Seeking arrangements: cell contact as a cleavage-stage biomarker.

RESEARCH QUESTION: What can three-dimensional cell contact networks tell us about the developmental potential of cleavage-stage human embryos? DESIGN: This pilot study was a retrospective analysis of two Embryoscope imaging datasets from two clinics. An artificial intelligence system was used to reconstruct the three-dimensional structure of embryos from 11-plane focal stacks. Networks of cell contacts were extracted from the resulting embryo three-dimensional models and each embryo's mean contacts per cell was computed. Unpaired t-tests and receiver operating characteristic curve analysis were used to statistically analyse mean cell contact outcomes. Cell contact networks from different embryos were compared with identical embryos with similar cell arrangements. RESULTS: At t4, a higher mean number of contacts per cell was associated with greater rates of blastulation and blastocyst quality. No associations were found with biochemical pregnancy, live birth, miscarriage or ploidy. At t8, a higher mean number of contacts was associated with increased blastocyst quality, biochemical pregnancy and live birth. No associations were found with miscarriage or aneuploidy. Mean contacts at t4 weakly correlated with those at t8. Four-cell embryos fell into nine distinct cell arrangements; the five most common accounted for 97% of embryos. Eight-cell embryos, however, displayed a greater degree of variation with 59 distinct cell arrangements. CONCLUSIONS: Evidence is provided for the clinical relevance of cleavage-stage cell arrangement in the human preimplantation embryo beyond the four-cell stage, which may improve selection techniques for day-3 transfers. This pilot study provides a strong case for further investigation into spatial biomarkers and three-dimensional morphokinetics.

Embryo ranking agreement between embryologists and artificial intelligence algorithms.

OBJECTIVE: To evaluate the degree of agreement of embryo ranking between embryologists and eight artificial intelligence (AI) algorithms. DESIGN: Retrospective study. PATIENT(S): A total of 100 cycles with at least eight embryos were selected from the Weill Cornell Medicine database. For each embryo, the full-length time-lapse (TL) videos, as well as a single embryo image at 120 hours, were given to five embryologists and eight AI algorithms for ranking. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Kendall rank correlation coefficient (Kendall's τ). RESULT(S): Embryologists had a high degree of agreement in the overall ranking of 100 cycles with an average Kendall's tau (K-τ) of 0.70, slightly lower than the interembryologist agreement when using a single image or video (average K-τ = 0.78). Overall agreement between embryologists and the AI algorithms was significantly lower (average K-τ = 0.53) and similar to the observed low inter-AI algorithm agreement (average K-τ = 0.47). Notably, two of the eight algorithms had a very low agreement with other ranking methodologies (average K-τ = 0.05) and between each other (K-τ = 0.01). The average agreement in selecting the best-quality embryo (1/8 in 100 cycles with an expected agreement by random chance of 12.5%; confidence interval [CI]95: 6%-19%) was 59.5% among embryologists and 40.3% for six AI algorithms. The incidence of the agreement for the two algorithms with the low overall agreement was 11.7%. Agreement on selecting the same top two embryos/cycle (expected agreement by random chance corresponds to 25.0%; CI95: 17%-32%) was 73.5% among embryologists and 56.0% among AI methods excluding two discordant algorithms, which had an average agreement of 24.4%, the expected range of agreement by random chance. Intraembryologist ranking agreement (single image vs. video) was 71.7% and 77.8% for single and top two embryos, respectively. Analysis of average raw scores indicated that cycles with low diversity of embryo quality generally resulted in a lower overall agreement between the methods (embryologists and AI models). CONCLUSION(S): To our knowledge, this is the first study that evaluates the level of agreement in ranking embryo quality between different AI algorithms and embryologists. The different concordance methods were consistent and indicated that the highest agreement was intraembryologist agreement, followed by interembryologist agreement. In contrast, the agreement between some of the AI algorithms and embryologists was similar to the inter-AI algorithm agreement, which also showed a wide range of pairwise concordance. Specifically, two AI models showed intra- and interagreement at the level expected from random selection.

