Drugs that inhibit TMEM16 proteins block SARS-CoV-2 spike-induced syncytia.

COVID-19 is a disease with unique characteristics that include lung thrombosis1, frequent diarrhoea2, abnormal activation of the inflammatory response3 and rapid deterioration of lung function consistent with alveolar oedema4. The pathological substrate for these findings remains unknown. Here we show that the lungs of patients with COVID-19 contain infected pneumocytes with abnormal morphology and frequent multinucleation. The generation of these syncytia results from activation of the SARS-CoV-2 spike protein at the cell plasma membrane level. On the basis of these observations, we performed two high-content microscopy-based screenings with more than 3,000 approved drugs to search for inhibitors of spike-driven syncytia. We converged on the identification of 83 drugs that inhibited spike-mediated cell fusion, several of which belonged to defined pharmacological classes. We focused our attention on effective drugs that also protected against virus replication and associated cytopathicity. One of the most effective molecules was the antihelminthic drug niclosamide, which markedly blunted calcium oscillations and membrane conductance in spike-expressing cells by suppressing the activity of TMEM16F (also known as anoctamin 6), a calcium-activated ion channel and scramblase that is responsible for exposure of phosphatidylserine on the cell surface. These findings suggest a potential mechanism for COVID-19 disease pathogenesis and support the repurposing of niclosamide for therapy.

The astrovirus N-terminal nonstructural protein anchors replication complexes to the perinuclear ER membranes.

An essential aspect of positive-sense RNA virus replication is anchoring the replication complex (RC) to cellular membranes. Positive-sense RNA viruses employ diverse strategies, including co-translational membrane targeting through signal peptides and co-opting cellular membrane trafficking components. Often, N-terminal nonstructural proteins play a crucial role in linking the RC to membranes, facilitating the early association of the replication machinery. Astroviruses utilize a polyprotein strategy to synthesize nonstructural proteins, relying on subsequent processing to form replication-competent complexes. This study provides evidence for the perinuclear ER membrane association of RCs in five distinct human astrovirus strains. Using tagged recombinant classical human astrovirus 1 and neurotropic MLB2 strains, we establish that the N-terminal domain guides the ER membrane association. We identified di-arginine motifs responsible for the perinuclear ER retention and formation of functional RCs through mutational analysis of the N-terminal domain in replicon and reverse genetics systems. In addition, we demonstrate the association of key components of the astrovirus replication complex: double-stranded RNA, RNA-dependent RNA polymerase, protease, and N-terminal protein. Our findings highlight the intricate virus-ER interaction mechanism employed by astroviruses, potentially leading to the development of novel antiviral intervention strategies.

Attenuation hotspots in neurotropic human astroviruses.

During the last decade, the detection of neurotropic astroviruses has increased dramatically. The MLB genogroup of astroviruses represents a genetically distinct group of zoonotic astroviruses associated with gastroenteritis and severe neurological complications in young children, the immunocompromised, and the elderly. Using different virus evolution approaches, we identified dispensable regions in the 3' end of the capsid-coding region responsible for attenuation of MLB astroviruses in susceptible cell lines. To create recombinant viruses with identified deletions, MLB reverse genetics (RG) and replicon systems were developed. Recombinant truncated MLB viruses resulted in imbalanced RNA synthesis and strong attenuation in iPSC-derived neuronal cultures confirming the location of neurotropism determinants. This approach can be used for the development of vaccine candidates using attenuated astroviruses that infect humans, livestock animals, and poultry.

MicroRNA therapy stimulates uncontrolled cardiac repair after myocardial infarction in pigs.

Prompt coronary catheterization and revascularization have markedly improved the outcomes of myocardial infarction, but have also resulted in a growing number of surviving patients with permanent structural damage of the heart, which frequently leads to heart failure. There is an unmet clinical need for treatments for this condition1, particularly given the inability of cardiomyocytes to replicate and thereby regenerate the lost contractile tissue2. Here we show that expression of human microRNA-199a in infarcted pig hearts can stimulate cardiac repair. One month after myocardial infarction and delivery of this microRNA through an adeno-associated viral vector, treated animals showed marked improvements in both global and regional contractility, increased muscle mass and reduced scar size. These functional and morphological findings correlated with cardiomyocyte de-differentiation and proliferation. However, subsequent persistent and uncontrolled expression of the microRNA resulted in sudden arrhythmic death of most of the treated pigs. Such events were concurrent with myocardial infiltration of proliferating cells displaying a poorly differentiated myoblastic phenotype. These results show that achieving cardiac repair through the stimulation of endogenous cardiomyocyte proliferation is attainable in large mammals, however dosage of this therapy needs to be tightly controlled.

