Opposing effects of acellular and whole cell pertussis vaccines on Bordetella pertussis biofilm formation, Siglec-F+ neutrophil recruitment and bacterial clearance in mouse nasal tissues.

Despite global vaccination, pertussis caused by Bordetella pertussis (Bp) is resurging. Pertussis resurgence is correlated with the switch from whole cell vaccines (wPV) that elicit TH1/TH17 polarized immune responses to acellular pertussis vaccines (aPV) that elicit primarily TH2 polarized immune responses. One explanation for the increased incidence in aPV-immunized individuals is the lack of bacterial clearance from the nose. To understand the host and bacterial mechanisms that contribute to Bp persistence, we evaluated bacterial localization and the immune response in the nasal associated tissues (NT) of naïve and immunized mice following Bp challenge. Bp resided in the NT of unimmunized and aPV-immunized mice as biofilms. In contrast, Bp biofilms were not observed in wPV-immunized mice. Following infection, Siglec-F+ neutrophils, critical for eliminating Bp from the nose, were recruited to the nose at higher levels in wPV immunized mice compared to aPV immunized mice. Consistent with this observation, the neutrophil chemokine CXCL1 was only detected in the NT of wPV immunized mice. Importantly, the bacteria and immune cells were primarily localized within the NT and were not recovered by nasal lavage (NL). Together, our data suggest that the TH2 polarized immune response generated by aPV vaccination facilitates persistence in the NT by impeding the infiltration of immune effectors and the eradication of biofilms In contrast, the TH1/TH17 immune phenotype generated by wPV, recruits Siglec-F+ neutrophils that rapidly eliminate the bacterial burden and prevent biofilm establishment. Thus, our work shows that aPV and wPV have opposing effects on Bp biofilm formation in the respiratory tract and provides a mechanistic explanation for the inability of aPV vaccination to control bacterial numbers in the nose and prevent transmission.

Identification and risk stratification of coronary disease by artificial intelligence-enabled ECG.

Background: Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of death worldwide, driven primarily by coronary artery disease (CAD). ASCVD risk estimators such as the pooled cohort equations (PCE) facilitate risk stratification and primary prevention of ASCVD but their accuracy is still suboptimal. Methods: Using deep electronic health record data from 7,116,209 patients seen at 70+ hospitals and clinics across 5 states in the USA, we developed an artificial intelligence-based electrocardiogram analysis tool (ECG-AI) to detect CAD and assessed the additive value of ECG-AI-based ASCVD risk stratification to the PCE. We created independent ECG-AI models using separate neural networks including subjects without known history of ASCVD, to identify coronary artery calcium (CAC) score ≥300 Agatston units by computed tomography, obstructive CAD by angiography or procedural intervention, and regional left ventricular akinesis in ≥1 segment by echocardiogram, as a reflection of possible prior myocardial infarction (MI). These were used to assess the utility of ECG-AI-based ASCVD risk stratification in a retrospective observational study consisting of patients with PCE scores and no prior ASCVD. The study period covered all available digitized EHR data, with the first available ECG in 1987 and the last in February 2023. Findings: ECG-AI for identifying CAC ≥300, obstructive CAD, and regional akinesis achieved area under the receiver operating characteristic (AUROC) values of 0.88, 0.85, and 0.94, respectively. An ensembled ECG-AI identified 3, 5, and 10-year risk for acute coronary events and mortality independently and additively to PCE. Hazard ratios for acute coronary events over 3-years in patients without ASCVD that tested positive on 1, 2, or 3 versus 0 disease-specific ECG-AI models at cohort entry were 2.41 (2.14-2.71), 4.23 (3.74-4.78), and 11.75 (10.2-13.52), respectively. Similar stratification was observed in cohorts stratified by PCE or age. Interpretation: ECG-AI has potential to address unmet need for accessible risk stratification in patients in whom PCE under, over, or insufficiently estimates ASCVD risk, and in whom risk assessment over time periods shorter than 10 years is desired. Funding: Anumana.

