Search Results (6,839)

A thorough understanding of the resistivity response characteristics of ethanol-contaminated soil is of great significance for the development of non-destructive geophysical detection techniques and for supporting contaminated site investigation and assessment. This experimental study aims to systematically investigate the resistivity behavior of ethanol-contaminated sandy soils, with a focus on the coupled mechanisms of multiple factors, including water content, ethanol concentration, particle size distribution, and contamination time. It is hypothesized that water content serves as the dominant factor controlling resistivity, whereas ethanol concentration and contamination time regulate resistivity by altering the physicochemical properties of the pore fluid. Under laboratory conditions, silt, fine sand, and medium sand were selected as the test materials. Resistivity was systematically measured using a Miller Soil Box with increasing water content, Wenner array configuration across varying water contents (3–24%), ethanol concentrations (40–98%), and contamination durations (0–144 h). The experimental results indicate the following: (1) Regardless of the presence of ethanol contamination, the resistivity of sandy soil decreases with increasing water content following a power-law relationship. The decrease is most pronounced at low water contents (3–9%), and gradually stabilizes at higher water contents. The results show that, at a constant water content, resistivity systematically and consistently follows the order: silt > medium sand > fine sand. (2) The influence of ethanol concentration on resistivity is constrained by water content levels, and the overall increase in resistivity is primarily attributed to ion dilution and the obstruction of conductive pathways. (3) Over time, resistivity exhibits a two-stage increasing trend, associated with ethanol volatilization and water loss. Resistivity changes in fine sand samples contaminated with ethanol at concentrations ranging from 75% to 95% follow a two-stage pattern. The initial phase of growth is characterized by a gradual increase over a period of 0–48 h, followed by a more rapid increase during the subsequent phase, which extends from 48 to 144 h. The results show that higher initial ethanol concentrations enhance the sensitivity of resistivity to temporal changes. Comprehensive analysis indicates that the resistivity variation mechanism under multi-factor coupling conditions can be summarized as follows: the water content is the dominant factor in the regulation of the conductive pathways; the particle size distribution determines pore structure and the characteristics of the particle interface; ethanol concentration and contamination time dynamically alter pore fluid properties, collectively regulating the resistivity response. Although the experiments were conducted under controlled laboratory conditions and the results have certain limitations, they provide a preliminary reference for interpreting resistivity responses in relatively homogeneous sandy contaminated sites and offer theoretical support for the application of resistivity methods in contamination identification and dynamic monitoring. Full article

Background/Objectives: Carbonic anhydrase I (CAI) is a zinc-dependent metalloenzyme whose inhibitor discovery requires both effective navigation of chemical space and explicit evaluation of coordination-credible binding hypotheses. We aimed to develop an interpretable and reproducible QSAR-to-structure workflow for CAI inhibitor discovery. The workflow links potency prediction with zinc-site plausibility and early developability to support decision-oriented prioritization of new CAI inhibitor candidates. Methods: CAI inhibitors were retrieved from ChEMBL (CHEMBL261) and modeled as pK_i = 9 – log₁₀(K_i[nM]). AlvaDesc v3.0.8 generated 4224 2D descriptors, which were reduced using train-only preprocessing, variance filtering, correlation pruning, and bagged-tree ranking to a top-100 panel. Five regressors (elastic net, CART, bagging, GB, and XGB) were benchmarked on a held-out test set. Potent ChEMBL seeds (K_i ≤ 10 nM) were used for a 90% 2D similarity PubChem expansion. Predicted hits were then externally validated using independently available PubChem CAI K_i records. Ten novel candidates lacking CAI K_i data were docked to CAI (PDB: 1AZM) via SwissDock AutoDock Vina in neutral and relevant anionic states, with pose selection constrained by a Zn-donor filter (Zn-N/O ≤2.6 Å). SwissADME was used to profile physicochemical space, alerts, and absorption/distribution proxies. Results: The bagging model showed the best test generalization (R² = 0.646; RMSE = 0.61; MAE = 0.45). PFI and SHAP converged on sulfur/heteroatom connectivity and polar–lipophilic organization as dominant potency drivers. PubChem expansion yielded 25,315 analogs and 233 candidates at predicted pK_i ≥ 8.0; external validation on 145 CAI-measured hits gave R² = 0.358 (RMSE = 0.456; MAE = 0.320). Across 20 ligand/protomer docking runs, 12 produced canonical Zn-anchored poses (10 Zn-N; 2 Zn-O). SwissADME indicated consensus logP values from −0.65 to 3.21, 0/10 PAINS alerts, and predominantly favorable drug-likeness (8/10 with zero Lipinski violations), supporting tiered advancement. Conclusions: Integrating interpretable QSAR, external PubChem validation, coordination-aware docking, and SwissADME yields a practical triage framework for CAI inhibitor discovery. The resulting tiered shortlist identifies two Zn-N-anchored N-alkyl sulfamides (CIDs 103935964 and 112684680) and one Zn-O-anchored carboxylate control (CID 122367674) as highest-priority computational hypotheses for staged biochemical evaluation. Full article