Cross-Linking Mass Spectrometry Uncovers Interactions Between High-Density Lipoproteins and the SARS-CoV-2 Spike Glycoprotein.

High-density lipoprotein (HDL) levels are reduced in patients with coronavirus disease 2019 (COVID-19), and the extent of this reduction is associated with poor clinical outcomes. While lipoproteins are known to play a key role during the life cycle of the hepatitis C virus, their influence on coronavirus (CoV) infections is poorly understood. In this study, we utilize cross-linking mass spectrometry (XL-MS) to determine circulating protein interactors of the severe acute respiratory syndrome (SARS)-CoV-2 spike glycoprotein. XL-MS of plasma isolated from patients with COVID-19 uncovered HDL protein interaction networks, dominated by acute-phase serum amyloid proteins, whereby serum amyloid A2 was shown to bind to apolipoprotein (Apo) D. XL-MS on isolated HDL confirmed ApoD to interact with SARS-CoV-2 spike but not SARS-CoV-1 spike. Other direct interactions of SARS-CoV-2 spike upon HDL included ApoA1 and ApoC3. The interaction between ApoD and spike was further validated in cells using immunoprecipitation-MS, which uncovered a novel interaction between both ApoD and spike with membrane-associated progesterone receptor component 1. Mechanistically, XL-MS coupled with data-driven structural modeling determined that ApoD may interact within the receptor-binding domain of the spike. However, ApoD overexpression in multiple cell-based assays had no effect upon viral replication or infectivity. Thus, SARS-CoV-2 spike can bind to apolipoproteins on HDL, but these interactions do not appear to alter infectivity.

The environmental impact of heavy metals in sediments of main valleys in the eastern side of Mosul City, Iraq.

Analyzing the geochemical changes in stream sediments can reveal important surface processes on Earth, like weathering, transportation, and cation exchange. The study area is located on the eastern side of Mosul, where valleys named Al-Rashediya, Al-Kharrazi, Al-Khosar, Al-Danffilli, and Al-Shor flow towards the Tigris River. These valleys' sediments contain diverse components like clay minerals, organic matter, iron oxides, carbonates, and heavy metals (H.M.s), either as part of these substances or adsorbed onto them. In this study, 36 sediment samples were gathered from these valleys. They underwent chemical analysis through X-ray fluorescence to ascertain their chemical constituents of major oxides. To understand the distribution of H.M.s in these sediments, correlation coefficient analysis and factor analysis were utilized. The study employed the geoaccumulation index (Igeo) and enrichment factor (E.F.) to evaluate sediment contamination. The results of Igeo ranged from Cr = 0.24 to 1.83, Ni = -0.92 to 0.77, Cu = -2.41 to 0.05, Zn = -1.83 to 0.89, Pb = -1.54 to 0.36, and As = -2.84 to 0.80. These findings suggest that the valley sediments are generally in the range of deficiency to minimal enrichment and moderate enrichment. However, Al-Danffilli Valley shows strong contamination levels for Cu, Zn, and Pb. The E.F. values for Cr = 3.63-12.50, Ni = 1.95-4.19, Cu = 0.69-12.36, Zn = 1.08-16.19, Pb = 1.25-62.16, and As = 0.60-1.79 indicate levels ranging from deficiency to minimal and moderate enrichment. Al-Danffilli Valley, in particular, was identified as ranging from moderate to extremely high enrichment, attributed to its location near industrial areas and its tributaries.

Aquatic toxicity of hydroquinone and catechol following metal oxide treatment to Ceriodaphnia dubia and Pimephales promelas.

