Non-calcified plaque-based coronary stenosis grading in contrast enhanced CT.

BACKGROUND: The high mortality rate associated with coronary heart disease has led to state-of-the-art non-invasive methods for cardiac diagnosis including computed tomography and magnetic resonance imaging. However, stenosis computation and clinical assessment of non-calcified plaques has been very challenging due to their ambiguous intensity response in CT i.e. a significant overlap with surrounding muscle tissues and blood. Accordingly, this research presents an approach for computation of coronary stenosis by investigating cross-sectional lumen behaviour along the length of 3D coronary segments. METHODS: Non-calcified plaques are characterized by comparatively lower-intensity values with respect to the surrounding. Accordingly, segment-wise orthogonal volume was reconstructed in 3D space using the segmented coronary tree. Subsequently, the cross sectional volumetric data was investigated using proposed CNN-based plaque quantification model and subsequent stenosis grading in clinical context was performed. In the last step, plaque-affected orthogonal volume was further investigated by comparing vessel-wall thickness and lumen area obstruction w.r.t. expert-based annotations to validate the stenosis grading performance of model. RESULTS: The experimental data consists of clinical CT images obtained from the Rotterdam CT repository leading to 600 coronary segments and subsequent 15786 cross-sectional images. According to the results, the proposed method quantified coronary vessel stenosis i.e. severity of the non-calcified plaque with an overall accuracy of 83%. Moreover, for individual grading, the proposed model show promising results with accuracy equal to 86%, 90% and 79% respectively for severe, moderate and mild stenosis. The stenosis grading performance of the proposed model was further validated by performing lumen-area versus wall-thickness analysis as per annotations of manual experts. The statistical results for lumen area analysis precisely correlates with the quantification performance of the model with a mean deviation of 5% only. CONCLUSION: The overall results demonstrates capability of the proposed model to grade the vessel stenosis with reasonable accuracy and precision equivalent to human experts.

Insights into population adaptation and biodiversity of lactic acid bacteria in challenged date palm leaves silaging, using MALDI-TOF MS.

The study focused on isolating indigenous Qatari lactic acid bacteria (LAB) from various challenged date palm tree leaf silages to construct a comprehensive strain collection, useful to study the diversity of these strains following their adaptation to the uncommon silage. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) was employed for strain identification and differentiation. The diversity of LAB populations and strains was assessed through principal component analysis (PCA) and dendrogram analyses. A total of 88 LAB isolates were obtained from silages of fresh palm leaves, silage of mixed leaves and dairy feed, along with fresh palm tree leaves, and dairy feed, adapted to local harsh environments. These isolates were categorized according to the new classification of 2020, belonging to genera of Pediococcus, Lactiplantibacillus plantarum, Lacticaseibacillus paracasei, Companilactobacillus farciminis, Limosilactobacillus oris, Limosilactobacillus vaginalis, Lactiplantibacillus pentosus and Lactobacillus johnsonii. Pediococcus was the most prevalent genus, falling mostly within the species Pediococcus lolii. MALDI-TOF MS protein profiles, PCA, and dendrogram analyses successfully grouped the LAB isolates into five distinctive clusters based on the protein's similarities. The high diversity of the indigenous LAB in spontaneous palm leaf silages demonstrated their adaptation and mutualistic interactions, forming robust consortia that ensure the quality of the silage. The straightforward, quick, and accurate identification of LAB in this silage using MALDI-TOF MS presents a valuable approach for formulating LAB consortia for silaging harsh agricultural by-products.

Hemophagocytic Lymphohistiocytosis Associated With Human Herpesvirus-6 (HHV-6) Infection in an Immunocompetent Adult: A Case Report.

