p63 is an alternative p53 repressor in melanoma that confers chemoresistance and a poor prognosis.

The role of apoptosis in melanoma pathogenesis and chemoresistance is poorly characterized. Mutations in TP53 occur infrequently, yet the TP53 apoptotic pathway is often abrogated. This may result from alterations in TP53 family members, including the TP53 homologue TP63. Here we demonstrate that TP63 has an antiapoptotic role in melanoma and is responsible for mediating chemoresistance. Although p63 was not expressed in primary melanocytes, up-regulation of p63 mRNA and protein was observed in melanoma cell lines and clinical samples, providing the first evidence of significant p63 expression in this lineage. Upon genotoxic stress, endogenous p63 isoforms were stabilized in both nuclear and mitochondrial subcellular compartments. Our data provide evidence of a physiological interaction between p63 with p53 whereby translocation of p63 to the mitochondria occurred through a codependent process with p53, whereas accumulation of p53 in the nucleus was prevented by p63. Using RNA interference technology, both isoforms of p63 (TA and ΔNp63) were demonstrated to confer chemoresistance, revealing a novel oncogenic role for p63 in melanoma cells. Furthermore, expression of p63 in both primary and metastatic melanoma clinical samples significantly correlated with melanoma-specific deaths in these patients. Ultimately, these observations provide a possible explanation for abrogation of the p53-mediated apoptotic pathway in melanoma, implicating novel approaches aimed at sensitizing melanoma to therapeutic agents

Uncoupling of the LKB1-AMPKα Energy Sensor Pathway by Growth Factors and Oncogenic BRAFV600E

BACKGROUND: Understanding the biochemical mechanisms contributing to melanoma development and progression is critical for therapeutical intervention. LKB1 is a multi-task Ser/Thr kinase that phosphorylates AMPK controlling cell growth and apoptosis under metabolic stress conditions. Additionally, LKB1(Ser428) becomes phosphorylated in a RAS-Erk1/2-p90(RSK) pathway dependent manner. However, the connection between the RAS pathway and LKB1 is mostly unknown. METHODOLOGY/PRINCIPAL FINDINGS: Using the UV induced HGF transgenic mouse melanoma model to investigate the interplay among HGF signaling, RAS pathway and PI3K pathway in melanoma, we identified LKB1 as a protein directly modified by HGF induced signaling. A variety of molecular techniques and tissue culture revealed that LKB1(Ser428) (Ser431 in the mouse) is constitutively phosphorylated in BRAF(V600E) mutant melanoma cell lines and spontaneous mouse tumors with high RAS pathway activity. Interestingly, BRAF(V600E) mutant melanoma cells showed a very limited response to metabolic stress mediated by the LKB1-AMPK-mTOR pathway. Here we show for the first time that RAS pathway activation including BRAF(V600E) mutation promotes the uncoupling of AMPK from LKB1 by a mechanism that appears to be independent of LKB1(Ser428) phosphorylation. Notably, the inhibition of the RAS pathway in BRAF(V600E) mutant melanoma cells recovered the complex formation and rescued the LKB1-AMPKalpha metabolic stress-induced response, increasing apoptosis in cooperation with the pro-apoptotic proteins Bad and Bim, and the down-regulation of Mcl-1. CONCLUSIONS/SIGNIFICANCE: These data demonstrate that growth factor treatment and in particular oncogenic BRAF(V600E) induces the uncoupling of LKB1-AMPKalpha complexes providing at the same time a possible mechanism in cell proliferation that engages cell growth and cell division in response to mitogenic stimuli and resistance to low energy conditions in tumor cells. Importantly, this mechanism reveals a new level for therapeutical intervention particularly relevant in tumors harboring a deregulated RAS-Erk1/2 pathway

World Society of Emergency Surgery (WSES) guidelines for management of skin and soft tissue infections

Peer reviewe

Current concept of abdominal sepsis : WSES position paper

Peer reviewe

Development and Validation of a Model for Individualized Prediction of Hospitalization Risk in 4,536 Patients with COVID-19

Background: Coronavirus Disease 2019 is a pandemic that is straining healthcare resources, mainly hospital beds. Multiple risk factors of disease progression requiring hospitalization have been identified, but medical decision-making remains complex. Objective: To characterize a large cohort of patients hospitalized with COVID-19, their outcomes, develop and validate a statistical model that allows individualized prediction of future hospitalization risk for a patient newly diagnosed with COVID-19. Design: Retrospective cohort study of patients with COVID-19 applying a least absolute shrinkage and selection operator (LASSO) logistic regression algorithm to retain the most predictive features for hospitalization risk, followed by validation in a temporally distinct patient cohort. The final model was displayed as a nomogram and programmed into an online risk calculator. Setting: One healthcare system in Ohio and Florida. Participants: All patients infected with SARS-CoV-2 between March 8, 2020 and June 5, 2020. Those tested before May 1 were included in the development cohort, while those tested May 1 and later comprised the validation cohort. Measurements: Demographic, clinical, social influencers of health, exposure risk, medical co-morbidities, vaccination history, presenting symptoms, medications, and laboratory values were collected on all patients, and considered in our model development. Results: 4,536 patients tested positive for SARS-CoV-2 during the study period. Of those, 958 (21.1%) required hospitalization. By day 3 of hospitalization, 24% of patients were transferred to the intensive care unit, and around half of the remaining patients were discharged home. Ten patients died. Hospitalization risk was increased with older age, black race, male sex, former smoking history, diabetes, hypertension, chronic lung disease, poor socioeconomic status, shortness of breath, diarrhea, and certain medications (NSAIDs, immunosuppressive treatment). Hospitalization risk was reduced with prior flu vaccination. Model discrimination was excellent with an area under the curve of 0.900 (95% confidence interval of 0.886-0.914) in the development cohort, and 0.813 (0.786, 0.839) in the validation cohort. The scaled Brier score was 42.6% (95% CI 37.8%, 47.4%) in the development cohort and 25.6% (19.9%, 31.3%) in the validation cohort. Calibration was very good. The online risk calculator is freely available and found at https://riskcalc.org/COVID19Hospitalization/. Limitation: Retrospective cohort design. Conclusion: Our study crystallizes published risk factors of COVID-19 progression, but also provides new data on the role of social influencers of health, race, and influenza vaccination. In a context of a pandemic and limited healthcare resources, individualized outcome prediction through this nomogram or online risk calculator can facilitate complex medical decision-making