Sample Concentration Research Articles

Geochemical constraints on critical metals in the Jajarm bauxite deposit in Northeastern Iran

Two types of bauxite lenses and horizons are found in the Jajarm mining area: A-type and B-type. The B-type bauxite lenses and horizons occur as stratiform orebodies between the shallow-marine platform carbonate of the early–middle Triassic Elika Formation and the siliciclastic and molassic sedimentary rocks of the late Triassic Shahmirzad Formation from the Shemshak Group. Lithium concentrations in the ore samples vary significantly between profiles I and II, ranging from 7.9 to 3780 ppm (mean 593.6 ppm) in profile I and 94.8 to 723 ppm (mean 392.9 ppm) in profile II. Ore samples with moderate Al2O3 contents (30–40 wt%) and moderate Al2O3/SiO2 ratios (< 2) show markedly higher Li and REE contents. This enrichment is likely due to the preferential uptake of REE and Li by clay minerals via isomorphic substitution and surface adsorption during kaolinization and early-stage lateritization. The Jajarm deposit is thus not only a major source of Al, but also holds significant potential as a strategic resource for critical metals, such as Li, TiO2, Ga, Nb, Ta, V, and REE. This highlights its importance as a key exploration target for future mining endeavors focused on these essential elements.

Leveraging machine learning to extract insights and spatial patterns from hydrogeochemical datasets for major groundwater regions in the UAE

Groundwater samples were collected from 45 wells across three regions in the United Arab Emirates (UAE)—Jabel Hafeet, Fujairah, and Ras Al Khaimah; their major-ion chemistry and radon activity were analyzed to characterize hydrogeochemical facies. Machine learning (ML) techniques were employed to impute the missing chloride and sulfate concentrations for any missing samples. In this regard, the best models accuracy wise were optimized Random Forest and Extra-trees. The resultant complete dataset was subjected to unsupervised K-means clustering. Mapping the clusters using GeoZ library revealed distinct spatial patterns related to different geological settings. Most Fujairah and Ras Al Khaimah samples clustered together, indicating aquifer similarity, while the Jabel Hafeet samples clustered separately. Several Jabel Hafeet surface water samples were clear outliers. Within the clusters, radon exhibited variation related to groundwater source and could be a useful environmental tracer. The study demonstrates that machine learning could be used to extract meaningful information from incomplete geoscience data. Major findings were the hydrogeochemical similarities between the Fujairah and Ras Al Khaimah aquifers and their differences with the Hafeet aquifer, identification of the Jabel Hafeet surface water samples, and utility of radon in environmental tracing. This research provides valuable insights into major UAE aquifers and the ability of artificial intelligence to boost the value of imperfect datasets.

Effect of Heat Treatment on the Microstructure and Tensile Properties of CU‐Bearing Steel Rods

ABSTRACTThe U.S. automotive tire industry consumes more than 3.6 million tons of materials to manufacture 300 million tires annually. To ensure materials‐related sustainability, it is important to enable the use of a greater recycling rate of steel scrap in the manufacturing of new tires. However, impurities like copper compromise the performance of steel wires that are cold‐drawn from ~5.5 mm diameter rods. This article investigates copper precipitation in sensitized steel wire rods and their mechanical properties subjected to varying heat treatment procedures. The amount of copper precipitated in sensitized zones as a function of Cu concentration in the steel samples is quantified using scanning electron microscopy. Preliminary findings indicate that an increase in cooling rate during the heat treatment procedure increases the amount of Cu precipitated in the sensitized zones and increases the geometric footprint of the sensitized zones. Tensile tests on heat‐treated 5 mm‐diameter wire rods showed a higher strain and slightly lower peak stress as the cooling rate decreased. This research thus contributes to a better understanding of the influence of Cu concentration on the microstructure and performance of steel wires used in tire manufacturing, thereby allowing for the enhanced use of scrap steel.