Teaching Python programming for bioinformatics with Jupyter notebook in the Post-COVID-19 era.

The COVID-19 pandemic has forced the Bioinformatics course to switch from on-site teaching to remote learning. This shift has prompted a change in teaching methods and laboratory activities. Students need to have a basic understanding of DNA sequences and how to analyze them using custom scripts. To facilitate learning, we have modified the course to use Jupyter Notebook, which offers an alternative approach to writing custom scripts for basic DNA sequence analysis. This approach allows students to acquire the necessary skills while working remotely. It is a versatile and user-friendly platform that can be used to combine explanations, code, and results in a single document. This feature enables students to interact with the code and results, making the learning process more engaging and effective. Jupyter Notebook provides a hybrid approach to learning basic Python scripting and genomics that is effective for remote teaching and learning during the COVID-19 pandemic.

A critical role for erythropoietin on vagus nerve Schwann cells in intestinal motility.

BACKGROUND: Dysmotility and postoperative ileus (POI) are frequent major clinical problems post-abdominal surgery. Erythropoietin (EPO) is a multifunctional tissue-protective cytokine that promotes recovery of the intestine in various injury models. While EPO receptors (EPOR) are present in vagal Schwann cells, the role of EPOR in POI recovery is unknown because of the lack of EPOR antagonists or Schwann-cell specific EPOR knockout animals. This study was designed to explore the effect of EPO via EPOR in vagal nerve Schwann cells in a mouse model of POI. RESULTS: The structural features of EPOR and its activation by EPO-mediated dimerization were understood using structural analysis. Later, using the Cre-loxP system, we developed a myelin protein zero (Mpz) promoter-driven knockout mouse model of Schwann cell EPOR (MpzCre-EPORflox/flox / Mpz-EPOR-KO) confirmed using PCR and qRT-PCR techniques. We then measured the intestinal transit time (ITT) at baseline and after induction of POI with and without EPO treatment. Although we have previously shown that EPO accelerates functional recovery in POI in wild type mice, EPO treatment did not improve functional recovery of ITT in POI of Mpz-EPOR-KO mice. CONCLUSIONS: To the best of our knowledge, this is the first pre-clinical study to demonstrate a novel mouse model of EPOR specific knock out on Schwan cells with an effect in the gut. We also showed novel beneficial effects of EPO through vagus nerve Schwann cell-EPOR in intestinal dysmotility. Our findings suggest that EPO-EPOR signaling in the vagus nerve after POI is important for the functional recovery of ITT.

Mycolactone: A Broad Spectrum Multitarget Antiviral Active in the Picomolar Range for COVID-19 Prevention and Cure.

We have previously shown computationally that Mycolactone (MLN), a toxin produced by Mycobacterium ulcerans, strongly binds to Munc18b and other proteins, presumably blocking degranulation and exocytosis of blood platelets and mast cells. We investigated the effect of MLN on endocytosis using similar approaches, and it bound strongly to the N-terminal of the clathrin protein and a novel SARS-CoV-2 fusion protein. Experimentally, we found 100% inhibition up to 60 nM and 84% average inhibition at 30 nM in SARS-CoV-2 live viral assays. MLN was also 10× more potent than remdesivir and molnupiravir. MLN's toxicity against human alveolar cell line A549, immortalized human fetal renal cell line HEK293, and human hepatoma cell line Huh7.1 were 17.12%, 40.30%, and 36.25%, respectively. The cytotoxicity IC50 breakpoint ratio versus anti-SARS-CoV-2 activity was more than 65-fold. The IC50 values against the alpha, delta, and Omicron variants were all below 0.020 µM, and 134.6 nM of MLN had 100% inhibition in an entry and spread assays. MLN is eclectic in its actions through its binding to Sec61, AT2R, and the novel fusion protein, making it a good drug candidate for treating and preventing COVID-19 and other similarly transmitted enveloped viruses and pathogens.

Carborane-Containing Hydroxamate MMP Ligands for the Treatment of Tumors Using Boron Neutron Capture Therapy (BNCT): Efficacy without Tumor Cell Entry.