Metal oxides comprise a large group of chemicals used in water treatment to adsorb organic pollutants. The ability of titanium dioxide (TiO2) and iron (III) oxide (Fe2O3) to reduce the chronic toxicity of (phenolic) C6H6(OH)2 isomers, namely hydroquinone (HQ) and catechol (CAT) to Ceriodaphnia dubia and Pimephales promelas (less than 24 h-old) were investigated. The toxic endpoints following metal oxide treatment were compared to endpoints of untreated CAT and HQ. In chronic toxicity testing, HQ resulted in greater toxicity than CAT for both test organisms; the median lethal concentrations (LC50) for CAT were 3.66 to 12.36 mg.L-1 for C. dubia and P. promelas, respectively, while LC50 for HQ were 0.07 to 0.05 mg.L-1, respectively. Although both treated solutions presented lower toxic endpoints than those in the untreated solutions, Fe2O3 had a better potential to reduce the toxic effects of CAT and HQ than TiO2.

Blockchain-Powered Healthcare Systems: Enhancing Scalability and Security with Hybrid Deep Learning.

The rapid advancements in technology have paved the way for innovative solutions in the healthcare domain, aiming to improve scalability and security while enhancing patient care. This abstract introduces a cutting-edge approach, leveraging blockchain technology and hybrid deep learning techniques to revolutionize healthcare systems. Blockchain technology provides a decentralized and transparent framework, enabling secure data storage, sharing, and access control. By integrating blockchain into healthcare systems, data integrity, privacy, and interoperability can be ensured while eliminating the reliance on centralized authorities. In conjunction with blockchain, hybrid deep learning techniques offer powerful capabilities for data analysis and decision making in healthcare. Combining the strengths of deep learning algorithms with traditional machine learning approaches, hybrid deep learning enables accurate and efficient processing of complex healthcare data, including medical records, images, and sensor data. This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, integrating hybrid deep learning models. The framework ensures that only authorized entities can access and modify sensitive health information, preserving patient privacy while facilitating seamless data sharing and collaboration among healthcare providers. Additionally, the hybrid deep learning models enable real-time analysis of large-scale healthcare data, facilitating timely diagnosis, treatment recommendations, and disease prediction. The integration of blockchain and hybrid deep learning presents numerous benefits, including enhanced scalability, improved security, interoperability, and informed decision making in healthcare systems. However, challenges such as computational complexity, regulatory compliance, and ethical considerations need to be addressed for successful implementation. By harnessing the potential of blockchain and hybrid deep learning, healthcare systems can overcome traditional limitations, promoting efficient and secure data management, personalized patient care, and advancements in medical research. The proposed framework lays the foundation for a future healthcare ecosystem that prioritizes scalability, security, and improved patient outcomes.

Gene Therapy for the Heart Lessons Learned and Future Perspectives.

While clinical gene therapy celebrates its first successes, with several products already approved for clinical use and several hundreds in the final stages of the clinical approval pipeline, there is not a single gene therapy approach that has worked for the heart. Here, we review the past experience gained in the several cardiac gene therapy clinical trials that had the goal of inducing therapeutic angiogenesis in the ischemic heart and in the attempts at modulating cardiac function in heart failure. Critical assessment of the results so far achieved indicates that the efficiency of cardiac gene delivery remains a major hurdle preventing success but also that improvements need to be sought in establishing more reliable large animal models, choosing more effective therapeutic genes, better designing clinical trials, and more deeply understanding cardiac biology. We also emphasize a few areas of cardiac gene therapy development that hold great promise for the future. In particular, the transition from gene addition studies using protein-coding cDNAs to the modulation of gene expression using small RNA therapeutics and the improvement of precise gene editing now pave the way to applications such as cardiac regeneration after myocardial infarction and gene correction for inherited cardiomyopathies that were unapproachable until a decade ago.

Nuclear architecture dictates HIV-1 integration site selection.

