{"results":[{"id":"ss_0baf42065989ef74ed2938de79e33af239f8c29b","title":"Casarett and Doull's Toxicology. The Basic Science of Poisons","authors":null,"abstract":"","source":"Semantic Scholar","year":1981,"language":"en","subjects":null,"doi":"10.1136/pgmj.57.666.271","url":"https://www.semanticscholar.org/paper/0baf42065989ef74ed2938de79e33af239f8c29b","pdf_url":"https://pmj.bmj.com/content/postgradmedj/57/666/271.1.full.pdf","is_open_access":true,"citations":2795,"published_at":"","score":80},{"id":"ss_f838288bc89125f6c775681c0e43d33f2075e9b3","title":"Casarett and Doull's toxicology: The basic science of poisons","authors":[{"name":"J. Pickrell"}],"abstract":"","source":"Semantic Scholar","year":1996,"language":"en","subjects":["Biology","Engineering"],"doi":"10.1016/S0378-4274(96)90054-5","url":"https://www.semanticscholar.org/paper/f838288bc89125f6c775681c0e43d33f2075e9b3","is_open_access":true,"citations":1040,"published_at":"","score":80},{"id":"arxiv_2603.29608","title":"Learning Diagnostic Reasoning for Decision Support in Toxicology","authors":[{"name":"Nico Oberländer"},{"name":"David Bani-Harouni"},{"name":"Tobias Zellner"},{"name":"Nassir Navab"},{"name":"Florian Eyer"},{"name":"Matthias Keicher"}],"abstract":"Acute poly-substance intoxication requires rapid, life-saving decisions under substantial uncertainty, as clinicians must rely on incomplete ingestion details and nonspecific symptoms. Effective diagnostic reasoning in this chaotic environment requires fusing unstructured, non-medical narratives (e.g. paramedic scene descriptions and unreliable patient self-reports or known histories), with structured medical data like vital signs. While Large Language Models (LLMs) show potential for processing such heterogeneous inputs, they struggle in this setting, often underperforming simple baselines that rely solely on patient histories. To address this, we present DeToxR (Decision-support for Toxicology with Reasoning), the first adaptation of Reinforcement Learning (RL) to emergency toxicology. We design a robust data-fusion engine for multi-label prediction across 14 substance classes based on an LLM finetuned with Group Relative Policy Optimization (GRPO). We optimize the model's reasoning directly using a clinical performance reward. By formulating a multi-label agreement metric as the reward signal, the model is explicitly penalized for missing co-ingested substances and hallucinating absent poisons. Our model significantly outperforms its unadapted base LLM counterpart and supervised baselines. Furthermore, in a clinical validation study, the model indicates a clinical advantage by outperforming an expert toxicologist in identifying the correct poisons (Micro-F1: 0.644 vs. 0.473). These results demonstrate the potential of RL-aligned LLMs to synthesize unstructured pre-clinical narratives and structured medical data for decision support in high-stakes environments.","source":"arXiv","year":2026,"language":"en","subjects":["cs.CL"],"url":"https://arxiv.org/abs/2603.29608","pdf_url":"https://arxiv.org/pdf/2603.29608","is_open_access":true,"published_at":"2026-03-31T11:26:45Z","score":70},{"id":"arxiv_2602.06980","title":"Potential Role of Agentic Artificial Intelligence in Toxicologic Pathology","authors":[{"name":"Nasir Rajpoot"},{"name":"Richard Haworth"},{"name":"Xavier Palazzi"},{"name":"Alok Sharma"},{"name":"Manu Sebastian"},{"name":"Stephen Cahalan"},{"name":"Dinesh S. Bangari"},{"name":"Radhakrishna Sura"},{"name":"James Hartke"},{"name":"Marco Tecilla"},{"name":"Krishna Yekkala"},{"name":"Simon Graham"},{"name":"Dang Vu"},{"name":"David Snead"},{"name":"Mostafa Jahanifar"},{"name":"Adnan Khan"},{"name":"Erio Barale-Thomas"}],"abstract":"As the volume and complexity of nonclinical toxicology studies continue to increase, toxicologic pathology reporting faces persistent challenges, including fragmented sources of data (e.g., histopathology images, clinical pathology and other study data, adverse effects database, mechanistic literature), variable reporting timelines and heightened regulatory expectations. This white paper examines the emerging role of agentic artificial intelligence (AI) in addressing these issues