Automatic Detection of Freshwater Phytoplankton Specimens in Conventional Microscopy Images.

Water safety and quality can be compromised by the proliferation of toxin-producing phytoplankton species, requiring continuous monitoring of water sources. This analysis involves the identification and counting of these species which requires broad experience and knowledge. The automatization of these tasks is highly desirable as it would release the experts from tedious work, eliminate subjective factors, and improve repeatability. Thus, in this preliminary work, we propose to advance towards an automatic methodology for phytoplankton analysis in digital images of water samples acquired using regular microscopes. In particular, we propose a novel and fully automatic method to detect and segment the existent phytoplankton specimens in these images using classical computer vision algorithms. The proposed method is able to correctly detect sparse colonies as single phytoplankton candidates, thanks to a novel fusion algorithm, and is able to differentiate phytoplankton specimens from other image objects in the microscope samples (like minerals, bubbles or detritus) using a machine learning based approach that exploits texture and colour features. Our preliminary experiments demonstrate that the proposed method provides satisfactory and accurate results.

Loss of in vitro Efficacy of Topical Commercial Acaricides on Rhipicephalus microplus (Ixodida: Ixodidae) From Antioquian Farms, Colombia.

In Antioquia, the problems to control Rhipicephalus microplus (Canestrini, 1888) tick infestations have spread and ranchers claim conventional treatments are no longer effective. In this study, the in vitro efficacy of commercial topical products was tested with ticks obtained from two dairy farms in Antioquia with severe repeated infestations. About 800 engorged ticks were collected directly from animals in two separate visits at the beginning and end of the same month. The adult immersion test was used, which exposed groups of 40 ticks from each collection at the recommended concentration for five commercial products and combinations for 5 min. Efficacy was determined by comparing the reproductive index (fecundity × fertility) of each treated group to that of the control group. The values of all reproductive parameters obtained with ticks from the two collection dates were very similar. Cypermethrin (150 ppm) and amitraz (208 ppm) separately showed very low efficacies of only 10-20% at one farm, and zero at the other. The combination of chlorpyrifos + cypermethrin was the only product with an efficacy >50% at both farms and field observations corroborated to be still capable of eliminating infestations. Exposure to fluazuron at concentrations ranging from 0.05 to 500 ppm for 1 min reduced fertility in all groups by ≥99%, as would be expected for very susceptible strains. However, reduction of oviposition only occurred at the 500 ppm concentration. In conclusion, there is a high degree of resistance to all products tested except for fluazuron.

Evidence for and against manganese deficiency as causal for congenital joint deficiency disease or death in fetal and neonatal cattle.

Measures of manganese (Mn) status in cattle vary among studies, and no single criterion accurately predicts or diagnoses Mn deficiency and pathologic outcomes. Mn deficiency causes congenital joint laxity and dwarfism (CJLD) when total dietary intake is <20 ppm Mn dry matter (DM) for most of the pregnancy. However, the recommended dietary intake of 40 ppm DM can also result in clinical Mn deficiency. Some studies have found that CJLD occurs in calves from cows fed red clover or silage but not in calves from cows fed hay. The concentration of Mn in the liver is the best indicator of Mn status in neonates and adults but cannot be interpreted in fetuses. Serum, plasma, and whole blood concentrations of Mn are unreliable indicators of bovine Mn status. The primary objective of our report is to present evidence linking CJLD to a primary or secondary Mn deficiency. To predict and diagnose Mn deficiency in cattle, we propose using a combination of clinical signs, dietary Mn, liver Mn at birth and beyond, positive response to Mn supplementation or the replacement of silage with other forages, and ruling out other causes of malformations. By following these recommendations, we expect that CJLD and gestational death will decrease as hepatic Mn concentrations increase at birth. Many publications we reviewed are not statistically sound, and future research should include a statistician from the initial discussions of the study through the final publication.

Phytoplankton detection and recognition in freshwater digital microscopy images using deep learning object detectors.

