Sygdomme og velfærd

Siden anden verdenskrig er der sket store ændringer i fjerkræproduktionen. Indførslen af nye produktionssystemer, robuste og højtydende dyr, forbedret management og indførslen af biosecurity har medført en stor produktionsfremgang med lav mortalitet. I de senere år har forbrugerønsker medført at udvikling af udendørs produktionssystemer, hvor de klassiske fjerkræsygdomme nu er på fremmarch med en forhøjet mortalitet til følge. Forfatterne diskuterer, om de udendørs produktionssystemer reelt har betydet en forbedret velfærd for hønerne

HMGB1 – a possible cursor for sepsis

Pyometra är en vanlig sjukdom hos äldre, okastrerade tikar. Oftast uppträder sjukdomen i efterlöpet då tiken står under progesteronpåverkan vilket gör livmodern mer mottaglig för bakteriella infektioner. Enligt studier är Escherichia coli den bakterie som vanligen isoleras från livmodern. Bakterierna härstammar troligtvis från tikens normalflora eller från urinvägarna. Kliniska symptom på livmoderinflammation kan vara sänkt allmäntillstånd, variga flytningar (om cervix är öppen), polyuri, polydipsi, feber, diarré och kräkningar. Rekommenderad behandling är ovariohysterektomi men det finns också möjlighet till medicinsk behandling i kombination med antibiotika i särskilda fall. High Mobility Group Box 1 (HMGB1) är ett protein med både en intra- och extracellulär roll. Under inflammatoriska tillstånd eller vid skada frigörs proteinet från aktiverade makrofager och monocyter som har stimulerats med endotoxin eller proinflammatoriska cytokiner. Vid studier både inom human- och veterinärmedicin har det visats att plasmakoncentrationerna av HMGB1 stiger vid sjukdomar som bl a sepsis, pankreatit, akut lungskada och chock. HMGB1 koncentrationen ökade då successivt under de första 8 – 16 timmarna efter infektion hos sepsispatienter och kvarstod på den höjda nivån under minst 36 timmar. HMGB1 utpekas sålunda som en sen mediator av sepsis. Den ökade plasmakoncentrationen har associerats med allvarlighetsgraden på sjukdomen och hur bra patienten kommer att svara på behandling. Syftet med studien var att undersöka om HMGB1 kan användas som en prognostisk markör för sepsis (blodförgiftning). Tikar med diagnosticerad pyometra undersöktes kliniskt och bedömdes vara positiva eller negativa för systemic inflammatory response syndrome (SIRS) enligt kliniska kriterier. Även de friska tikarna undersöktes kliniskt. Blodprov togs från samtliga tikar och samtidigt sattes blododlingar från tikarna i pyometragruppen för bakterieverifiering. Tikar i pyometragruppen med konstaterad SIRS bedömdes, enligt definitionen SIRS orsakat av infektion, ha sepsis. Serumkoncentrationerna av HMGB1 hos alla sjuka och friska tikar mättes med ett sandwich ELISA kit och resultaten bearbetades sedan statistiskt. Hos en tredjedel av tikarna från pyometragruppen mättes HMGB1 koncentrationen även en dag efter operation Koncentrationen av HMGB1 var signifikant högre hos tikar med pyometra än hos friska kontrollhundar. Dock skilde sig inte HMGB1 koncentration hos de tikar som diagnosticerats med SIRS jämfört med de tikar som var SIRS negativa. Hundar med bakteriemi hade inte heller signifikant högre HMGB1 koncentrationer än tikar vars blododling var negativ. Resultaten visade också att HMGB1 nivåerna var signifikant högre vid proverna tagna en dag efter operation jämfört med dagen innan operation. Resultaten visade att man genom att mäta HMGB1 koncentrationen inte kan urskilja hundar med sepsis. Därmed verkar användningsområdet för HMGB1 som ensam biomarkör för sepsis vara begränsat. Då HMGB1 koncentrationen hos pyometratikar var högre än