through coordinated workflow orchestration, data integration, and pathologist-in-the-loop report generation. Based on a closed-door roundtable held during the 2025 Society of Toxicologic Pathology (STP) Annual Meeting and follow-on discussions, this paper synthesizes the perspectives of leading toxicologic pathologists, toxicologists, and AI developers. It outlines the key pain points in current reporting workflows, identifies realistic near-term use cases for agentic AI, and describes major adoption barriers including requirements for transparency, validation, and organizational readiness. A phased adoption roadmap and pilot design considerations are proposed to help support responsible evaluation and deployment of agentic AI system in nonclinical settings. The paper concludes by emphasizing the need for coordinated efforts across pharmaceutical organizations, CROs, academia, and regulators to establish shared standards, benchmarks, and governance frameworks that will lead to safe, transparent, and trustworthy integration of AI into toxicologic science.","source":"arXiv","year":2026,"language":"en","subjects":["cs.CY"],"url":"https://arxiv.org/abs/2602.06980","pdf_url":"https://arxiv.org/pdf/2602.06980","is_open_access":true,"published_at":"2026-01-26T16:59:10Z","score":70},{"id":"doaj_10.1002/efd2.70114","title":"Spectroscopic Methods of Edible Flower Authentication and Quality Control for Food Applications","authors":[{"name":"Fidele Benimana"},{"name":"Christopher Kucha"},{"name":"Anupam Roy"},{"name":"Anand Mohan"}],"abstract":"ABSTRACT The global demand for edible flowers has increased due to their diverse applications in food, nutraceuticals, and the medical field. However, issues of species identification, adulteration, contamination, and quality necessitate the use of advanced methods to authenticate product quality for edible flowers. Conventional methods are expensive, time‐consuming, and require highly skilled personnel and technical expertise. Spectroscopic methods, including Fourier transform infrared, near‐infrared, and Raman spectroscopy, are efficient, fast, and non‐destructive, providing rapid insight into the chemical structure and authenticity of edible flowers. This review systematically summarizes the recent advances in spectroscopic methods for authenticating edible flowers, including the detection of chemical changes and ensuring product integrity. The primary goal is to examine the applications of spectroscopic techniques for assessing quality changes in edible flowers during processing for food applications. Spectroscopic techniques, such as FT‐IR, NIR, and Raman spectroscopy, are rapid, accurate, and non‐destructive alternatives for authenticating the composition and quality of edible flowers. These methods enable the detection of bioactive compounds, differentiation of species, and identification of adulterants with minimal sample processing. Furthermore, chemometric models enhance data analysis, allowing for automated classification and real‐time quality monitoring of edible flowers.","source":"DOAJ","year":2026,"language":"","subjects":["Food processing and manufacture","Toxicology. Poisons"],"doi":"10.1002/efd2.70114","url":"https://doi.org/10.1002/efd2.70114","is_open_access":true,"published_at":"","score":70},{"id":"doaj_Self+directed+learning+%E2%80%93+preparing+current+learners+for+future+learners+%E2%80%93+issues+and+concerns+in+Indian+context+%E2%80%93+Part+1","title":"Self directed learning – preparing current learners for future learners – issues and concerns in Indian context – Part 1","authors":[{"name":"N.K. Gupta"},{"name":"Ayesha Ahmad"},{"name":"Uma Gupta"}],"abstract":"Education is derived 'Educatum' a Latin word, combination of 'e' and 'duco'. 