The radiodensity of cerebrospinal fluid and vitreous humor as indicator of the time since death.

PURPOSE: After death, a series of changes occur naturally in the human body in a fairly regular pattern. These postmortem changes are detectable on postmortem CT scans (PMCT) and may be useful in estimating the postmortem interval (PMI). The purpose of our study is to correlate the PMCT radiodensities of the cerebrospinal fluid (CSF) and vitreous humor (VH) to the PMI. METHODS: Three patient groups were included: group A consisted of 5 donated cadavers, group B, 100 in-hospital deceased patients, and group C, 12 out-of-hospital forensic cadavers. Group A were scanned every hour for a maximum of 36 h postmortem, and the tympanic temperature was measured prior to each scan. Groups B and C were scanned once after death (PMI range 0.2-63.8 h). Radiodensities of the VH and CSF were measured in Hounsfield units. Correlation between density and PMI was determined using linear regression and the influence of temperature was assessed by a multivariate regression model. Results from group A were validated in groups B and C. RESULTS: Group A showed increasing radiodensity of the CSF and VH over time (r (2) CSF, 0.65). PMI overruled the influence of temperature (r = 0.99 and p = 0.000). Groups B and C showed more diversity, with CSF and VH radiodensities below the mean regression line of Group A. The formula of this upper limit indicated the maximum PMI and was correct for >95 % of the cadavers. CONCLUSION: The results of group A showed a significant correlation between CSF radiodensity and PMI. The radiodensities in groups B and C were higher than in group A, therefore the maximum PMI can be estimated with the upper 95 % confidence interval of the correlation line of group A.

Methane prediction equations including genera of rumen bacteria as predictor variables improve prediction accuracy.

Methane (CH4) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH4. To address this limitation, we developed novel CH4 prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH4 production (g CH4/animal·d, ANIM-B models) and CH4 yield (g CH4/kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin's concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH4 prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH4 emissions from sheep, providing valuable insights for future research and mitigation strategies.

Comparing thoracoabdominal injuries of manual versus load-distributing band cardiopulmonary resuscitation.

OBJECTIVE: To identify and compare manual and load-distributing band (LDB) cardiopulmonary resuscitation (CPR)-related injuries. METHODS: Retrospective observational cohort study. Adult, nontraumatic deaths with a postmortem computed tomography scan (PMCT) performed were classified into two groups: deceased after LDB CPR or after manual CPR. PMCT scans were reviewed for thoracoabdominal injuries such as fractures, pneumothorax and hemorrhage. The injuries between groups were compared. RESULTS: LDB CPR (n = 43) showed increased incidences of posterior rib fractures (53 vs 18%, P = 0.006), pneumothorax (23 vs 4%, P = 0.04) and more pericardial fluid (median 12 vs 6 mm, P = 0.002) compared with manual CPR (n = 29). Multivariable regression analysis revealed that LDB CPR was significantly associated with posterior rib fractures [odds ratio (OR) 5.37, 95% confidence interval (CI): 1.44-20.09, P = 0.01). Pneumothorax (OR 6.80, 95% CI: 0.73-62.99, P = 0.09) and the amount of pericardial fluid (OR 3.40, 95% CI: 0.20-56.32) were not significantly associated with LDB CPR. No significant difference was found for anterolateral rib fractures, sternal fractures, vertebral fractures, pleural fluid, hemothorax, hemopericardium, pneumoperitoneum, perihepatic, perisplenic and perirenal hemorrhage. CONCLUSION: Rib fractures, sternal fractures, hemothorax and hemopericardium are common CPR-related injuries. LDB CPR is significantly associated with more posterior rib fractures and a trend toward more pneumothoraces is observed when compared with manual CPR. This knowledge is important for caretakers in the case of ongoing CPR, as a pneumothorax may attribute to not achieving persistent return of spontaneous circulation, and to improve postresuscitation care of survivors.