New carborane-bearing hydroxamate matrix metalloproteinase (MMP) ligands have been synthesized for boron neutron capture therapy (BNCT) with nanomolar potency against MMP-2, -9 and -13. New analogs are based on MMP inhibitor CGS-23023A, and two previously reported MMP ligands 1 (B1) and 2 (B2) were studied in vitro for BNCT activity. The boronated MMP ligands 1 and 2 showed high in vitro tumoricidal effects in an in vitro BNCT assay, exhibiting IC50 values for 1 and 2 of 2.04 × 10-2 mg/mL and 2.67 × 10-2 mg/mL, respectively. The relative killing effect of 1 to L-boronophenylalanine (BPA) is 0.82/0.27 = 3.0, and that of 2 is 0.82/0.32 = 2.6, whereas the relative killing effect of 4 is comparable to boronophenylalanine (BPA). The survival fraction of 1 and 2 in a pre-incubation boron concentration at 0.143 ppm 10B and 0.101 ppm 10B, respectively, were similar, and these results suggest that 1 and 2 are actively accumulated through attachment to the Squamous cell carcinoma (SCC)VII cells. Compounds 1 and 2 very effectively killed glioma U87 delta EGFR cells after BNCT. This study is noteworthy in demonstrating BNCT efficacy through binding to MMP enzymes overexpressed at the surface of the tumor cell without tumor cell penetration.

In Silico Binding of 2-Aminocyclobutanones to SARS-CoV-2 Nsp13 Helicase and Demonstration of Antiviral Activity.

The landscape of viral strains and lineages of SARS-CoV-2 keeps changing and is currently dominated by Delta and Omicron variants. Members of the latest Omicron variants, including BA.1, are showing a high level of immune evasion, and Omicron has become a prominent variant circulating globally. In our search for versatile medicinal chemistry scaffolds, we prepared a library of substituted ɑ-aminocyclobutanones from an ɑ-aminocyclobutanone synthon (11). We performed an in silico screen of this actual chemical library as well as other virtual 2-aminocyclobutanone analogs against seven SARS-CoV-2 nonstructural proteins to identify potential drug leads against SARS-CoV-2, and more broadly against coronavirus antiviral targets. Several of these analogs were initially identified as in silico hits against SARS-CoV-2 nonstructural protein 13 (Nsp13) helicase through molecular docking and dynamics simulations. Antiviral activity of the original hits as well as ɑ-aminocyclobutanone analogs that were predicted to bind more tightly to SARS-CoV-2 Nsp13 helicase are reported. We now report cyclobutanone derivatives that exhibit anti-SARS-CoV-2 activity. Furthermore, the Nsp13 helicase enzyme has been the target of relatively few target-based drug discovery efforts, in part due to a very late release of a high-resolution structure accompanied by a limited understanding of its protein biochemistry. In general, antiviral agents initially efficacious against wild-type SARS-CoV-2 strains have lower activities against variants due to heavy viral loads and greater turnover rates, but the inhibitors we are reporting have higher activities against the later variants than the wild-type (10-20X). We speculate this could be due to Nsp13 helicase being a critical bottleneck in faster replication rates of the new variants, so targeting this enzyme affects these variants to an even greater extent. This work calls attention to cyclobutanones as a useful medicinal chemistry scaffold, and the need for additional focus on the discovery of Nsp13 helicase inhibitors to combat the aggressive and immune-evading variants of concern (VOCs).

Blockchain-enabled immutable, distributed, and highly available clinical research activity logging system for federated COVID-19 data analysis from multiple institutions.