'e' means 'out of' or 'from inside' and 'duco' means 'to lead out' - means to lead out of what is there inside the mind and soul of learner. Medical education has undergone significant changes in the last few decades due to the technological explosion, and medical students need to be exposed in appropriate and calculated manner at that stage of education","source":"DOAJ","year":2026,"language":"","subjects":["Therapeutics. Pharmacology","Toxicology. Poisons"],"url":"https://ajms.alameenmedical.org/ArticlePDFs/2%20AJMS%20V19.N1.2026%20p%201-3.pdf","is_open_access":true,"published_at":"","score":70},{"id":"arxiv_2503.00002","title":"Failure of Optimal Design Theory? A Case Study in Toxicology Using Sequential Robust Optimal Design Framework","authors":[{"name":"Elvis Han Cui"},{"name":"Michael Collins"},{"name":"Jessica Munson"},{"name":"Weng Kee Wong"}],"abstract":"This paper presents a quasi-sequential optimal design framework for toxicology experiments, specifically applied to sea urchin embryos. The authors propose a novel approach combining robust optimal design with adaptive, stage-based testing to improve efficiency in toxicological studies, particularly where traditional uniform designs fall short. The methodology uses statistical models to refine dose levels across experimental phases, aiming for increased precision while reducing costs and complexity. Key components include selecting an initial design, iterative dose optimization based on preliminary results, and assessing various model fits to ensure robust, data-driven adjustments. Through case studies, we demonstrate improved statistical efficiency and adaptability in toxicology, with potential applications in other experimental domains.","source":"arXiv","year":2025,"language":"en","subjects":["stat.ME","stat.AP","stat.CO"],"url":"https://arxiv.org/abs/2503.00002","pdf_url":"https://arxiv.org/pdf/2503.00002","is_open_access":true,"published_at":"2025-02-11T02:37:11Z","score":69},{"id":"arxiv_2506.06518","title":"A Systematic Review of Poisoning Attacks Against Large Language Models","authors":[{"name":"Neil Fendley"},{"name":"Edward W. Staley"},{"name":"Joshua Carney"},{"name":"William Redman"},{"name":"Marie Chau"},{"name":"Nathan Drenkow"}],"abstract":"With the widespread availability of pretrained Large Language Models (LLMs) and their training datasets, concerns about the security risks associated with their usage has increased significantly. One of these security risks is the threat of LLM poisoning attacks where an attacker modifies some part of the LLM training process to cause the LLM to behave in a malicious way. As an emerging area of research, the current frameworks and terminology for LLM poisoning attacks are derived from earlier classification poisoning literature and are not fully equipped for generative LLM settings. We conduct a systematic review of published LLM poisoning attacks to clarify the security implications and address inconsistencies in terminology across the literature. We propose a comprehensive poisoning threat model applicable to categorize a wide range of LLM poisoning attacks. The poisoning threat model includes four poisoning attack specifications that define the logistics and manipulation strategies of an attack as well as six poisoning metrics used to measure key characteristics of an attack. Under our proposed framework, we organize our discussion of published LLM poisoning literature along four critical dimensions of LLM poisoning attacks: concept poisons, stealthy poisons, persistent poisons, and poisons for unique tasks, to better understand the current landscape of security risks.","source":"arXiv","year":2025,"language":"en","subjects":["cs.CR","cs.LG"],"url":"https://arxiv.org/abs/2506.06518","pdf_url":"https://arxiv.org/pdf/2506.06518","is_open_access":true,"published_at":"2025-06-06T20:32:43Z","score":69},{"id":"arxiv_2510.07192","title":"Poisoning Attacks on LLMs Require a Near-constant Number of Poison Samples","authors":[{"name":"Alexandra Souly"},{"name":"Javier Rando"},{"name":"Ed Chapman"},{"name":"Xander Davies"},{"name":"Burak Hasircioglu"},{"name":"Ezzeldin Shereen"},{"name":"Carlos Mougan"},{"name":"Vasilios Mavroudis"},{"name":"Erik Jones"},{"name":"Chris Hicks"},{"name":"Nicholas Carlini"},{"name":"Yarin Gal"},{"name":"Robert Kirk"}],"abstract":"Poisoning attacks can compromise the safety of large language models (LLMs) by injecting malicious documents into their training data. Existing work has studied pretraining poisoning assuming adversaries control a percentage of the training corpus. However, for large models, even small percentages translate to impractically