hos friska kontroller visar studien dock att HMGB1 kan komma att ha ett kliniskt värde som markör för inflammation ur ett övergripande perspektiv och tillsammans med andra markörer.Pyometra is a common disease of elderly, intact bitches. The illness mainly appears in metoestrus when the uterus is progesterone-stimulated and consequently is more sensitive for bacterial infections. The bacterial species most frequently isolated from the uterine content in pyometra is Escherichia coli which most probably originate from the normal flora of the bitch. The clinical manifestations of canine pyometra are associated with symptoms such as lethargy, purulent vaginal discharge (if the cervix is open), polydipsia, polyuria, fever, vomiting and diarrhea. Surgical treatment - ovariohysterectomy- is the treatment of choice but there is also a possibility in some cases to use medical treatment in combination with antibiotics. High Mobility Group Box 1 (HMGB1) is a protein with diverse cellular functions - both intracellular and extracellular. During inflammatory or injurious conditions HMGB1 can be released by activated macrophages and monocytes in response to stimulation with bacterial endotoxin or proinflammatory cytokines. It has been shown in studies both in human- and veterinary medicine that serum HMGB1 levels increase in a number of diseases such as sepsis, pancreatitis, acute lung injury and shock. In the trials HMGB1 serum concentration increased gradually for 8-16 hours and remained elevated at least 36 hours in septic patients. HMGB1 is hence indicated as a late mediator of sepsis. The increased levels are suggested to be correlated with the severity and course of the diseases and are presumed to have a predictive value for the outcome. The purpose of the present study was to explore HMGB1 as a prognostic biomarker for dogs with sepsis (blood poisoning). Bitches with a pyometra diagnosis were clinically examined and classified as either positive or negative for systemic inflammatory response syndrome (SIRS) according to established clinical criteria. The healthy bitches were similarly clinically examined. Blood samples were collected from all dogs in the study. Blood from bitches with pyometra were subjected to culturing to confirm bacterial growth and for typing of the bacterial strains. The SIRS-positive dogs with pyometra were classified as having sepsis according to the definition of SIRS caused by infection. The serum concentrations of HMGB1 were quantified with a sandwich ELISA kit for all dogs in the study, including all bitches with pyometra and healthy controls. The HMGB1 concentrations were also measured on one third of the dogs with pyometra one day after surgical treatment. The data were analyzed statistically. Dogs with pyometra had significantly higher concentrations of serum HMGB1 compared with healthy control bitches. However the concentrations of serum HMGB1 did not differ significantly between dogs in the pyometra group with or without SIRS. The difference in HMGB1 levels was not significant between dogs with confirmed bacteremia and those with negative blood cultures. It was also shown that serum HMGB1 levels were elevated one day after surgery. The results of this study showed that dogs with sepsis could not be detected by measuring serum concentrations of HMGB1. This indicates that HMGB1 has limited potential as a single marker for sepsis. Because the HMGB1 concentrations were elevated in bitches with pyometra compared with healthy controls HMGB1, however, may have a value as a potential screening parameter together with other markers of infection and inflammation

