Using a genetically encoded sensor to identify inhibitors of Toxoplasma gondii Ca2+ Signalling

This work was supported in part by National Institutes of Health Grants AI-110027 and AI-096836 (to S. N. J. M.) and 1DP5OD017892 (to S. L.).The life cycles of apicomplexan parasites progress in accordance with fluxes in cytosolic Ca2+. Such fluxes are necessary for events like motility and egress from host cells. We used genetically encoded Ca2+ indicators (GCaMPs) to develop a cell-based phenotypic screen for compounds that modulate Ca2+ signaling in the model apicomplexan Toxoplasma gondii. In doing so, we took advantage of the phosphodiesterase inhibitor zaprinast, which we show acts in part through cGMP-dependent protein kinase (protein kinase G; PKG) to raise levels of cytosolic Ca2+. We define the pool of Ca2+ regulated by PKG to be a neutral store distinct from the endoplasmic reticulum. Screening a library of 823 ATP mimetics, we identify both inhibitors and enhancers of Ca2+ signaling. Two such compounds constitute novel PKG inhibitors and prevent zaprinast from increasing cytosolic Ca2+. The enhancers identified are capable of releasing intracellular Ca2+ stores independently of zaprinast or PKG. One of these enhancers blocks parasite egress and invasion and shows strong antiparasitic activity against T. gondii. The same compound inhibits invasion of the most lethal malaria parasite, Plasmodium falciparum. Inhibition of Ca2+-related phenotypes in these two apicomplexan parasites suggests that depletion of intracellular Ca2+ stores by the enhancer may be an effective antiparasitic strategy. These results establish a powerful new strategy for identifying compounds that modulate the essential parasite signaling pathways regulated by Ca2+, underscoring the importance of these pathways and the therapeutic potential of their inhibition.Publisher PDFPeer reviewe

Using a Genetically Encoded Sensor to Identify Inhibitors of Toxoplasma gondii Ca 2+ Signaling

The life cycles of apicomplexan parasites progress in accordance with fluxes in cytosolic Ca2+. Such fluxes are necessary for events like motility and egress from host cells. We used genetically encoded Ca2+ indicators (GCaMPs) to develop a cell-based phenotypic screen for compounds that modulate Ca2+ signaling in the model apicomplexan Toxoplasma gondii. In doing so, we took advantage of the phosphodiesterase inhibitor zaprinast, which we show acts in part through cGMP-dependent protein kinase (protein kinase G; PKG) to raise levels of cytosolic Ca2+. We define the pool of Ca2+ regulated by PKG to be a neutral store distinct from the endoplasmic reticulum. Screening a library of 823 ATP mimetics, we identify both inhibitors and enhancers of Ca2+ signaling. Two such compounds constitute novel PKG inhibitors and prevent zaprinast from increasing cytosolic Ca2+. The enhancers identified are capable of releasing intracellular Ca2+ stores independently of zaprinast or PKG. One of these enhancers blocks parasite egress and invasion and shows strong antiparasitic activity against T. gondii. The same compound inhibits invasion of the most lethal malaria parasite, Plasmodium falciparum. Inhibition of Ca2+-related phenotypes in these two apicomplexan parasites suggests that depletion of intracellular Ca2+ stores by the enhancer may be an effective antiparasitic strategy. These results establish a powerful new strategy for identifying compounds that modulate the essential parasite signaling pathways regulated by Ca2+, underscoring the importance of these pathways and the therapeutic potential of their inhibition

A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review

Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks

Evaluation of Phage Therapy in the Context of Enterococcus faecalis and Its Associated Diseases

peer-reviewedBacteriophages (phages) or bacterial viruses have been proposed as natural antimicrobial agents to fight against antibiotic-resistant bacteria associated with human infections. Enterococcus faecalis is a gut commensal, which is occasionally found in the mouth and vaginal tract, and does not usually cause clinical problems. However, it can spread to other areas of the body and cause life-threatening infections, such as septicemia, endocarditis, or meningitis, in immunocompromised hosts. Although E. faecalis phage cocktails are not commercially available within the EU or USA, there is an accumulated evidence from in vitro and in vivo studies that have shown phage efficacy, which supports the idea of applying phage therapy to overcome infections associated with E. faecalis. In this review, we discuss the potency of bacteriophages in controlling E. faecalis, in both in vitro and in vivo scenarios. E. faecalis associated bacteriophages were compared at the genome level and an attempt was made to categorize phages with respect to their suitability for therapeutic application, using orthocluster analysis. In addition, E. faecalis phages have been examined for the presence of antibiotic-resistant genes, to ensure their safe use in clinical conditions. Finally, the domain architecture of E. faecalis phage-encoded endolysins are discussed

FACTIFY3M: A Benchmark for Multimodal Fact Verification with Explainability through 5W Question-Answering