A Cell‐Compatible Polyethylene Glycol Sensor for Dual‐Mode Cu2+ Tracking: In Vivo Biosensing and Smartphone‐Based High‐Sensitivity Detection In Vitro

ABSTRACTPolyethylene glycol (PEG) plays a key component in functional materials due to its exceptional water solubility, biocompatibility, and chemical versatility. However, the absence of π‐conjugation in PEG's molecular structure prevents fluorescence emission, limiting its utility in fluorescent material applications. In this study, a novel PEG‐based sensor (P) was synthesized by covalently linking PEG to a coumarin fluorophore. P exhibited significantly enhanced green fluorescence compared to unmodified PEG. It allows rapid (~5 s), highly sensitive, and specific copper (Cu2+) detection through fluorescence quenching, achieving 97.40% quenching efficiency with a 0.52 nM detection limit. Nuclear magnetic resonance (1H NMR) titration, inductively coupled plasma mass spectrometry (ICP‐MS), and density functional theory (DFT) calculations confirmed the detection mechanisms, revealing the P–Cu2+ interaction mechanism. Furthermore, fluorescence imaging results demonstrated that P exhibited a fluorescence quenching response upon the addition of Cu2+ to HeLa cells, providing a potential new method for detecting Cu2+ in vivo. In addition, the incorporation of a smartphone color recognition application enables approximate quantification of Cu2+ concentrations in test samples. This approach not only extends PEG's applicability in sensing and smart technology integration but also offers an efficient, low‐cost solution for on‐site Cu2+ detection.

High-resolution fecal pharmacokinetic modeling in mice with orally administered antibiotics

High-resolution fecal pharmacokinetics are crucial for optimizing therapeutic design and evaluating gastrointestinal motility. However, empirical studies with detailed fecal concentration over time data remain limited. This study aims to characterize fecal pharmacokinetics through high-frequency sampling and parallelized fecal concentration quantification, establishing a simple pharmacokinetics model with physiologically interpretable parameters. We quantified vancomycin concentrations in fecal samples collected at a minimum interval of 4 hours from C57BL/6N mice following a single oral administration of either a low (1 mg/mL) or high (20 mg/mL) dose. Fecal concentrations gradually increased and exhibited an exponential decay, leading to the development of a compartmental model with an absorption phase. This simple model accurately fit the experimental data and provided physiological explanations for intra- and inter-individual pharmacokinetics variability. The results suggest that inter-individual differences in pharmacokinetics are attributable to fecal elimination capacity, which may be influenced by drug dosage via changes in gastrointestinal motility. Since the model predicts antibiotic concentrations within the gastrointestinal tract, it can be applied to fundamental studies investigating the effects of antibiotics on the gut microbiome and gastrointestinal motility.

Prediction of Total Microelement and Total Heavy Metal Contents of Agricultural Soils of the Central-Southern Anatolian Region Using Artificial Intelligence: A Pilot Study

The estimation of total microelement and heavy metal concentrations in soil samples taken from the Central-Southern Anatolian Region of Turkiye was conducted using artificial intelligence models. The accurate prediction of microelement and heavy metal contents obtained from the soil is of great importance for agricultural productivity and environmental health. A total of 62 soil samples were analyzed for Boron (B), Iron (Fe), Zinc (Zn), Manganese (Mn), Copper (Cu), Cadmium (Cd), Chromium (Cr), Nickel (Ni), and Lead (Pb). The artificial intelligence models used in this study were Random Forest (RF), Gradient Boosting (GB), and Support Vector Regressor (SVR). Model performance was evaluated based on Mean Absolute Error (MAE), Mean Squared Error (MSE), and R² scores. The best performance was achieved for Boron (B) and Copper (Cu). In the case of Boron (B), the GB model provided the best results (MAE: 4.89, MSE: 28.01, R²: 0.55), while the RF model showed the highest performance for Copper (Cu) predictions (MAE: 3.20, MSE: 16.80, R²: 0.75). The results indicate that the artificial intelligence models used in this study hold promising potential for the prediction of microelement and heavy metal concentrations in soil samples.

Aurelia aurita as a Model for Ecotoxicologically Assessing Food Additives: 2-Methyl-1-phenylpropan-2-ol and 1-Phenylethan-1-ol

