New advances in the protective mechanisms of acidic pH after ischemia: Participation of NO.

BACKGROUND: It has been previously demonstrated that the maintenance of ischemic acidic pH or the delay of intracellular pH recovery at the onset of reperfusion decreases ischemic-induced cardiomyocyte death. OBJECTIVE: To examine the role played by nitric oxide synthase (NOS)/NO-dependent pathways in the effects of acidic reperfusion in a regional ischemia model. METHODS: Isolated rat hearts perfused by Langendorff technique were submitted to 40 min of left coronary artery occlusion followed by 60 min of reperfusion (IC). A group of hearts received an acid solution (pH = 6.4) during the first 2 min of reperfusion (AR) in absence or in presence of l-NAME (NOS inhibitor). Infarct size (IS) and myocardial function were determined. In cardiac homogenates, the expression of P-Akt, P-endothelial and inducible isoforms of NOS (P-eNOS and iNOS) and the level of 3-nitrotyrosine were measured. In isolated cardiomyocytes, the intracellular NO production was assessed by confocal microscopy, under control and acidic conditions. Mitochondrial swelling after Ca2+ addition and mitochondrial membrane potential (Δψ) were also determined under control and acidosis. RESULTS: AR decreased IS, improved postischemic myocardial function recovery, increased P-Akt and P-eNOS, and decreased iNOS and 3-nitrotyrosine. NO production increased while mitochondrial swelling and Δψ decreased in acidic conditions. l-NAME prevented the beneficial effects of AR. CONCLUSIONS: Our data strongly supports that a brief acidic reperfusion protects the myocardium against the ischemia-reperfusion injury through eNOS/NO-dependent pathways.

Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling.

In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity and scarcity of available biomedical data challenge the development of predictive models. Combining nonlinear machine learning together with multicondition descriptors offers a solution for using data from various assays to create a robust model. This work applies multicondition descriptors (MCDs) to develop a QSTR (Quantitative Structure-Toxicity Relationship) model based on a large toxicity data set comprising more than 80,000 compounds and 59 different end points (122,572 data points). The prediction capabilities of developed single-task multi-end point machine learning models as well as a novel data analysis approach with the use of Convolutional Neural Networks (CNN) are discussed. The results show that using MCDs significantly improves the model and using them with CNN-1D yields the best result (R2train = 0.93, R2ext = 0.70). Several structural features showed a high level of contribution to the toxicity, including van der Waals surface area (VSA), number of nitrogen-containing fragments (nN+), presence of S-P fragments, ionization potential, and presence of C-N fragments. The developed models can be very useful tools to predict the toxicity of various compounds under different conditions, enabling quick toxicity assessment of new compounds.

NANO.PTML model for read-across prediction of nanosystems in neurosciences. computational model and experimental case of study.

Neurodegenerative diseases involve progressive neuronal death. Traditional treatments often struggle due to solubility, bioavailability, and crossing the Blood-Brain Barrier (BBB). Nanoparticles (NPs) in biomedical field are garnering growing attention as neurodegenerative disease drugs (NDDs) carrier to the central nervous system. Here, we introduced computational and experimental analysis. In the computational study, a specific IFPTML technique was used, which combined Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) to select the most promising Nanoparticle Neuronal Disease Drug Delivery (N2D3) systems. For the application of IFPTML model in the nanoscience, NANO.PTML is used. IF-process was carried out between 4403 NDDs assays and 260 cytotoxicity NP assays conducting a dataset of 500,000 cases. The optimal IFPTML was the Decision Tree (DT) algorithm which shown satisfactory performance with specificity values of 96.4% and 96.2%, and sensitivity values of 79.3% and 75.7% in the training (375k/75%) and validation (125k/25%) set. Moreover, the DT model obtained Area Under Receiver Operating Characteristic (AUROC) scores of 0.97 and 0.96 in the training and validation series, highlighting its effectiveness in classification tasks. In the experimental part, two samples of NPs (Fe3O4_A and Fe3O4_B) were synthesized by thermal decomposition of an iron(III) oleate (FeOl) precursor and structurally characterized by different methods. Additionally, in order to make the as-synthesized hydrophobic NPs (Fe3O4_A and Fe3O4_B) soluble in water the amphiphilic CTAB (Cetyl Trimethyl Ammonium Bromide) molecule was employed. Therefore, to conduct a study with a wider range of NP system variants, an experimental illustrative simulation experiment was performed using the IFPTML-DT model. For this, a set of 500,000 prediction dataset was created. The outcome of this experiment highlighted certain NANO.PTML systems as promising candidates for further investigation. The NANO.PTML approach holds potential to accelerate experimental investigations and offer initial insights into various NP and NDDs compounds, serving as an efficient alternative to time-consuming trial-and-error procedures.

