Baroreflex sensitivity differs among same strain Wistar rats from the same laboratory

Previous studies showed that a proportion of normotensive Sprague-Dawley rats spontaneously exhibit lower baroreflex sensitivity. However, investigations have not yet been carried out on Wistar rats. We aimed to compare baroreflex sensitivity among rats from the same strain and the same laboratory. Male Wistar normotensive rats (300–400g) were studied. Cannulas were inserted into the abdominal aortic artery through the right femoral artery to measure mean arterial pressure and heart rate. Baroreflex was calculated as the derivative of the variation of heart rate in function of the mean arterial pressure variation (ΔHR/ΔMAP) tested with a depressor dose of sodium nitroprusside (50 µg/kg) and with a pressor dose of phenylephrine (8µg/kg) in the right femoral venous approach through an inserted cannula. We divided the rats into four groups: i) high bradycardic baroreflex, baroreflex gain less than −2 tested with phenylephrine; ii) low bradycardic baroreflex, baroreflex gain between −1 and −2 tested with phenylephrine; iii) high tachycardic baroreflex, baroreflex gain less than −3 tested with sodium nitroprusside; and iv) low tachycardic baroreflex, baroreflex gain between −1 and −3 tested with sodium nitroprusside. Approximately 71% of the rats presented a decrease in bradycardic reflex while around half showed an increase in tachycardic reflex. No significant changes in basal mean arterial pressure and heart rate, tachycardic and bradycardic peak and heart rate range were observed. There was a significant change in baroreflex sensitivity among rats from the same strain and the same laboratory

Explaining Binary Classifiers - Will Acute Pancreatitis be the End of You?

Machine learning techniques have demonstrated remarkable performance inmany fields but an understanding of the model's inner decision-making process isstill often lacking. To remedy this, techniques have been developed to explainmachine learning models and their predictions, constituting the field of explainability.In this report, an explainability method is proposed that describes the behavior of anarbitrary binary classification model in carefully selected regions of the data spaceinferred from analysis of an existing data set. As a case study, this method wasapplied to a model predicting hospital mortality for acute pancreatitis patients. Thecase study showed that seemingly good explanations could be extracted using themethod, although it is limited by a lack of guarantee that the explanations provide anexhaustive description of the model's behavior. The main results were presented as acollection of tables describing the model's local behavior for five different patientcohorts. From these tables both global and local trends were identified, showing theutility of the method. Even though no comprehensive description is assured by ourmethod, a partial characterization is still valuable to potentially detect unforeseenbehavior or to extract new domain knowledge from a model.Maskininlärningsmetoder har uppvisat anmärkningsvärd prestanda inommånga tillämpningsområden men en förståelse för modellernas inre beslutsfattandesaknas fortfarande ofta. För att förbättra detta har tekniker utvecklats som förklararmaskininlärningsmodeller och deras prediktioner. Detta utgör området förklarbarhet. Idenna rapport föreslås en förklarbarhetsmetod som beskriver en godtycklig binärklassificeringsmodell i noggrant utvalda områden i datarummet, där områdena ärhärledda från analys av ett existerande datamängd. Som en fallstudie tillämpasmetoden på en modell som predicerar sjukhusdödlighet hos patienter med akutpankreatit. Fallstudien visade att till synes goda förklaringar kunde extraheras genomden föreslagna metoden men att den begränsades av brist på försäkran attförklaringen ger en fullständig bild av modellens beteende. Huvudresultatenpresenterades som en samling tabeller som beskrev modellens lokala beteende förfem olika patientgrupper. Från dessa tabeller kunde globala och lokala trenderidentifieras, vilket påvisade nyttan av metoden. Trots att ingen fullständig förklaringgaranteras av metoden kan fortfarande en delvis förklaring vara användbar för attupptäcka oväntat beteende eller för att utvinna ny domänkunskap från en modell.Kandidatexjobb i elektroteknik 2023, KTH, Stockhol

Explaining Binary Classifiers - Will Acute Pancreatitis be the End of You?

