Outbreak of Dermatophyte Infections on the Head and Neck Related to Shave Haircuts: Description of a Multicenter Case Series

Tinea capitis; Brote; PeluqueríasTinea capitis; Outbreak; HairdresserTinea capitis; Brot; PerruqueriesIntroduction: Since 2021, an increase in cases of tinea capitis has been detected in adolescents who shave their hair with fade haircut. Patients and methods: Multicenter retrospective observational study of cases of cephalic pole dermatophytosis with a history of having been acquired after frequent shaving in hairdressing. A call was made to dermatologists from the Spanish Academy of Dermatology and Venereology (AEDV) to provide cases observed between January 2021 and December 2022. Patients with microbiological confirmation by culture or direct examination with KOH were included. Results: 107 cases were collected, 106 of which were male. 78 non-inflammatory forms were observed, compared to 29 inflammatory. The most frequently isolated fungus was Trichophyton tonsurans (75.7% of cases). The lesions appeared predominantly on the nape of the neck and temporal area. Conclusions: The distribution by sex, age and lesional location seems to indicate that a new social trend, in which male adolescents regularly go to hairdressers to shave the occipital and temporal areas, would be the cause of this grouping of cases of ringworm of the scalp. The most frequent microorganism in our study (T.tonsurans) coincides with the most prevalent in our environment. This study shows an accumulation of cases that can be taken into account by competent Public Health agencies, which are responsible for ensuring compliance with the rules of disinfection of the material used for shaving.Introducción: Desde 2021 se ha detectado un aumento de casos de tiñas del cuero cabelludo en adolescentes que se cortan el pelo mediante rasurado o degradado. Pacientes y métodos: Estudio observacional retrospectivo multicéntrico de casos de dermatofitosis del polo cefálico con el antecedente de haber sido contraídas tras el rasurado frecuente en peluquería. Se realizó una llamada a dermatólogos de la Academia Española de Dermatología y Venereología (AEDV) para que aportaran casos observados entre enero de 2021 y diciembre de 2022. Se incluyeron pacientes con confirmación microbiológica mediante cultivo o examen directo con KOH. Resultados: Se recogieron 107 casos, siendo 106 pacientes varones. Se observaron 78 formas no inflamatorias frente a 29 inflamatorias. El hongo aislado con mayor frecuencia fue Trichophyton tonsurans (75,7% de los casos). Las lesiones aparecieron predominantemente en la nuca y en el área temporal. Conclusiones La distribución por sexo, edad y localización lesional parece apuntar a que una nueva tendencia social, en la que adolescentes varones acuden asiduamente a peluquerías para el afeitado de las zonas occipital y temporal, sería la causante de esta agrupación de casos de tiña del cuero cabelludo. El microorganismo más frecuente en nuestro estudio (T. tonsurans) coincide con el más prevalente en nuestro medio. Con el presente estudio se evidencia un acúmulo de casos susceptible de ser tenido en cuenta por organismos competentes de salud pública, a los cuales corresponde velar por el cumplimiento de las normas de desinfección del material empleado para el rasurado

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Physiological characteristics of <i>Saccharomyces</i> strains and extracellular metabolites during the steady state of continuous cultures.

*<p>Significant differences (p value≤0.05) compared to the control condition (<i>Sc</i> 12°C).</p

Global biochemical profiles comparison made of the three species.

<p><b>A</b>) Hierarchical clustering of all the species is computed after standardizing metabolites to the Z-score. <b>B</b>) Biplot of the first two components of the PCA according to the metabolic composition. Isobar  =  isobaric compounds fructose 1,6-diphosphate, glucose 1,6-diphosphate and myo-inositol 1,4 or 1,3 diphosphate.</p

Principal metabolic differences between <i>S. cerevisiae</i> and <i>S. kudriavzevii</i> growing at 12°C.

<p>A) Class distribution of the different identified metabolites. n  =  number of metabolites in each class. B) The Random Forest statistical analysis of metabolomic data was used to identify the top 30 biochemicals with the greatest influence in distinguishing the different groups. Metabolites are listed on the y-axis in order of importance, with importance decreasing from top to bottom. The mean decrease in accuracy for each metabolite is plotted on the x-axis.</p

Carbohydrate metabolism: glycolytic pathway, trehalose synthesis, intermediates of the pentose phosphate pathway and protein mannosylation.

<p>Differentially produced metabolites within <i>Su</i> and <i>Sk</i> compared to <i>Sc</i>. Box legend: bar inside the box represents the median value, upper bar represents maximum of distribution, lower bar represents minimum of distribution, and the circle represents extreme data points.</p

Phosphatidylcholine biosynthesis and degradation in <i>S. cerevisiae</i>.

<p>Metabolic differences in the <i>Sc</i> growing at 12°C and 28°C. Box legend: bar inside the box represents the median value, upper bar represents maximum of distribution, lower bar represents minimum of distribution and the circle represents extreme data points.</p

Principal metabolic differences between <i>Sc</i> and <i>Su</i> at 12°C.

<p>A) Class distribution of the different identified metabolites. n  =  number of metabolites in each class. B) The Random Forest statistical analysis of metabolomic data was used to identify the top 30 biochemicals with the greatest influence in distinguishing the different groups. Metabolites are listed on the y-axis in order of importance, with importance decreasing from top to bottom. The mean decrease in accuracy for each metabolite is plotted on the x-axis.</p

The Shikimate pathway.

<p>Differentially produced metabolites within <i>Su</i> and <i>Sc</i>. Box legend: bar inside the box represents the median value, upper bar represents maximum of distribution, lower bar represents minimum of distribution and the circle represents extreme data points.</p

Principal metabolic differences between <i>Sc</i> growing at 12°C and at 28°C.

<p>A) Class distribution of the different identified metabolites. n  =  number of metabolites in each class. B) The Random Forest statistical analysis of the metabolomic data was used to identify the top 30 biochemicals with the greatest influence in distinguishing different groups. Metabolites are listed on the y-axis in order of importance, which importance decreasing from top to bottom. The mean decrease in accuracy for each metabolite is plotted on the x-axis.</p