Search Results (1,721)

Traditional wood anatomy provides the gold standard for timber identification, yet its reliance on centralized laboratory infrastructure severely limits its efficacy during real-time field inspections. This study addresses a critical research question: How can physical xylotheque resources, national timber extraction registries, and edge-computing computer vision be integrated into a cohesive framework to enable robust, forensic-level wood identification at field control stations? To resolve this, we implemented a three-tier methodology: first, we audited historical records from Ecuador’s Forest Administration System (SAF) encompassing 129 commercial timber species; second, we conducted a gap analysis using the Wood Anatomy Laboratory and Xylotheque (LAMX) repository (510 cataloged samples, 2267 histological preparations) to secure botanically validated references; and third, we leveraged a curated database of high-resolution digital cross-section captures (4900 images) to evaluate CNN architectures via k-fold cross-validation and a standard 70/15/15% training/validation/testing split. Benchmarking demonstrated that the lightweight MobileNetV2 architecture achieved a global accuracy of 94.04% and an F1-score of 0.976. External field validation conducted across commercial timber yards in Ibarra confirmed an offline inference latency of just 145 ms on mid-range Android devices, proving the framework’s operational transparency and low-cost scalability. Furthermore, Explainable AI analysis using Class Activation Maps (Grad-CAM) provided visual evidence indicating that the neural network targeted diagnostic xylotomic features (vessel distribution and axial parenchyma), minimizing reliance on external environmental noise. In conclusion, this study demonstrates that hybridizing physical taxonomic reference collections with targeted edge AI models provides a scalable, transparent, and low-cost solution that successfully bridges academic research and active forest law enforcement in tropical regions. Full article

Mongolian rangelands face interacting ecological and livelihood pressures, including livestock pressure, vegetation change, soil-carbon dynamics, household income variability, and inefficiencies in livestock by-product recovery. This paper examines whether observed administrative and household data, field-observed pilot-area audit evidence, satellite-derived/backcast vegetation indicators, model-reconstructed ecological trajectories, econometric associations, machine-learning diagnostics, Monte Carlo uncertainty outputs, and scenario-based carbon-finance calculations are consistent with a study-specific ecological–economic feedback framework in Mongolian pastoral rangelands. The analysis combines observed livestock and household data, satellite-derived vegetation indicators, field-anchored soil organic carbon (SOC) information, climate controls, and pilot-area by-product audit evidence in a dual-scale framework comprising nine pasture-user groups in Öndörshireet Soum, Töv Aimag, and a national soum-level panel for 2002–2024. SOC, above-ground biomass (AGB), and below-ground biomass (BGB) trajectories are treated as model-reconstructed series rather than independently observed annual field measurements. Fixed-effects panel models are used to estimate conditional associations, while machine-learning models assess predictive consistency within reconstructed data structures. Under the fitted full specification, the best-performing national-panel model reports an out-of-sample R² of 0.942 for model-reconstructed SOC; this value is interpreted as high internal predictive consistency within the reconstructed SOC panel, not as independent validation of observed annual SOC change. Because the SU/SOC ratio mechanically contains SOC, the full-specification predictive results are subject to leakage risk, and leakage-free validation is needed for a more conservative assessment of predictive performance. Panel estimates suggest that vegetation condition is positively associated with ln(household income), while the by-product waste ratio is negatively associated with ln(income), conditional on fixed effects and model specification. Scenario-based carbon-finance outputs, framed with reference to Verra’s VM0042 Improved Agricultural Land Management methodology, vary materially with compliance, carbon price, weighted average cost of capital, and revenue-sharing assumptions; these outputs are illustrative sensitivity calculations and do not demonstrate VM0042 compliance, project eligibility, project-registration readiness, verified emission reductions, or credit-issuance readiness. The findings are associational, reconstruction-dependent, and scenario-based. They support an analytical framework rather than establish a closed causal loop. Full article