Automatic Ploidy Prediction and Quality Assessment of Human Blastocyst Using Time-Lapse Imaging.

Assessing fertilized human embryos is crucial for in vitro-fertilization (IVF), a task being revolutionized by artificial intelligence and deep learning. Existing models used for embryo quality assessment and chromosomal abnormality (ploidy) detection could be significantly improved by effectively utilizing time-lapse imaging to identify critical developmental time points for maximizing prediction accuracy. Addressing this, we developed and compared various embryo ploidy status prediction models across distinct embryo development stages. We present BELA (Blastocyst Evaluation Learning Algorithm), a state-of-the-art ploidy prediction model surpassing previous image- and video-based models, without necessitating subjective input from embryologists. BELA uses multitask learning to predict quality scores that are used downstream to predict ploidy status. By achieving an AUC of 0.76 for discriminating between euploidy and aneuploidy embryos on the Weill Cornell dataset, BELA matches the performance of models trained on embryologists' manual scores. While not a replacement for preimplantation genetic testing for aneuploidy (PGT-A), BELA exemplifies how such models can streamline the embryo evaluation process, reducing time and effort required by embryologists.

Embryo ploidy in vitrified versus fresh oocytes: Is there a difference?

PURPOSE: To evaluate embryo ploidy in a cohort of patients who underwent preimplantation genetic testing for aneuploidy (PGT-A) with vitrified oocytes compared to fresh oocytes. METHODS: Patients who underwent their first autologous oocyte vitrification and warming followed by in vitro fertilization (IVF) and trophectoderm biopsy for PGT-A between 1/1/2017 and 12/31/2021 at a single academic institution were included. Patients were compared 1:3 to age-matched controls who underwent their first IVF cycle with fresh oocytes and subsequent trophectoderm biopsy for PGT-A. The primary outcome was the proportions of euploid, mosaic, and aneuploid embryos between those using vitrified versus fresh oocytes. RESULTS: 117 patients who cryopreserved a total of 1,272 mature oocytes were included in the study and were matched with 351 controls using fresh oocytes. The average age was 36.9 ± 2.6 years, and the median interval between oocyte vitrification and warming was 38 months. There were similar numbers of mature oocytes (10.9 ± 4.9 vs. 11.1 ± 6.3, P = .67), fertilized oocytes (7.8 ± 4.0 vs. 8.7 ± 5.5, P = .10), and blastocysts per patient (5.1 ± 3.1 vs. 5.8 ± 4.3, P = .10) between those using vitrified versus fresh oocytes. In terms of embryo ploidy results, there were no statistically significant differences in rates of euploidy (40.1% vs. 41.6%), mosaicism (15.7% vs. 12.0%), or aneuploidy (44.3% vs. 46.4%) (P = .06) between the two groups. CONCLUSIONS: Oocyte vitrification with subsequent warming, fertilization, and trophectoderm biopsy for PGT-A was not associated with adverse chromosomal competence when compared to age-matched controls utilizing fresh oocytes.

A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study.