Water quality can be negatively affected by the presence of some toxic phytoplankton species, whose toxins are difficult to remove by conventional purification systems. This creates the need for periodic analyses, which are nowadays manually performed by experts. These labor-intensive processes are affected by subjectivity and expertise, causing unreliability. Some automatic systems have been proposed to address these limitations. However, most of them are based on classical image processing pipelines with not easily scalable designs. In this context, deep learning techniques are more adequate for the detection and recognition of phytoplankton specimens in multi-specimen microscopy images, as they integrate both tasks in a single end-to-end trainable module that is able to automatize the adaption to such a complex domain. In this work, we explore the use of two different object detectors: Faster R-CNN and RetinaNet, from the one-stage and two-stage paradigms respectively. We use a dataset composed of multi-specimen microscopy images captured using a systematic protocol. This allows the use of widely available optical microscopes, also avoiding manual adjustments on a per-specimen basis, which would require expert knowledge. We have made our dataset publicly available to improve the reproducibility and to foment the development of new alternatives in the field. The selected Faster R-CNN methodology reaches maximum recall levels of 95.35%, 84.69%, and 79.81%, and precisions of 94.68%, 89.30% and 82.61%, for W. naegeliana, A. spiroides, and D. sociale, respectively. The system is able to adapt to the dataset problems and improves the results overall with respect to the reference state-of-the-art work. In addition, the proposed system improves the automation and abstraction from the domain and simplifies the workflow and adjustment.

ConKeD: multiview contrastive descriptor learning for keypoint-based retinal image registration.

Retinal image registration is of utmost importance due to its wide applications in medical practice. In this context, we propose ConKeD, a novel deep learning approach to learn descriptors for retinal image registration. In contrast to current registration methods, our approach employs a novel multi-positive multi-negative contrastive learning strategy that enables the utilization of additional information from the available training samples. This makes it possible to learn high-quality descriptors from limited training data. To train and evaluate ConKeD, we combine these descriptors with domain-specific keypoints, particularly blood vessel bifurcations and crossovers, that are detected using a deep neural network. Our experimental results demonstrate the benefits of the novel multi-positive multi-negative strategy, as it outperforms the widely used triplet loss technique (single-positive and single-negative) as well as the single-positive multi-negative alternative. Additionally, the combination of ConKeD with the domain-specific keypoints produces comparable results to the state-of-the-art methods for retinal image registration, while offering important advantages such as avoiding pre-processing, utilizing fewer training samples, and requiring fewer detected keypoints, among others. Therefore, ConKeD shows a promising potential towards facilitating the development and application of deep learning-based methods for retinal image registration.

Diagnosis of Anaplasma marginale in cattle at the Iowa State University veterinary diagnostic laboratory 2003-2021.

Anaplasma marginale is a blood-borne rickettsia-like organism that infects cattle erythrocytes and causes anaplasmosis. This study reviews diagnostic data of all A. marginale diagnostics performed from 2003 to August 2021 in the Iowa State Veterinary Diagnostic Laboratory. Typically, the referring veterinarian's initial tentative diagnosis was based on presenting clinical signs or necropsy findings. Confirmatory testing at the ISU-VDL consisted of light microscopy evaluation of stained blood smears or molecular diagnostic procedures. A total of 94 cases were submitted with tissue samples from deceased animals, of which 79 were from Iowa and 15 were from other states. The most typical gross lesions were widespread yellow adipose tissue and splenomegaly. Typical histopathological lesions included marked bile stasis and hemosiderin-laden macrophages in the liver and spleen, respectively. Starting in 2013, when PCR was implemented to confirm cases of anaplasmosis, 315/1125 (28%) were positive to A. marginale, and 810 were negative, using a cut-off of 35.0 Ct. The average (±SD) of the positive PCR Ct was 19.5 (±6.0), and the first and third quartiles were 14.9 and 23.4. Most cases occurred between August and November, peaking in September, whether from necropsies or positive blood samples by PCR. The most common tick observed in Iowa, Dermacentor variabilis, is likely the main vector for transmission. Further surveys should be conducted to estimate seroprevalence by geographical location, the density of cattle populations, distribution of known vectors according to season, and strains of A. marginale.

Joint keypoint detection and description network for color fundus image registration.

