Effect of pooled tracheal sample testing on the probability of Mycoplasma hyopneumoniae detection.

Tracheal pooling for Mycoplasma hyopneumoniae (M. hyopneumoniae) DNA detection allows for decreased diagnostic cost, one of the main constraints in surveillance programs. The objectives of this study were to estimate the sensitivity of pooled-sample testing for the detection of M. hyopneumoniae in tracheal samples and to develop probability of M. hyopneumoniae detection estimates for tracheal samples pooled by 3, 5, and 10. A total of 48 M. hyopneumoniae PCR-positive field samples were pooled 3-, 5-, and 10-times using field M. hyopneumoniae DNA-negative samples and tested in triplicate. The sensitivity was estimated at 0.96 (95% credible interval [Cred. Int.]: 0.93, 0.98) for pools of 3, 0.95 (95% Cred. Int: 0.92, 0.98) for pools of 5, and 0.93 (95% Cred. Int.: 0.89, 0.96) for pools of 10. All pool sizes resulted in PCR-positive if the individual tracheal sample Ct value was < 33. Additionally, there was no significant decrease in the probability of detecting at least one M. hyopneumoniae-infected pig given any pool size (3, 5, or 10) of tracheal swabs. Furthermore, this manuscript applies the probability of detection estimates to various real-life diagnostic testing scenarios. Combining increased total animals sampled with pooling can be a cost-effective tool to maximize the performance of M. hyopneumoniae surveillance programs.

BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices.

Introduction: Social media platforms serve as a valuable resource for users to share health-related information, aiding in the monitoring of adverse events linked to medications and treatments in drug safety surveillance. However, extracting drug-related adverse events accurately and efficiently from social media poses challenges in both natural language processing research and the pharmacovigilance domain. Method: Recognizing the lack of detailed implementation and evaluation of Bidirectional Encoder Representations from Transformers (BERT)-based models for drug adverse event extraction on social media, we developed a BERT-based language model tailored to identifying drug adverse events in this context. Our model utilized publicly available labeled adverse event data from the ADE-Corpus-V2. Constructing the BERT-based model involved optimizing key hyperparameters, such as the number of training epochs, batch size, and learning rate. Through ten hold-out evaluations on ADE-Corpus-V2 data and external social media datasets, our model consistently demonstrated high accuracy in drug adverse event detection. Result: The hold-out evaluations resulted in average F1 scores of 0.8575, 0.9049, and 0.9813 for detecting words of adverse events, words in adverse events, and words not in adverse events, respectively. External validation using human-labeled adverse event tweets data from SMM4H further substantiated the effectiveness of our model, yielding F1 scores 0.8127, 0.8068, and 0.9790 for detecting words of adverse events, words in adverse events, and words not in adverse events, respectively. Discussion: This study not only showcases the effectiveness of BERT-based language models in accurately identifying drug-related adverse events in the dynamic landscape of social media data, but also addresses the need for the implementation of a comprehensive study design and evaluation. By doing so, we contribute to the advancement of pharmacovigilance practices and methodologies in the context of emerging information sources like social media.

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery.

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein-ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure-activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure-activity relationships in ß2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein-ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.

Review of machine learning and deep learning models for toxicity prediction.

The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional in vitro and in vivo toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using various machine learning and deep learning algorithms such as support vector machine, random forest, k-nearest neighbors, ensemble learning, and deep neural network. This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. Support vector machine and random forest are the most popular machine learning algorithms, and hepatotoxicity, cardiotoxicity, and carcinogenicity are the frequently modeled toxicity endpoints in predictive toxicology. It is known that datasets impact model performance. The quality of datasets used in the development of toxicity prediction models using machine learning and deep learning is vital to the performance of the developed models. The different toxicity assignments for the same chemicals among different datasets of the same type of toxicity have been observed, indicating benchmarking datasets is needed for developing reliable toxicity prediction models using machine learning and deep learning algorithms. This review provides insights into current machine learning models in predictive toxicology, which are expected to promote the development and application of toxicity prediction models in the future.

Machine learning and deep learning for brain tumor MRI image segmentation.

Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic resonance imaging (MRI) is a commonly used imaging technique for capturing brain images. Both machine learning and deep learning techniques are popular in analyzing MRI images. This article reviews some commonly used machine learning and deep learning techniques for brain tumor MRI image segmentation. The limitations and advantages of the reviewed machine learning and deep learning methods are discussed. Even though each of these methods has a well-established status in their individual domains, the combination of two or more techniques is currently an emerging trend.

Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment.

The coronavirus disease 2019 (COVID-19) global pandemic resulted in millions of people becoming infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and close to seven million deaths worldwide. It is essential to further explore and design effective COVID-19 treatment drugs that target the main protease of SARS-CoV-2, a major target for COVID-19 drugs. In this study, machine learning was applied for predicting the SARS-CoV-2 main protease binding of Food and Drug Administration (FDA)-approved drugs to assist in the identification of potential repurposing candidates for COVID-19 treatment. Ligands bound to the SARS-CoV-2 main protease in the Protein Data Bank and compounds experimentally tested in SARS-CoV-2 main protease binding assays in the literature were curated. These chemicals were divided into training (516 chemicals) and testing (360 chemicals) data sets. To identify SARS-CoV-2 main protease binders as potential candidates for repurposing to treat COVID-19, 1188 FDA-approved drugs from the Liver Toxicity Knowledge Base were obtained. A random forest algorithm was used for constructing predictive models based on molecular descriptors calculated using Mold2 software. Model performance was evaluated using 100 iterations of fivefold cross-validations which resulted in 78.8% balanced accuracy. The random forest model that was constructed from the whole training dataset was used to predict SARS-CoV-2 main protease binding on the testing set and the FDA-approved drugs. Model applicability domain and prediction confidence on drugs predicted as the main protease binders discovered 10 FDA-approved drugs as potential candidates for repurposing to treat COVID-19. Our results demonstrate that machine learning is an efficient method for drug repurposing and, thus, may accelerate drug development targeting SARS-CoV-2.

Analyzing 3D structures of the SARS-CoV-2 main protease reveals structural features of ligand binding for COVID-19 drug discovery.

The severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) main protease has an essential role in viral replication and has become a major target for coronavirus 2019 (COVID-19) drug development. Various inhibitors have been discovered or designed to bind to the main protease. The availability of more than 550 3D structures of the main protease provides a wealth of structural details on the main protease in both ligand-free and ligand-bound states. Therefore, we examined these structures to ascertain the structural features for the role of the main protease in the cleavage of polyproteins, the alternative conformations during main protease maturation, and ligand interactions in the main protease. The structural features unearthed could promote the development of COVID-19 drugs targeting the SARS-CoV-2 main protease.

Development of a primate model to evaluate the effects of ketamine and surgical stress on the neonatal brain.

With advances in pediatric and obstetric surgery, pediatric patients are subject to complex procedures under general anesthesia. The effects of anesthetic exposure on the developing brain may be confounded by several factors including pre-existing disorders and surgery-induced stress. Ketamine, a noncompetitive N-methyl-d-aspartate (NMDA) receptor antagonist, is routinely used as a pediatric general anesthetic. However, controversy remains about whether ketamine exposure may be neuroprotective or induce neuronal degeneration in the developing brain. Here, we report the effects of ketamine exposure on the neonatal nonhuman primate brain under surgical stress. Eight neonatal rhesus monkeys (postnatal days 5-7) were randomly assigned to each of two groups: Group A (n = 4) received 2 mg/kg ketamine via intravenous bolus prior to surgery and a 0.5 mg/kg/h ketamine infusion during surgery in the presence of a standardized pediatric anesthetic regimen; Group B (n = 4) received volumes of normal saline equivalent to those of ketamine given to Group A animals prior to and during surgery, also in the presence of a standardized pediatric anesthetic regimen. Under anesthesia, the surgery consisted of a thoracotomy followed by closing the pleural space and tissue in layers using standard surgical techniques. Vital signs were monitored to be within normal ranges throughout anesthesia. Elevated levels of cytokines interleukin (IL)-8, IL-15, monocyte chemoattractant protein-1 (MCP-1), and macrophage inflammatory protein (MIP)-1ß at 6 and 24 h after surgery were detected in ketamine-exposed animals. Fluoro-Jade C staining revealed significantly higher neuronal degeneration in the frontal cortex of ketamine-exposed animals, compared with control animals. Intravenous ketamine administration prior to and throughout surgery in a clinically relevant neonatal primate model appears to elevate cytokine levels and increase neuronal degeneration. Consistent with previous data on the effects of ketamine on the developing brain, the results from the current randomized controlled study in neonatal monkeys undergoing simulated surgery show that ketamine does not provide neuroprotective or anti-inflammatory effects.