BACKGROUND: One challenge in the field of in-vitro fertilisation is the selection of the most viable embryos for transfer. Morphological quality assessment and morphokinetic analysis both have the disadvantage of intra-observer and inter-observer variability. A third method, preimplantation genetic testing for aneuploidy (PGT-A), has limitations too, including its invasiveness and cost. We hypothesised that differences in aneuploid and euploid embryos that allow for model-based classification are reflected in morphology, morphokinetics, and associated clinical information. METHODS: In this retrospective study, we used machine-learning and deep-learning approaches to develop STORK-A, a non-invasive and automated method of embryo evaluation that uses artificial intelligence to predict embryo ploidy status. Our method used a dataset of 10 378 embryos that consisted of static images captured at 110 h after intracytoplasmic sperm injection, morphokinetic parameters, blastocyst morphological assessments, maternal age, and ploidy status. Independent and external datasets, Weill Cornell Medicine EmbryoScope+ (WCM-ES+; Weill Cornell Medicine Center of Reproductive Medicine, NY, USA) and IVI Valencia (IVI Valencia, Health Research Institute la Fe, Valencia, Spain) were used to test the generalisability of STORK-A and were compared measuring accuracy and area under the receiver operating characteristic curve (AUC). FINDINGS: Analysis and model development included the use of 10 378 embryos, all with PGT-A results, from 1385 patients (maternal age range 21-48 years; mean age 36·98 years [SD 4·62]). STORK-A predicted aneuploid versus euploid embryos with an accuracy of 69·3% (95% CI 66·9-71·5; AUC 0·761; positive predictive value [PPV] 76·1%; negative predictive value [NPV] 62·1%) when using images, maternal age, morphokinetics, and blastocyst score. A second classification task trained to predict complex aneuploidy versus euploidy and single aneuploidy produced an accuracy of 74·0% (95% CI 71·7-76·1; AUC 0·760; PPV 54·9%; NPV 87·6%) using an image, maternal age, morphokinetic parameters, and blastocyst grade. A third classification task trained to predict complex aneuploidy versus euploidy had an accuracy of 77·6% (95% CI 75·0-80·0; AUC 0·847; PPV 76·7%; NPV 78·0%). STORK-A reported accuracies of 63·4% (AUC 0·702) on the WCM-ES+ dataset and 65·7% (AUC 0·715) on the IVI Valencia dataset, when using an image, maternal age, and morphokinetic parameters, similar to the STORK-A test dataset accuracy of 67·8% (AUC 0·737), showing generalisability. INTERPRETATION: As a proof of concept, STORK-A shows an ability to predict embryo ploidy in a non-invasive manner and shows future potential as a standardised supplementation to traditional methods of embryo selection and prioritisation for implantation or recommendation for PGT-A. FUNDING: US National Institutes of Health.

Pregnancy outcomes after oral and injectable ovulation induction in women with infertility with a low antimüllerian hormone level compared with those with a normal antimüllerian hormone level.

OBJECTIVE: To determine the ongoing pregnancy rate among patients with infertility with a low antimüllerian (AMH) level compared with those with a normal AMH level after oral and injectable ovulation induction (OI)/intrauterine insemination (IUI). DESIGN: Retrospective cohort. SETTING: Academic center. PATIENT(S): Patients completing ≥1 medicated OI/IUI cycle at our center between 2015 and 2019 were included. The AMH levels were measured within 12 months of treatment initiation. The cohort was stratified into low AMH (AMH level, <1.0 ng/mL) and normal AMH (AMH level, ≥1.0 ng/mL) groups. All subsequent medicated OI/IUI cycles occurring within 1 year of initial cycle start date were included up to the third completed cycle or until an ongoing pregnancy was recorded. Patients were stratified by age (<35, 35-40, and >40 years), and the relationship between the low and normal AMH groups and each binary endpoint were quantified as risk ratios using the age-adjusted Poisson models. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Ongoing pregnancy. RESULT(S): A total of 3,122 patients completed 5,539 oral antiestrogen cycles, and 1,060 completed 1,630 injectable gonadotropin cycles. For oral antiestrogen treatment, pregnancy outcomes, including ongoing pregnancy rate per cycle, for patients with a low AMH level were comparable with those for patients with a normal AMH level (<35 years, 15.4% vs. 14.9%; 35-40 years, 10.0% vs. 11.0%; and >40 years, 2.8% vs. 3.3%). For injectable gonadotropin treatment, the ongoing pregnancy rate was lower in the low AMH group than in the normal AMH group for the ages of <35 (12.1% vs. 23.5%; relative risk [RR], 0.52 [95% confidence interval {CI}, 0.28-0.97]) and 35-40 (12.5% vs. 18.5%; RR, 0.70 [95% CI, 0.49-0.99]) years but comparable with that for patients aged >40 years (3.0% vs. 4.0%; RR, 0.86 [95% CI, 0.31-2.35]). The proportion of multifetal gestations was similar between the low and normal AMH groups treated with oral antiestrogens (13.1% vs. 10.8%); however, for injectable gonadotropin treatment, patients with a normal AMH level had a higher proportion of multifetal gestations (18.6% vs. 31.1%). CONCLUSION(S): Compared with normal ovarian reserve, treatment with oral antiestrogens for OI/IUI for patients with low ovarian reserve results in comparable follicular development and ongoing pregnancy rates for all age groups. When patients with low ovarian reserve are treated with gonadotropins for OI/IUI, multifollicular recruitment is less likely resulting in a significantly decreased ongoing pregnancy rate for patients aged <35 and 35-40 years but also a decrease in multifetal gestations. Overall, the ongoing pregnancy rates of 8.7% per oral antiestrogen cycle and 8.1% per injectable gonadotropin cycle in patients with low ovarian reserve are comparable with the expected rates in the general infertility population.