Background: Retinal imaging is widely used to diagnose many diseases, both systemic and eye-specific. In these cases, image registration, which is the process of aligning images taken from different viewpoints or moments in time, is fundamental to compare different images and to assess changes in their appearance, commonly caused by disease progression. Currently, the field of color fundus registration is dominated by classical methods, as deep learning alternatives have not shown sufficient improvement over classic methods to justify the added computational cost. However, deep learning registration methods are still considered beneficial as they can be easily adapted to different modalities and devices following a data-driven learning approach. Methods: In this work, we propose a novel methodology to register color fundus images using deep learning for the joint detection and description of keypoints. In particular, we use an unsupervised neural network trained to obtain repeatable keypoints and reliable descriptors. These keypoints and descriptors allow to produce an accurate registration using RANdom SAmple Consensus (RANSAC). We train the method using the Messidor dataset and test it with the Fundus Image Registration Dataset (FIRE) dataset, both of which are publicly accessible. Results: Our work demonstrates a color fundus registration method that is robust to changes in imaging devices and capture conditions. Moreover, we conduct multiple experiments exploring several of the method's parameters to assess their impact on the registration performance. The method obtained an overall Registration Score of 0.695 for the whole FIRE dataset (0.925 for category S, 0.352 for P, and 0.726 for A). Conclusions: Our proposal improves the results of previous deep learning methods in every category and surpasses the performance of classical approaches in category A which has disease progression and thus represents the most relevant scenario for clinical practice as registration is commonly used in patients with diseases for disease monitoring purposes.

Automated inter-device 3D OCT image registration using deep learning and retinal layer segmentation.

Optical coherence tomography (OCT) is the most widely used imaging modality in ophthalmology. There are multiple variations of OCT imaging capable of producing complementary information. Thus, registering these complementary volumes is desirable in order to combine their information. In this work, we propose a novel automated pipeline to register OCT images produced by different devices. This pipeline is based on two steps: a multi-modal 2D en-face registration based on deep learning, and a Z-axis (axial axis) registration based on the retinal layer segmentation. We evaluate our method using data from a Heidelberg Spectralis and an experimental PS-OCT device. The empirical results demonstrated high-quality registrations, with mean errors of approximately 46 µm for the 2D registration and 9.59 µm for the Z-axis registration. These registrations may help in multiple clinical applications such as the validation of layer segmentations among others.

Antimicrobial Prescribing Practices in Dogs and Cats by Colombian Veterinarians in the City of Medellin.

This study surveyed the prescribing behavior of Colombian companion animal veterinarians and compared the responses to the current guidelines of the International Society for Companion Animals on Infectious Diseases (ISCAID). A convenience sample of 100 primary-care veterinary practitioners was selected from the city of Medellin. A questionnaire was designed to present hypothetical clinical scenarios regarding prescription choices for systemic antimicrobials. The numbers of veterinarians empirically prescribing a course of systemic antimicrobials for each scenario were-perioperative elective surgeries (86%), superficial pyoderma (90%), lower urinary tract disease (52%), acute hemorrhagic diarrhea (50%), and kennel cough (46%). For urinary tract disease, cultures and susceptibility testing were only performed by half of the respondents, suggesting lower diagnostic standards. In superficial pyoderma cases, cytology was performed in the following percent of cases-0% (24), 20% (30), 40% (17), 60% (11), 80% (8), and 100% (10). Antimicrobials were over-prescribed relative to emerging standard for elective surgeries (86%), kennel cough (46%), and acute hemorrhagic diarrhea (50%). Critically important antimicrobials, such as fluoroquinolones, were applied commonly for superficial pyoderma (18%), kennel cough (12%), and lower urinary tract disease in dogs (20%) and cats (26%). In conclusion, antimicrobial prescribing behavior was inconsistent with current guidelines, and antimicrobial use could be improved by appropriate diagnostic steps allowing choice of an optimal antimicrobial drug. Overall, we documented the widespread use of antimicrobials for the treatment of these four common disease conditions.

Color fundus image registration using a learning-based domain-specific landmark detection methodology.