large amounts of data. This work demonstrates for the first time that poisoning attacks instead require a near-constant number of documents regardless of dataset size. We conduct the largest pretraining poisoning experiments to date, pretraining models from 600M to 13B parameters on chinchilla-optimal datasets (6B to 260B tokens). We find that 250 poisoned documents similarly compromise models across all model and dataset sizes, despite the largest models training on more than 20 times more clean data. We also run smaller-scale experiments to ablate factors that could influence attack success, including broader ratios of poisoned to clean data and non-random distributions of poisoned samples. Finally, we demonstrate the same dynamics for poisoning during fine-tuning. Altogether, our results suggest that injecting backdoors through data poisoning may be easier for large models than previously believed as the number of poisons required does not scale up with model size, highlighting the need for more research on defences to mitigate this risk in future models.","source":"arXiv","year":2025,"language":"en","subjects":["cs.LG"],"url":"https://arxiv.org/abs/2510.07192","pdf_url":"https://arxiv.org/pdf/2510.07192","is_open_access":true,"published_at":"2025-10-08T16:25:05Z","score":69},{"id":"doaj_10.3390/jox15020043","title":"Green Carbon Dots from Pinecones and Pine Bark for Amoxicillin and Tetracycline Detection: A Circular Economy Approach","authors":[{"name":"Saheed O. Sanni"},{"name":"Ajibola A. Bayode"},{"name":"Hendrik G. Brink"},{"name":"Nils H. Haneklaus"},{"name":"Lin Fu"},{"name":"Jianping Shang"},{"name":"Hua-Jun Shawn Fan"}],"abstract":"Over the years, the abuse of antibiotics has increased, leading to their presence in the environment. Therefore, a sustainable method for detecting these substances is crucial. Researchers have explored biomass-based carbon dots (CDs) to detect various contaminants, due to their low cost, environmental friendliness, and support of a circular economy. In our study, we reported the synthesis of CDs using pinecones (PCs) and pinebark (PB) through a sustainable microwave method. We characterized the PCCDs and PBCDs using X-ray diffraction, Raman spectroscopy, Transmission Electron Microscope, and Fourier transform infrared, Ultraviolet-visible, and photoluminescence (PL) spectroscopy. The PCCDs and PBCDs were tested for the detection of amoxicillin (AMX) and tetracycline (TC). The results indicated that the sizes of the PCCDs and PBCDs were 19.2 nm and 18.39 nm, respectively, and confirmed the presence of the 002 plane of the graphitic carbon structure. They exhibited excitation wavelength dependence, good stability, and quantum yields ranging from 6% to 11%. PCCDs and PBCDs demonstrated “turn-off” detection for TC and AMX. The limits of detection (LOD) for TC across a broader concentration range were found to be 0.062 µM for PCCDs and 0.2237 µM for PBCDs. For AMX detection, PBCDs presented an LOD of 0.49 µM.","source":"DOAJ","year":2025,"language":"","subjects":["Therapeutics. Pharmacology","Toxicology. Poisons"],"doi":"10.3390/jox15020043","url":"https://www.mdpi.com/2039-4713/15/2/43","is_open_access":true,"published_at":"","score":69},{"id":"doaj_10.3390/jox15060208","title":"A Systematic Review on the Toxicology of European Union-Approved Triazole Fungicides in Cell Lines and Mammalian Models","authors":[{"name":"Constantina-Bianca Vulpe"},{"name":"Adina-Daniela Iachimov-Datcu"},{"name":"Andrijana Pujicic"},{"name":"Bianca-Vanesa Agachi"}],"abstract":"Triazole fungicides are widely used in agriculture but may pose risks to human health through occupational, accidental, or environmental exposure. This systematic review aimed to evaluate the toxicity of ten European Union-approved triazole fungicides in rodent models and cell lines. A total of 70 studies were included, reporting quantitative in vivo oral, dermal, or inhalation toxicity in mammals or quantitative in vitro cytotoxicity in human or mammalian cell lines; the exclusion criteria comprised publications not in English or not accessible. Literature searches were conducted in Web of Science, Google Scholar, and the Pesticide Properties