Heiße Spuren

ZusammenfassungDie Differenzialdiagnostik von Hautläsionen bei Lebenden gehört zum wesentlichen Aufgabenspektrum der „klinischen Rechtsmedizin“. Im vorliegenden Fall eines 10 Monate alten Jungen mit nichtjuckenden, jedoch geformt wirkenden Hautläsionen bat die behandelnde Kinderarztpraxis um rechtsmedizinische Mitbeurteilung wegen des Verdachts einer Misshandlung von Schutzbefohlenen mittels glühender Zigarettenspitzen. Auf Fotografien vom Tag der Erstvorstellung in der Kinderarztpraxis sowie von der Wiedervorstellung einen Tag später waren im linken oberen Rückenbereich mehrere rundliche Hautläsionen zu erkennen. Bei der nachfolgenden rechtsmedizinischen Untersuchung erschienen diese Läsionen nun mit goldgelb-braunen Krustenbildungen. Die Kenntnis der Läsionsentwicklung sowie der charakteristische Endbefund ermöglichten die sichere Diagnose einer Impetigo contagiosa. Der Fall verdeutlicht die Notwendigkeit der engen Kooperation zwischen Klinik und Rechtsmedizin bei Sachlagen und Befunden mit initial möglich erscheinender strafrechtlicher Relevanz.

The Use of Ozenoxacin in Pediatric Patients: Clinical Evidence, Efficacy and Safety

Impetigo is the most common childhood skin infection in the world. There are two patterns of impetigo: nonbullous (or impetigo contagiosa) and bullous. The nonbullous type is due to Staphylococcus aureus and group A beta-haemolytic Streptococcus and occurs in 70% of impetigo cases. Impetigo is often a self-limited disease, but complications can sometimes occur. Therapy depends on the extent and site of the lesions and on the presence of systemic symptoms. The increase in multidrug resistance pathogens, such as methicillin-resistant Staphylococcus aureus, mupirocin-resistant Staphylococcus aureus or quinolone-resistant Staphylococcus aureus, requires the development of new antibiotics against these agents. The aim of this review is to evaluate the efficacy and safety of ozenoxacin in children compared to those of other approved topical antimicrobial therapies. The bactericidal activity against both susceptible and resistant organisms is a relevant feature of ozenoxacin because the bacterial strain and potential for resistance are generally not known at the beginning of therapy. Additionally, its minimal dermal absorption and its capability to reach high concentrations in the upper layers of the epidermidis agrees with the recommended practice aimed at avoiding the emergence of bacterial resistance in presence of a good safety profile. Further studies with real-life analyses and pharmacoeconomic evaluation are needed to confirm its role as first-line and second-line therapy in children with impetigo.

Gray Level Co-Occurrence Matrix (GLCM) Parameters Analysis for Pyoderma Image Variants

Analysis of different visual textures present in the given images is one of the important perspectives of human vision for objects segregation and identification. Texture-based features are widely used in medical diagnosis for informal prediction of dermatological diseases. Dermatological diseases are the most universal diseases affecting all the living beings worldwide. Recent advancements in image processing have considerably improved the classification, identification, and treatment of various dermatological diseases. Present paper reports the results of Gray Level Co-occurrence Matrix (GLCM) based texture analysis of skin diseases for parametric variations. The investigations were carried out using three Pyoderma variants (Boil, Carbuncle, and Impetigo Contagiosa) using GLCM. GLCM parameters (Energy, Correlation, Contrast, and Homogeneity) were extracted for each colour component of the images taken for the investigation. Contrast, correlation, energy, and homogeneity represent the coarseness, linear dependency, textural uniformity, and pixel distribution of the texture, respectively. The analysis of the GLCM parameters and their histograms showed that the said textural features are disease dependent. The approach may be used for the identification of dermatological diseases with satisfactory accuracy by employing a suitable machine learning algorithm.