Hemophagocytic lymphohistiocytosis (HLH) is a rare disorder characterized by extreme immune activation and excessive inflammation. It has been reported in patients with familial cases, immunodeficiencies, malignancies, stem cell transplants, and viral etiologies. This report describes acquired HLH associated with Human herpesvirus-6 (HHV-6) infection in a 76-year-old previously healthy male. The patient was admitted to the hospital due to fever, chills, and abdominal pain. The diagnostic workup revealed gallbladder wall thickening on imaging, concerning for cholecystitis. The patient was started on treatment for sepsis. Further clinical deterioration led to an extensive infectious workup. The patient was found to have elevated soluble IL-2Ra levels, and a bone marrow biopsy was performed, which revealed HLH. A positive HHV-6 polymerase chain reaction in the cerebrospinal fluid and serum confirmed the viral infection. Treatment involved the initiation of high-dose steroids, etoposide, and ganciclovir. Despite these interventions, the patient's clinical status worsened, leading to the implementation of comfort measures, and the patient eventually died. This case underscores the importance of considering HHV-6 as a potential cause of HLH in immunocompetent adults. From this case, we infer that a heightened level of vigilance is necessary to recognize and intervene in this challenging condition promptly.

Four-dimensional quantitative analysis of cell plate development in Arabidopsis using lattice light sheet microscopy identifies robust transition points between growth phases.

Cell plate formation during cytokinesis entails multiple stages occurring concurrently and requiring orchestrated vesicle delivery, membrane remodelling, and timely deposition of polysaccharides, such as callose. Understanding such a dynamic process requires dissection in time and space; this has been a major hurdle in studying cytokinesis. Using lattice light sheet microscopy (LLSM), we studied cell plate development in four dimensions, through the behavior of yellow fluorescent protein (YFP)-tagged cytokinesis-specific GTPase RABA2a vesicles. We monitored the entire duration of cell plate development, from its first emergence, with the aid of YFP-RABA2a, in both the presence and absence of cytokinetic callose. By developing a robust cytokinetic vesicle volume analysis pipeline, we identified distinct behavioral patterns, allowing the identification of three easily trackable cell plate developmental phases. Notably, the phase transition between phase I and phase II is striking, indicating a switch from membrane accumulation to the recycling of excess membrane material. We interrogated the role of callose using pharmacological inhibition with LLSM and electron microscopy. Loss of callose inhibited the phase transitions, establishing the critical role and timing of the polysaccharide deposition in cell plate expansion and maturation. This study exemplifies the power of combining LLSM with quantitative analysis to decode and untangle such a complex process.

Nanoparticles: The Plant Saviour under Abiotic Stresses.

Climate change significantly affects plant growth and productivity by causing different biotic and abiotic stresses to plants. Among the different abiotic stresses, at the top of the list are salinity, drought, temperature extremes, heavy metals and nutrient imbalances, which contribute to large yield losses of crops in various parts of the world, thereby leading to food insecurity issues. In the quest to improve plants' abiotic stress tolerance, many promising techniques are being investigated. These include the use of nanoparticles, which have been shown to have a positive effect on plant performance under stress conditions. Nanoparticles can be used to deliver nutrients to plants, overcome plant diseases and pathogens, and sense and monitor trace elements that are present in soil by absorbing their signals. A better understanding of the mechanisms of nanoparticles that assist plants to cope with abiotic stresses will help towards the development of more long-term strategies against these stresses. However, the intensity of the challenge also warrants more immediate approaches to mitigate these stresses and enhance crop production in the short term. Therefore, this review provides an update of the responses (physiological, biochemical and molecular) of plants affected by nanoparticles under abiotic stress, and potentially effective strategies to enhance production. Taking into consideration all aspects, this review is intended to help researchers from different fields, such as plant science and nanoscience, to better understand possible innovative approaches to deal with abiotic stresses in agriculture.

Acute Cholecystitis in a Gastric Bypass Patient Complicated by Takotsubo Cardiomyopathy.

