GNUV201, a novel human/mouse cross-reactive and low pH-selective anti-PD-1 monoclonal antibody for cancer immunotherapy.

BACKGROUND: Several PD-1 antibodies approved as anti-cancer therapies work by blocking the interaction of PD-1 with its ligand PD-L1, thus restoring anti-cancer T cell activities. These PD-1 antibodies lack inter-species cross-reactivity, necessitating surrogate antibodies for preclinical studies, which may limit the predictability and translatability of the studies. RESULTS: To overcome this limitation, we have developed an inter-species cross-reactive PD-1 antibody, GNUV201, by utilizing an enhanced diversity mouse platform (SHINE MOUSE™). GNUV201 equally binds to human PD-1 and mouse PD-1, equally inhibits the binding of human PD-1/PD-L1 and mouse PD-1/PD-L1, and effectively suppresses tumor growth in syngeneic mouse models. The epitope of GNUV201 mapped to the "FG loop" of hPD-1, distinct from those of Keytruda® ("C'D loop") and Opdivo® (N-term). Notably, the structural feature where the protruding epitope loop fits into GNUV201's binding pocket supports the enhanced binding affinity due to slower dissociation (8.7 times slower than Keytruda®). Furthermore, GNUV201 shows a stronger binding affinity at pH 6.0 (5.6 times strong than at pH 7.4), which mimics the hypoxic and acidic tumor microenvironment (TME). This phenomenon is not observed with marketed antibodies (Keytruda®, Opdivo®), implying that GNUV201 achieves more selective binding to and better occupancy on PD-1 in the TME. CONCLUSIONS: In summary, GNUV201 exhibited enhanced affinity for PD-1 with slow dissociation and preferential binding in TME-mimicking low pH. Human/monkey/mouse inter-species cross-reactivity of GNUV201 could enable more predictable and translatable efficacy and toxicity preclinical studies. These results suggest that GNUV201 could be an ideal antibody candidate for anti-cancer drug development.

Assessment of skin sensitization potential of zinc oxide, aluminum oxide, manganese oxide, and copper oxide nanoparticles through the local lymph node assay: 5-bromo-deoxyuridine flow cytometry method.

The advent of nanotechnology has significantly spurred the utilization of nanoparticles (NPs) across diverse sectors encompassing industry, agriculture, engineering, cosmetics, and medicine. Metallic oxides including zinc oxide (ZnO), copper oxide (CuO), manganese oxide (Mn2O3), and aluminum oxide (Al2O3), in their NP forms, have become prevalent in cosmetics and various dermal products. Despite the expanding consideration of these compounds for dermal applications, their potential for initiating skin sensitization (SS) has not been comprehensively examined. An in vivo assay, local lymph node assay: 5-bromo-2-deoxyuridine-flow cytometry method (LLNA: BrdU-FCM) recognized as an alternative testing method for screening SS potential was used to address these issues. Following the OECD TG 442B guidelines, NPs suspensions smaller than 50 nm size were prepared for ZnO and Al2O3 at concentrations of 10, 25, and 50%, and Mn2O3 and CuO at concentrations of 5, 10, and 25%, and applied to the dorsum of each ear of female BALB/c mice on a daily basis for 3 consecutive days. Regarding the prediction of test substance to skin sensitizer if sensitization index (SI)≥2.7, all 4 NPs were classified as non-sensitizing. The SI values were below 2.06, 1.33, 1.42, and 0.99 for ZnO, Al2O3, Mn2O3, and CuO, respectively, at all test concentrations. Although data presented were negative with respect to adverse SS potential for these 4 NPs, further confirmatory tests addressing other key events associated with SS adverse outcome pathway need to be carried out to arrive at an acceptable conclusion on the skin safety for both cosmetic and dermal applications.

Oral subacute polypropylene microplastics administration effect on potential immunotoxicity in ICR mice.

