Psychological Health of Surgeons in a Time of COVID-19: A Global Survey

OBJECTIVE: To assess the degree of psychological impact among surgical providers during the COVID-19 pandemic. SUMMARY BACKGROUND DATA: The COVID-19 pandemic has extensively impacted global healthcare systems. We hypothesized that the degree of psychological impact would be higher for surgical providers deployed for COVID-19 work, certain surgical specialties, and for those who knew of someone diagnosed with, or who died, of COVID-19. METHODS: We conducted a global web-based survey to investigate the psychological impact of COVID-19. The primary outcomes were the Depression Anxiety Stress Scale-21 (DASS-21) and Impact of Event Scale-Revised (IES-R) scores. RESULTS: 4283 participants from 101 countries responded. 32.8%, 30.8%, 25.9% and 24.0% screened positive for depression, anxiety, stress and Post-Traumatic Stress Disorder (PTSD) respectively. Respondents who knew someone who died of COVID-19 were more likely to screen positive for depression, anxiety, stress and PTSD (OR 1.3, 1,6, 1.4, 1.7 respectively, all p < 0.05). Respondents who knew of someone diagnosed with COVID-19 were more likely to screen positive for depression, stress and PTSD (OR 1.2, 1.2 and 1.3 respectively, all p < 0.05). Surgical specialities that operated in the Head and Neck region had higher psychological distress among its surgeons. Deployment for COVID-19-related work was not associated with increased psychological distress. CONCLUSIONS: The COVID-19 pandemic may have a mental health legacy outlasting its course. The long-term impact of this ongoing traumatic event underscores the importance of longitudinal mental health care for healthcare personnel, with particular attention to those who know of someone diagnosed with, or who died of COVID-19

Molecular identification of adenovirus causing respiratory tract infection in pediatric patients at the University of Malaya Medical Center

<p>Abstract</p> <p>Background</p> <p>There are at least 51 adenovirus serotypes (AdV) known to cause human infections. The prevalence of the different human AdV (HAdV) serotypes varies among different regions. Presently, there are no reports of the prevalent HAdV types found in Malaysia. The present study was undertaken to identify the HAdV types associated primarily with respiratory tract infections (RTI) of young children in Malaysia.</p> <p>Methods</p> <p>Archived HAdV isolates from pediatric patients with RTI seen at the University of Malaya Medical Center (UMMC), Kuala Lumpur, Malaysia from 1999 to 2005 were used. Virus isolates were inoculated into cell culture and DNA was extracted when cells showed significant cytopathic effects. AdV partial hexon gene was amplified and the sequences together with other known HAdV hexon gene sequences were used to build phylogenetic trees. Identification of HAdV types found among young children in Malaysia was inferred from the phylograms.</p> <p>Results</p> <p>At least 2,583 pediatric patients with RTI sought consultation and treatment at the UMMC from 1999 to 2005. Among these patients, 48 (< 2%) were positive for HAdV infections. Twenty-seven isolates were recovered and used for the present study. Nineteen of the 27 (~70%) isolates belonged to HAdV species C (HAdV-C) and six (~22%) were of HAdV species B (HAdV-B). Among the HAdV-C species, 14 (~74%) of them were identified as HAdV type 1 (HAdV-1) and HAdV type 2 (HAdV-2), and among the HAdV-B species, HAdV type 3 (HAdV-3) was the most common serotype identified. HAdV-C species also was isolated from throat and rectal swabs of children with hand, foot, and mouth disease (HFMD). Two isolates were identified as corresponding to HAdV-F species from a child with HFMD and a patient with intestinal obstruction.</p> <p>Conclusions</p> <p>HAdV-1 and HAdV-2 were the most common HAdV isolated from pediatric patients who sought treatment for RTI at the UMMC from 1999 to 2005. HAdV-B, mainly HAdV-3, was recovered from ~22% of the patients. These findings provide a benchmark for future studies on the prevalence and epidemiology of HAdV types in Malaysia and in the region.</p

PPARgamma inhibits hepatocellular carcinoma metastases in vitro and in mice

Background: We have previously demonstrated that peroxisome proliferator-activated receptor (PPARγ) activation inhibits hepatocarcinogenesis. We aim to investigate the effect of PPARγ on hepatocellular carcinoma (HCC) metastatic potential and explore its underlying mechanisms. Methods: Human HCC cells (MHCC97L, BEL-7404) were infected with adenovirus-expressing PPARγ (Ad-PPARγ) or Ad-lacZ and treated with or without PPARγ agonist (rosiglitazone). The effects of PPARγ on cell migration and invasive activity were determined by wound healing assay and Matrigel invasive model in vitro, and in an orthotopic liver tumour metastatic model in mice.Results:Pronounced expression of PPARγ was demonstrated in HCC cells (MHCC97L, BEL-7404) treated with Ad-PPARγ, rosiglitazone or Ad-PPARγ plus rosiglitazone, compared with control (Ad-LacZ). Such induction markedly suppressed HCC cell migration. Moreover, the invasiveness of MHCC97L and BEL-7404 cells infected with Ad-PPARγ, or treated with rosiglitazone was significantly diminished up to 60%. Combination of Ad-PPARγ and rosiglitazone showed an additive effect. Activation of PPARγ by rosiglitazone significantly reduced the incidence and severity of lung metastasis in an orthotopic HCC mouse model. Key mechanisms underlying the effect of PPARγ in HCC include upregulation of cell adhesion genes, E-cadherin and SYK (spleen tyrosine kinase), extracellular matrix regulator tissue inhibitors of metalloproteinase (TIMP) 3, tumour suppressor gene retinoblastoma 1, and downregulation of pro-metastatic genes MMP9 (matrix metallopeptidase 9), MMP13, HPSE (heparanase), and Hepatocyte growth factor (HGF). Direct transcriptional regulation of TIMP3, MMP9, MMP13, and HPSE by PPARγ was shown by ChIP-PCR. Conclusion: Peroxisome proliferator-activated receptor-gamma exerts an inhibitory effect on the invasive and metastatic potential of HCC in vitro and in vivo, and is thus, a target for the prevention and treatment of HCC metastases. © 2012 Cancer Research UK All rights reserved.published_or_final_versio