Body temperature patterns during pregnancy and parturition in moose.

Gestation and lactation have high energetic requirements. Up to three-fourths of the gestation period in moose (Alces alces) overlaps with the food-scarce period in winter. During this period, moose deal with the limited forage resources available through hypometabolism with decreased heart rate and body temperature (Tb). Body temperature is also an indicator of oestrus, pregnancy and parturition, which is well documented in several domestic species. In this study, we sought to determine if moose displayed a similar Tb pattern during pregnancy and parturition to domesticated ruminants, and if we could detect parturition by combining Tb and activity data. We studied the Tb pattern of 30 free-ranging adult female moose (≥1.5 years old), equipped with ruminal temperature loggers and GPS collars. We documented a 0.13-0.19°C higher Tb in pregnant compared to non-pregnant moose, depending on the study area with the Tb difference increasing along a south-north gradient, and a drop in Tb and in activity when parturition was imminent. Detection of parturition was highly successful when combining Tb and activity data with an accuracy of 91.5%. Our findings demonstrate that Tb responses to pregnancy and parturition in a wild capital-breeding ruminant are similar to those of domesticated ruminants.

Characterization of an artificial intelligence model for ranking static images of blastocyst stage embryos.

OBJECTIVE: To perform a series of analyses characterizing an artificial intelligence (AI) model for ranking blastocyst-stage embryos. The primary objective was to evaluate the benefit of the model for predicting clinical pregnancy, whereas the secondary objective was to identify limitations that may impact clinical use. DESIGN: Retrospective study. SETTING: Consortium of 11 assisted reproductive technology centers in the United States. PATIENT(S): Static images of 5,923 transferred blastocysts and 2,614 nontransferred aneuploid blastocysts. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Prediction of clinical pregnancy (fetal heartbeat). RESULT(S): The area under the curve of the AI model ranged from 0.6 to 0.7 and outperformed manual morphology grading overall and on a per-site basis. A bootstrapped study predicted improved pregnancy rates between +5% and +12% per site using AI compared with manual grading using an inverted microscope. One site that used a low-magnification stereo zoom microscope did not show predicted improvement with the AI. Visualization techniques and attribution algorithms revealed that the features learned by the AI model largely overlap with the features of manual grading systems. Two sources of bias relating to the type of microscope and presence of embryo holding micropipettes were identified and mitigated. The analysis of AI scores in relation to pregnancy rates showed that score differences of ≥0.1 (10%) correspond with improved pregnancy rates, whereas score differences of <0.1 may not be clinically meaningful. CONCLUSION(S): This study demonstrates the potential of AI for ranking blastocyst stage embryos and highlights potential limitations related to image quality, bias, and granularity of scores.

Macronutrient balancing in free-ranging populations of moose.

