Summary Neuronal signals in the prefrontal cortex have been reported to predict upcoming decisions. Such activity patterns are often coupled to perceptual cues indicating correct choices or values of different options. How does the prefrontal cortex signal future decisions, when no cues are present, but when decisions are made based on internal valuations of past experiences with stochastic outcomes? We trained rats to perform a two-arm bandit-task, successfully adjusting choices between certain-small, or possible-big rewards with changing long-term advantages. We discovered specialized prefrontal neurons, whose firing during the encounter of no-reward predicted the subsequent choice of animals, even for unlikely or uncertain decisions and several seconds before choice-execution. Optogenetic silencing of the prelimbic cortex exclusively timed to encounters of no-reward, provoked animals to excessive gambling for large rewards. Firing of prefrontal neurons during outcome evaluation signal subsequent choices during gambling and is essential for dynamically adjusting decisions based on internal valuations.

Bacteraemia is a life-threating condition requiring immediate diagnostic and therapeutic actions. Blood culture (BC) analyses often result in a low true positive result rate, indicating its improper usage. A predictive model might assist clinicians in deciding for whom to conduct or to avoid BC analysis in patients having a relevant bacteraemia risk. Predictive models were established by using linear and non-linear machine learning methods. To obtain proper data, a unique data set was collected prior to model estimation in a prospective cohort study, screening 3,370 standard care patients with suspected bacteraemia. Data from 466 patients fulfilling two or more systemic inflammatory response syndrome criteria (bacteraemia rate: 28.8%) were finally used. A 29 parameter panel of clinical data, cytokine expression levels and standard laboratory markers was used for model training. Model tuning was performed in a ten-fold cross validation and tuned models were validated in a test set (80:20 random split). The random forest strategy presented the best result in the test set validation (ROC-AUC: 0.729, 95%CI: 0.679–0.779). However, procalcitonin (PCT), as the best individual variable, yielded a similar ROC-AUC (0.729, 95%CI: 0.679–0.779). Thus, machine learning methods failed to improve the moderate diagnostic accuracy of PCT.

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Abstract. The current study aimed to determine the optimum diagnostic imaging technique out of magnetic resonance imaging (MRI), 18 F-f ludeoxyglucose positron emission tomography/computed tomography ([ 18 F] FDG-PET/CT, otherwise known as PET/CT) and [18 F] FDG-PET/MRI (otherwise known as PET/MRI) for the pelvic lymph node staging (N-staging) of untreated cervical carcinoma (CC). A total of 27 patients were included in the present study. All patients had undergone pre-treatment with PET/CT and MRI ≤45 days prior to undergoing a lymphadenectomy. The results from PET (separated from PET/CT), MRI and the statistically combined results of (virtual) PET/MRI were compared to those from histological analyses (the gold standard). A per-patient-based analysis of the detection of pelvic lymph node metastases indicated that PET/MRI had a sensitivity of 64%. The specificity of PET/CT and MRI were 69 and 62%, respectively. The positive predictive value (PPV) was 69 and 64% for PET/CT and MRI, respectively. The negative predictive value (NPV) was 64 and 62% for PET/CT and MRI, respectively. The sensitivity of the PET-guided PET/MRI and the MRI-guided PET/MRI was 64% for both. The specificity of the PET-guided PET/MRI and the MRI-guided PET/MRI was 77 and 62%, respectively. The PPV was 75% for PET-guided PET/MRI and 64% for MRI-guided PET/MRI, and the NPV was 67 and 62%, respectively. PET/CT and the virtual PET/MRI exhibited the same low sensitivity (64%). PET/MRI exhibited slightly better results than PET/CT regarding specificity (77 vs. 69%, respectively), PPV (75 vs. 69%, respectively) and NPV (67 vs. 64%, respectively). The results of the present study suggested that PET/CT and MRI are not optimal diagnostic modalities, and that PET/MRI does not necessarily lead to better results than PET/CT, in the pelvic N-staging of CC.