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Drejer Lausen posted an update 6 months, 1 week ago
The AJCC 8th edition issued a dedicated staging system for head and neck soft tissue sarcomas (HN-STS) with 2 and 4cm tumor cut-off points, as well as a T4 classification based on invasion of adjacent structures. Stage groupings were not provided due to a paucity of data.
We identified HN-STS patients undergoing primary surgery without neoadjuvant therapy patients in the Surveillance, Epidemiology, and End Results (SEER) database. We used multivariable analysis to examine adverse prognosticators. Then, using, recursive partitioning analysis (RPA), we established a stage grouping system that was externally validated in the National Cancer Database (NCDB).
Multivariable analysis in the SEER cohort (N=546) demonstrated worsened survival with tumors invading adjacent structures (P<0.001) and increasing de-differentiation (P<0.001). There was no prognostic difference based on size for T1-3 tumors; however, when assessed as a continuous variable, a 5cm tumor size cut-off point was predictive of outcome. RPA generated a stage grouping system with the following five-year overall survival RPA Stage I (pT1-3N0-1G1-2M0) 71.2%, RPA Stage II (pT4abN0-1G1-2M0/pT1-3N0-1G3-4M0) 53.4%, and RPA Stage III (pT4abN0-1G3-4M0) 17.5%. This was successfully externally validated in the NCDB cohort (P<0.001).
We confirm the importance of structural invasion and grade and demonstrate that the currently used size cut-off points are not prognostic. We propose a novel stage grouping system. A 5cm tumor size cut-off point for tumor stage should be further evaluated.
We confirm the importance of structural invasion and grade and demonstrate that the currently used size cut-off points are not prognostic. We propose a novel stage grouping system. A 5 cm tumor size cut-off point for tumor stage should be further evaluated.Bacillus cereus is a gram-positive, anaerobic, spore-forming bacterium related to food poisoning in humans. Vomit and diarrhea are the symptoms of foodborne B. cereus infection caused by emetic toxins and three enterotoxins, respectively. This bacterium is broadly present in soil and foods such as vegetables, spices, milk, and meat. The antibiotics impenem, vancomycin, chloramphenicol, gentamicin, and ciprofloxacin are used for all susceptible strains of B. cereus. But these antibiotics cause side effects in the host due to the drug-host interaction; because the targeted proteins by the drugs are not pathogen specific proteins, they are similar to human proteins also. To overcome this problem, this study focused on identifying putative drug targets in the pathogen B. cereus and finding new drugs to inhibit the function of the pathogen. this website The identification of drug targets is a pipeline process, starting with the identification of targets non-homologous to human and gutmicrobiota proteins, finding essential proteins, finding other proteins that highly interact with these essential proteins that are also highly important for protein network stability, finding cytoplasmic proteins with a clear pathway and known molecular function, and finding non-druggable proteins. Through this process, two novel drug targets were identified in B. cereus. Among the various antibiotics, Gentamicin had showed good binding affinity with the identified novel targets through molecular modeling and docking studies using Prime and GLIDE module of Schrödinger. Hence, this study suggest that the identified novel drug targets may very useful in drug therapeutic field for finding inhibitors which are similar to Gentamicin and designing new formulation of drug molecules to control the function of the foodborne illness causing pathogen B. cereus.
Pseudomonas aeruginosa is a leading nosocomial Gram-negative bacteria associated with prolonged hospitalization, and increased morbidity and mortality. Limited data exist regarding P. aeruginosa infection and outcome in patients managed in intensive care units (ICUs) in the Gulf countries. We aimed to determine the risk factors, antimicrobial susceptibility pattern and patient outcomes of P. aeruginosa infection in ICU.
In this matched case-control study, all P. aeruginosa infections that occurred >48 h after hospital admission between January 31st 2016 and December 31st 2018 at ICUs affiliated with King Abdulaziz Medical City, Riyadh were included. P. aeruginosa was identified using MALDI-TOF (Vitek-MS) by biomérieux, and the antimicrobial susceptibility testing was performed using an automated biomérieux VITEK
2 Antimicrobial Susceptibility card.
The study included 90 cases and 90 controls. Compared with controls, cases had significantly higher mean ICU stay and higher proportions with previous hures.
The study identifies several potentially modifiable factors associated with P. aeruginosa infection in ICUs. Identification of these factors could facilitate case identification and enhance control measures.Borderline personality disorder is most consistently characterized as a disorder of the experience and regulation of emotions. Neuropathological models have predominantly explained these clinical traits with an imbalance between prefrontal regulatory and limbic emotion generating structures. Here, we review the current evidential state of the fronto-limbic imbalance hypothesis of borderline personality disorder, based on task-related functional magnetic resonance imaging research. In turn, we discuss challenges to the notion that (1) amygdala hyperreactivity underlies emotional hyperreactivity and deficits in (2) prefrontal activity or (3) fronto-limbic connectivity underly emotion regulation deficits. We offer several suggestions to improve consolidation and interpretation of research in this area.Background and ObjectivesSegmentation of mammographic lesions has been proven to be a valuable source of information, as it can assist in both extracting shape-related features and providing accurate localization of the lesion. In this work, a methodology is proposed for integrating mammographic mass segmentation information into a convolutional neural network (CNN), aiming to improve the diagnosis of breast cancer in mammograms. MethodsThe proposed methodology involves modification of each convolutional layer of a CNN, so that information of not only the input image but also the corresponding segmentation map is considered. Furthermore, a new loss function is introduced, which adds an extra term to the standard cross-entropy, aiming to steer the attention of the network to the mass region, penalizing strong feature activations based on their location. The segmentation maps are acquired either from the provided ground-truth or from an automatic segmentation stage. ResultsPerformance evaluation in diagnosis is conducted on two mammographic mass datasets, namely DDSM-400 and CBIS-DDSM, with differences in quality of the corresponding ground-truth segmentation maps.