MultiToxPred 1.0: a novel comprehensive tool for predicting 27 classes of protein toxins using an ensemble machine learning approach

Protein toxins are defense mechanisms and adaptations found in various organisms and microorganisms, and their use in scientific research as therapeutic candidates is gaining relevance due to their effectiveness and specificity against cellular targets. However, discovering these toxins is time-consuming and expensive. In silico tools, particularly those based on machine learning and deep learning, have emerged as valuable resources to address this challenge. Existing tools primarily focus on binary classification, determining whether a protein is a toxin or not, and occasionally identifying specific types of toxins. For the first time, we propose a novel approach capable of classifying protein toxins into 27 distinct categories based on their mode of action within cells. To accomplish this, we assessed multiple machine learning techniques and found that an ensemble model incorporating the Light Gradient Boosting Machine and Quadratic Discriminant Analysis algorithms exhibited the best performance. During the tenfold cross-validation on the training dataset, our model exhibited notable metrics: 0.840 accuracy, 0.827 F1 score, 0.836 precision, 0.840 sensitivity, and 0.989 AUC. In the testing stage, using an independent dataset, the model achieved 0.846 accuracy, 0.838 F1 score, 0.847 precision, 0.849 sensitivity, and 0.991 AUC. These results present a powerful next-generation tool called MultiToxPred 1.0, accessible through a web application. We believe that MultiToxPred 1.0 has the potential to become an indispensable resource for researchers, facilitating the efficient identification of protein toxins. By leveraging this tool, scientists can accelerate their search for these toxins and advance their understanding of their therapeutic potential.

VirusHound-I: prediction of viral proteins involved in the evasion of host adaptive immune response using the random forest algorithm and generative adversarial network for data augmentation

Throughout evolution, pathogenic viruses have developed different strategies to evade the response of the adaptive immune system. To carry out successful replication, some pathogenic viruses encode different proteins that manipulate the molecular mechanisms of host cells. Currently, there are different bioinformatics tools for virus research; however, none of them focus on predicting viral proteins that evade the adaptive system. In this work, we have developed a novel tool based on machine and deep learning for predicting this type of viral protein named VirusHound-I. This tool is based on a model developed with the multilayer perceptron algorithm using the dipeptide composition molecular descriptor. In this study, we have also demonstrated the robustness of our strategy for data augmentation of the positive dataset based on generative antagonistic networks. During the 10-fold cross-validation step in the training dataset, the predictive model showed 0.947 accuracy, 0.994 precision, 0.943 F1 score, 0.995 specificity, 0.896 sensitivity, 0.894 kappa, 0.898 Matthew's correlation coefficient and 0.989 AUC. On the other hand, during the testing step, the model showed 0.964 accuracy, 1.0 precision, 0.967 F1 score, 1.0 specificity, 0.936 sensitivity, 0.929 kappa, 0.931 Matthew's correlation coefficient and 1.0 AUC. Taking this model into account, we have developed a tool called VirusHound-I that makes it possible to predict viral proteins that evade the host's adaptive immune system. We believe that VirusHound-I can be very useful in accelerating studies on the molecular mechanisms of evasion of pathogenic viruses, as well as in the discovery of therapeutic targets.

Protein Modelling and Molecular Docking Analysis of Fasciola hepatica β-Tubulin's Interaction Sites, with Triclabendazole, Triclabendazole Sulphoxide and Triclabendazole Sulphone

PURPOSE

Fasciola hepatica is a globally distributed trematode that causes significant economic losses. Triclabendazole is the primary pharmacological treatment for this parasite. However, the increasing resistance to triclabendazole limits its efficacy. Previous pharmacodynamics studies suggested that triclabendazole acts by interacting mainly with the β monomer of tubulin.

METHODS

We used a high-quality method to model the six isotypes of F. hepatica β-tubulin in the absence of three-dimensional structures. Molecular dockings were conducted to evaluate the destabilization regions in the molecule against the ligands triclabendazole, triclabendazole sulphoxide and triclabendazole sulphone.

RESULTS

The nucleotide binding site demonstrates higher affinity than the binding sites of colchicine, albendazole, the T7 loop and pβVII (p < 0.05). We suggest that the binding of the ligands to the polymerization site of β-tubulin can lead a microtubule disruption. Furthermore, we found that triclabendazole sulphone exhibited significantly higher binding affinity than other ligands (p < 0.05) across all isotypes of β-tubulin.