Exposure to microplastics may be associated with damage of immune system. Polypropylene microplastics (PP-MPs) with a wide range of beneficial applications have not been extensively studied with respect to the immune system. The aim of this investigation is to examine the influence of two different sizes of PP-MPs (5.2 and 23.9 µm diameter) on immune system components in ICR mice. PP-MPs were administered orally to female and male mice at 0 (corn oil vehicle), 500, 1000, or 2000 mg/kg/d for single and daily for 4-week repeated toxicity test, respectively. No significant differences were observed in number of thymic CD4+, CD8+, CD4+CD8+ T lymphocytes, splenic helper T cells, cytotoxic T cells, and B cells. The ratio of interferon-γ to interleukin-4 in culture supernatants from activated splenocytes ex vivo (48 hr) was lower in females which were repeatedly administered with PP-MPs compared to vehicle irrespective of PP-MPs size and dose. In contrast, the opposite trend was observed in males. Production of tumor necrosis factor-α was upregulated in females that were repeatedly exposed to PP-MPs. The serum IgG2a/IgG1 ratio was lowered in female receiving large-size PP-MPs. Data suggest that immune disturbances resulting in predominant type-2 helper T cell reactivity may occur in mice, especially in females, when repeatedly exposed to PP-MPs. Further investigations with longer exposure periods are necessary to determine the immunotoxicities attributed to PP-MPs.

Synthetic RNA Therapeutics in Cancer.

Recent advances in the RNA delivery system have facilitated the development of a separate field of RNA therapeutics, with modalities including mRNA, microRNA (miRNA), antisense oligonucleotide (ASO), small interfering RNA, and circular (circRNA) that have been incorporated into oncology research. The main advantages of the RNA-based modalities are high flexibility in designing RNA and rapid production for clinical screening. It is challenging to eliminate tumors by tackling a single target in cancer. In the era of precision medicine, RNA-based therapeutic approaches potentially constitute suitable platforms for targeting heterogeneous tumors that possess multiple sub-clonal cancer cell populations. In this review, we discussed how synthetic coding and non-coding RNAs, such as mRNA, miRNA, ASO, and circRNA, can be applied in the development of therapeutics. SIGNIFICANCE STATEMENT: With development of vaccines against coronavirus, RNA-based therapeutics have received attention. Here, the authors discuss different types of RNA-based therapeutics potentially effective against tumor that are highly heterogeneous giving rise to resistance and relapses to the conventional therapeutics. Moreover, this study summarized recent findings suggesting combination approaches of RNA therapeutics and cancer immunotherapy.

Development of a transcriptome-based determination of innate immune suppressor (TDIS) assay as an in vitro test for immunotoxicity.

Immunotoxicity has been an important topic in toxicology since inadvertent exposures to xenobiotics were found to alter immune functions in humans. While rodent toxicity tests can reveal some levels of immunotoxicity, alternative methods must be developed to identify the detailed mechanisms. In this study, a method of in vitro prediction of innate immune suppression by substances was developed using a genomics approach. The primary selection of immune suppressors was based on their ability to downregulate MCP-1, CCL3, TNF, IL-8, and IL-12p40 expression levels in lipopolysaccharide (LPS)-stimulated THP-1 cells. Among 11 substances classified as potent immune suppressors, six including dexamethasone, tacrolimus, tofacitinib, prednisolone, sodium lauryl sulfate, and benzoic acid were used to create a dataset by transcriptomics of chemical-treated THP-1 cells using bulk RNA sequencing. We selected genes that were significantly upregulated by suppressor treatment while filtering out genes also upregulated in LPS-treated THP-1 cells. We identified a 226-gene immunosuppressive gene set (ISG). Innate immune suppressor signature scores were calculated as the median expression of the ISG. In a validation dataset, the signature score predicted acyclovir, cyclosporine, and mercuric chloride as immune suppressors, while selecting genistein as a non-immune suppressor. Although more dataset integration is needed in the future, our results demonstrated the possibility and utility of a novel genomics-based approach, the transcriptome-based determination of innate immune suppressor (TDIS) assay, to evaluate innate immune suppression by different substances. This provides insight into the development of future alternative testing methods because it reflects a comprehensive genetic signature derived from multiple substances rather than one cytokine.