DataBase (PPDB), and risk of bias in included studies was assessed using ToxRTool. Due to heterogeneity in study designs, reporting formats, and endpoints, data were synthesized descriptively. Quantitative endpoints included LD\u003csub\u003e50\u003c/sub\u003e/LC\u003csub\u003e50\u003c/sub\u003e values for in vivo studies and LOEC, IC\u003csub\u003e50\u003c/sub\u003e, LC\u003csub\u003e50\u003c/sub\u003e, and EC\u003csub\u003e50\u003c/sub\u003e values for in vitro studies, while mechanistic endpoints highlighted apoptosis, oxidative stress, genotoxicity, and endoplasmic reticulum stress. Difenoconazole and tebuconazole were the most extensively studied compounds, whereas several triazoles had limited data. The limitations included heterogeneity of data and incomplete reporting, which restrict cross-study comparisons. Overall, the findings provide a comprehensive overview of potential human health hazards associated with EU-approved triazole fungicides and highlight critical knowledge gaps. The review was registered in Open Science Framework.","source":"DOAJ","year":2025,"language":"","subjects":["Therapeutics. Pharmacology","Toxicology. Poisons"],"doi":"10.3390/jox15060208","url":"https://www.mdpi.com/2039-4713/15/6/208","is_open_access":true,"published_at":"","score":69},{"id":"arxiv_2407.13296","title":"Prediction intervals for overdispersed binomial endpoints and their application to toxicological historical control data","authors":[{"name":"Max Menssen"},{"name":"Jonathan Rathjens"}],"abstract":"For toxicology studies, the validation of the concurrent control group by historical control data (HCD) has become requirements. This validation is usually done by historical control limits (HCL), which should cover the observations of the concurrent control with a predefined level of confidence. In many applications, HCL are applied to dichotomous data, e.g. the number of rats with a tumor vs. the number of rats without a tumor (carcinogenicity studies) or the number of cells with a micronucleus out of a total number of cells. Dichotomous HCD may be overdispersed and can be heavily right- (or left-) skewed, which is usually not taken into account in the practical applications of HCL. To overcome this problem, four different prediction intervals (two frequentist, two Bayesian), that can be applied to such data, are proposed. Based on comprehensive Monte-Carlo simulations, the coverage probabilities of the proposed prediction intervals were compared to heuristical HCL typically used in daily toxicological routine (historical range, limits of the np-chart, mean plus minus 2 SD). Our simulations reveal, that frequentist bootstrap calibrated prediction intervals control the type-1-error best, but, also prediction intervals calculated based on Bayesian generalized linear mixed models appear to be practically applicable. Contrary, all heuristics fail to control the type-1-error. The application of HCL is demonstrated based on a real life data set containing historical controls from long-term carcinogenicity studies run on behalf of the U.S. National Toxicology Program. The proposed frequentist prediction intervals are publicly available from the R package predint, whereas R code for the computation of the two Bayesian prediction intervals is provided via GitHub.","source":"arXiv","year":2024,"language":"en","subjects":["stat.AP"],"url":"https://arxiv.org/abs/2407.13296","pdf_url":"https://arxiv.org/pdf/2407.13296","is_open_access":true,"published_at":"2024-07-18T08:54:45Z","score":68},{"id":"arxiv_2403.16365","title":"Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion","authors":[{"name":"Hossein Souri"},{"name":"Arpit Bansal"},{"name":"Hamid Kazemi"},{"name":"Liam Fowl"},{"name":"Aniruddha Saha"},{"name":"Jonas Geiping"},{"name":"Andrew Gordon Wilson"},{"name":"Rama Chellappa"},{"name":"Tom Goldstein"},{"name":"Micah Goldblum"}],"abstract":"Modern neural networks are often trained on massive datasets that are web scraped with minimal human inspection. As a result of this insecure curation pipeline, an adversary can poison or backdoor the resulting model by uploading malicious data to the internet and waiting for a victim to scrape and train on