Accurate paper fiber identification is essential for cultural heritage conservation. Traditional staining methods are destructive, while macroscopic AI models often lack physicochemical interpretability. This study explores the feasibility of a non-destructive analytical approach using micro-hyperspectral imaging (Micro-HSI) to overcome both limitations. Three traditional Japanese bast fibers, Kozo, Mitsumata, and Gampi, were analyzed as standard reference samples. Relative reflectance spectra were extracted from microscopic fiber regions using Micro-HSI. Dynamic normalization and Savitzky–Golay first-derivative filtering were applied to suppress scattering effects and baseline drift. Principal component analysis (PCA) and linear discriminant analysis (LDA) were applied in parallel for dimensionality reduction and supervised classification, respectively. The results indicated that unsupervised PCA exhibited substantial inter-class overlap because of the shared cellulose matrix among the fiber types. In contrast, supervised LDA amplified subtle chemical differences and achieved clear separation among the three fibers. Feature-loading analysis indicated that the classification was mainly associated with visible range reflectance characteristics, lignin π→π* absorption bands in the 400–450 nm region, and near-infrared O−H and C−H overtone vibrations near 835 nm. Leave-One-Specimen-Out Cross-Validation yielded an overall accuracy of 77.8%, with error-free classification of Kozo (F1 = 1.00) and misclassification limited to the chemically similar Gampi and Mitsumata pair. This proof-of-concept study demonstrates that combining Micro-HSI with chemometric analysis enables non-destructive fiber discrimination while retaining physicochemically interpretable spectral features. The findings also establish a microscopic spectral reference framework for future non-destructive analysis of historical paper materials. Full article

Polymer-based product use is rapidly increasing worldwide, resulting in critical social, environmental, ecological, economic, and health effects. Worldwide efforts have increasingly focused on solutions to the equilibrium consumption, production, and disposal of plastics to tackle these issues. The frontiers of biodegradable and bio-based polymers are continually advancing in pursuit of sustainability. Therefore, designing ecological bioplastics made of both biodegradable and bio-based polymers reveals chances to overcome plastic pollution and resource depletion. Polymeric materials are mainly used to manufacture different products at the beginning of their lifespans and which become waste after usage. Numerous sustainability strategies and polymer recycling methods are described and mostly classified into chemical, mechanical, and thermal recycling processes. This manuscript presents a New Polymers Frontier in Recycling and Sustainability Using an Ensemble of Deep Learning with a Heuristic Search Algorithm (NPFRS-EDLHSA). This work is devoted to computational polymer typology, which is based on machine learning algorithms applied to data on physicochemical properties. Although polymer classification can facilitate downstream materials research, the present study does not directly simulate recycling, environmental impacts, or sustainability. The main contributions made by this work include (i) an exploratory analysis of ensemble deep learning models to classify polymers by type on a small and unbalanced dataset; (ii) an evaluation of the effect of feature selection with a heuristic optimization methodology; and (iii) a comparison of the effects on classification performance under limited data conditions. This research sets out to provide a methodological explanation, not arguments for industrial-scale applicability. For the polymer-type classification process, the proposed NPFRS-EDLHSA model designs an ensemble of deep learning techniques, namely a bidirectional recurrent neural network (BiRNN) model, a bidirectional gated recurrent unit (BiGRU) method, and a graph autoencoder (GAE) technique. Finally, the grasshopper optimization algorithm (GOA) adjusts the hyperparameter values of the ensemble models optimally and results in an improved classification performance. A wide-ranging set of experiments was conducted to validate the performance of the NPFRS-EDLHSA method. The experimental results indicated that the NPFRS-EDLHSA technique achieved a better performance than an existing model. Full article