Deep Reinforcement Learning in Trading Algorithms

An algorithm that can learn an optimal policy to execute trade profitable is any market participant’s dream. In the project, we propose an algorithm that does just that: a Deep Reinforcement Learning trading algorithm. We design our algorithm by tuning the reward function to our specified constraints, taking into account unrealized Profits and Losses (PnL), Sharpe ratio, profits, and transaction costs. Additionally, we use a short 5-month moving average replay memory in order to ensure our algorithm is basing its decision on the most pertinent information. We combine the aforementioned concepts to make a theoretical Deep Reinforcement Learning trading algorithm

The ABCC4 gene is associated with pyometra in golden retriever dogs

Pyometra is one of the most common diseases in female dogs, presenting as purulent inflammation and bacterial infection of the uterus. On average 20% of intact female dogs are affected before 10 years of age, a proportion that varies greatly between breeds (3-66%). The clear breed predisposition suggests that genetic risk factors are involved in disease development. To identify genetic risk factors associated with the disease, we performed a genome-wide association study (GWAS) in golden retrievers, a breed with increased risk of developing pyometra (risk ratio: 3.3). We applied a mixed model approach comparing 98 cases, and 96 healthy controls and identified an associated locus on chromosome 22 (p = 1.2 x 10(-6), passing Bonferroni corrected significance). This locus contained five significantly associated SNPs positioned within introns of the ATP-binding cassette transporter 4 (ABCC4) gene. This gene encodes a transmembrane transporter that is important for prostaglandin transport. Next generation sequencing and genotyping of cases and controls subsequently identified four missense SNPs within the ABCC4 gene. One missense SNP at chr22:45,893,198 (p.Met787Val) showed complete linkage disequilibrium with the associated GWAS SNPs suggesting a potential role in disease development. Another locus on chromosome 18 overlapping the TESMIN gene, is also potentially implicated in the development of the disease

Dopamine Synthesis Capacity and GABA and Glutamate Levels Separate Antipsychotic-Naïve Patients With First-Episode Psychosis From Healthy Control Subjects in a Multimodal Prediction Model

Background Disturbances in presynaptic dopamine activity and levels of gamma-aminobutyric acid (GABA) and glutamate plus glutamine (Glx) collectively may have a role in the pathophysiology of psychosis, although separately they are poor diagnostic markers. We tested whether these neurotransmitters in combination improve the distinction of antipsychotic-naïve first episode psychotic patients from healthy controls. Methods We included 23 patients (mean age 22.3 years, nine males) and 20 controls (mean age 22.4 years, eight males). We determined dopamine metabolism in nucleus accumbens (NAcc) and striatum from 18F-FDOPA positron emission tomography. We measured GABA levels in anterior cingulate cortex (ACC) and Glx levels in ACC and left thalamus with 3 Tesla 1H-MRS. We used binominal logistic regression for unimodal prediction when we modelled neurotransmitters individually, and for multimodal prediction when we combined the three neurotransmitters. We selected the best combination based on Akaike information criterion. Results Individual neurotransmitters failed to predict group. Three triple neurotransmitter combinations significantly predicted group after Benjamini-Hochberg correction. The best model (Akaike information criterion 48.5) carried 93.5% of the cumulative model weight. It reached a classification accuracy of 83.7% (p=0.003) and included dopamine synthesis capacity (Ki 4p) in NAcc (p=0.664), GABA levels in ACC (p=0.019), Glx levels in thalamus (p=0.678), and the interaction-term Ki 4pxGABA (p=0.016). Conclusion Our multimodal approach proved superior classification accuracy implying that the pathophysiology of patients represents a combination of neurotransmitter disturbances rather than aberrations in a single neurotransmitter. Particularly aberrant interrelations between Ki 4p in NAcc and GABA values in ACC appeared to contribute diagnostic information.Background: Disturbances in presynaptic dopamine activity and levels of GABA (gamma-aminobutyric acid) and glutamate plus glutamine collectively may have a role in the pathophysiology of psychosis, although separately they are poor diagnostic markers. We tested whether these neurotransmitters in combination improve the distinction of antipsychotic-naïve patients with first-episode psychosis from healthy control subjects. Methods: We included 23 patients (mean age 22.3 years, 9 male) and 20 control subjects (mean age 22.4 years, 8 male). We determined dopamine metabolism in the nucleus accumbens and striatum from 18F-fluorodopa ( 18F-FDOPA) positron emission tomography. We measured GABA levels in the anterior cingulate cortex (ACC) and glutamate plus glutamine levels in the ACC and left thalamus with 3T proton magnetic resonance spectroscopy. We used binominal logistic regression for unimodal prediction when we modeled neurotransmitters individually and for multimodal prediction when we combined the 3 neurotransmitters. We selected the best combination based on Akaike information criterion. Results: Individual neurotransmitters failed to predict group. Three triple neurotransmitter combinations significantly predicted group after Benjamini-Hochberg correction. The best model (Akaike information criterion 48.5) carried 93.5% of the cumulative model weight. It reached a classification accuracy of 83.7% (p = .003) and included dopamine synthesis capacity (K i 4p) in the nucleus accumbens (p = .664), GABA levels in the ACC (p = .019), glutamate plus glutamine levels in the thalamus (p = .678), and the interaction term K i 4p × GABA (p = .016). Conclusions: Our multimodal approach proved superior classification accuracy, implying that the pathophysiology of patients represents a combination of neurotransmitter disturbances rather than aberrations in a single neurotransmitter. Particularly aberrant interrelations between K i 4p in the nucleus accumbens and GABA values in the ACC appeared to contribute diagnostic information.</p

