Effect of supplementation with organic acids on productive and reproductive parameters in guinea pigs

Guinea pig production systems demand organic alternatives to improve their productivity, due to the increased demands of this type of livestock operations. The present study aimed to evaluate the effect of organic acids on the productive and reproductive indices in reproductive Guinea pigs in an intensive breeding farm in Lima. 60 pools were used with 10 Guinea pigs in each one (9 females and one male). They were randomly divided into 4 treatment groups: T1: 1mL/L, T2: 2mL/L, T3: 4mL/L; all with 15 pools in total (5 pools each with dosage of 5, 10 and 15 days, respectively), and T4: Control (15 pools without dosage); the observation period was 3 months. No significant difference was found (p> 0.05) between treatments for weight gain, percentage of maternal mortality, percentage of abortions, percentage of pregnancy, number of offspring born and percentage of viability. For the percentage of mortality of pups and average weight of the offspring, a significant difference was found (p <0.05), demonstrating that T3 reached a higher weight index at birth of offspring (T3: 147.7 / 142.7 / 146.8g) and lower percentage of mortality in rabbits (T3: 0 / 1.8 / 3.0%). No significant difference was found in terms of dosing days. It is concluded that supplementation with organic acids with T3 (4mL/L) improved the weight at birth of the offspring and the mortality percentage in kits

Factores de riesgo para la presentación de bacteriuria en gatos con enfermedad del tracto urinario inferior: un análisis retrospectivo de 102 casos (2008 – 2015)

The aim of this retrospective study was to describe the results of bacterial isolates and to determine the risk factors involved in the presentation of bacteriuria in cats with lower urinary tract disease (FLUTD). Clinical records of feline patients (n=102) who underwent urinalysis and urine culture were collected. The following variables were considered: age, sex, reproductive status, breed, bodyweight, season, colour of urine, urine odour, urine density, pH, glucose, proteins, blood, leukocytes, erythrocytes, crystals and type of crystals. In 60.8% (62/102) of the samples, bacteria were isolated in urine, including E. coli 40.3% (25/62), Klebsiella sp 12.9% (8/62), Staphylococcus sp 12.9% (8 / 62), Proteus sp 8.1% (5/62), Enterobacter sp 8.1% (5/62) and Staphylococcus epidermidis 6.4% (4/62). Age, urinary density and presence of blood were statistically significant. In addition, it was determined that the urine density, the red colour of the urine, the presence of leukocytes and crystals represent risk factors (p &lt;0.05) for the occurrence of bacteriuria.El objetivo de este estudio retrospectivo fue describir los resultados de aislados bacterianos y determinar los factores de riesgo involucrados en la presentación de bacteriuria en gatos con enfermedad del tracto urinario inferior (FLUTD). Se recolectaron 102 historias clínicas de pacientes felinos a los cuales se les realizó análisis de orina y urocultivo. Se consideraron las variables edad, sexo, estado reproductivo, raza, peso, estación del año, color de orina, olor de orina, densidad urinaria, pH, glucosa, proteínas, sangre, leucocitos, eritrocitos, cristales y tipo de cristales. En el 60.8% (62/102) de las muestras se llegó a aislar bacterias en orina, entre ellas E. coli 40.3% (25/62), Klebsiella sp 12.9% (8/62), Staphylococcus sp 12.9% (8/62), Proteus sp 8.1% (5/62), Enterobacter sp 8.1% (5/62) y Staphylococcus epidermidis 6.4% (4/62). La edad, densidad urinaria y presencia de sangre fue estadísticamente significativa. Además, se determinó que, la densidad urinaria, el color rojo de la orina, la presencia de leucocitos y de cristales representan factores de riesgo (p&lt;0.05) para la ocurrencia de bacteriuria

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine

Prevalence of Borderline Personality Disorder in University Samples: Systematic Review, Meta-Analysis and Meta-Regression.

OBJECTIVE: To determine pooled prevalence of clinically significant traits or features of Borderline Personality Disorder among college students, and explore the influence of methodological factors on reported prevalence figures, and temporal trends. DATA SOURCES: Electronic databases (1994-2014: AMED; Biological Abstracts; Embase; MEDLINE; PsycARTICLES; CINAHL Plus; Current Contents Connect; EBM Reviews; Google Scholar; Ovid Medline; Proquest central; PsychINFO; PubMed; Scopus; Taylor & Francis; Web of Science (1998-2014), and hand searches. STUDY SELECTION: Forty-three college-based studies reporting estimates of clinically significant BPD symptoms were identified (5.7% of original search). DATA EXTRACTION: One author (RM) extracted clinically relevant BPD prevalence estimates, year of publication, demographic variables, and method from each publication or through correspondence with the authors. RESULTS: The prevalence of BPD in college samples ranged from 0.5% to 32.1%, with lifetime prevalence of 9.7% (95% CI, 7.7-12.0; p &lt; .005). Methodological factors contributing considerable between-study heterogeneity in univariate meta-analyses were participant anonymity, incentive type, research focus and participant type. Study and sample characteristics related to between study heterogeneity were sample size, and self-identifying as Asian or "other" race. The prevalence of BPD varied over time: 7.8% (95% CI 4.2-13.9) between 1994 and 2000; 6.5% (95% CI 4.0-10.5) during 2001 to 2007; and 11.6% (95% CI 8.8-15.1) from 2008 to 2014, yet was not a source of heterogeneity (p = .09). CONCLUSIONS: BPD prevalence estimates are influenced by the methodological or study sample factors measured. There is a need for consistency in measurement across studies to increase reliability in establishing the scope and characteristics of those with BPD engaged in tertiary study

Mapping genomic loci implicates genes and synaptic biology in schizophrenia

Schizophrenia has a heritability of 60-80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine

A communication model and partitioning algorithm for streaming applications for an embedded MPSoC

Energy efficient embedded computing enables new application scenarios in mobile devices like software-defined radio and video processing. The hierarchical multiprocessor considered in this work may contain dozens or hundreds of resource efficient VLIW CPUs. Programming this number of CPU cores is a complex task requiring compiler support. The stream programming paradigm provides beneficial properties that help to support automatic partitioning. This work describes a compiler for streaming applications targeting the self-build hierarchical CoreVA-MPSoC multiprocessor platform. The compiler is supported by a programming model that is tailored to fit the streaming programming paradigm. We present a novel simulated-annealing (SA) based partitioning algorithm, called Smart SA. The overall speedup of Smart SA is 12.84 for an MPSoC with 16 CPU cores compared to a single CPU implementation. Comparison with a state of the art partitioning algorithm shows an average performance improvement of 34.07%

It Is Time To Take A Stand For Medical Research And Against Terrorism Targeting Medical Scientists

Terrorists are attacking scientists who are attempting to alleviate human suffering. We need a concerted public effort to eliminate these acts, particularly the harassment of scientists studying nonhuman primates. This need is highlighted by the attacks upon the home of our friend and colleague, the noted medical scientist, Dr. Edythe London, professor of psychiatry and biobehavioral sciences and of molecular and medical pharmacology at the David Geffen School of Medicine at the University of California Los Angeles (UCLA). Her work exemplifies the unique role of research involving nonhuman primates in enabling the results of research in simple systems (oocytes, cell culture) and lower organisms to be applied to human diseases. The importance of Dr. London’s research was highlighted in a public letter issued on February 8, 2008 from the Director of the National Institutes of Health (NIH), Dr. Elias Zerhouni, who stated, “her work is a prime example of NIH’s efforts … to develop effective treatments for people suffering from addiction—a disease that devastates individuals, families, communities, and costs society more than half a trillion dollars annually in health and crime-related costs and losses in productivity.