Long-standing evidence indicates that human immunodeficiency virus type 1 (HIV-1) preferentially integrates into a subset of transcriptionally active genes of the host cell genome. However, the reason why the virus selects only certain genes among all transcriptionally active regions in a target cell remains largely unknown. Here we show that HIV-1 integration occurs in the outer shell of the nucleus in close correspondence with the nuclear pore. This region contains a series of cellular genes, which are preferentially targeted by the virus, and characterized by the presence of active transcription chromatin marks before viral infection. In contrast, the virus strongly disfavours the heterochromatic regions in the nuclear lamin-associated domains and other transcriptionally active regions located centrally in the nucleus. Functional viral integrase and the presence of the cellular Nup153 and LEDGF/p75 integration cofactors are indispensable for the peripheral integration of the virus. Once integrated at the nuclear pore, the HIV-1 DNA makes contact with various nucleoporins; this association takes part in the transcriptional regulation of the viral genome. These results indicate that nuclear topography is an essential determinant of the HIV-1 life cycle.

Mole Ratio Dependence of the Mutual Deliquescence Relative Humidity of Aqueous Salts of Atmospheric Importance.

The response of the mutual deliquescence relative humidity (MDRH) of several mixed salt systems to changes in mole ratio is presented here. The MDRH values of NH4Cl-NaCl, NH4Cl-(NH4)2SO4, and, for the first time, the NaCl-NaBr systems were acquired as a function of mole ratio. These changes were studied using attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy. The MDRH values of 1:1 salt mixtures were consistently found to be lower than the values of the individual deliquescence relative humidity (iDRH) of NH4Cl-NaCl and NH4Cl-(NH4)2SO4. The exception was the MDRH of the NaCl-NaBr system, which was found to be higher than the iDRH of NaBr particles, but lower than the iDRH of NaCl particles. When the mole ratio of the mixed system was varied, the MDRH of the mixtures showed a slight dependence on the mole ratio.

A comparative study of biopolymers and alum in the separation and recovery of pulp fibres from paper mill effluent by flocculation.

Recovery of cellulose fibres from paper mill effluent has been studied using common polysaccharides or biopolymers such as Guar gum, Xanthan gum and Locust bean gum as flocculent. Guar gum is commonly used in sizing paper and routinely used in paper making. The results have been compared with the performance of alum, which is a common coagulant and a key ingredient of the paper industry. Guar gum recovered about 3.86mg/L of fibre and was most effective among the biopolymers. Settling velocity distribution curves demonstrated that Guar gum was able to settle the fibres faster than the other biopolymers; however, alum displayed the highest particle removal rate than all the biopolymers at any of the settling velocities. Alum, Guar gum, Xanthan gum and Locust bean gum removed 97.46%, 94.68%, 92.39% and 92.46% turbidity of raw effluent at a settling velocity of 0.5cm/min, respectively. The conditions for obtaining the lowest sludge volume index such as pH, dose and mixing speed were optimised for guar gum which was the most effective among the biopolymers. Response surface methodology was used to design all experiments, and an optimum operational setting was proposed. The test results indicate similar performance of alum and Guar gum in terms of floc settling velocities and sludge volume index. Since Guar gum is a plant derived natural substance, it is environmentally benign and offers a green treatment option to the paper mills for pulp recycling.

Artificial intelligence in cancer diagnosis: Opportunities and challenges.

Since cancer is one of the world's top causes of death, early diagnosis is critical to improving patient outcomes. Artificial intelligence (AI) has become a viable technique for cancer diagnosis by using machine learning algorithms to examine large volumes of data for accurate and efficient diagnosis. AI has the potential to alter the way cancer is detected fundamentally. Still, it has several disadvantages, such as requiring a large amount of data, technological limitations, and ethical concerns. This overview looks at the possibilities and restrictions of AI in cancer detection, as well as current applications and possible future developments. We can better understand how to use AI to improve patient outcomes and reduce cancer mortality rates by looking at its potential for cancer detection.

Prevalence of chronic kidney disease in patients undergoing cardiac rhythm device removal.