Background: Gallbladder disease is a common condition after gastric bypass surgery. Even after weight loss, many bariatric patients continue to suffer from comorbid conditions. Takotsubo cardiomyopathy is a rare condition that mimics acute cardiac ischemia but seems to be caused by a catecholamine storm triggered by intense stress. Case Report. A 62-year-old female presented with acute right upper quadrant (RUQ) pain to the ER. She had a history of laparoscopic gastric bypass 5 years ago and had been noncompliant for 2 years. This noncompliance included missing follow-up appointments, gaining weight which caused poorly controlled DM, and not taking her vitamin supplements. Upon presentation, her WBC was elevated, her LFTs were normal, and imaging showed acute calculous cholecystitis. She was admitted and started on antibiotics with plans for laparoscopic cholecystectomy. The next day, she developed acute chest pain, and troponins were elevated with ST changes on EKG. Echocardiography showed a ballooned left ventricle indicative for Takotsubo cardiomyopathy. Symptomatic treatment including antibiotics, betablocker, and thiamine infusion was initiated. At three-month follow-up, ejection fraction had improved from <20% to >50%. The patient underwent interval laparoscopic cholecystectomy, which was technically very challenging due to severe ongoing acute and chronic cholecystitis. There were no cardiac issues, but the patient developed an abscess in the gallbladder fossa, which was successfully treated with oral antibiotics. Conclusions: Takotsubo cardiomyopathy complicating acute cholecystitis has thus far not been reported. Our patient had a history of gastric bypass and was noncompliant with vitamin supplementation. Thiamine deficiency may have contributed to the cardiac condition (wet beriberi).

A biophysical model for plant cell plate maturation based on the contribution of a spreading force.

Plant cytokinesis, a fundamental process of plant life, involves de novo formation of a "cell plate" partitioning the cytoplasm of dividing cells. Cell plate formation is directed by orchestrated delivery, fusion of cytokinetic vesicles, and membrane maturation to form a nascent cell wall by timely deposition of polysaccharides. During cell plate maturation, the fragile membrane network transitions to a fenestrated sheet and finally a young cell wall. Here, we approximated cell plate sub-structures with testable shapes and adopted the Helfrich-free energy model for membranes, including a stabilizing and spreading force, to understand the transition from a vesicular network to a fenestrated sheet and mature cell plate. Regular cell plate development in the model was possible, with suitable bending modulus, for a two-dimensional late stage spreading force of 2-6 pN/nm, an osmotic pressure difference of 2-10 kPa, and spontaneous curvature between 0 and 0.04 nm-1. With these conditions, stable membrane conformation sizes and morphologies emerged in concordance with stages of cell plate development. To reach a mature cell plate, our model required the late-stage onset of a spreading/stabilizing force coupled with a concurrent loss of spontaneous curvature. Absence of a spreading/stabilizing force predicts failure of maturation. The proposed model provides a framework to interrogate different players in late cytokinesis and potentially other membrane networks that undergo such transitions. Callose, is a polysaccharide that accumulates transiently during cell plate maturation. Callose-related observations were consistent with the proposed model's concept, suggesting that it is one of the factors involved in establishing the spreading force.

Multi-step rainfall forecasting using deep learning approach.

Rainfall prediction is immensely crucial in daily life routine as well as for water resource management, stochastic hydrology, rain run-off modeling and flood risk mitigation. Quantitative prediction of rainfall time series is extremely challenging as compared to other meteorological parameters due to its variability in local features that involves temporal and spatial scales. Consequently, this requires a highly complex system having an advance model to accurately capture the highly non linear processes occurring in the climate. The focus of this work is direct prediction of multistep forecasting, where a separate time series model for each forecasting horizon is considered and forecasts are computed using observed data samples. Forecasting in this method is performed by proposing a deep learning approach, i.e, Temporal Deep Belief Network (DBN). The best model is selected from several baseline models on the basis of performance analysis metrics. The results suggest that the temporal DBN model outperforms the conventional Convolutional Neural Network (CNN) specifically on rainfall time series forecasting. According to our experimentation, a modified DBN with hidden layes (300-200-100-10) performs best with 4.59E-05, 0.0068 and 0.94 values of MSE, RMSE and R value respectively on testing samples. However, we found that training DBN is more exhaustive and computationally intensive than other deep learning architectures. Findings of this research can be further utilized as basis for the advance forecasting of other weather parameters with same climate conditions.

Anxiety among Doctors during COVID-19 Pandemic in Secondary and Tertiary Care Hospitals.

