A Study of the Effect of Terlipressin with Albumin Vs Only Albumin in Patients Diagnosed with Hepatorenal Syndrome (HRS)

Background: Hepatorenal syndrome (HRS) is a potentially reversible, functional renal failure that occurs in patients with advanced liver disease. HRS is generally characterized by increased serum creatinine, azotemia, reduced diuresis, increased urine osmolarity, and reduced urine sodium values without signs of organic kidney damage1. AIM: The aim of the study is to investigate the efficacy, adverse effects, and outcomes of Terlipressin with Albumin vs. only Albumin in patients diagnosed with hepatorenal syndrome. Material and methods: A single centred hospital-based descriptive ,observational study was conducted in patients admitted with a diagnosis of hepatorenal syndrome for 18 months (October 2019 to March 2021). A total 80 cases were studied. Results: All the parameters including Total Bilirubin, SGPT ( Serum glutamate pyruvate transaminase ), INR ( Internationalised normalised ratio ) and Serum Albumin were found to be similar ion both the groups except except SGOT (p=0.002) which was found to be significantly lower in Terlipressin +Albumin treatment group (61.89±47.83) compared to only albumin .(96.80±109.30). Those who were treated with Terlipressin +Albumin showed significant decrease in serum creatinine. A significant decrease in serum creatinine was observed at Day 5 (1.73±1.49) from Day 1 (3.47±1.68) making a mean percentage decrease of 50.14%

Human Huntington’s Disease iPSC-Derived Cortical Neurons Display Altered Transcriptomics, Morphology, and Maturation

Summary: Huntington’s disease (HD) is a neurodegenerative disease caused by an expanded CAG repeat in the Huntingtin (HTT) gene. Induced pluripotent stem cell (iPSC) models of HD provide an opportunity to study the mechanisms underlying disease pathology in disease-relevant patient tissues. Murine studies have demonstrated that HTT is intricately involved in corticogenesis. However, the effect of mutant Hungtintin (mtHTT) in human corticogenesis has not yet been thoroughly explored. This examination is critical, due to inherent differences in cortical development and timing between humans and mice. We therefore differentiated HD and non-diseased iPSCs into functional cortical neurons. While HD patient iPSCs can successfully differentiate toward a cortical fate in culture, the resulting neurons display altered transcriptomics, morphological and functional phenotypes indicative of altered corticogenesis in HD. : Mehta et al. show that induced pluripotent stem cells (iPSCs) from Huntington’s disease (HD) patients can successfully differentiate into functional cerebral cortical neurons. However, the resulting HD cortical neurons display altered transcriptomics and morphological and functional phenotypes indicative of altered corticogenesis in HD. Keywords: Huntington’s disease, iPSC, differentiation, cerebral cortex, corticogenesis, Huntingti

Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists.

BackgroundChest radiograph interpretation is critical for the detection of thoracic diseases, including tuberculosis and lung cancer, which affect millions of people worldwide each year. This time-consuming task typically requires expert radiologists to read the images, leading to fatigue-based diagnostic error and lack of diagnostic expertise in areas of the world where radiologists are not available. Recently, deep learning approaches have been able to achieve expert-level performance in medical image interpretation tasks, powered by large network architectures and fueled by the emergence of large labeled datasets. The purpose of this study is to investigate the performance of a deep learning algorithm on the detection of pathologies in chest radiographs compared with practicing radiologists.Methods and findingsWe developed CheXNeXt, a convolutional neural network to concurrently detect the presence of 14 different pathologies, including pneumonia, pleural effusion, pulmonary masses, and nodules in frontal-view chest radiographs. CheXNeXt was trained and internally validated on the ChestX-ray8 dataset, with a held-out validation set consisting of 420 images, sampled to contain at least 50 cases of each of the original pathology labels. On this validation set, the majority vote of a panel of 3 board-certified cardiothoracic specialist radiologists served as reference standard. We compared CheXNeXt's discriminative performance on the validation set to the performance of 9 radiologists using the area under the receiver operating characteristic curve (AUC). The radiologists included 6 board-certified radiologists (average experience 12 years, range 4-28 years) and 3 senior radiology residents, from 3 academic institutions. We found that CheXNeXt achieved radiologist-level performance on 11 pathologies and did not achieve radiologist-level performance on 3 pathologies. The radiologists achieved statistically significantly higher AUC performance on cardiomegaly, emphysema, and hiatal hernia, with AUCs of 0.888 (95% confidence interval [CI] 0.863-0.910), 0.911 (95% CI 0.866-0.947), and 0.985 (95% CI 0.974-0.991), respectively, whereas CheXNeXt's AUCs were 0.831 (95% CI 0.790-0.870), 0.704 (95% CI 0.567-0.833), and 0.851 (95% CI 0.785-0.909), respectively. CheXNeXt performed better than radiologists in detecting atelectasis, with an AUC of 0.862 (95% CI 0.825-0.895), statistically significantly higher than radiologists' AUC of 0.808 (95% CI 0.777-0.838); there were no statistically significant differences in AUCs for the other 10 pathologies. The average time to interpret the 420 images in the validation set was substantially longer for the radiologists (240 minutes) than for CheXNeXt (1.5 minutes). The main limitations of our study are that neither CheXNeXt nor the radiologists were permitted to use patient history or review prior examinations and that evaluation was limited to a dataset from a single institution.ConclusionsIn this study, we developed and validated a deep learning algorithm that classified clinically important abnormalities in chest radiographs at a performance level comparable to practicing radiologists. Once tested prospectively in clinical settings, the algorithm could have the potential to expand patient access to chest radiograph diagnostics

