Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval is more challenging. We demonstrate an explainable deep learning model that predicts the time-specific risk separately for all-cause death, acute coronary syndrome (ACS), and revascularization directly from MPI and 15 clinical features. We train and test the model internally using 10-fold hold-out cross-validation (n = 20,418) and externally validate it in three separate sites (n = 13,988) with MACE follow-ups for a median of 3.1 years (interquartile range [IQR]: 1.6, 3.6). We evaluate the model using the cumulative dynamic area under receiver operating curve (cAUC). The best model performance in the external cohort is observed for short-term prediction - in the first six months after the scan, mean cAUC for ACS and all-cause death reaches 0.76 (95% confidence interval [CI]: 0.75, 0.77) and 0.78 (95% CI: 0.78, 0.79), respectively. The model outperforms conventional perfusion abnormality measures at all time points for the prediction of death in both internal and external validations, with improvement increasing gradually over time. Individualized patient explanations are visualized using waterfall plots, which highlight the contribution degree and direction for each feature. This approach allows the derivation of individual event probability as a function of time as well as patient- and event-specific risk explanations that may help draw attention to modifiable risk factors. Such a method could help present post-scan risk assessments to the patient and foster shared decision-making

Neonatal Co-Lesion by DSP-4 and 5,7-DHT Produces Adulthood Behavioral Sensitization to Dopamine D\u3csub\u3e2\u3c/sub\u3e Receptor Agonists

To assess the possible modulatory effects of noradrenergic and serotoninergic neurons on dopaminergic neuronal activity, the noradrenergic and serotoninergic neurotoxins DSP-4 N-(2-chlorethyl)-N-ethyl-2-bromobenzylamine (50.0 mg/kg, sc) and 5,7- dihydroxytryptamine (5,7-DHT) (37.5 μg icv, half in each lateral ventricle), respectively, were administered to Wistar rats on the first and third days of postnatal ontogeny, and dopamine (DA) agonist-induced behaviors were assessed in adulthood. At eight weeks, using an HPLC/ED technique, DSP-4 treatment was associated with a reduction in NE content of the corpus striatum (\u3e 60%), hippocampus (95%), and frontal cortex (\u3e 85%), while 5,7-DHT was associated with an 80-90% serotonin reduction in the same brain regions. DA content was unaltered in the striatum and the cortex. In the group lesioned with both DSP-4 and 5,7-DHT, quinpirole-induced (DA D2-agonist-agonist) yawning, 7-hydroxy-DPAT-induced (DA D3 agonist) yawning, and apomorphine-induced (non-selective DA agonist) stereotypies were enhanced. However, SKF 38393-induced (DA D1 agonist) oral activity was reduced in the DSP-4 + 5,7-DHT group. These findings demonstrate that DA D2- and D3-agonist-induced behaviors are enhanced while DA D1-agonist-induced behaviors are suppressed in adult rats in which brain noradrenergic and serotoninergic innervation of the brain has largely been destroyed. This study indicates that noradrenergic and serotoninergic neurons have a great impact on the development of DA receptor reactivity (sensitivity)

Security theory and practice: Management of Local Security – exogenous and endogenous factors

Z wprowadzenia: "Problemy bezpieczeństwa można rozpatrywać w kontekście rozwoju lokalnego i regionalnego, obejmującego sfery: gospodarczą, przestrzenną, kulturową, społeczną, ekologiczną i bezpieczeństwa. Na bezpieczeństwo lokalne wpływ mają uwarunkowania: geograficzne, ekonomiczne, społeczne, kulturowe oraz polityczne. Podkreśla się, że na efektywność działań w zakresie bezpieczeństwa publicznego wpływa współpraca samorządów lokalnych z organizacjami pozarządowymi i sektorem prywatnym, uwzględniająca obszar obywatelski1. Istotną rolę odgrywają współpraca, współdziałanie i koordynacja działań na poziomie lokalnym. Wskazuje się na bliskoznaczność pojęć „koordynacja” i „współdziałanie”, które dotyczą harmonizacji (uzgadniania) działań. W przypadku koordynacji występuje nadrzędność lub uprawnienia władcze organu koordynującego wobec podmiotów, których działania podlegają koordynowaniu. Z kolei współdziałanie wiąże się z równorzędnością partnerów (podmiotów). Równocześnie nauki o zarządzaniu przyjmują, że koordynacja stanowi uporządkowane współdziałanie prowadzące do harmonizacji działań dla realizacji określonych celów. Oznacza to synchronizację działań cząstkowych w czasie i przestrzeni."(...

Viruses in transplantology.

The 3 leading causes of death in patients after solid organ transplantation (SOT) include cardiovascular diseases, malignancies, and infections. According to our current understanding, the latter play the key role in the pathogenesis of atherosclerosis. Similarly, infections (mainly viral) are implicated in the pathogenesis of at least 20% of known neoplasms. In other words, the implications of acute and chronic infectious diseases in modern medicine, not only transplantology, are significant and ever‑increasing. Immunosuppressive treatment impairs the immune function, which renders the patient more susceptible to infections. Furthermore, treatment of infections in immunocompromised patients poses a challenge and SOT. The current publication provides a brief summary of the key information provided in 20 lectures on viral infections in patients after SOT delivered during the 9th Practical Transplantology Course in Warsaw, Poland on September 15-16, 2017

Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

Abstract Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval is more challenging. We demonstrate an explainable deep learning model that predicts the time-specific risk separately for all-cause death, acute coronary syndrome (ACS), and revascularization directly from MPI and 15 clinical features. We train and test the model internally using 10-fold hold-out cross-validation (n = 20,418) and externally validate it in three separate sites (n = 13,988) with MACE follow-ups for a median of 3.1 years (interquartile range [IQR]: 1.6, 3.6). We evaluate the model using the cumulative dynamic area under receiver operating curve (cAUC). The best model performance in the external cohort is observed for short-term prediction – in the first six months after the scan, mean cAUC for ACS and all-cause death reaches 0.76 (95% confidence interval [CI]: 0.75, 0.77) and 0.78 (95% CI: 0.78, 0.79), respectively. The model outperforms conventional perfusion abnormality measures at all time points for the prediction of death in both internal and external validations, with improvement increasing gradually over time. Individualized patient explanations are visualized using waterfall plots, which highlight the contribution degree and direction for each feature. This approach allows the derivation of individual event probability as a function of time as well as patient- and event-specific risk explanations that may help draw attention to modifiable risk factors. Such a method could help present post-scan risk assessments to the patient and foster shared decision-making