Prolonged Washout Period for Avoiding Azilsartan-Induced Refractory Hypotension During General Anesthesia for a Patient With Renal Impairment

Angiotensin receptor blockers (ARBs) are widely used to treat hypertension, but severe refractory hypotension during general anesthesia is a well-known complication associated with the continuation of ARBs during the perioperative period. It has therefore been recommended that ARBs be withheld for 24 hours before induction of general anesthesia. However, impaired renal function affects the pharmacokinetics of each ARB differently. The half-life of azilsartan is prolonged in accordance with the degree of renal impairment. Herein, we describe a patient with chronic kidney disease grade 3B who experienced severe refractory hypotension after induction of general anesthesia requiring administration of dopamine following inadequate responses to ephedrine and phenylephrine despite a 24-hour azilsartan washout period. When the same patient underwent general anesthesia for a subsequent surgery, azilsartan was withheld for 48 hours before induction, resulting in mild intraoperative hypotension that responded adequately to phenylephrine. Severe refractory hypotension during general anesthesia cannot always be avoided by holding azilsartan for 24 hours in patients with significant renal impairment. Therefore, a longer washout period may be preferable for patients regularly taking azilsartan who also have concurrent substantial renal impairment.

A deep learning based graph-transformer for whole slide image classification

Deep learning is a powerful tool for assessing pathology data obtained from digitized biopsy slides. In the context of supervised learning, most methods typically divide a whole slide image (WSI) into patches, aggregate convolutional neural network outcomes on them and estimate overall disease grade. However, patch-based methods introduce label noise in training by assuming that each patch is independent with the same label as the WSI and neglect the important contextual information that is significant in disease grading. Here we present a Graph-Transformer (GT) based framework for processing pathology data, called GTP, that interprets morphological and spatial information at the WSI-level to predict disease grade. To demonstrate the applicability of our approach, we selected 3,024 hematoxylin and eosin WSIs of lung tumors and with normal histology from the Clinical Proteomic Tumor Analysis Consortium, the National Lung Screening Trial, and The Cancer Genome Atlas, and used GTP to distinguish adenocarcinoma (LUAD) and squamous cell carcinoma (LSCC) from those that have normal histology. Our model achieved consistently high performance on binary (tumor versus normal: mean overall accuracy = 0.975+/-0.013) as well as three-label (normal versus LUAD versus LSCC: mean accuracy = 0.932+/-0.019) classification on held-out test data, underscoring the power of GT-based deep learning for WSI-level classification. We also introduced a graph-based saliency mapping technique, called GraphCAM, that captures regional as well as contextual information and allows our model to highlight WSI regions that are highly associated with the class label. Taken together, our findings demonstrate GTP as a novel interpretable and effective deep learning framework for WSI-level classification.

Common Sonographic Patterns of Kidney Diseases in Nnewi North, Anambra State, Nigeria

Introduction: Despite the increasing prevalence of kidney diseases in Nigeria, and the value of ultrasonography in the diagnosis of these diseases, there is a paucity of data on the sonographic patterns of kidney diseases peculiar to people in Nnewi North Anambra state, Nigeria.Objective: The objective of this study was to document the common sonographic patterns of kidney diseases in Nnewi-North, Anambra state, Nigeria and to correlate certain Ultrasound detectable kidney diseases of patients with age and sex.Methods: This study adopted a cross-sectional retrospective design. Secondary records from files, folders were retrieved from patient’s records from the Radiography departments of Waves diagnostic center and Nnamdi Azikiwe University teaching hospital both in Anambra State, Nigeria. A total of 400 patients were reviewed from the health institutions between April 2020 and April 2021. The data were analyzed using descriptive and inferential statistics.Results: Common kidney diseases were: hydronephrosis [74(17.8%)], nephrolithiasis [69 (17.3%)], renal cyst [39(9.8)], urolithiasis [9(75%)], renal parenchyma disease grade I [27(6.8%)], renal parenchyma disease grade II [12(3.0%)], renal parenchyma disease grade III [12(3.0%)],renal parenchyma disease grade I-II [6(1.5%)], renal parenchyma disease grade II-III [1(0.3)], nephritis [6(1.5%)], ectopic kidney [3(0.8)], polycystic kidney disease[ 9(2.3%)], pyelonephritis [14(3.5%)], nephrocalcinosis [29(7.2%)]. It was found out that normal patients had the highest occurrence [81 (21.3%)], the most prevalent kidney disease was hydronephrosis [74(17.8%)] and the least prevalent was renal parenchyma disease grade II-III [1(0.3%)]. The prevalence of hydronephrosis was seen more in male patients [42 (59.2%)] than their female counterparts [29 (40.8%)]. The subjects between the ages of 37-52 years were more likely to develop hydronephrosis than other age groups and also there was no significant relationship between kidney diseases and age. It is therefore recommended that Ultrasound should be used as the first line of diagnosis in kidney pathologies and suspected flank pain, because of its availability, cheapness, improved safety profile, and level of diagnostic accuracy.Conclusion: Common sonographic patterns of kidney diseases were those of hydronephrosis, nephrolithiasis, and renal parenchyma diseases grade I-III predominantly among male subjects. Age and sex had no significant effect on sonographic patterns of kidney diseases.