CONCLUSIONS

Our investigation has yielded new insight on the mechanism of action of triclabendazole and its sulphometabolites on F. hepatica β-tubulin through computational tools. These findings have significant implications for ongoing scientific research ongoing towards the discovery of novel therapeutics to treat F. hepatica infections.

Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides

Viruses constitute a constant threat to global health and have caused millions of human and animal deaths throughout human history. Despite advances in the discovery of antiviral compounds that help fight these pathogens, finding a solution to this problem continues to be a task that consumes time and financial resources. Currently, artificial intelligence (AI) has revolutionized many areas of the biological sciences, making it possible to decipher patterns in amino acid sequences that encode different functions and activities. Within the field of AI, machine learning, and deep learning algorithms have been used to discover antimicrobial peptides. Due to their effectiveness and specificity, antimicrobial peptides (AMPs) hold excellent promise for treating various infections caused by pathogens. Antiviral peptides (AVPs) are a specific type of AMPs that have activity against certain viruses. Unlike the research focused on the development of tools and methods for the prediction of antimicrobial peptides, those related to the prediction of AVPs are still scarce. Given the significance of AVPs as potential pharmaceutical options for human and animal health and the ongoing AI revolution, we have reviewed and summarized the current machine learning and deep learning-based tools and methods available for predicting these types of peptides.

Current Status of Omics in Biological Quality Elements for Freshwater Biomonitoring

Freshwater ecosystems have been experiencing various forms of threats, mainly since the last century. The severity of this adverse scenario presents unprecedented challenges to human health, water supply, agriculture, forestry, ecological systems, and biodiversity, among other areas. Despite the progress made in various biomonitoring techniques tailored to specific countries and biotic communities, significant constraints exist, particularly in assessing and quantifying biodiversity and its interplay with detrimental factors. Incorporating modern techniques into biomonitoring methodologies presents a challenging topic with multiple perspectives and assertions. This review aims to present a comprehensive overview of the contemporary advancements in freshwater biomonitoring, specifically by utilizing omics methodologies such as genomics, metagenomics, transcriptomics, proteomics, metabolomics, and multi-omics. The present study aims to elucidate the rationale behind the imperative need for modernization in this field. This will be achieved by presenting case studies, examining the diverse range of organisms that have been studied, and evaluating the potential benefits and drawbacks associated with the utilization of these methodologies. The utilization of advanced high-throughput bioinformatics techniques represents a sophisticated approach that necessitates a significant departure from the conventional practices of contemporary freshwater biomonitoring. The significant contributions of omics techniques in the context of biological quality elements (BQEs) and their interpretations in ecological problems are crucial for biomonitoring programs. Such contributions are primarily attributed to the previously overlooked identification of interactions between different levels of biological organization and their responses, isolated and combined, to specific critical conditions.

Current state of molecular and metabolic strategies for the improvement of L-asparaginase expression in heterologous systems

Heterologous expression of L-asparaginase (L-ASNase) has become an important area of research due to its clinical and food industry applications. This review provides a comprehensive overview of the molecular and metabolic strategies that can be used to optimize the expression of L-ASNase in heterologous systems. This article describes various approaches that have been employed to increase enzyme production, including the use of molecular tools, strain engineering, and in silico optimization. The review article highlights the critical role that rational design plays in achieving successful heterologous expression and underscores the challenges of large-scale production of L-ASNase, such as inadequate protein folding and the metabolic burden on host cells. Improved gene expression is shown to be achievable through the optimization of codon usage, synthetic promoters, transcription and translation regulation, and host strain improvement, among others. Additionally, this review provides a deep understanding of the enzymatic properties of L-ASNase and how this knowledge has been employed to enhance its properties and production. Finally, future trends in L-ASNase production, including the integration of CRISPR and machine learning tools are discussed. This work serves as a valuable resource for researchers looking to design effective heterologous expression systems for L-ASNase production as well as for enzymes production in general.

