, and patient outcomes, especially in treating refractory cancers. Full article

Smart cities and urban planning have succeeded in gathering the attention of researchers worldwide, especially in the last decade, as a result of a series of technological, social and economic developments that have shaped the need for evolution from the traditional way in which the cities were viewed. Technology has been incorporated in many sectors associated with smart cities, such as communications, transportation, energy, and water, resulting in increasing people’s quality of life and satisfying the needs of a society in continuous change. Furthermore, with the rise in machine learning (ML) and artificial intelligence (AI), as well as Geographic Information Systems (GIS), the applications of big data analytics in the context of smart cities and urban planning have diversified, covering a wide range of applications starting with traffic management, environmental monitoring, public safety, and adjusting power distribution based on consumption patterns. In this context, the present paper brings to the fore the papers written in the 2015–2024 period and indexed in Clarivate Analytics’ Web of Science Core Collection and analyzes them from a bibliometric point of view. As a result, an annual growth rate of 10.72% has been observed, showing an increased interest from the scientific community in this area. Through the use of specific bibliometric analyses, key themes, trends, prominent authors and institutions, preferred journals, and collaboration networks among authors, data are extracted and discussed in depth. Thematic maps and topic discovery through Latent Dirichlet Allocation (LDA) and doubled by a BERTopic analysis, n-gram analysis, factorial analysis, and a review of the most cited papers complete the picture on the research carried on in the last decade in this area. The importance of big data analytics in the area of urban planning and smart cities is underlined, resulting in an increase in their ability to enhance urban living by providing personalized and efficient solutions to everyday life situations. Full article

Extremely high temperatures can severely impact urban livability and public health safety. However, risk assessments for high temperatures in cold-region cities remain inadequate. This study focuses on Dalian, a coastal city in northeastern China. Utilizing multi-source data, we established a population density prediction model based on the random forest algorithm and a heat vulnerability index (HVI) framework following the “Exposure-Sensitivity-Adaptability” paradigm constructed using an indicator system method, thereby building a high-temperature risk assessment system suited for more refined research. The results indicate the following: (1) Strong positive correlations exist between nighttime light brightness (NL), Road Density (RD), the proportion of flat area (SLP), the land surface temperature (LST), and the population distribution density, with correlation coefficients reaching 0.963, 0.963, 0.956, and 0.954, respectively. (2) Significant disparities exist in the spatial distribution of different criterion layers within the study area. Areas characterized by high exposure, high sensitivity, and low adaptability account for 13.04%, 8.05%, and 21.44% of the total area, respectively, with exposure being the primary contributing factor to high-temperature risk. (3) Areas classified as high-risk or extremely high-risk for high temperatures constitute 31.57% of the study area. The spatial distribution exhibits a distinct pattern, decreasing gradually from east to west and from the coast inland. This study provides a valuable tool for decision-makers to propose targeted adaptation strategies and measures based on the assessment results, thereby better addressing the challenges posed by climate change-induced high-temperature risks and promoting sustainable urban development. Full article

This study develops and operationalizes a multi-dimensional framework for sustainable building retrofit that aligns with national 2050 net-zero objectives. First, we conduct a scoping review of international standards (e.g., ISO), sustainability reporting guidelines (GRI G4), and peer-reviewed studies to define an indicator system spanning three pillars—environmental (carbon neutrality, resource circulation, pollution management), social (habitability, durability/safety, regional impact), and economic (direct support, deregulation). Building on this structure, we propose a transparent 0–3 rubric at the sub-indicator level and introduce the Sustainable Building Retrofit Index (SRI) to enable cross-case comparability and over-time monitoring. We then apply the framework to seven countries (United States, Canada, United Kingdom, France, Germany, Japan, and South Korea), score their retrofit systems/policies, and synthesize results through radar plots and a composite SRI. The analysis shows broad emphasis on carbon neutrality and habitability but persistent gaps in resource circulation, pollution management, regional impacts, and deregulatory mechanisms. For South Korea, policies remain energy-centric, with relatively limited treatment of resource/pollution issues and place-based social outcomes; economic instruments predominantly favor direct financial support. To address these gaps, we propose (i) life-cycle assessment (LCA)–based reporting that covers greenhouse gas and six additional impact categories for retrofit projects; (ii) a support program requiring community and ecosystem-impact reporting with performance-linked incentives; and (iii) targeted deregulation to reduce uptake barriers. The paper’s novelty lies in translating diffuse sustainability principles into a replicable, quantitative index (SRI) that supports benchmarking, policy revision, and longitudinal tracking across jurisdictions. The framework offers actionable guidance for policymakers and a foundation for future extensions (e.g., additional countries, legal/municipal instruments, refined weights). Full article