The relative expression of miR-31, miR-29, miR-126, and miR-17 and their mRNA targets in the serum of COVID-19 patients with different grades during hospitalization

Background The aim of this study was to evaluate the expression of four up/down-regulated inflammatory miRNAs and their mRNA targets in the serum samples of COVID-19 patients with different grades. Also, we investigated the relative expression of these miRNAs and mRNAs during hospitalization. Methods In this cross-sectional study, 5 mL of blood sample were taken from COVID-19 patients with different grades and during hospitalization from several health centers of Yazd, Tehran, and Zahedan province of Iran from December 20, 2020 to March 2, 2021. The relative expression of miRNAs and mRNAs was evaluated by q-PCR. Results We found that the relative expression of hsa-miR-31-3p, hsa-miR-29a-3p, and hsa-miR-126-3p was significantly decreased and the relative expression of their mRNA targets (ZMYM5, COL5A3, and CAMSAP1) was significantly increased with the increase of disease grade. Conversely, the relative expression of hsa-miR-17-3p was significantly increased and its mRNA target (DICER1) was significantly decreased with the increase of disease grade. This pattern was exactly seen during hospitalization of COVID-19 patients who did not respond to treatment. In COVID-19 patients who responded to treatment, the expression of selected miRNAs and their mRNA targets returned to the normal level. A negative significant correlation was seen between (1) the expression of hsa-miR-31-3p and ZMYM5, (2) hsa-miR-29a-3p and COL5A3, (3) hsa-miR-126-3p and CAMSAP1, and (4) hsa-miR-17-3p and DICER1 in COVID-19 patients with any grade (P < 0.05) and during hospitalization. Conclusions In this study, we gained a more accurate understanding of the expression of up/down-regulated inflammatory miRNAs in the blood of COVID-19 patients. The obtained data may help us in the diagnosis and prognosis of COVID-19. Trial registration: The ethics committee of Zahedan University of Medical Sciences, Zahedan, Iran. (Ethical Code: IR.ZAUMS.REC.1399.316) was registered for this project.

BACHD Mice Recapitulate the Striatal Parvalbuminergic Interneuron Loss Found in Huntington’s Disease

Huntington’s disease (HD) is a dominantly inherited, adult-onset neurodegenerative disease characterized by motor, psychiatric, and cognitive abnormalities. Neurodegeneration is prominently observed in the striatum where GABAergic medium spiny neurons (MSN) are the most affected neuronal population. Interestingly, recent reports of pathological changes in HD patient striatal tissue have identified a significant reduction in the number of parvalbumin-expressing interneurons which becomes more robust in tissues of higher disease grade. Analysis of other interneuron populations, including somatostatin, calretinin, and cholinergic, did not reveal significant neurodegeneration. Electrophysiological experiments in BACHD mice have identified significant changes in the properties of parvalbumin and somatostatin expressing interneurons in the striatum. Furthermore, their interactions with MSNs are altered as the mHTT expressing mouse models age with increased input onto MSNs from striatal somatostatin and parvalbumin-expressing neurons. In order to determine whether BACHD mice recapitulate the alterations in striatal interneuron number as observed in HD patients, we analyzed the number of striatal parvalbumin, somatostatin, calretinin, and choline acetyltransferase positive cells in symptomatic 12–14 month-old mice by immunofluorescent labeling. We observed a significant decrease in the number of parvalbumin-expressing interneurons as well as a decrease in the area and perimeter of these cells. No significant changes were observed for somatostatin, calretinin, or cholinergic interneuron numbers while a significant decrease was observed for the area of cholinergic interneurons. Thus, the BACHD mice recapitulate the degenerative phenotype observed in the parvalbumin interneurons in HD patient striata without affecting the number of other interneuron populations in the striatum.

Using Transfer Learning for Diabetic Retinopathy Stages Classification

Purpose – Diabetes is a chronic disease, that leads to damage of many systems of the body. One of the dangerous complications of diabetes is diabetic retinopathy. Frequent inspection for diabetic retinopathy is essential to recognize patients at risk of visual impairment. The disease grade level must be tracked to manage its progress and to start the appropriate decision for treatment in time. Effective automated methods for the detection of diabetic retinopathy and the classification of its severity stage are necessary. It also helps to decrease the burden on ophthalmologists and reduce diagnostic contradictions among manual readers. Methods– In this research, a convolutional neural network (CNN) is used based on color retinal fundus images for the detection of diabetic retinopathy (DR) and classification of its stages. CNN can recognize sophisticated features on the retina and so provide an automatic diagnosis. The pre-trained CNN model Visual Geometry Group (VGG) is applied on DR data using a transfer learning approach to utilize the already learnt parameters based on 1,000,000 images of ImageNet with 1000 classes.Results – By conducting different experiments with different classes setting the built models achieved promising results. The best achieved accuracies for 2-ary, 3-ary, 4-ary, and 5-ary classification are 85.99, 80.5, 61.28, and 71, respectively.

Multiple strokes and cerebral microangiopathy in a patient with Churg-Strauss syndrome

Eosinophilic granulomatosis with polyangiitis (EGPA) is a multisystemic disease that mainly affects the lungs and skin. It is considered to be a small and medium-vessel vasculitis. Although neurologic manifestations of EGPA are reported, usually consisting of peripheral neuropathy, central nervous system manifestations are quite rare, those described being cerebral infarctions or hemorrhages. We present the case of a 79-year-old woman diagnosed in 2016 with EGPA, being treated with Prednisone and Azathioprine, who presented to the Neurological Emergency Department with right hemiplegia, dysmetria in the left arm and right hemi-hypoesthesia. CT (computed tomography) and MRI (Magnetic resonance imaging) findings on admission described lacunar strokes. The patient presented with low creatinine clearance on admission (positive for chronic renal disease), grade III hypertension, ischemic cardiomyopathy and right calf deep vein thrombosis. The patient was started on neuroprotective and neurotrophic treatment associated with parenteral hydration, anticoagulant and hypotensive drugs . The patient’s symptoms partially improved, with possibility of independently maintaining a sitting position and upright stance with unilateral sustenance at discharge. Patients suffering from vasculitides must be carefully observed in order to prevent or treat complications that may emerge.