Antibiotic discovery against Piscirickettsia salmonis using a combined in silico and in vitro approach

Piscirickettsia salmonis is one of the main pathogens causing considerable economic losses in salmonid farming. The DNA gyrase of several pathogenic bacteria has been the target of choice for antibiotic design and discovery for years, due to its key function during DNA replication. In this study, we carried out a combined in silico and in vitro approach to antibiotic discovery targeting the GyrA subunit of Piscirickettsia salmonis. The in silico results of this work showed that flumequine (-6.6 kcal/mol), finafloxacin (-7.2 kcal/mol), rosoxacin (-6.6 kcal/mol), elvitegravir (-6.4 kcal/mol), sarafloxacin (-8.3 kcal/mol), orbifloxacin (-7.9 kcal/mol), and sparfloxacin (-7.2 kcal/mol) are docked with good affinities in the DNA binding domain of the Piscirickettsia salmonis GyrA subunit. In the in vitro inhibition assay, it was observed that most of these molecules inhibit the growth of Piscirickettsia salmonis, except for elvitegravir. We believe that this methodology could help to significantly reduce the time and cost of antibiotic discovery trials to combat Piscirickettsia salmonis within the salmonid farming industry.

In Silico Design of a Chimeric Humanized L-asparaginase

Acute lymphoblastic leukemia (ALL) is the most common cancer among children worldwide, characterized by an overproduction of undifferentiated lymphoblasts in the bone marrow. The treatment of choice for this disease is the enzyme L-asparaginase (ASNase) from bacterial sources. ASNase hydrolyzes circulating L-asparagine in plasma, leading to starvation of leukemic cells. The ASNase formulations of E. coli and E. chrysanthemi present notorious adverse effects, especially the immunogenicity they generate, which undermine both their effectiveness as drugs and patient safety. In this study, we developed a humanized chimeric enzyme from E. coli L-asparaginase which would reduce the immunological problems associated with current L-asparaginase therapy. For these, the immunogenic epitopes of E. coli L-asparaginase (PDB: 3ECA) were determined and replaced with those of the less immunogenic Homo sapiens asparaginase (PDB:4O0H). The structures were modeled using the Pymol software and the chimeric enzyme was modeled using the SWISS-MODEL service. A humanized chimeric enzyme with four subunits similar to the template structure was obtained, and the presence of asparaginase enzymatic activity was predicted by protein-ligand docking.

Helicobacter pyloril-asparaginase: a study of immunogenicity from an in silico approach

Helicobacter pylori has become the causal agent of multiple forms of gastric disease worldwide, including gastric cancer. The enzyme l-asparaginase (ASNase) has been studied as a virulence factor. In this work, we performed an in silico investigation to characterize the immunological profile of H. pylori ASNase (HpASNase) to ascertain the possible implication of HpASNase immunogenicity in the H. pylori virulence mechanism. We applied a workflow based on bioinformatics tools, which, by calculating the relative frequency of immunogenic T-cell and B-cell epitopes, allowed us to predict the immunogenicity and allergenicity of HpASNase in silico. We also visualized the epitopes by mapping them into the native structure of the enzyme. We report for the first time the T-cell and B-cell epitope composition that contributes to the immunogenicity of this HpASNase, as well as the regions that could generate a hypersensitivity response in humans. ASNase from H. pylori resulted in highly immunogenic and allergenic. The high immunogenicity of HpASNase could imply the pathogenic mechanisms of H. pylori. This knowledge could be important for the development of new drugs against H. pylori infections.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03359-0.

Anthocyanins Found in Pinot Noir Waste Induce Target Genes Related to the Nrf2 Signalling in Endothelial Cells

Grape pomace is a source of anthocyanins, which can prevent cardiovascular diseases due to their antioxidant properties. Anthocyanin activity is associated with the ability to regulate oxidative stress through the transcription factor Nrf2. Thus, the present study aimed to evaluate if the anthocyanins found in Pinot noir pomace extract can affect the target genes related to the Nrf2 signalling pathway in endothelial cells. Our results highlight that the predominant anthocyanin in the Pinot noir pomace extract was malvidin-3-glucoside (3.7 ± 2.7 Eq. Malv-3-glu/kg). Molecular docking indicated that cyanidin-3-glucoside (-6.9 kcal/mol), malvidin-3-glucoside (-6.6 kcal/mol) and peonidin-3-glucoside (-6.6 kcal/mol) showed the highest affinities for the binding sites of the BTB domains in Keap1, suggesting that these components may modify the interaction of this protein with Nrf2. In addition, when HUVEC cells were exposed to different concentrations of Pinot noir pomace extract (100 µg/mL, 200 µg/mL, and 400 µg/mL), no changes in Nrf2 gene expression were observed. However, the gene expression of HO-1 and NQO1, which are in the signalling pathway of this transcription factor, increased according the concentrations of the extract (p = 0.0004 and p = 0.0084, respectively). In summary, our results show that anthocyanins play a very important role in Nrf2 activation and release, while at the same time not promoting its transcription. These preliminary results strongly suggest that the Pinot noir pomace extract can serve as a potent bioactive component source that protects cells against oxidative stress.