Industry currently generates numerous substances, such as food additives, whose environmental impacts, particularly in marine environments, remain inadequately assessed. This study employed Aurelia aurita for the first time as a model organism to evaluate the toxicity of such compounds. The main goal was to evaluate the toxicity of two food additives, 2-methyl-1-phenylpropan-2-ol (S1) and 1-phenylethan-1-ol (S2), on A. aurita ephyrae, comparing the results with other organisms representing different trophic levels, specifically the alga Phaeodactylum tricornutum and the crustacean Artemia salina. Acute toxicity tests were conducted on each organism. In A. aurita, S1 exhibited high toxicity (LC50 ≈ 10 mg/L), while S2 had lower toxicity (LC50 ≈ 80 mg/L). The pulsation frequency data for A. aurita revealed that S1 initially increased the pulsation rates at lower concentrations (maximum at 10 mg/L), followed by a significant decrease at higher concentrations. Conversely, S2 showed a steady decrease in pulsation rates up to 10 mg/L, with a slight increase at concentrations of 15, 20, and 25 mg/L. The results demonstrate varying sensitivities to the toxic effects of the two compounds across different trophic levels, with A. aurita ephyrae being the most sensitive. This suggests the potential efficacy of jellyfish as novel ecotoxicological models due to their heightened sensitivity, enabling the detection of lower contaminant concentrations in test samples. Further research is required to enhance the efficiency of ecotoxicological assays using this model.

Bioanalytically validated potentiometric method for determination of bisphenol A: Application to baby bibs, pacifiers, and Teethers’ saliva samples

Abstract A bioanalytically validated potentiometric method was proposed for the assay of bisphenol A (BPA) using a MWCNT-modified graphite ion selective electrode. The sensitivity of measurements was significantly enhanced by using MWCNTs as an ion-to-electron transducer layer. A precise linear analytical curve was developed to determine BPA in a specified linear range of 10,000–0.01 μmol·L−1, with a detection limit of 0.000104 μmol·L−1. After confirming that the results obtained with the proposed potentiometric sensor are not affected by the presence of possible interferents, the method was successfully applied to determine BPA concentration in different saliva samples, giving good accuracy and precision.

Hydro-geochemical assessment of the Nubian sandstone aquifer system for sustainable use in the western desert, Egypt

The vast Western Desert of Egypt may appear as an endless expanse of barren sand and rocky plateaus, but beneath this seemingly lifeless landscape lies one of the most significant water resources in North Africa i.e. the Nubian Sandstone Aquifer System (NSAS). Covering large portions of Egypt, Sudan, Chad, and Libya, the NSAS is a vast underground reservoir that plays a pivotal role in the water supply of Egypt's arid regions. The present study re-evaluates the hydro geochemical conditions of the Nubian Sandstone Aquifer, focusing on water quality and its suitability for various uses. Hydro chemical analysis of 350 groundwater wells across the region involved the assessment of physical and chemical properties, Results indicate that most samples EC, TDS, pH, and concentrations of major anions and cations generally conform to (WHO) standards for drinking water. The high content of iron in the groundwater samples of the NSAS may be attributed to the iron-oxide minerals (Hematite and Magnetite) which is rich in the rocks of the western desert. Therefore, water treatment is necessary to reduce iron concentrations to levels suitable for drinking. A hydro-geochemical evaluation was carried out depending on statistical analyses. Additionally, Piper, and Schoeller diagrams were also used. Factor analysis revealed key factors contributing to groundwater quality variance across different regions, accounting for 58.1% in Bahariya, 46.73% in Farafra, 75.6% in Dakhla, 38.6% in Kharga, and 45.6% in Siwa. This study found that over-exploitation of groundwater will cause deterioration of groundwater quality due to over-pumping and therefore, the Nubian sandstone aquifer should be withdrawn based on the safe yield calculation in the aquifer.

Determination of Plant Nutrition Status of Corn in Serpentine Soils with Different Diagnosing Methods

ABSTRACT Serpentine soils have challenging conditions for plant growth as a result of diverse factors. The main purpose of this investigation was to reveal the nutritional status of plants in serpentine soils. For this purpose, corn was selected as the test plant and grown in an area in the Eastern Mediterranean Basin of Türkiye by applying different doses of nitrogen (0, 120, 240, 360 kg N/ha), phosphorus (0, 80 kg P/ha), and potassium (0, 120 kg K/ha) for two years. Nutrient concentrations in leaf samples collected at various growth stages were analyzed and evaluated using the Sufficiency Range Approach (SRA) and the Diagnosis and Recommendation Integrated System (DRIS). The SRA method indicated that adequate levels were achieved with nitrogen doses of 360 kg/ha and phosphorus doses of 80 kg/ha, whereas adequate concentrations were not attained with potassium applications. Additionally, nitrogen and phosphorus applications have also resulted in positive DRIS indices and improved Nutritional Balance Index (NBI) values, resulting in high yields. Furthermore, DRIS analysis revealed that potassium was the yield limiting nutrient despite the application of potassium in the high-yielding population. Both diagnostic methods indicate that there is a problem with potassium nutrition in serpentinite soils. It is estimated that the underlying factor contributing to the issues observed in potassium nutrition is associated with cation imbalances, where the magnesium to potassium ratio exceeds optimal cation ratios. This was statistically supported by the negative correlation (−0.416** and −0.613**, respectively) determined between magnesium and potassium levels within the foliage for both years.