Exploring proteasome inhibition using atomic weighted vector indices and machine learning approaches.

Ubiquitin-proteasome system (UPS) is a highly regulated mechanism of intracellular protein degradation and turnover. The UPS is involved in different biological activities, such as the regulation of gene transcription and cell cycle. Several researchers have applied cheminformatics and artificial intelligence methods to study the inhibition of proteasomes, including the prediction of UPP inhibitors. Following this idea, we applied a new tool for obtaining molecular descriptors (MDs) for modeling proteasome Inhibition in terms of EC50 (µmol/L), in which a set of new MDs called atomic weighted vectors (AWV) and several prediction algorithms were used in cheminformatics studies. In the manuscript, a set of descriptors based on AWV are presented as datasets for training different machine learning techniques, such as linear regression, multiple linear regression (MLR), random forest (RF), K-nearest neighbors (IBK), multi-layer perceptron, best-first search, and genetic algorithm. The results suggest that these atomic descriptors allow adequate modeling of proteasome inhibitors despite artificial intelligence techniques, as a variant to build efficient models for the prediction of inhibitory activity.

Development of Activity Rules and Chemical Fragment Design for In Silico Discovery of AChE and BACE1 Dual Inhibitors against Alzheimer's Disease.

Multi-target drug development has become an attractive strategy in the discovery of drugs to treat of Alzheimer's disease (AzD). In this study, for the first time, a rule-based machine learning (ML) approach with classification trees (CT) was applied for the rational design of novel dual-target acetylcholinesterase (AChE) and ß-site amyloid-protein precursor cleaving enzyme 1 (BACE1) inhibitors. Updated data from 3524 compounds with AChE and BACE1 measurements were curated from the ChEMBL database. The best global accuracies of training/external validation for AChE and BACE1 were 0.85/0.80 and 0.83/0.81, respectively. The rules were then applied to screen dual inhibitors from the original databases. Based on the best rules obtained from each classification tree, a set of potential AChE and BACE1 inhibitors were identified, and active fragments were extracted using Murcko-type decomposition analysis. More than 250 novel inhibitors were designed in silico based on active fragments and predicted AChE and BACE1 inhibitory activity using consensus QSAR models and docking validations. The rule-based and ML approach applied in this study may be useful for the in silico design and screening of new AChE and BACE1 dual inhibitors against AzD.

Identification of serologically active Helicobacter pylori antigens related to alterations in serum pepsinogen levels.

Gastric adenocarcinoma is associated with Helicobacter pylori infection. The transition to a carcinogenic process is preceded by glandular atrophy and serum levels of pepsinogen I and II (PGI and PGII) correlate with this type of gastric lesions. Possible associations of serum PG levels in relation to the frequency of serological activity against H. pylori antigens were studied. Serum samples from patients with gastric pathology associated with H. pylori (n=26) and asymptomatic individuals as controls (n=37) were used. Seroactive antigens were identified by immunoblot using a protein extract of H. pylori. The antibody titers anti-H. pylori and the concentration of PGs in serum was determined by ELISA. Thirty-one seroactive antigens were identified, nine of which exhibited a differential frequency between both groups (116.7, 68.8, 61.9, 54.9, 45.6, 38.3, 36.5, 33.8 and 30.1kDa) and only 3 were related to altered levels of PGs in serum. In the control group, the seropositivity of the 33.8kDa antigen was related to an increase in PGII, while the 68.8kDa antigen was related to normal PG values (decreased PGII and elevated PGI/PGII levels) indicating that seropositivity to this antigen could be a protective factor to gastric pathology. The seropositivity of the 54.9kDa antigen was related to altered values of PGs indicative of inflammation and gastric atrophy (increased in PGII and decreased in PGI/PGII). The identification of serum alterations in pepsinogen levels related to seropositivity to H. pylori 33.8, 54.9 and 68.8kDa antigens sets a precedent for further study as possible prognostic serological biomarkers.

Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds.

Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD vs MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algorithm on a huge dataset from the ChEMBL database, which contains >155,000 AD assays vs >40 MNs of multiple bacteria species. We built a linear discriminant analysis (LDA) and 17 ML models centered on the linear index and based on atoms to predict antibacterial compounds. The IFPTML-LDA model presented the following results for the training subset: specificity (Sp) = 76% out of 70,000 cases, sensitivity (Sn) = 70%, and Accuracy (Acc) = 73%. The same model also presented the following results for the validation subsets: Sp = 76%, Sn = 70%, and Acc = 73.1%. Among the IFPTML nonlinear models, the k nearest neighbors (KNN) showed the best results with Sn = 99.2%, Sp = 95.5%, Acc = 97.4%, and Area Under Receiver Operating Characteristic (AUROC) = 0.998 in training sets. In the validation series, the Random Forest had the best results: Sn = 93.96% and Sp = 87.02% (AUROC = 0.945). The IFPTML linear and nonlinear models regarding the ADs vs MNs have good statistical parameters, and they could contribute toward finding new metabolic mutations in antibiotic resistance and reducing time/costs in antibacterial drug research.

The Hydrolysis Rate of Paraoxonase-1 Q and R Isoenzymes: An In Silico Study Based on In Vitro Data.

Human serum paraoxonase-1 (PON1) is an important hydrolase-type enzyme found in numerous tissues. Notably, it can exist in two isozyme-forms, Q and R, that exhibit different activities. This study presents an in silico (QSAR, Docking, MD and QM/MM) study of a set of compounds on the activity towards the PON1 isoenzymes (QPON1 and RPON1). Different rates of reaction for the Q and R isoenzymes were analyzed by modelling the effect of Q192R mutation on active sites. It was concluded that the Q192R mutation is not even close to the active site, while it is still changing the geometry of it. Using the combined genetic algorithm with multiple linear regression (GA-MLR) technique, several QSAR models were developed and relative activity rates of the isozymes of PON1 explained. From these, two QSAR models were selected, one each for the QPON1 and RPON1. Best selected models are four-variable MLR models for both Q and R isozymes with squared correlation coefficient R2 values of 0.87 and 0.83, respectively. In addition, the applicability domain of the models was analyzed based on the Williams plot. The results were discussed in the light of the main factors that influence the hydrolysis activity of the PON1 isozymes.

Correlation between three assessment pain tools in subacromial pain syndrome.

OBJECTIVE: To compare the correlation of Visual Analog Scale with pain subsections of Shoulder Pain and Disability Index and Constant-Murley Score in subacromial pain syndrome patients. DESIGN: Single cross-sectional analysis. SETTING: Hospital Rehabilitation Department. METHODS: The assessment tools were applied at baseline. Correlations between Visual Analog Scale, Shoulder Pain and Disability Index and Constant-Murley Score pain subsections were assessed by Pearson correlation coefficient. Linear regression models were calculated between scales. Statistical significance was set at two-sided p < 0.05. RESULTS: Forty-three patients were included. Pearson's correlation between assessments was for Visual Analog Scale-Shoulder Pain Disability Index-pain (r = 0.61, p < 0.001) and for Visual Analog Scale-Constant Murley Score-pain were (r = -0.74, p < 0.001). Visual Analog Scale-Shoulder Pain and Disability Index-pain determination coefficient was r2 = 0.37 and r2 = 0.54 for Visual Analog Scale-Constant-Murley Score-pain. CONCLUSIONS: Visual Analog Scale showed better correlation with Constant Murley Score-pain than with Shoulder Pain and Disability Index-pain in subacromial pain syndrome patients.

Characterisation by Excitation-Emission Matrix Fluorescence Spectroscopy of Pigments in Mucus Secreted of Earthworm Eisenia foetida Exposed to Lead.

