Gastric cancer is not a top‐10 malignancy in the United States but represents one of the most common causes of cancer death worldwide. Biological differences between tumors from Eastern and Western ...countries add to the complexity of identifying standard‐of‐care therapy based on international trials. Systemic chemotherapy, radiotherapy, surgery, immunotherapy, and targeted therapy all have proven efficacy in gastric adenocarcinoma; therefore, multidisciplinary treatment is paramount to treatment selection. Triplet chemotherapy for resectable gastric cancer is now accepted and could represent a plateau of standard cytotoxic chemotherapy for localized disease. Classification of gastric cancer based on molecular subtypes is providing an opportunity for personalized therapy. Biomarkers, in particular microsatellite instability (MSI), programmed cell death ligand 1 (PD‐L1), human epidermal growth factor receptor 2 (HER2), tumor mutation burden, and Epstein‐Barr virus, are increasingly driving systemic therapy approaches and allowing for the identification of populations most likely to benefit from immunotherapy and targeted therapy. Significant research opportunities remain for the less differentiated histologic subtypes of gastric adenocarcinoma and those without markers of immunotherapy activity.
Summary
Carbapenem is an important therapy for serious hospital‐acquired infections and for the care of patients affected by multidrug‐resistant organisms, specifically Acinetobacter baumannii; ...however, with the global increase of carbapenem‐resistant A. baumannii, this pathogen has significantly threatened public health. Thus, there is a pressing need to better understand this pathogen in order to develop novel treatments and control strategies for dealing with A. baumannii. In this review, we discuss an overview of carbapenem, including its discovery, development, classification and biological characteristics, and its importance in hospital medicine especially in critical care units. We also describe the peculiarity of bacterial pathogen, A. baumannii, including its commonly reported virulence factors, environmental persistence and carbapenem resistance mechanisms. In closing, we discuss various control strategies for overcoming carbapenem resistance in hospitals and for limiting outbreaks. With the appearance of strains that resist carbapenem, the aim of this review is to highlight the importance of understanding this increasingly problematic healthcare‐associated pathogen that creates significant concern in the field of nosocomial infections and overall public health.
Many bacteria have the capability to form a three-dimensional, strongly adherent network called 'biofilm'. Biofilms provide adherence, resourcing nutrients and offer protection to bacterial cells. ...They are involved in pathogenesis, disease progression and resistance to almost all classical antibiotics. The need for new antimicrobial therapies has led to exploring applications of gold and silver nanoparticles against bacterial biofilms. These nanoparticles and their respective ions exert antimicrobial action by damaging the biofilm structure, biofilm components and hampering bacterial metabolism via various mechanisms. While exerting the antimicrobial activity, these nanoparticles approach the biofilm, penetrate it, migrate internally and interact with key components of biofilm such as polysaccharides, proteins, nucleic acids and lipids via electrostatic, hydrophobic, hydrogen-bonding, Van der Waals and ionic interactions. Few bacterial biofilms also show resistance to these nanoparticles through similar interactions. The nature of these interactions and overall antimicrobial effect depend on the physicochemical properties of biofilm and nanoparticles. Hence, study of these interactions and participating molecular players is of prime importance, with which one can modulate properties of nanoparticles to get maximal antibacterial effects against a wide spectrum of bacterial pathogens. This article provides a comprehensive review of research specifically directed to understand the molecular interactions of gold and silver nanoparticles with various bacterial biofilms.