Three-Dimensional Structural Insights Have Revealed the Distinct Binding Interactions of Agonists, Partial Agonists, and Antagonists with the µ Opioid Receptor.

The United States is experiencing the most profound and devastating opioid crisis in history, with the number of deaths involving opioids, including prescription and illegal opioids, continuing to climb over the past two decades. This severe public health issue is difficult to combat as opioids remain a crucial treatment for pain, and at the same time, they are also highly addictive. Opioids act on the opioid receptor, which in turn activates its downstream signaling pathway that eventually leads to an analgesic effect. Among the four types of opioid receptors, the µ subtype is primarily responsible for the analgesic cascade. This review describes available 3D structures of the µ opioid receptor in the protein data bank and provides structural insights for the binding of agonists and antagonists to the receptor. Comparative analysis on the atomic details of the binding site in these structures was conducted and distinct binding interactions for agonists, partial agonists, and antagonists were observed. The findings in this article deepen our understanding of the ligand binding activity and shed some light on the development of novel opioid analgesics which may improve the risk benefit balance of existing opioids.

Transdiscal instrumentation in single-level lumbosacral fusion for high-grade isthmic pediatric spondylolisthesis: Technical note and review of the literature.

Pediatric spondylolisthesis is a common cause of back pain in children, typically managed conservatively with bracing and non-steroidal anti-inflammatory drugs. When posterolateral fusion is performed for refractory pain, pseudarthrosis and implant failure may occur, necessitating reoperation. To improve patient outcomes, there is a need for alternative surgical techniques to effectively manage high-grade isthmic slips. Here, the authors report the case of a child with Meyerding grade III anterolisthesis of L5 on S1 who was treated with a single-level, instrumented fusion using bilateral S1-L5 transdiscal screws, supported with L5-S1 posterolateral instrumentation and arthrodesis. Postoperatively, there was improvement in the patient's symptoms with good clinical and radiographic outcomes. The patient continues to be symptom free with radiographic evidence of hardware stability and bony fusion across the segment. The authors detail a novel surgical technique in children as well as a review of lumbosacral transdiscal screw fixation. Further evidence is required to definitively establish the safety, outcomes, and biomechanical strength of this technique.

CACNA1S mutation-associated dental anomalies: A calcium channelopathy.

OBJECTIVES: To identify the molecular etiology of distinct dental anomalies found in eight Thai patients and explore the mutational effects on cellular functions. MATERIALS AND METHODS: Clinical and radiographic examinations were performed for eight patients. Whole exome sequencing, mutant protein modelling, qPCR, western blot analysis, scratch assays, immunofluorescence, confocal analysis, in situ hybridization, and scanning electron micrography of teeth were done. RESULTS: All patients had molars with multiple supernumerary cusps, single-cusped premolars, and a reduction in root number. Mutation analysis highlighted a heterozygous c.865A>G; p.Ile289Val mutation in CACNA1S in the patients. CACNA1S is a component of the slowly inactivating L-type voltage-dependent calcium channel. Mutant protein modeling suggested that the mutation might allow leakage of Ca2+ or other cations, or a tightening, to restrict calcium flow. Immunohistochemistry analysis showed expression of Cacna1s in the developing murine tooth epithelium during stages of crown and root morphogenesis. In cell culture, the mutation resulted in abnormal cell migration of transfected CHO cells compared to wildtype CACNA1S, with changes to the cytoskeleton and markers of focal adhesion. CONCLUSIONS: The malformations observed in our patients suggest a role for calcium signaling in organization of both cusps and roots, affecting cell dynamics within the dental epithelium.

A conserved tooth resorption mechanism in modern and fossil snakes.

Whether snakes evolved their elongated, limbless bodies or their specialized skulls and teeth first is a central question in squamate evolution. Identifying features shared between extant and fossil snakes is therefore key to unraveling the early evolution of this iconic reptile group. One promising candidate is their unusual mode of tooth replacement, whereby teeth are replaced without signs of external tooth resorption. We reveal through histological analysis that the lack of resorption pits in snakes is due to the unusual action of odontoclasts, which resorb dentine from within the pulp of the tooth. Internal tooth resorption is widespread in extant snakes, differs from replacement in other reptiles, and is even detectable via non-destructive µCT scanning, providing a method for identifying fossil snakes. We then detected internal tooth resorption in the fossil snake Yurlunggur, and one of the oldest snake fossils, Portugalophis, suggesting that it is one of the earliest innovations in Pan-Serpentes, likely preceding limb loss.