GammaGAN: Gamma-Scaled Class Embeddings for Conditional Video Generation.

In this paper, we propose a new model for conditional video generation (GammaGAN). Generally, it is challenging to generate a plausible video from a single image with a class label as a condition. Traditional methods based on conditional generative adversarial networks (cGANs) often encounter difficulties in effectively utilizing a class label, typically by concatenating a class label to the input or hidden layer. In contrast, the proposed GammaGAN adopts the projection method to effectively utilize a class label and proposes scaling class embeddings and normalizing outputs. Concretely, our proposed architecture consists of two streams: a class embedding stream and a data stream. In the class embedding stream, class embeddings are scaled to effectively emphasize class-specific differences. Meanwhile, the outputs in the data stream are normalized. Our normalization technique balances the outputs of both streams, ensuring a balance between the importance of feature vectors and class embeddings during training. This results in enhanced video quality. We evaluated the proposed method using the MUG facial expression dataset, which consists of six facial expressions. Compared with the prior conditional video generation model, ImaGINator, our model yielded relative improvements of 1.61%, 1.66%, and 0.36% in terms of PSNR, SSIM, and LPIPS, respectively. These results suggest potential for further advancements in conditional video generation.

Image Recommendation System Based on Environmental and Human Face Information.

With the advancement of computer hardware and communication technologies, deep learning technology has made significant progress, enabling the development of systems that can accurately estimate human emotions. Factors such as facial expressions, gender, age, and the environment influence human emotions, making it crucial to understand and capture these intricate factors. Our system aims to recommend personalized images by accurately estimating human emotions, age, and gender in real time. The primary objective of our system is to enhance user experiences by recommending images that align with their current emotional state and characteristics. To achieve this, our system collects environmental information, including weather conditions and user-specific environment data through APIs and smartphone sensors. Additionally, we employ deep learning algorithms for real-time classification of eight types of facial expressions, age, and gender. By combining this facial information with the environmental data, we categorize the user's current situation into positive, neutral, and negative stages. Based on this categorization, our system recommends natural landscape images that are colorized using Generative Adversarial Networks (GANs). These recommendations are personalized to match the user's current emotional state and preferences, providing a more engaging and tailored experience. Through rigorous testing and user evaluations, we assessed the effectiveness and user-friendliness of our system. Users expressed satisfaction with the system's ability to generate appropriate images based on the surrounding environment, emotional state, and demographic factors such as age and gender. The visual output of our system significantly impacted users' emotional responses, resulting in a positive mood change for most users. Moreover, the system's scalability was positively received, with users acknowledging its potential benefits when installed outdoors and expressing a willingness to continue using it. Compared to other recommender systems, our integration of age, gender, and weather information provides personalized recommendations, contextual relevance, increased engagement, and a deeper understanding of user preferences, thereby enhancing the overall user experience. The system's ability to comprehend and capture intricate factors that influence human emotions holds promise in various domains, including human-computer interaction, psychology, and social sciences.

Molecular basis of PD-1 blockade by dostarlimab, the FDA-approved antibody for cancer immunotherapy.

Targeting of programmed cell death 1 (PD-1) with monoclonal antibodies to block the interaction with its ligand PD-L1 has been successful in immunotherapy of multiple types of cancer, and their mechanism involves the restoration of the T-cell immune response. April 2021, the US FDA approved dostarlimab, a therapeutic antibody against PD-1, for the treatment of endometrial cancer. Here, we report the crystal structure of the extracellular domain of PD-1 in complex with the dostarlimab Fab at the resolution of 1.53 Å. Although the interaction between PD-1 and dostarlimab involves mainly the residues within the heavy chain of dostarlimab, the steric occlusion of PD-L1 binding is primarily contributed by the light chain. Dostarlimab induces conformational rearrangements of the BC, C'D and FG loops of PD-1 to achieve a high affinity. Significantly, the residue R86 within the C'D loop of PD-1 plays a critical role for dostarlimab binding by occupying the concave surface on the heavy chain via multiple interactions. This high-resolution structure can provide helpful information for designing improved anti-PD-1 biologics or effective combination strategies for cancer immunotherapy.