The earthworm exposed to toxics shows physiological responses as: avoidance and mucus secretion. Heavy metals are particularly toxic to earthworms and the mucus secretion has been considered as a defence mechanism against undesirable substance. The chromophores present in the mucus secretion of Eisenia foetida have been poorly studied. Mucus secretion of E. foetida was induced by PbCl2. High PbCl2 concentrations provoked abundant mucus secretion which showed fluorescence when illuminated by UV light. Dialysis membrane separation, UV Visible and Excitation-Emission Matrix Fluorescence (EEM) spectroscopy were used to characterise the fluorescent pigments. EEM spectroscopy analysis of the mucus secretion signalled three excitation-emission peaks at: 310/380 nm, 370/520 nm and 440/520 nm. Two fluorophores were separated by dialysis. One of them matched the fluorescent compound riboflavin excitation-emission profile; the other is a protein with a peak 290/350 nm. Native-PAGE electrophoresis was conducted to assess the riboflavin-biding ability of the coelomic fluid protein produced by Eisenia foetida showing a high riboflavin-biding ability.

Efficacy of high-intensity laser therapy in subacromial impingement syndrome: a three-month follow-up controlled clinical trial.

OBJECTIVES: To evaluate the effectiveness of high-intensity laser therapy on shoulder pain and function in subacromial impingement syndrome. DESIGN: Clinical controlled trial with alternate allocation. SETTING: Hospital Department of Rehabilitation. SUBJECTS: A total of 46 participants with subacromial impingement syndrome. INTERVENTION: Participants were sequence allocated to an intervention group (high-intensity laser therapy + exercise therapy) and control group (sham-laser + exercise therapy) and received 15 sessions (five days a week during three weeks). MAIN MEASURES: Patiens were evaluated at baseline, after 15 sessions, and at one month and at three months after completing the intervention. The main outcome variables were pain and functionality as measured by visual analogue scale; pressure pain threshold; Shoulder Pain and Disability Index; Constant-Murley Score; and QuickDASH. Secondary outcomes were number of sessions at discharge and drug use. RESULTS: A total of 21 patients in high-intensity laser therapy group (56.7 ± 8.9 years) and 22 patients in sham-laser group (61.3 ± 8.9 years) concluded the study. Visual analogue scale (cm) at baseline, one-month, and three-months were 6.2 ± 0.5, 3 ± 2.6, and 2.6 ± 2.4 for the control group and 5.4 ± 1.5, 3.6 ± 1.3, and 1.8 ± 1.7 for experimental group, respectively. Shoulder Pain and Disability Index (points) at baseline, one-month, and three-monts were 51.8 ± 16.1, 16.3 ± 16.1, and 13.6 ± 17.1 in the control group and 41.8 ± 20.6, 20.5 ± 19.7, 11 ± 14.5 in experimental group, respectively. No differences were found between groups ( P > 0.05). CONCLUSION: The effect of high-intensity laser therapy plus exercise is not higher than exercise alone to reduce pain and improve functionality in patients with subacromial syndrome.

Spanish Version of the Whiplash Disability Questionnaire in Adults With Acute Whiplash-Associated Disorders.

OBJECTIVE: The purpose of this study was to develop and validate a Spanish version of the Whiplash Disability Questionnaire (WDQ) for the Spanish population with acute whiplash-associated disorder (WAD). METHODS: This was a cross-sectional questionnaire validation study. Adults with acute WAD (grade I to III) were enrolled within 3 weeks of their injury. A blinded forward and back translation of the WDQ was made from English to Spanish, and the resulting back-translation version was compared with the original. Patients with WAD completed the Spanish version of the 13-item WDQ. The developed questionnaire was assessed using psychometric statistical analysis including correlation with the numerical rating score for pain, Northwick Park Neck Pain Questionnaire, Neck Disability Index, and 36-item Short Form Health Survey. RESULTS: Fifty-six patients completed the questionnaire, the mean age was 33.9 years (standard deviation [SD] = 10.5), and 76.8% were women. Participants were enrolled 13.9 days (SD 4.9) after the injury, with 14.3% presenting with WAD grade I and 85.7% with WAD grade II. The mean WDQ score was 62 (SD = 31). Two factors were detected, and the factor structure remained stable after translation. Positive correlations were identified between the total WDQ score and the numerical rating score, Neck Pain Questionnaire, and Neck Disability Index results, with a strong negative correlation with the 36-item Short Form Health Survey. CONCLUSION: The Spanish version of WDQ is psychometrically reliable and a valid instrument to measure the disability status in patients with acute WAD within the clinic.

Age- and size-dependent resistance to chytridiomycosis in the invasive cane toad Rhinella marina.