Background/Objectives: Several subunit vaccines for tuberculosis (TB), such as MVA85A and H4:IC3, have not demonstrated ideal protective efficacy in clinical trials, which may be attributed to their limited antigenic profile and lack of effective latency-associated antigens. In this study, we combined bioinformatics with experimental validation to screen for latency-associated antigens that have immune-protective effects. Methods: Highly expressed antigens were identified from models related to latent infections, such as hypoxia and nutritional starvation. Their physicochemical properties and immunogenicity were predicted using online tools such as Expasy-ProParam, IEBD, and VaxiJen. The immunogenicity of these antigens was then evaluated in multiple mycobacterium infection models. Finally, a systematic evaluation of the immune response and protective effects induced by the candidate antigens was performed in a mouse model using intracellular cytokine detection, mycobacterium growth inhibition assays (MGIAs), antibody-dependent cellular phagocytosis (ADCP), and a latent tuberculosis infection (LTBI) mouse model. Results: The antigen Rv2656c is highly expressed in the nutritional starvation model and demonstrates strong immunogenicity in both infected humans and cattle. Moreover, Rv2656c exerted a significant inhibitory effect against Mycobacterium tuberculosis (M. tuberculosis) and Mycobacterium avium (M. avium) infections in MGIA. The humoral immune response elicited by Rv2656c enhanced the phagocytosis and killing of Mycobacteria by macrophages in vitro. Furthermore, in a mouse model of LTBI established using the attenuated M. tuberculosis H37Ra strain, treatment with Rv2656c significantly decreased the bacterial load in the lungs of the mice. Conclusions: Latency-associated Rv2656c may serve as an immune-protective antigen, offering potential for the development of novel multi-stage antigen subunit vaccine against TB. Full article

(1) The growing interest in the use of natural and sustainable ingredients highlights the investigation of vegetable oils in dermato-cosmetic applications. In this context, the vegetable oil obtained from walnut (Juglans regia L.) is of actual interest due to its composition rich in unsaturated fatty acids. The aim of the present study was to investigate and characterize walnut oil from a physicochemical, structural, and rheological point of view. (2) The oil was obtained by a cold pressing process from walnut seeds, with a yield of about 51.03 ± 1.41%, and subsequently analyzed by complementary methods. (3) The results show an acceptable physicochemical profile, characterized by appropriate values of density, pH, and spreadability. The oxidative stability indicated a moderate resistance to degradation, specific to oils rich in polyunsaturated fatty acids. Fourier infrared transform spectrometry (FTIR) analysis confirmed the presence of functional groups characteristic of triglycerides, without indications of advanced oxidation, and atomic force microscopy (AFM) investigations revealed a heterogeneous morphology. The rheological properties indicated a pseudoplastic behavior, favorable for topical application. The determination of heavy metals confirmed the safety of the raw material for the intended dermato-cosmetic use. While arsenic levels were slightly above the strict Codex Alimentarius limits for foodstuffs, all values remained within the safety ranges established for cosmetic ingredients. A total of six fatty acids were found in cold-pressed walnut oil, determined using GC-MS methods. The number of compounds identified in the silylated sample was found to be 17. The antioxidant activity determined using DPPH and ABTS methods was generally considered good and relatively stable over time. The measured sun protection value (SPF) demonstrates a favorable capacity to act as a photoprotective ingredient against ultraviolet (UV) radiation. (4) Overall, the results demonstrate that walnut oil presents adequate physicochemical and structural properties, supporting its further use as a potential cosmetic raw material. Full article

Ozone micro- and nanobubbles have emerged as a promising platform for advanced oxidation processes owing to their distinctive physicochemical characteristics, including exceptional stability, prolonged gas residence time, and highly active gas–liquid interfaces. Compared with conventional ozonation, micro/nanobubble-assisted systems significantly enhance ozone dissolution and utilization efficiency. They achieve this by creating a unique interfacial microenvironment that promotes localized and sustained oxidative reactions. Increasing evidence suggests that ozone oxidation is not dominated solely by homogeneous bulk-phase reactions but is strongly coupled with processes occurring at the bubble/water interface, particularly hydroxyl radical generation and surface-localized oxidation. This review provides an application-oriented overview of ozone micro/nanobubble technology by summarizing representative preparation methods and characterization techniques, elucidating their distinctive interfacial physicochemical properties, and critically examining their performance in oxidative cleaning, microbial inactivation, and complex environmental remediation. Special emphasis is placed on interpreting these phenomena from the perspective of gas–liquid reactions and surface-induced radical generation, with the aim of establishing a unified mechanistic framework that bridges fundamental properties with engineering performance. Finally, current challenges and future research directions for translating ozone micro/nanobubble systems into large-scale and long-term applications are discussed. Full article