Efficient Multi-Scale Stereo-Matching Network Using Adaptive Cost Volume Filtering.

While recent deep learning-based stereo-matching networks have shown outstanding advances, there are still some unsolved challenges. First, most state-of-the-art stereo models employ 3D convolutions for 4D cost volume aggregation, which limit the deployment of networks for resource-limited mobile environments owing to heavy consumption of computation and memory. Although there are some efficient networks, most of them still require a heavy computational cost to incorporate them to mobile computing devices in real-time. Second, most stereo networks indirectly supervise cost volumes through disparity regression loss by using the softargmax function. This causes problems in ambiguous regions, such as the boundaries of objects, because there are many possibilities for unreasonable cost distributions which result in overfitting problem. A few works deal with this problem by generating artificial cost distribution using only the ground truth disparity value that is insufficient to fully regularize the cost volume. To address these problems, we first propose an efficient multi-scale sequential feature fusion network (MSFFNet). Specifically, we connect multi-scale SFF modules in parallel with a cross-scale fusion function to generate a set of cost volumes with different scales. These cost volumes are then effectively combined using the proposed interlaced concatenation method. Second, we propose an adaptive cost-volume-filtering (ACVF) loss function that directly supervises our estimated cost volume. The proposed ACVF loss directly adds constraints to the cost volume using the probability distribution generated from the ground truth disparity map and that estimated from the teacher network which achieves higher accuracy. Results of several experiments using representative datasets for stereo matching show that our proposed method is more efficient than previous methods. Our network architecture consumes fewer parameters and generates reasonable disparity maps with faster speed compared with the existing state-of-the art stereo models. Concretely, our network achieves 1.01 EPE with runtime of 42 ms, 2.92M parameters, and 97.96G FLOPs on the Scene Flow test set. Compared with PSMNet, our method is 89% faster and 7% more accurate with 45% fewer parameters.

Semantic Segmentation Using Pixel-Wise Adaptive Label Smoothing via Self-Knowledge Distillation for Limited Labeling Data.

To achieve high performance, most deep convolutional neural networks (DCNNs) require a significant amount of training data with ground truth labels. However, creating ground-truth labels for semantic segmentation requires more time, human effort, and cost compared with other tasks such as classification and object detection, because the ground-truth label of every pixel in an image is required. Hence, it is practically demanding to train DCNNs using a limited amount of training data for semantic segmentation. Generally, training DCNNs using a limited amount of data is problematic as it easily results in a decrease in the accuracy of the networks because of overfitting to the training data. Here, we propose a new regularization method called pixel-wise adaptive label smoothing (PALS) via self-knowledge distillation to stably train semantic segmentation networks in a practical situation, in which only a limited amount of training data is available. To mitigate the problem caused by limited training data, our method fully utilizes the internal statistics of pixels within an input image. Consequently, the proposed method generates a pixel-wise aggregated probability distribution using a similarity matrix that encodes the affinities between all pairs of pixels. To further increase the accuracy, we add one-hot encoded distributions with ground-truth labels to these aggregated distributions, and obtain our final soft labels. We demonstrate the effectiveness of our method for the Cityscapes dataset and the Pascal VOC2012 dataset using limited amounts of training data, such as 10%, 30%, 50%, and 100%. Based on various quantitative and qualitative comparisons, our method demonstrates more accurate results compared with previous methods. Specifically, for the Cityscapes test set, our method achieved mIoU improvements of 0.076%, 1.848%, 1.137%, and 1.063% for 10%, 30%, 50%, and 100% training data, respectively, compared with the method of the cross-entropy loss using one-hot encoding with ground truth labels.

Recent Advances in Antibody Therapeutics.

Antibody-based therapeutics have achieved unprecedented success in treating various diseases, including cancers, immune disorders, and infectious diseases [...].

Crystal structure of CD38 in complex with daratumumab, a first-in-class anti-CD38 antibody drug for treating multiple myeloma.