Analysis of Muscle Lipidome in Juvenile Rainbow Trout Fed Rapeseed Oil and Cochayuyo Meal

This study aimed to analyze the effects on the lipidome of juvenile Oncorhynchus mykiss muscle fed 90% Brassica napus “rapeseed” oil and different amounts of Durvillaea antarctica “Cochayuyo” meal (1.5, 3 and 6%) as a replacement for cellulose. The analysis allowed for the identification of 329 lipids, mainly represented by phospholipids and fatty esters. The inclusion of Brassica napus oil significantly increased the levels of C18:2 species and fatty esters of hydroxylated fatty acids, which could play a bioactive role in human health. One of the most abundant lipids in all fillets was Phosphatidylcholine 33:6, which, according to the literature, could be considered a biomarker for the identification of Oncorhynchus mykiss. In all experimental diets, the species Phosphatidylethanolamine 15:1-18:24 showed four-fold higher levels than the control; increments of n-3- and n-6-rich phospholipids were also observed. Diets containing Durvillaea antarctica meal did not generate more significant variation in fish muscle phospholipids relative to the muscle of the rapeseed-oil-only group. These lipid species consist of medium- and long-chain fatty acids with different degrees of unsaturation. Still, it appears that the rapeseed oil masks the lipid contribution of the meal, possibly due to the low levels of total lipids in the macroalgae.

A bioinformatics analysis of the CatSper channel in the class Actinopterygii

The regulation of sperm motility is controlled by several variables, including mainly ion concentrations. In fish, Ca²⁺ concentrations play an important role in the regulation of sperm motility, and several reports highlight the importance of certain Ca²⁺ channels in the regulation of this cell function. CatSper is a calcium channel scarcely studied in fish. In the species Salmo salar, it has been shown that it is key in the regulation of sperm motility. Taking into account the relevance of this channel in sperm activation in fish, in this study we evaluated the presence and probable functionality of this channel in the class Actinopterygii. For this purpose, a rational bioinformatic analysis was carried out, which had been previously validated using in vitro techniques by our group. The bioinformatic analysis of the present work revealed that the functionality of CatSper of the species of the class Actinopterygii could be exclusive to freshwater and anadromous fish species. The results of this study showed that only some anadromous and freshwater fish species contain 11 subunits of the CatSper channel, which are enough to trigger sperm motility. Consequently, this study provides new data for a better understanding of the sperm activation mechanism in fish.

Neurobiological Promises of the Bitter Diterpene Lactone Andrographolide

Andrographolide (ANDRO), a bitter diterpene lactone found in Andrographis paniculata (Burm.f.) Nees, possesses several biological effects such as antioxidant, anti-inflammatory, and organo-protective effects. Scientific reports suggest that it also has neuroprotective capacity in various test systems. The purpose of this review was to synthesize the neuropharmacological properties of ANDRO and highlight the molecular mechanisms of action that highlight these activities. A careful search was done in PubMed and Google Scholar databases using specific keywords. Findings suggest that ANDRO possess neuroprotective, analgesic, and antifatigue effects. Prominent effects were stated on neuro-inflammation, cerebral ischemia, Alzheimer's and Parkinson's diseases, multiple sclerosis, and brain cancer in mice and rats. Furthermore, ANDRO and its derivatives can enhance memory and learning capacity in experimental animals (rats) without causing any toxicity in the brain. Thus, ANDRO may be one of the most promising plant-based psychopharmacological lead compounds for new drug development.

VirVACPRED: A Web Server for Prediction of Protective Viral Antigens

Viral antigens are key in the development of vaccines that prevent or eradicate infections caused by these pathogens. Bioinformatics tools are modern alternatives that facilitate the discovery of viral antigens, reducing the costs of experimental assays. We developed a bioinformatics tool called VirVACPRED, which is highly efficient in predicting viral antigens. In this study, we obtained a model based on the gradient boosting classifier, which showed high performance during the training, leave-one-out cross-validation (accuracy = 0.7402, sensitivity = 0.7319, precision = 0.7503, F1 = 0.7251, kappa = 0.4774, Matthews correlation coefficient = 0.4981) and testing (accuracy = 0.8889, sensitivity = 1.0, precision = 0.8276, F1 = 0.9057, kappa = 0.7734, Matthews correlation coefficient = 0.7941). VirVACPRED is a robust tool that can be of great help in the search and proposal of new viral antigens, which can be considered in the development of future vaccines against infections caused by viruses.