The physicochemical mechanisms and numerical characterization of amine-ether gemini surfactant emulsion flooding remain insufficient, limiting its field application in low-permeability reservoirs. This study developed a refined numerical simulation method that integrates full-process emulsion kinetics, including generation, coalescence, dispersion-assisted oil displacement, and demulsification, with graded emulsion characterization using the differentiated inaccessible pore volume (IPV) and residual resistance factor (RRF). Core-flooding validation demonstrated that the model accurately reproduced the key dynamic responses of water cut reduction and oil production increase, with a relative error of about 3.0%. Mechanistic analysis showed that the enhanced oil recovery performance arose from the combined effects of ultralow interfacial tension and emulsion-induced profile control. Relative to conventional surfactant flooding, emulsion flooding increased oil recovery by an additional 4.8–5.0% and lowered water cut by about 12 percentage points. For the Shengli Oilfield pilot block, the optimized injection design involved a surfactant concentration of 1.2 wt.%, an injection rate of 60 m³/d, a slug size of 0.01 PV, an injection–production ratio of 0.95, and a stepwise concentration-decline strategy. The field pilot further confirmed the applicability of the method: daily oil production of the well group increased by 46.5%, while comprehensive water cut decreased by 8.6 percentage points. These results demonstrate the value of the proposed method for both mechanistic characterization and field design of amine-ether gemini surfactant emulsion flooding in heterogeneous low-permeability reservoirs. Full article

Understanding the environmental drivers underlying the spatial heterogeneity of soil organic carbon (SOC) in mountainous regions remains a major challenge in digital soil mapping. This study investigated the spatial distribution and driving mechanisms of SOC contents in a typical subtropical mountainous area using an integrated modeling and interpretation framework based on 132 soil samples. The SOC content in Yangshan County ranged from 3.33 to 50.00 g kg⁻¹, with a coefficient of variation of 48.64%, indicating a moderate level of variability across the study area. Six mainstream modeling approaches were compared, including multiple linear regression (MLR), geographically weighted regression (GWR), Cubist, eXtreme Gradient Boosting (XGBoost), random forest (RF), and a hybrid RF-GWR model. The results showed that RF outperformed traditional linear methods and other machine learning approaches, achieving an R² of 0.45 and RMSE of 7.78 g kg⁻¹, while the hybrid model further improved prediction accuracy (R² = 0.48). Then, spatial mapping revealed a clear elevational gradient, with higher SOC values concentrated in forested mountainous areas in the north and lower values distributed across low-elevation cultivated and disturbed zones. SHAP analysis identified intrinsic soil properties, particularly total nitrogen (TN) and cation-exchange capacity (CEC), as dominant controls on SOC contents. When extended to prediction datasets, relative humidity (RH) and mean annual precipitation (MAP) showed greater importance on SOC, suggesting an amplification of climatic factors at the broader scale. Subsequently, hotspot analysis of GeoShapley components further revealed the spatial differentiations in group indicators, with overall contributions ranked as soil physicochemical properties (36.4%) > geographic conditions (21.1%) > climate (17.4%) > organisms (12.9%) > parent material (12.1%). Soil properties formed clustered hotspots overlaid on carbonate-dominated areas, while geographic conditions and climate primarily acted as spatial modulators, generating localized zones of intensified or weakened influence across the landscape. The integrated framework proposed in this study has potential applicability across broader regions. These findings provided a scientific basis for the localized interpretation of environmental drivers of SOC and offered valuable support for region-specific land management and sustainable decision-making. Full article

Background: Mental health disorders are a major global public health challenge. Psychobiotics have emerged as a promising strategy to modulate the microbiota–gut–brain axis. Whey and blueberry bagasse are agro-industrial by-products with potential for functional fermented matrices. Objective: To develop whey biotic blend (WBB) formulations enriched with blueberry bagasse and evaluate the impact of incorporation timing on functional properties, and to explore the neurobiological effects of a selected formulation. Methods: Three WBB formulations were prepared with Lactiplantibacillus plantarum 299v and PS128, differing in the timing of blueberry bagasse incorporation: Control, WBB-Before, and WBB-After. Physicochemical properties, microbial viability, composition, γ-aminobutyric acid (GABA), phenolic profile, and antioxidant and anti-inflammatory capacities were evaluated. Based on these results, WBB-After was selected as the sole formulation and advanced for testing in a lipopolysaccharide (LPS)-induced murine model. Results: All formulations supported probiotic growth (>8.6 log CFU/mL). Blueberry bagasse incorporation significantly influenced functional properties. WBB-After showed ~2.1-fold higher total phenolic content than WBB-Before and enrichment in anthocyanins and hydroxycinnamic acids, with higher antioxidant (≈24% by DPPH) and anti-inflammatory potential (≈19%), whereas WBB-Before exhibited ~20% higher GABA levels. In vivo, WBB-After showed improved recognition performance under baseline conditions (Control vs. WBB), although the overall group effect was marginal. No significant differences were observed in hippocampal cytokines or neuronal integrity markers under LPS-induced inflammation. Conclusions: The timing of blueberry bagasse incorporation shapes WBB functional properties. The selected formulation showed limited neurobiological effects under the evaluated in vivo conditions, highlighting the need for further studies. Full article

