Residential exposure to petrochemical industrial complexes and the risk of leukemia: A systematic review and exposure-response meta-analysis.

Exposure to chemicals produced by petrochemical industrial complexes (PICs), such as benzene, ionizing radiation, and particulate matters, may contribute to the development of leukemia. However, epidemiological studies showed controversial results. This systematic review and meta-analysis aimed to summarize the association between residential exposure to PICs and the risk of leukemia incidence, focusing on exposure-response effects. We searched PubMed, Embase, Web of Science, and Cochrane Library databases for studies published before September 1st, 2019. Observational studies investigating residential exposure to PICs and the risk of leukemia were included. The outcome of interest was the incidence of leukemia comparing to reference groups. Relative risk (RR) was used as the summary effect measure, synthesized by characteristics of populations, distance to PICs, and calendar time in meta-regression. We identified 7 observational studies, including 2322 leukemia cases and substantial reference groups, in this meta-analysis. Residential exposure to PICs within a maximal 8-km distance had a 36% increased risk of leukemia (pooled RR = 1.36, 95% CI = 1.14-1.62) compared to controls, regardless of sex and age. In terms of leukemia subtypes, residential exposure to PICs was associated with the risks of acute myeloid leukemia (AML, pooled RR = 1.61, 95% CI = 1.12-2.31) and chronic lymphocytic leukemia (CLL, pooled RR = 1.85, 95% CI = 1.11-6.42). In meta-regression, the positive association occurred after 10 years of follow-up with a pooled RRs of 1.21 (95% CI = 1.02-1.44) and then slightly increased to 1.77 (95% CI = 1.35-2.33) at 30 years after follow-up. No effect modification was found by sex, age, and geographic locations.

Clinical Predictive Models of Sudden Cardiac Arrest: A Survey of the Current Science and Analysis of Model Performances.

Background More than 500 000 sudden cardiac arrests (SCAs) occur annually in the United States. Clinical predictive models (CPMs) may be helpful tools to differentiate between patients who are likely to survive or have good neurologic recovery and those who are not. However, which CPMs are most reliable for discriminating between outcomes in SCA is not known. Methods and Results We performed a systematic review of the literature using the Tufts PACE (Predictive Analytics and Comparative Effectiveness) CPM Registry through February 1, 2020, and identified 81 unique CPMs of SCA and 62 subsequent external validation studies. Initial cardiac rhythm, age, and duration of cardiopulmonary resuscitation were the 3 most commonly used predictive variables. Only 33 of the 81 novel SCA CPMs (41%) were validated at least once. Of 81 novel SCA CPMs, 56 (69%) and 61 of 62 validation studies (98%) reported discrimination, with median c-statistics of 0.84 and 0.81, respectively. Calibration was reported in only 29 of 62 validation studies (41.9%). For those novel models that both reported discrimination and were validated (26 models), the median percentage change in discrimination was -1.6%. We identified 3 CPMs that had undergone at least 3 external validation studies: the out-of-hospital cardiac arrest score (9 validations; median c-statistic, 0.79), the cardiac arrest hospital prognosis score (6 validations; median c-statistic, 0.83), and the good outcome following attempted resuscitation score (6 validations; median c-statistic, 0.76). Conclusions Although only a small number of SCA CPMs have been rigorously validated, the ones that have been demonstrate good discrimination.

The Spatial Structure of Neural Encoding in Mouse Posterior Cortex during Navigation.

Navigation engages many cortical areas, including visual, parietal, and retrosplenial cortices. These regions have been mapped anatomically and with sensory stimuli and studied individually during behavior. Here, we investigated how behaviorally driven neural activity is distributed and combined across these regions. We performed dense sampling of single-neuron activity across the mouse posterior cortex and developed unbiased methods to relate neural activity to behavior and anatomical space. Most parts of the posterior cortex encoded most behavior-related features. However, the relative strength with which features were encoded varied across space. Therefore, the posterior cortex could be divided into discriminable areas based solely on behaviorally relevant neural activity, revealing functional structure in association regions. Multimodal representations combining sensory and movement signals were strongest in posterior parietal cortex, where gradients of single-feature representations spatially overlapped. We propose that encoding of behavioral features is not constrained by retinotopic borders and instead varies smoothly over space within association regions.