Kasur's Silent Threat: Heavy Metal Intrusion In Meat Supplies

Researchers have studied heavy metals within mutton specimens collected from Kasur, which exhibits extensive industrial operations, especially leather tanning operations. A study was conducted to analyze chromium (Cr) and nickel (Ni) concentrations in mutton samples from five different locations throughout Kasur using atomic absorption spectrophotometry. International guidelines were used to evaluate results. The tested samples contained chromium (0.885±1.252 to 5.525±2.157 mg/L) and nickel (0.41±0.580 to 3.00±0.707 mg/L) levels that surpassed the international safety standards. The highest concentrations were detected in samples from Shehzad Road (Cr) and Rao Khan Wala (Ni). The study showed that heavy metal pollutants had infiltrated meat products sold in Kasur markets because of industrial pollution. Public health risks become substantial because Cr and Ni showed constant elevation across all sampling points. This research highlights the urgent requirement to establish better environmental monitoring systems and enforce strict industrial waste management and complete food safety protocols for Pakistan's industrial areas.

Growth rates and response to nutrient variability in the green tide forming alga Ulva stenophylloides from Algarrobo Bay, Chile.

Growth rates and response to nutrient variability in the green tide forming alga Ulva stenophylloides from Algarrobo Bay, Chile.

Honey and bee pollen as bioindicators of mercury and thallium exposure in Kosovo

Honey and pollen are nutritious natural foods, but their contamination risk has increased, potentially turning them into health hazards. In Kosovo, data on mercury (Hg) and thallium (Tl) levels in honey and pollen are limited, especially across the entire territory. This study used a dense sampling grid and Inductively Coupled Plasma–Mass Spectrometry (ICP-MS) to assess Hg and Tl concentrations in honey and pollen samples from across Kosovo. Results revealed significant spatial variation. Median concentrations in honey were 0.014 mg/kg for Hg and 0.001 mg/kg for Tl; in pollen, 0.02 mg/kg Hg and 0.0013 mg/kg Tl. Maximum values reached 0.025 mg/kg Hg and 0.05 mg/kg Tl in honey, and 0.1 mg/kg Hg and 0.07 mg/kg Tl in pollen. Interestingly, the highest levels did not always align with known pollution sources, as confirmed by principal component analysis (PCA). Risk assessment based on a daily intake of 20g showed that the Hazard Index (HI) exceeded 1 (unsafe threshold) in several samples: honey from Zhegër-Gjilan, Janjevë-Lipjan, and Artanë (Novobërdë); and pollen from Voksh-Deçan and Artanë (Novobërdë). These findings highlight potential health risks and the need for regular monitoring of these widely consumed natural products.

Development and validation of a LC-MS/MS method for the simultaneous determination of simnotrelvir and ritonavir in human serum and bronchoalveolar lavage fluid

“Xiannuoxin” (simnotrelvir/ritonavir) is a novel anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) drug developed in China, which plays an antiviral role by inhibiting 3C-like protease (3CLpro). At present, it has been put into clinical use, while a simple, accurate and sensitive detection method is urgently needed for the quantification of simnotrelvir/ritonavir in human serum and bronchoalveolar lavage fluid (BALF) to ensure safe and efficacious antiviral therapeutics. In this study, we developed a liquid chromatography tandem mass spectrophotometry (LC-MS/MS) method for the simultaneous determination of simnotrelvir, ritonavir and urea concentrations in human serum and BALF samples. Prior to LC-MS/MS analysis, a user-friendly, one-step pre-analytical process was conducted, followed by a rapid chromatographic run lasting 3 min. This was then succeeded by positive and negative electrospray ionization and detection using a triple quadrupole tandem mass spectrometer in the multiple reaction monitoring mode. Subsequently, the LC-MS/MS method underwent a comprehensive validation in aspects such as sensitivity (LoQs of 2.5 ng/mL, 0.1 ng/mL, and 1 μg/mL for simnotrelvir, ritonavir and urea), linearity, carryover, precision, trueness (recovery rates of simnotrelvir, ritonavir and urea were between 85 and 115%), matrix effect (within 85-115%) and stability (stable for 72 h at room temperature). The validation results demonstrated that this LC-MS/MS method was robust and reliable. Notably, we can use the urea dilution correction method to calculate the concentrations of simnotrelvir and ritonavir in epithelial lining fluid (ELF), which is of great significance for evaluating the effectiveness and safety of antiviral drug treatment.Graphical