We aimed to develop kombucha-vinegar beverages inspired by switchel (a beverage that combines apple cider vinegar and ginger extract), using pecan shell aqueous extract (PSE) and green tea infusion (GTI) in the preparation of kombucha vinegar, and to assess its effects on physicochemical characteristics, antioxidant activity, and sensory acceptance. Combinations of PSE and GTI (100:0, 75:25, 50:50, 25:75, and 0:100) were tested as substrates to produce kombucha vinegar with an initial sugar concentration of 80 g/L. After, the initial sucrose concentration was tested (80 to 60 g/L) using two of the previous formulations (50% of PSE and 50% of GTI; 25% of PSE and 75% of GTI), that showed better results in antioxidant capacity and sensory characteristics, particularly bitterness, which was attributed to the addition of higher amounts of pecan nutshell extract (100 and 75%). The formulation with 60 g/L of sucrose and higher pecan shell extract (50%) was chosen, allowing a beverage with less sugar at the end of kombucha fermentation. An increase in antioxidant potential was observed during the fermentations, with this being a highlight of this study. Kombucha vinegar beverages inspired by switchel were developed (50% PSE and 50% GTI, 60 g/L of sucrose), with the use of ginger extract or juice fruits (apple, pineapple, or white grape), in order to make the beverage palatable to consumers. The samples without ginger showed the highest antioxidant capacity values. In the sensory evaluation using acceptability and the check all that apply method (CATA), the beverages without ginger showed acceptability ranging from 74.4% (addition of white grape juice) to 84.0% (addition of pineapple juice), being described as refreshing, healthy, and energizing. Full article

The increasing emission of harmful gases into the atmosphere represents a major environmental challenge, driving the need for efficient air purification materials. Poly(methyl methacrylate) (PMMA) has emerged as a promising candidate due to its favorable physicochemical properties and adsorption potential. In this study, the interactions between PMMA and selected sulfur-containing pollutants (CH₃SH, COS, CS₂, H₂S, and SO₂) were systematically investigated using a multiscale computational approach. Initial structural exploration was performed using extended tight-binding (xTB) methods, followed by refinement at the density functional theory (DFT) level, while molecular dynamics (MD) simulations were employed to capture the dynamic behavior of the systems. The results suggest that all investigated gases exhibit attractive interactions with PMMA, with interaction strength strongly dependent on molecular polarity and electronic structure. Among the studied systems, SO₂ shows the strongest binding, while CS₂ exhibits the weakest interaction. Energy decomposition based on symmetry-adapted perturbation theory (SAPT) and electronic structure analyses suggest that electrostatic and donor–acceptor interactions play a dominant role for strongly interacting systems, whereas weaker interactions are primarily governed by dispersion forces. Full article

Continuous-flow microwave (MW) pasteurization is a relatively new and still poorly understood preservation method with great potential for industrial applications. The raw material for the research was blue honeysuckle berries (Lonicera caerulea var. kamtschatica Sevast.), which are considered a rich source of bioactive compounds. This study investigated the effects of various MW power (2100 W, 2400 W, 2700 W, 3000 W), traditional pasteurization parameters (90 °C/10 min), and blue honeysuckle berry varieties (Aurora and Indigo) on the quality of nectars after the preservation process and during 16 weeks of cold storage (4 °C). Physicochemical measurements were performed (pH, titratable acidity, total soluble solids, nephelometric turbidity), together with spectrophotometric (total polyphenol content, antioxidant activity, color parameters) and chromatographic (L-ascorbic acid, anthocyanins, phenolic acids, iridoids) analyses. A slight effect of MW power on pH, total soluble solids, total titratable acidity, turbidity, and color parameters was demonstrated. Immediately after preservation, the ∆E* values of the samples subjected to MW ranged from 0.48 to 1.06, while after PT they ranged from 1.90 to 5.83. Considering the content of bioactive components, it has been proven that the MW method is more beneficial than traditional pasteurization due to a higher retention of anthocyanins (1–6% reduction or 1–5% increase after MW or 5–16% reduction after PT—values for the individual anthocyanins) and partially higher antioxidant activity. After 16 weeks of storage, MW-treated samples contained more anthocyanins and total polyphenols than untreated samples. The study showed the negligible effect of MW processing on iridoid content; these compounds were the most stable bioactive compounds present in the preserved nectars during storage (changes of up to 5%). Full article