Tropical mid-size cities on alluvial floodplains face compounded flood challenges combining pluvial accumulation from intense convective storms, regulated river overflow, and aging drainage networks. This study presents an integrated framework for Monteria, Colombia (~450,000 inhabitants; Sinu River, Caribbean lowlands), within Colombian Decree 1807/2014 and structured in four phases. (1) Hazard: A Rain-on-Grid 2D HEC-RAS 6.6 model covering 4090 ha, calibrated against four gauged events, identifies three dominant pluvial mechanisms (poor hydraulic connectivity, limited evacuation capacity, downstream channel overflow), plus 17 critical fluvial erosion points affecting ~289 properties at 100-year return period. (2) Vulnerability: Depth-damage functions from 1465 household surveys yield 36.36% of 3015 assets in high risk and 57.77% in medium risk. (3) Measures: Scenario M2 (channel widening plus dikes, land-raising, retention lagoons) removes 80 ha of flooding while displacing 28 ha at COP 845 million pre-design cost. Non-structural measures include a Sustainable Urban Drainage Master Plan, IoT-based Early Warning System, minimum construction-elevation map, and land-management instruments. A Monte Carlo residual-risk model reduces baseline risk to 19.9% under full implementation. (4) Emergency: A February 2026 cold-front event was addressed with a 4300 m perimeter dike and six pump stations deployed jointly by the Regional Environmental Authority (CVS) and Municipal Administration. Full article

Objectives: Surgical site infections (SSIs) represent a significant postoperative challenge. Although timely perioperative prophylaxis with cephalosporins is essential to prevention, adherence to the recommended 30–60 min administration window may be challenging with traditional intravenous infusion (IVI) in settings with high surgical turnover, as is the case in China. Intravenous push (IVP) has been proposed as a more time-efficient alternative. This scoping review aims to map the available evidence comparing IVP with IVI for perioperative cephalosporin administration across four domains: safety, pharmacokinetics/pharmacodynamics (PK/PD), efficacy, and economic impact. Methods: A systematic search was conducted across PubMed, Embase, Web of Science, the Cochrane Library, and gray literature up to February 2026. Data were systematically charted and extracted using a standardized form. Results: Of the 14 included sources, only 3 were peer-reviewed comparative studies; the remaining 11 (78.6%) were gray literature documents. Among the gray literature, 72.7% (8/11) permitted or recommended IVP for cephalosporin prophylaxis; however, this proportion reflected practice patterns of heterogeneous methodological rigor. The 3 peer-reviewed studies focused on the safety, PK/PD, and economic outcomes. Two studies—in orthopedic and bariatric surgery, respectively—found no significant difference in adverse event rates between IVP and IVI, though both were limited by small samples. A single small study suggested similar PK/PD target attainment between IVI and IVP cefazolin. No study directly compared SSI rates between the two routes. One study suggested potential cost savings with IVP, but the evidence was dated and based on limited patient numbers. Conclusions: The available evidence for IVP is predominantly derived from gray literature, while peer-reviewed articles suggest that safety and PK/PD profiles do not differ markedly from IVI in the limited populations, surgical procedures, and agents studied; economic data are suggestive but dated. Direct comparative data on clinical efficacy outcomes, such as SSI rates, are absent. Well-powered, multi-center comparative studies comparing IVP and IVI with SSI as a primary endpoint are needed. Full article

Background/Objectives: Iatrogenic nerve injury is a significant challenge in urologic surgery, with radical prostatectomy posing a high risk due to complex pelvic neural anatomy. Despite advances in robotic-assisted and nerve-sparing techniques, postoperative urinary incontinence and erectile dysfunction remain prevalent, adversely affecting patients’ quality of life and imposing substantial healthcare costs. Methods: A narrative review was conducted using PubMed, MEDLINE, and the Cochrane Library (searches through February 2026) for studies on radical prostatectomy epidemiology, mechanisms of nerve injury, functional outcomes, and economic burden. Emerging intraoperative fluorescence imaging technologies, surgical strategies to mitigate iatrogenic nerve injuries, and the financial costs of post-prostatectomy complications were assessed. Results: Robotic-assisted radical prostatectomy now accounts for >80% of procedures in the United States, and has been associated in observational studies with improved early recovery of erectile function compared with open and laparoscopic approaches. However, the lack of real-time nerve visualization remains a limiting factor. Recent milestones (January 2026) include the Food and Drug Administration Investigational New Drug clearance for the nerve-specific fluorophore LGW16-03 (NerveTrace), which enables real-time identification of sub-millimeter nerve branches, and the 510(k) premarket clearance of Dendrite imaging (November 2025). Conclusions: Enhanced intraoperative nerve discrimination via molecularly targeted imaging has the potential to reduce iatrogenic complications and improve long-term functional and economic outcomes in prostate cancer surgery, although these benefits have yet to be demonstrated in prospective clinical and health-economic studies. Full article

