Skin regeneration,repair, and reconstruction: present and future

European Surgery - Large skin defects caused by trauma (e.g., burns) or due to other reasons (e.g., tumor-related skin resections) require sufficient skin replacement. The constant improvement of...     

Praxis der perioperativen Prävention von Phantomschmerz: eine deutschlandweite Umfrage

Die Anaesthesiologie - Phantomschmerzen haben eine hohe Prävalenz nach Majoramputationen und sind mit einer zusätzlichen Einschränkung der Lebensqualität verbunden....     

PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia?

Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus.     

New frontiers in applied veterinary point‐of‐capture diagnostics: Toward early detection and control of zoonotic influenza

Among the chief limitations in achieving early detection and control of animal‐origin influenza of pandemic potential in high‐risk livestock populations is the existing lag time between sample collection and diagnostic result. Advances in molecular diagnostics are permitting deployment of affordable, rapid, highly sensitive, and specific point‐of‐capture assays, providing opportunities for targeted surveillance driving containment strategies with potentially compelling returns on investment. Interrupting disease transmission at source holds promise of disrupting cycles of animal‐origin influenza incursion to endemicity and limiting impact on animal production, food security, and public health. Adoption of new point‐of‐capture diagnostics should be undertaken in the context of promoting robust veterinary services systems and parallel support for operationalizing pre‐authorized plans and communication strategies that will ensure that the full potential of these new platforms is realized.     

The case for allele‐specific recognition by the TCR

There is a sharp difference in how one views TCR structure–function–behaviour dependent on whether its recognition of major histocompatibility complex‐encoded restriction elements (R) is germline selected or somatically generated. The generally accepted or Standard model is built on the assumption that recognition of R is by the V regions of the αβ TCR, which is not driven by allele specificity, whereas the competing model posits that recognition of R is allele‐specific. The establishing of allele‐specific recognition of R by the TCR would rule out the Standard model and clear the road to a consideration of a competing construct, the Tritope model. Here, the case for allele‐specific recognition (germline selected) is detailed making it obvious that the Standard model is untenable.