At northern latitudes, large spatial and temporal variation in the nutritional composition of available foods poses challenges to wild herbivores trying to satisfy their nutrient requirements. Studies conducted in mostly captive settings have shown that animals from a variety of taxonomic groups deal with this challenge by adjusting the amounts and proportions of available food combinations to achieve a target nutrient balance. In this study, we used proportions-based nutritional geometry to analyze the nutritional composition of rumen samples collected in winter from 481 moose (Alces alces) in southern Sweden and examine whether free-ranging moose show comparable patterns of nutrient balancing. Our main hypothesis was that wild moose actively regulate their rumen nutrient composition to offset ecologically imposed variation in the nutritional composition of available foods. To test this, we assessed the macronutritional composition (protein, carbohydrates, and lipids) of rumen contents and commonly eaten foods, including supplementary feed, across populations with contrasting winter diets, spanning an area of approximately 10,000 km2. Our results suggest that moose balanced the macronutrient composition of their rumen, with the rumen contents having consistently similar proportional relationship between protein and nonstructural carbohydrates, despite differences in available (and eaten) foods. Furthermore, we found that rumen macronutrient balance was tightly related to ingested levels of dietary fiber (cellulose and hemicellulose), such that the greater the fiber content, the less protein was present in the rumen compared with nonstructural carbohydrates. Our results also suggest that moose benefit from access to a greater variety of trees, shrubs, herbs, and grasses, which provides them with a larger nutritional space to maneuver within. Our findings provide novel theoretical insights into a model species for ungulate nutritional ecology, while also generating data of direct relevance to wildlife and forest management, such as silvicultural or supplementary feeding practices.

Cardiac Anomalies in Two White-Tailed Eagles (Haliaeetus albicilla) in Sweden.

Severe cardiomegaly with an atrial septal defect was discovered during necropsy of a subadult White-tailed Eagle (Haliaeetus albicilla) found dead in the wild. A thin membrane composed of fibromuscular tissue separated the left atrium into two chambers, most consistent with that described for cor triatriatum sinister (CTS) in other species. Seventeen months later, necropsy of an adult White-tailed Eagle again revealed CTS. This lesion has not been reported previously in raptors.

Detection and Genetic Characterization of Viruses Present in Free-Ranging Snow Leopards Using Next-Generation Sequencing.

Snow leopards inhabit the cold, arid environments of the high mountains of South and Central Asia. These living conditions likely affect the abundance and composition of microbes with the capacity to infect these animals. It is important to investigate the microbes that snow leopards are exposed to detect infectious disease threats and define a baseline for future changes that may impact the health of this endangered felid. In this work, next-generation sequencing is used to investigate the fecal (and in a few cases serum) virome of seven snow leopards from the Tost Mountains of Mongolia. The viral species to which the greatest number of sequences reads showed high similarity was rotavirus. Excluding one animal with overall very few sequence reads, four of six animals (67%) displayed evidence of rotavirus infection. A serum sample of a male and a rectal swab of a female snow leopard produced sequence reads identical or closely similar to felid herpesvirus 1, providing the first evidence that this virus infects snow leopards. In addition, the rectal swab from the same female also displayed sequence reads most similar to feline papillomavirus 2, which is the first evidence for this virus infecting snow leopards. The rectal swabs from all animals also showed evidence for the presence of small circular DNA viruses, predominantly Circular Rep-Encoding Single-Stranded (CRESS) DNA viruses and in one case feline anellovirus. Several of the viruses implicated in the present study could affect the health of snow leopards. In animals which are under environmental stress, for example, young dispersing individuals and lactating females, health issues may be exacerbated by latent virus infections.

Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?

Artificial intelligence (AI) systems have been proposed for reproductive medicine since 1997. Although AI is the main driver of emergent technologies in reproduction, such as robotics, Big Data, and internet of things, it will continue to be the engine for technological innovation for the foreseeable future. What does the future of AI research look like?

Divergence in parturition timing and vegetation onset in a large herbivore-differences along a latitudinal gradient.

