Clinical characteristics and predictors of mortality in patients with melioidosis: the Kapit experience.

OBJECTIVES: Melioidosis, caused by Burkholderia pseudomallei, is prevalent in rural areas of Malaysia. The aim of this study is to delineate the epidemiology and predictors of mortality from melioidosis in Kapit district, Sarawak. METHODS: For this retrospective study of patients with culture-confirmed melioidosis admitted to Kapit Hospital, Sarawak, Malaysia, between July 2016 and July 2019, epidemiological, clinical and microbiological data were obtained. Univariate and multivariate logistic regression analyses were used to determine predictors of mortality. RESULTS: Seventy three patients met inclusion criteria. Diabetes mellitus (28.8%) and hypertension (27.4%) were primary co-morbidities. Clinical spectrum of melioidosis ranged from bacteraemia (64.4%), pneumonia (61.6%) and internal organ abscesses (49.3%) to localised soft tissue (21.9%) and joint abscesses (6.9%). Mortality rate was 12.3%. Bacteraemia and pneumonia were significantly associated with septic shock, whereas patients with soft tissue abscesses tended to present with a milder form of melioidosis without septic shock. Septic shock, mechanical ventilation, intensive care unit admission, serum urea, creatinine, bicarbonate, albumin and aspartate transaminase were all significantly associated with increased mortality on univariate analysis (all P < 0.05). Multivariate analysis revealed that low serum bicarbonate (P = 0.004, OR 0.64, 95% CI 0.48-0.87) and albumin (P = 0.031, OR 0.73, 95% CI 0.54-0.97) could be associated with a higher mortality. CONCLUSION: Melioidosis remains a fatal infection and commonly presents with septic shock, in the form of bacteraemia and pneumonia. Two routine clinical parameters, serum bicarbonate and serum albumin, may have important prognostic implications in septicaemic melioidosis.

A multiplicative reinforcement learning model capturing learning dynamics and interindividual variability in mice.

Both in humans and in animals, different individuals may learn the same task with strikingly different speeds; however, the sources of this variability remain elusive. In standard learning models, interindividual variability is often explained by variations of the learning rate, a parameter indicating how much synapses are updated on each learning event. Here, we theoretically show that the initial connectivity between the neurons involved in learning a task is also a strong determinant of how quickly the task is learned, provided that connections are updated in a multiplicative manner. To experimentally test this idea, we trained mice to perform an auditory Go/NoGo discrimination task followed by a reversal to compare learning speed when starting from naive or already trained synaptic connections. All mice learned the initial task, but often displayed sigmoid-like learning curves, with a variable delay period followed by a steep increase in performance, as often observed in operant conditioning. For all mice, learning was much faster in the subsequent reversal training. An accurate fit of all learning curves could be obtained with a reinforcement learning model endowed with a multiplicative learning rule, but not with an additive rule. Surprisingly, the multiplicative model could explain a large fraction of the interindividual variability by variations in the initial synaptic weights. Altogether, these results demonstrate the power of multiplicative learning rules to account for the full dynamics of biological learning and suggest an important role of initial wiring in the brain for predispositions to different tasks.