Cardiac implantable electronic device (CIED) leads can cause central venous stenosis (CVS). In addition, these devices can get infected. Both are critically important considerations in patients with chronic kidney disease (CKD) for at least two reasons: (i) central veins serve as the final pathway should these patients need an arteriovenous access to provide dialysis therapy; and (ii) the presence of renal failure increases the risk of CIED infection. In this analysis, we investigated the prevalence as well as the degree of chronic kidney disease in patients harboring a CIED. Patients undergoing CIED removal were evaluated from 2001 to 2011. The patients were categorized into CKD stage I-V based on National Kidney Foundation-Dialysis Outcomes Quality Initiative guidelines. A total of 503 patients underwent CIED removal. Demographic characteristics revealed that 30% had hypertension, 44% were diabetics, 77% had coronary artery disease, and 84% suffered from congestive heart failure. Ninety percent (452/503) of the patients had CKD (stage I = I9 [4.2%], stage II = 189 [41.8%], stage III A = 96 [21.2%], stage III B = 59 [13.0%], stage IV = 45 [9.9%], and stage V = 44 [9.7%]). Overall, 148 (32.7%) patients (stage III B, stage IV, and stage V) of 452 had advanced renal failure. The results of this study reveal that one-third of CIED patients undergoing device removal have advanced chronic kidney disease.

Comparison of a plant based natural surfactant with SDS for washing of As(V) from Fe rich soil.

This study explores the possible application of a biodegradable plant based surfactant, obtained from Sapindus mukorossi, for washing low levels of arsenic (As) from an iron (Fe) rich soil. Natural association of As(V) with Fe(III) makes the process difficult. Soapnut solution was compared to anionic surfactant sodium dodecyl sulfate (SDS) in down-flow and a newly introduced suction mode for soil column washing. It was observed that soapnut attained up to 86% efficiency with respect to SDS in removing As. Full factorial design of experiment revealed a very good fit of data. The suction mode generated up to 83 kPa pressure inside column whilst down-flow mode generated a much higher pressure of 214 kPa, thus making the suction mode more efficient. Micellar solubilisation was found to be responsible for As desorption from the soil and it followed 1st order kinetics. Desorption rate coefficient of suction mode was found to be in the range of 0.005 to 0.01, much higher than down-flow mode values. Analysis of the FT-IR data suggested that the soapnut solution did not interact chemically with As, offering an option for reusing the surfactant. Soapnut can be considered as a soil washing agent for removing As even from soil with high Fe content.

Driving drowsiness detection using spectral signatures of EEG-based neurophysiology.

Introduction: Drowsy driving is a significant factor causing dire road crashes and casualties around the world. Detecting it earlier and more effectively can significantly reduce the lethal aftereffects and increase road safety. As physiological conditions originate from the human brain, so neurophysiological signatures in drowsy and alert states may be investigated for this purpose. In this preface, A passive brain-computer interface (pBCI) scheme using multichannel electroencephalography (EEG) brain signals is developed for spatially localized and accurate detection of human drowsiness during driving tasks. Methods: This pBCI modality acquired electrophysiological patterns of 12 healthy subjects from the prefrontal (PFC), frontal (FC), and occipital cortices (OC) of the brain. Neurological states are recorded using six EEG channels spread over the right and left hemispheres in the PFC, FC, and OC of the sleep-deprived subjects during simulated driving tasks. In post-hoc analysis, spectral signatures of the δ, θ, α, and ß rhythms are extracted in terms of spectral band powers and their ratios with a temporal correlation over the complete span of the experiment. Minimum redundancy maximum relevance, Chi-square, and ReliefF feature selection methods are used and aggregated with a Z-score based approach for global feature ranking. The extracted drowsiness attributes are classified using decision trees, discriminant analysis, logistic regression, naïve Bayes, support vector machines, k-nearest neighbors, and ensemble classifiers. The binary classification results are reported with confusion matrix-based performance assessment metrics. Results: In inter-classifier comparison, the optimized ensemble model achieved the best results of drowsiness classification with 85.6% accuracy and precision, 89.7% recall, 87.6% F1-score, 80% specificity, 70.3% Matthews correlation coefficient, 70.2% Cohen's kappa score, and 91% area under the receiver operating characteristic curve with 76-ms execution time. In inter-channel comparison, the best results were obtained at the F8 electrode position in the right FC of the brain. The significance of all the results was validated with a p-value of less than 0.05 using statistical hypothesis testing methods. Conclusions: The proposed scheme has achieved better results for driving drowsiness detection with the accomplishment of multiple objectives. The predictor importance approach has reduced the feature extraction cost and computational complexity is minimized with the use of conventional machine learning classifiers resulting in low-cost hardware and software requirements. The channel selection approach has spatially localized the most promising brain region for drowsiness detection with only a single EEG channel (F8) which reduces the physical intrusiveness in normal driving operation. This pBCI scheme has a good potential for practical applications requiring earlier, more accurate, and less disruptive drowsiness detection using the spectral information of EEG biosignals.