In northern environments, the period of access to high-quality forage is limited, exerting strong selective pressure to optimize the timing of parturition. We analysed timing and variation in moose (Alces alces) parturition dates of 555 females at 18 study sites across 12° of latitude (56-68° N, 1350 km) in Sweden. We found evidence for a spatial match of parturition timing to vegetation onset, but no evidence that moose adjust parturition to vegetation onset in a given year. We found a breakpoint at 64° N. Despite adaptation across latitudes, temporal divergences occurred. Females below 64° N calved after vegetation onset and females above 64° N calved before. Here, parturition before vegetation onset might be a strategy to optimize forage utilization time with the very short growing season. Highly seasonal environments such as at higher latitudes may make it advantageous to adapt parturition towards long-term climatic patterns by matching the most favourable period. Given the direction of temporal divergence, our study suggests that climate change may have less of an impact on moose parturition at northern latitudes than southern latitudes.

Varied diets, including broadleaved forage, are important for a large herbivore species inhabiting highly modified landscapes.

Diet quality is an important determinant of animal survival and reproduction, and can be described as the combination of different food items ingested, and their nutritional composition. For large herbivores, human landscape modifications to vegetation can limit such diet-mixing opportunities. Here we use southern Sweden's modified landscapes to assess winter diet mixtures (as an indicator of quality) and food availability as drivers of body mass (BM) variation in wild moose (Alces alces). We identify plant species found in the rumen of 323 moose harvested in Oct-Feb, and link variation in average calf BM among populations to diets and food availability. Our results show that variation in calf BM correlates with variation in diet composition, diversity, and food availability. A varied diet relatively rich in broadleaves was associated with higher calf BM than a less variable diet dominated by conifers. A diet high in shrubs and sugar/starch rich agricultural crops was associated with intermediate BM. The proportion of young production forest (0-15 yrs) in the landscape, an indicator of food availability, significantly accounted for variation in calf BM. Our findings emphasize the importance of not only diet composition and forage quantity, but also variability in the diets of large free-ranging herbivores.

Blastocyst score, a blastocyst quality ranking tool, is a predictor of blastocyst ploidy and implantation potential.

OBJECTIVE: To convert blastocyst (BL) morphological grade and BL day into a numeric blastocyst score (BS). DESIGN: Retrospective cohort study. SETTING: Academic center. PATIENTS: A total of 5,653 BL of known implantation (fetal heart, FH) and 11,348 biopsied BL. INTERVENTIONS: Based on their FH rates and/or significance, a score (1-4) was assigned to each BL grade component. The BL morphological score (BMS) is the sum (BS = BMS on day 5; BS = BMS + 2 on day 6). MAIN OUTCOME MEASURES: Statistics characterized the FH and euploidy odds with BS. RESULTS: All three morphology grade components and BL day were associated with implantation and euploidy probability. The FH rate and euploidy odds decrease with larger BS. The BS was the most important factor (odds ratio [OR] per unit change = 0.807, 95% confidence interval [CI] 0.784, 0.831) for untested and euploid BL implantation, and the sole one for euploid BL (OR/unit change = 0.845, 95% CI 0.803, 0.889). The BS is the second most significant factor after maternal age for euploidy probability (OR/unit change = 0.808, 95% CI 0.795, 0.822). In training and validation sets (75:25), the BS can predict implantation with similar area under the curve [AUC] (training = 0.628, 95% CI 0.613, 0.643; validation = 0.606, 95% CI 0.581, 0.631). The BS has better euploidy prediction ability (training AUC = 0.683, 95% CI 0.673, 0.693; validation AUC = 0.698, 95% CI 0.681, 0.715). The BS can stratify BL into good (3-5), fair (6-9), and poor (10-14) groups, reflecting their FH, live birth rates, and ploidy status. Advanced maternal age was associated with lower untested BL implantation and lower euploidy odds across all groups. CONCLUSIONS: The BS is a predictor of BL ploidy and FH implantation.

Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization.