Background: A novel series of pyrazolo[3,4-d]pyrimidinone derivatives were synthesized, characterized, and examined for their anti-inflammatory effects. Results: The findings indicated that compounds 5d, 5j, 5k, and 5m demonstrated significant anti-inflammatory effects through the selective inhibition of the COX-2 isozyme, with IC₅₀ values ranging from 0.27 to 2.34 μM, compared to celecoxib (IC₅₀ = 0.29 μM). Compound 5k emerged as the most potent, exhibiting a selectivity index (SI) of 95.8 for COX-2 relative to COX-1. In vivo tests additionally validated that compounds 5j and 5k demonstrated significant anti-inflammatory efficacy, exhibiting greater suppression percentages of generated paw edema than indomethacin, comparable to celecoxib, while preserving excellent safety profiles with intact gastric tissue. Mechanistic studies demonstrated that the anti-inflammatory efficacy of the target compounds was associated with a substantial decrease in serum levels of TNF-α and IL-6. Moreover, molecular modeling investigations corroborated the in vitro findings. Compound 5k displayed a binding free energy ΔG of −10.57 kcal/mol, comparable to that of celecoxib, which showed a ΔG of −10.19 kcal/mol. The intensified binding contacts in the COX-2 isozyme indicated the augmented inhibitory efficacy of 5k. Conclusions: Compound 5k exhibited dual activity by inhibiting the COX-2 isozyme and suppressing the pro-inflammatory cytokines TNF-α and IL-6, therefore providing a remarkable anti-inflammatory effect with increased therapeutic potential. Full article

Nitrosamines, including N-nitroso diethylamine (NDEA) have emerged as pharmaceutical impurities and carcinogenic environmental contaminants of grave public health safety concerns. This study reports on the preparation and first use of cysteine–gold nanoparticles (CysAuNPs) for colorimetric detection of NDEA in human serum albumin (HSA) under physiological conditions. Molecular docking (MD) and molecular dynamic simulation (MDS) were performed to probe the interaction between NDEA and serum albumin. UV–visible absorption and fluorescence spectroscopy, dynamic light scattering (DLS), and transmission electron microscopy (TEM) imaging were used to characterize the synthesized CysAuNPs. These CysAuNPs show a UV–visible absorbance wavelength maxima (λ_max) at 377 nm and emission λ_max at 623 nm. Results from DLS measurement revealed the CysAuNPs’ uniform size distribution and high polydispersity index of 0.8. Microscopic imaging using TEM showed that CysAuNPs have spherical to nanoplate-like morphology. The addition of NDEA to HSA in the presence of CysAuNPs resulted in a remarkable increase in the absorbance of human serum albumin. The interaction of NDEA–CysAuNPs–HSA is plausibly facilitated by hydrogen bonding, sulfur linkages, or by Cys–NDEA-induced electrostatic and van der Waal interactions. These are due to the disruption of the disulfide bond linkage in Cys–Cys upon the addition of NDEA, causing the unfolding of the serum albumin and the dispersion of CysAuNPs. The combined use of molecular dynamic simulation and colorimetric experiment provided complementary data that allows robust analysis of NDEA in serum samples. In addition, the low cost of the UV–visible spectrophotometer and the easy preparation and optical sensitivity of CysAuNPs sensors are desirable, allowing the low detection limit of the CysAuNPs sensors, which are capable of detecting as little as 0.35 µM NDEA in serum albumin samples, making the protocol an attractive sensor for rapid detection of nitrosamines in biological samples. Full article