Multiple myeloma is a blood cancer characterized by the plasma cell malignancy in the bone marrow, resulting in the destruction of bone tissue. Recently, the US FDA approved two antibody drugs for the treatment of multiple myeloma, daratumumab and isatuximab, targeting CD38, a type II transmembrane glycoprotein highly expressed in plasma cells and multiple myeloma cells. Here, we report the crystal structure of CD38 in complex with the Fab fragment of daratumumab, providing its exact epitope on CD38 and the structural insights into the mechanism of action of the antibody drug. Daratumumab binds to a specific discontinuous region on CD38 that includes residues located opposite to the active site of CD38. All the six complementarity determining regions of daratumumab are involved in the CD38 interaction. The epitopes of daratumumab and isatuximab do not overlap at all and their bindings to CD38 induce different structural changes within the CD38 protein. This structural study can facilitate the design of improved biologics or effective combination therapies for the treatment of multiple myeloma.

High-resolution structure of the vWF A1 domain in complex with caplacizumab, the first nanobody-based medicine for treating acquired TTP.

von Willebrand factor (vWF) is a huge oligomeric glycoprotein involved in blood homeostasis. However, this protein is also implicated in acquired thrombotic thrombocytopenic purpura (TTP). The blocking of its binding with platelets has been recognized as an attractive therapeutic strategy for treating acquired TTP. Caplacizumab, a bivalent single-domain antibody (VHH), is the first FDA-approved nanobody drug against vWF for the treatment of acquired TTP. Here, we describe the crystal structure of the A1 domain of vWF in complex with the caplacizumab nanobody at the resolution of 1.60 Å. This structure elucidates the precise epitope and binding mode of caplacizumab. Unexpectedly, caplacizumab binds to the bottom face of the vWF A1 domain and does not create any steric clash with platelet-receptor glycoprotein Ib (GPIb) bound to vWF. However, its binding can stabilize the different conformation within the N-terminus and α1ß2 loop from the GPIb bound structure, suggesting that the mechanisms of caplacizumab would not be the direct competition of GPIb binding to vWF A1 domain but the conformational arrestment of vWF in an inappropriate state to platelet adhesion. This high-resolution structure would provide helpful information for the design of improved anti-vWF therapeutics for the treatment of acquired TTP.

Exposure to lead on expression levels of brain immunoglobulins, inflammatory cytokines, and brain-derived neurotropic factor in fetal and postnatal mice with autism-like characteristics.

Autism spectrum disorders (ASD) are neurodevelopmental disorders, and their incidence is increasing worldwide. Increased exposure to environmental metal lead (Pb) has been proposed as a risk factor associated with ASD. In the present study, BTBR T+ tf/J (BTBR) mice with ASD-like behavioral characteristics and control FVB mice were exposed gestationally and/or neonatally to Pb, and compared with highly social FVB mice to investigate neuroimmunological abnormalities. IgG1 and IgG2a levels in fetal brains from BTBR dams exposed to Pb (BTBR-Pb) were significantly higher than those of BTBR-controls (BTBR-C). However, this change did not occur in FVB mice exposed to Pb. The IgG1:IgG2a ratio was higher in both fetal and postnatal brains of BTBR mice compared to FVB animals regardless of Pb exposure. The IL-4:IFN-γ ratio was elevated in BTBR-Pb relative to BTBR-C mice, but this ratio was not markedly affected following Pb exposure in FVB animals. These findings suggest the potential for a Pb-driven predominant TH2-like reactivity profile in brain microenvironment present in BTBR mice. Brain-derived neurotrophic factor was decreased in fetal and postnatal BTBR-Pb brains relative to BTBR-C brains but not in FVB-Pb relative to FVB-C mice. Taken together, data demonstrate that Pb exposure might contribute to developmental brain abnormalities associated with ASD, particularly in individuals with genetic susceptibility to ASD.

Prediction of the skin sensitization potential of polyhexamethylene guanidine and triclosan and mixtures of these compounds with the excipient propylene glycol through the human Cell Line Activation Test.