Machine learning techniques have demonstrated remarkable performance inmany fields but an understanding of the model's inner decision-making process isstill often lacking. To remedy this, techniques have been developed to explainmachine learning models and their predictions, constituting the field of explainability.In this report, an explainability method is proposed that describes the behavior of anarbitrary binary classification model in carefully selected regions of the data spaceinferred from analysis of an existing data set. As a case study, this method wasapplied to a model predicting hospital mortality for acute pancreatitis patients. Thecase study showed that seemingly good explanations could be extracted using themethod, although it is limited by a lack of guarantee that the explanations provide anexhaustive description of the model's behavior. The main results were presented as acollection of tables describing the model's local behavior for five different patientcohorts. From these tables both global and local trends were identified, showing theutility of the method. Even though no comprehensive description is assured by ourmethod, a partial characterization is still valuable to potentially detect unforeseenbehavior or to extract new domain knowledge from a model.Maskininlärningsmetoder har uppvisat anmärkningsvärd prestanda inommånga tillämpningsområden men en förståelse för modellernas inre beslutsfattandesaknas fortfarande ofta. För att förbättra detta har tekniker utvecklats som förklararmaskininlärningsmodeller och deras prediktioner. Detta utgör området förklarbarhet. Idenna rapport föreslås en förklarbarhetsmetod som beskriver en godtycklig binärklassificeringsmodell i noggrant utvalda områden i datarummet, där områdena ärhärledda från analys av ett existerande datamängd. Som en fallstudie tillämpasmetoden på en modell som predicerar sjukhusdödlighet hos patienter med akutpankreatit. Fallstudien visade att till synes goda förklaringar kunde extraheras genomden föreslagna metoden men att den begränsades av brist på försäkran attförklaringen ger en fullständig bild av modellens beteende. Huvudresultatenpresenterades som en samling tabeller som beskrev modellens lokala beteende förfem olika patientgrupper. Från dessa tabeller kunde globala och lokala trenderidentifieras, vilket påvisade nyttan av metoden. Trots att ingen fullständig förklaringgaranteras av metoden kan fortfarande en delvis förklaring vara användbar för attupptäcka oväntat beteende eller för att utvinna ny domänkunskap från en modell.Kandidatexjobb i elektroteknik 2023, KTH, Stockhol

Pharmacotherapy for alcohol use disorders - unequal provision across sociodemographic factors and co-morbid conditions. A cohort study of the total population in Sweden.

BACKGROUND: Pharmacotherapy for alcohol use disorders (AUD) is effective. However, knowledge about utilization of, and patient characteristics associated with prescriptions is scarce. The aim is to investigate prescriptions of pharmacotherapy for AUD in Sweden across time, sociodemographics, domicile and comorbid conditions. METHOD: This is a national cohort study, comprising 132 733 adult patients with AUD diagnosis between 2007 and 2015. The exposure variables were age, sex, income, education, family constellation, domicile, origin, concurrent psychiatric and somatic co-morbid diagnoses. Logistic regression analyses were used to obtain odds ratios (OR) for any filled prescription of AUD pharmacotherapy; Acamprosate, Disulfiram, Naltrexone or Nalmefene during 12 months after AUD diagnosis. RESULTS: During the study period, the proportion of individuals who received pharmacotherapy ranged between 22.80 and 23.94 % (χ2(64) = 72.00, p = .23). Female sex, age 31-45, higher education and income, living in a big city, co-habiting and born in Sweden, bar Norway, Denmark and Iceland, were associated with higher odds of pharmacotherapy. Concurrent somatic diagnosis was associated with lower odds of pharmacotherapy but psychiatric diagnosis higher (aOR = 0.61 95 % CI 0.59-0.63 and aOR = 1.61 95 % CI 1.57-1.66 respectively). CONCLUSIONS: Pharmacotherapy for AUD is underutilized. The proportion of individuals with a prescription did not change between 2007 and 2015. Provision of treatment is unequal across different groups in society, where especially older age, lower income and education, and co-morbid somatic diagnosis were associated with lower odds of prescription. There is a need to develop treatment provision, particularly for individuals with co-morbid somatic conditions

Risk of readmission among individuals with cannabis use disorder during a 15-year cohort study: the impact of socio-economic factors and psychiatric comorbidity.

BACKGROUND AND AIM: Cannabis use disorder (CUD) is one of the main reasons for seeking substance treatment in the Nordic countries, but there are few studies on readmission to care. We aimed to characterize CUD readmission and estimate the magnitude of how socio-economic factors and psychiatric comorbidity influence the risk of CUD readmission. DESIGN, SETTING AND PARTICIPANTS: This was a nation-wide cohort study carried out between 2001 and 2016 in Sweden. The participants were individuals with CUD, aged 17 years and above (n = 12 143). MEASUREMENTS: Information on predictors was obtained from registers and included education, income and psychiatric comorbidity assessed by six disease groups. The outcome measure was readmission, defined as a CUD visit to health-care at least 6 months after initial CUD diagnosis. Hazard ratios (HR) were estimated using Cox survival analyses and flexible parametric survival analyses to assess risk of readmission and how the risk varied with age. FINDINGS: The vast majority of CUD visits took place in outpatient care (~80%). Approximately 23% of the included individuals were readmitted to care during follow-up. The fully adjusted model showed an increased risk of readmission among those with schizophrenia and other psychotic disorders, low education, personality disorders or mood disorders. Flexible parametric modeling revealed increased risk of readmission mainly in individuals aged 18-35 years. CONCLUSIONS: The risk of readmission was highest among those with low education, schizophrenia and other psychotic disorders, mood-related disorders or personality disorders. Individuals aged 18-35 years showed the highest risk of readmission. Our findings highlight individuals with complex health-care needs