Comparative phytochemical analysis and bioactive potential of white and black garlic

The phenolic and organosulfur compound profiles of black garlic samples produced at different temperatures, humidity and durations were determined using liquid chromatography-electrospray tandem mass spectrometry (LC–ESI-MS/MS) and their bioactive properties were compared with those of the white garlic samples. The total concentrations of the phenolic compounds in the white (fresh) garlic samples were observed at 24.15 mg/kg and it was found that caffeic and ferulic acids, epicatechin, apigenin and p-hydroxybenzoic acid were dominant. Resveratrol, which is present in trace amounts in fresh garlic, has shown a significant increase in black garlic. Allicin was found to be the predominant organosulfur compound in white garlic, whereas S-allyl-L-cysteine was dominant in black garlic. Antimicrobial activity and minimum inhibitory concentration of the garlic samples were evaluated on two test microorganisms (Staphylococcus auereus and Escherichia coli). The effects of the garlic extracts on cancer cell lines (A549, HuH-7, MCF-7, HCT-116) were also assessed. The anticancer effect of the black garlic extracts was observed at higher concentrations than the white garlic water extracts, but the white garlic and black garlic methanolic extracts were effective on all cancer cell lines within the concentration range used in the study.

Comparison of glucose concentrations in samples frozen for varying durations and subjected to freeze-thaw cycles using 2 assay systems

Comparison of glucose concentrations in samples frozen for varying durations and subjected to freeze-thaw cycles using 2 assay systems

Deep Learning Driven Volatile Organic Compounds Analysis for Lung Cancer Detection Using HC-PCF and Convolutional Neural Networks

A Volatile Organic Compounds (VOC) detection system for lung cancer diagnosis through deep learning (DL) technology is implemented in a special Hollow-Core Photonic Crystal Fibre (HC-PCF) sensor platform. COMSOL Multiphysics is used to simulate the HC-PCF. A hexagonal lattice structure of silica material with 1 μm pitch dimensions and 0.5 μm air hole diameters allow for exceptional light guidance and VOC interaction when detecting exhaled breath components. The sensor achieves a remarkable refractive index sensitivity of 920 nm/RIU for detecting cancerous and non-cancerous VOC profiles. The refractive index measurements of lung cancer-related VOC samples fell within 1.380 to 1.392, while VOC samples from healthy patients ranged from 1.350 to 1.360. Sensor spectral response data processing relied on a Convolutional Neural Network (CNN) model that was trained to distinguish different VOC signature patterns. When applied to a dataset of 1,200 breath samples consisting of 600 cancer-positive and 600 healthy specimens, the CNN architecture reached a 96.3% overall classification accuracy combined with 94.7% sensitivity and 97.8% specificity. Full Text: PDF References S. Sharma, L. Tharani, "Photonic Crystal Fiber Sensor Design for Enhanced Tumor Detection: Structural Optimization and Sensitivity Analysis", Photonics Lett. Poland 16(2), 25 (2024). CrossRef A. Yasli, "Cancer Detection with Surface Plasmon Resonance-Based Photonic Crystal Fiber Biosensor", Plasmonics 16, 1605 (2021). CrossRef S. Sharma, S. Das, C.S. Shieh et al. "Design and Numerical Analysis of a Gold-Coated Photonic Crystal Fiber Sensor for Metabolic Disorder Detection with Deep Learning Assistance", Plasmonics (2025). CrossRef N. Ayyanar, G.T. Raja, M. Sharma, D.S. Kumar, "Photonic Crystal Fiber-Based Refractive Index Sensor for Early Detection of Cancer", IEEE Sensors Journal 18(17), 7093 (2018). CrossRef S. Sharma, L. Tharani, "Photonics for AI and AI for photonics integration : Materials and characteristics", J. Information and Optimization Sciences 45(3), 805 (2024). CrossRef M. Babińska, A. Władziński, "Enhanced Sensitivity of Absorption Spectroscopy Glucose Detection by Machine Learning", Photonics Lett. Poland 17(1), 16 (2025). CrossRef M. Babińska, A. Władziński, T. Talaśka, M. Szczerska, "Machine Learning Enhanced Optical Fiber Sensor For Detection Of Glucose Low Concentration In Samples Mimicking Tissue", Photonics Lett. Poland 17(1), 20 (2025). CrossRef