Medical imaging, and particularly retinal imaging, allows to accurately diagnose many eye pathologies as well as some systemic diseases such as hypertension or diabetes. Registering these images is crucial to correctly compare key structures, not only within patients, but also to contrast data with a model or among a population. Currently, this field is dominated by complex classical methods because the novel deep learning methods cannot compete yet in terms of results and commonly used methods are difficult to adapt to the retinal domain. In this work, we propose a novel method to register color fundus images based on previous works which employed classical approaches to detect domain-specific landmarks. Instead, we propose to use deep learning methods for the detection of these highly-specific domain-related landmarks. Our method uses a neural network to detect the bifurcations and crossovers of the retinal blood vessels, whose arrangement and location are unique to each eye and person. This proposal is the first deep learning feature-based registration method in fundus imaging. These keypoints are matched using a method based on RANSAC (Random Sample Consensus) without the requirement to calculate complex descriptors. Our method was tested using the public FIRE dataset, although the landmark detection network was trained using the DRIVE dataset. Our method provides accurate results, a registration score of 0.657 for the whole FIRE dataset (0.908 for category S, 0.293 for category P and 0.660 for category A). Therefore, our proposal can compete with complex classical methods and beat the deep learning methods in the state of the art.

ISODRIP, a model to transfer the δ18O signal of precipitation to drip water - Implementation of the model for Eagle Cave (central Spain).

The isotope signature of cave waters provides an excellent opportunity to better understand the recharge in karst regions and the complexity of drainage systems in the vadose zone. We have developed a cave isotope hydrological model (ISODRIP) that requires entering basic hydrometeorological information and a precipitation δ18O record to simulate the discharge and δ18O signals of different drip sites. The model includes four different modules to simulate various flow route regimes: continuous and discontinuous drips under diffuse or preferential flows. We use precipitation and cave water δ18O records that were obtained in Eagle Cave (central Spain) during a 5-year period to test our model and to better understand the dynamics of karst aquifers. Eagle Cave waters do not record evaporation. The δ18O signals do not have seasonality, although they record intra-annual and inter-annual variability. Additionally, cave water δ18O signal falls within the range of the annual average weighted isotope composition of precipitation. Well-mixed cave waters, that characterize diffuse flows, record 1‰ δ18O variability, whereas partially-mixed waters, that flow along preferential drainage routes, have up to 3‰ δ18O variability. The results suggest that precipitation takes on average 15 months to reach the cave through the diffuse flow network, whereas under preferential flow the transit time is highly variable depending on the previous condition of the system. ISODRIP includes a soil layer above the vadose zone that controls large recharge events, together with direct recharge components that bypass the soil layer enabling at least some recharge all year round. Thus, the simulations reproduce the observed lack of seasonal bias in the cave water δ18O composition in relation to the average weighted isotope composition of precipitation. This research highlights the importance of understanding recharge dynamics and the configuration of particular drips sites to properly interpret speleothem δ18O records.

Fully automatic detection and classification of phytoplankton specimens in digital microscopy images.

BACKGROUND AND OBJECTIVE: The proliferation of toxin-producing phytoplankton species can compromise the quality of the water sources. This contamination is difficult to detect, and consequently to be neutralised, since normal water purification techniques are ineffective. Currently, the water analyses about phytoplankton are commonly performed by the specialists with manual routine analyses, which represents a major limitation. The adequate identification and classification of phytoplankton specimens requires intensive training and expertise. Additionally, the performed analysis involves a lengthy process that exhibits serious problems of reliability and repeatability as inter-expert agreement is not always reached. Considering all those factors, the automatization of these analyses is, therefore, highly desirable to reduce the workload of the specialists and facilitate the process. METHODS: This manuscript proposes a novel fully automatic methodology to perform phytoplankton analyses in digital microscopy images of water samples taken with a regular light microscope. In particular, we propose a method capable of analysing multi-specimen images acquired using a simplified systematic protocol. In contrast with prior approaches, this enables its use without the necessity of an expert taxonomist operating the microscope. The system is able to detect and segment the different existing phytoplankton specimens, with high variability in terms of visual appearances, and to merge them into colonies and sparse specimens when necessary. Moreover, the system is capable of differentiating them from other similar objects like zooplankton, detritus or mineral particles, among others, and then classify the specimens into defined target species of interest using a machine learning-based approach. RESULTS: The proposed system provided satisfactory and accurate results in every step. The detection step provided a FNR of 0.4%. Phytoplankton detection, that is, differentiating true phytoplankton from similar objects (zooplankton, minerals, etc.), provided a result of 84.07% of precision at 90% of recall. The target species classification, reported an overall accuracy of 87.50%. The recall levels for each species are, 81.82% for W. naegeliana, 57.15% for A. spiroides, 85.71% for D. sociale and 95% for the "Other" group, a set of relevant toxic and interesting species widely spread over the samples. CONCLUSIONS: The proposed methodology provided accurate results in all the designed steps given the complexity of the problem, particularly in terms of specimen identification, phytoplankton differentiation as well as the classification of the defined target species. Therefore, this fully automatic system represents a robust and consistent tool to aid the specialists in the analysis of the quality of the water sources and potability.