OBJECTIVE: We aimed to develop a distributed, immutable, and highly available cross-cloud blockchain system to facilitate federated data analysis activities among multiple institutions. MATERIALS AND METHODS: We preprocessed 9166 COVID-19 Structured Query Language (SQL) code, summary statistics, and user activity logs, from the GitHub repository of the Reliable Response Data Discovery for COVID-19 (R2D2) Consortium. The repository collected local summary statistics from participating institutions and aggregated the global result to a COVID-19-related clinical query, previously posted by clinicians on a website. We developed both on-chain and off-chain components to store/query these activity logs and their associated queries/results on a blockchain for immutability, transparency, and high availability of research communication. We measured run-time efficiency of contract deployment, network transactions, and confirmed the accuracy of recorded logs compared to a centralized baseline solution. RESULTS: The smart contract deployment took 4.5 s on an average. The time to record an activity log on blockchain was slightly over 2 s, versus 5-9 s for baseline. For querying, each query took on an average less than 0.4 s on blockchain, versus around 2.1 s for baseline. DISCUSSION: The low deployment, recording, and querying times confirm the feasibility of our cross-cloud, blockchain-based federated data analysis system. We have yet to evaluate the system on a larger network with multiple nodes per cloud, to consider how to accommodate a surge in activities, and to investigate methods to lower querying time as the blockchain grows. CONCLUSION: Blockchain technology can be used to support federated data analysis among multiple institutions.

Structure-Based Identification of Natural-Product-Derived Compounds with Potential to Inhibit HIV-1 Entry.

Broadly neutralizing antibodies (bNAbs) are potent in neutralizing a wide range of HIV strains. VRC01 is a CD4-binding-site (CD4-bs) class of bNAbs that binds to the conserved CD4-binding region of HIV-1 envelope (env) protein. Natural products that mimic VRC01 bNAbs by interacting with the conserved CD4-binding regions may serve as a new generation of HIV-1 entry inhibitors by being broadly reactive and potently neutralizing. This study aimed to identify compounds that mimic VRC01 by interacting with the CD4-bs of HIV-1 gp120 and thereby inhibiting viral entry into target cells. Libraries of purchasable natural products were virtually screened against clade A/E recombinant 93TH057 (PDB: 3NGB) and clade B (PDB ID: 3J70) HIV-1 env protein. Protein-ligand interaction profiling from molecular docking and dynamics simulations showed that the compounds had intermolecular hydrogen and hydrophobic interactions with conserved amino acid residues on the CD4-binding site of recombinant clade A/E and clade B HIV-1 gp120. Four potential lead compounds, NP-005114, NP-008297, NP-007422, and NP-007382, were used for cell-based antiviral infectivity inhibition assay using clade B (HXB2) env pseudotype virus (PV). The four compounds inhibited the entry of HIV HXB2 pseudotype viruses into target cells at 50% inhibitory concentrations (IC50) of 15.2 µM (9.7 µg/mL), 10.1 µM (7.5 µg/mL), 16.2 µM (12.7 µg/mL), and 21.6 µM (12.9 µg/mL), respectively. The interaction of these compounds with critical residues of the CD4-binding site of more than one clade of HIV gp120 and inhibition of HIV-1 entry into the target cell demonstrate the possibility of a new class of HIV entry inhibitors.

Protein structure-based in-silico approaches to drug discovery: Guide to COVID-19 therapeutics.

With more than 5 million fatalities and close to 300 million reported cases, COVID-19 is the first documented pandemic due to a coronavirus that continues to be a major health challenge. Despite being rapid, uncontrollable, and highly infectious in its spread, it also created incentives for technology development and redefined public health needs and research agendas to fast-track innovations to be translated. Breakthroughs in computational biology peaked during the pandemic with renewed attention to making all cutting-edge technology deliver agents to combat the disease. The demand to develop effective treatments yielded surprising collaborations from previously segregated fields of science and technology. The long-standing pharmaceutical industry's aversion to repurposing existing drugs due to a lack of exponential financial gain was overrun by the health crisis and pressures created by front-line researchers and providers. Effective vaccine development even at an unprecedented pace took more than a year to develop and commence trials. Now the emergence of variants and waning protections during the booster shots is resulting in breakthrough infections that continue to strain health care systems. As of now, every protein of SARS-CoV-2 has been structurally characterized and related host pathways have been extensively mapped out. The research community has addressed the druggability of a multitude of possible targets. This has been made possible due to existing technology for virtual computer-assisted drug development as well as new tools and technologies such as artificial intelligence to deliver new leads. Here in this article, we are discussing advances in the drug discovery field related to target-based drug discovery and exploring the implications of known target-specific agents on COVID-19 therapeutic management. The current scenario calls for more personalized medicine efforts and stratifying patient populations early on for their need for different combinations of prognosis-specific therapeutics. We intend to highlight target hotspots and their potential agents, with the ultimate goal of using rational design of new therapeutics to not only end this pandemic but also uncover a generalizable platform for use in future pandemics.