Performance of the ATLAS muon trigger in pp collisions at [Formula: see text] TeV

The performance of the ATLAS muon trigger system is evaluated with proton-proton collision data collected in 2012 at the Large Hadron Collider at a centre-of-mass energy of 8 TeV. It is primarily evaluated using events containing a pair of muons from the decay of [Formula: see text] bosons. The efficiency of the single-muon trigger is measured for muons with transverse momentum [Formula: see text] GeV, with a statistical uncertainty of less than 0.01 % and a systematic uncertainty of 0.6 %. The [Formula: see text] range for efficiency determination is extended by using muons from decays of [Formula: see text] mesons, [Formula: see text] bosons, and top quarks. The muon trigger shows highly uniform and stable performance. The performance is compared to the prediction of a detailed simulation

Combination of searches for heavy spin-1 resonances using 139 fb−1 of proton-proton collision data at s = 13 TeV with the ATLAS detector

A combination of searches for new heavy spin-1 resonances decaying into different pairings of W, Z, or Higgs bosons, as well as directly into leptons or quarks, is presented. The data sample used corresponds to 139 fb−1 of proton-proton collisions at = 13 TeV collected during 2015–2018 with the ATLAS detector at the CERN Large Hadron Collider. Analyses selecting quark pairs (qq, bb, , and tb) or third-generation leptons (τν and ττ) are included in this kind of combination for the first time. A simplified model predicting a spin-1 heavy vector-boson triplet is used. Cross-section limits are set at the 95% confidence level and are compared with predictions for the benchmark model. These limits are also expressed in terms of constraints on couplings of the heavy vector-boson triplet to quarks, leptons, and the Higgs boson. The complementarity of the various analyses increases the sensitivity to new physics, and the resulting constraints are stronger than those from any individual analysis considered. The data exclude a heavy vector-boson triplet with mass below 5.8 TeV in a weakly coupled scenario, below 4.4 TeV in a strongly coupled scenario, and up to 1.5 TeV in the case of production via vector-boson fusion

Combined measurement of the Higgs boson mass from the H → γγ and H → ZZ∗ → 4ℓ decay channels with the ATLAS detector using √s = 7, 8, and 13 TeV pp collision data

A measurement of the mass of the Higgs boson combining the H → Z Z ∗ → 4 ℓ and H → γ γ decay channels is presented. The result is based on 140     fb − 1 of proton-proton collision data collected by the ATLAS detector during LHC run 2 at a center-of-mass energy of 13 TeV combined with the run 1 ATLAS mass measurement, performed at center-of-mass energies of 7 and 8 TeV, yielding a Higgs boson mass of 125.11 ± 0.09 ( stat ) ± 0.06 ( syst ) = 125.11 ± 0.11     GeV . This corresponds to a 0.09% precision achieved on this fundamental parameter of the Standard Model of particle physics

Deep Cross-Output Knowledge Transfer Using Stacked-Structure Least-Squares Support Vector Machines.

This article presents a new deep cross-output knowledge transfer approach based on least-squares support vector machines, called DCOT-LS-SVMs. Its aim is to improve the generalizability of least-squares support vector machines (LS-SVMs) while avoiding the complicated parameter tuning process that occurs in many kernel machines. The proposed approach has two significant characteristics: 1) DCOT-LS-SVMs is inspired by a stacked hierarchical architecture that combines several layer-by-layer LS-SVMs modules. The module that forms the higher layer has additional input features that consider the predictions from all previous modules and 2) cross-output knowledge transfer is used to leverage knowledge from the predictions of the previous module to improve the learning process in the current module. With this approach, the model's parameters, such as a tradeoff parameter C and a kernel width δ, can be randomly assigned to each module in order to greatly simplify the learning process. Moreover, DCOT-LS-SVMs is able to autonomously and quickly decide the extent of the cross-output knowledge transfer between adjacent modules through a fast leave-one-out cross-validation strategy. In addition, we present an imbalanced version of DCOT-LS-SVMs, called IDCOT-LS-SVMs, given that imbalanced datasets are common in real-world scenarios. The effectiveness of the proposed approaches is demonstrated through a comparison with five comparative methods on UCI datasets and with a case study on the diagnosis of prostate cancer