Visual morphology assessment is routinely used for evaluating of embryo quality and selecting human blastocysts for transfer after in vitro fertilization (IVF). However, the assessment produces different results between embryologists and as a result, the success rate of IVF remains low. To overcome uncertainties in embryo quality, multiple embryos are often implanted resulting in undesired multiple pregnancies and complications. Unlike in other imaging fields, human embryology and IVF have not yet leveraged artificial intelligence (AI) for unbiased, automated embryo assessment. We postulated that an AI approach trained on thousands of embryos can reliably predict embryo quality without human intervention. We implemented an AI approach based on deep neural networks (DNNs) to select highest quality embryos using a large collection of human embryo time-lapse images (about 50,000 images) from a high-volume fertility center in the United States. We developed a framework (STORK) based on Google's Inception model. STORK predicts blastocyst quality with an AUC of >0.98 and generalizes well to images from other clinics outside the US and outperforms individual embryologists. Using clinical data for 2182 embryos, we created a decision tree to integrate embryo quality and patient age to identify scenarios associated with pregnancy likelihood. Our analysis shows that the chance of pregnancy based on individual embryos varies from 13.8% (age ≥41 and poor-quality) to 66.3% (age <37 and good-quality) depending on automated blastocyst quality assessment and patient age. In conclusion, our AI-driven approach provides a reproducible way to assess embryo quality and uncovers new, potentially personalized strategies to select embryos.

Health and zoonotic Infections of snow leopards Panthera unica in the South Gobi desert of Mongolia.

Background: Snow leopards, Panthera uncia, are a threatened apex predator, scattered across the mountains of Central and South Asia. Disease threats to wild snow leopards have not been investigated.Methods and Results: Between 2008 and 2015, twenty snow leopards in the South Gobi desert of Mongolia were captured and immobilised for health screening and radio-collaring. Blood samples and external parasites were collected for pathogen analyses using enzyme-linked immunosorbent assay (ELISA), microscopic agglutination test (MAT), and next-generation sequencing (NGS) techniques. The animals showed no clinical signs of disease, however, serum antibodies to significant zoonotic pathogens were detected. These pathogens included, Coxiella burnetii, (25% prevalence), Leptospira spp., (20%), and Toxoplasma gondii (20%). Ticks collected from snow leopards contained potentially zoonotic bacteria from the genera Bacillus, Bacteroides, Campylobacter, Coxiella, Rickettsia, Staphylococcus and Streptococcus.Conclusions: The zoonotic pathogens identified in this study, in the short-term did not appear to cause illness in the snow leopards, but have caused illness in other wild felids. Therefore, surveillance for pathogens should be implemented to monitor for potential longer- term disease impacts on this snow leopard population.

Rate of Cooling in a Moose (Alces alces) Carcass.

Postmortem body temperature is used to estimate time of death in humans, but the available models are not validated for most nonhuman species. Here, we report that cooling in an adult female moose (Alces alces) equipped with a rumen temperature monitor was extremely slow, with a rumen temperature of 27-28 C as late as 40 h postmortem.

Vector-Borne Zoonotic Pathogens in Eurasian Moose (Alces alces alces).

Climate change, with warmer temperatures and altered precipitation patterns, has affected the distribution of vectors and vector-borne diseases. In the northern hemisphere, vectors are spreading north, and with them, pathogens of zoonotic and animal health impact. Eurasian moose (Alces alces alces) are physiologically and anatomically adapted for cold climate, and are rarely considered ideal hosts of vectors, apart from deer keds (Lipoptena cervi). To investigate the presence of vector-borne pathogens, spleen samples from 615 moose were collected in southern Sweden from 2008 to 2015. The samples were analyzed with a high-throughput PCR method for 24 bacterial, and 12 parasitic pathogens. Anaplasma (82%), Borrelia (3%), Babesia (3%), and Bartonella (1%) DNA was found, showing that moose are exposed to, and can act as hosts of some of these pathogens, which can have an impact of both animal and human health. These results show that Swedish moose are exposed to pathogens that in some instances are more commonly found in regions with warmer climate, and highlights the importance of also considering moose as sentinels of vector-borne pathogens. Further research is needed to understand the effect of these pathogens on the health of individual moose and to elucidate whether climate change and moose population density interact to create the pattern observed.