Data structuring may prevent ambiguity and improve personalized medical prognosis.

Topics expected to influence personalized medicine (PM), where medical decisions, practices, and treatments are tailored to the individual patient, are reviewed. Lack of discrimination due to different biological conditions that express similar values of numerical variables (ambiguity) is regarded to be a major potential barrier for PM. This material explores possible causes and sources of ambiguity and offers suggestions for mitigating the impacts of uncertainties. Three causes of ambiguity are identified: (1) delayed adoption of innovations, (2) inadequate emphases, and (3) inadequate processes used when new medical practices are developed and validated. One example of the first problem is the relative lack of medical research on "compositional data" -the type that characterizes leukocyte data. This omission results in erroneous use of data abundantly utilized in medicine, such as the blood cell differential. Emphasis on data output ‒not biomedical interpretation that facilitates the use of clinical data‒ exemplifies the second type of problems. Reliance on tools generated in other fields (but not validated within biomedical contexts) describes the last limitation. Because reductionism is associated with these problems, non-reductionist alternatives are reviewed as potential remedies. Data structuring (converting data into information) is considered a key element that may promote PM. To illustrate a process that includes data-information-knowledge and decision-making, previously published data on COVID-19 are utilized. It is suggested that ambiguity may be prevented or ameliorated. Provided that validations are grounded on biomedical knowledge, approaches that describe certain criteria - such as non-overlapping data intervals of patients that experience different outcomes, immunologically interpretable data, and distinct graphic patterns - can inform, at personalized bases, earlier and/or with fewer observations.

Deep learning model based multimedia retrieval and its optimization in augmented reality applications.

With the uproar of touchless technology, the Virtual Continuum has seen some spark in the upcoming products. Today numerous gadgets support the use of Mixed Reality / Augmented Reality (AR)/ Virtual Reality. The Head Mounted Displays (HMDs) like that of Hololens, Google Lens, Jio Glass manifested reality into virtuality. Other than the HMDs many organizations tend to develop mobile AR applications to support umpteen number of industries like medicine, education, construction. Currently, the major issue lies in the performance parameters of these applications, while deploying for mobile application's graphics performance, latency, and CPU functioning. Many industries pose real-time computation requirements in AR but do not implement an efficient algorithm in their frameworks. Offloading the computation of deep learning models involved in the application to the cloud servers will highly affect the processing parameters. For our use case, we will be using Multi-Task Cascaded Convolutional Neural Network (MTCNN) which is a modern tool for face detection, using a 3-stage neural network detector. Therefore, the optimization of communication between local application and cloud computing frameworks needs to be optimized. The proposed framework defines how the parameters involving the complete deployment of a mobile AR application can be optimized in terms of retrieval of multimedia, its processing, and augmentation of graphics, eventually enhancing the performance. To implement the proposed algorithm a mobile application is created in Unity3D. The mobile application virtually augments a 3D model of a skeleton on a target face. After the mentioned experimentation, it is found that average Media Retrieval Time (1.1471 µ s) and Client Time (1.1207 µ s) in the local application are extremely low than the average API process time (288.934ms). The highest time latency is achieved at the frame rate higher than 80fps.

Optimization and application of loop-mediated isothermal amplification technique for sex identification in red-whiskered bulbul (Pycnonotus jocosus).