An orally effective dihydropyrimidone (DHPM) analogue induces apoptosis-like cell death in clinical isolates of Leishmania donovani overexpressing pteridine reductase 1

The protozoan parasite Leishmania donovani is the causative agent of visceral leishmaniasis. The enzyme pteridine reductase 1 (PTR1) of L. donovani acts as a metabolic bypass for drugs targeting dihydrofolate reductase (DHFR); therefore, for successful antifolate chemotherapy to be developed against Leishmania, it must target both enzyme activities. Leishmania cells overexpressing PTR1 tagged at the N-terminal with green fluorescent protein were established to screen for proprietary dihydropyrimidone (DHPM) derivatives of DHFR specificity synthesised in our laboratory. A cell-permeable molecule with impressive antileishmanial in vitro and in vivo oral activity was identified. Structure activity relationship based on homology model drawn on our recombinant enzyme established the highly selective inhibition of the enzyme by this analogue. It was seen that the leishmanicidal effect of this analogue is triggered by programmed cell death mediated by the loss of plasma membrane integrity as detected by binding of annexin V and propidium iodide (PI), loss of mitochondrial membrane potential culminating in cell cycle arrest at the sub-G0/G1 phase and oligonucleosomal DNA fragmentation. Hence, this DHPM analogue [(4-fluoro-phenyl)-6-methyl-2-thioxo-1, 2, 3, 4- tetrahydropyrimidine-5-carboxylic acid ethyl ester] is a potent antileishmanial agent that merits further pharmacological investigation

The ATLAS experiment at the CERN Large Hadron Collider: a description of the detector configuration for Run 3

Abstract The ATLAS detector is installed in its experimental cavern at Point 1 of the CERN Large Hadron Collider. During Run 2 of the LHC, a luminosity of  ℒ = 2 × 1034 cm-2 s-1 was routinely achieved at the start of fills, twice the design luminosity. For Run 3, accelerator improvements, notably luminosity levelling, allow sustained running at an instantaneous luminosity of  ℒ = 2 × 1034 cm-2 s-1, with an average of up to 60 interactions per bunch crossing. The ATLAS detector has been upgraded to recover Run 1 single-lepton trigger thresholds while operating comfortably under Run 3 sustained pileup conditions. A fourth pixel layer 3.3 cm from the beam axis was added before Run 2 to improve vertex reconstruction and b-tagging performance. New Liquid Argon Calorimeter digital trigger electronics, with corresponding upgrades to the Trigger and Data Acquisition system, take advantage of a factor of 10 finer granularity to improve triggering on electrons, photons, taus, and hadronic signatures through increased pileup rejection. The inner muon endcap wheels were replaced by New Small Wheels with Micromegas and small-strip Thin Gap Chamber detectors, providing both precision tracking and Level-1 Muon trigger functionality. Trigger coverage of the inner barrel muon layer near one endcap region was augmented with modules integrating new thin-gap resistive plate chambers and smaller-diameter drift-tube chambers. Tile Calorimeter scintillation counters were added to improve electron energy resolution and background rejection. Upgrades to Minimum Bias Trigger Scintillators and Forward Detectors improve luminosity monitoring and enable total proton-proton cross section, diffractive physics, and heavy ion measurements. These upgrades are all compatible with operation in the much harsher environment anticipated after the High-Luminosity upgrade of the LHC and are the first steps towards preparing ATLAS for the High-Luminosity upgrade of the LHC. This paper describes the Run 3 configuration of the ATLAS detector.</jats:p

The ATLAS experiment at the CERN Large Hadron Collider: a description of the detector configuration for Run 3

The ATLAS detector is installed in its experimental cavern at Point 1 of the CERN Large Hadron Collider. During Run 2 of the LHC, a luminosity of ℒ = 2 × 1034 cm-2 s-1 was routinely achieved at the start of fills, twice the design luminosity. For Run 3, accelerator improvements, notably luminosity levelling, allow sustained running at an instantaneous luminosity of ℒ = 2 × 1034 cm-2 s-1, with an average of up to 60 interactions per bunch crossing. The ATLAS detector has been upgraded to recover Run 1 single-lepton trigger thresholds while operating comfortably under Run 3 sustained pileup conditions. A fourth pixel layer 3.3 cm from the beam axis was added before Run 2 to improve vertex reconstruction and b-tagging performance. New Liquid Argon Calorimeter digital trigger electronics, with corresponding upgrades to the Trigger and Data Acquisition system, take advantage of a factor of 10 finer granularity to improve triggering on electrons, photons, taus, and hadronic signatures through increased pileup rejection. The inner muon endcap wheels were replaced by New Small Wheels with Micromegas and small-strip Thin Gap Chamber detectors, providing both precision tracking and Level-1 Muon trigger functionality. Trigger coverage of the inner barrel muon layer near one endcap region was augmented with modules integrating new thin-gap resistive plate chambers and smaller-diameter drift-tube chambers. Tile Calorimeter scintillation counters were added to improve electron energy resolution and background rejection. Upgrades to Minimum Bias Trigger Scintillators and Forward Detectors improve luminosity monitoring and enable total proton-proton cross section, diffractive physics, and heavy ion measurements. These upgrades are all compatible with operation in the much harsher environment anticipated after the High-Luminosity upgrade of the LHC and are the first steps towards preparing ATLAS for the High-Luminosity upgrade of the LHC. This paper describes the Run 3 configuration of the ATLAS detector