it. Existing approaches for creating poisons and backdoors start with randomly sampled clean data, called base samples, and then modify those samples to craft poisons. However, some base samples may be significantly more amenable to poisoning than others. As a result, we may be able to craft more potent poisons by carefully choosing the base samples. In this work, we use guided diffusion to synthesize base samples from scratch that lead to significantly more potent poisons and backdoors than previous state-of-the-art attacks. Our Guided Diffusion Poisoning (GDP) base samples can be combined with any downstream poisoning or backdoor attack to boost its effectiveness. Our implementation code is publicly available at: https://github.com/hsouri/GDP .","source":"arXiv","year":2024,"language":"en","subjects":["cs.LG","cs.CR","cs.CV"],"url":"https://arxiv.org/abs/2403.16365","pdf_url":"https://arxiv.org/pdf/2403.16365","is_open_access":true,"published_at":"2024-03-25T02:03:38Z","score":68},{"id":"arxiv_2409.08509","title":"Exploiting Supervised Poison Vulnerability to Strengthen Self-Supervised Defense","authors":[{"name":"Jeremy Styborski"},{"name":"Mingzhi Lyu"},{"name":"Yi Huang"},{"name":"Adams Kong"}],"abstract":"Availability poisons exploit supervised learning (SL) algorithms by introducing class-related shortcut features in images such that models trained on poisoned data are useless for real-world datasets. Self-supervised learning (SSL), which utilizes augmentations to learn instance discrimination, is regarded as a strong defense against poisoned data. However, by extending the study of SSL across multiple poisons on the CIFAR-10 and ImageNet-100 datasets, we demonstrate that it often performs poorly, far below that of training on clean data. Leveraging the vulnerability of SL to poison attacks, we introduce adversarial training (AT) on SL to obfuscate poison features and guide robust feature learning for SSL. Our proposed defense, designated VESPR (Vulnerability Exploitation of Supervised Poisoning for Robust SSL), surpasses the performance of six previous defenses across seven popular availability poisons. VESPR displays superior performance over all previous defenses, boosting the minimum and average ImageNet-100 test accuracies of poisoned models by 16% and 9%, respectively. Through analysis and ablation studies, we elucidate the mechanisms by which VESPR learns robust class features.","source":"arXiv","year":2024,"language":"en","subjects":["cs.CV"],"url":"https://arxiv.org/abs/2409.08509","pdf_url":"https://arxiv.org/pdf/2409.08509","is_open_access":true,"published_at":"2024-09-13T03:12:58Z","score":68},{"id":"doaj_10.1016/j.toxrep.2023.12.010","title":"Anticancer and anti-angiogenic activities of mannooligosaccharides extracted from coconut meal on colorectal carcinoma cells in vitro","authors":[{"name":"Patthra Pason"},{"name":"Chakrit Tachaapaikoon"},{"name":"Waralee Suyama"},{"name":"Rattiya Waeonukul"},{"name":"Rong Shao"},{"name":"Molin Wongwattanakul"},{"name":"Temduang Limpaiboon"},{"name":"Chirapond Chonanant"},{"name":"Nipaporn Ngernyuang"}],"abstract":"Colorectal carcinoma (CRC) is one of the most common malignancies, though there are no effective therapeutic regimens at present. This study aimed to investigate the inhibitory effects of mannooligosaccharides extracted from coconut meal (CMOSs) on the proliferation and migration of human colorectal cancer HCT116 cells in vitro. The results showed that CMOSs exhibited significant inhibitory activity against HCT116 cell proliferation in a concentration-dependent manner with less cytotoxic effects on the Vero normal cells. CMOSs displayed the ability to increase the activation of caspase-8, –9, and –3/7, as well as the generation of reactive oxygen species (ROS). Moreover, CMOSs suppressed HCT116 cell migration in vitro. Interestingly, treatment of human microvascular endothelial cells (HMVECs) with CMOSs resulted in the inhibition of cell proliferation, cell migration, and capillary-like tube formation, suggesting its anti-vascular angiogenesis. In summary, the results of this study indicate that CMOSs could be a valuable therapeutic candidate for CRC