High-plasticity soils pose significant problems in road infrastructure, adversely affecting structural safety due to their unfavorable engineering properties. Lime stabilization is one of the most widely used methods for improving such soils. However, lime addition may cause brittleness of these soils, resulting in a sudden loss of strength. To overcome this weakness, this study investigated using polypropylene fibers in combination with lime stabilization. Accordingly, the plasticity, compressibility, and strength properties of soil mixtures containing 3%, 6%, 9%, and 12% lime, along with mixtures prepared with a constant 0.5% polypropylene fiber content, were systematically evaluated in a laboratory environment. Additionally, the influence of freeze–thaw cycles (FTCs), which induce long-term strength degradation in highway subgrades, on these mixtures was investigated. The results indicated that lime addition reduced the plasticity index by up to 38% without causing a significant change in dry unit weight. It was also determined that FTCs could lead to a strength loss of up to 84% in natural soil, and this loss was substantially reduced by adding lime and fibers. These findings highlight that the lime-fiber combination represents an effective and sustainable method for increasing the performance of high-plasticity soils. Full article

A pressing scientific task is the development of modern extractants that meet the increased requirements for efficiency and safety. In this work, a new three-component eutectic solvent based on bis(2,4,4-trimethylpentyl)phosphinic acid (BTMPPA), tributyl phosphate (TBP) and phenol was proposed. The formation of the eutectic solvent was confirmed by IR and ³¹P NMR spectroscopy. The temperature dependences of the main physical properties of the proposed eutectic solvent—the refractive index, density and viscosity—were determined. For the first time, the extraction properties of the eutectic solvent BTMPPA/TBP/phenol (1:1:2) were studied using the example of the extraction of metal ions from aqueous nitrate solutions. The extraction efficiencies of Pr, Nd and Dy in a single stage were 34, 38 and 81%, respectively. The extraction behaviour of Pr, Nd and Dy with the eutectic solvent BTMPPA/TBP/phenol was studied as a function of pH, salting-out agent concentration, component ratio in the eutectic mixture, phase volume ratio, etc. Nitric acid with a concentration of 0.5 mol/L was chosen as a stripping agent, and the chemical stability of the eutectic solvent BTMPPA/TBP/phenol during extraction–stripping cycles was evaluated. In summary, the proposed hydrophobic eutectic solvent has good physical characteristics and enables a more efficient recovery of rare-earth elements from nitrate solutions. Full article

The unique ability of oncolytic viruses (OVs) to replicate in and destroy malignant cells while leaving healthy cells intact and activating the host immune response makes them powerful targeted anti-cancer therapeutic agents. Vesicular stomatitis virus (VSV) only causes mild and asymptomatic infection, lacks pre-existing immunity, can be genetically engineered for enhanced efficiency and improved safety, and has a broad cell tropism. VSV can facilitate targeted delivery of immunostimulatory cytokines for an enhanced immune response against cancer cells, thus decreasing the possible toxicity frequently observed as a result of systemic delivery. In this study, the oncolytic potency of the two rVSV versions, rVSV-dM51-GFP, delivering green fluorescent protein (GFP), and rVSV-dM51-mIL12-mGMCSF, delivering mouse interleukin-12 (mIL-12) and granulocyte-macrophage colony-stimulating factor (mGMCSF), was compared on the four murine cancer cell lines of different origin and healthy mesenchymal stem cells (MSCs) at 24 h post-infection by flow cytometry. Lewis lung carcinoma (LL/2) cells were demonstrated to be more susceptible to the lytic effects of both rVSV versions compared to melanoma (B16-F10) cells. Detection of expression levels of antiviral and pro-apoptotic genes in response to the rVSV-dM51-GFP infection by quantitative PCR (qPCR) showed lower levels of IFIT, RIG-I, and N-cadherin and higher levels of IFNβ and p53 in LL/2 cells. Subsequently, C57BL/6 mice, infused subcutaneously with the LL/2 cells, were injected intratumorally with the rVSV-dM51-mIL12-mGMCSF 7 days later to assess the synergistic effect of rVSV and immunostimulatory factors. The in vivo study demonstrated that treatment with two rVSV-dM51-mIL12-mGMCSF doses 3 days apart resulted in a tumor growth inhibition index (TGII) of over 50%. Full article