SARS-CoV-2 Syncytium under the Radar: Molecular Insights of the Spike-Induced Syncytia and Potential Strategies to Limit SARS-CoV-2 Replication.

SARS-CoV-2 infection induces non-physiological syncytia when its spike fusogenic protein on the surface of the host cells interacts with the ACE2 receptor on adjacent cells. Spike-induced syncytia are beneficial for virus replication, transmission, and immune evasion, and contribute to the progression of COVID-19. In this review, we highlight the properties of viral fusion proteins, mainly the SARS-CoV-2 spike, and the involvement of the host factors in the fusion process. We also highlight the possible use of anti-fusogenic factors as an antiviral for the development of therapeutics against newly emerging SARS-CoV-2 variants and how the fusogenic property of the spike could be exploited for biomedical applications.

Multidisiplinary design optimization of a power generation system based on waste energy recovery from an internal combustion engine using organic Rankine cycle and thermoelectric generator.

The research paper mainly deals with waste heat recovery from internal combustion engines (ICE) using the organic Rankine cycle (ORC) and Thermoelectric generator (TEG). Simultaneously recovering the wasted heat of both exhaust gases and coolant, a novel configuration named two-stage is proposed. Then a comprehensive thermo-economic analysis and optimization are conducted. Produced power and total cost rate are selected as the objective function of the optimization. Also, the first and second stage pressures of the ORC system are considered as decision variables. Finally, a sensitivity analysis is performed to study the effect of expander inlet temperature, pumps isentropic efficiency, and expander isentropic efficiency on the objective function.

Prediction of geomechanical bearing capacity using autoregressive deep neural network in carbon capture and storage systems.

Carbon Capture and Storage (CCS) field is growing rapidly as a means to mitigate the accumulation of greenhouse gas emissions. However, the geomechanical stability of CCS systems, particularly related to bearing capacity, remains a critical challenge that requires accurate prediction models. In this research paper, we investigate the efficacy of employing an Autoregressive Deep Neural Network (ARDNN) algorithm to predict the geomechanical bearing capacity in CCS systems through shear wave velocity prediction as an index for bearing capacity evaluation of deep rock formations. The model utilizes a dataset consisting of 23,000 data points to train and test the ARDNN algorithm. Its scalability, use of deep learning techniques, automatic feature extraction, adaptability to changes in data, and versatility in various prediction tasks make it an attractive option for accurate predictions. The results demonstrate exceptional performance, as evidenced by an R-squared value of 0.9906 and a mean squared error of 0.0438 for the test data compared to the measured data. This research has significant practical implications for effectively predicting geomechanical stability in CCS systems, thus mitigating potential risks associated with their operation.

High brachial artery bifurcation: clinical considerations and practical implications for an arteriovenous access.

High brachial artery bifurcation (HiBAB) is not a rare occurrence. Recent data have emphasized that HiBAB can have major clinical implications including high failure rate and decreased functional patency of an arteriovenous (AV) fistula. In this retrospective study, we investigated the incidence of HiBAB. Patients with advanced chronic kidney disease and end-stage renal disease on chronic hemodialysis undergoing preoperative vascular mapping for the creation of an AV access were included in this analysis. Ultrasound examination was used to map the arteries of the upper extremities. Four hundred and eighty-one arms in 340 patients were examined (right arm = 181, left arm = 300). Sixty-nine of the 481 (12.3%) demonstrated HiBAB. The internal diameter of the radial and ulnar arteries measured at the elbow region was found to be 2.9 ± 0.8 and 3.6 ± 1.0 mm, respectively (p = 0.0001). There were no statistically significant differences in terms of race, gender, and right versus left arms regarding the incidence of HiBAB. As HiBAB can be present in a significant number of patients and have an impact on the AV access, its presence should be evaluated during vascular mapping prior to an AV access creation.