treatment.","source":"DOAJ","year":2024,"language":"","subjects":["Toxicology. Poisons"],"doi":"10.1016/j.toxrep.2023.12.010","url":"http://www.sciencedirect.com/science/article/pii/S2214750023001488","is_open_access":true,"published_at":"","score":68},{"id":"doaj_10.1016/j.toxrep.2024.101778","title":"Synthesis and evaluation of amyloid beta peptide/Ruthenium III-based complex drugs as drug delivery and anticancer activity","authors":[{"name":"Rethinam Senthil"}],"abstract":"The development and characterization of anticancer complex drugs (ACD), specifically Amyloid Beta Peptide (ABP) - Ruthenium III (Ru III) - nivolumab (NB), were explored through analytical techniques. Fourier-transform infrared (FTIR) spectroscopy demonstrated the structural transformation of peptides from α-helical to β-sheet formations, aligning with amyloid fibril aggregation. Ruthenium (III) complex synthesis was confirmed through distinct absorption peaks in FTIR analysis. High-resolution scanning electron microscopy (HRSEM) revealed the fibrous and smooth morphology of ACD, while thermogravimetric analysis (TGA) confirmed the decomposition stages and stability of the ruthenium complexes. The encapsulation efficiency and in vitro release profile of nivolumab (NB) within ABP-RuIII-NB were investigated, showing a two-phase release over 40 h. Cytotoxicity studies using acridine orange and ethidium bromide staining techniques indicated significant apoptosis in human oral squamous cell carcinoma (OSCC) -treated cells. These findings highlight the potential of ABP-RuIII-NB as an effective cancer treatment with controlled drug release and high cytotoxicity against cancer cells.","source":"DOAJ","year":2024,"language":"","subjects":["Toxicology. Poisons"],"doi":"10.1016/j.toxrep.2024.101778","url":"http://www.sciencedirect.com/science/article/pii/S2214750024001616","is_open_access":true,"published_at":"","score":68},{"id":"doaj_Prevalence+of+vitamin+D+deficiency+in+PLHIV+and+its+relation+to+CD4+count+and+ART%3A+A+cross+sectional+study","title":"Prevalence of vitamin D deficiency in PLHIV and its relation to CD4 count and ART: A cross sectional study","authors":[{"name":"Himeshwari Verma"},{"name":"Devpriya Lakra"},{"name":"Vyom Agarwal"}],"abstract":"Introduction: HIV (Human Immunodeficiency Virus) continues to be a major global public health issue with no cure. Vitamin D is a fat-soluble hormone that is majorly involved in the classical function of calcium and phosphorus hemostasis and bone mineralization as well as non-classical functions of immune modulation in various viral and autoimmune diseases. A combination of both traditional risk factors, HIV- specific and antiretroviral therapy (ART)-specific contributors leave HIV-infected persons (PLHIV) at a greater risk for low 25-OH-Vitamin D levels and frank vitamin D deficiency. Aims and Setting: The current study was conducted to assess and characterize the prevalence of Vitamin D deficiency in PLHIV-on-ART attending a tertiary care hospital and assess the factors that may be affecting it. Methods: 95 PLHIV registered at an ART center were selected over a period of 6 months based on Inclusion and Exclusion criteria.  Flow cytometry estimation of CD4 count and ELISA based quantitative assessment of serum 25-OH Vitamin D3 were done along with detailed clinical examination. P\u003c0.05 was considered to be statistically significant. Results: About half of the PLHIV assessed were deficient in vitamin D. Severe vitamin D deficiency was noted in one-fourth of subjects. Serum vitamin D levels were significantly less in subjects on ZLN regime compared to TLE regime. No significant difference was found between vitamin D deficiency and duration of treatment, different treatment regimens or differing CD4 counts.  