Regional drought monitoring based on the Temperature Vegetation Drought Index (TVDI) holds significant potential in efforts to ensure food safety. However, its empirical determination of dry and wet edges introduces subjectivity and uncertainty, limiting its accuracy and applicability. An improved TVDI (iTVDI) was developed by optimizing boundary parameters using reinforcement learning, based on maximizing the correlation between the TVDI and the ERA5-Land soil moisture dataset. The findings are as follows: (1) The enclosed area and the absolute value of dry edge slope of iTVDI was 34.83–39.97% and 0.79–33.75% larger than TVDI, indicating that the iTVDI can be used to achieve better representation of drought conditions. (2) The iTVDI showed stronger correlations with ERA5 soil moisture (r: −0.416 to −0.174), with average |r| values 17.25% higher than TVDI; its correlations with Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and the Vegetation Condition Index (VCI) were also 12.69–75.43% higher. (3) From 2005 to 2024, the spring drought in the Huaihe Basin intensified, with the annual iTVDI increasing by 0.008–0.011, primarily driven by rising temperature, potential evapotranspiration, and vapor pressure deficit. Overall, the iTVDI is proved to be more accurate and reliable for monitoring drought dynamics and driving factors. Full article

Background/Objectives: Coronary artery disease (CAD) is a major cause of mortality worldwide, accounting for 7.3% of all deaths in Germany. Invasive coronary angiography (ICA) remains the gold standard for diagnosing CAD, yet coronary computed tomography angiography (CTCA) is gaining recognition as a non-invasive alternative. Recent clinical trials have confirmed CTCA’s diagnostic accuracy, leading to its inclusion in the 2019 European Society of Cardiology (ESC) guidelines. Despite this, its adoption in Germany has been slow. Methods: This single-center, non-randomized study at the Heart and Diabetes Center North Rhine-Westphalia (HDZ NRW) evaluated CTCA’s safety and diagnostic performance. We included patients with low to intermediate pre-test probability (PTP) referred for cardiac catheterization between 2019 and 2022. The primary outcome was the change in the Wall Motion Score Index (ΔWMSI), with a threshold of 0.37 indicating significant mortality risk. Secondary outcomes included cardiovascular mortality, myocardial infarction, angina at follow-up, and myocardial revascularization procedures. Results: A total of 100 patients were enrolled; 30 underwent CTCA, and 70 had ICA. The mean patient age was 63 years, with 33% female. Of the 63 patients who completed follow-up (41 ICA, 22 CTCA), no significant differences in cardiovascular outcomes or mortality were observed. CTCA effectively ruled out CAD in low-risk patients, with a sensitivity of 75% and specificity of 77%. CTCA was faster (4.7 vs. 20.2 h) but had a higher radiation dose (2.3 vs. 1.5 mSv). Conclusions: CTCA is a viable, non-invasive alternative for diagnosing low- to intermediate-risk CAD patients. Further studies are needed to confirm its clinical benefits. Full article