The red-whiskered bulbul (Pycnonotus jocosus) is a popular avian species in Thailand and many other countries. The red-whiskered bulbul has a high economic value, but breeding is challenging since sex identification is difficult. The PCR method is now used for sex identification. However, PCR amplification and post-PCR analysis necessitate the use of a laboratory equipped with specialized scientific instruments, which is inconvenient for field operations. This research describes a method for amplification of DNA samples using the loop-mediated isothermal amplification (LAMP) approach, which is a molecular biology methodology for isothermal amplification that is extremely sensitive, fast, and easy for post-LAMP product visualization. Herein, total of 23 blood samples were collected and DNA was extracted. Two sets of LAMP primers were designed for CHD-Z and CHD-W genes. The colorimetric assay was used to investigate the best conditions for LAMP reactions and post-LAMP product visualization. LAMP reactions for sex identification were compared to traditional PCR in terms of sensitivity and specificity. LAMP reactions were found to be 10-fold more sensitive than PCR at 1 ng of DNA. When compared to electrophoresis analysis, the visualization with colorimetric assay using GelRed® and SYTO™ 9 was 100% accurate. The optimal LAMP condition tested simple DNA extracted from bird feathers using the HotSHOT technique. The result showed that the optimal condition could distinguish the sex of red-whiskered bulbuls totally and accurately. A powerful method for red-whiskered bulbul sex identification is demonstrated in this study, which can be used in field studies because it is quick and easy to perform, has high sensitivity, and does not require advanced scientific equipment.

The complete mitochondrial genome of giant cricket, Tarbinskiellus portentosus (Orthoptera: Gryllidae) and its curation.

Tarbinskiellus portentosus, commonly known as giant cricket one of the important edible cricket species. However, the genetic information of these species is still limited. Therefore, we have assembled and annotated the first mitochondrial genome of T. portentosus. The mitogenome is 15710 bp long and has GC content of 27.19%. The nucleotide composition is similar with other insect mitogenomes (A 40.6%; T 32.2%; C 17.3%; G 9.9%). The gene organization in the mitogenome of T. portentosus is identical to the mitogenome of other cricket species. The complete mitogenome of T. portentosus consisted 37 genes including 13 protein coding genes, 22 tRNA genes, and two rRNA genes. The newly assembled mitogenome will help molecular biology research on edible crickets. Since mitogenome genes are traditionally used for DNA barcoding and phylogenetic analysis, comparative analysis of T. portentosus mitogenome with other related cricket species will also aid researchers in developing universal primers for species identification toward food security. Apart from the main goal of providing full mitogenome of T. portentosus, paper also provides conceptual workflow based on de novo assembly and its correction for final mitogenome construction.

The Multistage Antimalarial Compound Calxinin Perturbates P. falciparum Ca2+ Homeostasis by Targeting a Unique Ion Channel.

Malaria elimination urgently needs novel antimalarial therapies that transcend resistance, toxicity, and high costs. Our multicentric international collaborative team focuses on developing multistage antimalarials that exhibit novel mechanisms of action. Here, we describe the design, synthesis, and evaluation of a novel multistage antimalarial compound, 'Calxinin'. A compound that consists of hydroxyethylamine (HEA) and trifluoromethyl-benzyl-piperazine. Calxinin exhibits potent inhibitory activity in the nanomolar range against the asexual blood stages of drug-sensitive (3D7), multidrug-resistant (Dd2), artemisinin-resistant (IPC4912), and fresh Kenyan field isolated Plasmodium falciparum strains. Calxinin treatment resulted in diminished maturation of parasite sexual precursor cells (gametocytes) accompanied by distorted parasite morphology. Further, in vitro liver-stage testing with a mouse model showed reduced parasite load at an IC50 of 79 nM. A single dose (10 mg/kg) of Calxinin resulted in a 30% reduction in parasitemia in mice infected with a chloroquine-resistant strain of the rodent parasite P. berghei. The ex vivo ookinete inhibitory concentration within mosquito gut IC50 was 150 nM. Cellular in vitro toxicity assays in the primary and immortalized human cell lines did not show cytotoxicity. A computational protein target identification pipeline identified a putative P. falciparum membrane protein (Pf3D7_1313500) involved in parasite calcium (Ca2+) homeostasis as a potential Calxinin target. This highly conserved protein is related to the family of transient receptor potential cation channels (TRP-ML). Target validation experiments showed that exposure of parasitized RBCs (pRBCs) to Calxinin induces a rapid release of intracellular Ca2+ from pRBCs; leaving de-calcinated parasites trapped in RBCs. Overall, we demonstrated that Calxinin is a promising antimalarial lead compound with a novel mechanism of action and with potential therapeutic, prophylactic, and transmission-blocking properties against parasites resistant to current antimalarials.