Background/Objectives: Cardiovascular disease increases with ageing and remains the leading cause of death worldwide. Cellular senescence contributes to cardiac dysfunction in the older population by secreting the senescence-associated secretory phenotype (SASP). Cardiac injury models induced by surgery have been shown to induce senescence in young adult rodents. However, surgical models are complex and associated with high mortality. Methods: We established a rat model of injury and senescence using isoproterenol (ISO). Male SD rats received ISO (100 mg/kg) for five days, then hearts were collected on days 10 and 28 after the first ISO dose. Results: ISO administration caused cardiac injury, manifested by inflammatory infiltration, fibrosis, and increased cardiomyocyte cross-sectional area. Cardiac injury was accompanied by an increase in the senescence markers SA-β-gal, p16 and p21, and DNA damage marker γH2AX. Moreover, the mRNA levels of p21 increased on day 10, along with several SASP factors, whereas the mRNA levels of p16 increased on day 28. Fibrosis, hypertrophy, and senescence persisted until day 28, indicating long-lasting cardiac remodeling and senescent cell accumulation. Conclusions: These findings suggest that ISO can provide a simple, cost-effective platform for studying senescence and cardiac injury. This model facilitates the study of timing, dosage, mechanisms and efficacy of senolytic interventions and may contribute to the development of senescence-targeted therapies. Full article

Background/Objectives: Standardised institutional patient satisfaction surveys are the primary quality-monitoring tool in Romanian public hospitals, but their ability to capture the full range of patient experiences remains uncertain. This study quantifies the discrepancy between institutional patient satisfaction scores and an independent, unmediated national feedback instrument, the Patient Feedback Mechanism (MFP), at Targoviste County Emergency Hospital (SJUT) over a six-year period (2019–2024), and examines item-level MFP results across eight dimensions of the patient experience, including dimensions not captured by the institutional indicators routinely reported by SMCSP. Methods: A sequential design combined six years of institutional satisfaction data (2019–2024) from SJUT (N = 32,176 questionnaires) with item-level MFP results for the same period, covering eight questions on medical services, cleanliness, out-of-pocket medication costs, staff involvement, communication, recommendation intent, self-reported health outcome, and willingness to report requests for money from staff. Hypotheses were tested using two-proportion z-tests with Wilson confidence intervals, Mann–Kendall trend analysis, and Cohen’s h for effect sizes. Results: Institutional satisfaction remained consistently high (96.88–97.45%), while MFP satisfaction with medical services ranged from 70.7% to 88.9% across the same years, yielding gaps of 7.9 to 26.7 percentage points, significant in every year (p < 0.001; Cohen’s h ranging from 0.32 to 0.82). The gap did not follow a monotonic trend (Mann–Kendall p = 0.469); instead, it widened to a peak in 2021 and narrowed progressively through 2024. A parallel comparison between the Quality and Patient Safety Management Service (SMCSP) overall impression item (exceeding 99%) and the MFP recommendation item (69.9–76.3%) showed even larger gaps, of 23.3 to 29.6 percentage points. The MFP item on willingness to report requests for money from staff, which is not part of SMCSP’s reported institutional indicators, remained in a narrow 4.0–5.5% range between 2019 and 2023 with no significant trend (Mann–Kendall p = 0.82); a higher 2024 value (6.9%) coincides with a national redesign of this item and is not directly comparable to earlier years. Conclusions: Institutional surveys and an independent national feedback instrument offer structurally distinct perspectives on hospital performance, reflecting differences in administration rather than equivalent estimates of patient satisfaction. The discrepancy between sources is significant and persistent, though not monotonic, widening sharply during 2021 before narrowing. One item with no institutional equivalent documents a measurable, non-trivial proportion of patients willing to report informal payment requests every year, although the available data do not establish whether this proportion is rising over time. Systematic use of existing MFP data, already collected nationally, can complement institutional surveys at minimal additional cost, provided the two instruments are interpreted as structurally different rather than as alternative estimates of the same quantity. Full article

Peru’s tax dispute system—administrative claim, Tax Court appeal, and contentious-administrative review—has increasingly migrated toward electronic files, e-invoicing, interoperable databases, and data-driven oversight. This article examines whether artificial intelligence can reduce avoidable tax litigation without weakening taxpayers’ rights and identifies the institutional conditions required to reconcile administrative efficiency with due process, reason-giving, and effective contestation. Using a legal-doctrinal and policy-analytical design, the study analyzes Peru’s tax dispute architecture, digital evidence environment, and AI-related risks in compliance and administrative litigation. The findings show that only bounded decision-support applications are institutionally appropriate, including audit triage, anomaly detection, document classification, workflow prioritization, compliance assistance, and consistency checks, provided that they do not replace legally attributable human judgment. AI is compatible with digital tax justice only when six safeguards are institutionalized: legally meaningful explainability, evidentiary and computational traceability, meaningful human oversight with override authority, lifecycle auditability, effective contestation, and distributional equality. The analysis further demonstrates that facially neutral digital requirements and risk models may generate unequal effects when disparities in connectivity, digital literacy, record-keeping capacity, and access to professional assistance translate into differences in audit exposure, compliance costs, evidentiary burdens, and practical contestability. The article proposes a rights-compatible framework for AI-supported tax enforcement in Peru. Full article

The rapidly urbanizing peripheries of the Global South face significant demographic pressures, leading to governance deficits that often neglect the long-term structural safety of new buildings. While regulatory frameworks predominantly emphasize initial construction quality, they frequently overlook the critical “post-occupancy” phase, during which distinct structural risks accumulate. This study introduces a reproducible, open-data risk identification framework designed to trace theoretical “windows of vulnerability” in Çekmeköy, a peripheral district of Istanbul. By triangulating temporal, spatial, and demographic municipal administrative records from 2018 to 2024, we illustrated how low-cost data can serve as proxies for prioritizing structural risk assessments. The findings demonstrate that a 103% population increase between 2008 and 2023, coupled with a 21% reduction in the average household size, has generated urgent housing demand that outpaces supply. We hypothesize that these conditions create high-probability zones for “informality creep,” where demographic pressures induce informal practices, such as unauthorized structural modifications within ostensibly formal high-rise settings. The primary contribution is a transferable algorithmic tool, the Weighted Post-Occupancy Vulnerability Index (POVI). Rather than serving as a deterministic building-level diagnostic, this framework operates much like an epidemiological screening process; it acts as a macroscopic prioritization heuristic that allows resource-constrained municipalities to proactively direct their inspection efforts. By mathematically quantifying the conditions under which post-occupancy risks develop, this framework provides an essential resource for enhancing urban resilience during reactive urbanism planning. Full article

Accurate tropical cyclone (TC) track prediction remains challenging, as numerical models suffer from high computational cost, substantial storage requirements, and physical parameterization uncertainties, while data-driven large AI models depend heavily on training data volume and high-resolution inputs, resulting in prohibitive computational overhead. To address these issues, this paper proposes TCN-GAN-DM, a three-stage deep learning framework based on the China Meteorological Administration (CMA) Tropical Cyclone Best Track Dataset. Specifically, a dual-stream temporal convolutional network (TCN) first extracts temporal features from track and meteorological sequences, respectively. A generative adversarial network (GAN) then takes these features and produces multiple physically plausible candidate tracks via noise injection. Finally, a conditional diffusion model (DM) refines the predicted positions through progressive denoising. Experimental results for TCs in 2024 show that under the fair deterministic comparison using a single fixed candidate, the model achieves a 6 h track error of 49.10 km, which is comparable to CMA-GFS (49.75 km) and HWRF (44.34 km), and substantially lower than the large AI model FuXi (120.44 km). When evaluating the oracle metric (best-of-K, K = 6) as an upper bound of coverage, the model achieves the smallest errors among all models at 6 h (24.04 km) and 12 h (55.81 km). In addition, the proposed model has advantages over CMA-GFS, HWRF, and FuXi in terms of computational resource consumption and hardware deployment cost. However, its mean track error increases more rapidly beyond 12 h, and at lead times of 18 h and 24 h the model is outperformed by HWRF, FuXi, and CMA-GFS, indicating that its current strength lies primarily in short-term prediction. Consequently, the practical utility of TCN-GAN-DM is currently demonstrated for 6–12 h TC track prediction, offering a new solution for disaster prevention and mitigation that balances accuracy and deployment cost at these specific time scales. Full article

Against the backdrop of the deep advancement of the carbon peak and carbon neutrality goals and the superposition of the transportation power strategy, leveraging the spatial restructuring of highway networks to optimize the low-carbon layout of county-level industries has become a crucial lever for balancing economic quality improvement with carbon intensity control. This study selects panel data from 129 counties in Yunnan Province spanning 2015–2024, constructing a comprehensive highway network development index from four dimensions: highway density, road network connectivity, weighted hierarchical structure, and county accessibility. Using a two-way fixed effects benchmark model, a stepwise mediation effect testing framework, and a regional heterogeneity identification strategy, the paper systematically examines the marginal effects, transmission pathways, and spatially differentiated characteristics of highway network development on county-level industrial carbon emission intensity. Key findings are as follows: Enhanced highway network development significantly suppresses the increase in county-level industrial carbon emission intensity, and a well-developed road network can provide long-term empowerment for the low-carbon transformation of county-level industries. Mechanism analysis confirms that highway network development reduces emissions through two core pathways: first, a direct emission reduction effect achieved by optimizing the county-wide freight organization system, reducing inefficient transport energy consumption, and improving overall transport efficiency; second, an indirect low-carbon enabling effect realized by breaking down administrative barriers in county markets, lowering cross-regional business transaction costs, deepening industrial division of labor and collaboration, and forcing resource allocation improvements. Heterogeneity analysis reveals that the low-carbon dividends of highway network development exhibit significant gradient differentiation: the emission reduction enabling effect is strongest in counties within the Central Yunnan urban agglomeration, followed by cultural tourism counties in western Yunnan and border counties in southern Yunnan, with the weakest marginal enabling effect observed in traditional agricultural counties in northeastern Yunnan. Full article

Efficient and financially sustainable social protection systems are essential under conditions of economic instability and increasing social demand. However, traditional administrative models are often characterized by high operational costs, procedural complexity, and delayed benefit delivery. This study examines the role of digitalization, process automation, and AI-driven administrative solutions in reducing administrative expenses while enhancing the sustainability and resilience of social protection systems. An integrated Automation Index is developed using standardized proxy indicators that reflect reductions in operational and transaction costs associated with digital and automated technologies. To assess future trajectories of administrative expenses, scenario-based modelling is applied under three digital transformation paths—baseline, moderate, and intensive. Administrative efficiency is estimated using a translog Stochastic Frontier Analysis (SFA) framework. The results indicate that digitalization and automation significantly reduce administrative costs only when supported by favorable institutional conditions, including decentralized governance, effective inter-agency coordination, and clearly regulated administrative procedures. Under the intensive digital transformation scenario, administrative expenses decline substantially relative to the baseline, while system responsiveness and beneficiary coverage improve. In contrast, weak institutional environments limit the efficiency gains of technological solutions. The study concludes that AI agents and automated systems should be viewed not as substitutes for human decision-making but as tools for optimizing administrative architectures. This transition from resource-intensive to technology-intensive models is particularly important for developing countries seeking sustainable social protection under constrained fiscal conditions. Full article

The energy performance of a building reflects its typical energy use and is influenced by factors such as the building envelope (insulation and windows), system efficiency (particularly for heating, cooling, and domestic hot water), and the integration of renewable energy sources. Improving energy performance helps save energy, boost energy independence and security, lower energy costs, and reduce the need for grid investments. Standardizing energy performance assessments enables benchmarking and comparison of building efficiency, encouraging informed decision-making. In this context, the paper presents a knowledge discovery-driven intelligent decision-making system, designed, developed, and tested to identify the best strategies for prioritizing buildings in the envelope process. The system combines data mining techniques with statistical analysis to precisely rank and thoroughly evaluate low-energy-performance buildings and to develop scenario-based strategies for enveloping the buildings to achieve high energy efficiency (associated with nearly zero-energy buildings) under real-world conditions. Testing of the proposed intelligent decision-making system was conducted using a real building database of approximately 3900 records, uploaded from the Romanian central administration website. Under the highest-performance scenario of the envelope-priority strategy, which includes nearly zero-energy building standards, energy savings exceeded 50% across all categories: 51.70% for healthcare, 53.40% for residential, 60.11% for administrative and office buildings, and 69.92% for educational institutions. Overall, the average savings across all building types were 59.81% (644.86 GWh/year). Full article