Image-based PM2.5 estimation has emerged as a promising complementary approach to traditional physicochemical monitoring. However, achieving accurate predictions in severely polluted environments remains a critical challenge, as existing deep learning models tend to prioritize luminance variations induced by PM2.5 while neglecting the impact of complex atmospheric light interference, leading to substantial estimation errors. To address this issue, this paper proposes a novel luminance–spatial decoupling (LSD) module constructed based on L2–Lp Retinex theory and integrated into a VGG16 backbone. By establishing a prior knowledge module linking luminance to PM2.5, the proposed method achieves high-fidelity separation of atmospheric luminance (AL) and target luminance (TL) during feature extraction. TL represents the luminance variation induced by PM2.5 concentrations, whereas AL characterizes the luminance contribution arising from atmospheric light. Simulation experiments validate the reliability of the L2–Lp Retinex-based decomposition. Ablation studies reveal that the LSD module effectively mitigates haze interference in high-pollution conditions while minimizing influence on the backbone network in clear weather, thereby resolving the conflict between dehazing and feature extraction. Comparative experiments demonstrate that LSD-VGG16 significantly outperforms traditional methods and standard convolutional neural networks, achieving a minimum prediction error of 12.42 while exhibiting stronger stability against temporal variations. Furthermore, evaluation on the unseen RHID-AQI dataset without retraining confirms the model’s robust generalization capability under abrupt illumination fluctuations and diverse weather conditions. Full article

Background/Objectives: The efficacy of topical therapies in dermatology is often limited by the barrier function of the stratum corneum, which restricts drug penetration. Iontophoresis is a non-invasive transdermal delivery technique that uses a low-intensity electrical current to enhance the transport of charged and polar molecules across the skin. It has emerged as a strategy to improve local drug bioavailability while minimizing systemic exposure. We systematically reviewed the clinical evidence on the efficacy, safety, and pharmacologic performance of iontophoresis-assisted topical drug delivery in dermatologic diseases. Methods: This systematic review followed PRISMA guidelines and was prospectively registered in PROSPERO (CRD420251234877). PubMed, Embase, Web of Science, CENTRAL, and ClinicalTrials.gov were searched through 19 November 2025 without language restrictions. Records were screened against predefined eligibility criteria, and data were extracted on study design, participants, dermatologic indications, intervention/comparator, iontophoresis parameters, efficacy outcomes, and adverse events. The risk of bias was assessed using RoB 2 for randomized trials and the JBI checklist for non-randomized studies. Because of substantial clinical and methodological heterogeneity, the findings were synthesized narratively and no meta-analysis was performed. Results: Twenty-one studies published between 1990 and 2025 met the inclusion criteria, including 15 randomized and 6 non-randomized studies. Investigated conditions included psoriasis, eczema, melasma, post-inflammatory hyperpigmentation, herpes labialis, onychomycosis, chronic ulcers, systemic sclerosis-related digital ulcers, acne scarring, and actinic keratosis. Across studies, findings were mixed. The most consistent signals of benefit were observed in pigmentary disorders and infectious diseases, whereas results were more heterogeneous in inflammatory dermatoses and some studies did not show superiority over active comparators. Tolerability was generally favorable, with adverse events limited to mild, reversible local reactions such as erythema, tingling, burning, or transient irritation. No serious treatment-related adverse events were reported. Conclusions: Iontophoresis may represent a useful non-invasive delivery-enhancement strategy in selected dermatologic settings, particularly when topical efficacy is limited by anatomical or physicochemical barriers. However, heterogeneity in protocols, formulations, outcomes, and clinical indications limits direct comparison and does not support broad conclusions of efficacy across all dermatologic conditions. Larger, standardized trials are needed to clarify its therapeutic role, long-term efficacy, and indication-specific benefit. Full article