No significant association of serum levels of Vitamin D with duration of treatment or varying CD4 count was found. Conclusion: There is greater prevalence of subnormal levels of Vitamin D in PLHIV on ART. ZLN regime appears to have a negative impact on Vitamin D levels in comparison to TLE regimen. More research needs to be done to further evaluate the physiology of Vitamin D in PLHIV on ART.","source":"DOAJ","year":2024,"language":"","subjects":["Therapeutics. Pharmacology","Toxicology. Poisons"],"url":"http://ajms.alameenmedical.org/ArticlePDFs/16%20AJMS%20V17.N1.2024%20p%2090-95.pdf","is_open_access":true,"published_at":"","score":68},{"id":"doaj_10.11836/JEOM23355","title":"Survey and analysis on fertility status of female employees aged 22-35 years by industries","authors":[{"name":"Changyan YU"},{"name":"Jiarui XIN"},{"name":"Ming XU"},{"name":"Zhenxia KOU"},{"name":"Wenlan YU"},{"name":"Meibian ZHANG"},{"name":"Xuefei LI"}],"abstract":"BackgroundAs the population ages, there has been a growing focus on the decline in fertility. Research has identified age and fertility history as the primary influencing factors. Nevertheless, there is a deficiency in fundamental data regarding the fertility status among different industries. ObjectiveTo investigate the fertility status and influencing factors among female workers aged 22-35 years in different industries. MethodsFrom July 2020 to February 2021, a cross-sectional survey was conducted using a staged sampling approach. This survey specifically targeted 22-35-year-old married female workers with a history of pregnancy in industries such as education, healthcare, finance, and telecommunications, totaling 22903 participants. The survey encompassed industry, demographic characteristics, pregnancy history, time to pregnancy (TTP), and other influencing factors. The influencing factors of decline in fertility were identified by chi-square test and Cox proportional hazards regression. Subsequent industry-specific Cox proportional hazards regression models were used to compared fertility decline patterns across a spectrum of industries after selected influencing factors were adjusted. ResultsAmong the 22903 respondents, 19194 valid questionnaires were collected, with a valid recovery rate of 83.8%. The cumulative pregnancy rates (CRP) of 1-6 months and 1-12 months for the 22-35-year-old female workers were 67.23% and 91.33% respectively. The multivariate analysis showed that region, age, education level, personal annual income, housework time, coping style, gravidity, parity, and spontaneous abortion were influencing factors of fertility decline (P\u003c0.05). Female workers with ≥3 gravidities and ≥2 spontaneous abortions had a higher risk of fertility decline, with hazard ratios (HR) and associated 95% confidence interval (95%CI) of 0.633 (0.582, 0.688) and 0.785 (0.670, 0.921) respectively (P\u003c0.01). Compared to the education industry, the healthcare and finance industries showed a higher risk of fertility decline, with HR (95%CI) values of 0.876 (0.834, 0.920) and 0.909 (0.866, 0.954), respectively (P\u003c0.05). These two HR (95%CI) values remained statistically significant [0.899 (0.852, 0.948) and 0.882 (0.833, 0.934) respectively, P\u003c0.05)] after further adjustment with nine influencing factors such as region and age. ConclusionRegions, age, education level, personal annual income, housework time, coping style, pregnancy and childbirth times, and natural abortion times are influencing factors of fertility decline in female workers. Compared to the education industry, the healthcare and finance industries have a higher risk of declining fertility.","source":"DOAJ","year":2024,"language":"","subjects":["Medicine (General)","Toxicology. Poisons"],"doi":"10.11836/JEOM23355","url":"http://www.jeom.org/article/cn/10.11836/JEOM23355","is_open_access":true,"published_at":"","score":68},{"id":"crossref_10.1007/978-981-99-9283-6_216","title":"Arrow Poisons","authors":null,"abstract":"","source":"CrossRef","year":2024,"language":"en","subjects":null,"doi":"10.1007/978-981-99-9283-6_216","url":"https://doi.org/10.1007/978-981-99-9283-6_216","is_open_access":true,"published_at":"","score":68},{"id":"crossref_10.1007/978-981-99-9283-6_588","title":"Contact Poisons","authors":null,"abstract":"","source":"CrossRef","year":2024,"language":"en","subjects":null,"doi":"10.1007/978-981-99-9283-6_588","url":"https://doi.org/10.1007/978-981-99-9283-6_588","is_open_access":true,"published_at":"","score":68}],"total":800901,"page":1,"page_size":20,"sources":["arXiv","DOAJ","Semantic Scholar","CrossRef"],"query":"Toxicology. Poisons"}