Combating disinformation is one of the burning societal crises -- about 67% of the American population believes that disinformation produces a lot of uncertainty, and 10% of them knowingly propagate disinformation. Evidence shows that disinformation can manipulate democratic processes and public opinion, causing disruption in the share market, panic and anxiety in society, and even death during crises. Therefore, disinformation should be identified promptly and, if possible, mitigated. With approximately 3.2 billion images and 720,000 hours of video shared online daily on social media platforms, scalable detection of multimodal disinformation requires efficient fact verification. Despite progress in automatic text-based fact verification (e.g., FEVER, LIAR), the research community lacks substantial effort in multimodal fact verification. To address this gap, we introduce FACTIFY 3M, a dataset of 3 million samples that pushes the boundaries of the domain of fact verification via a multimodal fake news dataset, in addition to offering explainability through the concept of 5W question-answering. Salient features of the dataset include: (i) textual claims, (ii) ChatGPT-generated paraphrased claims, (iii) associated images, (iv) stable diffusion-generated additional images (i.e., visual paraphrases), (v) pixel-level image heatmap to foster image-text explainability of the claim, (vi) 5W QA pairs, and (vii) adversarial fake news stories.Comment: arXiv admin note: text overlap with arXiv:2305.0432

Biochar-based fertilizer: Supercharging root membrane potential and biomass yield of rice

Biochar-based compound fertilizers (BCF) and amendments have proven to enhance crop yields and modify soil properties (pH, nutrients, organic matter, structure etc.) and are now in commercial production in China. While there is a good understanding of the changes in soil properties following biochar addition, the interactions within the rhizosphere remain largely unstudied, with benefits to yield observed beyond the changes in soil properties alone. We investigated the rhizosphere interactions following the addition of an activated wheat straw BCF at an application rates of 0.25% (g·g−1 soil), which could potentially explain the increase of plant biomass (by 67%), herbage N (by 40%) and P (by 46%) uptake in the rice plants grown in the BCF-treated soil, compared to the rice plants grown in the soil with conventional fertilizer alone. Examination of the roots revealed that micron and submicron-sized biochar were embedded in the plaque layer. BCF increased soil Eh by 85 mV and increased the potential difference between the rhizosphere soil and the root membrane by 65 mV. This increased potential difference lowered the free energy required for root nutrient accumulation, potentially explaining greater plant nutrient content and biomass. We also demonstrate an increased abundance of plant-growth promoting bacteria and fungi in the rhizosphere. We suggest that the redox properties of the biochar cause major changes in electron status of rhizosphere soils that drive the observed agronomic benefits

Genetic structure of Plasmodium falciparum field isolates in eastern and north-eastern India

<p>Abstract</p> <p>Background</p> <p>Molecular techniques have facilitated the studies on genetic diversity of <it>Plasmodium </it>species particularly from field isolates collected directly from patients. The <it>msp-1 </it>and <it>msp-2 </it>are highly polymorphic markers and the large allelic polymorphism has been reported in the block 2 of the <it>msp-1 </it>gene and the central repetitive domain (block3) of the <it>msp-2 </it>gene. Families differing in nucleotide sequences and in number of repetitive sequences (length variation) were used for genotyping purposes. As limited reports are available on the genetic diversity existing among <it>Plasmodium falciparum </it>population of India, this report evaluates the extent of genetic diversity in the field isolates of <it>P. falciparum </it>in eastern and north-eastern regions of India.</p> <p>Methods</p> <p>A study was designed to assess the diversity of <it>msp-1 </it>and <it>msp-2 </it>among the field isolates from India using allele specific nested PCR assays and sequence analysis. Field isolates were collected from five sites distributed in three states namely, Assam, West Bengal and Orissa.</p> <p>Results</p> <p><it>P. falciparum </it>isolates of the study sites are highly diverse in respect of length as well as sequence motifs with prevalence of all the reported allelic families of <it>msp-1 </it>and <it>msp-2</it>. Prevalence of identical allelic composition as well as high level of sequence identity of alleles suggest a considerable amount of gene flow between the <it>P. falciparum </it>populations of different states. A comparatively higher proportion of multiclonal isolates as well as multiplicity of infection (MOI) was observed among isolates of highly malarious districts Karbi Anglong (Assam) and Sundergarh (Orissa). In all the five sites, R033 family of <it>msp-1 </it>was observed to be monomorphic with an allele size of 150/160 bp. The observed 80–90% sequence identity of Indian isolates with data of other regions suggests that Indian <it>P. falciparum </it>population is a mixture of different strains.</p> <p>Conclusion</p> <p>The present study shows that the field isolates of eastern and north-eastern regions of India are highly diverse in respect of <it>msp-1 </it>(block 2) and <it>msp-2 </it>(central repeat region, block 3). As expected Indian isolates present a picture of diversity closer to southeast Asia, Papua New Guinea and Latin American countries, regions with low to meso-endemicity of malaria in comparison to African regions of hyper- to holo-endemicity.</p

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients