Postmortem metabolomics: influence of time since death on the level of endogenous compounds in human femoral blood. Necessary to be considered in metabolome study planning?

Introduction: The (un)targeted analysis of endogenous compounds has gained interest in the field of forensic postmortem investigations. The blood metabolome is influenced by many factors, and postmortem specimens are considered particularly challenging due to unpredictable decomposition processes.

Objectives: This study aimed to systematically investigate the influence of the time since death on endogenous compounds and its relevance in designing postmortem metabolome studies.

Inborn errors of immunity in adulthood.

Inborn errors of immunity (IEIs) are a group of conditions whereby parts of the immune system are missing or dysfunctional. Once thought to primarily be a pediatric disorder, it is now estimated that more than 50% of worldwide incident IEI cases are accounted for by adults. Delayed diagnosis, late symptom onset, and IEI phenocopies can all lead to adult-onset recognition of IEIs.

Radiographic density changes may be associated with overloading and implant loss on short implants: A 5-year analysis of a randomized controlled clinical trial.

Objectives: To analyze changes in radiographic bone density around short implants with and without cantilevers at 5 years post-loading.

Materials And Methods: Thirty-six patients with two adjacent posterior missing teeth participated in this randomized controlled clinical trial. All patients were randomly allocated to receive either two short implants (6 mm) with single-unit restorations (group TWO) or one single short implant (6 mm) with a cantilever restoration (group ONE-C).

Recognition of gasoline in fire debris using machine learning: Part II, application of a neural network.

The recognition of ignitable liquid (IL) residues in fire debris is a resource intensive but key part of an arson investigation. Due to the highly diverse and heavily loaded chemical matrix of fire debris samples, combined with the broad chemical composition of IL, the interpretation of the laboratory analysis results is a very challenging task for the forensic examiner. Fire debris samples are commonly analyzed using gas chromatography coupled to mass spectrometry (GC-MS).

Recognition of gasoline in fire debris using machine learning: Part I, application of random forest, gradient boosting, support vector machine, and naïve bayes.

The detection and identification of ignitable liquid (IL) residues in fire debris are two very challenging tasks in a fire investigation. To this day, the recognition of IL in fire debris includes the chemical analysis of the fire debris composition, followed by the examination and interpretation of the analysis result by a trained forensic examiner. Throughout the last decade, chemometrics and artificial intelligence have become increasingly important.