Plasma and brain cholinesterase in methomyl-intoxicated free-ranging pigeons (Columba livia f. domestica).

A mortality event caused by exposure to the carbamate insecticide methomyl was diagnosed in several hundred pigeons fed treated corn kernels in a city park. A cholinesterase inhibitor insecticide was initially suspected based on clinical signs and a significant inhibition (P < 0.05) of brain cholinesterase (ChE) activity compared with normal values for the species. However, brain ChE activity was within the normal range in birds subsequently submitted in an advanced stage of autolysis. Two groups of 10 healthy pigeons were allocated into a control group and an experimental group, which was offered corn samples retrieved from the incident site. Within minutes of ingesting the contaminated corn, the birds became immobile, had transient wing fluttering, and developed profuse salivation immediately followed by death. Plasma ChE activity at death had declined by more than 95% of preexposure levels (0.04 +/- 0.02 vs. 1.56 +/- 0.23 micromol/min per milliliter). Brain activity in the sagittal brain sections that were immediately frozen after death was inhibited by > or =50% of control birds (13.5 +/- 2.2 vs. 27.5 +/- 1.8 micromol/min per gram). However, the sagittal sections left for 1.5 days at ambient temperature of 25 degrees C had normal or higher activity, an effect that was attributed to a combination of spontaneous reactivation and dehydration. After incubation of both plasma and brain homogenates for 1 hr at 37 degrees C, ChE activity recovered by 2- and 1.46-fold, respectively. An organophosphorus and carbamate screen conducted by 2 independent laboratories identified and quantified methomyl in treated kernels at 400 ppm. These results indicate that spontaneous reactivation and dehydration can mask previous reductions in ChE activity.

Interpretation of the larval immersion test with ivermectin in populations of the cattle tick Rhipicephalus (Boophilus) microplus from Colombian farms.

Interpreting in vitro bioassays used to determine resistance against acaricides in Rhipicephalus microplus can be challenging without parallel in vivo studies that assess for lost efficacy. The larval immersion test (LIT) is currently the most widely used bioassay to detect ivermectin resistance. The objective of this study was to compare results of the LIT and a field trial using ivermectin in naturally infested cattle. Criteria to consider ticks as resistant with the LIT were based on discriminating doses (DD) and the ratio of lethal concentration (LC) in test populations over the LC of the susceptible Deutch strain, known as the resistance ratio (RR). Ticks were collected from 4 farms, two where ivermectin provided good control of tick infestations and two that claimed lack of efficacy. In two farms where administration of a long-acting ivermectin formulation reduced body tick counts to 45 and 25% of the initial counts at 10-days post-treatment, the RR50 and RR99 were approximately 6 and 20, respectively. The LC50 value approximated the DD for the two farms with claimed resistance, suggesting that about half of the population in each farm was resistant. These LIT values are equal to those reported for the most resistant ticks, which supports the use of the LIT to predict control failure in field situations. The two farms where ivermectin provided good control of tick infestations had LC50s similar to the susceptible strain, although for one farm the LC99 and RR99 suggested incipient resistance.

Prevalence of Antimicrobial Resistance in Bacterial Isolates from Dogs and Cats in a Veterinary Diagnostic Laboratory in Colombia from 2016-2019.