TAP 1.0: A robust immunoinformatic tool for the prediction of tumor T-cell antigens based on AAindex properties

Immunotherapy is a research area with great potential in drug discovery for cancer treatment. Because of the capacity of tumor antigens to activate the immune response and promote the destruction of tumor cells, they are considered excellent immunotherapeutic drugs. In this work, we evaluated fifteen machine learning algorithms for the classification of tumor antigens. For this purpose, we build robust datasets, carefully selected from the TANTIGEN and IEDB databases. The feature computation of all antigens in this study was performed by developing a script written in Python 3.8, which allowed the calculation of 544 physicochemical and biochemical properties extracted from the AAindex database. All classifiers were subjected to the training, 10-fold cross-validation, and testing on an independent dataset. The results of this study showed that the quadratic discriminant classifier presented the best performance measures over the independent dataset, accuracy = 0.7384, AUC = 0.817, recall = 0.676, precision = 0.7857, F1 = 0.713, kappa = 0.4764, and Matthews correlation coefficient = 0.4834, outperforming common machine learning classifiers used in the bioinformatics area. We believe that our prediction model could be of great importance in the field of cancer immunotherapy for the search of potential tumor antigens. Taking all aspects mentioned before, we developed an immunoinformatic tool called TAP 1.0 with a friendly interface for tumor antigens prediction, available at https://tapredictor.herokuapp.com/.

The voltage-gated T-type Ca2+ channel is key to the sperm motility of Atlantic salmon (Salmo salar)

Ca²⁺ is a key element in the sperm activation process of Salmo salar. However, the molecular mechanisms by which this ion enters the sperm cell have been poorly studied. In this study, we examined, for the first time, the role of the voltage-gated T-type Ca²⁺ channel in the activation of sperm motility of Salmo salar. Using an in vitro inhibition assay, a significant decrease in total and progressive motility (P < 0.0001) was observed in Salmo salar sperm when they were treated with NNC-55-0396, a highly selective blocker. The in silico analysis showed that this blocker is docked with a strong affinity for the pore of the voltage-gated T-type calcium channel suggesting the blocking of Ca²⁺ ions. The results show that the T-type voltage-gated Ca²⁺ channel is key to sperm motility in Salmo salar.

Permanent genetic resources added to Molecular Ecology Resources Database 1 December 2010-31 January 2011

This article documents the addition of 238 microsatellite marker loci to the Molecular Ecology Resources Database. Loci were developed for the following species: Alytes dickhilleni, Arapaima gigas, Austropotamobius italicus, Blumeria graminis f. sp. tritici, Cobitis lutheri, Dendroctonus ponderosae, Glossina morsitans morsitans, Haplophilus subterraneus, Kirengeshoma palmata, Lysimachia japonica, Macrolophus pygmaeus, Microtus cabrerae, Mytilus galloprovincialis, Pallisentis (Neosentis) celatus, Pulmonaria officinalis, Salminus franciscanus, Thais chocolata and Zootoca vivipara. These loci were cross-tested on the following species: Acanthina monodon, Alytes cisternasii, Alytes maurus, Alytes muletensis, Alytes obstetricans almogavarii, Alytes obstetricans boscai, Alytes obstetricans obstetricans, Alytes obstetricans pertinax, Cambarellus montezumae, Cambarellus zempoalensis, Chorus giganteus, Cobitis tetralineata, Glossina fuscipes fuscipes, Glossina pallidipes, Lysimachia japonica var. japonica, Lysimachia japonica var. minutissima, Orconectes virilis, Pacifastacus leniusculus, Procambarus clarkii, Salminus brasiliensis and Salminus hilarii.

Flow cytometric analysis of peroxidative activity in granulocytes from coronary and peripheral blood in acute myocardial ischemia and reperfusion in dogs: protective effect of methionine

BACKGROUND

Methionine has shown protective effects in experimental models of myocardial infarction and is highly reactive to oxidative compounds produced by polymorphonuclear leukocytes (PMN), which in turn have been associated with myocardial damage. We have investigated the effect of methionine administration on spontaneous leukocyte peroxidative activity in myocardial ischemia and reperfusion.