In Australia, the cane toad Rhinella marina and chytrid fungus Batrachochytrium dendrobatidis (Bd) are examples of invasive species that have had dramatic impacts on native fauna. However, little is known about the interaction between Bd and cane toads. We aimed to explore the interaction of these 2 species in 3 parts. First, we collated data from the literature on Bd infection in wild cane toads. Second, we tested the susceptibility of recently metamorphosed cane toads to Bd infection. Finally, we modelled the distribution of the 2 species in Australia to identify where they overlap and, therefore, might interact. Through our data collation, we found that adult cane toads are infrequently infected and do not carry high infection burdens; however, our infection experiment showed that metamorphs are highly susceptible to infection and disease, but resistance appears to increase with increasing toad size. Niche modelling revealed overlapping distributions and the potential for cane toads to be affected by chytridiomycosis in the wild. While Bd can cause mortality in small juveniles in the laboratory, warm microhabitats used by wild toads likely prevent infection, and furthermore, high mortality of juveniles is unlikely to affect the adult populations because they are highly fecund. However, to demonstrate the impact of Bd on wild cane toad populations, targeted field studies are required to assess (1) the overall impact of chytridiomycosis on recruitment especially in cooler areas more favourable for Bd and (2) whether cane toad juveniles can amplify Bd exposure of native amphibian species in these areas.

The role of Omega-3 and Omega-9 fatty acids for the treatment of neuropathic pain after neurotrauma.

Omega-3 polyunsaturated fatty acids (PUFAs), such as docosaexaenoic acid (DHA) and eicosapentaenoic acid (EPA), mediate neuroactive effects in experimental models of traumatic peripheral nerve and spinal cord injury. Cellular mechanisms of PUFAs include reduced neuroinflammation and oxidative stress, enhanced neurotrophic support, and activation of cell survival pathways. Bioactive Omega-9 monounsaturated fatty acids, such as oleic acid (OA) and 2-hydroxy oleic acid (2-OHOA), also show therapeutic effects in neurotrauma models. These FAs reduces noxious hyperreflexia and pain-related anxiety behavior following peripheral nerve injury and improves sensorimotor function following spinal cord injury (SCI), including facilitation of descending inhibitory antinociception. The relative safe profile of neuroactive fatty acids (FAs) holds promise for the future clinical development of these molecules as analgesic agents. This article is part of a Special Issue entitled: Membrane Lipid Therapy: Drugs Targeting Biomembranes edited by Pablo V. Escribá.

Microclimates Might Limit Indirect Spillover of the Bat Borne Zoonotic Hendra Virus.

Infectious diseases are transmitted when susceptible hosts are exposed to pathogen particles that can replicate within them. Among factors that limit transmission, the environment is particularly important for indirectly transmitted parasites. To try and assess a pathogens' ability to be transmitted through the environment and mitigate risk, we need to quantify its decay where transmission occurs in space such as the microclimate harbouring the pathogen. Hendra virus, a Henipavirus from Australian Pteropid bats, spills-over to horses and humans, causing high mortality. While a vaccine is available, its limited uptake has reduced opportunities for adequate risk management to humans, hence the need to develop synergistic preventive measures, like disrupting its transmission pathways. Transmission likely occurs shortly after virus excretion in paddocks; however, no survival estimates to date have used real environmental conditions. Here, we recorded microclimate conditions and fitted models that predict temperatures and potential evaporation, which we used to simulate virus survival with a temperature-survival model and modification based on evaporation. Predicted survival was lower than previously estimated and likely to be even lower according to potential evaporation. Our results indicate that transmission should occur shortly after the virus is excreted, in a relatively direct way. When potential evaporation is low, and survival is more similar to temperature dependent estimates, transmission might be indirect because the virus can wait several hours until contact is made. We recommend restricting horses' access to trees during night time and reducing grass under trees to reduce virus survival.

Longitudinal estimation of intramuscular Tibialis Anterior coherence during subacute spinal cord injury: relationship with neurophysiological, functional and clinical outcome measures.