Definitive Taylor Spatial Frame management for the treatment of high-energy open tibial fractures: Clinical and patient-reported outcomes.

BACKGROUND: High energy open tibial fractures are complex injuries with no consensus on the optimal method of fixation. Treatment outcomes are often reported with union and re-operation rates, often without specific definitions being provided.  We sought to describe union, reoperation rates, and patient reported outcomes, using the validated EQ-VAS and Disability Rating Index (DRI) scores, following stabilisation with a Taylor Spatial Frame (TSF) and a combined orthoplastic approach for the management of soft tissues. A literature review is also provided. METHOD: A prospective cross-sectional follow up of open tibial fractures, treated at a level 1 major trauma centre, managed with a TSF using a one ring per segment technique between January 2014 and December 2019 were identified. Demographic, injury and operative data were recorded, along with Patient Reported Outcome Measures (PROM) scores, specifically the EQ-VAS and Disability Rating Index (DRI). Union rates, defined by radiographic union scale in tibia (RUST) scores, and re-operation rates were recorded. Appropriate statistical analyses were performed, with a p<0.05 considered statistically significant. RESULTS: Overall, 51 patients were included. Mean age was 51.2 ± 17.4 years, with a 4:1 male preponderance. Diaphyseal and distal fractures accounted for 76% of cases. Mean time in frame was 206.7 ± 149.4 days. Union was defined and was achieved in 41/51 (80.4%) patients. Deep infection occurred in 6/51 (11.8%) patients. Amputation was performed in 1 case (1.9%). Overall re-operation rate was 33%. Time to union were significantly longer if re-operation was required for any reason (uncomplicated 204±189 vs complicated 304±155 days; p = 0.0017) . EQ-VAS and DRI scores significantly deteriorated at 1 year follow-up (EQVAS 87.5 ± 11.7 vs 66.5 ± 20.4;p<0.0001 and DRI 11.9 ± 17.8 vs 39.3 ± 23.3;p<0.0001). At 1 year post op, 23/51(45.1%) required a walking aid, and 17/29 (58.6%) of those working pre-injury had returned to work. CONCLUSION: Open tibial fracture have significant morbidity and long recovery periods as determined by EQVAS and DRI outcome measures.  We report the largest series of open tibial feature treated primarily with a TSF construct, which has similar outcomes to other techniques, and should therefore be considered as a useful technique for managing these injuries.

Machine learning models for rat multigeneration reproductive toxicity prediction.

Reproductive toxicity is one of the prominent endpoints in the risk assessment of environmental and industrial chemicals. Due to the complexity of the reproductive system, traditional reproductive toxicity testing in animals, especially guideline multigeneration reproductive toxicity studies, take a long time and are expensive. Therefore, machine learning, as a promising alternative approach, should be considered when evaluating the reproductive toxicity of chemicals. We curated rat multigeneration reproductive toxicity testing data of 275 chemicals from ToxRefDB (Toxicity Reference Database) and developed predictive models using seven machine learning algorithms (decision tree, decision forest, random forest, k-nearest neighbors, support vector machine, linear discriminant analysis, and logistic regression). A consensus model was built based on the seven individual models. An external validation set was curated from the COSMOS database and the literature. The performances of individual and consensus models were evaluated using 500 iterations of 5-fold cross-validations and the external validation data set. The balanced accuracy of the models ranged from 58% to 65% in the 5-fold cross-validations and 45%-61% in the external validations. Prediction confidence analysis was conducted to provide additional information for more appropriate applications of the developed models. The impact of our findings is in increasing confidence in machine learning models. We demonstrate the importance of using consensus models for harnessing the benefits of multiple machine learning models (i.e., using redundant systems to check validity of outcomes). While we continue to build upon the models to better characterize weak toxicants, there is current utility in saving resources by being able to screen out strong reproductive toxicants before investing in vivo testing. The modeling approach (machine learning models) is offered for assessing the rat multigeneration reproductive toxicity of chemicals. Our results suggest that machine learning may be a promising alternative approach to evaluate the potential reproductive toxicity of chemicals.

Presence and magnitude of anterior physeal separation in slipped upper femoral epiphysis helps identifying those at high risk for avascular necrosis.