Repurposing Lansoprazole and Posaconazole to treat leishmaniasis: Integration of in vitro testing, pharmacological corroboration, and mechanisms of action.

Leishmaniasis remains a serious public health problem in many tropical regions of the world. Among neglected tropical diseases, the mortality rate of leishmaniasis is second only to malaria. All currently approved therapeutics have toxic side effects and face rapidly increasing resistance. To identify existing drugs with antileishmanial activity and predict the mechanism of action, we designed a drug-discovery pipeline utilizing both in-silico and in-vitro methods. First, we screened compounds from the Selleckchem Bio-Active Compound Library containing ~1622 FDA-approved drugs and narrowed these down to 96 candidates based on data mining for possible anti-parasitic properties. Next, we completed preliminary in-vitro testing of compounds against Leishmania amastigotes and selected the most promising active compounds, Lansoprazole and Posaconazole. We identified possible Leishmania drug targets of Lansoprazole and Posaconazole using several available servers. Our in-silico screen identified likely Lansoprazole targets as the closely related calcium-transporting ATPases (LdBPK_352080.1, LdBPK_040010.1, and LdBPK_170660.1), and the Posaconazole target as lanosterol 14-alpha-demethylase (LdBPK_111100.1). Further validation showed LdBPK_352080.1 to be the most plausible target based on induced-fit docking followed by long (100ns) MD simulations to confirm the stability of the docked complexes. We present a likely ion channel-based mechanism of action of Lansoprazole against Leishmania calcium-transporting ATPases, which are essential for parasite metabolism and infectivity. The LdBPK_111100.1 interaction with Posaconazole is very similar to the known fungal orthologue. Herein, we present two novel anti-leishmanial agents, Posaconazole and Lansoprazole, already approved by the FDA for different indications and propose plausible mechanisms of action for their antileishmanial activity.

Intelligent Real-Time Face-Mask Detection System with Hardware Acceleration for COVID-19 Mitigation.

This paper proposes and implements a dedicated hardware accelerated real-time face-mask detection system using deep learning (DL). The proposed face-mask detection model (MaskDetect) was benchmarked on three embedded platforms: Raspberry PI 4B with either Google Coral USB TPU or Intel Neural Compute Stick 2 VPU, and NVIDIA Jetson Nano. The MaskDetect was independently quantised and optimised for each hardware accelerated implementation. An ablation study was carried out on the proposed model and its quantised implementations on the embedded hardware configurations above as a comparison to other popular transfer-learning models, such as VGG16, ResNet-50V2, and InceptionV3, which are compatible with these acceleration hardware platforms. The ablation study revealed that MaskDetect achieved excellent average face-mask detection performance with accuracy above 94% across all embedded platforms except for Coral, which achieved an average accuracy of nearly 90%. With respect to detection performance (accuracy), inference speed (frames per second (FPS)), and product cost, the ablation study revealed that implementation on Jetson Nano is the best choice for real-time face-mask detection. It achieved 94.2% detection accuracy and twice greater FPS when compared to its desktop hardware counterpart.

Multi-Cellular Immunological Interactions Associated With COVID-19 Infections.