METHODS

In anesthetized dogs, with coronary occlusion (90 min) and reperfusion (90 min), PMN activation was measured by flow cytometric determination of H(2)O(2) with dihydrorhodamine 123, and correlated to hemodynamic parameters and infarct presence. To assess a possible direct effect of methionine, H(2)O(2) and superoxide were measured by flow cytometry in dog leukocyte suspensions following in vitro stimulation with f-MLP.

RESULTS

PMN peroxidative activity in saline-treated dogs increased significantly after coronary occlusion and after reperfusion. These changes were greater in coronary venous blood than in femoral blood. Methionine administration (150 mg/kg, i.v.) before occlusion totally suppressed PMN activation, both after occlusion and reperfusion.

CONCLUSIONS

PMN are promptly activated in myocardial ischemia, and methionine administration prevents such activation. However, methionine has no direct effect on spontaneous peroxidative activity, and f-MLP induced peroxidative activity. These in vivo effects of methionine, may additionally contribute to explain its protective role in experimental -788-877-7QQ8-8-7-88-8-8778--8Q78-----8--8-Q-7-Q7----- --------------8888 888888-7777777777777777777777777777777----------------888888888888888888 8877777--87--------8-----------------7-8888-887-----------8----8-8-87777 7777777------------------------------------------------------T7OW

Immobilization of ocelots and bobcats with ketamine hydrochloride and xylazine hydrochloride

We immobilized 10 ocelots (Felis pardalis), and 21 bobcats (F. rufus) in south Texas (USA) during March to November 1991 with a mixture of ketamine hydrochloride (KH) and xylazine hydrochloride (XH); two ocelots were immobilized twice. Species were immobilized with (mean +/- SE) 14.7 +/- 1.6 mg KH/kg body mass for ocelots, 13.3 +/- 1.8 mg KH/kg for bobcats, and 1.1 +/- 0.1 mg XH/kg and 1.2 +/- 0.1 mg XH/kg for ocelots and bobcats, respectively. Immobilization times in bobcats were longer (P = 0.08) than in ocelots. Adult female ocelots (18.5 +/- 2.6 mg/kg) needed larger (P < 0.05) doses of KH than adult males (12.0 +/- 1.7 mg/kg). Bobcats were immobilized during summer with lower initial (8.6 +/- 0.9 mg/kg, P < 0.001) and total (10.1 +/- 1.3 mg/kg, P = 0.02) doses of KH than bobcats immobilized in winter (14.5 +/- 1.0 mg/kg, and 18.5 +/- 3.8 mg/kg, respectively); summer immobilization times (44.3 +/- 3.8 min) were also shorter (P = 0.03) than during winter (59.1 +/- 5.2 min). Bobcats immobilized during summer had lower (P < 0.01) initial rectal temperatures (39.4 +/- 0.2 C) than bobcats trapped in winter (41.1 +/- 0.4 C). Overall, we observed no effects of KH-XH dose on body temperature.

Immobilization of coypus (Myocastor coypus) with ketamine hydrochloride and xylazine hydrochloride

A combination of 100 mg/ml of ketamine hydrochloride (Ket) and 20 mg/ml of xylazine hydrochloride (Xyl) was used to immobilize coypus (Myocastor coypus). Eight mature coypus (four males and four females) were injected intramuscularly with doses ranging from 2.33 to 6.25 mg/kg of KET and 0.25 to 0.86 mg/kg of Xyl. The mean (+/- SE) time for induction, arousal, and recovery were 7.3 +/- 2 min, 23.5 +/- 0.3 min and 46 +/- 2.5 min, respectively. The mean +/- SE doses injected were 4.07 +/- 0.52 mg/kg Ket (range, 2.33 to 6.25 mg/kg) and 0.5 +/- 0.08 mg/kg Xyl (range, 0.25 to 0.86 mg/kg). No adverse responses were observed in any of the animals treated.

Immobilization of the endangered Iberian lynx with xylazine- and ketamine-hydrochloride

A combination of the dissociative anesthetic ketamine hydrochloride (KH) and the sedative xylazine hydrochloride (XH) was used to immobilize 31 wild Iberian lynx (Felis pardina) 45 times at Doñana National Park, Spain. A mean (+/- SE) dose of 4.6 (+/- 0.2) mg/kg KH and 4.0 (+/- 0.2) mg/kg XH resulted in mean (+/- SE) induction time of 5.6 (+/- 0.3) min and mean (+/- SE) first reaction time of 59.3 (+/- 6.5) min. Convulsions occurred four times (9%), but with no noteworthy consequences.