Household products often contain an antimicrobial agent such as biocides, polyhexamethylene guanidine (PHMG), triclosan (TCS), and propylene glycol (PG) as an excipient to dissolve the active ingredients. The skin sensitization (SS) potentials of each of these substances or mixtures of PHMG or TCS with PG have not been investigated through in vitro alternative test methods. The in vitro alternative assay called human Cell Line Activation Test (h-CLAT) served to address these issues. The h-CLAT assay was conducted in accordance with OECD TG 442E. On three independent runs, all the three substances were predicted to be sensitizers according to the SS positivity with relative fluorescence intensity of CD86 ≥ 150% and/or CD54 ≥ 200% at any tested concentrations. Mixtures of PHMG or TCS with PG at ratios of 9:1, 4:1, or 1:4 weight/volume were all positive in terms of SS potential. Since humans can be occupationally or environmentally exposed to mixtures of excipients with active ingredients of biocides, the present study may give insights into further investigations of the SS potentials of various chemical mixtures.

Dysregulation of murine immune functions on inhalational exposure to ammonia, dimethyl disulfide, 3-methylindole, or propionic acid.

Animal husbandry workers are exposed to various malodorous compounds in the workplace. Although these compounds cause severe nuisance, no systemic investigation of their effects on the immune system has been conducted. To address this issue, we evaluated the effects of inhalational exposure to ammonia, dimethyl disulfide, 3-methylindole (3-MI), and propionic acid (PA), representing four major groups of malodorous compounds, on humoral and cellular immunity in mice. Mice were exposed to the substances (low dose: 10 µL and high dose: 200 µL) for 10 min/day for 4 weeks in a modified standard mouse cage. Neutrophil% and splenic cytotoxic T cell% were significantly lower in the high-dose ammonia group than in the vehicle control. Exposure to ammonia and 3-MI increased immature thymic T lymphocyte% relative to control and concomitantly decreased both mature helper and cytotoxic T-cell populations in the thymus. In the ammonia exposure group, levels of serum immunoglobulin E and immunoglobulin A were elevated, and the IgG2a:IgG1 ratio in the serum was reduced in a dose-dependent manner. Splenic natural killer cell activity was significantly less in the PA exposure group than in the control. Overall, our findings suggest that inhalational exposure to these malodorous substances disturbs immune homeostasis in vivo.

Crystal structure of PD-1 in complex with an antibody-drug tislelizumab used in tumor immune checkpoint therapy.

Blocking of the interaction between Programmed cell death 1 (PD-1) and its ligand PD-L1 by monoclonal antibodies has elicited unprecedented therapeutic benefits and achieved a major breakthrough in immunotherapy of multiple types of tumors. Here, we determined the crystal structure of PD-1 in complex with the Fab fragment of tislelizumab. This monoclonal antibody was approved in December 2019 by the China National Medical Product Administration for Hodgkin's lymphoma and is under multiple clinical trials in China and the US. While the three complementarity determining regions (CDRs) in the light chain are involved in the target interaction, only CDR3 within the heavy chain interacts with PD-1. Tislelizumab binds the front ß-sheet of PD-1 in a very similar way as PD-L1 binds to PD-1, thereby blocking the PD-1/PD-L1 interaction with a higher affinity. A comparative analysis of PD-1 interactions with therapeutic antibodies targeting PD-1 provides a better understanding of the blockade mechanism of PD-1/PD-L1 interaction in addition to useful information for the improvement of therapeutic antibodies capable of diminishing checkpoint signaling for cancer immunotherapy.

Knowledge Distillation for Semantic Segmentation Using Channel and Spatial Correlations and Adaptive Cross Entropy.