Macroscale EEG characteristics in antipsychotic-naïve patients with first-episode psychosis and healthy controls

Electroencephalography in patients with a first episode of psychosis (FEP) may contribute to the diagnosis and treatment response prediction. Findings in the literature vary due to small sample sizes, medication effects, and variable illness duration. We studied macroscale resting-state EEG characteristics of antipsychotic naïve patients with FEP. We tested (1) for differences between FEP patients and controls, (2) if EEG could be used to classify patients as FEP, and (3) if EEG could be used to predict treatment response to antipsychotic medication. In total, we studied EEG recordings of 62 antipsychotic-naïve patients with FEP and 106 healthy controls. Spectral power, phase-based and amplitude-based functional connectivity, and macroscale network characteristics were analyzed, resulting in 60 EEG variables across four frequency bands. Positive and Negative Symptom Scale (PANSS) were assessed at baseline and 4–6 weeks follow-up after treatment with amisulpride or aripiprazole. Mann-Whitney U tests, a random forest (RF) classifier and RF regression were used for statistical analysis. Our study found that at baseline, FEP patients did not differ from controls in any of the EEG characteristics. A random forest classifier showed chance-level discrimination between patients and controls. The random forest regression explained 23% variance in positive symptom reduction after treatment in the patient group. In conclusion, in this largest antipsychotic- naïve EEG sample to date in FEP patients, we found no differences in macroscale EEG characteristics between patients with FEP and healthy controls. However, these EEG characteristics did show predictive value for positive symptom reduction following treatment with antipsychotic medication

Advancing treatment response prediction in first-episode psychosis: integrating clinical and electroencephalography features

AIMS: Prompt diagnosis and intervention are crucial for first-episode psychosis (FEP) outcomes, but predicting the response to antipsychotics remains challenging. We studied whether adding electroencephalography (EEG) characteristics improves clinical prediction models for treatment response and whether EEG-based predictors are influenced by initial treatment. METHODS: We included 115 antipsychotic-naïve patients with FEP. Positive and Negative Syndrome Scale (PANSS) and sociodemographic items were included as clinical features. Additionally, we analyzed resting-state EEG data (n = 45) for (relative) power, functional connectivity, and network organization. Treatment response, measured as change in PANSS positive subscale scores (∆PANSS+), was predicted using a random forest regression model. We analyzed whether the most predictive EEG characteristics were influenced after treatment. RESULTS: The clinical model explained 12% variance in symptom reduction in the training set and 32% in the validation set. Including EEG variables in the model led to a nonsignificant increase of 2% (total 34%) explained variance in symptom reduction. High hallucination symptom scores and a more hierarchical organization of alpha band networks (tree hierarchy) were associated with ∆PANSS+ reduction. The tree hierarchy in the alpha band decreased after medication. EEG source analysis revealed that this change was driven by alterations in the degree and centrality of frontal and parietal nodes in the functional brain network. CONCLUSIONS: Both clinical and EEG characteristics can inform treatment response prediction in patients with FEP, but the combined model may not be beneficial over a clinical model. Nevertheless, adding a more objective marker such as EEG could be valuable in selected cases