To rapidly prognosticate and generate hypotheses on pathogenesis, leukocyte multi-cellularity was evaluated in SARS-CoV-2 infected patients treated in India or the United States (152 individuals, 384 temporal observations). Within hospital (<90-day) death or discharge were retrospectively predicted based on the admission complete blood cell counts (CBC). Two methods were applied: (i) a "reductionist" one, which analyzes each cell type separately, and (ii) a "non-reductionist" method, which estimates multi-cellularity. The second approach uses a proprietary software package that detects distinct data patterns generated by complex and hypothetical indicators and reveals each data pattern's immunological content and associated outcome(s). In the Indian population, the analysis of isolated cell types did not separate survivors from non-survivors. In contrast, multi-cellular data patterns differentiated six groups of patients, including, in two groups, 95.5% of all survivors. Some data structures revealed one data point-wide line of observations, which informed at a personalized level and identified 97.8% of all non-survivors. Discovery was also fostered: some non-survivors were characterized by low monocyte/lymphocyte ratio levels. When both populations were analyzed with the non-reductionist method, they displayed results that suggested survivors and non-survivors differed immunologically as early as hospitalization day 1.

Iron dysregulation in COVID-19 and reciprocal evolution of SARS-CoV-2: Natura nihil frustra facit.

After more than a year of the COVID-19 pandemic, SARS-CoV-2 infection rates with newer variants continue to devastate much of the world. Global healthcare systems are overwhelmed with high positive patient numbers. Silent hypoxia accompanied by rapid deterioration and some cases with septic shock is responsible for COVID-19 mortality in many hospitalized patients. There is an urgent need to further understand the relationships and interplay with human host components during pathogenesis and immune evasion strategies. Currently, acquired immunity through vaccination or prior infection usually provides sufficient protection against the emerging variants of SARS-CoV-2 except Omicron variant requiring recent booster. New strains have shown higher viral loads and greater transmissibility with more severe disease presentations. Notably, COVID-19 has a peculiar prognosis in severe patients with iron dysregulation and hypoxia which is still poorly understood. Studies have shown abnormally low serum iron levels in severe infection but a high iron overload in lung fibrotic tissue. Data from our in-silico structural analysis of the spike protein sequence along with host proteolysis processing suggests that the viral spike protein fragment mimics Hepcidin and is resistant to the major human proteases. This functional spike-derived peptide dubbed "Covidin" thus may be intricately involved with host ferroportin binding and internalization leading to dysregulated host iron metabolism. Here, we propose the possible role of this potentially allogenic mimetic hormone corresponding to severe COVID-19 immunopathology and illustrate that this molecular mimicry is responsible for a major pathway associated with severe disease status. Furthermore, through 3D molecular modeling and docking followed by MD simulation validation, we have unraveled the likely role of Covidin in iron dysregulation in COVID-19 patients. Our meta-analysis suggests the Hepcidin mimetic mechanism is highly conserved among its host range as well as among all new variants to date including Omicron. Extensive analysis of current mutations revealed that new variants are becoming alarmingly more resistant to selective human proteases associated with host defense.

The emerging paradigm of calcium homeostasis as a new therapeutic target for protozoan parasites.

Calcium channels (CCs), a group of ubiquitously expressed membrane proteins, are involved in many pathophysiological processes of protozoan parasites. Our understanding of CCs in cell signaling, organelle function, cellular homeostasis, and cell cycle control has led to improved insights into their structure and functions. In this article, we discuss CCs characteristics of five major protozoan parasites Plasmodium, Leishmania, Toxoplasma, Trypanosoma, and Cryptosporidium. We provide a comprehensive review of current antiparasitic drugs and the potential of using CCs as new therapeutic targets. Interestingly, previous studies have demonstrated that human CC modulators can kill or sensitize parasites to antiparasitic drugs. Still, none of the parasite CCs, pumps, or transporters has been validated as drug targets. Information for this review draws from extensive data mining of genome sequences, chemical library screenings, and drug design studies. Parasitic resistance to currently approved therapeutics is a serious and emerging threat to both disease control and management efforts. In this article, we suggest that the disruption of calcium homeostasis may be an effective approach to develop new anti-parasite drug candidates and reduce parasite resistance.