AIMS: We sought to determine if the magnitude of anterior physeal separation (APS) in slipped upper femoral epiphysis was a predictor for the subsequent development of avascular necrosis (AVN). Anterior Physeal Separation (APS) is defined as the distance between the anterior lip of the bony capital epiphysis and the lateral corresponding point of the adjacent bony metaphysis on the Lauenstein radiographic view. It represents hinging of the posterior aspect of the metaphysis with the anterior epiphysis lifting away from its adjacent metaphysis, indicating instability and potential vulnerability of the vessels. PATIENTS AND METHODS: A retrospective review of all patients treated regionally for slipped upper femoral epiphysis over a 9 year period (2010-2018 inclusive) were identified. Data regarding demographics, radiological parameters and outcomes was recorded. APS was measured on a Launestein radiograph view, with the patient supine, the hip and knee are flexed to 40°, and the hip externally rotated 45°, with film-focus distance of 100 cm. Analysis of the APS was performed to validate a threshold above which AVN occurs. RESULTS: We identified 147 hips in 142 patients, of which 5 had bilateral slips at the time of presentation. Average anterior physeal separation was 3.8 ± 1.8 mm, with higher grade slips having significantly greater APS values. Increased APS values were seen with Loder "unstable" slips. Seven hips (4.8%) developed AVN, and in these cases the APS was significantly larger than those who did not (8.5 ± 1.4 Vs 3.9 ± 1.7; p < 0.001). Receiver operator curve analysis demonstrated a critical value of 7.5 mm was associated with a 100% sensitivity and 98.6% specificity for AVN. We identified some grade II/moderate slips with high APS values had similar outcomes to grade III/severe slips, and therefore suggest that APS may serve to alert the surgeon on counselling patients on the risk of developing AVN and to consider strategies to minimise the risk of AVN. CONCLUSIONS: APS is sensitive, specific, accurate and reliable for the association with AVN in SUFE. Its values closely reflect the high AVN rates seen in acute and unstable SUFE. This risk is greatest if the magnitude of APS exceeds the critical value of 7.5 mm.

Application of Nonhuman Primate Models in the Studies of Pediatric Anesthesia Neurotoxicity.

Numerous animal models have been used to study developmental neurotoxicity associated with short-term or prolonged exposure of common general anesthetics at clinically relevant concentrations. Pediatric anesthesia models using the nonhuman primate (NHP) may more accurately reflect the human condition because of their phylogenetic similarity to humans with regard to reproduction, development, neuroanatomy, and cognition. Although they are not as widely used as other animal models, the contribution of NHP models in the study of anesthetic-induced developmental neurotoxicity has been essential. In this review, we discuss how neonatal NHP animals have been used for modeling pediatric anesthetic exposure; how NHPs have addressed key data gaps and application of the NHP model for the studies of general anesthetic-induced developmental neurotoxicity. The appropriate application and evaluation of the NHP model in the study of general anesthetic-induced developmental neurotoxicity have played a key role in enhancing the understanding and awareness of the potential neurotoxicity associated with pediatric general anesthetics.

Machine Learning Models for Predicting Liver Toxicity.

Liver toxicity is a major adverse drug reaction that accounts for drug failure in clinical trials and withdrawal from the market. Therefore, predicting potential liver toxicity at an early stage in drug discovery is crucial to reduce costs and the potential for drug failure. However, current in vivo animal toxicity testing is very expensive and time consuming. As an alternative approach, various machine learning models have been developed to predict potential liver toxicity in humans. This chapter reviews current advances in the development and application of machine learning models for prediction of potential liver toxicity in humans and discusses possible improvements to liver toxicity prediction.

Machine Learning Models for Predicting Cytotoxicity of Nanomaterials.

The wide application of nanomaterials in consumer and medical products has raised concerns about their potential adverse effects on human health. Thus, more and more biological assessments regarding the toxicity of nanomaterials have been performed. However, the different ways the evaluations were performed, such as the utilized assays, cell lines, and the differences of the produced nanoparticles, make it difficult for scientists to analyze and effectively compare toxicities of nanomaterials. Fortunately, machine learning has emerged as a powerful tool for the prediction of nanotoxicity based on the available data. Among different types of toxicity assessments, nanomaterial cytotoxicity was the focus here because of the high sensitivity of cytotoxicity assessment to different treatments without the need for complicated and time-consuming procedures. In this review, we summarized recent studies that focused on the development of machine learning models for prediction of cytotoxicity of nanomaterials. The goal was to provide insight into predicting potential nanomaterial toxicity and promoting the development of safe nanomaterials.