In this paper, we propose an efficient knowledge distillation method to train light networks using heavy networks for semantic segmentation. Most semantic segmentation networks that exhibit good accuracy are based on computationally expensive networks. These networks are not suitable for mobile applications using vision sensors, because computational resources are limited in these environments. In this view, knowledge distillation, which transfers knowledge from heavy networks acting as teachers to light networks as students, is suitable methodology. Although previous knowledge distillation approaches have been proven to improve the performance of student networks, most methods have some limitations. First, they tend to use only the spatial correlation of feature maps and ignore the relational information of their channels. Second, they can transfer false knowledge when the results of the teacher networks are not perfect. To address these two problems, we propose two loss functions: a channel and spatial correlation (CSC) loss function and an adaptive cross entropy (ACE) loss function. The former computes the full relationship of both the channel and spatial information in the feature map, and the latter adaptively exploits one-hot encodings using the ground truth labels and the probability maps predicted by the teacher network. To evaluate our method, we conduct experiments on scene parsing datasets: Cityscapes and Camvid. Our method presents significantly better performance than previous methods.

Major environmental characteristics of swine husbandry that affect exposure to dust and airborne endotoxins.

Inhalation of organic dust or endotoxin in the dust is considered a major risk factor for occupational respiratory illnesses. Eighteen environmental characteristics associated with animal husbandry were surveyed at 36 swine farms in seven provinces throughout South Korea. Association of these factors with levels of indoor inhalable or respirable dust or endotoxin in each type of dust was analyzed using backward stepwise multiple linear regression models. Mean levels of inhalable and respirable dust were 0.5 ± 0.35 and 0.13 ± 0.12 mg/m3 air, respectively, and mean endotoxin levels were 676 ± 463 and 48.4 ± 68.2 EU/m3, respectively, in each dust. Factors negatively associated with inhalable dust levels included pig age, indoor farm temperature, number of pigs in the building, hr/week of indoor farm work, and partly slatted floor. Factors positively associated with inhalable dust levels included floor cleaning by manual scraping and slurry deposit duration. Factors negatively associated with the level of endotoxin in inhalable dust included pig age, temperature, number of pigs, hr/week of indoor farm work, and partly slatted floor. Factors negatively associated with respirable dust level included area of the confinement building, whereas factors positively associated with respirable dust level included the number of pigs and stocking density. Endotoxin levels in respirable dust were negatively associated with h/week of indoor farm work and partly slatted floor. Overall, data suggest that husbandry variables may be adjusted to control dust and airborne endotoxin levels in swine farms.

Prediction of the skin sensitization potential of polyhexamethylene guanidine phosphate, oligo(2-(2-ethoxy)ethoxyethyl) guanidinium chloride, triclosan, and mixtures of these compounds with the excipient propylene glycol through the local lymph node assay: BrdU-FCM.

The guanidine family of antimicrobial agents, which includes polyhexamethylene guanidine phosphate (PHMG) and oligo(2-(2-ethoxy)ethoxyethyl) guanidinium chloride (PGH), and chlorophenol biocidal chemicals such as 2,4,4'-trichloro-2'-hydroxydiphenyl ether (triclosan) are used in various occupational and environmental biocidal applications. The excipient propylene glycol (PG) is used to dissolve the active ingredients. The skin sensitization (SS) potential of these substances has not been systemically investigated and is still debated. Moreover, mixtures of PHMG, PGH, or triclosan with PG have not been evaluated for SS potency. An in vivo assay known as the local lymph node assay: 5-bromo-2-deoxyuridine-flow cytometry method (LLNA: BrdU-FCM) was recently adopted as an alternative testing method and was used to address these issues. Via the LLNA: BrdU-FCM, PHMG, PGH, and triclosan were predicted to be sensitizers, while PG was predicted to be a nonsensitizer. In addition, d-limonene, which is used as a flavoring in various consumer products, was also predicted to be a sensitizer, although no unanimous conclusion has been reached regarding its SS potential. Mixtures of PHMG, PGH, triclosan, or d-limonene with PG at ratios of 9:1, 4:1, and 1:4 (w/w) were all positive in terms of SS potential, indicating that the PG excipient does not influence the SS predictions of these chemicals. Since humans can be occupationally and environmentally exposed to mixtures of excipients with active ingredients, the present study may give insight into further investigations of the SS potentials of various chemical mixtures.