Quantitative Risk Assessment (QRA) Prenatal Mercury Exposure of Whitening Cosmetic and Infant Neurodevelopmental Risk

ntroduction: Mercury compounds are divided into elemental mercury, inorganic mercury, and organic mercury. Exposure to mercury through both direct and indirect pathways can have serious impacts on environmental and health issues, especially sensitive populations such as pregnant women, infants, and children. According to the Global Nielsen institution, 45% of women in Indonesia use whitening cosmetics. It should be noted that the content of whitening cosmetics contains mercury and its derivatives. Pregnant women are exposed to mercury through the use of cosmetics and the consumption of seafood. The impact caused if accumulated in the placenta will cause neonatal neurodevelopmental risk. This study aimed to analysed the quantitative risk assessment of prenatal mercury exposure and neurodevelopmental risk. Methods: The study design is an observational analytic with a cross-sectional study. Statistical analysis of Hg concentration in scalp hair samples was performed using SPSS software. The correlation between Hg concentration in scalp hair and skin whitening cosmetics was determined by chi-square correlation analysis, with p &lt; 0.05 considered significant. The quantitative risk assessment (QRA) method analyses hazard identification, assessment exposure, hazard characterization, and risk characterization. The sample used was 20 pregnant women and 20 babies. The mercury concentration in the scalp hair of pregnant women and their babies will be examined in the BBLK Laboratory. Results: The results of this study indicate that hazard identifications were mercury concentrations in the scalp hair of pregnant women is average of 7.04 (range 0.18-41.70 ppm, the mercury concentration in scalp hair is average of 0.71 (range 0.24-1.68), mercury whitening cosmetic is average 17.34 (range 3.5-86.0 ppm). Hazard characteristics are divided according to HBM 3 categories: normal at 60%, alert at 15%, and high at 25% for mercury concentration in pregnant women. In comparison, the baby concentration is divided into two categories: normal 70% and alert 30%. Risk characteristics found that around nine people, or 45% of babies, experienced the risk of neurodevelopmental symptoms, and mothers exposed to mercury had an impact on their babies. Conclusion: This study highlights the occurrence of Hg in skin-whitening cosmetics and the accumulation of Hg in the human body through their application. There is a correlation between mercury in pregnant women's scalp hair and the mercury concentration in their babies. Babies exposed to mercury with alert concentration levels have a neurodevelopmental risk. Therefore, it is better not to use whitening cosmetics during prenatal periods because mercury exposure can affect the baby.

Changes of Cytokines in Aqueous Humor of Retinoblastoma with Intravitreal Melphalan.

To explore the levels of cytokines in the aqueous humor (AH) of patients diagnosed with retinoblastoma (RB) who received intravitreal melphalan (IVM). A total of 28 eyes from 27 patients diagnosed with RB who received IVM were enrolled, along with 14 eyes from 14 patients with congenital cataracts as controls. The clinical features of the patients were recorded before and after IVM. 27 cytokines concentrations in AH samples collected from these patients were measured by a suspension array system. The levels of IL-8, IL-17A, Eotaxin, G-CSF, and PDGF-BB increased significantly following IVM administration in RB patients (p<0.05). Post IVM treatments, the levels of IL-5, IL-8, IL-9, IL-12p70, IL-15, IL-17A, G-CSF, IP-10, MIP-1α, PDGF-BB, TNF-α, and VEGF were higher in eyes that developed ocular toxicity than those eyes didn't (p<0.05). The ocular toxicity group received a higher cumulative dose and mean per dose of intravitreal melphalan. The elevated cytokines subsequent to IVM were associated with the number of treatments and cumulative dose of intravitreal melphalan. The results revealed a notable increase in cytokine levels subsequent to IVM interventions. Elevated cytokine concentrations in the eyes of RB patients post-IVM may be associated with ocular toxicities.

Hair analysis as a non-invasive method for assessing the exposure of wildlife to per- and poly-fluoroalkyl substances (PFAS).

Hair analysis as a non-invasive method for assessing the exposure of wildlife to per- and poly-fluoroalkyl substances (PFAS).