OBJECTIVE: To assess the level of anxiety among doctors during COVID-19 pandemic and the associated risk factors. METHODS: This cross-sectional study was conducted from 30th April to 16th May, 2020 in Karachi, Pakistan. The data was collected via an online web-based questionnaire. Questionnaire was used to assess anxiety level using GAD-7 scale among health-care professionals and the risk factors playing role in it. RESULTS: One hundred and fifty-one doctors participated in our study. Out of these 151 participants, 69 (45.7%) had mild, 22 (14.6%) had moderate, and 5 (3.3%) had severe symptoms of anxiety, whereas the remaining 55 (36.4%) had no anxiety according to GAD-7 scale. The median [interquartile range (IQR)] GAD-7 scale scores are 6.0 [3.00-9.00]. Females showed more severe degrees of measurement of anxiety symptoms than males. Doctors dealing with COVID-19 patients showed higher level of anxiety as compared to the doctors who were not dealing with COVID-19 patients, having a significant difference (U = 9.697, p = 0.008). One hundred and forty-one (93.4%) participants were concerned about being exposed to COVID-19 at work and 112 (74.2 %) thought they have inadequate protective equipment for safety. CONCLUSIONS: During COVID-19 pandemic, doctors exhibited different grades of anxiety. In order for healthcare workers to perform to the best of their capability, certain guidelines and interventions are needed.

Framework for detection and localization of coronary non-calcified plaques in cardiac CTA using mean radial profiles.

BACKGROUND AND OBJECTIVE: The high mortality rate associated with coronary heart disease (CHD) has driven intensive research in cardiac imaging and image analysis. The advent of computed tomography angiography (CTA) has turned non-invasive diagnosis of cardiovascular anomalies into reality as calcified coronary plaques can be easily identified due to their high intensity values. However, the detection of non-calcified plaques in CTA is still a challenging problem because of lower intensity values, which are often similar to the nearby blood and muscle tissues. In this work, we propose the use of mean radial profiles for the detection of non-calcified plaques in CTA imagery. METHODS: Accordingly, we computed radial profiles by averaging the image intensity in concentric rings around the vessel centreline in a first stage. In the subsequent stage, an SVM classifier is applied to identify the abnormal coronary segments. For occluded segments, we further propose a derivative-based method to localize the position and length of the plaque inside the segment. RESULTS: A total of 32 CTA volumes were analysed and a detection accuracy of 88.4% with respect to the manual expert was achieved. The plaque localization accuracy was computed using the Dice similarity coefficient and a mean of 83.2% was achieved. CONCLUSION: The consistent performance for multi-vendor, multi-institution data demonstrates the reproducibility of our method across different CTA datasets with a good agreement with manual expert annotations.

A hybrid energy model for region based curve evolution - Application to CTA coronary segmentation.

BACKGROUND AND OBJECTIVE: State-of-the-art medical imaging techniques have enabled non-invasive imaging of the internal organs. However, high volumes of imaging data make manual interpretation and delineation of abnormalities cumbersome for clinicians. These challenges have driven intensive research into efficient medical image segmentation. In this work, we propose a hybrid region-based energy formulation for effective segmentation in computed tomography angiography (CTA) imagery. METHODS: The proposed hybrid energy couples an intensity-based local term with an efficient discontinuity-based global model of the image for optimal segmentation. The segmentation is achieved using a level set formulation due to the computational robustness. After validating the statistical significance of the hybrid energy, we applied the proposed model to solve an important clinical problem of 3D coronary segmentation. An improved seed detection method is used to initialize the level set evolution. Moreover, we employed an auto-correction feature that captures the emerging peripheries during the curve evolution for completeness of the coronary tree. RESULTS: We evaluated the segmentation accuracy of the proposed energy model against the existing techniques in two stages. Qualitative and quantitative results demonstrate the effectiveness of the proposed framework with a consistent mean sensitivity and specificity measures of 80% across the CTA data. Moreover, a high degree of agreement with respect to the inter-observer differences justifies the generalization of the proposed method. CONCLUSIONS: The proposed method is effective to segment the coronary tree from the CTA volume based on hybrid image based energy, which can improve the clinicians ability to detect arterial abnormalities.