Autonomous vehicle trajectory tracking control is one of the hot topics in the autonomous driving field. One of the most widely used control methods is MPC (Model Predictive Control). As the control system generally becomes more nonlinear and complex, more nonlinear system factors are added to the MPC method. However, tracking accuracy and the amount of calculation needed are both dependent on a lot of contradictions for NMPC (Nonlinear Model Predictive Control). This research proposes a control algorithm for MPC-fused PID (Proportional-Integral-Derivative) control that ensures tracking accuracy under different high-speed driving conditions on wet and slippery road surfaces. The objective of the algorithm is twofold: first, to enhance trajectory tracking accuracy, and second, to ensure real-time control and optimize the vehicle’s comfort, economy, and safety indexes. The results of the joint simulation in Carsim/MATLAB Simulink show that trajectory tracking accuracy is improved by at least 22.2% under high-speed driving conditions of a vehicle on a wet and slippery road. At the same time, the comfort, economy, and safety of the vehicle are improved by at least 9.4%, 19.8%, and 5.3%, respectively. Full article

As the region with the earliest housing stock market and the most advanced development in China, the Pearl River Delta has experienced extensive housing demolition and construction, leading to buildings having short lifespans. The environmental pollution generated during this process has brought attention to the concept of green buildings. However, whether due to previous patterns of demolition and construction or the significant impacts of social and economic changes in the current and future housing stock contexts, the comprehensive adaptability of human-centered living spaces remains a crucial issue. This focus is strongly related to the residents’ psychological responses, such as sense of belonging, safety, and atmosphere, across different scales of physical environment. However, most housing evaluation systems regarding sustainable issues are green building evaluation systems. And their concept and practice are often accompanied by a neglect of the interrelationship between people and the built environment, as well as a lack of an appropriate methodological framework to integrate these elements in the temporal dimension. This paper primarily tries to provide new answers to old questions about housing durability by reconceptualizing evaluation systems beyond ecological metrics, while simultaneously challenging accepted answers that privilege material and energy indicators over sociocultural embeddedness. Moreover, an effective housing evaluation framework must transcend purely technical or ecological indicators to systematically integrate the temporal and sociocultural factors that sustain long-term residential quality, particularly in rapidly transforming urban contexts. Therefore, theories closely related to building longevity, such as open building and the “level” strategy, were introduced. Based on this combined methodological framework, selected cases of local traditional housing and green building evaluation systems were studied, aiming to identify valuable longevity factors and improved evaluation methods. Furthermore, two rounds of expert consultation and a data analysis were conducted. The first round helped determine the local indexes and preliminary evaluation methods, while the second round helped confirm the weighting value of each index through a questionnaire study and data analysis. This systematic study ultimately established a preliminary long-term housing evaluation system for the Pearl River Delta. Full article

The anti-slip performance of road surfaces directly affects traffic safety, yet existing evaluation methods based on texture features often suffer from limited interpretability and low accuracy. To overcome these limitations, a portable 3D laser surface analyzer was used to acquire road texture data, while a dynamic friction coefficient tester provided friction measurements. A multi-view fractal dimension index was developed to comprehensively describe the complexity of texture across spatial, cross-sectional, and depth dimensions. Combined with road surface temperature, this index was integrated into an XGBoost-based prediction model to evaluate friction at driving speeds of 10 km/h and 70 km/h. Comparative analysis with linear regression, decision tree, support vector machine, random forest, and backpropagation (BP) neural network models confirmed the superior predictive performance of the proposed approach. The model achieved backpropagation (R²) values of 0.80 and 0.82, with root mean square errors (RMSEs) of 0.05 and 0.04, respectively. Feature importance analysis indicated that fractal characteristics from multiple texture perspectives, together with temperature, significantly influence anti-slip performance. The results demonstrate the feasibility of using non-contact texture-based methods to replace traditional contact-based friction testing. Compared with traditional statistical indices and alternative machine learning algorithms, the proposed model achieved improvements in R² (up to 0.82) and reduced RMSE (as low as 0.04). This study provides a robust indicator system and predictive model to advance road surface safety assessment technologies. Full article