BACKGROUND: Estimation of surface intramuscular coherence has been used to indirectly assess pyramidal tract activity following spinal cord injury (SCI), especially within the 15-30 Hz bandwidth. However, change in higher frequency (>40 Hz) muscle coherence during SCI has not been characterised. Thus, the objective of this study was to identify change of high and low frequency intramuscular Tibialis Anterior (TA) coherence during incomplete subacute SCI. METHODS: Fifteen healthy subjects and 22 subjects with motor incomplete SCI (American Spinal Injury Association Impairment Scale, AIS, C or D grade) were recruited and tested during 4 sessions performed at 2-week intervals up to 8 months after SCI. Intramuscular TA coherence estimation was calculated within the 10-60 Hz bandwidth during controlled maximal isometric and isokinetic foot dorsiflexion. Maximal voluntary dorsiflexion torque, gait function measured with the WISCI II scale, and TA motor evoked potentials (MEP) were recorded. RESULTS: During subacute SCI, significant improvement in total lower limb manual muscle score, TA muscle strength and gait function were observed. No change in TA MEP amplitude was identified. Significant increase in TA coherence was detected in the 40-60 Hz, but not the 15-30 Hz bandwidth. The spasticity syndrome was associated with lower 15-30 Hz TA coherence during maximal isometric dorsiflexion and higher 10-60 Hz coherence during fast isokinetic movement (p < 0.05). CONCLUSIONS: Longitudinal estimation of neurophysiological and clinical measures during subacute SCI suggest that estimation of TA muscle coherence during controlled movement provides indirect information regarding adaptive and maladaptive motor control mechanisms during neurorehabilitation.

Exploring different strategies for imbalanced ADME data problem: case study on Caco-2 permeability modeling.

In many absorption, distribution, metabolism, and excretion (ADME) modeling problems, imbalanced data could negatively affect classification performance of machine learning algorithms. Solutions for handling imbalanced dataset have been proposed, but their application for ADME modeling tasks is underexplored. In this paper, various strategies including cost-sensitive learning and resampling methods were studied to tackle the moderate imbalance problem of a large Caco-2 cell permeability database. Simple physicochemical molecular descriptors were utilized for data modeling. Support vector machine classifiers were constructed and compared using multiple comparison tests. Results showed that the models developed on the basis of resampling strategies displayed better performance than the cost-sensitive classification models, especially in the case of oversampling data where misclassification rates for minority class have values of 0.11 and 0.14 for training and test set, respectively. A consensus model with enhanced applicability domain was subsequently constructed and showed improved performance. This model was used to predict a set of randomly selected high-permeability reference drugs according to the biopharmaceutics classification system. Overall, this study provides a comparison of numerous rebalancing strategies and displays the effectiveness of oversampling methods to deal with imbalanced permeability data problems.

Hendra virus survival does not explain spillover patterns and implicates relatively direct transmission routes from flying foxes to horses.

Hendra virus (HeV) is lethal to humans and horses, and little is known about its epidemiology. Biosecurity restrictions impede advances, particularly on understanding pathways of transmission. Quantifying the environmental survival of HeV can be used for making decisions and to infer transmission pathways. We estimated HeV survival with a Weibull distribution and calculated parameters from data generated in laboratory experiments. HeV survival rates based on air temperatures 24 h after excretion ranged from 2 to 10 % in summer and from 12 to 33 % in winter. Simulated survival across the distribution of the black flying fox (Pteropus alecto), a key reservoir host, did not predict spillover events. Based on our analyses we concluded that the most likely pathways of transmission did not require long periods of virus survival and were likely to involve relatively direct contact with flying fox excreta shortly after excretion.

Ecological dynamics of emerging bat virus spillover.

Viruses that originate in bats may be the most notorious emerging zoonoses that spill over from wildlife into domestic animals and humans. Understanding how these infections filter through ecological systems to cause disease in humans is of profound importance to public health. Transmission of viruses from bats to humans requires a hierarchy of enabling conditions that connect the distribution of reservoir hosts, viral infection within these hosts, and exposure and susceptibility of recipient hosts. For many emerging bat viruses, spillover also requires viral shedding from bats, and survival of the virus in the environment. Focusing on Hendra virus, but also addressing Nipah virus, Ebola virus, Marburg virus and coronaviruses, we delineate this cross-species spillover dynamic from the within-host processes that drive virus excretion to land-use changes that increase interaction among species. We describe how land-use changes may affect co-occurrence and contact between bats and recipient hosts. Two hypotheses may explain temporal and spatial pulses of virus shedding in bat populations: episodic shedding from persistently infected bats or transient epidemics that occur as virus is transmitted among bat populations. Management of livestock also may affect the probability of exposure and disease. Interventions to decrease the probability of virus spillover can be implemented at multiple levels from targeting the reservoir host to managing recipient host exposure and susceptibility.