The susceptibility to antimicrobials of bacterial isolates from dogs (n = 1256) and cats (n = 94) was retrospectively evaluated in a veterinary diagnostic laboratory over a 4-year period (2016-2019). Out of 1316 isolates in dogs, 771 were Staphylococcus spp. distributed as follows: Staph. pseudointermedius (n = 406), Staph. intermedius (n = 160), Staph. aureus (n = 104), and Staph. coagulase-negative (n = 101). In common, all Staphylococcus spp. showed a high prevalence (20-50%) of resistance to ampicillin, cephalosporin, enrofloxacin, gentamicin, tetracycline, and trimethoprim-sulfonamide, but a low prevalence (1-10%) of resistance to amoxicillin-clavulanate. With regards to the other families of bacteria, the number of antimicrobials for which resistance was high (>20%) in dogs was: Enterobacteriaceae (7/12), Enterococcus spp. (10/16), E. coli (11/15), Pseudomonas spp. (10/13), and Streptococcus spp. (4/9). For urinary tract infections caused by E. coli or Enterobacteriaceae (Klebsiella spp., Proteus spp.), amikacin and florfenicol were the only drugs that demonstrated 100% in vitro efficacy. Multi-drug resistance was observed in 18.7% (246/1316) and 22% (21/97) of the isolates from dogs and cats, respectively. Except for Pseudomonas spp., known for intrinsic resistance, resistance in other bacteria was likely attributed to high selection pressure. In conclusion, empirical antimicrobial use cannot be recommended to treat most common infections, and selection should be based on results from susceptibility testing.

Millennial climate oscillations controlled the structure and evolution of Termination II.

The controls that affect the structure and timing of terminations are still poorly understood. We studied a tufa deposit from the Iberian Peninsula that covers Termination II (T-II) and whose chronology was synchronized to speleothem records. We used the same chronology to synchronize ocean sediments from the North Atlantic to correlate major climate events in a common timescale. We identify two stages within T-II. The first stage started with the increase of boreal summer integrated solar insolation, and during this stage three millennial climate oscillations were recorded. These oscillations resulted from complex ocean-atmosphere interactions in the Nordic seas, caused by the progressive decay of Northern Hemisphere ice-sheets. The second stage commenced after a glacial outburst that caused the collapse of the Thermohaline Circulation, a massive Heinrich event, and the onset of the Bipolar Seesaw Mechanism (BSM) that eventually permitted the completion of T-II. The pace of the millennial oscillations during the first stage of T-II controlled the onset of the second stage, when the termination became a non-reversible and global phenomenon that accelerated the deglaciation. During the last the two terminations, the BSM was triggered by different detailed climate interactions, which suggests the occurrence of different modes of terminations.

Disarmament is the New War, Gold is the New Opium, and Ecohealth is the Historic Victim.

In Colombia, the convergence of drug trafficking, illegal armed groups, and gold production and trade threatens peace and stability in the post-FARC (Revolutionary Armed Forces of Colombia) era, as had the narcotics trade previously. Armed groups and criminal organizations have increased and consolidated their influence over illegal mining and may be diverting US$5 billion from Colombia's annual economy. As of 2014, 46% of the total area (78 939 ha) exploited for alluvial gold was in the Afro-Colombian Pacific States, in which unregulated mining was the main driver of deforestation. The informal job market represents 49% of the workforce and absent other economic alternatives, this workforce of ex-guerrillas, organized crime groups and corrupt officials will sustain the black markets that permeate gold mining. Human health consequences of unregulated gold mining are largely unrecognized, but include the spread of malaria and other insect-borne diseases, and we suggest diseases such as babesiosis.

Survey of Pain Knowledge and Analgesia in Dogs and Cats by Colombian Veterinarians.

A questionnaire study was conducted among 131 veterinarians practicing in the city of Medellin, Colombia, to assess views on pain evaluation and management in dogs and cats. When pain recognition and quantification abilities were used as a perceived competence of proper pain assessment, only 83/131 (63.4%, confidence interval (CI) 0.55⁻0.72) were deemed to have satisfactory skills, with the rest considered to be deficient. There were 49/131 (37.4) veterinarians who had participated in continuing education programs and were more confident assessing pain, with an odds ratio (±standard error) of 2.84 ± 1.15 (p = 0.01; CI 1.27⁻6.32). In addition, the odds of using pain scales was 4.28 ± 2.17 (p < 0.01, CI 1.58⁻11.55) greater if they had also participated in continuing education programs. The term multimodal analgesia was familiar to 77 (58.7%) veterinarians who also claimed to use more than one approach to pain control. Nevertheless, homeopathy was the preferred alternative approach in 71/77 (92%). There were major misconceptions on side effects and/or contraindications for use of opioids and non-steroidal anti-inflammatory drugs (NSAIDs) by most veterinarians. In addition, the lack of multimodal analgesia by at least 40% of the practitioners, combined with heavy reliance on weak analgesics (i.e., tramadol) or those with no proven record of efficacy (homeopathic remedies), denotes major deficits in education at the undergraduate level and a need for additional continuing education designed to fulfill the gaps in knowledge identified in this study, and overcome ideological convictions not supported by scientific evidence.

