Marinobufagenin (MBG) is a cardiotonic steroid that increases in the circulation in preeclampsia. Preeclampsia and eclampsia are associated with cerebral edema. Therefore, we examined the effects of MBG on human brain microvascular endothelial cells (HBMEC) in vitro. MBG enhanced the permeability of HBMEC monolayers at 1-, 10-, and 100-nM doses, but had no effect at 0.1 nM. Agilent Human Gene Expression microarrays were utilized in these studies. MBG treatment (10 nM for 12 h) downregulated concentrations of the soluble VEGFR transcript sFLT by 59% but did not alter those of FLTv3 mRNA (determined by quantitative PCR). When treated and control HBMEC transcriptomes were interrogated on microarrays, 1,069 genes appeared to be regulated by MBG. Quantitative RT-PCR confirmed that MBG treatment upregulated ENKUR mRNA concentrations by 57%. Its protein product interacts with calmodulin and calcium channel proteins. MBG treatment downregulated several genes whose protein products are involved in cell adhesion (ITGA2B, FERMT1, CLDN16, and TMEM207) and cell signaling (GRIN2C, SLC8A1, and ESR1). The level of downregulation ranged from 22 to 66%. Altogether, MBG actively enhanced the permeability of HBMEC monolayers while downregulating genes involved in adhesion. MBG treatment had variable effects on ENKUR, GRIN2C, and SLC8A1 genes, all associated with calcium transport. These studies provide the basis for future investigations of MBG actions in normal physiology and disease.
AIM: To share clinical pattern of presentation, the modalities of surgical intervention and the one month post-surgical outcome of rhino-orbito-mucormycosis (ROCM) cases. METHODS: All COVID associated mucormycosis (CAM) patients underwent comprehensive multidisciplinary examination by ophthalmologist, otorhinolaryngologist and physician. Patients with clinical and radiological evidence of orbital apex involvement were included in the study. Appropriate medical and surgical intervention were done to each patient. Patients were followed up one-month post intervention. RESULTS: Out of 89 CAM patients, 31 (34.8%) had orbital apex syndrome. Sixty-six (74.2%) of such patients had pre-existing diabetes mellitus, 18 (58%) patients had prior documented use of steroid use, and 55 (61.8%) had no light perception (LP) presenting vision. Blepharoptosis, proptosis, complete ophthalmoplegia were common clinical findings. Seventeen (19.1%) of such patients had variable amount of cavernous sinus involvement. Endoscopic debridement of paranasal sinuses and orbit with or without eyelid sparing limited orbital exenteration was done in most cases, 34 (38.2%) patients could retain vision in the affected eye. CONCLUSION: Orbital apex involvement in CAM patients occur very fast. It not only leads to loss of vision but also sacrifice of the eyeball, orbital contents and eyelids. Early diagnosis and prompt intervention can preserve life, vision and spare mutilating surgeries.
The impairment of glucose metabolism leads to hyperglycemia and type-2 diabetes mellitus. Glucokinase enzyme is the key regulator of glucose homeostasis that catalyzes the conversion of glucose to glucose-6-phosphate in liver and pancreatic cells. In hepatocytes, GK controls the glucose uptake and glycogen synthesis. The action of liver GK is controlled by glucokinase regulatory protein (GKRP) partially. In fasting conditions the GKRP binds with GK and inactivate it from carbohydrate metabolism and serve as new target for treatment of diabetes mellitus. However, the GK activators as potential antidiabetic agents but results in increased risks of hypoglycemia. The allosteric inhibitors of the GK-GKRP interaction are coming as alternative agents that can mitigate the risk associated with GK activators. This review discusses the recent advances and current status of potential molecules targeted to GK activators and GK-GKRP disrupters.
An emerging crisis of antibiotic resistance for microbial pathogens is alarming all the nations, posing a global threat to human health. The production of the metallo-β-lactamase enzyme is the most powerful strategy of bacteria to produce resistance. An efficient way to combat this global health threat is the development of broad/non-specific type of metallo-β-lactamase inhibitors, which can inhibit the different isoforms of the enzyme. Till date, there are no clinically active drugs against metallo- β-lactamase. The lack of efficient drug molecules against MBLs carrying bacteria requires continuous research efforts to overcome the problem of multidrug-resistance bacteria. The present review will discuss the clinically potent molecules against different variants of B1 metallo-β-lactamase.
ML “machine learning” is an ever-expanding research field with plenty of possibilities for study and implementation. Mr. James Collin stated at MIT that ML is the technology defining this decade, even though it has had a meagre effect on healthcare. Several fresh businesses in the ML industry are applying themselves earnestly on healthcare. Even google has jumped in to the race and it has designed a ML application for identification of cancer tumour on mammograms. To identify skin cancer, Stanford uses a Deep Learning algorithm. Around one trillion GB of data is getting generated per year by the USA health system. Various academic experts and scientists have worked out various characteristics and several factors of risk involved in of chronic illness. Additional data stands for more learning for machine, but for higher precision, these many features need a huge quantity samples. So if machines can harvest clinically greater risk oriented feature it would definitely be better. Precision is improved when the data in the form of exploratory data analysis and feature engineering is pre-processed. The multi-class classification might be capable of evaluating a patient’s different disease risk levels. In health care, correct identification of the percentage of diseased individuals “sensitivity” is a primary concern rather than correct identification of the percentage of healthy individuals “ specificity”. Our paper introduces one of ML’s super challenger and emergent application i.e. Healthcare. The careful and sympathetic relation with care providers will always be necessary for the patients. ML cannot remove this, but will become instrumental for health professionals in improving and strengthening on-going care. Our paper discusses the several established models of ML along with its applications in healthcare system. We also bring up directions for imparting more efficiency to ML model. In addition to this we have also discussed ML’s case study for Brest cancer.
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