Smoking is an established risk factor for pancreatic cancer (PC), but late diagnosis limits the evaluation of its mechanistic role in the progression of PC. We used a well-established genetically ...engineered mouse model (LSL-K-ras(G12D)) of PC to elucidate the role of smoking during initiation and development of pancreatic intraepithelial neoplasia (PanIN). The 10-week-old floxed mice (K-ras(G12D); Pdx-1cre) and their control unfloxed (LSL-K-ras(G12D)) littermates were exposed to cigarette smoke (total suspended particles: 150 mg/m(3)) for 20 weeks. Smoke exposure significantly accelerated the development of PanIN lesions in the floxed mice, which correlated with tenfold increase in the expression of cytokeratin19. The systemic accumulation of myeloid-derived suppressor cells (MDSCs) decreased significantly in floxed mice compared with unfloxed controls (P<0.01) after the smoke exposure with the concurrent increase in the macrophage (P<0.05) and dendritic cell (DCs) (P<0.01) population. Further, smoking-induced inflammation (IFN-γ, CXCL2; P<0.05) was accompanied by enhanced activation of pancreatic stellate cells and elevated levels of serum retinoic acid-binding protein 4, indicating increased bioavailability of retinoic acid which contributes to differentiation of MDSCs to tumor-associated macrophages (TAMs) and DCs. TAMs predominantly contribute to the increased expression of heparin-binding epidermal growth factor-like growth factor (EGFR ligand) in pre-neoplastic lesions in smoke-exposed floxed mice that facilitate acinar-to-ductal metaplasia (ADM). Further, smoke exposure also resulted in partial suppression of the immune system early during PC progression. Overall, the present study provides a novel mechanism of smoking-induced increase in ADM in the presence of constitutively active K-ras mutation.
We consider the problem of choosing a set of k sensor measurements, from a set of m possible or potential sensor measurements, that minimizes the error in estimating some parameters. Solving this ...problem by evaluating the performance for each of the ( m k ) possible choices of sensor measurements is not practical unless m and k are small. In this paper, we describe a heuristic, based on convex optimization, for approximately solving this problem. Our heuristic gives a subset selection as well as a bound on the best performance that can be achieved by any selection of k sensor measurements. There is no guarantee that the gap between the performance of the chosen subset and the performance bound is always small; but numerical experiments suggest that the gap is small in many cases. Our heuristic method requires on the order of m 3 operations; for m = 1000 possible sensors, we can carry out sensor selection in a few seconds on a 2-GHz personal computer.
Alzheimer's disease is the primary cause of dementia worldwide, with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods ...integrate patient history, neuropsychological testing and MRI to identify likely cases, yet effective practices remain variably applied and lacking in sensitivity and specificity. Here we report an interpretable deep learning strategy that delineates unique Alzheimer's disease signatures from multimodal inputs of MRI, age, gender, and Mini-Mental State Examination score. Our framework linked a fully convolutional network, which constructs high resolution maps of disease probability from local brain structure to a multilayer perceptron and generates precise, intuitive visualization of individual Alzheimer's disease risk en route to accurate diagnosis. The model was trained using clinically diagnosed Alzheimer's disease and cognitively normal subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n = 417) and validated on three independent cohorts: the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) (n = 382), the Framingham Heart Study (n = 102), and the National Alzheimer's Coordinating Center (NACC) (n = 582). Performance of the model that used the multimodal inputs was consistent across datasets, with mean area under curve values of 0.996, 0.974, 0.876 and 0.954 for the ADNI study, AIBL, Framingham Heart Study and NACC datasets, respectively. Moreover, our approach exceeded the diagnostic performance of a multi-institutional team of practicing neurologists (n = 11), and high-risk cerebral regions predicted by the model closely tracked post-mortem histopathological findings. This framework provides a clinically adaptable strategy for using routinely available imaging techniques such as MRI to generate nuanced neuroimaging signatures for Alzheimer's disease diagnosis, as well as a generalizable approach for linking deep learning to pathophysiological processes in human disease.
Photophysical properties of a supramolecular amphiphile of calix4arene having benzofurazan moiety at the lower rim,
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, has been studied. Electronic absorption and fluorescence spectra of
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have been ...recorded in wide range of solvents of different polarities and data were used to study solvatochromic properties. The ground state and the excited state dipole moment of
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were estimated from the Bakhshiev’s and Bilot-Kawaski’s equations. High value of dipole moment is observed for excited state as compared to ground state value and this is attributed to more polar excited state of molecule. Also, fluorescence emission peak undergoes a bathochromic shift with increase in the polarity of the solvent, confirming π → π* transition. Scanning electron microscopy reveals that the aggregation of
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is increased on going from the polar to non polar solvents.