Giardia is the most prevalent parasitic infection in dogs and cats with diarrhea in the city of Medellín, Colombia.

Parasitic agents are a common cause of diarrhea in dogs and cats and, thus, determining their prevalence is essential to establish preventive and control measures. This retrospective study examined the fecal tests records from 1111 dogs and 203 cats with diarrhea submitted to a diagnostic laboratory in the city of Medellin between January and May 2018. The detection of parasites was carried out by direct smears and simple flotation methods. Parasitic organisms were detected in feces from 464 (41.7%) dogs and 96 (47.3%) cats. In order of decreasing prevalence, the parasites detected in dogs were: Giardia intestinalis (13%), ancylostomids (12.6%), Entamoeba spp. (6.1%), coccidian oocysts (5.8%), Toxocara spp. (5.6%) and Dipylidium caninum (1.3%). In cats, the prevalence was: Giardia intestinalis (20%), coccidian oocysts (8.9%), Entamoeba spp. (7.9%), ancylostomids (6.4%), Toxocara spp. (2.5%) and Dipylidium caninum (2%). Age, but not gender, was a predisposing factor, as puppies and kittens had significantly higher infection rates that older age categories. The majority of Giardia intestinalis positive cases occurred in puppies (109/145, 75.2%) and kittens (19/36, 52.8%), making this parasite the most prevalent in amongst animals with diarrhea. Out of 117 positive infections in the adult dog population, ancylostomids accounted for 56 cases (47.9%) and was the most common parasite in this age group. In conclusion, although these results do not imply a cause and effect relationship, they are an estimate of the type of parasites that may be most commonly associated with diarrhea in dogs and cats. The lower diagnostic sensitivity of the traditional methods used here as compared to more contemporary techniques like fecal flotation with centrifugation and PCR, may have underestimated the actual prevalence and diminished the detection of co-infections. Future studies should aim to have diagnostic panels that also screen for other enteric pathogens, including bacterial and viral agents.

Clinical presentation and pathology of sarcocystosis in psittaciform birds: 11 cases.

Sarcocystosis in psittaciform birds occurs in several different presentations, making ante-mortem diagnosis challenging without specific laboratory tests. This study followed the course of 11 birds diagnosed with sarcocystosis by serologic analysis and/or post-mortem examinations during a 10-month period in 2006-07. The disease presented in three different clinical forms: an acute pulmonary disease (three birds), muscular disease (five), and neurological disease (three). Early diagnosis of sarcocystosis was possible through the combined used of plasma protein electrophoresis and indirect fluorescent antibody serology in birds presented with the neurological and muscular forms of the disease. In three of these birds the plasma electrophoretic patterns revealed marked hypergammaglobulinemia. All of the birds that presented with the acute pulmonary form developed similar gross and microscopic lesions. Definitive diagnosis was ultimately made by microscopic observation of intravascular pulmonary schizonts containing merozoites. Schizonts were identified in the cerebellum and brainstem in two birds with the neurological form of disease. Those birds that initially presented with severe lethargy and weakness were considered to suffer from the muscular form of disease if they had extreme elevations of muscle enzyme activities (creatine phosphokinase, aspartate aminotransferase) and beta and gamma globulins concentrations, and were seropositive for antibodies to Sarcocystis falcatula. In this group the progression of the disease varied. Two birds recovered completely, and secondary aspergillosis was diagnosed in three birds. The histopathological lesions observed are discussed and interpreted in light of earlier findings from experimental infections in budgerigars, which provide insights into the natural course of sarcocystosis in psittaciform birds.