Background/Objectives: Human papillomavirus (HPV) infection is the leading cause of cervical cancer and is associated with several anogenital and oropharyngeal malignancies. Although licensed HPV vaccines are highly effective, access remains limited in many low- and middle-income countries due to cost, supply shortages, and implementation barriers. In this study, we evaluated the immunogenicity and safety of Gardisun, a newly developed quadrivalent prophylactic HPV vaccine, compared with Gardasil^®. Methods: This Phase III randomized, double-blind, active-controlled, parallel-group non-inferiority trial enrolled 450 healthy participants stratified by sex and randomized (1:1) to receive three 0.5 mL intramuscular doses of Gardisun or Gardasil^® on Days 0, 60, and 180. Participants were followed through to Day 210. The primary endpoint was the geometric mean titer (GMT) of antibodies against HPV types 6, 11, 16, and 18 one month after the administration of the third dose. Non-inferiority was defined as the lower bound of the 95% confidence interval (CI) for the GMT ratio exceeding 0.67. Safety was assessed through adverse event monitoring. Results: Of the 450 randomized participants, 422 completed the Month 7 visit and 429 received all three doses. Both vaccines induced antibody responses and seroconversion rates for all HPV types. The primary analysis met the non-inferiority criterion for HPV-6, while prespecified sensitivity analyses supported the existence of non-inferiority across all evaluated HPV types. Most adverse events were mild and transient, with no vaccine-related serious adverse events reported. Conclusions: Gardisun demonstrated robust immunogenicity and a safety profile comparable to that of Gardasil^®, supporting its potential as an accessible alternative quadrivalent HPV vaccine for broader vaccination programs in resource-limited settings. Full article

Background: Alcohol use disorder (AUD) is a grave mental health condition that can result in significant health and social consequences. The medications Naltrexone and Nalmefene are indicated for the treatment of AUD, with Naltrexone having received the most extensive research attention. Methods: The majority of papers assessing universal measures of alcohol consumption employed two primary metrics: total alcohol consumption (TAC) and the number of days per month where individuals engaged in heavy drinking (HDD). Indicators pertaining to the maintenance of complete abstinence were excluded due to the absence of sufficient data. The safety of both substances was also assessed, as were the frequency of side effects and independent patient dropout. The study also incorporated practical factors of the therapy, such as the route of administration, dosage regimen, and the drug’s patient convenience, which can have a significant impact on adherence to therapy. Results: Nalmefene, administered in an “as needed” regimen, demonstrated statistically significant activity in reducing HDD and total alcohol consumption (TAC) among patients with AUD, particularly those with elevated World Health Organization (WHO) DRL risk. Preliminary findings from the ESENSE1 (Efficacy of Nalmefene in Alcohol Dependence; the first phase III study), ESENSE 2 (Efficacy of Nalmefene in Alcohol Dependence, the second phase III study), and SENSE (the final phase III long term-safety and cost-effectiveness study) studies indicate a substantial decrease in HDD and TAC following the initial month of treatment. These effects persist throughout the subsequent follow-up period. Several Japanese studies have corroborated the effectiveness of Nalmefene, demonstrating its efficacy across both short-term and long-term applications. Furthermore, these studies have substantiated its safety profile, indicating that there is no inherent risk of addiction or the emergence of withdrawal symptoms. The mild nature of adverse events (most commonly nausea and dizziness) led to a relatively low discontinuation rate of Nalmefene treatment. A subsequent study, employing a recognized methodology, corroborated the efficacy of psychosocial support in enhancing treatment outcomes. Meta-analyses demonstrate that Naltrexone exhibits comparable efficacy in reducing the frequency and severity of alcohol consumption. In select populations, the injectable form (LAI) of this pharmaceutical agent facilitates less frequent dosing, which is advantageous for the treatment process. A comparison of Nalmefene and Naltrexone reveals that the latter does not demonstrate a significant impact on the likelihood of individuals returning to heavy alcohol consumption. Conclusions: In the treatment of AUD, both naltrexone and nalmefene have been shown to yield positive outcomes, particularly in terms of reducing the HDD and TAC. According to the World Health Organization (WHO) classification, Nalmefene is indicated for individuals with a high risk of developing serious conditions. It has been demonstrated to produce rapid and sustained results while exhibiting a favorable safety profile, characterized by the absence of significant adverse effects. Naltrexone is a medication that has proven to be effective. LAI may have a positive impact on the efficacy of treatment. Full article